Merge remote-tracking branch 'origin/master' into fix/adaptive-embed-batch-sizing

# Conflicts:
#	CHANGELOG.md
#	VERSION
#	package.json

.github/workflows/actionlint.yml

@@ -1,32 +0,0 @@
-name: Actionlint
-
-# Lints the GitHub Actions workflow YAML on every change so a malformed
-# workflow / bad action ref / missing-permission bug is caught before it ships
-# a broken pipeline. gbrain edits .github/workflows/* often (sharding, cache,
-# timeouts); this is the cheap guard that keeps those edits honest.
-
-on:
-  push:
-    branches: [master]
-    paths:
-      - '.github/workflows/**'
-  pull_request:
-    branches: [master]
-    paths:
-      - '.github/workflows/**'
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  actionlint:
-    runs-on: ubuntu-latest
-    timeout-minutes: 5
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: rhysd/actionlint@393031adb9afb225ee52ae2ccd7a5af5525e03e8  # v1.7.11

.github/workflows/e2e.yml

@@ -12,61 +12,10 @@ on:
 permissions:
   contents: read
 
-# Cancel a superseded run when a newer commit lands on the same PR/branch.
-# PR number for pull_request events (fork-safe), github.ref fallback for
-# push/scheduled runs.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
 jobs:
-  jsonb-parity:
-    # Dedicated required guard for the JSONB double-encode bug-class (#2339).
-    # PGLite parses a double-encoded jsonb string silently, so this assertion can
-    # ONLY be made on real Postgres — a normal gated e2e file would skip without
-    # DATABASE_URL and let the bug ship green (as #2339 did). This job provisions
-    # Postgres and HARD-FAILS if DATABASE_URL is missing, so the guard can never
-    # silently skip.
-    name: JSONB parity (#2339 regression guard)
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    services:
-      postgres:
-        image: pgvector/pgvector:pg16
-        env:
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: postgres
-          POSTGRES_DB: gbrain_test
-        ports:
-          - 5432:5432
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: 1.3.13
-      - run: bun install
-      - name: Require DATABASE_URL (no silent skip)
-        env:
-          DATABASE_URL: postgresql://postgres:postgres@localhost:5432/gbrain_test
-        run: |
-          if [ -z "$DATABASE_URL" ]; then
-            echo "::error::DATABASE_URL must be set for the jsonb-parity job — the #2339 guard would silently skip (the exact failure PGLite hides). Failing the job." >&2
-            exit 1
-          fi
-      - name: Run JSONB double-encode parity tests on real Postgres
-        env:
-          DATABASE_URL: postgresql://postgres:postgres@localhost:5432/gbrain_test
-        run: bun test test/e2e/op-checkpoint-jsonb-parity.test.ts test/e2e/jsonb-roundtrip.test.ts
-
   tier1:
     name: Tier 1 (Mechanical)
     runs-on: ubuntu-latest
-    timeout-minutes: 20
     services:
       postgres:
         image: pgvector/pgvector:pg16
@@ -85,7 +34,7 @@ jobs:
       - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
       - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
         with:
-          bun-version: 1.3.13
+          bun-version: latest
       - run: bun install
       - name: Run Tier 1 E2E tests
         run: bun test test/e2e/mechanical.test.ts test/e2e/mcp.test.ts
@@ -100,7 +49,6 @@ jobs:
     # from repo/org secrets. Nightly + manual triggers still supported via
     # the workflow-level `on:` list.
     needs: tier1
-    timeout-minutes: 30
     services:
       postgres:
         image: pgvector/pgvector:pg16
@@ -119,25 +67,10 @@ jobs:
       - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
       - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
         with:
-          bun-version: 1.3.13
+          bun-version: latest
       - run: bun install
       - name: Install OpenClaw
-        # Bound + retry the install: a transient npm/registry stall here used to
-        # hang unbounded and (since the v0.42.50.0 job timeout) burn the entire
-        # 30m Tier 2 budget before failing — even though the install normally
-        # finishes in well under a minute. `timeout` kills a hung attempt fast;
-        # up to 3 attempts ride out a flaky registry. Step cap is a backstop.
-        timeout-minutes: 8
-        run: |
-          for attempt in 1 2 3; do
-            if timeout 120 npm install -g openclaw@2026.4.9; then
-              exit 0
-            fi
-            echo "::warning::openclaw install attempt $attempt failed or timed out; retrying in 10s" >&2
-            sleep 10
-          done
-          echo "::error::openclaw install failed after 3 attempts" >&2
-          exit 1
+        run: npm install -g openclaw@2026.4.9
       - name: Configure OpenClaw MCP
         run: |
           mkdir -p ~/.openclaw
@@ -155,13 +88,8 @@ jobs:
           }
           EOF
       - name: Run Tier 2 skill tests
-        run: bun test test/e2e/skills.test.ts test/e2e/zeroentropy-live.test.ts
+        run: bun test test/e2e/skills.test.ts
         env:
           DATABASE_URL: postgresql://postgres:postgres@localhost:5432/gbrain_test
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          # v0.33.3.0: ZE live API tests skip gracefully when this is unset,
-          # so forks without the secret stay green. The test exercises the
-          # zeroEntropyCompatFetch response-rewriter + URL rewrite + flexible
-          # dim handling + gateway.rerank against the real provider.
-          ZEROENTROPY_API_KEY: ${{ secrets.ZEROENTROPY_API_KEY }}

.github/workflows/heavy-tests.yml

@@ -1,89 +0,0 @@
-name: Heavy Tests
-
-# Heavy ops-shape tests under tests/heavy/. Cost minutes per run; NOT part
-# of default PR CI. Two triggers:
-#   - Nightly schedule (catches regressions within 24h of merge to master).
-#   - On-demand opt-in via PR label `heavy-tests` (slow loop kept off by default).
-#   - Manual workflow_dispatch for triage.
-#
-# See CLAUDE.md "tests/heavy/*.sh" entry and tests/heavy/README.md.
-
-on:
-  schedule:
-    - cron: '17 8 * * *'  # 08:17 UTC daily — staggered to avoid noisy slots
-  pull_request:
-    # `synchronize` + `reopened` fire on subsequent pushes / reopens — without
-    # them, a PR labeled `heavy-tests` would NEVER re-run heavy on later
-    # commits. The job-level `if:` below filters to PRs that still carry the
-    # label so we don't fan out on unrelated label changes.
-    types: [labeled, synchronize, reopened]
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-# When a PR gets the heavy-tests label, cancel any in-flight heavy-tests run on
-# the same ref so we only ever measure the latest commit.
-concurrency:
-  group: heavy-tests-${{ github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  heavy:
-    name: Heavy tests
-    # On pull_request: only run when the PR currently carries the `heavy-tests`
-    # label. Works for all three trigger types (labeled, synchronize, reopened)
-    # because `contains(labels.*.name, ...)` reads the live label set, not the
-    # event payload's `label.name` (which is only populated for `labeled`).
-    if: |
-      github.event_name != 'pull_request' ||
-      contains(github.event.pull_request.labels.*.name, 'heavy-tests')
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    services:
-      postgres:
-        image: pgvector/pgvector:pg16
-        env:
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: postgres
-          POSTGRES_DB: gbrain_test
-        ports:
-          - 5432:5432
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: 1.3.13
-      - run: bun install
-
-      - name: Run heavy tests
-        env:
-          DATABASE_URL: postgresql://postgres:postgres@localhost:5432/gbrain_test
-        run: bun run test:heavy
-
-      # The heavy runner writes per-script logs to ~/.gbrain/audit/ on every
-      # run. Upload those + the rss workload JSON on failure for triage
-      # without re-running locally.
-      #
-      # actions/upload-artifact runs as a node action — `~` is NOT expanded by
-      # the shell here. Stage logs into the workspace first, then upload from
-      # the stable workspace-relative path.
-      - name: Stage heavy-test logs into workspace
-        if: always()
-        run: |
-          mkdir -p heavy-artifacts
-          cp -r "$HOME/.gbrain/audit"/heavy-* heavy-artifacts/ 2>/dev/null || true
-          cp tests/heavy/rss-baseline.json heavy-artifacts/ 2>/dev/null || true
-      - name: Upload heavy-test artifacts
-        if: always()
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02  # v4
-        with:
-          name: heavy-tests-${{ github.run_id }}-${{ github.run_attempt }}
-          path: heavy-artifacts/
-          retention-days: 14
-          if-no-files-found: ignore

.github/workflows/osv-scanner.yml

@@ -1,33 +0,0 @@
-name: OSV-Scanner
-
-# Dependency vulnerability scan (#2182) via Google's official reusable
-# workflow. Runs weekly and on any PR that touches the dependency manifests.
-# Tokenless: needs zero secrets. Findings are reported in the job log and as
-# a SARIF artifact on the run; code-scanning upload is deliberately disabled
-# so the workflow stays read-only (no security-events: write).
-
-on:
-  pull_request:
-    branches: [master]
-    paths:
-      - 'bun.lock'
-      - 'package.json'
-  schedule:
-    - cron: '30 6 * * 1'  # weekly, Monday 06:30 UTC
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  osv-scan:
-    permissions:
-      actions: read
-      contents: read
-      # Required by the reusable workflow's own top-level permissions block —
-      # GitHub validates the caller grants a superset AT STARTUP, even with
-      # upload-sarif: false (nothing is actually uploaded; see #2117 upstream).
-      security-events: write
-    uses: google/osv-scanner-action/.github/workflows/osv-scanner-reusable.yml@9a498708959aeaef5ef730655706c5a1df1edbc2  # v2.3.8
-    with:
-      upload-sarif: false

.github/workflows/release.yml

@@ -19,23 +19,14 @@ jobs:
             target: bun-linux-x64
             artifact: gbrain-linux-x64
     runs-on: ${{ matrix.os }}
-    permissions:
-      contents: read
-      id-token: write      # for attest-build-provenance (Sigstore OIDC)
-      attestations: write  # for attest-build-provenance
     steps:
       - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
       - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
         with:
-          bun-version: 1.3.13
+          bun-version: latest
       - run: bun install
       - run: bun test
-      - run: bun run verify
       - run: bun build --compile --target=${{ matrix.target }} --outfile bin/${{ matrix.artifact }} src/cli.ts
-      - name: Attest build provenance
-        uses: actions/attest-build-provenance@0f67c3f4856b2e3261c31976d6725780e5e4c373  # v4.1.1
-        with:
-          subject-path: bin/${{ matrix.artifact }}
       - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02  # v4
         with:
           name: ${{ matrix.artifact }}

.github/workflows/semgrep.yml

@@ -1,36 +0,0 @@
-name: Semgrep
-
-# Static analysis (SAST) with Semgrep Community Edition (#2272). Tokenless:
-# uses the public registry rulesets, needs zero secrets. Findings print in
-# the job log; no code-scanning/SARIF upload by design (keeps permissions
-# read-only, no security-events: write).
-
-on:
-  pull_request:
-    branches: [master]
-  schedule:
-    - cron: '30 7 * * 1'  # weekly, Monday 07:30 UTC
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  semgrep:
-    runs-on: ubuntu-latest
-    timeout-minutes: 20
-    container:
-      image: semgrep/semgrep:1.170.0@sha256:c98f8829eea377274ee4b10656458b078b88232469b2ff913f091c2317347c9d
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      # Non-blocking initially (continue-on-error): the first runs establish a
-      # baseline without failing unrelated PRs. Graduation path: once the
-      # baseline findings are triaged (fixed or `# nosemgrep`'d), remove
-      # continue-on-error so new findings block PRs.
-      - name: Semgrep scan (report-only)
-        run: semgrep scan --config p/default --config p/typescript --error
-        continue-on-error: true

.github/workflows/test.yml

@@ -5,84 +5,13 @@ on:
     branches: [master]
   pull_request:
     branches: [master]
-  # Manual dispatch lets a local dev/agent offload the suite to GitHub's
-  # on-demand runners from ANY branch (see scripts/ship-remote-tests.sh).
-  # Frees a load-saturated local machine (e.g. many Conductor agents running
-  # their own bun-test suites at once — load avg 120 on 16 cores).
-  workflow_dispatch:
 
 permissions:
   contents: read
 
-# Cancel a superseded run when a newer commit lands on the same PR/branch.
-# Keyed on the PR number for pull_request events (unique per PR, so two PRs
-# from forks sharing a branch name don't cancel each other) and falls back to
-# github.ref for push/scheduled runs. Mirrors heavy-tests.yml; frees runners
-# and stops a stale-SHA run from reporting a flaky failure on an obsolete commit.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
 jobs:
-  # ──────────────────────────────────────────────────────────────────────
-  # cache-check: runs first, computes the content hash of every tracked
-  # file EXCEPT the deny-list (CHANGELOG.md, README.md, docs/**/*.md, etc.
-  # — see scripts/ci-cache-hash.sh for the full list). Looks up
-  # `ci-pass-<hash>` in actions/cache; if hit, the test matrix + verify
-  # + serial jobs all skip and test-status reports green immediately.
-  # If miss, the full suite runs and cache-write seals it on success.
-  #
-  # Hit rate covers re-pushes (same SHA twice), branch rebases that
-  # don't touch tracked code, and any branch update that touches only
-  # the deny-listed doc files.
-  # ──────────────────────────────────────────────────────────────────────
-  cache-check:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    outputs:
-      hit: ${{ steps.lookup.outputs.cache-hit }}
-      hash: ${{ steps.compute.outputs.hash }}
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - name: Compute content hash
-        id: compute
-        run: |
-          # --verbose writes the "X/Y files in hash" diagnostic to stderr;
-          # stdout carries the 16-char hash. Capture both.
-          HASH=$(bash scripts/ci-cache-hash.sh --verbose 2>/tmp/cache-diag)
-          cat /tmp/cache-diag
-          echo "Computed cache hash: $HASH"
-          echo "hash=$HASH" >> "$GITHUB_OUTPUT"
-      - name: Lookup actions/cache for ci-pass-<hash>
-        id: lookup
-        uses: actions/cache/restore@5a3ec84eff668545956fd18022155c47e93e2684  # v4.2.3
-        with:
-          key: ci-pass-${{ steps.compute.outputs.hash }}
-          path: .ci-cache-marker
-          # `lookup-only: true` means we only probe whether the cache
-          # entry exists — we don't download it (the marker contents
-          # don't matter, only the key match does). `cache-hit` returns
-          # true only on EXACT key match (per actions/cache docs); a
-          # restore-keys prefix fallback would set cache-hit=false, so
-          # it's deliberately omitted here. Cross-branch scoping works
-          # naturally: PR branches can read default-branch (master)
-          # cache entries via exact key match when the content hash
-          # matches, which happens whenever the tree is doc-only
-          # different from a green master run.
-          lookup-only: true
-      - name: Cache status
-        run: |
-          if [ "${{ steps.lookup.outputs.cache-hit }}" = "true" ]; then
-            echo "✓ cache HIT for hash ${{ steps.compute.outputs.hash }} — test jobs will skip"
-          else
-            echo "✗ cache MISS for hash ${{ steps.compute.outputs.hash }} — full suite will run"
-          fi
-
   gitleaks:
-    needs: cache-check
-    if: needs.cache-check.outputs.hit != 'true'
     runs-on: ubuntu-latest
-    timeout-minutes: 10
     steps:
       - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
         with:
@@ -91,196 +20,23 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-  verify:
-    # Pre-test gates: privacy/jsonb/source-id/etc + typecheck + admin-build.
-    # Lives in its own runner so the matrix shards aren't carrying ~2-3min
-    # of verify work in addition to their test files (the old shape stuffed
-    # this into `test (1)` via `if: matrix.shard == 1`, which made shard 1
-    # the slowest matrix worker). scripts/run-verify-parallel.sh fans out
-    # the 20 checks via & + wait (~5s vs ~15-25s sequential).
-    needs: cache-check
-    if: needs.cache-check.outputs.hit != 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 12
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: 1.3.13
-      - uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684  # v4.2.3
-        with:
-          path: ~/.bun/install/cache
-          key: bun-cache-${{ runner.os }}-${{ hashFiles('bun.lock') }}
-      - run: bun install
-      - run: bun run verify
-
-  serial-tests:
-    # *.serial.test.ts at --max-concurrency=1. Lives in its own runner so
-    # the matrix shards aren't carrying the serial-pass tail (the old shape
-    # stuffed this into `test (1)` after the matrix work, which compounded
-    # shard 1's overload).
-    needs: cache-check
-    if: needs.cache-check.outputs.hit != 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: 1.3.13
-      - uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684  # v4.2.3
-        with:
-          path: ~/.bun/install/cache
-          key: bun-cache-${{ runner.os }}-${{ hashFiles('bun.lock') }}
-      - run: bun install
-      - run: bun run test:serial
-
-  slow-eval-longmemeval:
-    # Dedicated runner for the LongMemEval end-to-end test file. The file
-    # was originally 359s. TODO #1 (engine-sharing in runEvalLongMemEval
-    # via RunOpts.engine) cut it to ~200s by amortizing PGLite cold-create
-    # across all 13 runEvalLongMemEval calls in one beforeAll-shared brain.
-    # Pulled out of the matrix (see scripts/test-shard.sh) so a single 200s
-    # atom doesn't dominate a shard's wallclock. Companion file
-    # test/eval-longmemeval.slow.test.ts (the pure-bucket half) stays in
-    # the matrix because it's light (~42s).
-    needs: cache-check
-    if: needs.cache-check.outputs.hit != 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 12
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: 1.3.13
-      - uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684  # v4.2.3
-        with:
-          path: ~/.bun/install/cache
-          key: bun-cache-${{ runner.os }}-${{ hashFiles('bun.lock') }}
-      - run: bun install
-      - run: bun test test/eval-longmemeval-e2e.slow.test.ts --timeout=60000
-
-  slow-entity-resolve-perf:
-    # Dedicated runner for the entity-resolve perf test (~159s, single perf
-    # describe with one test that builds 5000+ pages and asserts the NEW
-    # tryPrefixExpansion shape is 5x faster than the OLD shape — not
-    # subdivisible without weakening the perf guarantee). Pulled out of the
-    # matrix (see scripts/test-shard.sh) so a single 159s atom doesn't
-    # dominate a shard's wallclock. Runs in parallel with the matrix.
-    needs: cache-check
-    if: needs.cache-check.outputs.hit != 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 12
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: 1.3.13
-      - uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684  # v4.2.3
-        with:
-          path: ~/.bun/install/cache
-          key: bun-cache-${{ runner.os }}-${{ hashFiles('bun.lock') }}
-      - run: bun install
-      - run: bun test test/entity-resolve-perf.slow.test.ts --timeout=300000
-
   test:
-    # Pure matrix shard — no verify, no serial. Each shard runs its slice
-    # of the unit test set under one `bun test` invocation.
-    #
-    # 10 shards (was 6) drops per-shard total from 532s → 287s. With the two
-    # dedicated jobs (slow-eval-longmemeval, slow-entity-resolve-perf) also
-    # pulled out, the matrix is bounded by ~287s ≈ 4.8 min. Total CI ≈ max
-    # of matrix + slow-eval (~3.3 min after engine-sharing in TODO #1) +
-    # slow-entity-resolve-perf (~2.6 min) ≈ 4.8 min.
-    #
-    # Concurrency budget: 10 shards + verify + serial + slow-eval +
-    # slow-entity-resolve-perf + gitleaks + cache-check + cache-write +
-    # test-status = ~18 jobs × 2 concurrent PRs = 36. GitHub free-tier
-    # caps at ~20 concurrent jobs, so multi-PR days will see some queue
-    # pressure. Single-PR runs are unaffected.
-    #
-    # Partition policy is weight-aware LPT bin-packing via scripts/sharding.ts
-    # (replaces FNV-1a path hash). Weights live in scripts/test-weights.json,
-    # mined from real CI logs via scripts/mine-shard-weights.ts. Missing
-    # weights fall back to corpus median — new test files work immediately.
-    needs: cache-check
-    if: needs.cache-check.outputs.hit != 'true'
+    # ubuntu-latest is free 2-core/7GB. Larger runners (16-cores, etc.) require
+    # a provisioned runner pool in repo settings. Falling back to default keeps
+    # the matrix shard speedup (~5-6x via parallelism) at zero cost.
     runs-on: ubuntu-latest
-    timeout-minutes: 15
     strategy:
       fail-fast: false
       matrix:
-        shard: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        shard: [1, 2, 3, 4]
     steps:
       - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
       - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
         with:
-          bun-version: 1.3.13
-      - uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684  # v4.2.3
-        with:
-          path: ~/.bun/install/cache
-          key: bun-cache-${{ runner.os }}-${{ hashFiles('bun.lock') }}
+          bun-version: latest
       - run: bun install
-      - name: Run test shard ${{ matrix.shard }}/10
-        run: scripts/test-shard.sh ${{ matrix.shard }} 10
-
-  # ──────────────────────────────────────────────────────────────────────
-  # cache-write: ONLY runs when every gated job succeeded. Writes the
-  # cache entry under `ci-pass-<hash>` so future runs at the same hash
-  # hit cache. Codex's load-bearing correctness point: writing the
-  # cache before the matrix completes would permanently bless bad states
-  # (a future run at the same hash would skip tests because of a cache
-  # entry written when tests hadn't actually passed).
-  # ──────────────────────────────────────────────────────────────────────
-  cache-write:
-    needs: [cache-check, gitleaks, verify, serial-tests, slow-eval-longmemeval, slow-entity-resolve-perf, test]
-    if: success() && needs.cache-check.outputs.hit != 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 5
-    steps:
-      - name: Create cache marker
-        run: |
-          mkdir -p .ci-cache-marker
-          echo "${{ needs.cache-check.outputs.hash }}" > .ci-cache-marker/hash
-          echo "$GITHUB_SHA" > .ci-cache-marker/sha
-          echo "$GITHUB_REF" > .ci-cache-marker/ref
-      - uses: actions/cache/save@5a3ec84eff668545956fd18022155c47e93e2684  # v4.2.3
-        with:
-          key: ci-pass-${{ needs.cache-check.outputs.hash }}
-          path: .ci-cache-marker
-
-  # ──────────────────────────────────────────────────────────────────────
-  # test-status: the single user-visible "did CI pass?" check.
-  # Runs always (if: always()), succeeds when EITHER cache-check.hit==true
-  # OR all gated jobs (gitleaks, verify, serial-tests, test) succeeded.
-  # Branch protection (when configured) gates on this single job name.
-  # ──────────────────────────────────────────────────────────────────────
-  test-status:
-    needs: [cache-check, gitleaks, verify, serial-tests, slow-eval-longmemeval, slow-entity-resolve-perf, test]
-    if: always()
-    runs-on: ubuntu-latest
-    timeout-minutes: 5
-    steps:
-      - name: Aggregate result
-        run: |
-          HIT="${{ needs.cache-check.outputs.hit }}"
-          GITLEAKS="${{ needs.gitleaks.result }}"
-          VERIFY="${{ needs.verify.result }}"
-          SERIAL="${{ needs.serial-tests.result }}"
-          SLOW_EVAL="${{ needs.slow-eval-longmemeval.result }}"
-          SLOW_PERF="${{ needs.slow-entity-resolve-perf.result }}"
-          TEST="${{ needs.test.result }}"
-          echo "cache-check.hit=$HIT"
-          echo "gitleaks=$GITLEAKS  verify=$VERIFY  serial-tests=$SERIAL  slow-eval-longmemeval=$SLOW_EVAL  slow-entity-resolve-perf=$SLOW_PERF  test=$TEST"
-          if [ "$HIT" = "true" ]; then
-            echo "✓ cache HIT for hash ${{ needs.cache-check.outputs.hash }} — CI green"
-            exit 0
-          fi
-          # Cache miss: every gated job must have succeeded.
-          for r in "$GITLEAKS" "$VERIFY" "$SERIAL" "$SLOW_EVAL" "$SLOW_PERF" "$TEST"; do
-            if [ "$r" != "success" ]; then
-              echo "✗ gated job did not succeed (got $r) — CI fail"
-              exit 1
-            fi
-          done
-          echo "✓ all gated jobs succeeded — CI green"
+      - name: Pre-test gates (shard 1 only — they're not test files)
+        if: matrix.shard == 1
+        run: bun run verify
+      - name: Run test shard ${{ matrix.shard }}/4
+        run: scripts/test-shard.sh ${{ matrix.shard }} 4

.gitignore

@@ -38,6 +38,3 @@ export/
 # Tier 3 PGLite snapshot fixture (built on demand by build:pglite-snapshot)
 test/fixtures/pglite-snapshot.tar
 test/fixtures/pglite-snapshot.version
-
-# Private brain reports — never check these in (per CLAUDE.md privacy rule)
-reports/network-intelligence/

AGENTS.md

@@ -6,38 +6,18 @@ start here.
 
 ## Install (5 min)
 
-1. Install gbrain via Bun (the canonical path):
-   ```bash
-   curl -fsSL https://bun.sh/install | bash
-   export PATH="$HOME/.bun/bin:$PATH"
-   bun install -g github:garrytan/gbrain
-   ```
-   If `bun install -g` aborts or `gbrain doctor` reports `schema_version: 0`,
-   the CLI prints a recovery hint pointing at [#218](https://github.com/garrytan/gbrain/issues/218).
-   Run `gbrain apply-migrations --yes` to recover, or fall back to the
-   deterministic install: `git clone https://github.com/garrytan/gbrain.git ~/gbrain && cd ~/gbrain && bun install && bun link`.
-2. Init the brain: `gbrain init` (defaults to PGLite, zero-config). For 1000+ files or
+1. Clone: `git clone https://github.com/garrytan/gbrain ~/gbrain && cd ~/gbrain`
+2. Install: `bun install`
+3. Init the brain: `gbrain init` (defaults to PGLite, zero-config). For 1000+ files or
    multi-machine sync, init suggests Postgres + pgvector via Supabase.
-3. **STOP — ask the user about search mode.** `gbrain init` auto-applied a
-   default but printed a 9-cell cost matrix (mode × downstream model)
-   preceded by `[AGENT]` markers. You MUST relay the matrix to the operator
-   and confirm their choice before continuing. Cost spread between corners
-   is 25x — silent acceptance is the wrong default. See
-   [`./INSTALL_FOR_AGENTS.md`](./INSTALL_FOR_AGENTS.md) Step 3.5 for the
-   exact ask-the-user protocol. Same banner fires on `gbrain post-upgrade`
-   for existing users (search modes were added in v0.32.3).
 4. Read [`./INSTALL_FOR_AGENTS.md`](./INSTALL_FOR_AGENTS.md) for the full 9-step flow
    (API keys, identity, cron, verification).
 
 ## Read this order
 
 1. `./AGENTS.md` (this file) — install + operating protocol.
-2. [`./CLAUDE.md`](./CLAUDE.md) — orientation + resolver: architecture, cross-cutting
-   invariants, the reference map, inline ship rules. It routes to on-demand detail docs:
-   [`./docs/architecture/KEY_FILES.md`](./docs/architecture/KEY_FILES.md) (per-file index —
-   read a file's entry before editing it), [`./docs/TESTING.md`](./docs/TESTING.md) (test
-   tiers + isolation lint + E2E lifecycle), and
-   [`./docs/architecture/thin-client.md`](./docs/architecture/thin-client.md) (remote-MCP seam).
+2. [`./CLAUDE.md`](./CLAUDE.md) — architecture reference, key files, trust boundaries,
+   test layout.
 3. [`./docs/architecture/brains-and-sources.md`](./docs/architecture/brains-and-sources.md)
    — the two-axis mental model (brain = which DB, source = which repo in the DB). Every
    query routes on both axes. Read before writing anything that touches brain ops.
@@ -61,42 +41,13 @@ writing or reviewing an operation, consult `src/core/operations.ts` for the cont
   [`docs/mcp/DEPLOY.md`](./docs/mcp/DEPLOY.md).
 - **Debug:** [`docs/GBRAIN_VERIFY.md`](./docs/GBRAIN_VERIFY.md),
   [`docs/guides/minions-fix.md`](./docs/guides/minions-fix.md), `gbrain doctor --fix`.
-- **Migrate / upgrade:** `gbrain upgrade` (binary self-update + schema migrations + post-upgrade prompts),
-  [`docs/UPGRADING_DOWNSTREAM_AGENTS.md`](./docs/UPGRADING_DOWNSTREAM_AGENTS.md),
-  [`skills/migrations/`](./skills/migrations/), `gbrain apply-migrations --yes` (manual schema-only).
+- **Migrate:** [`docs/UPGRADING_DOWNSTREAM_AGENTS.md`](./docs/UPGRADING_DOWNSTREAM_AGENTS.md),
+  [`skills/migrations/`](./skills/migrations/), `gbrain apply-migrations`.
 - **Eval retrieval changes:** capture is off by default. To benchmark a
   retrieval change against real captured queries, set
   `GBRAIN_CONTRIBUTOR_MODE=1`, then `gbrain eval export --since 7d > base.ndjson`
-  and `gbrain eval replay --against base.ndjson`. For public benchmark
-  coverage (LongMemEval, ground-truth scoring), `gbrain eval longmemeval
-  <dataset.jsonl>` (v0.28.8) runs against an isolated in-memory PGLite
-  per question — your `~/.gbrain` is never opened. Full guide:
+  and `gbrain eval replay --against base.ndjson`. Full guide:
   [`docs/eval-bench.md`](./docs/eval-bench.md).
-- **Drive the brain to a target health score (v0.36.4.0):** the one-command
-  loop. `gbrain doctor --remediation-plan --json` previews what would be
-  fixed; `gbrain doctor --remediate --yes --target-score 90 --max-usd 5`
-  walks a dependency-ordered plan (sync before extract, embed after
-  consolidate), re-checking score between every step, refusing to spend
-  past the cost cap. Empty brains (no entity pages) or unconfigured embedding
-  keys hit a `max_reachable_score` ceiling and bail with what's missing.
-  Three phase handlers (synthesize / patterns / consolidate) are
-  PROTECTED — only trusted local callers can submit them; MCP cannot.
-  Reference: [`docs/architecture/topologies.md`](./docs/architecture/topologies.md)
-  and the CHANGELOG entry for v0.36.4.0.
-- **Track a founder/company over time (v0.35.7):** when an entity has
-  typed metric claims in its `## Facts` fence (`metric: mrr`, `value: 50000`,
-  `unit: USD`, `period: monthly` columns), run
-  `gbrain eval trajectory <entity-slug>` for the chronological history
-  with regressions auto-flagged, or `gbrain founder scorecard <entity-slug>`
-  for a four-signal JSON rollup (claim_accuracy / consistency /
-  growth_trajectory / red_flags). MCP op `find_trajectory` exposes the
-  same data — read scope, visibility-filtered for remote callers. **v0.40.2.0:**
-  `gbrain think` now uses this substrate automatically on temporal /
-  knowledge_update intent (default ON; flip `think.trajectory_enabled=false`
-  to opt out). Migration v82 added `facts.event_type` so non-metric event
-  rows (`meeting`, `job_change`, `location_change`) ride through the same
-  pipeline; pass `kind: 'event'` or `'all'` to `find_trajectory` to query
-  them.
 - **Everything else:** [`./llms.txt`](./llms.txt) is the full documentation map.
   [`./llms-full.txt`](./llms-full.txt) is the same map with core docs inlined for
   single-fetch ingestion.
@@ -104,18 +55,15 @@ writing or reviewing an operation, consult `src/core/operations.ts` for the cont
 ## Before shipping
 
 Easiest path: `bun run ci:local` runs the full CI gate inside Docker (gitleaks,
-guards + typecheck, then 4-shard parallel unit + E2E against four pgvector
-containers plus a transaction-mode PgBouncer; unit phase keeps `DATABASE_URL`
-unset) and tears down. Use `bun run ci:local:diff` for the
+unit tests with `DATABASE_URL` unset, then all 29 E2E files sequentially against a
+fresh pgvector container) and tears down. Use `bun run ci:local:diff` for the
 diff-aware subset during fast iteration on a focused branch. Requires Docker
 (Docker Desktop / OrbStack / Colima) and `gitleaks` (`brew install gitleaks`).
 
 Manual path: `bun test` plus the E2E lifecycle described in `./CLAUDE.md` (spin
 up the test Postgres container, run `bun run test:e2e`, tear it down).
 
-Ship via the `/ship` skill, not by hand. The full release + contributor process
-(CHANGELOG voice, version-locations sync, PR conventions, community-PR-wave) lives in
-[`./docs/RELEASING.md`](./docs/RELEASING.md); read it before shipping.
+Ship via the `/ship` skill, not by hand.
 
 ## Privacy
 

CONTRIBUTING.md

@@ -57,7 +57,7 @@ bun run test                      # parallel 8-shard fan-out + serial post-pass
 bun test test/markdown.test.ts    # specific unit test
 
 # Pre-push gate (matches what CI runs on shard 1 + typecheck)
-bun run verify                    # privacy + jsonb + progress + test-isolation + wasm + admin-build + resolver + typecheck
+bun run verify                    # privacy + jsonb + progress + test-isolation + wasm + admin-build + typecheck
 
 # Pre-merge sanity (everything CI runs)
 bun run test:full                 # verify + parallel unit + slow + smart e2e
@@ -81,12 +81,9 @@ patterns (`scripts/check-jsonb-pattern.sh`), `\r` progress bleed to stdout
 (`scripts/check-progress-to-stdout.sh`), test-isolation rule violations
 (`scripts/check-test-isolation.sh` — see "Writing tests that survive the parallel
 loop" below), silent fallback to recursive chunking in the compiled binary
-(`scripts/check-wasm-embedded.sh`), stale admin-dashboard build artifacts
-(`scripts/check-admin-build.sh`), and resolver drift on bundled skills
-(`bun run check:resolver` — strict-mode `check-resolvable` that exit-1s on any
-warning, added in v0.41.14.0 to catch SKILL.md frontmatter ↔ RESOLVER.md drift
-before merge). `bun run check:all` runs the full historical sweep including the
-trailing-newline and exports-count checks.
+(`scripts/check-wasm-embedded.sh`), and stale admin-dashboard build artifacts
+(`scripts/check-admin-build.sh`). `bun run check:all` runs the full historical
+sweep including the trailing-newline and exports-count checks.
 
 ### Writing tests that survive the parallel loop
 
@@ -193,7 +190,7 @@ See `docs/ENGINES.md` for the full guide. In short:
 3. Run the test suite against your engine
 4. Document in `docs/`
 
-The original SQLite engine plan was superseded by PGLite (embedded Postgres 17 via WASM), which uses the same SQL dialect as Postgres and eliminates the need for a separate FTS5/sqlite-vss translation layer. See [`docs/ENGINES.md`](docs/ENGINES.md) for the engine architecture and the rationale.
+The SQLite engine is designed and ready for implementation. See `docs/SQLITE_ENGINE.md`.
 
 ## CONTRIBUTOR_MODE — turn on the dev loop
 
@@ -273,16 +270,6 @@ without captured data can still replay), and cost considerations. The
 NDJSON wire format is documented in
 [`docs/eval-capture.md`](./docs/eval-capture.md).
 
-For public benchmark coverage on top of replay, `gbrain eval longmemeval
-<dataset.jsonl>` (v0.28.1) runs LongMemEval against gbrain's hybrid
-retrieval. One in-memory PGLite per question, runtime-enumerated
-`TRUNCATE` between questions, ground-truth scoring via LongMemEval's
-published `evaluate_qa.py`. Use it alongside replay when changes affect
-retrieval quality on long-context conversational data — replay catches
-regressions on YOUR queries, LongMemEval catches them on a public set the
-benchmark community already cites. See the "Public benchmarks: LongMemEval"
-section in [`docs/eval-bench.md`](./docs/eval-bench.md).
-
 ## Welcome PRs
 
 - SQLite engine implementation

DESIGN.md

@@ -1,148 +0,0 @@
-# DESIGN.md
-
-The design system source of truth for gbrain. Born from the de facto tokens
-that landed in `admin/src/index.css` during the v0.26.0 admin SPA work and
-formalized during the v0.36.1.0 Hindsight calibration wave's design review.
-
-This doc is the calibration target for `/plan-design-review` and `/design-review`.
-When a question is "does this UI fit the system?", the answer is here.
-
-## Voice
-
-GBrain talks like a smart friend who knows your past, not a clinical scoring
-system. Every user-facing string passes through this filter:
-
-- Second person, contractions allowed.
-- Grounded in concrete data the user can verify ("2 of 3 missed" beats
-  "Brier 0.31").
-- Never preachy. Never "we recommend." Never "according to your data."
-- Short. Under 25 words for narrative; under one line for status.
-- Numbers grounded in real outcomes, never abstract metrics without
-  translation.
-
-Five surfaces use this voice (v0.36.1.0+):
-`pattern_statement`, `nudge`, `forecast_blurb`, `dashboard_caption`,
-`morning_pulse`. All five pass through `gateVoice()` in
-`src/core/calibration/voice-gate.ts` with mode-specific rubrics. A Haiku
-judge rejects academic-sounding candidates; up to 2 regens; then fall
-back to a hand-written template from `src/core/calibration/templates.ts`.
-
-## Color tokens
-
-CSS variables in `admin/src/index.css`. SVG renderer inlines literals
-matching these tokens (`src/core/calibration/svg-renderer.ts`).
-
-| Token              | Value     | Use                                       |
-|--------------------|-----------|-------------------------------------------|
-| `--bg-primary`     | `#0a0a0f` | Page background                           |
-| `--bg-secondary`   | `#14141f` | Sidebar, cards                            |
-| `--bg-tertiary`    | `#1e1e2e` | Subtle surfaces, borders                  |
-| `--text-primary`   | `#e0e0e0` | Body text                                 |
-| `--text-secondary` | `#888`    | Headings, labels                          |
-| `--text-muted`     | `#777`    | Tertiary text — TD2 bumped from #555 for WCAG AA contrast (~5.5:1) |
-| `--accent`         | `#3b82f6` | Active states, links, primary CTAs        |
-| `--success`        | `#22c55e` | Healthy / ok status                       |
-| `--warning`        | `#f59e0b` | Doctor warnings                           |
-| `--error`          | `#ef4444` | Failures, destructive confirmations       |
-
-Dark theme is the only theme. No light mode toggle planned — admin is an
-operator tool, not a marketing surface. Users live in the terminal with a
-dark theme already.
-
-WCAG contrast:
-- Body text (#e0e0e0 on #0a0a0f) → ~14:1, AAA
-- Muted text (#777 on #0a0a0f) → ~5.5:1, AA (was 4.0 / fail before TD2)
-- Accent links (#3b82f6 on #0a0a0f) → ~5.7:1, AA
-
-## Typography
-
-| Variable           | Value                       | Use                            |
-|--------------------|-----------------------------|---------------------------------|
-| `--font-sans`      | `Inter, system-ui, sans-serif` | UI text, headings, body         |
-| `--font-mono`      | `JetBrains Mono, monospace` | Numbers, slugs, code, terminal-ish data |
-
-Type scale (de facto, not formalized yet):
-- 18px: sidebar logo / page title
-- 14px: body
-- 13px: nav items
-- 12px: chart captions, secondary labels
-- 11px: tertiary labels in dense charts
-
-Numbers in tables and metrics use JetBrains Mono so column alignment is
-mechanical. Avoid mixing Inter and JetBrains Mono in the same line.
-
-## Spacing scale
-
-4 / 8 / 16 / 24 / 32px. Linear-app-style density: 24-32px between major
-sections, 16px between row groups, 8px within a row. The Calibration tab
-(approved variant-B mockup) is the canonical example.
-
-## Layout
-
-- Sidebar 200px on the left. Active item gets a 3px left-border in `--accent`.
-- Main content area uses the remaining width.
-- Max content width: 720px for text-heavy pages (Calibration), 960px for
-  data tables (Request Log).
-- No 3-column feature grids. No icons in colored circles. No decorative blobs.
-- Cards earn their existence — heading + content works without a card frame
-  in most cases.
-
-## Charts
-
-Server-rendered SVG via `src/core/calibration/svg-renderer.ts`. Pure
-functions: data → SVG string. No DOM, no React component, no chart library.
-
-XSS posture: server-side `escapeXml()` on every caller-controlled string.
-Numeric inputs `.toFixed()`-coerced. Admin SPA renders via
-`<TrustedSVG>` wrapper with `dangerouslySetInnerHTML`. Endpoint gated by
-`requireAdmin` middleware.
-
-Why server-rendered SVG (per D23):
-- Chart logic stays close to the data math.
-- Zero new client-side chart-library dep.
-- SVG is accessible (text labels), scalable, copy-paste-friendly to PR
-  descriptions and docs.
-- Sets the precedent for future admin charts (contradictions trend, takes
-  scorecard, etc.).
-
-Four chart renderers in v0.36.1.0:
-- `renderBrierTrend({ series })` — sparkline + baseline reference at 0.25
-- `renderDomainBars({ bars })` — horizontal accuracy bars
-- `renderAbandonedThreadsCard(threads)` — text rows + "revisit now" links
-- `renderPatternStatementsCard(statements)` — clickable drill-down anchors
-
-## Interaction patterns
-
-- Keyboard navigation is REQUIRED for all CLI interaction surfaces. The
-  propose-queue review uses J/K/space/u/q shortcuts (gmail-style).
-- Loading states: "Loading...". Don't show spinners on sub-200ms operations.
-- Empty states ARE features: warmth + primary action + context. Cold-brain
-  Calibration page tells the user EXACTLY how to build a profile, not
-  "no data available."
-- Error states: name what failed + name the next step. Never "an error
-  occurred — please try again."
-
-## What's NOT here yet (v0.37+ roadmap)
-
-- Type scale formalization (current values are de facto, not enforced)
-- Animation tokens (admin SPA has zero animations on purpose; v0.37 may
-  add subtle progress / loading transitions)
-- Print stylesheet
-- Light mode (NOT planned — see "Dark theme is the only theme" above)
-- Component library extraction (the React components live inline in admin/src/pages/;
-  no `<Button>` / `<Card>` abstraction layer yet)
-
-## How to use this document
-
-When adding a new UI surface to gbrain:
-
-1. Pick existing tokens before introducing new ones. New tokens go through
-   `/plan-design-review`.
-2. Match the voice rules. Run candidates through `gateVoice()` before
-   shipping any user-facing string in the calibration surfaces.
-3. Match the spacing scale and density. Linear-calm-clarity over
-   dashboard-card-mosaic.
-4. Match the typography: Inter for UI, JetBrains Mono for numbers.
-
-When updating this document: it's a living target, not a frozen spec.
-Major changes go through `/plan-design-review` to keep the system coherent.

INSTALL_FOR_AGENTS.md

@@ -16,43 +16,32 @@ If you fetched this file by URL without cloning yet, the companion files live at
 
 ## Step 1: Install GBrain
 
-Default path (Bun is required — gbrain is a Bun + TypeScript runtime):
-
 ```bash
+git clone https://github.com/garrytan/gbrain.git ~/gbrain && cd ~/gbrain
 curl -fsSL https://bun.sh/install | bash
 export PATH="$HOME/.bun/bin:$PATH"
-bun install -g github:garrytan/gbrain
+bun install && bun link
 ```
 
 Verify: `gbrain --version` should print a version number. If `gbrain` is not found,
 restart the shell or add the PATH export to the shell profile.
 
-> **If `bun install -g` aborts or `gbrain doctor` reports `schema_version: 0`** (Bun
-> occasionally blocks the top-level postinstall hook on global installs, so schema
-> migrations don't run automatically), the CLI prints a recovery hint pointing at
-> [#218](https://github.com/garrytan/gbrain/issues/218). Run `gbrain apply-migrations --yes`
-> to recover. If that doesn't work, fall back to the deterministic install path:
->
-> ```bash
-> git clone https://github.com/garrytan/gbrain.git ~/gbrain && cd ~/gbrain
-> bun install && bun link
-> ```
+> **Do NOT use `bun install -g github:garrytan/gbrain`.** Bun blocks the top-level
+> postinstall hook on global installs, so schema migrations never run and the CLI
+> aborts with `Aborted()` when it opens PGLite. Use the `git clone + bun link` path
+> above. Tracking issue: [#218](https://github.com/garrytan/gbrain/issues/218).
 
 ## Step 2: API Keys
 
-Ask the user for these. gbrain defaults to the ZeroEntropy embedding + reranker stack
-(as of v0.36.2.0); OpenAI/Voyage are still supported as fallbacks via `gbrain config
-set embedding_model <provider:model>`.
+Ask the user for these:
 
 ```bash
-export ZEROENTROPY_API_KEY=ze-...     # default embedding + reranker (v0.36.2.0+)
-export OPENAI_API_KEY=sk-...          # fallback for vector search; also used for chat models
-export ANTHROPIC_API_KEY=sk-ant-...   # optional, improves search quality via query expansion
+export OPENAI_API_KEY=sk-...          # required for vector search
+export ANTHROPIC_API_KEY=sk-ant-...   # optional, improves search quality
 ```
 
-Save to shell profile or `.env`. Keys are picked up by `gbrain config set` automatically
-or can be stored in `~/.gbrain/config.json` (file plane). Without any embedding provider,
-keyword search still works. Without Anthropic, search works but skips query expansion.
+Save to shell profile or `.env`. Without OpenAI, keyword search still works.
+Without Anthropic, search works but skips query expansion.
 
 ## Step 3: Create the Brain
 
@@ -72,63 +61,6 @@ Read `~/gbrain/docs/GBRAIN_RECOMMENDED_SCHEMA.md` and set up the MECE directory
 structure (people/, companies/, concepts/, etc.) inside the user's brain repo,
 NOT inside ~/gbrain.
 
-## Step 3.5: Confirm search mode with the user (DO NOT SKIP)
-
-`gbrain init` auto-applied a default search mode (`tokenmax` unless your subagent
-tier is Haiku-class or no OpenAI key is configured). The init output included the
-cost matrix below preceded by `[AGENT]` markers. You must NOT silently accept the
-default. Stop and ask the operator.
-
-**Present this matrix verbatim:**
-
-```
-Per-query cost @ 10K queries/mo (typical single-user volume):
-
-                  Haiku 4.5     Sonnet 4.6    Opus 4.7
-                  ($1/M)        ($3/M)        ($5/M)
-  conservative    $40/mo        $120/mo       $200/mo
-  balanced        $100/mo       $300/mo       $500/mo
-  tokenmax        $200/mo       $600/mo       $1,000/mo
-
-(scales linearly: ×10 for 100K/mo, ÷10 for 1K. 25x corner-to-corner spread.
- Natural diagonal pairings — cheap/cheap → frontier/frontier — span ~4x.)
-```
-
-**Ask the operator (paraphrase if needed):**
-
-> Your gbrain just installed with search mode `<auto-applied default>`. This is
-> a one-time setup decision that controls retrieval payload size. Which mode
-> do you want?
->
->   1) conservative — tight 4K budget, no LLM expansion, 10 chunks max.
->      Best for Haiku subagents, cost-sensitive setups, high-volume loops.
->
->   2) balanced — 12K budget, no expansion, 25 chunks. Sonnet-tier sweet spot.
->
->   3) tokenmax (recommended default — preserves v0.31.x retrieval shape) —
->      no budget, LLM expansion ON, 50 chunks. Best for Opus/frontier models.
->
-> Cost depends on BOTH the mode AND the downstream model you run. See the
-> matrix above for the 9-cell breakdown.
-
-If the operator picks a non-default mode, run:
-```bash
-gbrain config set search.mode <mode>
-```
-
-If they pick tokenmax AND want to preserve the literal v0.31.x default
-(limit=20 instead of tokenmax's 50), also run:
-```bash
-gbrain config set search.searchLimit 20
-```
-
-Verify the choice with `gbrain search modes` before continuing.
-
-**Why this matters:** the cost spread between corners of the matrix is 25x.
-An agent that silently accepts the default and starts running queries against
-a user who didn't expect tokenmax-class context loads can rack up surprise
-spend. Confirm before continuing.
-
 ## Step 4: Import and Index
 
 ```bash
@@ -161,49 +93,10 @@ After this step:
 If a user has a very large brain (>10K pages), `extract --source db` is idempotent
 and supports `--since YYYY-MM-DD` for incremental runs.
 
-### Obsidian-style bare wikilinks (opt-in)
-
-If the user imported an Obsidian or Notion vault that uses **bare** `[[note-name]]`
-wikilinks — where `[[struktura]]` written in one folder means the page that lives
-at `projects/struktura.md` in another — GBrain does NOT connect those by default.
-Out of the box it only resolves path-qualified refs like `[[projects/struktura]]`,
-so a vault full of bare links shows up as a thin, broken graph. Turn on basename
-resolution so the cross-folder links connect:
-
-```bash
-gbrain config set link_resolution.global_basename true
-gbrain extract links --source db          # re-run so the new edges land
-```
-
-`gbrain doctor` surfaces a `link_resolution_opportunity` hint with the exact count
-("47 of 60 bare wikilinks would resolve") so you know whether it's worth enabling
-before you flip it. When a bare name matches more than one page (`[[struktura]]` →
-both `projects/struktura` and `archive/struktura`), GBrain emits one edge to each
-rather than guessing a winner — review and prune the duplicates with
-`gbrain graph-query <slug>`. The mode is also honored on the filesystem-walk path
-(`gbrain extract links` with no `--source db`) and by auto-link on every future
-`put_page`.
-
 ## Step 5: Load Skills
 
-If you're running an agent platform (OpenClaw, Hermes, or any repo with a workspace),
-scaffold the bundled skills into it:
-
-```bash
-cd /path/to/agent/workspace
-gbrain skillpack scaffold --all       # copy 43 curated skills + RESOLVER.md
-```
-
-Scaffolded skills are first-class files in your repo. Edit freely; re-running scaffold
-refuses to overwrite anything that exists. Use `gbrain skillpack reference <name>` to
-diff against gbrain's bundle when you want upstream improvements. (The legacy
-`gbrain skillpack install` managed-block model was retired in v0.36.0.0 — run
-`gbrain skillpack migrate-fence` once if upgrading from an older release.)
-
-Whether you scaffolded or not, read `skills/RESOLVER.md` (in your workspace, or the
-bundled copy at `~/gbrain/skills/RESOLVER.md` when running from the cloned repo). It's
-the skill dispatcher — tells you which skill to read for any task. Save this to your
-memory permanently.
+Read `~/gbrain/skills/RESOLVER.md`. This is the skill dispatcher. It tells you which
+skill to read for any task. Save this to your memory permanently.
 
 The three most important skills to adopt immediately:
 
@@ -231,17 +124,14 @@ If skipped, minimal defaults are installed automatically.
 
 ## Step 7: Recurring Jobs
 
-Set up using your platform's scheduler (OpenClaw cron, Railway cron, crontab), or skip the
-platform glue entirely with `gbrain autopilot --install` (built-in self-maintaining daemon):
+Set up using your platform's scheduler (OpenClaw cron, Railway cron, crontab):
 
 - **Live sync** (every 15 min): `gbrain sync --repo ~/brain && gbrain embed --stale`
-  — or `gbrain sync --watch` for a continuous loop.
-- **Auto-update** (daily): `gbrain check-update --json` (tell user, never auto-install).
-- **Dream cycle** (nightly): `gbrain dream` runs the 8-phase overnight maintenance cycle.
+- **Auto-update** (daily): `gbrain check-update --json` (tell user, never auto-install)
+- **Dream cycle** (nightly): read `docs/guides/cron-schedule.md` for the full protocol.
   Entity sweep, citation fixes, memory consolidation, plus (v0.23+) overnight conversation
-  synthesis and cross-session pattern detection. One cron-friendly command. This is what
-  makes the brain compound. Do not skip it. See `docs/guides/cron-schedule.md` for the
-  full protocol.
+  synthesis and cross-session pattern detection. 8 phases, one cron-friendly command. This
+  is what makes the brain compound. Do not skip it.
 - **Weekly**: `gbrain doctor --json && gbrain embed --stale`
 
 ## Step 8: Integrations
@@ -259,18 +149,9 @@ actually works) is the most important.
 
 ## Upgrade
 
-If you installed via `bun install -g`:
-
-```bash
-gbrain upgrade                        # self-updates the binary, runs schema migrations,
-                                      # and prints post-upgrade notes for the version range
-```
-
-If you installed via `git clone + bun link`:
-
 ```bash
 cd ~/gbrain && git pull origin master && bun install
-gbrain apply-migrations --yes         # apply schema migrations (idempotent)
+gbrain init                           # apply schema migrations (idempotent)
 gbrain post-upgrade                   # show migration notes for the version range
 ```
 
@@ -278,15 +159,6 @@ Then read `~/gbrain/skills/migrations/v<NEW_VERSION>.md` (and any intermediate
 versions you skipped) and run any backfill or verification steps it lists. Skipping
 this is how features ship in the binary but stay dormant in the user's brain.
 
-**v0.32.3 search modes (one-time upgrade prompt):** if the user's brain was
-created before v0.32.3, `gbrain post-upgrade` prints a banner including the
-9-cell cost matrix (mode × downstream model) preceded by `[AGENT]` markers.
-**Do NOT silently move past the banner.** Present the matrix to the operator
-verbatim, ask which mode they want (recommended default: `tokenmax` to preserve
-v0.31.x retrieval shape), then run `gbrain config set search.mode <mode>`. See
-Step 3.5 above for the full ask-the-user protocol — the upgrade path uses the
-same matrix and same default.
-
 For v0.12.0+ specifically: if your brain was created before v0.12.0, run
 `gbrain extract links --source db && gbrain extract timeline --source db` to
 backfill the new graph layer (see Step 4.5 above).
@@ -297,58 +169,3 @@ automatically during `gbrain post-upgrade` to fix the double-encoded JSONB
 columns. PGLite brains no-op. If wiki-style imports were truncated by the old
 `splitBody` bug, run `gbrain sync --full` after upgrading to rebuild
 `compiled_truth` from source markdown.
-
-## v0.42.0+ onboard surface (NEW)
-
-`gbrain onboard` is the activation surface gbrain did not have before.
-Once your brain has any content, run `gbrain onboard --check --json` to
-see structured recommendations across 5 brain-health axes (orphans,
-stale embeddings, entity link coverage, timeline coverage, takes count).
-
-**On first connect (after `gbrain init`):**
-```bash
-gbrain onboard --check --json
-```
-The JSON envelope (`schema_version: 1`) carries `recommendations[]` with
-`apply_policy` per item: `auto_apply` (safe to run unattended),
-`prompt_required` (needs explicit user consent), or `manual_only`
-(LLM-bearing, user must run themselves).
-
-**After every `gbrain upgrade`:**
-```bash
-gbrain onboard --check --json
-```
-New versions may surface new opportunities. The post-upgrade banner
-nudges the user when it runs, but agents should re-probe as a hygiene
-step regardless.
-
-**Unattended remediation (cron / autopilot):**
-```bash
-gbrain onboard --auto --max-usd 5
-```
-Refuses without `--max-usd N`. Runs auto-eligible items only. The
-autopilot daemon also consults onboard recommendations on its tick — no
-explicit agent action needed for the autonomous path.
-
-**Remote / federated brain installs (MCP):**
-The `run_onboard` MCP op (admin scope) lets thin-client agents probe
-brain health + drive remediation over OAuth-authenticated MCP. Protected
-LLM-bearing handlers (synthesize, patterns, consolidate, takes-bootstrap,
-contextual_reindex_per_chunk) require the additional `run_protected_onboard`
-scope — admin alone is insufficient. The MCP op returns
-`skipped_missing_scope[]` listing what would have run with the right
-grants.
-
-**Privacy + consent gates:**
-- `gbrain takes extract --from-pages` sends concept/atom/lore/briefing/
-  writing/originals page content to your configured chat model (default
-  Anthropic Haiku). Refuses to run unless `takes.bootstrap_enabled=true`
-  is set in config AND `--yes` is passed. Two-gate opt-in by design.
-- Autopilot's auto-apply tier for takes-bootstrap stays `manual_only`
-  until v0.42.1's eval gate (do not bypass).
-
-**Suppress nudges in CI / scripted environments:**
-```bash
-export GBRAIN_NO_ONBOARD_NUDGE=1
-```
-Init + upgrade banners auto-skip in non-TTY too.

SECURITY.md

@@ -50,55 +50,6 @@ exclusively via `gbrain auth create/list/revoke`.
 4. **Log all token issuance** — alert on unexpected registrations
 5. **Rate-limit registration and token endpoints**
 
-### Pre-registering claude.ai / ChatGPT clients without DCR (v0.41.3+)
-
-The recommended hardening posture above is: ship `gbrain serve --http`
-**without** `--enable-dcr` and pre-register every client manually. As of
-v0.41.3, `gbrain auth register-client` accepts the OAuth fields
-browser-based clients need:
-
-```bash
-# Pre-register claude.ai (confidential client; two redirect URIs)
-gbrain auth register-client claude-ai \
-  --scopes "read write" \
-  --redirect-uri https://claude.ai/api/mcp/auth_callback \
-  --redirect-uri https://claude.com/api/mcp/auth_callback
-# --grant-types is auto-set to authorization_code,refresh_token when
-# --redirect-uri is passed; pass --grant-types explicitly to override.
-
-# Pre-register ChatGPT (public PKCE client; no client_secret minted)
-gbrain auth register-client chatgpt \
-  --scopes "read write" \
-  --redirect-uri https://chatgpt.com/connector/oauth/<HASH> \
-  --token-endpoint-auth-method none
-```
-
-Auth methods (`--token-endpoint-auth-method`):
-
-- `client_secret_post` (default) — confidential client, secret in body
-- `client_secret_basic` — confidential client, secret in `Authorization` header
-- `none` — public PKCE-only client (no secret minted; ChatGPT custom
-  connector, Claude Code, Cursor)
-
-The validator rejects unknown methods at the registration boundary, and
-the same gate applies to the admin endpoint `POST /admin/api/register-client`
-and the DCR `POST /register` path. Pre-v0.41.3 the CLI hard-coded
-`redirect_uris = []` and `token_endpoint_auth_method = NULL`, forcing
-operators to UPDATE `oauth_clients` rows by hand to make claude.ai work
-without `--enable-dcr`. That footgun is gone.
-
-### DCR consent default (v0.42.55+)
-
-The "disable `client_credentials`, only allow `authorization_code`" guidance
-above is now the built-in default for the DCR path, not just advice for custom
-wrappers. With `--enable-dcr` on, a self-registered client defaults to the
-`authorization_code` (browser-approval) grant, and an explicit
-`client_credentials` request is rejected with `invalid_client_metadata`.
-Operators who genuinely need the machine-to-machine grant on the registration
-endpoint opt in with `--enable-dcr-insecure` (which implies `--enable-dcr`); a
-startup WARNING prints whenever DCR is enabled, and a second when the insecure
-grant is allowed. Pre-registering clients via the CLI / admin API is unchanged.
-
 ### Token Management
 
 ```bash
@@ -117,16 +68,6 @@ The built-in HTTP transport ships with several layers of hardening on by
 default. All env vars below are optional; the defaults are intentionally
 conservative.
 
-### Bind address (v0.34: loopback by default)
-
-`gbrain serve --http` listens on `127.0.0.1` by default. Personal-laptop
-installs cannot accidentally publish the brain to the LAN. Self-hosted
-deployments that need remote access pass `--bind 0.0.0.0` (all
-interfaces) or `--bind <interface-ip>` (specific NIC). A stderr WARN
-fires when `--public-url` is set without `--bind` so the operator sees
-the binding before the first request — common cause of "ngrok forwards
-to me but the agent can't reach the upstream" misconfigurations.
-
 ### Postgres-only
 
 `gbrain serve --http` requires a Postgres engine. PGLite is local-only by
@@ -150,19 +91,6 @@ When the request `Origin` matches the allowlist, the server echoes it
 back in `Access-Control-Allow-Origin` (with `Vary: Origin`). Otherwise no
 CORS header is sent and the browser blocks the request.
 
-**v0.41.3:** the same allowlist now gates every OAuth endpoint (`/mcp`,
-`/token`, `/authorize`, `/register`, `/revoke`). Pre-v0.41.3 these used
-default-wide-open `cors()` middleware, leaking
-`Access-Control-Allow-Origin: *` on every response — any web origin could
-complete a token exchange from a logged-in operator's browser. The CORS
-preflight handler in the legacy bearer transport was also asymmetric
-(actual-request path correctly default-deny, but OPTIONS preflight leaked
-`Access-Control-Allow-Methods` + `Access-Control-Allow-Headers` to every
-Origin); both are now consolidated through a single allowlist-gated path.
-A startup stderr WARN fires when `--bind 0.0.0.0` is set without
-`GBRAIN_HTTP_CORS_ORIGIN`, surfacing the default-deny posture before the
-first request.
-
 ### Rate limiting
 
 Two buckets, both stored in a bounded LRU map (default 10K keys, evicts
@@ -186,51 +114,30 @@ deployments.
 
 ### Reverse-proxy trust
 
-**Loopback-only by default** (v0.41.3+ Express server agrees with the
-legacy transport; pre-v0.41.3 the Express server hardcoded `'loopback'`
-while docs claimed "disabled by default" — that disagreement is gone).
-The default trusts only same-host proxies (127.0.0.1, ::1, fc00::/7);
-external forwarded-for headers are ignored regardless. To widen or
-narrow trust:
+Disabled by default. To honor `X-Forwarded-For` (or `X-Real-IP`) when
+gbrain runs behind a trusted reverse proxy:
 
 ```bash
-# Trust exactly one hop — Fly.io, Render, Vercel, single-layer nginx
 GBRAIN_HTTP_TRUST_PROXY=1 gbrain serve --http --port 8787
-
-# Trust N hops — Cloudflare → nginx → gbrain
-GBRAIN_HTTP_TRUST_PROXY=2 gbrain serve --http --port 8787
-
-# Disable entirely — direct-exposure deployment with no proxy
-GBRAIN_HTTP_TRUST_PROXY=0 gbrain serve --http --port 8787
-
-# Named Express modes (uniquelocal, linklocal) or CIDR lists pass through
-GBRAIN_HTTP_TRUST_PROXY=uniquelocal gbrain serve --http --port 8787
-GBRAIN_HTTP_TRUST_PROXY="10.0.0.0/8,192.168.1.0/24" gbrain serve --http --port 8787
 ```
 
-Both transports (Express OAuth server in `src/commands/serve-http.ts` and
-the legacy bearer transport in `src/mcp/http-transport.ts`) read the same
-env var, so single source of truth.
-
-**Critical safety contract:** only widen past `'loopback'` when **both**
-of these are true:
+**Critical safety contract:** only set `GBRAIN_HTTP_TRUST_PROXY=1` when
+**both** of these are true:
 
 1. gbrain is reachable only via a trusted reverse proxy (not directly
-   exposed to the internet on the configured port). As of v0.34
-   `gbrain serve --http` binds `127.0.0.1` by default, so the
-   reverse-proxy-only posture is the out-of-the-box shape; only
-   override with `--bind 0.0.0.0` (or a specific interface IP) when
-   gbrain itself needs to accept remote connections directly.
+   exposed to the internet on the configured port). The simplest
+   guarantee is to bind gbrain to `127.0.0.1` or a private interface
+   and have the proxy forward to it.
 2. The proxy strips any client-supplied `X-Forwarded-For` and `X-Real-IP`
    headers, then sets them itself. (nginx with `proxy_set_header
    X-Forwarded-For $remote_addr` does this; Cloudflare and most cloud
    load balancers handle it automatically.)
 
-If gbrain is reachable directly AND `GBRAIN_HTTP_TRUST_PROXY=1` (or any
-non-loopback value) is set, clients can spoof their IP by sending
-arbitrary `X-Forwarded-For` headers, defeating the pre-auth IP rate
-limit. The `'loopback'` default protects against this by ignoring all
-forwarded-for headers and using the socket peer address.
+If gbrain is reachable directly AND `GBRAIN_HTTP_TRUST_PROXY=1` is set,
+clients can spoof their IP by sending arbitrary `X-Forwarded-For`
+headers, defeating the pre-auth IP rate limit. Without the flag, gbrain
+ignores all forwarded-for headers and uses the socket peer address,
+which is the safe default for direct-exposure deployments.
 
 ### Body size cap
 

VERSION

@@ -1 +1 @@
-0.42.64.0
+0.28.7

admin/bun.lock

@@ -13,52 +13,48 @@
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         "@vitejs/plugin-react": "^4.4.1",
         "typescript": "^5.8.3",
-        "vite": "^6.4.3",
+        "vite": "^6.3.3",
       },
     },
   },
-  "overrides": {
-    "@babel/core": "^7.29.6",
-    "postcss": "^8.5.10",
-  },
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+    "@babel/code-frame": ["@babel/code-frame@7.29.0", "", { "dependencies": { "@babel/helper-validator-identifier": "^7.28.5", "js-tokens": "^4.0.0", "picocolors": "^1.1.1" } }, "sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw=="],
 
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+    "@babel/compat-data": ["@babel/compat-data@7.29.0", "", {}, "sha512-T1NCJqT/j9+cn8fvkt7jtwbLBfLC/1y1c7NtCeXFRgzGTsafi68MRv8yzkYSapBnFA6L3U2VSc02ciDzoAJhJg=="],
 
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-    "@babel/helper-module-imports": ["@babel/helper-module-imports@7.29.7", "", { "dependencies": { "@babel/traverse": "^7.29.7", "@babel/types": "^7.29.7" } }, "sha512-ejHwrQQYcm9xnTivShn2IDOlIzInN34AXskvq9QicvCtEzq1Vzclu/tKF8Jq1Cg8JG2GL6/EmjgsCT7lXepE3g=="],
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-    "@babel/helper-module-transforms": ["@babel/helper-module-transforms@7.29.7", "", { "dependencies": { "@babel/helper-module-imports": "^7.29.7", "@babel/helper-validator-identifier": "^7.29.7", "@babel/traverse": "^7.29.7" }, "peerDependencies": { "@babel/core": "^7.0.0" } }, "sha512-UPUVSyXbOh627KiCIGQSgwWzGeBKLkaJ9PJEdrngIwMSzxLR4jS4+f1f1jb7VzBbg8nFLaYotvVPFCTqdrmTAg=="],
+    "@babel/helper-module-transforms": ["@babel/helper-module-transforms@7.28.6", "", { "dependencies": { "@babel/helper-module-imports": "^7.28.6", "@babel/helper-validator-identifier": "^7.28.5", "@babel/traverse": "^7.28.6" }, "peerDependencies": { "@babel/core": "^7.0.0" } }, "sha512-67oXFAYr2cDLDVGLXTEABjdBJZ6drElUSI7WKp70NrpyISso3plG9SAGEF6y7zbha/wOzUByWWTJvEDVNIUGcA=="],
 
     "@babel/helper-plugin-utils": ["@babel/helper-plugin-utils@7.28.6", "", {}, "sha512-S9gzZ/bz83GRysI7gAD4wPT/AI3uCnY+9xn+Mx/KPs2JwHJIz1W8PZkg2cqyt3RNOBM8ejcXhV6y8Og7ly/Dug=="],
 
-    "@babel/helper-string-parser": ["@babel/helper-string-parser@7.29.7", "", {}, "sha512-Pb5ijPrZ89GDH8223L4UP8i6QApWxs04RbPQJTeWDV0/keR2E36MeKnyr6LYmUUvqRRI+Iv87SuF1W6ErINzYw=="],
+    "@babel/helper-string-parser": ["@babel/helper-string-parser@7.27.1", "", {}, "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA=="],
 
-    "@babel/helper-validator-identifier": ["@babel/helper-validator-identifier@7.29.7", "", {}, "sha512-qehxGkRj55h/ff8EMaJ+cYhyaKlHIxqYDn682wQD7RNp9UujOQsHog2uS0r2vzr4pW+sXf90NeeayjcNaX3fFg=="],
+    "@babel/helper-validator-identifier": ["@babel/helper-validator-identifier@7.28.5", "", {}, "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q=="],
 
-    "@babel/helper-validator-option": ["@babel/helper-validator-option@7.29.7", "", {}, "sha512-N9ZErrD+yW5geCDtBqnOoxmR8+tNKiGuxKlDpuJxfsqpa2dFcexaziGAE/qoHLiDDreVNMupxGmSoNlyvsA3gw=="],
+    "@babel/helper-validator-option": ["@babel/helper-validator-option@7.27.1", "", {}, "sha512-YvjJow9FxbhFFKDSuFnVCe2WxXk1zWc22fFePVNEaWJEu8IrZVlda6N0uHwzZrUM1il7NC9Mlp4MaJYbYd9JSg=="],
 
-    "@babel/helpers": ["@babel/helpers@7.29.7", "", { "dependencies": { "@babel/template": "^7.29.7", "@babel/types": "^7.29.7" } }, "sha512-1k2lAGRMfHTcwuNYcCNUmaUffmQv8KWMfh2iJUUeRlwlwH4FdNG7mfPI10NPfLHJFThE4Tyr4mv7kTNZOiPuBg=="],
+    "@babel/helpers": ["@babel/helpers@7.29.2", "", { "dependencies": { "@babel/template": "^7.28.6", "@babel/types": "^7.29.0" } }, "sha512-HoGuUs4sCZNezVEKdVcwqmZN8GoHirLUcLaYVNBK2J0DadGtdcqgr3BCbvH8+XUo4NGjNl3VOtSjEKNzqfFgKw=="],
 
-    "@babel/parser": ["@babel/parser@7.29.7", "", { "dependencies": { "@babel/types": "^7.29.7" }, "bin": "./bin/babel-parser.js" }, "sha512-hnORnjP/1P/zFEndoeX+n+t1RwWRJiJpM/jO7FW32Kn9r5+sJB2JWOdYo4L6k78j15eCwY3Gm/7364B1EMwtNg=="],
+    "@babel/parser": ["@babel/parser@7.29.2", "", { "dependencies": { "@babel/types": "^7.29.0" }, "bin": "./bin/babel-parser.js" }, "sha512-4GgRzy/+fsBa72/RZVJmGKPmZu9Byn8o4MoLpmNe1m8ZfYnz5emHLQz3U4gLud6Zwl0RZIcgiLD7Uq7ySFuDLA=="],
 
     "@babel/plugin-transform-react-jsx-self": ["@babel/plugin-transform-react-jsx-self@7.27.1", "", { "dependencies": { "@babel/helper-plugin-utils": "^7.27.1" }, "peerDependencies": { "@babel/core": "^7.0.0-0" } }, "sha512-6UzkCs+ejGdZ5mFFC/OCUrv028ab2fp1znZmCZjAOBKiBK2jXD1O+BPSfX8X2qjJ75fZBMSnQn3Rq2mrBJK2mw=="],
 
     "@babel/plugin-transform-react-jsx-source": ["@babel/plugin-transform-react-jsx-source@7.27.1", "", { "dependencies": { "@babel/helper-plugin-utils": "^7.27.1" }, "peerDependencies": { "@babel/core": "^7.0.0-0" } }, "sha512-zbwoTsBruTeKB9hSq73ha66iFeJHuaFkUbwvqElnygoNbj/jHRsSeokowZFN3CZ64IvEqcmmkVe89OPXc7ldAw=="],
 
-    "@babel/template": ["@babel/template@7.29.7", "", { "dependencies": { "@babel/code-frame": "^7.29.7", "@babel/parser": "^7.29.7", "@babel/types": "^7.29.7" } }, "sha512-puq+Gf35oI24FeN11LkoUQFqv9uwNeWpxXZi/Ji3rRIoKAzKnxRaZ+Gkj0vKS9ZCiTESfng1N9LyOyXvo+m+Gg=="],
+    "@babel/template": ["@babel/template@7.28.6", "", { "dependencies": { "@babel/code-frame": "^7.28.6", "@babel/parser": "^7.28.6", "@babel/types": "^7.28.6" } }, "sha512-YA6Ma2KsCdGb+WC6UpBVFJGXL58MDA6oyONbjyF/+5sBgxY/dwkhLogbMT2GXXyU84/IhRw/2D1Os1B/giz+BQ=="],
 
-    "@babel/traverse": ["@babel/traverse@7.29.7", "", { "dependencies": { "@babel/code-frame": "^7.29.7", "@babel/generator": "^7.29.7", "@babel/helper-globals": "^7.29.7", "@babel/parser": "^7.29.7", "@babel/template": "^7.29.7", "@babel/types": "^7.29.7", "debug": "^4.3.1" } }, "sha512-EhlfNQtZ+NK22w5BM61ciuiq1m58ed33Wr1Xan//ZRTy6hgjnwyCffRYwzsGXdASJSUJ1guZILsErh1eQcl+zw=="],
+    "@babel/traverse": ["@babel/traverse@7.29.0", "", { "dependencies": { "@babel/code-frame": "^7.29.0", "@babel/generator": "^7.29.0", "@babel/helper-globals": "^7.28.0", "@babel/parser": "^7.29.0", "@babel/template": "^7.28.6", "@babel/types": "^7.29.0", "debug": "^4.3.1" } }, "sha512-4HPiQr0X7+waHfyXPZpWPfWL/J7dcN1mx9gL6WdQVMbPnF3+ZhSMs8tCxN7oHddJE9fhNE7+lxdnlyemKfJRuA=="],
 
-    "@babel/types": ["@babel/types@7.29.7", "", { "dependencies": { "@babel/helper-string-parser": "^7.29.7", "@babel/helper-validator-identifier": "^7.29.7" } }, "sha512-4zBIxpPzowiZpusoFkyGVwakdRJUyuH5PxQ/PrqghfdFWWasvnCdPfQXHrenDai+gyLARulZjZowCOj6fjT4pA=="],
+    "@babel/types": ["@babel/types@7.29.0", "", { "dependencies": { "@babel/helper-string-parser": "^7.27.1", "@babel/helper-validator-identifier": "^7.28.5" } }, "sha512-LwdZHpScM4Qz8Xw2iKSzS+cfglZzJGvofQICy7W7v4caru4EaAmyUuO6BGrbyQ2mYV11W0U8j5mBhd14dd3B0A=="],
 
     "@esbuild/aix-ppc64": ["@esbuild/aix-ppc64@0.25.12", "", { "os": "aix", "cpu": "ppc64" }, "sha512-Hhmwd6CInZ3dwpuGTF8fJG6yoWmsToE+vYgD4nytZVxcu1ulHpUQRAB1UJ8+N1Am3Mz4+xOByoQoSZf4D+CpkA=="],
 
@@ -224,7 +220,7 @@
 
     "ms": ["ms@2.1.3", "", {}, "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA=="],
 
-    "nanoid": ["nanoid@3.3.16", "", { "bin": { "nanoid": "bin/nanoid.cjs" } }, "sha512-bzlKTyNJ7+LdGIIwy8ijFpIqEQIvafahV7eYykJ8Cvh42EdJeODoJ6gUJXpQJvej1BddH8OqTXZNE/KfbWAu8Q=="],
+    "nanoid": ["nanoid@3.3.11", "", { "bin": { "nanoid": "bin/nanoid.cjs" } }, "sha512-N8SpfPUnUp1bK+PMYW8qSWdl9U+wwNWI4QKxOYDy9JAro3WMX7p2OeVRF9v+347pnakNevPmiHhNmZ2HbFA76w=="],
 
     "node-releases": ["node-releases@2.0.37", "", {}, "sha512-1h5gKZCF+pO/o3Iqt5Jp7wc9rH3eJJ0+nh/CIoiRwjRxde/hAHyLPXYN4V3CqKAbiZPSeJFSWHmJsbkicta0Eg=="],
 
@@ -232,7 +228,7 @@
 
     "picomatch": ["picomatch@4.0.4", "", {}, "sha512-QP88BAKvMam/3NxH6vj2o21R6MjxZUAd6nlwAS/pnGvN9IVLocLHxGYIzFhg6fUQ+5th6P4dv4eW9jX3DSIj7A=="],
 
-    "postcss": ["postcss@8.5.19", "", { "dependencies": { "nanoid": "^3.3.12", "picocolors": "^1.1.1", "source-map-js": "^1.2.1" } }, "sha512-Mz8SaolMd8nB+G13WkORcxQKHZ/NE4xXevtkJHVuG+guo9/wYKlIMTKAqGdEmYOXR2ijPjTYNHssizdaVSUNdQ=="],
+    "postcss": ["postcss@8.5.9", "", { "dependencies": { "nanoid": "^3.3.11", "picocolors": "^1.1.1", "source-map-js": "^1.2.1" } }, "sha512-7a70Nsot+EMX9fFU3064K/kdHWZqGVY+BADLyXc8Dfv+mTLLVl6JzJpPaCZ2kQL9gIJvKXSLMHhqdRRjwQeFtw=="],
 
     "react": ["react@19.2.5", "", {}, "sha512-llUJLzz1zTUBrskt2pwZgLq59AemifIftw4aB7JxOqf1HY2FDaGDxgwpAPVzHU1kdWabH7FauP4i1oEeer2WCA=="],
 
@@ -254,36 +250,8 @@
 
     "update-browserslist-db": ["update-browserslist-db@1.2.3", "", { "dependencies": { "escalade": "^3.2.0", "picocolors": "^1.1.1" }, "peerDependencies": { "browserslist": ">= 4.21.0" }, "bin": { "update-browserslist-db": "cli.js" } }, "sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w=="],
 
-    "vite": ["vite@6.4.3", "", { "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.4.4", "picomatch": "^4.0.2", "postcss": "^8.5.3", "rollup": "^4.34.9", "tinyglobby": "^0.2.13" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", "jiti": ">=1.21.0", "less": "*", "lightningcss": "^1.21.0", "sass": "*", "sass-embedded": "*", "stylus": "*", "sugarss": "*", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-NTKlcQjlAK7MlQoyb6LgaqHc8sso/pVyUJYWMws3jg21uTJw/LddqIFPcPqP6PzpgbIcZyKI85sFE4HBrQDA8A=="],
+    "vite": ["vite@6.4.2", "", { "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.4.4", "picomatch": "^4.0.2", "postcss": "^8.5.3", "rollup": "^4.34.9", "tinyglobby": "^0.2.13" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", "jiti": ">=1.21.0", "less": "*", "lightningcss": "^1.21.0", "sass": "*", "sass-embedded": "*", "stylus": "*", "sugarss": "*", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-2N/55r4JDJ4gdrCvGgINMy+HH3iRpNIz8K6SFwVsA+JbQScLiC+clmAxBgwiSPgcG9U15QmvqCGWzMbqda5zGQ=="],
 
     "yallist": ["yallist@3.1.1", "", {}, "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g=="],
-
-    "@types/babel__core/@babel/parser": ["@babel/parser@7.29.2", "", { "dependencies": { "@babel/types": "^7.29.0" }, "bin": "./bin/babel-parser.js" }, "sha512-4GgRzy/+fsBa72/RZVJmGKPmZu9Byn8o4MoLpmNe1m8ZfYnz5emHLQz3U4gLud6Zwl0RZIcgiLD7Uq7ySFuDLA=="],
-
-    "@types/babel__core/@babel/types": ["@babel/types@7.29.0", "", { "dependencies": { "@babel/helper-string-parser": "^7.27.1", "@babel/helper-validator-identifier": "^7.28.5" } }, "sha512-LwdZHpScM4Qz8Xw2iKSzS+cfglZzJGvofQICy7W7v4caru4EaAmyUuO6BGrbyQ2mYV11W0U8j5mBhd14dd3B0A=="],
-
-    "@types/babel__generator/@babel/types": ["@babel/types@7.29.0", "", { "dependencies": { "@babel/helper-string-parser": "^7.27.1", "@babel/helper-validator-identifier": "^7.28.5" } }, "sha512-LwdZHpScM4Qz8Xw2iKSzS+cfglZzJGvofQICy7W7v4caru4EaAmyUuO6BGrbyQ2mYV11W0U8j5mBhd14dd3B0A=="],
-
-    "@types/babel__template/@babel/parser": ["@babel/parser@7.29.2", "", { "dependencies": { "@babel/types": "^7.29.0" }, "bin": "./bin/babel-parser.js" }, "sha512-4GgRzy/+fsBa72/RZVJmGKPmZu9Byn8o4MoLpmNe1m8ZfYnz5emHLQz3U4gLud6Zwl0RZIcgiLD7Uq7ySFuDLA=="],
-
-    "@types/babel__template/@babel/types": ["@babel/types@7.29.0", "", { "dependencies": { "@babel/helper-string-parser": "^7.27.1", "@babel/helper-validator-identifier": "^7.28.5" } }, "sha512-LwdZHpScM4Qz8Xw2iKSzS+cfglZzJGvofQICy7W7v4caru4EaAmyUuO6BGrbyQ2mYV11W0U8j5mBhd14dd3B0A=="],
-
-    "@types/babel__traverse/@babel/types": ["@babel/types@7.29.0", "", { "dependencies": { "@babel/helper-string-parser": "^7.27.1", "@babel/helper-validator-identifier": "^7.28.5" } }, "sha512-LwdZHpScM4Qz8Xw2iKSzS+cfglZzJGvofQICy7W7v4caru4EaAmyUuO6BGrbyQ2mYV11W0U8j5mBhd14dd3B0A=="],
-
-    "@types/babel__core/@babel/types/@babel/helper-string-parser": ["@babel/helper-string-parser@7.27.1", "", {}, "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA=="],
-
-    "@types/babel__core/@babel/types/@babel/helper-validator-identifier": ["@babel/helper-validator-identifier@7.28.5", "", {}, "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q=="],
-
-    "@types/babel__generator/@babel/types/@babel/helper-string-parser": ["@babel/helper-string-parser@7.27.1", "", {}, "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA=="],
-
-    "@types/babel__generator/@babel/types/@babel/helper-validator-identifier": ["@babel/helper-validator-identifier@7.28.5", "", {}, "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q=="],
-
-    "@types/babel__template/@babel/types/@babel/helper-string-parser": ["@babel/helper-string-parser@7.27.1", "", {}, "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA=="],
-
-    "@types/babel__template/@babel/types/@babel/helper-validator-identifier": ["@babel/helper-validator-identifier@7.28.5", "", {}, "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q=="],
-
-    "@types/babel__traverse/@babel/types/@babel/helper-string-parser": ["@babel/helper-string-parser@7.27.1", "", {}, "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA=="],
-
-    "@types/babel__traverse/@babel/types/@babel/helper-validator-identifier": ["@babel/helper-validator-identifier@7.28.5", "", {}, "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q=="],
   }
 }

admin/dist/index.html

@@ -7,8 +7,8 @@
   <link rel="preconnect" href="https://fonts.googleapis.com" />
   <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
   <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet" />
-  <script type="module" crossorigin src="/admin/assets/index-CoGEje3-.js"></script>
-  <link rel="stylesheet" crossorigin href="/admin/assets/index-GxkWX7v3.css">
+  <script type="module" crossorigin src="/admin/assets/index-CDv6_ml5.js"></script>
+  <link rel="stylesheet" crossorigin href="/admin/assets/index-BOifXQpQ.css">
 </head>
 <body>
   <div id="root"></div>

admin/package.json

@@ -15,11 +15,7 @@
     "@types/react": "^19.1.2",
     "@types/react-dom": "^19.1.2",
     "@vitejs/plugin-react": "^4.4.1",
-    "vite": "^6.4.3",
+    "vite": "^6.3.3",
     "typescript": "^5.8.3"
-  },
-  "overrides": {
-    "@babel/core": "^7.29.6",
-    "postcss": "^8.5.10"
   }
 }

admin/src/App.tsx

@@ -3,15 +3,13 @@ import { LoginPage } from './pages/Login';
 import { DashboardPage } from './pages/Dashboard';
 import { AgentsPage } from './pages/Agents';
 import { RequestLogPage } from './pages/RequestLog';
-import { CalibrationPage } from './pages/Calibration';
-import { JobsWatchPage } from './pages/JobsWatch';
 import { api } from './api';
 
-type Page = 'login' | 'dashboard' | 'agents' | 'log' | 'calibration' | 'jobs';
+type Page = 'login' | 'dashboard' | 'agents' | 'log';
 
 function getPage(): Page {
   const hash = window.location.hash.replace('#', '') || 'dashboard';
-  if (['login', 'dashboard', 'agents', 'log', 'calibration', 'jobs'].includes(hash)) return hash as Page;
+  if (['login', 'dashboard', 'agents', 'log'].includes(hash)) return hash as Page;
   return 'dashboard';
 }
 
@@ -56,10 +54,6 @@ export function App() {
              onClick={() => navigate('agents')}>Agents</a>
           <a className={`nav-item ${page === 'log' ? 'active' : ''}`}
              onClick={() => navigate('log')}>Request Log</a>
-          <a className={`nav-item ${page === 'calibration' ? 'active' : ''}`}
-             onClick={() => navigate('calibration')}>Calibration</a>
-          <a className={`nav-item ${page === 'jobs' ? 'active' : ''}`}
-             onClick={() => navigate('jobs')}>Jobs Watch</a>
         </div>
         <div style={{ marginTop: 'auto', padding: '16px 12px', borderTop: '1px solid var(--border)' }}>
           <button
@@ -84,8 +78,6 @@ export function App() {
         {page === 'dashboard' && <DashboardPage />}
         {page === 'agents' && <AgentsPage />}
         {page === 'log' && <RequestLogPage />}
-        {page === 'calibration' && <CalibrationPage />}
-        {page === 'jobs' && <JobsWatchPage />}
       </main>
     </div>
   );

admin/src/api.ts

@@ -22,17 +22,6 @@ async function apiFetch(path: string, options?: RequestInit) {
   return res.json();
 }
 
-// v0.36.1.0 (T15 / E6) — SVG fetch (text/plain payload, NOT JSON).
-async function apiFetchText(path: string) {
-  const res = await fetch(`${BASE}${path}`, { credentials: 'same-origin' });
-  if (res.status === 401) {
-    window.location.hash = '#login';
-    throw new Error('Unauthorized');
-  }
-  if (!res.ok) throw new Error(`HTTP ${res.status}`);
-  return res.text();
-}
-
 export const api = {
   login: (token: string) => apiFetch('/admin/login', { method: 'POST', body: JSON.stringify({ token }) }),
   signOutEverywhere: () => apiFetch('/admin/api/sign-out-everywhere', { method: 'POST' }),
@@ -45,11 +34,4 @@ export const api = {
   revokeApiKey: (name: string) => apiFetch('/admin/api/api-keys/revoke', { method: 'POST', body: JSON.stringify({ name }) }),
   updateClientTtl: (clientId: string, tokenTtl: number | null) => apiFetch('/admin/api/update-client-ttl', { method: 'POST', body: JSON.stringify({ clientId, tokenTtl }) }),
   revokeClient: (clientId: string) => apiFetch('/admin/api/revoke-client', { method: 'POST', body: JSON.stringify({ clientId }) }),
-  // v0.36.1.0 (T15 / E6) — calibration endpoints.
-  calibrationProfile: (holder?: string) =>
-    apiFetch(`/admin/api/calibration/profile${holder ? `?holder=${encodeURIComponent(holder)}` : ''}`),
-  calibrationChart: (type: string, holder?: string) =>
-    apiFetchText(`/admin/api/calibration/charts/${encodeURIComponent(type)}${holder ? `?holder=${encodeURIComponent(holder)}` : ''}`),
-  // v0.41 D2 — live minion-jobs dashboard snapshot.
-  jobsWatch: () => apiFetch('/admin/api/jobs/watch'),
 };

admin/src/index.css

@@ -4,10 +4,7 @@
   --bg-tertiary: #1e1e2e;
   --text-primary: #e0e0e0;
   --text-secondary: #888;
-  /* v0.36.1.0 TD2 — bumped from #555 (contrast 4.0 on #0a0a0f bg, below WCAG AA
-     4.5 for body text) to #777 (contrast ~5.5, passes AA). Applies globally
-     to Dashboard, Agents, RequestLog, and the new Calibration tab. */
-  --text-muted: #777;
+  --text-muted: #555;
   --accent: #3b82f6;
   --success: #22c55e;
   --warning: #f59e0b;

admin/src/lib/scope-constants.ts

@@ -10,14 +10,11 @@
  * or `bun run verify` will reject the change.
  */
 
-export type Scope = 'read' | 'write' | 'admin' | 'sources_admin' | 'users_admin' | 'agent';
+export type Scope = 'read' | 'write' | 'admin' | 'sources_admin' | 'users_admin';
 
 // MIRROR OF src/core/scope.ts ALLOWED_SCOPES_LIST — keep alphabetically sorted.
-// v0.38: 'agent' added for the submit_agent remote-MCP op (sibling to admin,
-// NOT implied — existing admin clients must re-register to opt in).
 export const ALLOWED_SCOPES_LIST: ReadonlyArray<Scope> = [
   'admin',
-  'agent',
   'read',
   'sources_admin',
   'users_admin',

admin/src/pages/Calibration.tsx

@@ -1,174 +0,0 @@
-/**
- * v0.36.1.0 (T15 / E6) — Calibration tab.
- *
- * Fetches the active calibration profile + 4 server-rendered SVG charts.
- * Layout: Linear calm clarity (per D23 mockup variant-B) — single column,
- * generous whitespace, ONE big sparkline as hero, then patterns, then
- * domain bars, then abandoned threads.
- *
- * Per D23 — SVG markup comes from the server (image/svg+xml endpoint).
- * Admin SPA renders inside a TrustedSVG wrapper that uses
- * dangerouslySetInnerHTML. XSS posture: server-side escapeXml() on all
- * caller-controlled strings + requireAdmin middleware on the endpoint.
- */
-
-import React, { useEffect, useState } from 'react';
-import { api } from '../api';
-
-interface CalibrationProfileSummary {
-  holder: string;
-  source_id: string;
-  generated_at: string;
-  published: boolean;
-  total_resolved: number;
-  brier: number | null;
-  accuracy: number | null;
-  partial_rate: number | null;
-  grade_completion: number;
-  pattern_statements: string[];
-  active_bias_tags: string[];
-  voice_gate_passed: boolean;
-  voice_gate_attempts: number;
-}
-
-interface ChartSvgProps {
-  type: string;
-  ariaLabel: string;
-}
-
-function TrustedSVG({ markup }: { markup: string }) {
-  return (
-    <div
-      style={{ width: '100%', overflow: 'auto' }}
-      // Server-rendered SVG (image/svg+xml) gated by requireAdmin middleware.
-      // All caller-controlled strings pass through escapeXml() server-side.
-      dangerouslySetInnerHTML={{ __html: markup }}
-    />
-  );
-}
-
-function ChartSvg({ type, ariaLabel }: ChartSvgProps) {
-  const [markup, setMarkup] = useState<string>('');
-  const [error, setError] = useState<string>('');
-
-  useEffect(() => {
-    let cancelled = false;
-    api
-      .calibrationChart(type)
-      .then(svg => {
-        if (!cancelled) setMarkup(svg);
-      })
-      .catch(err => {
-        if (!cancelled) setError(err.message ?? 'fetch failed');
-      });
-    return () => {
-      cancelled = true;
-    };
-  }, [type]);
-
-  if (error) {
-    return (
-      <div style={{ padding: 16, color: 'var(--error)' }} role="alert">
-        {ariaLabel}: {error}
-      </div>
-    );
-  }
-  if (!markup) {
-    return <div style={{ padding: 16, color: 'var(--text-muted)' }}>{ariaLabel} loading...</div>;
-  }
-  return <TrustedSVG markup={markup} />;
-}
-
-export function CalibrationPage() {
-  const [profile, setProfile] = useState<CalibrationProfileSummary | null>(null);
-  const [loading, setLoading] = useState(true);
-  const [error, setError] = useState<string>('');
-
-  useEffect(() => {
-    api
-      .calibrationProfile()
-      .then(p => {
-        setProfile(p);
-        setLoading(false);
-      })
-      .catch(err => {
-        setError(err.message ?? 'fetch failed');
-        setLoading(false);
-      });
-  }, []);
-
-  if (loading) {
-    return <div style={{ padding: 24, color: 'var(--text-secondary)' }}>Loading calibration profile…</div>;
-  }
-  if (error) {
-    return (
-      <div style={{ padding: 24, color: 'var(--error)' }} role="alert">
-        Could not load calibration profile: {error}
-      </div>
-    );
-  }
-  if (!profile) {
-    return (
-      <div style={{ padding: 24, maxWidth: 700 }}>
-        <h1 style={{ marginBottom: 16 }}>Calibration</h1>
-        <p style={{ color: 'var(--text-secondary)' }}>
-          No calibration profile yet. Builds after 5+ resolved takes.
-        </p>
-        <pre
-          style={{
-            background: 'var(--bg-secondary)',
-            padding: 12,
-            borderRadius: 4,
-            color: 'var(--text-primary)',
-            marginTop: 12,
-            fontFamily: 'var(--font-mono)',
-          }}
-        >
-          gbrain dream --phase calibration_profile
-        </pre>
-      </div>
-    );
-  }
-
-  const generated = new Date(profile.generated_at);
-  const generatedAgo = Math.floor((Date.now() - generated.getTime()) / (1000 * 60 * 60 * 24));
-
-  return (
-    <div style={{ padding: 32, maxWidth: 720 }}>
-      <h1 style={{ marginBottom: 8 }}>Calibration</h1>
-      <div style={{ color: 'var(--text-muted)', fontSize: 13, marginBottom: 24 }}>
-        Holder: {profile.holder}
-        {' · '}
-        Updated {generatedAgo === 0 ? 'today' : `${generatedAgo}d ago`}
-        {profile.published && ' · published'}
-        {profile.grade_completion < 0.9 && ` · ~${Math.round(profile.grade_completion * 100)}% graded`}
-        {!profile.voice_gate_passed && ' · voice gate fell back to template'}
-      </div>
-
-      <section style={{ marginBottom: 32 }}>
-        <ChartSvg type="brier-trend" ariaLabel="Brier trend" />
-      </section>
-
-      <section style={{ marginBottom: 32 }}>
-        <h2 style={{ fontSize: 14, color: 'var(--text-secondary)', marginBottom: 12, fontWeight: 400 }}>
-          Pattern statements
-        </h2>
-        <ChartSvg type="pattern-statements" ariaLabel="Pattern statements" />
-      </section>
-
-      <section style={{ marginBottom: 32 }}>
-        <ChartSvg type="domain-bars" ariaLabel="Per-domain accuracy" />
-      </section>
-
-      <section style={{ marginBottom: 32 }}>
-        <ChartSvg type="abandoned-threads" ariaLabel="Abandoned threads" />
-      </section>
-
-      {profile.active_bias_tags.length > 0 && (
-        <section style={{ marginBottom: 32, color: 'var(--text-muted)', fontSize: 13 }}>
-          Active bias tags: {profile.active_bias_tags.join(', ')}
-        </section>
-      )}
-    </div>
-  );
-}

admin/src/pages/Dashboard.tsx

@@ -21,7 +21,7 @@ export function DashboardPage() {
     api.stats().then(setStats).catch(() => {});
     api.health().then(setHealth).catch(() => {});
 
-    const es = new EventSource('/admin/events', { withCredentials: true });
+    const es = new EventSource('/admin/events');
     eventSourceRef.current = es;
     es.onopen = () => setSseStatus('connected');
     es.onmessage = (e) => {

admin/src/pages/JobsWatch.tsx

@@ -1,174 +0,0 @@
-import React, { useEffect, useState } from 'react';
-import { api } from '../api';
-
-/**
- * v0.41 D2 — live jobs dashboard. Browser counterpart to the TTY
- * `gbrain jobs watch` command. Polls `/admin/api/jobs/watch` every
- * 1s (matches TTY refresh cadence; SSE upgrade is a v0.42 follow-up
- * once the same wiring lands in serve-http for the TTY command).
- *
- * Layout intentionally matches the TTY 1:1 so an operator looking at
- * both surfaces sees the same panels in the same order.
- */
-
-interface WatchSnapshot {
-  ts_ms: number;
-  by_type: Array<{ name: string; total: number; completed: number; failed: number; dead: number }>;
-  queue_health: { waiting: number; active: number; stalled: number };
-  lease_pressure_1h: number;
-  top_errors: Array<{ cluster: string; count: number }>;
-  budget_owners: Array<{ owner_id: number; remaining_cents: number; total_spent_cents: number }>;
-}
-
-function leasePressureColor(n: number): string {
-  if (n === 0) return 'var(--accent-success, #2ea043)';
-  if (n >= 100) return 'var(--accent-danger, #f85149)';
-  return 'var(--accent-warn, #d29922)';
-}
-
-function dollars(cents: number): string {
-  return `$${(cents / 100).toFixed(2)}`;
-}
-
-export function JobsWatchPage() {
-  const [snap, setSnap] = useState<WatchSnapshot | null>(null);
-  const [err, setErr] = useState<string | null>(null);
-
-  useEffect(() => {
-    let alive = true;
-    let timer: ReturnType<typeof setTimeout> | null = null;
-
-    const tick = async () => {
-      try {
-        const data = await api.jobsWatch();
-        if (alive) {
-          setSnap(data);
-          setErr(null);
-        }
-      } catch (e) {
-        if (alive) setErr(e instanceof Error ? e.message : String(e));
-      }
-      if (alive) timer = setTimeout(tick, 1000);
-    };
-
-    tick();
-    return () => {
-      alive = false;
-      if (timer) clearTimeout(timer);
-    };
-  }, []);
-
-  if (err) {
-    return (
-      <div style={{ padding: 24, color: 'var(--accent-danger, #f85149)' }}>
-        <h2>Jobs Watch — error</h2>
-        <pre style={{ whiteSpace: 'pre-wrap' }}>{err}</pre>
-      </div>
-    );
-  }
-
-  if (!snap) {
-    return <div style={{ padding: 24, color: 'var(--text-muted, #777)' }}>Loading jobs watch…</div>;
-  }
-
-  const ts = new Date(snap.ts_ms).toLocaleTimeString();
-
-  return (
-    <div style={{ padding: 24, fontFamily: 'var(--font-mono, "JetBrains Mono", monospace)' }}>
-      <h1 style={{ fontSize: 18, marginBottom: 4 }}>
-        Jobs Watch
-        <span style={{ marginLeft: 12, color: 'var(--text-muted, #777)', fontSize: 12, fontWeight: 'normal' }}>
-          updated {ts}
-        </span>
-      </h1>
-
-      <section style={{ marginTop: 24 }}>
-        <h2 style={{ fontSize: 14, marginBottom: 8 }}>Queue</h2>
-        <div>
-          waiting=<b>{snap.queue_health.waiting}</b>{'  '}
-          active=<b>{snap.queue_health.active}</b>{'  '}
-          stalled=<b style={{ color: snap.queue_health.stalled > 0 ? 'var(--accent-warn, #d29922)' : undefined }}>
-            {snap.queue_health.stalled}
-          </b>
-        </div>
-      </section>
-
-      {snap.by_type.length > 0 && (
-        <section style={{ marginTop: 24 }}>
-          <h2 style={{ fontSize: 14, marginBottom: 8 }}>By type (24h)</h2>
-          <table style={{ borderCollapse: 'collapse' }}>
-            <thead>
-              <tr style={{ color: 'var(--text-muted, #777)', fontSize: 12 }}>
-                <th style={{ textAlign: 'left', padding: '4px 12px 4px 0' }}>name</th>
-                <th style={{ textAlign: 'right', padding: '4px 12px' }}>total</th>
-                <th style={{ textAlign: 'right', padding: '4px 12px' }}>done</th>
-                <th style={{ textAlign: 'right', padding: '4px 12px' }}>fail</th>
-                <th style={{ textAlign: 'right', padding: '4px 12px' }}>dead</th>
-              </tr>
-            </thead>
-            <tbody>
-              {snap.by_type.slice(0, 6).map(t => (
-                <tr key={t.name}>
-                  <td style={{ padding: '4px 12px 4px 0' }}>{t.name}</td>
-                  <td style={{ textAlign: 'right', padding: '4px 12px' }}>{t.total}</td>
-                  <td style={{ textAlign: 'right', padding: '4px 12px' }}>{t.completed}</td>
-                  <td style={{ textAlign: 'right', padding: '4px 12px' }}>{t.failed}</td>
-                  <td style={{ textAlign: 'right', padding: '4px 12px' }}>{t.dead}</td>
-                </tr>
-              ))}
-            </tbody>
-          </table>
-        </section>
-      )}
-
-      <section style={{ marginTop: 24 }}>
-        <h2 style={{ fontSize: 14, marginBottom: 8 }}>Lease pressure (1h)</h2>
-        <div style={{ color: leasePressureColor(snap.lease_pressure_1h) }}>
-          {snap.lease_pressure_1h} bounce{snap.lease_pressure_1h === 1 ? '' : 's'}
-        </div>
-      </section>
-
-      {snap.top_errors.length > 0 && (
-        <section style={{ marginTop: 24 }}>
-          <h2 style={{ fontSize: 14, marginBottom: 8 }}>Top errors (24h)</h2>
-          <table style={{ borderCollapse: 'collapse' }}>
-            <tbody>
-              {snap.top_errors.slice(0, 5).map(e => (
-                <tr key={e.cluster}>
-                  <td style={{ textAlign: 'right', padding: '4px 12px 4px 0', color: 'var(--text-muted, #777)' }}>
-                    {e.count}×
-                  </td>
-                  <td style={{ padding: '4px 12px 4px 0' }}>{e.cluster}</td>
-                </tr>
-              ))}
-            </tbody>
-          </table>
-        </section>
-      )}
-
-      {snap.budget_owners.length > 0 && (
-        <section style={{ marginTop: 24 }}>
-          <h2 style={{ fontSize: 14, marginBottom: 8 }}>Budget owners</h2>
-          <table style={{ borderCollapse: 'collapse' }}>
-            <thead>
-              <tr style={{ color: 'var(--text-muted, #777)', fontSize: 12 }}>
-                <th style={{ textAlign: 'left', padding: '4px 12px 4px 0' }}>owner</th>
-                <th style={{ textAlign: 'right', padding: '4px 12px' }}>spent</th>
-                <th style={{ textAlign: 'right', padding: '4px 12px' }}>remaining</th>
-              </tr>
-            </thead>
-            <tbody>
-              {snap.budget_owners.slice(0, 5).map(b => (
-                <tr key={b.owner_id}>
-                  <td style={{ padding: '4px 12px 4px 0' }}>{b.owner_id}</td>
-                  <td style={{ textAlign: 'right', padding: '4px 12px' }}>{dollars(b.total_spent_cents)}</td>
-                  <td style={{ textAlign: 'right', padding: '4px 12px' }}>{dollars(b.remaining_cents)}</td>
-                </tr>
-              ))}
-            </tbody>
-          </table>
-        </section>
-      )}
-    </div>
-  );
-}

bun.lock

@@ -13,21 +13,15 @@
         "@aws-sdk/client-s3": "^3.1028.0",
         "@dqbd/tiktoken": "^1.0.22",
         "@electric-sql/pglite": "0.4.3",
-        "@jsquash/avif": "^2.1.1",
-        "@jsquash/png": "^3.1.1",
         "@modelcontextprotocol/sdk": "1.29.0",
         "ai": "^6.0.168",
-        "chokidar": "^4.0.3",
         "cookie-parser": "^1.4.7",
         "cors": "^2.8.5",
         "eventsource-parser": "^3.0.8",
-        "exifr": "^7.1.3",
         "express": "^5.1.0",
         "express-rate-limit": "^7.5.0",
         "gray-matter": "^4.0.3",
-        "heic-decode": "^2.1.0",
-        "js-yaml": "^3.15.0",
-        "marked": "^18.0.2",
+        "marked": "^18.0.0",
         "openai": "^4.0.0",
         "pgvector": "^0.2.0",
         "postgres": "^3.4.0",
@@ -40,9 +34,7 @@
         "@types/cookie-parser": "^1.4.7",
         "@types/cors": "^2.8.19",
         "@types/express": "^5.0.6",
-        "@types/js-yaml": "^3.12.10",
         "bun-types": "^1.3.13",
-        "fast-check": "^4.8.0",
         "typescript": "^5.6.0",
       },
     },
@@ -50,17 +42,6 @@
   "trustedDependencies": [
     "@electric-sql/pglite",
   ],
-  "overrides": {
-    "@hono/node-server": "^1.19.13",
-    "fast-uri": "^3.1.2",
-    "fast-xml-builder": "^1.1.7",
-    "fast-xml-parser": "^5.7.0",
-    "form-data": "^4.0.6",
-    "hono": "^4.12.25",
-    "ip-address": "^10.1.1",
-    "js-yaml": "^3.15.0",
-    "qs": "^6.15.2",
-  },
   "packages": {
     "@ai-sdk/anthropic": ["@ai-sdk/anthropic@3.0.74", "", { "dependencies": { "@ai-sdk/provider": "3.0.10", "@ai-sdk/provider-utils": "4.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-Xew9rfz9WWhDSyF8rNhjT/XWOWelNfJrMlmG0Ahw210hStisRpQZ1s+7VeI9JTJOZ5y5tXqBi5kfPwYnCfyRTA=="],
 
@@ -162,16 +143,10 @@
 
     "@electric-sql/pglite": ["@electric-sql/pglite@0.4.3", "", {}, "sha512-ichuWTgtd4mOM1G4SpyGJa5trT03lWbMypDV0fUXUCXg5hiHqVAz/bZyV68NqmkLB7WcYmj1RMJVSp8HV/v/ZQ=="],
 
-    "@hono/node-server": ["@hono/node-server@1.19.14", "", { "peerDependencies": { "hono": "^4" } }, "sha512-GwtvgtXxnWsucXvbQXkRgqksiH2Qed37H9xHZocE5sA3N8O8O8/8FA3uclQXxXVzc9XBZuEOMK7+r02FmSpHtw=="],
-
-    "@jsquash/avif": ["@jsquash/avif@2.1.1", "", { "dependencies": { "wasm-feature-detect": "^1.2.11" } }, "sha512-LMRxd0fMgfCLtobDh0/sFYJMMiRJTNYSEEWvRDKXlAeZ08t3gI5V+1thIT0XjXJ+SVG7Zug9B0XPyx0Ti5VRNA=="],
-
-    "@jsquash/png": ["@jsquash/png@3.1.1", "", {}, "sha512-C10pc+0H6j0h8fENOfnGOvkXCmvpSQTDGlfGd0sHphZhPSGTyLjIrHba0FaZZdsKqA/wlmhYicUHb92vfZphaw=="],
+    "@hono/node-server": ["@hono/node-server@1.19.12", "", { "peerDependencies": { "hono": "^4" } }, "sha512-txsUW4SQ1iilgE0l9/e9VQWmELXifEFvmdA1j6WFh/aFPj99hIntrSsq/if0UWyGVkmrRPKA1wCeP+UCr1B9Uw=="],
 
     "@modelcontextprotocol/sdk": ["@modelcontextprotocol/sdk@1.29.0", "", { "dependencies": { "@hono/node-server": "^1.19.9", "ajv": "^8.17.1", "ajv-formats": "^3.0.1", "content-type": "^1.0.5", "cors": "^2.8.5", "cross-spawn": "^7.0.5", "eventsource": "^3.0.2", "eventsource-parser": "^3.0.0", "express": "^5.2.1", "express-rate-limit": "^8.2.1", "hono": "^4.11.4", "jose": "^6.1.3", "json-schema-typed": "^8.0.2", "pkce-challenge": "^5.0.0", "raw-body": "^3.0.0", "zod": "^3.25 || ^4.0", "zod-to-json-schema": "^3.25.1" }, "peerDependencies": { "@cfworker/json-schema": "^4.1.1" }, "optionalPeers": ["@cfworker/json-schema"] }, "sha512-zo37mZA9hJWpULgkRpowewez1y6ML5GsXJPY8FI0tBBCd77HEvza4jDqRKOXgHNn867PVGCyTdzqpz0izu5ZjQ=="],
 
-    "@nodable/entities": ["@nodable/entities@3.0.0", "", {}, "sha512-8L9xFeTYKhm49xfIypoe2W5wV1m/3Z58kT+7kR9A8OyFxcPduI4VmxaUMQyKYrRjUoLLSXv6EKKID5Tvj9cUVw=="],
-
     "@opentelemetry/api": ["@opentelemetry/api@1.9.0", "", {}, "sha512-3giAOQvZiH5F9bMlMiv8+GSPMeqg0dbaeo58/0SlA9sxSqZhnUtxzX9/2FzyhS9sWQf5S0GJE0AKBrFqjpeYcg=="],
 
     "@smithy/chunked-blob-reader": ["@smithy/chunked-blob-reader@5.2.2", "", { "dependencies": { "tslib": "^2.6.2" } }, "sha512-St+kVicSyayWQca+I1rGitaOEH6uKgE8IUWoYnnEX26SWdWQcL6LvMSD19Lg+vYHKdT9B2Zuu7rd3i6Wnyb/iw=="],
@@ -292,8 +267,6 @@
 
     "@types/http-errors": ["@types/http-errors@2.0.5", "", {}, "sha512-r8Tayk8HJnX0FztbZN7oVqGccWgw98T/0neJphO91KkmOzug1KkofZURD4UaD5uH8AqcFLfdPErnBod0u71/qg=="],
 
-    "@types/js-yaml": ["@types/js-yaml@3.12.10", "", {}, "sha512-/Mtaq/wf+HxXpvhzFYzrzCqNRcA958sW++7JOFC8nPrZcvfi/TrzOaaGbvt27ltJB2NQbHVAg5a1wUCsyMH7NA=="],
-
     "@types/node": ["@types/node@25.5.2", "", { "dependencies": { "undici-types": "~7.18.0" } }, "sha512-tO4ZIRKNC+MDWV4qKVZe3Ql/woTnmHDr5JD8UI5hn2pwBrHEwOEMZK7WlNb5RKB6EoJ02gwmQS9OrjuFnZYdpg=="],
 
     "@types/node-fetch": ["@types/node-fetch@2.6.13", "", { "dependencies": { "@types/node": "*", "form-data": "^4.0.4" } }, "sha512-QGpRVpzSaUs30JBSGPjOg4Uveu384erbHBoT1zeONvyCfwQxIkUshLAOqN/k9EjGviPRmWTTe6aH2qySWKTVSw=="],
@@ -320,8 +293,6 @@
 
     "ajv-formats": ["ajv-formats@3.0.1", "", { "dependencies": { "ajv": "^8.0.0" } }, "sha512-8iUql50EUR+uUcdRQ3HDqa6EVyo3docL8g5WJ3FNcWmu62IbkGUue/pEyLBW8VGKKucTPgqeks4fIU1DA4yowQ=="],
 
-    "anynum": ["anynum@1.0.1", "", {}, "sha512-N6//FLET/tXYNM/F6ABca1oH6fWB+KlTt909Le28WMDBk8oaT4vY17DCrwg2MvmuqUKt3Ni4N5dGJ/EoBgcO6A=="],
-
     "argparse": ["argparse@1.0.10", "", { "dependencies": { "sprintf-js": "~1.0.2" } }, "sha512-o5Roy6tNG4SL/FOkCAN6RzjiakZS25RLYFrcMttJqbdd8BWrnA+fGz57iN5Pb06pvBGvl5gQ0B48dJlslXvoTg=="],
 
     "asynckit": ["asynckit@0.4.0", "", {}, "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q=="],
@@ -338,8 +309,6 @@
 
     "call-bound": ["call-bound@1.0.4", "", { "dependencies": { "call-bind-apply-helpers": "^1.0.2", "get-intrinsic": "^1.3.0" } }, "sha512-+ys997U96po4Kx/ABpBCqhA9EuxJaQWDQg7295H4hBphv3IZg0boBKuwYpt4YXp6MZ5AmZQnU/tyMTlRpaSejg=="],
 
-    "chokidar": ["chokidar@4.0.3", "", { "dependencies": { "readdirp": "^4.0.1" } }, "sha512-Qgzu8kfBvo+cA4962jnP1KkS6Dop5NS6g7R5LFYJr4b8Ub94PPQXUksCw9PvXoeXPRRddRNC5C1JQUR2SMGtnA=="],
-
     "combined-stream": ["combined-stream@1.0.8", "", { "dependencies": { "delayed-stream": "~1.0.0" } }, "sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg=="],
 
     "content-disposition": ["content-disposition@1.0.1", "", {}, "sha512-oIXISMynqSqm241k6kcQ5UwttDILMK4BiurCfGEREw6+X9jkkpEe5T9FZaApyLGGOnFuyMWZpdolTXMtvEJ08Q=="],
@@ -388,27 +357,23 @@
 
     "eventsource-parser": ["eventsource-parser@3.0.8", "", {}, "sha512-70QWGkr4snxr0OXLRWsFLeRBIRPuQOvt4s8QYjmUlmlkyTZkRqS7EDVRZtzU3TiyDbXSzaOeF0XUKy8PchzukQ=="],
 
-    "exifr": ["exifr@7.1.3", "", {}, "sha512-g/aje2noHivrRSLbAUtBPWFbxKdKhgj/xr1vATDdUXPOFYJlQ62Ft0oy+72V6XLIpDJfHs6gXLbBLAolqOXYRw=="],
-
     "express": ["express@5.2.1", "", { "dependencies": { "accepts": "^2.0.0", "body-parser": "^2.2.1", "content-disposition": "^1.0.0", "content-type": "^1.0.5", "cookie": "^0.7.1", "cookie-signature": "^1.2.1", "debug": "^4.4.0", "depd": "^2.0.0", "encodeurl": "^2.0.0", "escape-html": "^1.0.3", "etag": "^1.8.1", "finalhandler": "^2.1.0", "fresh": "^2.0.0", "http-errors": "^2.0.0", "merge-descriptors": "^2.0.0", "mime-types": "^3.0.0", "on-finished": "^2.4.1", "once": "^1.4.0", "parseurl": "^1.3.3", "proxy-addr": "^2.0.7", "qs": "^6.14.0", "range-parser": "^1.2.1", "router": "^2.2.0", "send": "^1.1.0", "serve-static": "^2.2.0", "statuses": "^2.0.1", "type-is": "^2.0.1", "vary": "^1.1.2" } }, "sha512-hIS4idWWai69NezIdRt2xFVofaF4j+6INOpJlVOLDO8zXGpUVEVzIYk12UUi2JzjEzWL3IOAxcTubgz9Po0yXw=="],
 
     "express-rate-limit": ["express-rate-limit@7.5.1", "", { "peerDependencies": { "express": ">= 4.11" } }, "sha512-7iN8iPMDzOMHPUYllBEsQdWVB6fPDMPqwjBaFrgr4Jgr/+okjvzAy+UHlYYL/Vs0OsOrMkwS6PJDkFlJwoxUnw=="],
 
     "extend-shallow": ["extend-shallow@2.0.1", "", { "dependencies": { "is-extendable": "^0.1.0" } }, "sha512-zCnTtlxNoAiDc3gqY2aYAWFx7XWWiasuF2K8Me5WbN8otHKTUKBwjPtNpRs/rbUZm7KxWAaNj7P1a/p52GbVug=="],
 
-    "fast-check": ["fast-check@4.8.0", "", { "dependencies": { "pure-rand": "^8.0.0" } }, "sha512-GOJ158CUMnN6cSahsv4+ExARvIDuzzinFjkp0E9WtiBa5zcVeLozVkWaE4IzFcc+Y48Wp1EDlUZsXRyAztQcSg=="],
-
     "fast-deep-equal": ["fast-deep-equal@3.1.3", "", {}, "sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q=="],
 
-    "fast-uri": ["fast-uri@3.1.3", "", {}, "sha512-i70LwGWUduXqzicKXWshooq+sWL1K3WUU5rKZNG/0i3a1OSoX3HqhH5WbWwTmqWfor4urUakGPiRQcleRZTwOg=="],
+    "fast-uri": ["fast-uri@3.1.0", "", {}, "sha512-iPeeDKJSWf4IEOasVVrknXpaBV0IApz/gp7S2bb7Z4Lljbl2MGJRqInZiUrQwV16cpzw/D3S5j5Julj/gT52AA=="],
 
-    "fast-xml-builder": ["fast-xml-builder@1.3.0", "", { "dependencies": { "path-expression-matcher": "^1.6.2", "xml-naming": "^0.3.0" } }, "sha512-F74cZEdCvuw9P41GAC3rod4X04jjWGM1JPEv/GWSqFTWLsdyMSBMBMlm9Hk3GLBgLBbdBNY8yee0pQh2RBVESQ=="],
+    "fast-xml-builder": ["fast-xml-builder@1.1.4", "", { "dependencies": { "path-expression-matcher": "^1.1.3" } }, "sha512-f2jhpN4Eccy0/Uz9csxh3Nu6q4ErKxf0XIsasomfOihuSUa3/xw6w8dnOtCDgEItQFJG8KyXPzQXzcODDrrbOg=="],
 
-    "fast-xml-parser": ["fast-xml-parser@5.10.1", "", { "dependencies": { "@nodable/entities": "^3.0.0", "fast-xml-builder": "^1.2.0", "is-unsafe": "^2.0.0", "path-expression-matcher": "^1.6.2", "strnum": "^2.4.1", "xml-naming": "^0.3.0" }, "bin": { "fxparser": "src/cli/cli.js" } }, "sha512-IEMIf7298kXuZSRFoGfMYrl7is8LpavODgbNz1cwIudv7KwVFnuU+UsMporfq6PD6aXSlawZlARiA3UywCTfMw=="],
+    "fast-xml-parser": ["fast-xml-parser@5.5.8", "", { "dependencies": { "fast-xml-builder": "^1.1.4", "path-expression-matcher": "^1.2.0", "strnum": "^2.2.0" }, "bin": { "fxparser": "src/cli/cli.js" } }, "sha512-Z7Fh2nVQSb2d+poDViM063ix2ZGt9jmY1nWhPfHBOK2Hgnb/OW3P4Et3P/81SEej0J7QbWtJqxO05h8QYfK7LQ=="],
 
     "finalhandler": ["finalhandler@2.1.1", "", { "dependencies": { "debug": "^4.4.0", "encodeurl": "^2.0.0", "escape-html": "^1.0.3", "on-finished": "^2.4.1", "parseurl": "^1.3.3", "statuses": "^2.0.1" } }, "sha512-S8KoZgRZN+a5rNwqTxlZZePjT/4cnm0ROV70LedRHZ0p8u9fRID0hJUZQpkKLzro8LfmC8sx23bY6tVNxv8pQA=="],
 
-    "form-data": ["form-data@4.0.6", "", { "dependencies": { "asynckit": "^0.4.0", "combined-stream": "^1.0.8", "es-set-tostringtag": "^2.1.0", "hasown": "^2.0.4", "mime-types": "^2.1.35" } }, "sha512-vKatAh4SlVfgbv+YtmhiRjhEMJsYpsG1Y2rMQtR+SVSbytsSD1YGzDIcrAJmdFec88u/+VoGmxnl+80gL1tRCQ=="],
+    "form-data": ["form-data@4.0.5", "", { "dependencies": { "asynckit": "^0.4.0", "combined-stream": "^1.0.8", "es-set-tostringtag": "^2.1.0", "hasown": "^2.0.2", "mime-types": "^2.1.12" } }, "sha512-8RipRLol37bNs2bhoV67fiTEvdTrbMUYcFTiy3+wuuOnUog2QBHCZWXDRijWQfAkhBj2Uf5UnVaiWwA5vdd82w=="],
 
     "form-data-encoder": ["form-data-encoder@1.7.2", "", {}, "sha512-qfqtYan3rxrnCk1VYaA4H+Ms9xdpPqvLZa6xmMgFvhO32x7/3J/ExcTd6qpxM0vH2GdMI+poehyBZvqfMTto8A=="],
 
@@ -432,11 +397,9 @@
 
     "has-tostringtag": ["has-tostringtag@1.0.2", "", { "dependencies": { "has-symbols": "^1.0.3" } }, "sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw=="],
 
-    "hasown": ["hasown@2.0.4", "", { "dependencies": { "function-bind": "^1.1.2" } }, "sha512-T2UbfbBEF32wiepXIsMlTW9+dDYC6wMh/t/vYA4tuOMKqWz/n3vr1NFSxQiyP+zk2mXsoMA/i/7qV6LKut1t1A=="],
+    "hasown": ["hasown@2.0.2", "", { "dependencies": { "function-bind": "^1.1.2" } }, "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ=="],
 
-    "heic-decode": ["heic-decode@2.1.0", "", { "dependencies": { "libheif-js": "^1.19.8" } }, "sha512-0fB3O3WMk38+PScbHLVp66jcNhsZ/ErtQ6u2lMYu/YxXgbBtl+oKOhGQHa4RpvE68k8IzbWkABzHnyAIjR758A=="],
-
-    "hono": ["hono@4.12.30", "", {}, "sha512-emn+JoJjrN9YTpRDS5it/UI2SO9BAE37T6I3d963RxcZ81G9A4pr2SZTEiiaiKbzx+NKRg5BZ89fCL7gCJCUog=="],
+    "hono": ["hono@4.12.10", "", {}, "sha512-mx/p18PLy5og9ufies2GOSUqep98Td9q4i/EF6X7yJgAiIopxqdfIO3jbqsi3jRgTgw88jMDEzVKi+V2EF+27w=="],
 
     "http-errors": ["http-errors@2.0.1", "", { "dependencies": { "depd": "~2.0.0", "inherits": "~2.0.4", "setprototypeof": "~1.2.0", "statuses": "~2.0.2", "toidentifier": "~1.0.1" } }, "sha512-4FbRdAX+bSdmo4AUFuS0WNiPz8NgFt+r8ThgNWmlrjQjt1Q7ZR9+zTlce2859x4KSXrwIsaeTqDoKQmtP8pLmQ=="],
 
@@ -446,7 +409,7 @@
 
     "inherits": ["inherits@2.0.4", "", {}, "sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ=="],
 
-    "ip-address": ["ip-address@10.2.0", "", {}, "sha512-/+S6j4E9AHvW9SWMSEY9Xfy66O5PWvVEJ08O0y5JGyEKQpojb0K0GKpz/v5HJ/G0vi3D2sjGK78119oXZeE0qA=="],
+    "ip-address": ["ip-address@10.1.0", "", {}, "sha512-XXADHxXmvT9+CRxhXg56LJovE+bmWnEWB78LB83VZTprKTmaC5QfruXocxzTZ2Kl0DNwKuBdlIhjL8LeY8Sf8Q=="],
 
     "ipaddr.js": ["ipaddr.js@1.9.1", "", {}, "sha512-0KI/607xoxSToH7GjN1FfSbLoU0+btTicjsQSWQlh/hZykN8KpmMf7uYwPW3R+akZ6R/w18ZlXSHBYXiYUPO3g=="],
 
@@ -454,13 +417,11 @@
 
     "is-promise": ["is-promise@4.0.0", "", {}, "sha512-hvpoI6korhJMnej285dSg6nu1+e6uxs7zG3BYAm5byqDsgJNWwxzM6z6iZiAgQR4TJ30JmBTOwqZUw3WlyH3AQ=="],
 
-    "is-unsafe": ["is-unsafe@2.0.0", "", {}, "sha512-2LdV822R+wmI86unXA93WCFpL6g+av8ynWk0nrHyJqGop5VoocYsSLFgN8jrfalT6iGeLNM4KXuVSsULP53kEA=="],
-
     "isexe": ["isexe@2.0.0", "", {}, "sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw=="],
 
     "jose": ["jose@6.2.2", "", {}, "sha512-d7kPDd34KO/YnzaDOlikGpOurfF0ByC2sEV4cANCtdqLlTfBlw2p14O/5d/zv40gJPbIQxfES3nSx1/oYNyuZQ=="],
 
-    "js-yaml": ["js-yaml@3.15.0", "", { "dependencies": { "argparse": "^1.0.7", "esprima": "^4.0.0" }, "bin": { "js-yaml": "bin/js-yaml.js" } }, "sha512-ttBQIIQPDeLjpPOohtUdXuXUVoA2uIB6fEH9HyJ7234s5mBJ5wTx20njxplLZQgLaOfpmPQA7X2t5AX6tIPbog=="],
+    "js-yaml": ["js-yaml@3.14.2", "", { "dependencies": { "argparse": "^1.0.7", "esprima": "^4.0.0" }, "bin": { "js-yaml": "bin/js-yaml.js" } }, "sha512-PMSmkqxr106Xa156c2M265Z+FTrPl+oxd/rgOQy2tijQeK5TxQ43psO1ZCwhVOSdnn+RzkzlRz/eY4BgJBYVpg=="],
 
     "json-schema": ["json-schema@0.4.0", "", {}, "sha512-es94M3nTIfsEPisRafak+HDLfHXnKBhV3vU5eqPcS3flIWqcxJWgXHXiey3YrpaNsanY5ei1VoYEbOzijuq9BA=="],
 
@@ -470,9 +431,7 @@
 
     "kind-of": ["kind-of@6.0.3", "", {}, "sha512-dcS1ul+9tmeD95T+x28/ehLgd9mENa3LsvDTtzm3vyBEO7RPptvAD+t44WVXaUjTBRcrpFeFlC8WCruUR456hw=="],
 
-    "libheif-js": ["libheif-js@1.19.8", "", {}, "sha512-vQJWusIxO7wavpON1dusciL8Go9jsIQ+EUrckauFYAiSTjcmLAsuJh3SszLpvkwPci3JcL41ek2n+LUZGFpPIQ=="],
-
-    "marked": ["marked@18.0.6", "", { "bin": { "marked": "bin/marked.js" } }, "sha512-MrV5puXBfuiy6wl6DLaq3BtIJQAJToAd5zt/ZKhRfGRAuFPALE7/4Y7jnxRQoEgK/pBgurGqLyAuRgZ2xOjr6w=="],
+    "marked": ["marked@18.0.0", "", { "bin": { "marked": "bin/marked.js" } }, "sha512-2e7Qiv/HJSXj8rDEpgTvGKsP8yYtI9xXHKDnrftrmnrJPaFNM7VRb2YCzWaX4BP1iCJ/XPduzDJZMFoqTCcIMA=="],
 
     "math-intrinsics": ["math-intrinsics@1.1.0", "", {}, "sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g=="],
 
@@ -504,7 +463,7 @@
 
     "parseurl": ["parseurl@1.3.3", "", {}, "sha512-CiyeOxFT/JZyN5m0z9PfXw4SCBJ6Sygz1Dpl0wqjlhDEGGBP1GnsUVEL0p63hoG1fcj3fHynXi9NYO4nWOL+qQ=="],
 
-    "path-expression-matcher": ["path-expression-matcher@1.6.2", "", {}, "sha512-enSlaiat05iasnzmgNxRj8reFdj3puY2QpNgP1aPIaVfT6nn9ICuPoFlKHk8EN22HcwewshO+mN2DGbkCEOtqQ=="],
+    "path-expression-matcher": ["path-expression-matcher@1.5.0", "", {}, "sha512-cbrerZV+6rvdQrrD+iGMcZFEiiSrbv9Tfdkvnusy6y0x0GKBXREFg/Y65GhIfm0tnLntThhzCnfKwp1WRjeCyQ=="],
 
     "path-key": ["path-key@3.1.1", "", {}, "sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q=="],
 
@@ -518,16 +477,12 @@
 
     "proxy-addr": ["proxy-addr@2.0.7", "", { "dependencies": { "forwarded": "0.2.0", "ipaddr.js": "1.9.1" } }, "sha512-llQsMLSUDUPT44jdrU/O37qlnifitDP+ZwrmmZcoSKyLKvtZxpyV0n2/bD/N4tBAAZ/gJEdZU7KMraoK1+XYAg=="],
 
-    "pure-rand": ["pure-rand@8.4.0", "", {}, "sha512-IoM8YF/jY0hiugFo/wOWqfmarlE6J0wc6fDK1PhftMk7MGhVZl88sZimmqBBFomLOCSmcCCpsfj7wXASCpvK9A=="],
-
-    "qs": ["qs@6.15.3", "", { "dependencies": { "es-define-property": "^1.0.1", "side-channel": "^1.1.1" } }, "sha512-O9gl3zCl5h5blw1KGUzQKhA5oUXSl8rwUIM5o0S3nCXMliSvy5Dzx7/DJcI+SwgICv+IneSZwhBh1oSyEHA71A=="],
+    "qs": ["qs@6.15.0", "", { "dependencies": { "side-channel": "^1.1.0" } }, "sha512-mAZTtNCeetKMH+pSjrb76NAM8V9a05I9aBZOHztWy/UqcJdQYNsf59vrRKWnojAT9Y+GbIvoTBC++CPHqpDBhQ=="],
 
     "range-parser": ["range-parser@1.2.1", "", {}, "sha512-Hrgsx+orqoygnmhFbKaHE6c296J+HTAQXoxEF6gNupROmmGJRoyzfG3ccAveqCBrwr/2yxQ5BVd/GTl5agOwSg=="],
 
     "raw-body": ["raw-body@3.0.2", "", { "dependencies": { "bytes": "~3.1.2", "http-errors": "~2.0.1", "iconv-lite": "~0.7.0", "unpipe": "~1.0.0" } }, "sha512-K5zQjDllxWkf7Z5xJdV0/B0WTNqx6vxG70zJE4N0kBs4LovmEYWJzQGxC9bS9RAKu3bgM40lrd5zoLJ12MQ5BA=="],
 
-    "readdirp": ["readdirp@4.1.2", "", {}, "sha512-GDhwkLfywWL2s6vEjyhri+eXmfH6j1L7JE27WhqLeYzoh/A3DBaYGEj2H/HFZCn/kMfim73FXxEJTw06WtxQwg=="],
-
     "require-from-string": ["require-from-string@2.0.2", "", {}, "sha512-Xf0nWe6RseziFMu+Ap9biiUbmplq6S9/p+7w7YXP/JBHhrUDDUhwa+vANyubuqfZWTveU//DYVGsDG7RKL/vEw=="],
 
     "router": ["router@2.2.0", "", { "dependencies": { "debug": "^4.4.0", "depd": "^2.0.0", "is-promise": "^4.0.0", "parseurl": "^1.3.3", "path-to-regexp": "^8.0.0" } }, "sha512-nLTrUKm2UyiL7rlhapu/Zl45FwNgkZGaCpZbIHajDYgwlJCOzLSk+cIPAnsEqV955GjILJnKbdQC1nVPz+gAYQ=="],
@@ -546,9 +501,9 @@
 
     "shebang-regex": ["shebang-regex@3.0.0", "", {}, "sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A=="],
 
-    "side-channel": ["side-channel@1.1.1", "", { "dependencies": { "es-errors": "^1.3.0", "object-inspect": "^1.13.4", "side-channel-list": "^1.0.1", "side-channel-map": "^1.0.1", "side-channel-weakmap": "^1.0.2" } }, "sha512-6x6dK6zJdpTzF4sQeNYxwtvBzf6Eg4GtlesS94HOvTudUeyK2WXAaIfmDgsyslYrRBeFIlsi54AYsFGUuhmvrQ=="],
+    "side-channel": ["side-channel@1.1.0", "", { "dependencies": { "es-errors": "^1.3.0", "object-inspect": "^1.13.3", "side-channel-list": "^1.0.0", "side-channel-map": "^1.0.1", "side-channel-weakmap": "^1.0.2" } }, "sha512-ZX99e6tRweoUXqR+VBrslhda51Nh5MTQwou5tnUDgbtyM0dBgmhEDtWGP/xbKn6hqfPRHujUNwz5fy/wbbhnpw=="],
 
-    "side-channel-list": ["side-channel-list@1.0.1", "", { "dependencies": { "es-errors": "^1.3.0", "object-inspect": "^1.13.4" } }, "sha512-mjn/0bi/oUURjc5Xl7IaWi/OJJJumuoJFQJfDDyO46+hBWsfaVM65TBHq2eoZBhzl9EchxOijpkbRC8SVBQU0w=="],
+    "side-channel-list": ["side-channel-list@1.0.0", "", { "dependencies": { "es-errors": "^1.3.0", "object-inspect": "^1.13.3" } }, "sha512-FCLHtRD/gnpCiCHEiJLOwdmFP+wzCmDEkc9y7NsYxeF4u7Btsn1ZuwgwJGxImImHicJArLP4R0yX4c2KCrMrTA=="],
 
     "side-channel-map": ["side-channel-map@1.0.1", "", { "dependencies": { "call-bound": "^1.0.2", "es-errors": "^1.3.0", "get-intrinsic": "^1.2.5", "object-inspect": "^1.13.3" } }, "sha512-VCjCNfgMsby3tTdo02nbjtM/ewra6jPHmpThenkTYh8pG9ucZ/1P8So4u4FGBek/BjpOVsDCMoLA/iuBKIFXRA=="],
 
@@ -560,7 +515,7 @@
 
     "strip-bom-string": ["strip-bom-string@1.0.0", "", {}, "sha512-uCC2VHvQRYu+lMh4My/sFNmF2klFymLX1wHJeXnbEJERpV/ZsVuonzerjfrGpIGF7LBVa1O7i9kjiWvJiFck8g=="],
 
-    "strnum": ["strnum@2.4.1", "", { "dependencies": { "anynum": "^1.0.1" } }, "sha512-M9eUSMT2dCB2cTNPG7UYj6KuK7RJR2SN2+yCV/fTW3xzTCS6EaGZ5pSMgDIjB7r8zSfTGk+dvvn9rTjpVS9Mwg=="],
+    "strnum": ["strnum@2.2.3", "", {}, "sha512-oKx6RUCuHfT3oyVjtnrmn19H1SiCqgJSg+54XqURKp5aCMbrXrhLjRN9TjuwMjiYstZ0MzDrHqkGZ5dFTKd+zg=="],
 
     "toidentifier": ["toidentifier@1.0.1", "", {}, "sha512-o5sSPKEkg/DIQNmH43V0/uerLrpzVedkUh8tGNvaeXpfpuwjKenlSox/2O/BTlZUtEe+JG7s5YhEz608PlAHRA=="],
 
@@ -580,8 +535,6 @@
 
     "vary": ["vary@1.1.2", "", {}, "sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg=="],
 
-    "wasm-feature-detect": ["wasm-feature-detect@1.8.0", "", {}, "sha512-zksaLKM2fVlnB5jQQDqKXXwYHLQUVH9es+5TOOHwGOVJOCeRBCiPjwSg+3tN2AdTCzjgli4jijCH290kXb/zWQ=="],
-
     "web-streams-polyfill": ["web-streams-polyfill@4.0.0-beta.3", "", {}, "sha512-QW95TCTaHmsYfHDybGMwO5IJIM93I/6vTRk+daHTWFPhwh+C8Cg7j7XyKrwrj8Ib6vYXe0ocYNrmzY4xAAN6ug=="],
 
     "web-tree-sitter": ["web-tree-sitter@0.22.6", "", {}, "sha512-hS87TH71Zd6mGAmYCvlgxeGDjqd9GTeqXNqTT+u0Gs51uIozNIaaq/kUAbV/Zf56jb2ZOyG8BxZs2GG9wbLi6Q=="],
@@ -594,8 +547,6 @@
 
     "wrappy": ["wrappy@1.0.2", "", {}, "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="],
 
-    "xml-naming": ["xml-naming@0.3.0", "", {}, "sha512-ghig2TBE/H11aOVgmahA3MhimvkBr6JIYknH/Dhdk10nXwdbIqBJsbfMxpvFPG8bAw77gN29aQWvKpmVoPlvPQ=="],
-
     "zod": ["zod@4.3.6", "", {}, "sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg=="],
 
     "zod-to-json-schema": ["zod-to-json-schema@3.25.2", "", { "peerDependencies": { "zod": "^3.25.28 || ^4" } }, "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA=="],
@@ -614,16 +565,12 @@
 
     "@types/bun/bun-types": ["bun-types@1.3.11", "", { "dependencies": { "@types/node": "*" } }, "sha512-1KGPpoxQWl9f6wcZh57LvrPIInQMn2TQ7jsgxqpRzg+l0QPOFvJVH7HmvHo/AiPgwXy+/Thf6Ov3EdVn1vOabg=="],
 
-    "es-set-tostringtag/hasown": ["hasown@2.0.2", "", { "dependencies": { "function-bind": "^1.1.2" } }, "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ=="],
-
     "eventsource/eventsource-parser": ["eventsource-parser@3.0.6", "", {}, "sha512-Vo1ab+QXPzZ4tCa8SwIHJFaSzy4R6SHf7BY79rFBDf0idraZWAkYrDjDj8uWaSm3S2TK+hJ7/t1CEmZ7jXw+pg=="],
 
     "express/cookie-signature": ["cookie-signature@1.2.2", "", {}, "sha512-D76uU73ulSXrD1UXF4KE2TMxVVwhsnCgfAyTg9k8P6KGZjlXKrOLe4dJQKI3Bxi5wjesZoFXJWElNWBjPZMbhg=="],
 
     "form-data/mime-types": ["mime-types@2.1.35", "", { "dependencies": { "mime-db": "1.52.0" } }, "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw=="],
 
-    "get-intrinsic/hasown": ["hasown@2.0.2", "", { "dependencies": { "function-bind": "^1.1.2" } }, "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ=="],
-
     "openai/@types/node": ["@types/node@18.19.130", "", { "dependencies": { "undici-types": "~5.26.4" } }, "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg=="],
 
     "@anthropic-ai/sdk/@types/node/undici-types": ["undici-types@5.26.5", "", {}, "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="],

bunfig.toml

@@ -7,15 +7,3 @@
 # also pass `--timeout=60000` explicitly so the ceiling is consistent
 # whether tests are invoked through the wrapper or directly via bun test.
 timeout = 60_000
-
-# v0.37 fix wave: pin gateway defaults to legacy OpenAI/1536 BEFORE any
-# test runs, so the 20+ test files with hardcoded 1536-d Float32Array
-# fixtures still match the schema. v0.37's production default is ZE/1280;
-# tests that want the new default call configureGateway() explicitly in
-# their own beforeAll.
-#
-# #2823: redirect GBRAIN_AUDIT_DIR to a per-run scratch dir BEFORE any test
-# runs, so audit-emitting code paths (content-sanity, shell-audit, etc.)
-# can't leak fixture events into the operator's real ~/.gbrain/audit/. See
-# test/helpers/audit-dir-preload.ts for the full rationale.
-preload = ["./test/helpers/legacy-embedding-preload.ts", "./test/helpers/audit-dir-preload.ts"]

docker-compose.ci.yml

@@ -85,40 +85,6 @@ services:
     volumes:
       - gbrain-ci-pg-data-4:/var/lib/postgresql/data
 
-  # v0.43 (#2084 / eng-review TD1): PgBouncer in TRANSACTION pooling mode
-  # fronting postgres-1 — the production topology (Supabase direct :5432 +
-  # pooled :6543) behind three consecutive pooler-teardown waves
-  # (#1972 → #2015 → #2084) that CI could never reproduce.
-  # test/e2e/pgbouncer-teardown.test.ts uses a DEDICATED database
-  # (gbrain_pgbouncer) on postgres-1 so it never races shard 1's
-  # TRUNCATE-based fixtures; pgbouncer's wildcard [databases] section
-  # forwards any dbname to DB_HOST.
-  pgbouncer:
-    image: edoburu/pgbouncer:latest
-    environment:
-      DB_HOST: postgres-1
-      DB_PORT: "5432"
-      DB_USER: postgres
-      DB_PASSWORD: postgres
-      POOL_MODE: transaction
-      # plain (CI-only): pg16 stores SCRAM verifiers, and pgbouncer can only
-      # answer the server's SCRAM challenge when its userlist holds the
-      # PLAINTEXT password — an md5-hashed userlist fails with
-      # "server login failed: wrong password type".
-      AUTH_TYPE: plain
-      MAX_CLIENT_CONN: "200"
-      DEFAULT_POOL_SIZE: "10"
-      # gbrain's client sets statement_timeout + idle_in_transaction_session_timeout
-      # as startup parameters (db.ts buildConnectionParams); the Supabase pooler
-      # whitelists them, so this pooler must too or every connection is refused
-      # before the teardown path is even reached.
-      IGNORE_STARTUP_PARAMETERS: extra_float_digits,statement_timeout,idle_in_transaction_session_timeout,search_path
-    ports:
-      - "${GBRAIN_CI_PGBOUNCER_PORT:-6543}:5432"
-    depends_on:
-      postgres-1:
-        condition: service_healthy
-
   runner:
     image: oven/bun:1
     working_dir: /app
@@ -131,8 +97,6 @@ services:
         condition: service_healthy
       postgres-4:
         condition: service_healthy
-      pgbouncer:
-        condition: service_started
     # No global DATABASE_URL — scripts/ci-local.sh sets per-shard URL via -e.
     # Unit phase explicitly unsets DATABASE_URL so test/e2e/* gracefully skip.
     volumes:

docs/ENGINES.md

@@ -94,7 +94,7 @@ export interface BrainEngine {
 
 **Slug-based API, not ID-based.** Every method takes slugs, not numeric IDs. The engine resolves slugs to IDs internally. This keeps the interface portable... slugs are strings, IDs are database-specific.
 
-**Embedding is NOT in the engine.** The engine stores embeddings and searches by vector, but it doesn't generate embeddings. `src/core/embedding.ts` handles that (a thin delegation to the provider-agnostic AI gateway in `src/core/ai/gateway.ts`). This is intentional: embedding is an external API call (OpenAI, Voyage, a local Ollama — whichever provider you configured), not a storage concern. All engines share the same embedding service.
+**Embedding is NOT in the engine.** The engine stores embeddings and searches by vector, but it doesn't generate embeddings. `src/core/embedding.ts` handles that. This is intentional: embedding is an external API call (OpenAI), not a storage concern. All engines share the same embedding service.
 
 **Chunking is NOT in the engine.** Same logic. `src/core/chunkers/` handles chunking. The engine stores and retrieves chunks. All engines share the same chunkers.
 
@@ -148,51 +148,6 @@ RRF fusion, multi-query expansion, and 4-layer dedup are engine-agnostic. They o
 
 **Why not self-hosted for v0:** The brain should be infrastructure agents use, not something you maintain. Self-hosted Postgres with Docker is a welcome community PR, but v0 optimizes for zero ops.
 
-### Opt-in RLS source-scope binding (`GBRAIN_RLS_SCOPE_BINDING`)
-
-Defense-in-depth layer for Postgres deployments that want the database itself
-to enforce source isolation, in addition to the mandatory app-layer filters
-(`sourceScopeOpts` — layer 1, always on).
-
-**Mechanism.** With `GBRAIN_RLS_SCOPE_BINDING=1` (or `true`), the engine's
-source-scoped read methods wrap their queries in a transaction that first runs
-`SELECT set_config('app.scopes', $1, true)` — the value is a bound parameter
-(federated `sourceIds` CSV > scalar `sourceId` > `'*'` for unscoped internal
-reads), transaction-local (equivalent to `SET LOCAL`, which itself can't take
-bound params). An RLS policy can then filter rows by
-`current_setting('app.scopes', true)`.
-
-**Default off.** With the env var unset, reads call through on the shared pool
-exactly as before — no per-read transaction, no pool-slot hold (the search
-methods keep the transaction they always had for their `SET LOCAL
-statement_timeout`). Existing operators see zero behavior change.
-
-**Enabling it** (operator-managed SQL; gbrain ships no DDL for this):
-
-```sql
-ALTER TABLE pages ENABLE ROW LEVEL SECURITY;
-CREATE POLICY pages_scope_filter ON pages
-  USING (current_setting('app.scopes', true) = '*'
-         OR source_id = ANY(string_to_array(current_setting('app.scopes', true), ',')));
-
--- Required: connections that don't run through the scoped read helper
--- (admin, autopilot, cycle, writes) must default to unscoped, or they
--- see zero rows once the policy exists:
-ALTER ROLE <runtime-role> SET app.scopes = '*';
-
--- If the runtime role OWNS the table, RLS is skipped for it unless forced:
-ALTER TABLE pages FORCE ROW LEVEL SECURITY;
-```
-
-Safe to enable in either order: the env var without a policy is a no-op
-setting; a policy without the env var is enforced only via the role default.
-
-**Honest caveat:** only read paths routed through the scoped helper carry a
-per-request scope binding — unwrapped paths (writes, admin/maintenance reads)
-run under the role default and are not backstopped per caller. This is layer 2;
-the app-layer source filters remain layer 1 and stay mandatory. Behavioral pins
-live in `test/postgres-engine-rls-scope.test.ts`.
-
 ## PGLiteEngine (v0.7, ships)
 
 **Dependencies:** `@electric-sql/pglite` (v0.4.4+)
@@ -221,39 +176,6 @@ live in `test/postgres-engine-rls-scope.test.ts`.
 
 **Migration:** `gbrain migrate --to supabase` exports everything (pages, chunks, embeddings, links, tags, timeline) and imports into Supabase. `gbrain migrate --to pglite` goes the other direction. Bidirectional, lossless.
 
-## JSONB writes: never double-encode (the #2339 trap)
-
-Writing a JS value into a `jsonb` column has exactly two correct forms. Get this
-wrong and the write succeeds on PGLite but stores a **jsonb string scalar** on
-real Postgres — `col ->> 'k'` returns NULL, `jsonb_array_elements` throws, and a
-`jsonb_typeof = 'array'` CHECK rejects the row (this aborted every sync in #2339).
-
-| Form | Verdict |
-|---|---|
-| Template tag: `` sql`... ${sql.json(obj)}` `` (postgres-engine only) | ✅ native jsonb serialization |
-| Positional raw call, raw object: `executeRawJsonb(engine, sql, scalars, [obj])` | ✅ object reaches the wire as jsonb |
-| Positional raw call, stringified: `executeRaw(\`... $N::text::jsonb\`, [JSON.stringify(x)])` | ✅ binds as text, the cast parses it |
-| Positional raw call, BARE cast: `executeRaw(\`... $N::jsonb\`, [JSON.stringify(x)])` | ❌ **double-encodes** under postgres.js `.unsafe()` |
-| Template literal interpolation: `` `... ${JSON.stringify(x)}::jsonb` `` | ❌ double-encodes |
-
-**Why:** postgres.js `.unsafe(sql, params)` (the path behind `executeRaw` /
-`executeRawDirect`) binds a JS **string** as a text param. A bare `$N::jsonb`
-cast then wraps that already-JSON string into a jsonb scalar string instead of
-parsing it. Casting through `$N::text::jsonb` forces a text→jsonb parse.
-**PGLite's `db.query` parses text→jsonb natively, so it hides the bug** — which is
-why a regression only shows up on Postgres (and why the parity test must run there).
-
-**Two CI guards enforce this, both wired into `scripts/check-jsonb-pattern.sh`:**
-- the template-tag grep (`${JSON.stringify(x)}::jsonb`), and
-- `scripts/check-jsonb-params.mjs`, an AST-lite scanner for the positional
-  `$N::jsonb` + `JSON.stringify` form the grep misses. Sanctioned escapes:
-  `$N::text::jsonb`, `$N::text[]`, `executeRawJsonb`, `sql.json`, or an inline
-  `jsonb-guard-ok` comment.
-
-The real backstop is `test/e2e/op-checkpoint-jsonb-parity.test.ts` +
-`test/e2e/jsonb-roundtrip.test.ts`, which round-trip writes through real Postgres
-and assert `jsonb_typeof` — the assertion PGLite cannot make.
-
 ## Adding a new engine
 
 1. Create `src/core/<name>-engine.ts` implementing `BrainEngine`

docs/GBRAIN_SKILLPACK.md

@@ -102,16 +102,6 @@ Keeping it running and up to date.
 | [Upgrades & Auto-Update](guides/upgrades-auto-update.md) | check-update, agent notifications, migration files |
 | [Live Sync](guides/live-sync.md) | Keep the index current: cron, --watch, webhook approaches |
 
-## Getting Started
-
-After setup, the brain is empty. The cold-start skill sequences the highest-leverage
-data sources to populate it:
-
-| Guide | What It Covers |
-|-------|---------------|
-| [Cold Start](../skills/cold-start/SKILL.md) | Day-one bootstrapping: contacts, calendar, email, conversations, social, archives. Uses ClawVisor for safe credential handling — agents never hold raw API keys. |
-| [Ask User](../skills/ask-user/SKILL.md) | Choice-gate pattern for human input at decision points. Used by cold-start and other skills. |
-
 ---
 
 ## Appendix: GBrain CLI Quick Reference

docs/GBRAIN_V0.md

@@ -1,12 +1,5 @@
 # GBrain v0: Postgres-Native Personal Knowledge Brain
 
-> **Historical design doc.** This is the original v0 spec from before PGLite landed. Several
-> forward-looking sections — most notably the SQLite engine plan — were superseded by
-> PGLite (embedded Postgres via WASM), which uses the same SQL dialect as Postgres and
-> eliminates the need for a separate FTS5/sqlite-vss translation layer. Kept here for
-> historical context; see [`ENGINES.md`](ENGINES.md) for the current engine architecture and
-> the [`CHANGELOG.md`](../CHANGELOG.md) for the actual implementation history.
-
 ## What this is
 
 GBrain is a compiled intelligence system. Not a note-taking app. Not "chat with your notes."
@@ -524,7 +517,7 @@ See `docs/ENGINES.md` for the pluggable engine architecture and future backend p
 - **Intelligence compiler.** Treat every fact as a first-class claim with source span, entity links, validity window, confidence, and contradiction status. "What changed, why, and what evidence would flip it again?" From Codex review. Builds on compiled truth model.
 - **Active skills via Trigger.dev.** Application-specific briefings, meeting prep. Belongs in OpenClaw, not generic brain infra.
 - **Multi-user access.** Supabase RLS + per-user API keys. v0 is single-user.
-- **SQLite engine.** Superseded by PGLite (embedded Postgres 17 via WASM) before v1. See [`ENGINES.md`](ENGINES.md) for the current engine architecture.
+- **SQLite engine.** Community PRs welcome. See `docs/SQLITE_ENGINE.md`.
 - **Docker Compose for self-hosted Postgres.** Community PRs welcome.
 - **Web UI.** Optional Vercel-hosted dashboard for browsing brain pages.
 
@@ -538,7 +531,7 @@ This means:
 - A future DuckDB engine could implement analytics-heavy workloads
 - The CLI, MCP server, and library consumers never know which engine runs underneath
 
-See [`ENGINES.md`](ENGINES.md) for the full interface spec. (The original SQLite engine plan was superseded by PGLite; the contract-first `BrainEngine` interface made that swap clean.)
+See `docs/ENGINES.md` for the full interface spec and `docs/SQLITE_ENGINE.md` for the SQLite implementation plan.
 
 ## Review history
 

docs/GBRAIN_VERIFY.md

@@ -88,15 +88,14 @@ find /data/brain -name '*.md' \
 Some difference is normal (files added since last sync), but if page count is
 less than half the file count, sync is silently skipping pages.
 
-**If page count is way too low:** The #1 cause is an unreachable direct
-connection on an IPv4-only host. GBrain uses the Transaction pooler (port 6543)
-for reads, but routes migrations, DDL, and sync transactions to a derived direct
-connection (`db.<ref>.supabase.co:5432`), which is IPv6-only.
-- On an IPv4-only host, reads work but sync transactions fail and silently skip
-  pages.
-- Fix: set `GBRAIN_DIRECT_DATABASE_URL` to the **Session pooler** string (port
-  5432 on the `pooler.supabase.com` host, IPv4), or enable Supabase's IPv4
-  add-on. Then run `gbrain sync --full` to reimport everything.
+**If page count is way too low:** The #1 cause is the connection pooler bug.
+Check your `DATABASE_URL`:
+- If it contains `pooler.supabase.com:6543`, verify it's using **Session mode**,
+  not Transaction mode.
+- Transaction mode breaks `engine.transaction()` and causes `.begin() is not a
+  function` errors.
+- Fix: switch to Session mode pooler string, then run `gbrain sync --full`
+  to reimport everything.
 
 ### 4b. Embed Check
 
@@ -143,8 +142,7 @@ gbrain search "<text from the correction>"
 - Is `gbrain sync --watch` still alive (if using watch mode)?
 - Run `gbrain config get sync.last_run` to see when sync last ran.
 - Run `gbrain sync --repo /data/brain` manually and check for errors.
-- If sync errors mention an unreachable host or connection timeout, the direct
-  connection isn't reachable on IPv4 (see 4a above).
+- If you see `.begin() is not a function`, fix the pooler (see 4a above).
 
 ---
 

docs/INSTALL.md

@@ -1,148 +0,0 @@
-# Install
-
-Three install paths. Pick one. Mix later if needed.
-
-## 1. Run with an agent platform (recommended)
-
-Already running [OpenClaw](https://github.com/garrytan/openclaw) or [Hermes](https://github.com/garrytan/hermes)?
-
-```bash
-bun install -g github:garrytan/gbrain
-gbrain init --pglite                  # 2 seconds; no server
-gbrain skillpack scaffold --all       # 43 skills scaffolded into your agent workspace
-gbrain doctor                         # green checks all the way down
-```
-
-Your agent now reads `skills/RESOLVER.md` once per request, routes intent to the right skill, executes. New entity mentions create new pages. Daily cron runs enrichment overnight.
-
-Scaffolded skills are first-class files in your agent repo — edit freely. To pull upstream gbrain improvements later, `gbrain skillpack reference <name>` diffs your local copy vs the bundle. The legacy `skillpack install` managed-block model was retired in v0.36.0.0; if you're upgrading from an older release, run `gbrain skillpack migrate-fence` once to strip the legacy fence and keep your existing skill rows.
-
-To upgrade later: `gbrain upgrade` runs schema migrations + post-upgrade prompts (chunker bumps, the v0.36.2.0 ZeroEntropy switch). Always TTY-only; non-TTY upgrades skip prompts with informational stderr lines.
-
-## 2. CLI standalone
-
-No agent platform, just shell + MCP-aware editor.
-
-```bash
-bun install -g github:garrytan/gbrain
-gbrain init --pglite
-```
-
-> **If `bun install -g` hits a postinstall error** (Bun blocks postinstall hooks in some environments), the CLI prints a recovery hint pointing at [#218](https://github.com/garrytan/gbrain/issues/218). Run `gbrain doctor` to diagnose, then `gbrain apply-migrations --yes` manually. The deterministic fallback is `git clone https://github.com/garrytan/gbrain.git ~/gbrain && cd ~/gbrain && bun install && bun link`.
-
-The init flow detects your repo size and suggests Supabase for brains > 1000 markdown files. To switch later:
-
-```bash
-gbrain migrate --to supabase     # PGLite → Postgres
-gbrain migrate --to pglite       # Postgres → PGLite (rare)
-```
-
-For shared / large / multi-machine deployments (a team or company brain with multiple users hitting one server over HTTP MCP with OAuth scoping per user), follow the dedicated walkthrough: **[Tutorial: set up GBrain as your company brain](tutorials/company-brain.md)**.
-
-API keys live in `~/.gbrain/config.json` (file plane) or env vars (`OPENAI_API_KEY`, `ZEROENTROPY_API_KEY`, `VOYAGE_API_KEY`, `ANTHROPIC_API_KEY`). Set via CLI:
-
-```bash
-gbrain config set zeroentropy_api_key sk-...
-gbrain config set anthropic_api_key sk-ant-...
-```
-
-Common follow-ups:
-
-```bash
-gbrain import ~/my-knowledge      # bulk-import a markdown folder
-gbrain sync --watch               # live-sync a git repo (autopilot mode)
-gbrain autopilot --install        # background daemon for nightly enrichment
-```
-
-**Wire this same local brain into your coding agent** — zero server, zero token:
-
-```bash
-claude mcp add gbrain -- gbrain serve    # Claude Code
-codex  mcp add gbrain -- gbrain serve    # Codex
-```
-
-The agent spawns `gbrain serve` as a stdio subprocess against your local brain. Full walkthrough (both this local path and connecting to a remote brain), plus the brain-first protocol to paste into `CLAUDE.md` / `AGENTS.md`: **[Give your coding agent a memory](tutorials/connect-coding-agent.md)**.
-
-## 3. MCP server (any MCP client)
-
-```bash
-gbrain serve                      # stdio MCP (Claude Desktop / Code / Cursor)
-gbrain serve --http               # HTTP MCP with OAuth 2.1 + admin dashboard
-```
-
-**Wire a coding agent to a remote brain in one command** (when you have an HTTP
-server + a bearer token): `gbrain connect` prints a paste-ready setup block, or
-`--install` runs it and smoke-tests the token.
-
-```bash
-gbrain auth create "claude-code"
-gbrain connect https://your-host/mcp --token gbrain_xxx                      # Claude Code (default)
-gbrain connect https://your-host/mcp --token gbrain_xxx --agent codex        # Codex (env-var bearer)
-gbrain connect https://your-host/mcp --agent perplexity --oauth --register   # Perplexity (OAuth)
-```
-
-Per-client setup guides live in [`docs/mcp/`](mcp/):
-
-- [`docs/mcp/CLAUDE_CODE.md`](mcp/CLAUDE_CODE.md)
-- [`docs/mcp/CODEX.md`](mcp/CODEX.md)
-- [`docs/mcp/CLAUDE_DESKTOP.md`](mcp/CLAUDE_DESKTOP.md)
-- [`docs/mcp/CHATGPT.md`](mcp/CHATGPT.md)
-- [`docs/mcp/PERPLEXITY.md`](mcp/PERPLEXITY.md)
-- [`docs/mcp/DEPLOY.md`](mcp/DEPLOY.md) — production deploy patterns
-
-The HTTP server ships with an admin SPA at `/admin`, an SSE activity feed at `/admin/events`, DCR-style client registration, scope-gated `read`/`write`/`admin` access, and rate limiting.
-
-## Thin-client mode
-
-Connect to someone else's brain without running a local engine:
-
-```bash
-gbrain init --mcp-only            # configures remote MCP, skips local DB
-```
-
-Useful for: team mounts, brain-as-a-service deployments, dev machines without disk space. Most local commands refuse with a paste-ready hint. See [`docs/architecture/topologies.md`](architecture/topologies.md).
-
-## Verifying the install
-
-```bash
-gbrain doctor --json              # full health check
-gbrain models                     # which AI models are configured for what
-gbrain models doctor              # 1-token probe per configured model
-```
-
-If anything's yellow, `gbrain doctor` names the fix command in the message. Most issues are missing API keys or stale schema (`gbrain upgrade --force-schema`).
-
-## Troubleshooting
-
-### PGLite crashes on macOS 26.x (Tahoe)
-
-PGLite's embedded WASM engine is incompatible with macOS 26.x (Tahoe) on Apple Silicon. If `gbrain init --pglite` crashes during engine initialization, switch to native Homebrew PostgreSQL:
-
-```bash
-# Install PostgreSQL + pgvector
-brew install postgresql@17
-brew services start postgresql@17
-createdb gbrain
-
-# Build pgvector from source (required for vector search)
-cd /tmp && git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git
-cd pgvector && make && make install
-psql gbrain -c "CREATE EXTENSION IF NOT EXISTS vector;"
-
-# Point gbrain at your local Postgres
-cat > ~/.gbrain/config.json << 'EOF'
-{
-  "engine": "postgres",
-  "database_url": "postgresql://localhost:5432/gbrain",
-  "schema_pack": "gbrain-base-v2"
-}
-EOF
-
-# Run migrations and verify
-gbrain apply-migrations --yes
-gbrain doctor
-```
-
-All 102 migrations run on first try. Once `gbrain doctor` shows green, the brain works identically to PGLite — same commands, same skills, same data model. The only difference is the storage backend.
-
-> **Note:** This workaround is temporary. When the upstream WASM runtime fix ships (likely via a Bun update), `--pglite` will work on Tahoe again.

docs/RELEASING.md

@@ -1,435 +0,0 @@
-# Releasing & contributing (gbrain)
-
-The full release + contributor process. CLAUDE.md keeps the ship-critical IRON RULES
-inline (the Version-locations table, branch=workspace, post-ship `/document-release`,
-the Privacy + Responsible-disclosure rules, PR-title-version-first, never-hand-roll-ship)
-and points here for everything else. **Before any ship, read this in full. Use `/ship` —
-never hand-roll a release.**
-
-## Pre-ship requirements
-
-Before shipping (/ship) or reviewing (/review), always run the full test suite.
-Two equivalent paths:
-
-**Path A — local CI gate (recommended, v0.23.1+):**
-- `bun run ci:local` runs the entire stack inside Docker: gitleaks (host),
-  guards + typecheck, then 4-shard parallel unit + E2E against four pgvector
-  containers plus a transaction-mode PgBouncer service (unit phase keeps
-  `DATABASE_URL` unset; `--no-shard` for the legacy sequential flow). Stronger
-  than PR CI's 2-file Tier 1 set; closer to what nightly Tier 1 catches. Spins
-  up + tears down postgres automatically via `docker-compose.ci.yml`. Override
-  the host port with `GBRAIN_CI_PG_PORT=5435 bun run ci:local` if 5434 collides.
-- `bun run ci:local:diff` runs only the E2E files matched by the diff selector
-  (`scripts/select-e2e.ts`), falling back to ALL E2E files on unmapped src/
-  paths or schema/skills/package.json changes. Fast iteration during a focused
-  branch.
-
-**Path B — manual lifecycle (still supported):**
-- `bun test` — unit tests (no database required)
-- Follow the "E2E test DB lifecycle" steps above to spin up the test DB,
-  run `bun run test:e2e`, then tear it down.
-
-Both must pass. Do not ship with failing E2E tests. Do not skip E2E tests.
-
-**Always run typecheck before pushing.** `bun test` (the bun runner)
-skips TypeScript type checking — it only enforces runtime behavior.
-Three ways to actually gate on types:
-
-1. `bun run test` (npm script in `package.json`) — includes `bun run typecheck`
-   plus the four shell pre-checks (`check-jsonb-pattern.sh`,
-   `check-progress-to-stdout.sh`, `check-trailing-newline.sh`,
-   `check-wasm-embedded.sh`) before the runner. Use this mid-branch.
-2. `bun run typecheck` — `tsc --noEmit` standalone. Fast (~5s on this repo).
-3. `bun run ci:local` — the full local CI gate from Path A.
-
-The trap is: writing a new test, running `bun test test/foo.test.ts`,
-seeing it pass, pushing — and CI's separate typecheck stage rejects an
-invalid type literal that the runner accepted. Caught one of these
-shipping the v0.23.2 round-trip E2E (`type: 'reflection'` is not a
-member of `PageType`). Run `bun run typecheck` once before push, even
-when only test files changed.
-
-
-## CHANGELOG + VERSION are branch-scoped
-
-**VERSION and CHANGELOG describe what THIS branch adds vs master, not how we got
-here.** Every feature branch that ships gets its own version bump and CHANGELOG
-entry. The entry is product release notes for users; it is not a log of internal
-decisions, review rounds, or codex findings.
-
-**Write the CHANGELOG entry at /ship time, not during development.** Mid-branch
-iterations, review rounds (CEO/Eng/Codex/DX), and implementation detours belong
-in the plan file at `~/.claude/plans/`, not in the CHANGELOG. One unified entry
-per branch, covering what the branch added vs the base branch.
-
-**Never edit a CHANGELOG entry that already landed on master.** If master has
-v0.18.2 and your branch adds features, bump to the next version (v0.19.0, not
-editing master's v0.18.2). When merging master into your branch, master may
-bring new CHANGELOG entries above yours — push your entry above master's
-latest and verify:
-
-- Does CHANGELOG have your branch's own entry separate from master's entries?
-- Is VERSION higher than master's VERSION?
-- Is your entry the topmost `## [X.Y.Z]` entry?
-- `grep "^## \[" CHANGELOG.md` shows a contiguous version sequence?
-
-If any answer is no, fix it before continuing.
-
-**CHANGELOG is for users, not contributors.** Write like product release notes:
-
-- Lead with what the user can now **do** that they couldn't before. Sell the capability.
-- Plain language, not implementation details. "You can now..." not "Refactored the..."
-- **Never mention internal artifacts**: plan file IDs, decision tags (D-CX-#, F-ENG-#),
-  review rounds, codex findings, subcontractor credits. These are invisible to users.
-- Put contributor-facing changes in a separate `### For contributors` section at the bottom.
-- Every entry should make someone think "oh nice, I want to try that."
-
-**What to omit:**
-- "Codex caught X that the CEO review missed" — private process detail.
-- "D-CX-3 split errors/warnings" — tag is meaningless to users; name the feature instead.
-- "Fix-wave PR #N supersedes #M" — supersede chains belong in PR bodies, not release notes.
-- "215 new cases, 3 decisions applied, 7 reviews cleared" — these are planning-mode metrics.
-
-**What to keep:**
-- The user-facing change: what commands exist now, what flag was added, what behavior fixed.
-- Numbers that mean something to the user: TTHW, commands that timed out before, detection counts.
-- Upgrade instructions: `gbrain upgrade` + any manual step if needed.
-- Credit to external contributors when a community PR was incorporated.
-
-## CHANGELOG voice + release-summary format
-
-**IRON RULE: the CHANGELOG describes what the user gets, not how the work
-happened.** Nobody reading release notes cares that codex caught a bug, that
-the plan went through CEO + eng review, that the migration was originally
-numbered v68 and renumbered to v79 during master merge, or that two
-review rounds caught architectural mistakes. The reader cares what
-`gbrain brainstorm` does and how to use it. If a fact only exists because
-of the development process, it does NOT belong in the CHANGELOG.
-
-**Specifically forbidden in CHANGELOG entries:**
-
-- Any mention of review processes (CEO review, eng review, codex review,
-  plan-eng-review, outside voice, adversarial review, autoplan, /review).
-- "What we caught and fixed before merging" sections. Bugs found pre-merge
-  are not changes — they're things that didn't ship.
-- Plan file references, plan IDs, plan decision tags (D1, D14, D-CDX-3).
-- Migration version drama ("originally v68", "renumbered to v77", "claimed
-  by parallel waves") — just say "Migration v79 adds X." If the user
-  cares about migration ordering, they read the diff.
-- Round counts, finding counts, decision counts ("25 findings across 2
-  rounds", "8 architectural decisions", "5/6 expansions accepted").
-- Names of internal collaborators ("codex caught", "the reviewer flagged",
-  "Claude noticed").
-- "Plan + reviews" summary bullets. The plan lives in `~/.claude/plans/`;
-  if a future reader wants the backstory they can grep there.
-- Any wording that frames a shipped feature as a *recovery* from a planning
-  mistake ("the first plan was wrong", "we corrected the approach", "the
-  shipped version supersedes the original design").
-
-**Smell test:** read the entry as a stranger who has never touched gbrain.
-If any sentence makes them think "why are you telling me this?", cut it.
-Every sentence in the release-summary AND in the itemized changes must
-answer one of three questions: *What can I now do? How do I use it? What
-should I watch for after I upgrade?*
-
-Every version entry in `CHANGELOG.md` MUST start with a release-summary section in
-the GStack/Garry voice — one viewport's worth of prose + tables that lands like a
-verdict, not marketing. The itemized changelog (subsections, bullets, files) goes
-BELOW that summary, separated by a `### Itemized changes` header.
-
-The release-summary section gets read by humans, by the auto-update agent, and by
-anyone deciding whether to upgrade. The itemized list is for agents that need to
-know exactly what changed.
-
-### Release-summary template
-
-**Iron rule: lead ELI10, get precise after.** The first ~150 words of every entry
-must be readable by someone who does NOT know gbrain's internals. No file paths,
-no function names, no internal constants, no acronyms (no "RRF", no "knobsHash",
-no "MODE_BUNDLES", no "CDX-4"), no jargon that requires reading the codebase to
-parse. Lead with the user-visible behavior change, in everyday English, like
-you're explaining it to a smart engineer who has never opened the repo.
-
-THEN, once the reader knows what shipped and why they'd care, drill into the
-precise details: real file paths, real function names, real config keys, real
-numbers. The precision part is required (the entry is also the technical record
-of what changed), but it lives AFTER the plain-English lead, never before it.
-
-The shape:
-
-1. **One-line bold headline.** What changed for the user, in human English. No
-   jargon. No internal terms. Example good: "Your search stops boosting weak
-   pages just because they have a lot of links pointing at them." Example bad:
-   "PostFusionOpts gains floorRatio; KNOBS_HASH_VERSION bumped 2→3."
-2. **Plain-English opener** (~3-5 sentences). Describe the problem this fixes in
-   everyday terms. Pretend the reader has a brain full of meeting notes and
-   people pages and wants to know if this release helps them. Concrete example
-   beats abstract description.
-3. **A "How to turn it on" or "How to use it" section** with paste-ready
-   commands. Real flags, real config keys. This is where precision starts.
-4. **A "What you'd see in a concrete example" or "The X numbers that matter"
-   section** with a table. Use everyday-language column headers ("Page",
-   "Match quality", "Has many backlinks?") even when the underlying mechanism
-   is technical. The table teaches what the feature does without requiring the
-   reader to understand how.
-5. **A "What's safe to know about" or "Things to watch" section** for caveats,
-   side effects, cache invalidation, mid-deploy notes. Still in plain language.
-6. **A "What we caught and fixed before merging" section** if the work went
-   through review (CEO/eng/codex/outside-voice). Translate review findings into
-   plain English. "We caught a stale-cache bug" beats "knobsHash() did not
-   include floorRatio in the v=2 hash input."
-7. **`### Itemized changes`** (precision lives here). File paths, function
-   names, types, constants, line numbers. This section is for engineers who
-   need to know exactly what moved.
-
-Voice rules (apply throughout):
-- No em dashes (use commas, periods, "...").
-- No AI vocabulary (delve, robust, comprehensive, nuanced, fundamental, etc.) or
-  banned phrases ("here's the kicker", "the bottom line", etc.).
-- Real numbers, real file names, real commands AFTER the ELI10 lead. Not "fast"
-  but "~30s on 30K pages." In the ELI10 lead, "fast enough that you won't
-  notice" or "~30 seconds even on a big brain."
-- Short paragraphs, mix one-sentence punches with 2-3 sentence runs.
-- Connect to user outcomes: "the agent does ~3x less reading" beats "improved
-  precision."
-- Be direct about quality. "Well-designed" or "this is a mess." No dancing.
-
-**The smell test:** if someone who has never opened gbrain reads the first 150
-words and walks away knowing what shipped and whether they care, the entry
-passes. If they need to grep the codebase to follow along, rewrite the lead.
-
-**Canonical examples in this CHANGELOG:** v0.35.6.0 (floor-ratio gate, written
-ELI10-lead-first), v0.34.4.0 (embed stale fix wave). Use those shapes when in
-doubt. Avoid the shape of entries that lead with internal constants or release
-mechanics; those exist in older history but should not be the model for new
-work.
-
-Source material to pull from:
-- CHANGELOG.md previous entry for prior context
-- Latest `gbrain-evals/docs/benchmarks/[latest].md` for headline numbers (sibling repo)
-- Recent commits (`git log <prev-version>..HEAD --oneline`) for what shipped
-- Don't make up numbers. If a metric isn't in a benchmark or production data, don't
-  include it. Say "no measurement yet" if asked.
-
-Target length: ~250-350 words for the summary. Should render as one viewport.
-
-### "To take advantage of v[version]" block (required, v0.13+)
-
-After the release-summary and BEFORE `### Itemized changes`, every `## [X.Y.Z]`
-entry MUST include a human-readable self-repair block under the heading
-`## To take advantage of v[version]`.
-
-Why: `gbrain upgrade` runs `gbrain post-upgrade` which runs `gbrain apply-migrations`.
-This chain has a known weak link — `upgrade.ts` catches post-upgrade failures as
-best-effort (so the binary still works). When that chain silently fails, users end
-up with half-upgraded brains. The self-repair block gives them a paste-ready
-recovery path; the v0.13+ `~/.gbrain/upgrade-errors.jsonl` trail + `gbrain doctor`
-integration close the loop.
-
-Template (adapt the verify commands per release):
-
-```markdown
-## To take advantage of v[version]
-
-`gbrain upgrade` should do this automatically. If it didn't, or if `gbrain doctor`
-warns about a partial migration:
-
-1. **Run the orchestrator manually:**
-   ```bash
-   gbrain apply-migrations --yes
-   ```
-2. **Your agent reads `skills/migrations/v[version].md` the next time you interact with it.**
-   [One sentence on whether headless agents need manual action, or whether the
-   orchestrator already handled the mechanical side.]
-3. **Verify the outcome:**
-   ```bash
-   [release-specific verify commands, e.g. `gbrain graph ... --depth 2`]
-   gbrain stats
-   ```
-4. **If any step fails or the numbers look wrong,** please file an issue:
-   https://github.com/garrytan/gbrain/issues with:
-   - output of `gbrain doctor`
-   - contents of `~/.gbrain/upgrade-errors.jsonl` if it exists
-   - which step broke
-
-   This feedback loop is how the gbrain maintainers find fragile upgrade paths. Thank you.
-```
-
-**Skip this block** for patches that are pure bug fixes with zero user-facing action
-(rare). If the release has a schema migration, data backfill, or new feature the
-user needs to verify, the block is required.
-
-The v0.13.0 entry in CHANGELOG.md is the canonical example.
-
-### Itemized changes (the existing rules)
-
-Below the release summary, write `### Itemized changes` and continue with the
-detailed subsections (Knowledge Graph Layer, Schema migrations, Security hardening,
-Tests, etc.). Same rules as before:
-
-- Lead with what the user can now DO that they couldn't before
-- Frame as benefits and capabilities, not files changed or code written
-- Make the user think "hell yeah, I want that"
-- Bad: "Added GBRAIN_VERIFY.md installation verification runbook"
-- Good: "Your agent now verifies the entire GBrain installation end-to-end, catching
-  silent sync failures and stale embeddings before they bite you"
-- Bad: "Setup skill Phase H and Phase I added"
-- Good: "New installs automatically set up live sync so your brain never falls behind"
-- **Always credit community contributions.** When a CHANGELOG entry includes work from
-  a community PR, name the contributor with `Contributed by @username`. Contributors
-  did real work. Thank them publicly every time, no exceptions.
-
-### Reference: v0.12.0 entry as canonical example
-
-The v0.12.0 entry in CHANGELOG.md is the canonical example of the format. Match its
-structure for every future version: bold headline, lead paragraph, "numbers that
-matter" with BrainBench-style before/after table, "what this means" closer, then
-`### Itemized changes` with the detailed sections below.
-
-## Version migrations
-
-Create a migration file at `skills/migrations/v[version].md` when a release
-includes changes that existing users need to act on. The auto-update agent
-reads these files post-upgrade (Section 17, Step 4) and executes them.
-
-**You need a migration file when:**
-- New setup step that existing installs don't have (e.g., v0.5.0 added live sync,
-  existing users need to set it up, not just new installs)
-- New SKILLPACK section with a MUST ADD setup requirement
-- Schema changes that require `gbrain init` or manual SQL
-- Changed defaults that affect existing behavior
-- Deprecated commands or flags that need replacement
-- New verification steps that should run on existing installs
-- New cron jobs or background processes that should be registered
-
-**You do NOT need a migration file when:**
-- Bug fixes with no behavior changes
-- Documentation-only improvements (the agent re-reads docs automatically)
-- New optional features that don't affect existing setups
-- Performance improvements that are transparent
-
-**The key test:** if an existing user upgrades and does nothing else, will their
-brain work worse than before? If yes, migration file. If no, skip it.
-
-Write migration files as agent instructions, not technical notes. Tell the agent
-what to do, step by step, with exact commands. See `skills/migrations/v0.5.0.md`
-for the pattern.
-
-## Migration is canonical, not advisory
-
-GBrain's job is to deliver a canonical, working setup to every user on upgrade.
-Anything that looks like a "host-repo change" — AGENTS.md, cron manifests,
-launchctl units, config files outside `~/.gbrain/` — is a GBrain migration
-step, not a nudge we leave for the host-repo maintainer. Migrations edit host
-files (with backups) to make the canonical setup real. Exceptions: changes
-that require human judgment (content edits, renames that break semantics,
-host-specific handler registration where shell-exec would be an RCE surface).
-Everything mechanical ships in the migration.
-
-**Test:** if shipping a feature requires a sentence that starts with "in
-your AGENTS.md, add…" or "in your cron/jobs.json, rewrite…", the migration
-orchestrator should be doing that edit, not the user.
-
-**The exception is host-specific code.** For custom Minion handlers
-(host-specific integrations like inbox sweeps or third-party API scanners), shipping them as a
-data file the worker would exec is an RCE surface. Those get registered in
-the host's own repo via the plugin contract (`docs/guides/plugin-handlers.md`);
-the migration orchestrator emits a structured TODO to
-`~/.gbrain/migrations/pending-host-work.jsonl` + the host agent walks the
-TODOs using `skills/migrations/v0.11.0.md` — stays host-agnostic, still
-canonical.
-
-
-## Schema state tracking
-
-`~/.gbrain/update-state.json` tracks which recommended schema directories the user
-adopted, declined, or added custom. The auto-update agent (SKILLPACK Section 17)
-reads this during upgrades to suggest new schema additions without re-suggesting
-things the user already declined. The setup skill writes the initial state during
-Phase C/E. Never modify a user's custom directories or re-suggest declined ones.
-
-## GitHub Actions SHA maintenance
-
-All GitHub Actions in `.github/workflows/` are pinned to commit SHAs. Before shipping
-(`/ship`) or reviewing (`/review`), check for stale pins and update them:
-
-```bash
-for action in actions/checkout oven-sh/setup-bun actions/upload-artifact actions/download-artifact softprops/action-gh-release gitleaks/gitleaks-action; do
-  tag=$(grep -r "$action@" .github/workflows/ | head -1 | grep -o '#.*' | tr -d '# ')
-  [ -n "$tag" ] && echo "$action@$tag: $(gh api repos/$action/git/ref/tags/$tag --jq .object.sha 2>/dev/null)"
-done
-```
-
-If any SHA differs from what's in the workflow files, update the pin and version comment.
-
-
-## PR descriptions cover the whole branch
-
-Pull request titles and bodies must describe **everything in the PR diff against the
-base branch**, not just the most recent commit you made. When you open or update a
-PR, walk the full commit range with `git log --oneline <base>..<head>` and write the
-body to cover all of it. Group by feature area (schema, code, tests, docs) — not
-chronologically by commit.
-
-This matters because reviewers read the PR body to understand what's shipping. If
-the body only covers your last commit, they miss everything else and can't review
-properly. A 7-commit PR with a body that describes commit 7 is worse than no body
-at all — it actively misleads.
-
-When in doubt, run `gh pr view <N> --json commits --jq '[.commits[].messageHeadline]'`
-to see what's actually in the PR before writing the body.
-
-## Community PR wave process
-
-Never merge external PRs directly into master. Instead, use the "fix wave" workflow:
-
-1. **Categorize** — group PRs by theme (bug fixes, features, infra, docs)
-2. **Deduplicate** — if two PRs fix the same thing, pick the one that changes fewer
-   lines. Close the other with a note pointing to the winner.
-3. **Collector branch** — create a feature branch (e.g. `garrytan/fix-wave-N`), cherry-pick
-   or manually re-implement the best fixes from each PR. Do NOT merge PR branches directly —
-   read the diff, understand the fix, and write it yourself if needed.
-4. **Test the wave** — verify with `bun test && bun run test:e2e` (full E2E lifecycle).
-   Every fix in the wave must have test coverage.
-5. **Close with context** — every closed PR gets a comment explaining why and what (if
-   anything) supersedes it. Contributors did real work; respect that with clear communication
-   and thank them.
-6. **Ship as one PR** — single PR to master with all attributions preserved via
-   `Co-Authored-By:` trailers. Include a summary of what merged and what closed.
-
-**Community PR guardrails:**
-- Always AskUserQuestion before accepting commits that touch voice, tone, or
-  promotional material (README intro, CHANGELOG voice, skill templates).
-- Never auto-merge PRs that remove YC references or "neutralize" the founder perspective.
-- Preserve contributor attribution in commit messages.
-
-## Checking out PRs from garrytan-agents
-
-`garrytan-agents` is the AI-authored PR account and is NOT a collaborator on
-this repo. Its PRs live in a fork, so GitHub Actions triggered by
-`pull_request` events on those PRs do not receive base-repo secrets. Any CI
-job that needs `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, or similar will fail
-with empty-env auth errors, regardless of what's set on the base repo. This
-is a GitHub security default, not a config bug.
-
-When the user says "check out <PR link>" and the PR is from `garrytan-agents`
-(or any other non-collaborator fork), move the branch into the base repo
-before running CI:
-
-1. `gh pr checkout <N>` — pull down the fork's branch. Note the PR number and
-   head branch name (`gh pr view <N> --json headRefName --jq .headRefName`).
-2. `git push origin HEAD:<branch-name>` — push the same branch to the base
-   repo (origin points at `garrytan/gbrain`, not the fork). This is the move
-   that gives CI access to secrets.
-3. `gh pr close <N> --comment "moving to base-repo branch for secret access"`
-   — close the fork PR so the queue stays clean.
-4. `gh pr create --base master --head <branch-name>` — open the replacement
-   PR from the base-repo branch. **Preserve the original PR's title and body
-   verbatim** (`gh pr view <N> --json title,body`); contributor attribution
-   moves to a `Co-Authored-By:` trailer if needed.
-
-Why this over alternatives: adding `garrytan-agents` as a collaborator, or
-flipping the repo-wide "send secrets to fork PRs" toggle, both broaden
-secret distribution to every fork PR from that account or any fork. Moving
-the branch keeps secret scope tight to just the one PR being shipped.
-

docs/TESTING.md

@@ -1,308 +0,0 @@
-# Testing (gbrain repo)
-
-On-demand reference (see CLAUDE.md Reference map). Current behavior + invariants
-only.
-
-`test/e2e/serve-http-oauth.test.ts` additionally pins confidential POST/Basic revocation, public-client SDK fallthrough, malformed/mixed authentication rejection, cross-client isolation, unknown-token opacity, metadata auth methods, no-store responses, strict post-revoke `401`, and retryable backend `503` semantics.
-
-### Test command tiers
-
-Seven test command tiers, each with a clear scope:
-
-| Command | What it runs | Wallclock | When to use |
-|---|---|---|---|
-| `bun run test` | Parallel unit-test fast loop. 8-shard fan-out via `scripts/run-unit-parallel.sh`, then a serial pass over `*.serial.test.ts`. Excludes `*.slow.test.ts` and `test/e2e/*`. No pre-checks, no typecheck. | ~85s on a Mac dev box (3650+ tests) | Inner edit loop. Default. |
-| `bun run verify` | CI's authoritative pre-test gate set, fanned out in parallel by `scripts/run-verify-parallel.sh`: the full `check:*` battery (~30 checks — privacy, jsonb, progress, source-id, test-isolation, wasm, …) plus `bun run typecheck`. The `CHECKS` array in that script is the single source of truth — CI literally calls `bun run verify` in a dedicated job. | ~16s (parallel; typecheck dominates) | Before pushing; before `/ship`. |
-| `bun run test:full` | `verify && bun run test && bun run test:slow && [smart e2e]`. The local equivalent of "everything CI runs." Smart e2e: runs e2e only when `DATABASE_URL` is set; else loud skip notice to stderr. | ~3-5min depending on slow + e2e | Pre-merge sanity, before opening a PR. |
-| `bun run test:slow` | Just the `*.slow.test.ts` set (intentional cold-path correctness checks). | seconds-to-minutes | When touching slow-path code. |
-| `bun run test:serial` | Just the `*.serial.test.ts` set (cross-file-contention quarantine; one bun process per file for true module-registry isolation). | ~1s per quarantined file | Debugging a specific quarantined file. |
-| `bun run test:e2e` | Real Postgres E2E. Requires Docker + `DATABASE_URL`. Sequential. | ~5-10min | Pre-ship; nightly. |
-| `bun run check:all` | The historical pre-check scripts (22, chained sequentially in package.json). Overlaps `verify` heavily but is NOT a superset — `verify`'s `CHECKS` array in `scripts/run-verify-parallel.sh` (~30 entries incl. typecheck) is the authoritative gate; `check:all` keeps a few local-only extras (trailing-newline, exports-count, no-legacy-getconnection). | ~10s | Local-only sweep for the extras. |
-
-### CI vs local: intentionally divergent file sets
-
-- **CI matrix** (`.github/workflows/test.yml`) runs `scripts/test-shard.sh` across 10 matrix shards partitioned by weight-aware LPT bin-packing (`scripts/sharding.ts`) and INCLUDES `*.slow.test.ts` (the two outlier slow files run as dedicated jobs alongside the matrix). CI EXCLUDES `*.serial.test.ts` from the shards and runs them in a dedicated job via `bun run test:serial`, one bun process per file — keeping serial files out of the shard processes is what preserves the `mock.module` quarantine (a top-level mock in one file leaks into every other file sharing its process). `bun run verify` gets its own job too. CI is the ground truth for "did everything pass."
-- **Local fast loop** (`scripts/run-unit-shard.sh` via the parallel wrapper) uses round-robin-by-index sharding and EXCLUDES `*.slow.test.ts` AND `*.serial.test.ts`. Local trades coverage for inner-loop speed; CI catches what local skips.
-
-This divergence is intentional. Don't try to make them equal — the two scripts deliberately solve different problems. The regression test at `test/scripts/run-unit-shard.test.ts` pins what the local fast loop should and shouldn't include.
-
-### Failure-first logging
-
-When `bun run test` finds any failure, the wrapper:
-
-1. Writes failure blocks (each prefixed with `--- shard N: <test name> ---`) to `.context/test-failures.log` (workspace-local, gitignored). On systems without a writable `.context/`, falls back to `/tmp/gbrain-test-failures.log`.
-2. Prints a loud stderr banner with the absolute log path, plus the last 30 lines of the failure log inlined. Banner survives `| head` / `| tail` / agent-side log truncation.
-3. Writes a one-line-per-shard summary to `.context/test-summary.txt` (`shard N/M: pass=X fail=Y skip=Z rc=W`).
-4. Exits non-zero. Empty failure log + non-zero exit = infrastructure problem (wedged shard, killed child); the banner says so.
-
-If a shard wedges (per-shard `GBRAIN_TEST_SHARD_TIMEOUT` cap, default 600s), the wrapper writes `--- shard N: WEDGED after ${SHARD_TIMEOUT}s ---` to the failure log, includes the last 50 lines of the shard log, and proceeds with other shards' results.
-
-### File taxonomy
-
-- `*.test.ts` → fast loop (parallel 8-shard fan-out).
-- `*.slow.test.ts` → run via `bun run test:slow` only (intentional cold-path tests; would dominate the fast loop's wallclock).
-- `*.serial.test.ts` → run via `bun run test:serial` after the parallel pass completes; one bun process per file (`--max-concurrency=1` within a shared process is not enough — the module registry still leaks `mock.module`). Quarantine for tests that share file-wide state and race when run alongside other files in the same `bun test` process. Several dozen files, discovered by the `*.serial.test.ts` glob — no list to maintain. Typical residents: `mock.module(...)` users (top-level mocks leak across files in a shard process, e.g. `test/embed.serial.test.ts`), env-coupled files (e.g. `test/brain-registry.serial.test.ts`), and process-lifecycle suites that assert on `process.exitCode` (e.g. `test/pglite-engine-disconnect.serial.test.ts`). **Do not put the parallelism back on a serial file unless you've fixed the contention root cause** (it just re-introduces the flake).
-- `test/e2e/*.test.ts` → real-Postgres E2E. Skipped when `DATABASE_URL` is unset.
-- `tests/heavy/*.sh` → ops-shape shell scripts. Cost minutes per run; NOT in default `bun test`. Run via `bun run test:heavy` or scheduled nightly via `.github/workflows/heavy-tests.yml`. Examples: pg_upgrade matrix (boot legacy brain → walk to head), RSS budget gate (measure peak worker RSS vs committed baseline), read-latency-under-sync (p50/p95/p99 under concurrent writer load), sync lock regression (N concurrent syncs assert 1 winner + N-1 lock-busy + zero leaked `gbrain_cycle_locks` rows). See `tests/heavy/README.md` for when to add a script here vs `*.slow.test.ts`. Files prefixed with `_` (e.g. `tests/heavy/_build_legacy_fixtures.sh`) are helpers/libs invoked by sibling tests — the runner skips them.
-- `test/fuzz/*.test.ts` → property-based fuzz harness. Pure-validator targets in `pure-validators.test.ts` are guarded by `scripts/check-fuzz-purity.sh` (in `bun run verify`), which `bun build --target=bun` bundles each target and greps the resulting bundle for banned transitive imports (`node:fs`, `node:child_process`, engine modules). Anything that fails the guard moves to `mixed-validators.test.ts` (still property-tested, but no purity guarantee) or `filesystem-validators.test.ts` (fs-backed, uses temp dirs). Fuzz tests run in the default `bun test` loop because they're fast (~3s for ~12 properties × 1000 runs each).
-
-### Test-isolation lint and helpers
-
-The cross-file flake class is enforced statically by `scripts/check-test-isolation.sh`, wired into `bun run verify` and `bun run check:all`. Rules (non-serial unit files only; `*.serial.test.ts` and `test/e2e/*` are skipped):
-
-| Rule | What it bans | Fix |
-|---|---|---|
-| **R1** | `process.env.X = ...`, bracket assignment, `delete process.env.X`, `Object.assign(process.env, ...)`, `Reflect.set(process.env, ...)` | Use `withEnv()` from `test/helpers/with-env.ts`, OR rename file to `*.serial.test.ts` |
-| **R2** | `mock.module(...)` anywhere in the file | Rename file to `*.serial.test.ts` (no DI on production code for testability) |
-| **R3** | `new PGLiteEngine(` outside ~50 lines after a `beforeAll(` line | Use the canonical block (below) inside `beforeAll(` |
-| **R4** | Files creating `new PGLiteEngine(` without `engine.disconnect(` inside an `afterAll(` block | Add `afterAll(() => engine.disconnect())` |
-
-Files that violated these rules at the isolation-lint baseline are listed in `scripts/check-test-isolation.allowlist`. **The allow-list MUST shrink over time** — never add new entries.
-
-#### Canonical PGLite block (R3 + R4 compliant)
-
-Every test file that needs a PGLite engine should use this exact pattern:
-
-```ts
-import { PGLiteEngine } from '../src/core/pglite-engine.ts';
-import { resetPgliteState } from './helpers/reset-pglite.ts';
-
-let engine: PGLiteEngine;
-
-beforeAll(async () => {
-  engine = new PGLiteEngine();
-  await engine.connect({});
-  await engine.initSchema();
-});
-
-afterAll(async () => {
-  await engine.disconnect();
-});
-
-beforeEach(async () => {
-  await resetPgliteState(engine);
-});
-```
-
-Why this exact shape: `beforeAll` creates a single engine per file (PGLite WASM cold-start + initSchema is ~20s); `beforeEach` truncates user data via `resetPgliteState` ("two orders of magnitude faster" than fresh-engine-per-test); `afterAll` disconnects so the engine doesn't leak across file boundaries within a shard process.
-
-#### `withEnv` pattern (R1 fix)
-
-```ts
-import { withEnv } from './helpers/with-env.ts';
-
-test('reads OPENAI_API_KEY', async () => {
-  await withEnv({ OPENAI_API_KEY: 'sk-test' }, async () => {
-    expect(loadConfig().openai_key).toBe('sk-test');
-  });
-});
-
-// Delete a var (override is undefined):
-await withEnv({ GBRAIN_HOME: undefined }, fn);
-
-// Multiple keys:
-await withEnv({ A: '1', B: '2', C: undefined }, fn);
-```
-
-`withEnv` saves the prior value of every key it touches and restores via try/finally — including when the callback throws. **It is cross-test safe but NOT intra-file concurrent-safe.** `process.env` is process-global; two `test.concurrent()` calls in the same file both touching the same key will race. Files using `withEnv` stay outside the `test.concurrent()` codemod's eligibility filter.
-
-#### When to quarantine instead of fix
-
-Rename to `*.serial.test.ts` when:
-- The file uses `mock.module(...)` (R2 — there's no clean fix without changing production code).
-- The file is genuinely env-coupled (e.g. `gbrain-home-isolation.test.ts`, `claw-test-cli.test.ts`) — module-load env readers + ESM caching defeat dynamic-import-after-env tricks.
-- The file's tests intentionally share state across `it()` boundaries.
-
-The quarantine has grown to dozens of files — treat it as debt: every addition needs a reason from the list above, and prefer fixing the contention root cause when one exists.
-
-### Unit test inventory
-
-`bun test` runs all tests without a database. E2E tests skip gracefully when `DATABASE_URL` is not set.
-
-Unit tests and what they cover:
-
-- `test/markdown.test.ts` — frontmatter parsing; `splitBody` sentinel precedence, horizontal-rule preservation, `inferType` wiki subtypes.
-- `test/chunkers/recursive.test.ts` — chunking.
-- `test/parity.test.ts` — operations contract parity.
-- `test/cli.test.ts` — CLI structure.
-- `test/cli-finish-teardown.test.ts` — the #2084 teardown contract: `computeTeardownDeadlineMs` formula/floor/live-registry scaling + `GBRAIN_TEARDOWN_DEADLINE_MS` override (garbage/zero/negative values fall back to the formula); `finishCliTeardown` clean path (drain BEFORE disconnect, no exit, no warn), backstop on hung drain or disconnect (honors an errored op's exit code), throwing drain/disconnect warned + swallowed; the gbrain-owned verdict channel is immune to PGLite WASM `process.exitCode` writes; `flushThenExit` unit coverage with mocked streams (exits once after both stream callbacks, non-TTY aliveness grace, blocked-pipe guard, EPIPE-safe, `GBRAIN_FLUSH_GRACE_MS` override).
-- `test/flush-then-exit-harness.test.ts` — real spawned-Bun pipe semantics for `flushThenExit` (fixture: `test/fixtures/flush-then-exit-harness.ts`): a 4MB piped stdout payload arrives byte-complete with the exit code even with a late reader, small output survives exit with a concurrent reader, and the fence resolves promptly (wall time well under the guard + grace ceiling).
-- `test/cli-should-force-exit.test.ts` — `shouldForceExitAfterMain` daemon-survival gate: `serve` (stdio and `--http`) never force-exits, including with preceding global flags; op commands / empty / flag-only argv do; the #2084 case that space-separated global-flag VALUES can't fake a command (`--timeout 30s serve` resolves to the `serve` daemon, not a `30s` command).
-- `test/cli-exit-verdict-pin.test.ts` — #2084 structural class pin: greps `src/` so the NEXT raw `process.exitCode =` write fails CI (a raw write bypasses the gbrain-owned verdict channel and gets silently zeroed by the deliberate flush-exit — the bug that made doctor's FAIL path exit 0). Runtime variants live in `test/cli-finish-teardown.test.ts`; this is the review-time guard.
-- `test/cli-pipe-truncation.test.ts` — real-CLI pipe completeness (the #1959 incident class), implementation-agnostic: the actual CLI run the way agents run it (piped stdout) produces complete, parseable, byte-stable `--tools-json` output and exits deliberately, well under the teardown backstop. Synthetic flush-mechanism coverage stays in `test/flush-then-exit-harness.test.ts`.
-- `test/volunteer-context.test.ts` — push-based context core (#2095), hermetic in-memory PGLite: `parseWindow` lenient `user:`/`assistant:` parsing, multi-turn window extraction, confidence-gated volunteering (arm confidences, multi-turn/newest-turn boosts, `min_confidence` gate, max-pages cap), slug-only suppression, privacy (rationales are deterministic templates; synopses pass the takes/facts fence), and the approximate usage-stats join.
-- `test/watch-command.test.ts` — `gbrain watch` push transport (#2095): streaming loop, rolling window, session dedupe, `--json` JSONL shape, `channel: 'watch'` event logging, clean EOF return. Hermetic PGLite + injected line/write deps (no subprocess, no real stdin).
-- `test/watch-sigint.serial.test.ts` — `gbrain watch` SIGINT lifecycle against a real spawned CLI subprocess with a tmpdir brain. SERIAL: parallel unit shards flake on concurrent subprocess spawns (same rationale as `apply-migrations-pglite-spawn.serial.test.ts`).
-- `test/cli-format-volunteer.test.ts` — `formatResult`'s `volunteer_context` human rendering: pointer lines with confidence/arm/rationale, the empty-result message, the approximate stats summary.
-- `test/config.test.ts` — config redaction.
-- `test/files.test.ts` — MIME/hash.
-- `test/import-file.test.ts` — import pipeline.
-- `test/upgrade.test.ts` — schema migrations.
-- `test/file-migration.test.ts` — file migration.
-- `test/file-resolver.test.ts` — file resolution.
-- `test/import-resume.test.ts` — import checkpoints.
-- `test/migrate.test.ts` — migration: v8/v9 helper-btree-index SQL structural assertions; 1000-row wall-clock fixtures guarding the O(n²)→O(n log n) fix; v12/v13 SQL shape; `sqlFor` + `transaction:false` runner semantics; the `max_stalled DEFAULT 1` regression guard; v24 `sqlFor.pglite: ''` no-op assertion; v117 `context_volunteer_events` (named + idempotent entry, documented columns + both source-scoped indexes after `initSchema`, insert + 90-day `purgeStaleVolunteerEvents` round-trip).
-- `test/bootstrap.test.ts` — bootstrap contract: no-op on fresh install, idempotent across two `initSchema()` calls, no-op on modern brain that already has every probed column, full bootstrap path on a simulated legacy brain, fresh-install regression guard, legacy `links` shape coverage.
-- `test/schema-bootstrap-coverage.test.ts` — CI guard. `REQUIRED_BOOTSTRAP_COVERAGE` lists every forward reference in `PGLITE_SCHEMA_SQL`; the test fails loudly if `applyForwardReferenceBootstrap` skips one (extend both arrays when adding a column-with-index to the embedded schema blob). Also parses `src/core/migrate.ts` source text for every `ALTER TABLE ... ADD COLUMN` (top-level `sql:`, `sqlFor.{postgres,pglite}` overrides, AND handler-body `engine.runMigration(N, \`ALTER TABLE ...\`)`) and asserts each (table, column) pair is covered by the bootstrap OR by the schema blob's CREATE TABLE bodies — catching the column-only forward-reference class (e.g. `sources.archived`, `oauth_clients.source_id`) that a CREATE INDEX parser alone can't see. `parseBaseTableColumns` strips SQL line + block comments before identifying column names so commented-out lines don't hide adjacent columns.
-- `test/helpers/schema-diff.ts` + `test/helpers/schema-diff.test.ts` + `test/e2e/schema-drift.test.ts` — cross-engine schema parity gate. Helper exports pure `snapshotSchema(query)` / `diffSnapshots(pg, pglite, opts)` / `formatDiffForFailure(diff)` / `isCleanDiff(diff)` over a four-tuple per column (`data_type`, `udt_name`, `is_nullable`, `column_default`). E2E test spins up fresh PGLite + Postgres, runs `engine.initSchema()` on each, snapshots `information_schema.columns`, then diffs. 2-table allowlist (`files`, `file_migration_ledger`) — every other Postgres table must reach PGLite via `PGLITE_SCHEMA_SQL` or a migration's `sqlFor.pglite` branch. Sentinels for `oauth_clients`, `mcp_request_log`, `access_tokens`, `eval_candidates` give tighter blame messages. Skips without `DATABASE_URL`. Wired into `scripts/e2e-test-map.ts` so changes to `src/schema.sql`, `src/core/pglite-schema.ts`, or `src/core/migrate.ts` trigger it. The failure message names every drift with a paste-ready hint pointing at `src/core/pglite-schema.ts`.
-- `test/setup-branching.test.ts` — setup flow.
-- `test/slug-validation.test.ts` — slug validation.
-- `test/storage.test.ts` — storage backends.
-- `test/supabase-admin.test.ts` — Supabase admin.
-- `test/yaml-lite.test.ts` — YAML parsing.
-- `test/check-update.test.ts` — version check + update CLI.
-- `test/pglite-engine.test.ts` — PGLite engine, all BrainEngine methods including `addLinksBatch` / `addTimelineEntriesBatch` (empty batch, missing optionals, within-batch dedup via ON CONFLICT, missing-slug rows dropped by JOIN, half-existing batch, batch of 100) plus `connect()` error-wrap assertion (original error nested, #223 link in message, lock released).
-- `test/links-timeline-jsonb-poison.test.ts` — gbrain#1861 PGLite half (always-on, no `DATABASE_URL`). Locks the `jsonb_to_recordset` batch-insert path for links/timeline/takes against free-text "poison" payloads (commas, quotes, backslashes, braces, em-dashes) and asserts NUL is stripped from free-text body fields but rejected in identity fields. gbrain#2011 adds lone-UTF-16-surrogate cases: every free-text field (link context; timeline summary/detail/source; take claim/source) well-forms to U+FFFD across batch + scalar write paths, while a surrogate in an identity field (slug) still fail-closed rejects the batch. The Postgres lane (`test/e2e/jsonb-batch-poison-postgres.test.ts`) is the one that actually reproduced the original crash.
-- `test/engine-factory.test.ts` — engine factory + dynamic imports.
-- `test/integrations.test.ts` — recipe parsing, CLI routing, recipe validation.
-- `test/publish.test.ts` — content stripping, encryption, password generation, HTML output.
-- `test/backlinks.test.ts` — entity extraction, back-link detection, timeline entry generation.
-- `test/lint.test.ts` — LLM artifact detection, code fence stripping, frontmatter validation.
-- `test/report.test.ts` — report format, directory structure.
-- `test/skills-conformance.test.ts` — skill frontmatter + required sections validation.
-- `test/resolver.test.ts` — RESOLVER.md coverage, routing validation; round-trip that every quoted RESOLVER.md trigger matches a frontmatter `triggers:` entry in the target skill, and every `name="<word>"` reference in any SKILL.md resolves to a declared op in `src/core/operations.ts` or a Minions handler in `PROTECTED_JOB_NAMES`.
-- `test/search.test.ts` — RRF normalization, compiled truth boost, cosine similarity, dedup key.
-- `test/sql-ranking.test.ts` — source-boost helpers: longest-prefix-match in SQL CASE, `detail=high` temporal-bypass, three-meta-char LIKE escape (`%`, `_`, `\`), single-quote SQL-literal doubling, env override parsing for `GBRAIN_SOURCE_BOOST` + `GBRAIN_SEARCH_EXCLUDE`, `resolveBoostMap` / `resolveHardExcludes` merge semantics.
-- `test/dedup.test.ts` — source-aware dedup, compiled truth guarantee, layer interactions.
-- `test/intent.test.ts` — query intent classification: entity/temporal/event/general.
-- `test/eval.test.ts` — retrieval metrics: `precisionAtK`, `recallAtK`, `mrr`, `ndcgAtK`, `parseQrels`.
-- `test/check-resolvable.test.ts` — resolver reachability, MECE overlap, gap detection, proximity-based DRY detection, `extractDelegationTargets` coverage.
-- `test/dry-fix.test.ts` — auto-fix: three shape-aware expander pure-function tests; five guards (working-tree-dirty, no-git-backup, inside-code-fence, already-delegated within 40 lines, ambiguous-multi-match, block-is-callout).
-- `test/doctor-fix.test.ts` — `gbrain doctor --fix` CLI integration: dry-run preview, apply path, JSON output shape.
-- `test/backoff.test.ts` — load-aware throttling, concurrency limits, active hours.
-- `test/fail-improve.test.ts` — deterministic/LLM cascade, JSONL logging, test generation, rotation.
-- `test/transcription.test.ts` — provider detection, format validation, API key errors.
-- `test/enrichment-service.test.ts` — entity slugification, extraction, tier escalation.
-- `test/data-research.test.ts` — recipe validation, MRR/ARR extraction, dedup, tracker parsing, HTML stripping.
-- `test/minions.test.ts` — Minions job queue: CRUD, state machine, backoff, stall detection, dependencies, worker lifecycle, lock management, claim mechanics, depth/child-cap, timeouts, cascade kill, idempotency, `child_done` inbox, attachments, removeOnComplete/Fail, `max_stalled` clamp/default/plumbing coverage.
-- `test/extract.test.ts` — link extraction, timeline extraction, frontmatter parsing, directory type inference.
-- `test/extract-db.test.ts` — `gbrain extract --source db`: typed link inference, idempotency, `--type` filter, `--dry-run` JSON output.
-- `test/extract-fs.test.ts` — `gbrain extract --source fs`: first-run inserts + second-run reports zero, dry-run dedups candidates across files, second-run perf regression guard for the N+1 dedup bug.
-- `test/link-extraction.test.ts` — canonical `extractEntityRefs` both formats, `extractPageLinks` dedup, `inferLinkType` heuristics, `parseTimelineEntries` date variants, `isAutoLinkEnabled` config.
-- `test/graph-query.test.ts` — direction in/out/both, type filter, indented tree output.
-- `test/features.test.ts` — feature scanning, brain_score calculation, CLI routing, persistence.
-- `test/file-upload-security.test.ts` — symlink traversal, cwd confinement, slug + filename allowlists, remote vs local trust.
-- `test/query-sanitization.test.ts` — prompt-injection stripping, output sanitization, structural boundary.
-- `test/search-limit.test.ts` — `clampSearchLimit` default/cap behavior across `list_pages` and `get_ingest_log`.
-- `test/repair-jsonb.test.ts` — JSONB repair: TARGETS list, idempotency, engine-awareness.
-- `test/migrations-v0_12_2.test.ts` — JSONB-repair orchestrator phases: schema → repair → verify → record.
-- `test/orphans.test.ts` — orphans command: detection, pseudo filtering, text/json/count outputs, MCP op.
-- `test/postgres-engine.test.ts` — `statement_timeout` scoping: `sql.begin` + `SET LOCAL` shape, source-level grep guardrail against a reintroduced bare `SET statement_timeout`.
-- `test/sync.test.ts` — sync logic + regression guard asserting top-level `engine.transaction` is not called.
-- `test/sync-concurrency.test.ts` — `autoConcurrency()` thresholds + PGLite-forces-serial + explicit-override clamping; `shouldRunParallel()` explicit-bypasses-floor contract; `parseWorkers()` validation rejecting `'0'`/`'-3'`/`'foo'`/`'1.5'`/trailing chars.
-- `test/sync-parallel.test.ts` — PGLite-routed coverage of the bookmark gate under concurrency, head-drift gate, vanished-file failure capture, PGLite-stays-serial, and the `gbrain-sync` writer-lock contract.
-- `test/sync-failures.test.ts` — `classifyErrorCode` regex coverage for all 12 codes against literal production message strings from `markdown.ts` and `import-file.ts`; `summarizeFailuresByCode` sort + pre-classified-honor; `recordSyncFailures` code-field persistence; `acknowledgeSyncFailures` `AcknowledgeResult` shape + backfill on legacy entries.
-- `test/doctor.test.ts` — doctor command; assertions that `jsonb_integrity` scans the four JSONB write sites and `markdown_body_completeness` is present.
-- `test/utils.test.ts` — shared SQL utilities + `tryParseEmbedding` null-return and single-warn semantics.
-- `test/build-llms.test.ts` — `llms.txt`/`llms-full.txt` generator: path resolution, idempotence, spec shape, regen-drift guard, content contract, AGENTS.md install-path mirror, size-budget enforcement.
-- `test/oauth.test.ts` — OAuth 2.1 provider: register, getClient, `client_credentials` grant exchange, `authorization_code` flow with PKCE challenge/verifier, refresh token rotation, `verifyAccessToken` with both OAuth + legacy `access_tokens` fallback, `revokeToken`, `sweepExpiredTokens`; contract test asserting `scope` + `localOnly` annotations on all operations; `coerceTimestamp` unit cases (null/undefined/string/number/throw-on-NaN); NULL-`expires_at`-as-expired contract for both refresh + access token paths; cascade-delete contract asserting `revoke-client` purges `oauth_tokens` + `oauth_codes` via FK CASCADE; cross-client isolation (wrong-client attempt MUST reject AND rightful owner MUST still succeed atomically afterward); empty-string `redirect_uri` bypass guard; PKCE DCR public-client gate (`token_endpoint_auth_method: "none"` returns no `client_secret`, default `client_secret_post` clients get the one-time-reveal secret, `getClient` NULL→undefined normalization, full PKCE `/authorize` → `/token` round-trip against a public client).
-- `test/mcp-dispatch-summarize.test.ts` — `summarizeMcpParams` invariants: declared-keys allow-list intersection, attacker-key-name leak guard (unknown keys counted not named), 1KB byte bucketing for size-probe defense, missing op falls through to fully-redacted shape, declared-keys sorted for deterministic output.
-- `test/trust-boundary-contract.test.ts` — fail-closed trust semantics under cast bypass: `ctx.remote === undefined` treated as remote/untrusted at every flipped call site; `as any` and `Partial<>` spreads can't downgrade trust by accident.
-- `test/check-resolvable-cli.test.ts` — CLI wrapper: exit codes, JSON envelope shape, AGENTS.md fallback chain.
-- `test/regression-v0_16_4.test.ts` — `findRepoRoot` regression guard, hermetic startDir parameterization.
-- `test/repo-root.test.ts` — `findRepoRoot` walk semantics + default-arg parity; the 4-tier `autoDetectSkillsDir` fallback chain (`$OPENCLAW_WORKSPACE` → `~/.openclaw/workspace` → repo-root → `./skills`); RESOLVER.md/AGENTS.md filename precedence; explicit-env-wins-over-repo-root; tier-0 `$GBRAIN_SKILLS_DIR` valid/invalid/precedence-over-`OPENCLAW_WORKSPACE`; the install-path walk in `autoDetectSkillsDirReadOnly`; no-drift on primary success; `AUTO_DETECT_HINT` + `AUTO_DETECT_HINT_READ_ONLY` content; regression guard asserting the shared `autoDetectSkillsDir` MUST NEVER return `'install_path'` source (how the read-path/write-path split stays safe).
-- `test/resolver-merge.test.ts` — multi-file resolver merge: `findAllResolverFiles` empty / RESOLVER.md-only / AGENTS.md-only / both-present (RESOLVER.md first); `checkResolvable` merge semantics across `skills/RESOLVER.md` + `../AGENTS.md` for the OpenClaw layout where the skillpack ships a thin RESOLVER.md and the real dispatcher lives at the workspace root; dedup by `skillPath` (first occurrence wins); AGENTS.md-at-workspace-root works alone.
-- `test/filing-audit.test.ts` — filing audit: `writes_pages` / `writes_to` frontmatter, filing-rules JSON validation.
-- `test/skill-brain-first.test.ts` — shared frontmatter parser; `analyzeSkillBrainFirst` compliance ladder across 9 fixtures under `test/fixtures/brain-first-skills/` (compliant-callout, compliant-phase, compliant-position, exempt-frontmatter, missing-brain-first, multi-pattern, negation-prose, no-external, typo-frontmatter); offset helpers; external-lookup regex shape; audit snapshot+diff transition logic; `FORMERLY_HARDCODED_EXEMPT` regression absorption.
-- `test/routing-eval.test.ts` — fixture parsing, structural routing, `ambiguous_with`, Haiku tie-break layer.
-- `test/skill-manifest.test.ts` — skill manifest parser: drift detection, managed-block markers.
-- `test/skillify-scaffold.test.ts` — `gbrain skillify scaffold` stubs: SKILL.md, script, tests, routing-eval fixtures.
-- `test/skillpack-install.test.ts` — `gbrain skillpack install` managed-block install / update / no-clobber semantics.
-- `test/skillpack-sync-guard.test.ts` — sync-guard: bundled skills stay byte-identical to `skills/` source.
-- `test/http-transport.test.ts` — HTTP transport: bearer auth + missing/no-Bearer/unknown/revoked + `/health` bypass; dispatch.ts round-trip; invalid_params; application/json response shape (not SSE); CORS default-deny + allowlist; body cap on Content-Length AND chunked; two-bucket rate limit (refill, exhaust+Retry-After, LRU eviction, TTL prune, pre-auth IP fires before DB); `mcp_request_log` audit on success + auth_failed.
-- `test/restart-sweep.test.ts` — `recipes/restart-sweep.md` inlined script: sentinel-anchored fenced-block extraction with salted tmp filenames to bypass ESM cache; constructor-time env reads (proves no module-load snapshot); idempotency layer load/save/atomic-tmp-rename/corrupt-JSON-recovery/30-day-prune; `(sessionKey, lastAlertedAt)` cooldown gate with 6h threshold; AGGRESSIVE-gate two-state tests; execFile argv shape proving shell metachars in `OPENCLAW_TELEGRAM_GROUP` cannot reach `/bin/sh`; real-`\n`-not-literal alert formatting; `GBRAIN_HOME` state path override.
-- `test/eval-longmemeval.test.ts` — LongMemEval harness, hermetic with no `DATABASE_URL` and no API keys: PGLite create + reset over runtime-enumerated `pg_tables`, infrastructure-table preservation across resets, JSONL question parsing, retrieval-only and answer-gen modes via stubbed `ThinkLLMClient`, `--limit` cutoff, `--keyword-only` vs hybrid, default `--expansion=off` behavior, perf gate (p50 < 30ms / p99 < 50ms warm reset+import+search on Apple Silicon), `--help` works without a configured brain, fixture round-trip via `test/fixtures/longmemeval-mini.jsonl`.
-- `test/longmemeval-sanitize.test.ts` — sanitization parity pinning that `INJECTION_PATTERNS` from `src/core/think/sanitize.ts` is the single source of truth (adding a pattern there must cover both `<take>` framing and `<chat_session>` framing, no per-surface regex drift).
-- `test/openai-compat-multimodal.test.ts` — gateway's openai-compatible multimodal path: happy-path single + multi-input embedding, unauthenticated proxy mode, dimension-mismatch guard (throws `AIConfigError` with model id + observed + expected pre-storage), default-dim fallback when recipe declares `default_dims`, HTTP 401 / 400 / malformed-JSON / non-array error paths, regression that the existing Voyage `/multimodalembeddings` recipe still routes through its dedicated path. Hermetic via the `__setEmbedTransportForTests` seam.
-- `test/serve-stdio-lifecycle.test.ts` — `MCP_STDIO=1` env guard: stdin EOF does NOT trigger shutdown when the env is set, SIGTERM still does (guard scope is correct), unset env preserves the CLI lifecycle. Exercises the `ServeOptions.mcpStdio?: boolean` test seam directly so tests don't mutate `process.env`.
-
-### E2E test inventory
-
-E2E tests live in `test/e2e/` and run against real Postgres+pgvector (require `DATABASE_URL`), except where noted as PGLite in-memory (no `DATABASE_URL` needed).
-
-- `bun run test:e2e` runs Tier 1 (mechanical, all operations, no API keys). Includes dedicated cases for the postgres-engine `addLinksBatch` / `addTimelineEntriesBatch` bind path — postgres-js's JSONB bind (`jsonb_to_recordset(($1::jsonb)->'rows')`) differs from PGLite's and gets its own coverage.
-- `test/e2e/search-quality.test.ts` — search quality against PGLite (no API keys, in-memory).
-- `test/e2e/graph-quality.test.ts` — knowledge graph pipeline (auto-link via put_page, reconciliation, traversePaths) against PGLite in-memory.
-- `test/e2e/jsonb-batch-poison-postgres.test.ts` — gbrain#1861 regression, the engine that actually crashed. Seeds free-text "poison" context (Zoom URL with `?pwd=`, commas, quotes, Windows backslash path, braces, em-dash) and asserts the links/timeline/takes batch writers no longer error with "malformed array literal"; also asserts NUL is stripped from free-text bodies (`context`/`summary`/`detail`/`claim`) and still rejected in identity fields. gbrain#2011 adds the lone-surrogate crash lock: a lone UTF-16 surrogate in free text (the value that aborted `extract --stale` with `22P02` on Supabase) well-forms to U+FFFD across batch + scalar paths (incl. timeline + take `source`), while a surrogate in an identity field still rejects the batch. `DATABASE_URL`-gated.
-- `test/e2e/postgres-jsonb.test.ts` — round-trips all 5 JSONB write sites (`pages.frontmatter`, `raw_data.data`, `ingest_log.pages_updated`, `files.metadata`, `page_versions.frontmatter`) against real Postgres and asserts `jsonb_typeof='object'` plus `->>'key'` returns the expected scalar. Guards against the double-encode bug.
-- `test/e2e/integrity-batch.test.ts` — parity for `scanIntegrity`'s batch-load fast path vs sequential. Cases (dedup, hits, validate, topPages) seed a fixture and assert both paths return identical results. Dedup case uses raw SQL via `getConn().unsafe()` to seed a `(test-source-2, people/alice)` row alongside the default-source row, since `engine.putPage` doesn't take a `source_id`. Pins multi-source overcounting; the "multi-source duplicate slugs scan once" case expects both batch + sequential paths to report 2.
-- `test/e2e/jsonb-roundtrip.test.ts` — companion regression against the 4 doctor-scanned JSONB sites. Assertion-level overlap with `postgres-jsonb.test.ts` is intentional defense-in-depth: if doctor's scan surface drifts from the actual write surface, one of these tests catches it.
-- `test/e2e/sync.test.ts` — `--skip-failed` failure-loop test alongside happy-path tests: broken file → `performSync` returns `blocked_by_failures` with grouped breakdown → `performSync({skipFailed: true})` advances bookmark and returns `AcknowledgeResult` with code summary → second broken file → second cycle. Saves and restores the user's real `~/.gbrain/sync-failures.jsonl` so the test is hermetic. Asserts bookmark gating, JSONL state, dedup across paths, summary aggregation, and the literal doctor-rendering string format.
-- `test/e2e/upgrade.test.ts` — check-update against real GitHub API (network required).
-- `test/e2e/minions-shell-pglite.test.ts` — PGLite `--follow` inline shell-job path (in-memory, no `DATABASE_URL` required) — the path the minion-orchestrator skill documents for dev use.
-- `test/e2e/pglite-cli-exit.serial.test.ts` — real spawned-CLI exit behavior on PGLite (in-memory, no `DATABASE_URL`): read commands (`search`/`get`/`query`) exit 0 promptly; CLI_ONLY `capture` exits clean and frees the single-writer lock; the `#2084` describes pin every swept disconnect site — a failed op exits 1 with the error on stderr, and the dashboard, read-only-timeout, doctor, and `dream --dry-run` paths all exit with no force-exit banner.
-- `test/e2e/pgbouncer-teardown.test.ts` — PgBouncer TRANSACTION-mode teardown (#2084 / the #1972→#2015→#2084 class). Pins the bug CLASS, not timings: a CLI op against a txn-mode pooled URL exits 0 with intact stdout and does NOT ride the 10s hard-deadline backstop (the `engine.disconnect() did not return` banner is the smoking gun — pre-#2084 it printed on 100% of query-shaped ops). Gated by `GBRAIN_PGBOUNCER_URL` + `GBRAIN_PGBOUNCER_DIRECT_URL` (NOT `DATABASE_URL`) — set automatically by `bun run ci:local`'s `pgbouncer` compose service; skips gracefully elsewhere. Uses a DEDICATED `gbrain_pgbouncer` database so it never races the `gbrain_test` TRUNCATE fixtures.
-- `test/e2e/volunteer-context-postgres.test.ts` — `volunteer_context` on REAL Postgres (#2095; engine parity beyond the hermetic PGLite unit suite): resolution arms through the actual op handler, the fire-and-forget volunteer-event sink landing rows, the stats join, and the RLS pin that `context_volunteer_events` has ROW LEVEL SECURITY enabled (keeps the v35 auto-RLS event trigger honest for migration-created tables). `DATABASE_URL`-gated.
-- `test/e2e/openclaw-reference-compat.test.ts` — `check-resolvable` + `skillpack install` against a minimal AGENTS.md workspace fixture (`test/fixtures/openclaw-reference-minimal/`), regression guard for the OpenClaw deployment shape.
-- `test/e2e/search-swamp.test.ts` — reproduces the source-swamp case. Seeds a curated `originals/talks/article-outline-fat-code` page against two `<fork>/chat/` pages stuffed with the same multi-word phrase. Asserts the article wins keyword AND vector ranking, that `detail=high` lets the chat swamp re-surface, and that `source_id` passes through the two-stage CTE intact. PGLite in-memory.
-- `test/e2e/search-exclude.test.ts` — `test/` + `archive/` pages hidden by default, `include_slug_prefixes` opts back in, caller-supplied `exclude_slug_prefixes` adds to defaults. Both keyword and vector search paths.
-- `test/e2e/engine-parity.test.ts` — Postgres ↔ PGLite top-result and result-set parity for `searchKeyword` + `searchVector` (Postgres ranks pages then picks best chunk while PGLite returns chunks directly, so the source-boost behavior needs parity coverage). Skips without `DATABASE_URL`.
-- `test/e2e/postgres-bootstrap.test.ts` — exercises `PostgresEngine.initSchema()` directly against a fresh real Postgres database. Asserts the bootstrap path is no-op on fresh installs and that SCHEMA_SQL replays cleanly through the engine path (not via the standalone `db.initSchema` from `src/core/db.ts`).
-- `test/e2e/http-transport.test.ts` — `gbrain serve --http` end-to-end against real Postgres: bearer auth round-trip, `last_used_at` SQL-level debounce, `mcp_request_log` row insertion on success and auth_failed paths, `/health` DB-down → 503 (DB-probing health check), and the dispatch round-trip with a real operation. Skips without `DATABASE_URL`.
-- `test/e2e/serve-http-oauth.test.ts` — real-Postgres E2E against `gbrain serve --http` with full OAuth 2.1. Spawns a subprocess server, registers a client via the CLI, mints `client_credentials` tokens, exercises the `/mcp` JSON-RPC pipeline. Real DCR `/register` HTTP-level response-shape test (asserts `typeof body.client_id_issued_at === 'number'` over the wire, RFC 7591 §3.2.1); real CLI subprocess test for `revoke-client` (registers → mints token → revokes via `execSync` → asserts token rejected at `/mcp` → asserts re-run exits 1); server fixture flips on `--enable-dcr` so `/register` is reachable. **bun execSync env-inheritance contract:** bun's `execSync` does NOT inherit env mutations done via `process.env.X = ...`, only OS-level env from before bun started. helpers.ts loads `.env.testing` and sets `DATABASE_URL` via `process.env` mutation, which is invisible to subprocesses unless `env: { ...process.env }` is passed explicitly — every subprocess call in this file passes `env: { ...process.env }`. Reference fix for the same failure mode in sibling sync/cycle/dream/claw-test E2Es. `afterAll` cleanup is guarded on `clientId` (won't throw if `beforeAll` failed before registration); cleanup errors surface to stderr without throwing so real test failures aren't masked. Also covers the trust-boundary fix: an HTTP MCP `submit_job` for `name: "shell"` MUST reject with a permission error (request handler sets `remote: true` and `submit_job`'s protected-name guard fires), and the same guard rejects subagent submission. Skips without `DATABASE_URL`.
-- `test/e2e/sync-parallel.test.ts` — `DATABASE_URL`-gated. 60-file Postgres sync at concurrency=4 imports all + no connection leak (probes `pg_stat_activity` before/after to confirm worker engines disconnected). 120-file serial-vs-parallel benchmark prints `SYNC_PARALLEL_BENCH N files | serial=Xms | parallel(4)=Yms | speedup=Zx`. Asserts parallel ≤ serial × 1.5 (CI-noise tolerant; not a strict speedup gate).
-- `test/e2e/multi-source-bug-class.test.ts` — PGLite in-memory regression suite pinning every multi-source bug site: `listAllPageRefs` ordering by `(source_id, slug)`, `getPage` with sourceId picks the right `(source, slug)` row, `extract-takes` processes both overlapping `people/alice` rows independently, `listPages` filters correctly with `PageFilters.sourceId`, `addLinksBatch` with `from/to_source_id` targets the right rows, `validateSourceId` rejects path traversal, reverse-write disk layout uses `brainDir/.sources/<id>/<slug>.md` for non-default sources, `copyMigrationSources` lands source metadata before overlapping-slug pages. No `DATABASE_URL` needed. Wired into `scripts/e2e-test-map.ts` so changes to extract-takes / patterns / synthesize / embed / extract / migrate-engine auto-trigger it.
-- `test/e2e/migrate-engine-sources-postgres.test.ts` — `DATABASE_URL`-gated companion for `gbrain migrate --to`: migrates a PGLite brain carrying two non-default sources with overlapping slugs into real Postgres and asserts `copyMigrationSources` created every `sources` FK parent (config JSONB intact, not double-encoded) before any page write. Unit-level manifest identity (crash manifest resumes only against the SAME target; legacy engine-only manifests start fresh) is `test/migrate-engine-resume.test.ts`.
-- `test/e2e/facts-fence-reconcile-postgres.test.ts` — `DATABASE_URL`-gated round-trip for the escape-aware fence parser: renders a `## Facts` fence whose cells carry literal pipes, backslashes (Windows paths), and empty cells via `renderFactsTable`, runs the wipe-and-reinsert reconcile (`runExtractFacts`) on real Postgres, and asserts every cell survives byte-identically with no column shift.
-- `test/e2e/source-isolation-pglite.test.ts` — PGLite in-memory regression suite pinning the source-isolation seal at two layers. Engine layer: `searchKeyword` / `searchVector` / `searchKeywordChunks` / `listPages` / `getPage` / `traverseGraph` / `traversePaths` apply `sourceId` (scalar fast path) and `sourceIds` (array path) correctly across both engines. Op-handler layer: routes through `sourceScopeOpts(ctx)` so a `read+write`-scoped OAuth client bound to `--source dept-x` cannot see rows from neighboring sources via `search`, `query`, `list_pages`, `get_page`, or `find_experts`. Covers both `ctx.sourceId` (single-source clients) and `ctx.auth.allowedSources` (federated_read clients) precedence; federated array wins over scalar wins over nothing. No `DATABASE_URL` needed.
-- `test/e2e/think-source-isolation-pglite.test.ts` — PGLite in-memory suite pinning the `think` gather stage's source scope: seeds three sources with cross-source links and embedded takes, then asserts `runGather` under a federated `sourceIds` grant (and under a scalar `sourceId`) keeps every stream — hybrid retrieval, takes keyword + vector (`searchTakes`/`searchTakesVector`), and the `traversePaths` graph walk — inside the grant while still reaching authorized neighboring sources. No `DATABASE_URL` needed.
-- `test/e2e/skill-brain-first.test.ts` — doctor reports `skill_brain_first` check with structured issues; `--fix --dry-run` previews insertion without writing; `--fix` applies the canonical Convention callout idempotently; `brain_first: exempt` frontmatter resolves the warn; `brain_first_typo` surfaces a paste-ready hint; audit JSONL records `detected` / `resolved` / `fixed` transitions; stable brain emits 0 audit lines/run.
-- Tier 2 (`test/e2e/skills.test.ts`) requires OpenClaw + API keys, runs nightly in CI.
-- If `.env.testing` doesn't exist in this directory, check sibling worktrees: `find ../ -maxdepth 2 -name .env.testing -print -quit` and copy it here if found.
-- **Run E2E tests without asking permission.** When you want to verify behavior, there's a relevant E2E test, or you're shipping anything covered by an E2E suite — spin up the test DB, run the tests, tear down. Don't ask, don't propose it, don't defer. The lifecycle is short (~2-30s startup, sub-minute tests, instant teardown) and the gate value is high. Skipping with "DATABASE_URL unset" is silent regression, not caution.
-
-### API keys and running ALL tests
-
-ALWAYS source the user's shell profile before running tests:
-
-```bash
-source ~/.zshrc 2>/dev/null || true
-```
-
-This loads `OPENAI_API_KEY` and `ANTHROPIC_API_KEY`. Without these, Tier 2 tests
-skip silently. Do NOT skip Tier 2 tests just because they require API keys — load
-the keys and run them.
-
-When asked to "run all E2E tests" or "run tests", that means ALL tiers:
-- Tier 1: `bun run test:e2e` (mechanical, sync, upgrade — no API keys needed)
-- Tier 2: `test/e2e/skills.test.ts` (requires OpenAI + Anthropic + openclaw CLI)
-- Always spin up the test DB, source zshrc, run everything, tear down.
-
-### E2E test DB lifecycle (ALWAYS follow this)
-
-You are responsible for spinning up and tearing down the test Postgres container.
-Do not leave containers running after tests. Do not skip E2E tests, do not ask
-permission to run them — see the "run without asking" rule above.
-
-1. **Check for `.env.testing`** — if missing, copy from sibling worktree.
-   Read it to get the DATABASE_URL (it has the port number).
-2. **Check if the port is free:**
-   `docker ps --filter "publish=PORT"` — if another container is on that port,
-   pick a different port (try 5435, 5436, 5437) and start on that one instead.
-3. **Start the test DB:**
-   ```bash
-   docker run -d --name gbrain-test-pg \
-     -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres \
-     -e POSTGRES_DB=gbrain_test \
-     -p PORT:5432 pgvector/pgvector:pg16
-   ```
-   Wait for ready: `docker exec gbrain-test-pg pg_isready -U postgres`
-4. **Bootstrap the schema** (required — fresh containers have no `oauth_clients`,
-   `mcp_request_log`, `pages` etc.; tests like `serve-http-oauth.test.ts` will fail
-   with `relation "oauth_clients" does not exist` if you skip this):
-   ```bash
-   DATABASE_URL=postgresql://postgres:postgres@localhost:PORT/gbrain_test \
-     bun run src/cli.ts doctor --json > /dev/null 2>&1
-   ```
-   `gbrain doctor` triggers `initSchema()` on first connect, which is the canonical
-   way to bring a fresh DB to head. `apply-migrations --yes` alone does NOT seed
-   the base schema — it runs ALTER-style migrations on top of `initSchema`. Tests
-   that bypass the engine (raw `execSync`-spawned `auth register-client`) hit the
-   schema directly and need this step to have run first.
-5. **Run E2E tests:**
-   `DATABASE_URL=postgresql://postgres:postgres@localhost:PORT/gbrain_test bun run test:e2e`
-6. **Tear down immediately after tests finish (pass or fail):**
-   `docker stop gbrain-test-pg && docker rm gbrain-test-pg`
-
-Never leave `gbrain-test-pg` running. If you find a stale one from a previous run,
-stop and remove it before starting a new one.

docs/UPGRADING_DOWNSTREAM_AGENTS.md

@@ -538,75 +538,3 @@ To check what your fork is missing:
 diff <(grep -A3 "Based on gbrain" ~/<your-fork>/skills/brain-ops/SKILL.md) \
      <(grep "v[0-9]" ~/gbrain/skills/migrations/ | tail -3)
 ```
-
-
-## v0.36.5.0 — Free-form secret inheritance for shell jobs calling `gbrain` CLI
-
-**The change.** Shell-job params get a new `inherit:` field. Pass any
-snake_case config-key name on it; the worker resolves the value from its
-`loadConfig()` at child-spawn time and injects it into the child env. Names
-land in the row; values never persist from `inherit:`. Validation runs
-**pre-enqueue** in both submit paths (CLI + `submit_job` op), so a malformed
-payload never lands in `minion_jobs.data`.
-
-**Why.** Pre-v0.36.5.0, agents that wanted to call `gbrain` from shell jobs
-had to either write `database_url` to `~/.gbrain/config.json` plaintext or
-pass `env: { GBRAIN_DATABASE_URL: "..." }` per-job. Both left plaintext
-secrets somewhere — disk or DB row. `inherit:` keeps names in the row and
-resolves values at spawn time.
-
-**What your agent can do.** `inherit:` is free-form. Pass any config-key:
-
-```jsonc
-{
-  "cmd": "gbrain sync --skip-failed && gbrain embed --stale",
-  "cwd": "/data/gbrain",
-  "inherit": ["database_url", "anthropic_api_key", "voyage_api_key"]
-}
-```
-
-The env-key name in the child is derived by uppercasing the config-key:
-`database_url` → `GBRAIN_DATABASE_URL`, `anthropic_api_key` →
-`ANTHROPIC_API_KEY`, `voyage_api_key` → `VOYAGE_API_KEY`, etc. The validator
-does NOT police which config keys you inherit — the agent is in the same
-uid as the worker, so it's the agent's call.
-
-**You can still use `env:`.** v0.36.5.0 does not forbid `env:{ ANYTHING }`.
-If you have a reason to put a value in the row plaintext (a non-secret
-correlation token, or a secret you know is OK to persist), pass it via
-`env:`. Prefer `inherit:` when you want the value out of the row.
-
-**Worker setup** (one-time, per host):
-
-- `gbrain config set database_url postgresql://...` (or any other key you
-  want available for inherit)
-- OR put the key in `~/.gbrain/config.json` directly
-- OR set `GBRAIN_DATABASE_URL` / `DATABASE_URL` / per-provider env on the
-  worker process
-
-If the worker can't resolve a requested name, the validator fail-fasts at
-submit time with `gbrain config set <X>` hint. No more silent "No database
-URL" failures in child stderr minutes after submission.
-
-**Also new.** A `gbrain doctor` check `home_dir_in_worktree` warns if
-`~/.gbrain/` lives inside a git worktree. A retroactive `~/.gbrain/.gitignore`
-(single line `*`) is now laid down by every `saveConfig()` call AND by
-`gbrain post-upgrade`, so existing users get coverage without re-running
-`gbrain init`. Honest scope: the `.gitignore` covers casual `git add` but does
-NOT cover already-tracked files, screenshots, backups, or `git add -f`.
-
-**Strategy framing.** For agent-to-gbrain calls, the new canonical guide is
-`docs/guides/agent-to-gbrain.md`. Two distinct surfaces: HTTP MCP via OAuth
-for ops with MCP equivalents (`search`, `query`, `put_page`, etc.), and shell
-job + `inherit:` for `localOnly` admin ops (`sync`, `embed`, `dream`,
-`doctor`, etc.). Not a fallback hierarchy — pick by op.
-
-**Errors to handle** (your agent submits shell jobs; surface these clearly):
-
-| Error | What it means | Agent action |
-|---|---|---|
-| `shell: inherit must be an array of config-key names` | `inherit` wasn't an array. | Pass `"inherit": ["database_url", ...]`. |
-| `shell: inherit entries must be non-empty strings` | Element was empty, non-string, or null. | Use snake_case config-key names. |
-| `shell: inherit name "<X>" must match [a-z][a-z0-9_]*` | Name failed snake_case regex (uppercase, leading underscore, etc.). | Use the config-key verbatim — `database_url`, not `DATABASE_URL`. |
-| `shell: inherit requested "<X>" but worker has no <X> configured` | Worker can't resolve the name from its `loadConfig()`. | Run `gbrain config set <X> <value>` on the worker host. |
-

docs/ai-providers/llama-server-reranker.md

@@ -1,161 +0,0 @@
-# llama-server reranker (local) — Qwen3-Reranker, self-hosted ZE, any ZE-wire-shape provider
-
-[`llama-server`](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md)
-is the HTTP wrapper that ships with llama.cpp. With `--reranking`, it
-exposes an OpenAI-style `POST /v1/rerank` endpoint that returns
-`{results: [{index, relevance_score}]}` — exactly the wire shape gbrain
-already drives for ZeroEntropy's hosted reranker. The
-`llama-server-reranker` recipe (added in v0.40.6.1) routes
-`gateway.rerank()` at your local llama.cpp instance instead of ZE.
-
-Two flavors of "local" this recipe covers:
-
-- **Qwen3-Reranker** (0.6B / 4B / 8B) — open-weight cross-encoder; pull
-  the GGUF from HuggingFace and serve.
-- **Self-hosted ZeroEntropy** (`zerank-2`, `zerank-1-small`) — the
-  weights are on HuggingFace too. GGUF-convert them and serve them the
-  same way. **Quality is not guaranteed to match ZE-hosted:** GGUF
-  conversion + quantization + pooling/rank metadata + tokenizer special
-  tokens all affect scores. If you self-host ZE for production
-  retrieval, pin your own brain-relevant eval (
-  [docs/eval-bench.md](../eval-bench.md)) as a regression guard.
-
-This recipe is the path override + recipe shape. Any provider whose
-request/response wire matches ZE/llama.cpp can use it by just pointing
-at a different base URL. Providers whose wire shape differs (Voyage uses
-`top_k` not `top_n`, returns `data[]` not `results[]`) need a separate
-recipe with adapter hooks — that lands in a follow-up plan.
-
-## Setup
-
-### 1. Build llama.cpp (or download a release)
-
-```bash
-# Clone and build (CPU only; add `-DGGML_CUDA=ON` for GPU)
-git clone https://github.com/ggml-org/llama.cpp.git
-cd llama.cpp
-cmake -B build
-cmake --build build --config Release -j
-```
-
-Pin a specific commit when you ship — `llama-server`'s path aliases
-(`/rerank`, `/v1/rerank`, `/reranking`, `/v1/reranking`) have shifted
-across releases. The recipe sends to `/v1/rerank`.
-
-### 2. Pull a reranker GGUF
-
-For Qwen3-Reranker-4B (quantized Q4_K_M is the sweet spot for CPU):
-
-```bash
-# Pick a quant level — Q4_K_M is the usual CPU sweet spot.
-huggingface-cli download \
-  Qwen/Qwen3-Reranker-4B-GGUF qwen3-reranker-4b-q4_k_m.gguf \
-  --local-dir ./models
-```
-
-For self-hosted ZeroEntropy weights, find a community GGUF conversion
-or convert from the HuggingFace weights yourself (out of scope of this
-doc — see llama.cpp's `convert_hf_to_gguf.py`).
-
-### 3. Launch llama-server with --reranking AND --alias
-
-```bash
-./build/bin/llama-server \
-  --model ./models/qwen3-reranker-4b-q4_k_m.gguf \
-  --alias qwen3-reranker-4b \
-  --reranking \
-  --port 8081
-```
-
-The `--alias` matters: without it, llama-server's `/v1/models` (and the
-`model` field rerank requests echo) defaults to the full gguf file
-path, which makes the gbrain config string ugly and brittle. With
-`--alias qwen3-reranker-4b`, your config string is short and stable.
-
-`--reranking` and `--embeddings` are mutually exclusive at server
-launch. If you also run a local embedder via the
-[`llama-server`](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md)
-recipe, run two separate llama-server processes on two different ports
-(typically 8080 for embeddings, 8081 for reranking — gbrain's defaults
-match that convention).
-
-### 4. Wire gbrain at your server
-
-```bash
-# Point gbrain at the llama.cpp host (skip if running locally on default port)
-gbrain config set provider_base_urls.llama-server-reranker http://your-host:8081/v1
-
-# Tell search to use this reranker
-gbrain config set search.reranker.model llama-server-reranker:qwen3-reranker-4b
-gbrain config set search.reranker.enabled true
-```
-
-The `qwen3-reranker-4b` after the colon is your `--alias` value from
-step 3. Any string works as long as it matches your server's alias.
-
-Env vars work too as an alternative to the config set above:
-
-```bash
-export LLAMA_SERVER_RERANKER_BASE_URL=http://your-host:8081/v1
-# Optional: if you front llama-server with nginx + bearer auth
-export LLAMA_SERVER_RERANKER_API_KEY=your-bearer-token
-```
-
-### 5. Verify
-
-```bash
-gbrain models doctor
-# Expect: ✔ reranker_config llama-server-reranker:qwen3-reranker-4b ok
-#         ✔ reranker_config llama-server-reranker:qwen3-reranker-4b ok (reachability)
-
-gbrain search "some query" --json | jq '.[].rerank_score'
-# Expect: rerank_score on every row
-```
-
-If `gbrain models doctor` reports the reachability probe as `network`
-status, two common causes:
-
-1. The server is reachable but in embedding mode, not reranking mode.
-   `--reranking` and `--embeddings` are mutually exclusive at launch
-   — relaunch the right one.
-2. The recipe path doesn't match what your llama.cpp version serves.
-   This recipe sends `/v1/rerank`; older llama.cpp installs may only
-   serve `/rerank`. Pin to a recent llama.cpp commit.
-
-## Cold-start headroom
-
-CPU-only first-call warmup on a 4B reranker can take 8-15 seconds. The
-recipe declares `default_timeout_ms: 30000` so the first call after a
-server restart doesn't fail-open silently. That value flows through
-search-mode resolution unless you override it:
-
-```bash
-# Tighten or loosen per-search timeout (overrides recipe default):
-gbrain config set search.reranker.timeout_ms 60000
-```
-
-Per-call overrides in `SearchOpts.reranker_timeout_ms` still win for
-any single call.
-
-## Budget caps + local rerank
-
-The recipe declares `cost_per_1m_tokens_usd: 0` and registers under
-`FREE_LOCAL_RERANK_PROVIDERS` in the budget tracker, so
-`--max-cost`-bounded callers (autopilot loops, batch jobs) do NOT
-hard-fail when configured for local rerank. Local rerank costs
-electricity, not API tokens.
-
-```bash
-GBRAIN_MAX_USD=0.01 gbrain search "..." --reranker llama-server-reranker:qwen3-reranker-4b
-# Works: rerank fires, recorded at $0, cumulative cap untouched.
-```
-
-## Fail-open contract preserved
-
-`applyReranker` in `src/core/search/rerank.ts` still has the
-fail-open posture: any error class (network, timeout, malformed
-response) logs to `~/.gbrain/audit/rerank-failures-*.jsonl` and
-returns the original RRF order unchanged. Search reliability beats
-reranker quality. If your llama.cpp host goes down, your searches keep
-working — they just stop ranking against the cross-encoder until you
-restart the server.

docs/ai-providers/zeroentropy.md

@@ -1,173 +0,0 @@
-# ZeroEntropy — zembed-1 + zerank-2
-
-[ZeroEntropy](https://zeroentropy.dev) ships two specialized small models
-for retrieval pipelines:
-
-- **`zembed-1`** — multilingual embedding distilled from zerank-2.
-  Flexible Matryoshka dims (2560/1280/640/320/160/80/40), 32K context,
-  asymmetric `input_type: query|document` encoding. $0.025/1M tokens
-  (sale) / $0.05 regular.
-- **`zerank-2`** — SOTA multilingual cross-encoder reranker.
-  $0.025/1M tokens (~50% cheaper than Cohere/Voyage rerankers).
-  Plus `zerank-1` and `zerank-1-small` for legacy / open-source needs.
-
-Both land in gbrain v0.35.0.0 behind the openai-compatible recipe path,
-alongside OpenAI and Voyage.
-
-## Setup
-
-1. Get an API key at
-   [dashboard.zeroentropy.dev](https://dashboard.zeroentropy.dev).
-2. Export it:
-   ```bash
-   export ZEROENTROPY_API_KEY=<your-key>
-   ```
-
-## Embedding switch — zembed-1
-
-**Important:** `gbrain config set embedding_model …` is NOT a live
-gateway switch. `embedding_model` and `embedding_dimensions` size the
-schema and must be stable across engine connects, so they only resolve
-from the **file plane** (`~/.gbrain/config.json`) and the **env plane**
-(`GBRAIN_EMBEDDING_MODEL` / `GBRAIN_EMBEDDING_DIMENSIONS`). The DB plane
-is intentionally ignored for these two keys (same posture as today's
-Voyage setup).
-
-### Option A — file plane (recommended for stable installs)
-
-Edit `~/.gbrain/config.json`:
-
-```json
-{
-  "embedding_model": "zeroentropyai:zembed-1",
-  "embedding_dimensions": 2560
-}
-```
-
-Valid dims: `2560` (default), `1280`, `640`, `320`, `160`, `80`, `40`.
-Matryoshka-style — smaller trades quality for storage monotonically.
-Pick the largest that fits your column width.
-
-### Option B — env plane (CI / Docker)
-
-```bash
-export GBRAIN_EMBEDDING_MODEL=zeroentropyai:zembed-1
-export GBRAIN_EMBEDDING_DIMENSIONS=2560
-```
-
-### Re-embed
-
-Switching embedding models invalidates the vector index. Re-embed:
-
-```bash
-gbrain embed --stale --limit 50    # smoke a small batch
-gbrain embed --stale               # full re-embed
-```
-
-### Verify
-
-```bash
-gbrain models doctor --json | jq '.probes[] | select(.touchpoint=="embedding_config")'
-```
-
-Expected: `status: "ok"`. Invalid dims (e.g. `1024`, `1536`, `3072`)
-surface as `status: "config"` with a paste-ready
-`gbrain config set embedding_dimensions <one of 2560|1280|640|320|160|80|40>` fix hint.
-
-## Reranker switch — zerank-2
-
-The reranker is the bigger story: gbrain had no cross-encoder reranker
-stage before v0.35.0.0. It slots between RRF dedup and token-budget
-enforcement in hybrid search.
-
-### Default-on with `tokenmax` mode
-
-`tokenmax` mode now defaults `search.reranker.enabled = true` with
-`zerank-2`. If you already use `tokenmax` AND have `ZEROENTROPY_API_KEY`
-set, reranker fires automatically. Without the key, every rerank call
-fails-open (audit-logged) and search returns RRF order — same UX as
-before, just with an observable failure surfaced via `gbrain doctor`.
-
-### Opt-in on `conservative` or `balanced` mode
-
-```bash
-gbrain config set search.reranker.enabled true
-```
-
-The override sits above the mode-bundle default; opt-out is one flip.
-
-### Cost anchor
-
-At 30 candidates × ~400 tokens/chunk × $0.025/1M = **~$0.0003/query**.
-Rounding error against the `tokenmax + Opus` pairing's ~$700/mo at
-single-user volume per the CLAUDE.md cost matrix.
-
-### Verify
-
-```bash
-gbrain models doctor --json | jq '.probes[] | select(.touchpoint=="reranker_config")'
-```
-
-Two probes run for reranker:
-- `reranker_config` (zero-network) — validates the model resolves
-  through the recipe registry and is in the touchpoint's allowlist.
-- A reachability probe sends a minimal `{query: "probe", documents:
-  ["probe"]}` rerank to verify auth + URL.
-
-## Knobs reference
-
-| Config key | Default | Notes |
-|---|---|---|
-| `search.reranker.enabled` | `true` for tokenmax, `false` for others | One-flip opt-in/out |
-| `search.reranker.model` | `zeroentropyai:zerank-2` | Try `zerank-1` (older SOTA) or `zerank-1-small` (Apache-2.0 open) |
-| `search.reranker.top_n_in` | `30` | Candidates sent to reranker (caps API spend) |
-| `search.reranker.top_n_out` | `null` (no truncate) | Truncate reranked output to this many; `null` preserves full length |
-| `search.reranker.timeout_ms` | `5000` | HTTP timeout; long stalls degrade UX worse than RRF fallback |
-
-## Failure observability
-
-Reranker is fail-open by construction: every error class (auth, rate-limit,
-network, timeout, payload-too-large, unknown) returns the original RRF
-order unchanged. Failures log to
-`~/.gbrain/audit/rerank-failures-YYYY-Www.jsonl` (ISO-week rotation).
-
-`gbrain doctor` reads the audit and surfaces:
-- **auth failures** — any single one warns (config-time problem doctor's
-  own probe should have caught)
-- **payload-too-large** — any single one warns (workload-mismatch signal)
-- **transient (network/timeout/rate_limit)** — warns at >=5 in 7 days
-
-Query text is SHA-256 hashed in the audit; never logged raw.
-
-## Asymmetric input_type
-
-ZE zembed-1 (and Voyage v3+) use asymmetric query/document encoding for
-better retrieval. The gateway's `embedQuery(text)` companion threads
-`input_type: 'query'`; standard `embed(texts)` defaults to
-`'document'`. Hybrid search's two query-side embed sites use
-`embedQuery()` automatically; all ingest paths use `embed()`.
-
-Symmetric providers (OpenAI text-embedding-3, fixed-dim Voyage models)
-ignore the field — no behavior change.
-
-## Cache key versioning
-
-v0.35.0.0 bumped `KNOBS_HASH_VERSION` 1 → 2 to fold reranker config into
-the `query_cache.knobs_hash` column. During a rolling deploy:
-
-- Expect a temporary cache hit-rate dip (~1 hour at default
-  `cache.ttl_seconds = 3600s`)
-- Hot queries may briefly double their cache row count (one row per
-  version)
-
-Both clear naturally; no operator action required.
-
-## Troubleshooting
-
-| Symptom | Likely cause | Fix |
-|---|---|---|
-| `embedding_config` probe says invalid dim | Defaulting to 1536 (OpenAI default) | Set `embedding_dimensions` to one of 2560/1280/640/320/160/80/40 |
-| `reranker_config` probe says model not in allowlist | Typo in `search.reranker.model` | Use one of `zerank-2` / `zerank-1` / `zerank-1-small` |
-| `reranker_health` doctor warns about auth | `ZEROENTROPY_API_KEY` not set or invalid | Re-export the env var; `gbrain models doctor` to verify |
-| `reranker_health` doctor warns about transient failures | Upstream flake or rate limit | Reranker fails open to RRF; check ZE status page if persistent |
-| Cache hit rate dipped after upgrade | Expected during rolling deploy | Clears within `cache.ttl_seconds` (default 3600s) |

docs/architecture/RETRIEVAL.md

@@ -1,165 +0,0 @@
-# Why the hybrid + graph stack works
-
-Vector search alone underdelivers on real personal-knowledge queries. This doc explains why gbrain layers four strategies together and how they compound.
-
-## The four strategies in concert
-
-1. **Vector (HNSW on pgvector)** — semantic similarity. Catches "who works on retrieval quality at YC?" → pages mentioning "Garry Tan + retrieval" even when the user never typed "YC".
-2. **BM25 keyword** — lexical match. Catches names, exact phrases, code identifiers, anything where the user remembers the literal token. Survives the cases where vector search drifts into thematic neighbors.
-3. **Reciprocal-rank fusion (RRF)** — merges vector + keyword rankings without weighting one over the other globally. Each strategy gets to vote.
-4. **Knowledge graph traversal** — follows typed edges. Catches "what did Bob invest in this quarter?" by walking `bob ── invested_in ──> company ── dated ──> Q1`. Vector search can't see causal chains; the graph can.
-
-## Why each one alone fails
-
-**Vector only.** Returns chunks semantically close to the query. Misses any factual relationship not directly encoded in the embedding. "Companies in Garry's portfolio" returns essays about portfolios, not company pages.
-
-**Keyword only (ripgrep-style).** Brittle to phrasing. "Who works on retrieval?" misses pages that say "search ranking" instead of "retrieval." Garbage on synonyms, near-misses, or paraphrases.
-
-**Graph only.** Excellent at "neighbors of Alice" but blind to anything not yet linked. Sparse on fresh pages until backlinks accumulate.
-
-**Hybrid (vector + keyword + RRF), no graph.** Decent at "what is X?" type queries. Fails on "what is Y's relationship to X?" — those are graph queries and no amount of embedding tuning recovers them.
-
-## The benchmark
-
-BrainBench (corpus + harness in the sibling [gbrain-evals](https://github.com/garrytan/gbrain-evals) repo) measures retrieval P@5, R@5, MRR, nDCG@5 on a 240-page Opus-generated rich-prose corpus.
-
-| Strategy | P@5 | R@5 | Notes |
-|---|---|---|---|
-| ripgrep BM25 only | ~18 | ~75 | Lexical-only baseline |
-| vector-only RAG | ~18 | ~80 | Standard RAG implementation |
-| gbrain graph-disabled (hybrid + RRF, no graph traversal) | ~18 | ~85 | Hybrid alone |
-| **gbrain default (full stack)** | **49.1** | **97.9** | Graph + extract-quality lift |
-
-**+31 P@5 points** from the graph + extract quality work. The graph isn't a marginal feature; it's the load-bearing wall.
-
-## Auto-link: why zero-LLM-call edge extraction works
-
-Every `put_page` runs `extractEntityRefs` on the markdown body. It matches:
-
-- Standard markdown links: `[Garry Tan](wiki/people/garry-tan)`
-- Obsidian wikilinks: `[[wiki/people/garry-tan|Garry Tan]]`
-- Typed-link blockquotes: `> **Convention:** see [path](path).`
-
-Three regexes, zero LLM tokens, single SQL `addLinksBatch` call with `INSERT ... SELECT FROM jsonb_to_recordset(($1::jsonb)->'rows') JOIN pages ON CONFLICT DO NOTHING RETURNING 1` (free-text-safe; the prior `unnest(${arr}::text[])` form crashed on calendar/Zoom context per gbrain#1861). The graph grows on every write at near-zero cost. On a 17K-page brain, full graph extract completes in seconds.
-
-Heuristic link-type inference (`attended`, `works_at`, `invested_in`, `founded`, `advises`) fires from surrounding sentence context — also LLM-free. Power users who want richer types add them via the typed-link blockquote convention.
-
-## ZeroEntropy as reranker: 60% top-1 reshuffle
-
-v0.36.0.0 ships ZeroEntropy's `zerank-2` as the default reranker (on for the `balanced` mode bundle). On a real-corpus benchmark across 20 queries, zerank-2 reshuffles **60% of top-1 results** after the hybrid + RRF + graph stack. That's the headline number.
-
-The mechanical reason: hybrid ranking is locally optimal per strategy but globally suboptimal. A cross-encoder reranker reads the query + each candidate document jointly, with full attention. It catches the cases where the vector + keyword + graph signals all agreed on a document that's semantically related but topically wrong.
-
-The cost: +150ms p50 latency, ~$0.025/M tokens. Disabled with `gbrain config set search.reranker.enabled false`. For agent loops that do downstream LLM work after retrieval, the latency is invisible.
-
-## Source-aware ranking
-
-Hybrid search applies a source-factor CASE expression at the SQL layer (lives in `src/core/search/sql-ranking.ts`). Curated content like `originals/`, `concepts/`, `writing/` outranks bulk content like `your-openclaw/chat/`, `daily/`, `media/x/`. Hard-exclude prefixes (`test/`, `attachments/`, `.raw/`) filter at retrieval, not post-rank.
-
-`archive/` is deliberately NOT hard-excluded (issue #1777): it holds high-signal historical content users expect to find, so it is demoted (`0.5x` in `DEFAULT_SOURCE_BOOSTS`), not hidden. The demote is a prior applied in the outer SQL re-rank; the cross-encoder reranker (balanced/tokenmax modes) can still PROMOTE an archive page that survives the demote into the rerank candidate window — it is not an unconditional suppression. `gbrain doctor`'s `hidden_by_search_policy` check reports how many chunked pages remain hidden by the surviving exclude prefixes.
-
-The boost map is configurable via `GBRAIN_SOURCE_BOOST` env var or per-call `SearchOpts.exclude_slug_prefixes`. Temporal queries (`detail: 'high'`) bypass the boost so chat pages re-surface for time-sensitive lookups.
-
-## Named-thing retrieval (per-page pool + title + alias + evidence)
-
-A brain organized around *chosen names* (Mingtang, Hall of Light) needs more than
-embedding proximity. Four layers, added after the incident in
-[`RETRIEVAL_MAXPOOL_INCIDENT.md`](./RETRIEVAL_MAXPOOL_INCIDENT.md):
-
-- **Per-page max-pool** — `searchVector` (both engines) collapses chunk-grain
-  candidates to the best chunk per page (`DISTINCT ON (slug)`) over the full
-  candidate set before the user `LIMIT`, via the shared `buildBestPerPagePoolCte`
-  in `sql-ranking.ts`. The vector side returns N distinct pages by best chunk,
-  not N chunks that collapse to fewer pages downstream.
-- **Title-phrase boost** — when the normalized query is a contiguous token-run
-  inside `page.title` (or an exact full-title match), a floor-ratio-gated,
-  bounded multiplier fires (`applyTitleBoost`, `search.title_boost` knob). A
-  query that is a phrase from the title can't lose to a body chunk by luck.
-- **Alias hop** — free-text `aliases:` frontmatter is projected into a
-  `page_aliases` table (separate from the `slug_aliases` wikilink redirect) and
-  consulted at query time: a full normalized-query match injects/boosts the
-  canonical page (`applyAliasHop`). The only layer that bridges true synonyms
-  with zero surface overlap ("Hall of Light" → the Mingtang page). Backfill
-  existing pages with `gbrain reindex --aliases`.
-- **Evidence contract** — every result carries `evidence`
-  (`alias_hit | exact_title_match | high_vector_match | keyword_exact |
-  weak_semantic`) and `create_safety` (`exists | probable | unknown`). An agent
-  deciding "is this page already here, safe to NOT write a duplicate?" keys off
-  `create_safety`, not a raw blended score.
-
-The `search` MCP/CLI op is **cheap-hybrid** (vector + keyword + RRF + pool +
-title + alias, expansion off); `query` is the full-control variant. NamedThingBench
-(`gbrain eval retrieval-quality`) gates these families on every PR. Diagnose a
-specific miss with `gbrain search diagnose "<q>" --target <slug>`.
-
-## Intent-aware query rewriting
-
-`src/core/search/intent.ts` classifies queries into `entity`, `temporal`, `event`, or `general`. Each routes through different ranking knobs:
-
-- **Entity** queries ("who works at X?") apply a higher graph-traversal weight.
-- **Temporal** queries ("what happened last week?") bypass source-boost so chat/daily pages surface.
-- **Event** queries ("Acme AI Series A") engage the timeline index.
-- **General** queries hit the standard hybrid stack.
-
-The classifier is deterministic (no LLM call). Wrong classification degrades gracefully — the hybrid stack still works without it.
-
-## Multi-query expansion
-
-For `detail: 'high'` searches, `src/core/search/expansion.ts` runs a Haiku-class LLM call to produce 2-3 query variants. Each variant runs through the full hybrid stack; results merge via RRF. Catches synonym misses without recall loss.
-
-Expansion is opt-in per mode bundle (`tokenmax` on by default; `balanced` + `conservative` off). Default off in the cheap tiers because the LLM call adds ~$0.001/query and ~200ms — real money at scale.
-
-## Putting it together
-
-The full pipeline for a `query` op:
-
-```
-intent classify
-       │
-       ▼
-expansion (if enabled)
-       │
-       ▼
-hybrid search:
-   ├── vector  (HNSW on chunk embeddings)
-   ├── keyword (BM25 via tsvector)
-   ├── relational (v0.42.34.0: typed-edge recall arm — relational queries only)
-   ├── source-aware re-rank (CASE in SQL)
-   └── RRF fusion → top 30
-       │
-       ▼
-graph augment (typed-edge traversal from any seed)
-       │
-       ▼
-reranker (zerank-2 cross-encoder, top 30 → reordered)
-       │
-       ▼
-token-budget enforcement (per mode bundle)
-       │
-       ▼
-deduplication (same slug, different chunks → keep best)
-       │
-       ▼
-results
-```
-
-Each stage is testable in isolation. Each stage is replaceable. The whole pipeline is < 1ms of orchestration cost; the latency budget goes to the upstream HTTP calls (embedding, rerank) and the index scans.
-
-## How to verify on your own brain
-
-```bash
-# Run the public LongMemEval benchmark
-gbrain eval longmemeval datasets/longmemeval_s.jsonl
-
-# Capture your own queries and replay against retrieval changes
-export GBRAIN_CONTRIBUTOR_MODE=1
-# ... use gbrain normally ...
-gbrain eval export > before.ndjson
-# ... change something ...
-gbrain eval replay --against before.ndjson
-
-# A/B retrieval strategies on a labeled fixture
-gbrain eval --qrels labels.tsv --config balanced.json
-```
-
-Methodology + metric glossary in [`docs/eval/SEARCH_MODE_METHODOLOGY.md`](../eval/SEARCH_MODE_METHODOLOGY.md).

docs/architecture/RETRIEVAL_MAXPOOL_INCIDENT.md

@@ -1,97 +0,0 @@
-# Retrieval Incident: a chosen-name page was missed, and the fix
-
-**Status:** Resolved (retrieval-cathedral wave). Supersedes the docs-only RFC in
-closed PR #1616 — the diagnosis there was directionally right about the disease
-but wrong on several mechanics; this is the corrected record + what shipped.
-**Original author:** Garry Tan's OpenClaw. **Severity at the time:** High.
-**Related:** [`RETRIEVAL.md`](./RETRIEVAL.md), [`../eval/METRIC_GLOSSARY.md`](../eval/METRIC_GLOSSARY.md).
-
----
-
-## 1. What happened
-
-The agent was asked to log that Garry "wants to build a Greek amphitheater." It
-ran a retrieval for the concept, the canonical concept page (titled "...Indoor
-Greek Amphitheater...") did **not** surface with enough confidence to be
-recognized as the existing page, and the agent wrote a **duplicate stub** on top
-of a fully-developed concept doc. Garry caught it: "It's in the brain. It's the
-Hall of Light. Why did you forget?"
-
-The page is *about* a Greek amphitheater — the phrase is in its title and first
-sentence. A healthy index returns it at the top. It didn't.
-
-## 2. The disease (the RFC got this right)
-
-The brain is stored by **meaning and chosen name** (Mingtang, Hall of Light) but
-was retrieved by **literal embedding proximity to a body chunk**, and the agent's
-"is this already here?" decision keyed off a single fuzzy blended score. Three
-retrieval gaps plus one contract gap produced the miss.
-
-## 3. Verified ground truth (corrections to the RFC)
-
-These were checked in code during the fix; several change the remedy:
-
-1. **`gbrain search` was keyword-only**, not hybrid — so the RFC's cosine scores
-   (0.64/0.98) came from the hybrid `query`/MCP path the agent actually hit, not
-   `gbrain search`. The repro command in the RFC was mislabeled.
-2. **`--mode` was never a CLI param** — mode resolves server-side from the
-   `search.mode` config key, which is why all three "modes" returned identical
-   results (the flag was silently dropped; `thorough` isn't a real mode).
-3. **`hybridSearch` already max-pooled per page at the dedup layer.** So the
-   per-page max-pool fix's real win is *candidate-set page recall* (the vector
-   side returned N chunks that could collapse to fewer pages), and it is
-   necessary-but-not-sufficient: if a page's title chunk scores below a body
-   chunk on a 2-word query, or falls outside the candidate pool, pooling alone
-   doesn't rescue it.
-4. **Frontmatter `aliases:` was dead to search** — stored in `pages.frontmatter`
-   JSONB, never consulted. `slug_aliases` is a *slug→slug* wikilink redirect, a
-   different concept.
-
-## 4. The fix that shipped (four layers + a contract)
-
-| Layer | Fixes | Where |
-|---|---|---|
-| **Per-page max-pool** (T1) | a page scored by its weakest chunk; vector page-recall | `searchVector` both engines, shared `buildBestPerPagePoolCte` |
-| **Title-phrase boost** (T2) | query is a phrase in the title but matched a body chunk | `applyTitleBoost` (reads `page.title`), `title_boost` mode knob |
-| **Alias hop** (T3) | true synonyms with zero surface overlap ("Hall of Light" → Mingtang) | `page_aliases` table, `applyAliasHop`, ingest projection + `reindex --aliases` backfill |
-| **Evidence contract** (T4) | the agent keyed "don't duplicate" off a fuzzy score | `evidence` + `create_safety` on every result; the agent keys off `create_safety='exists'`, not a threshold |
-
-Plus: `gbrain search "<text>"` is now cheap-hybrid (the obvious verb gives the
-good path); `modes/stats/tune` stay subcommands; `--mode` works per-call for
-local callers; rank-1 score drift telemetry; and **NamedThingBench**, a CI gate
-that hard-gates the families that ARE this incident.
-
-## 5. How to confirm / triage a recurrence
-
-```
-# Which layer surfaces (or misses) the target page?
-gbrain search diagnose "Greek amphitheater" --target projects/new-greek-theater/concept_v0
-
-# Backfill aliases for existing pages whose frontmatter predates the alias layer:
-gbrain reindex --aliases
-
-# Watch retrieval quality over time (a downward avg rank-1 score = regressing):
-gbrain search stats --days 30
-
-# The gate that prevents silent reintroduction:
-gbrain eval retrieval-quality test/fixtures/retrieval-quality/namedthing.jsonl
-```
-
-For a page to be reliably found by its chosen name, give it `aliases:` frontmatter:
-
-```yaml
----
-title: The Mingtang — Indoor Greek Amphitheater
-aliases:
-  - Hall of Light
-  - 明堂
----
-```
-
-## 6. The discipline this teaches
-
-A benchmark that scores 97.9 R@5 while production returns a flagship page at 0.64
-means the benchmark and the shipped path diverged. NamedThingBench runs the same
-families through the real pipeline on every PR, and the evidence contract means
-the agent's duplicate-or-not decision is grounded in *why* a page matched, not a
-number that was never a calibrated probability.

docs/architecture/calibration-quality-gate-spec.md

@@ -1,215 +0,0 @@
-# Calibration Quality Gate — Falsifiability Filter + Category Classification
-
-> **Historical context.** This is the source spec absorbed from PR #1191 into
-> two waves of implementation:
->
-> - **v0.37.2.0 hotfix** (this release): widens the `takes_resolution_consistency`
->   CHECK constraint to accept `quality='unresolvable'` as a 4th valid state.
->   Unblocks the production grading script. Adds `unresolvable_count` +
->   `unresolvable_rate` to `TakesScorecard` as sibling fields (preserves
->   v0.36.1.0 historical comparison semantics). Migration renumbered v74→v79→v80
->   during successive master merges — v0.37.0.0's autonomous-remediation wave
->   claimed v68-v78, then v0.37.1.0 (brainstorm/lsd) claimed v79.
-> - **Follow-up minor** (forthcoming): falsifiability + category extraction at
->   `propose_takes`, SQL-side grade gate, per-category calibration scorecards,
->   pg_trgm-based proposal dedup. Wave-blocking on cat15 F1 re-validation
->   against the v0.36.1.0 fixtures.
->
-> Preserved here per the hotfix plan's PR #1191 close protocol so the
-> production context (96K-page brain, 6.8% falsifiability rate, category
-> breakdown) doesn't get lost in the CHANGELOG → release-notes condensation.
-
-## Problem
-
-v0.36.1.0 ships `propose_takes`, `grade_takes`, and `calibration_profile` as a
-connected pipeline: extract claims → grade them against outcomes → build a
-calibration profile showing systematic biases.
-
-In production on a 96K-page brain with 36K takes across 6,239 holders, the
-grade_takes phase produces noisy results:
-
-- **6.8% falsifiability rate**: Of 500 candidate takes (weight ≥ 0.7), only 34
-  passed an LLM falsifiability filter. The other 93% were philosophical beliefs,
-  present-state observations, advice, logistics, or vague vibes.
-- **50% unresolvable**: Even after filtering, 17/34 predictions couldn't be
-  graded because evidence was insufficient or the claim was too ambiguous.
-- **Duplicates**: Same claim from the same page extracted multiple times with
-  slightly different wording.
-
-The root cause: `propose_takes` extracts everything that looks like a belief or
-assertion. That's correct for the *takes* table (epistemological layer), but
-`grade_takes` needs a much narrower subset: **falsifiable predictions about
-future outcomes** where we can check what actually happened.
-
-### Example classifications from production testing
-
-**Genuine predictions (grade-worthy):**
-- "X will reach $1M ARR very soon" → company_outcome
-- "X is going to leave Y" → people_move  
-- "AI will make authentic authorship more important" → technology
-- "X was convinced Y would win the Z market" → market_call
-
-**Not predictions (should skip grading):**
-- "Desire is mimetic" → philosophical belief
-- "X should charge 10x more" → advice
-- "Return from Toronto on Monday" → logistics
-- "Something is going to happen there" → vague/unfalsifiable
-- "X is growing very quickly" → present-state observation
-
-## Solution
-
-### 1. Falsifiability score at extraction time
-
-Add a `falsifiability` column to the `takes` table (real, 0.0–1.0, nullable,
-default null). `propose_takes` sets this during extraction using the same LLM
-call that already produces the take — one additional field in the JSON schema.
-
-```sql
-ALTER TABLE takes ADD COLUMN IF NOT EXISTS falsifiability real;
-ALTER TABLE takes ADD COLUMN IF NOT EXISTS falsifiability_category text;
-```
-
-The LLM prompt addition (appended to the existing propose_takes extraction prompt):
-
-```
-For each claim, also assess:
-- falsifiability (0.0-1.0): Can this claim be checked against future reality?
-  1.0 = specific, measurable, time-bounded prediction about an outcome
-  0.5 = directional claim that's partially checkable
-  0.0 = philosophical belief, advice, observation, or unfalsifiable assertion
-- falsifiability_category: one of
-  company_outcome | fundraising | technology | people_move | market_call | other_prediction | not_prediction
-```
-
-Cost: ~0 incremental tokens (the claim is already being extracted; this adds
-two fields to the JSON output schema).
-
-### 2. Grade gate in `grade_takes`
-
-Before attempting grading, filter:
-
-```typescript
-const gradeable = candidates.filter(t =>
-  t.falsifiability !== null && t.falsifiability >= 0.7
-  && t.falsifiability_category !== 'not_prediction'
-);
-```
-
-This reduces grading volume by ~93% in production, which means:
-- LLM cost for grading drops proportionally
-- Evidence retrieval load drops (each grade attempt triggers hybrid search)
-- Calibration profiles are built on real predictions, not noise
-
-### 3. Deduplication at extraction
-
-`propose_takes` should check for near-duplicate claims before inserting:
-
-```typescript
-// Before inserting a new take, check if a similar claim exists
-// for the same holder from the same page
-const existing = await engine.sql`
-  SELECT id, claim FROM takes
-  WHERE holder = ${holder}
-  AND page_id = ${pageId}
-  AND similarity(claim, ${newClaim}) > 0.8
-  LIMIT 1
-`;
-if (existing.length > 0) {
-  // Skip — near-duplicate
-  continue;
-}
-```
-
-Requires `pg_trgm` extension (already available on most Postgres installations).
-Falls back gracefully: if `similarity()` isn't available, skip the dedup check.
-
-### 4. Category-aware calibration profiles
-
-The `calibration_profile` phase can now group resolved takes by
-`falsifiability_category` to produce per-domain scorecards:
-
-```
-"Your company_outcome calls are 73% accurate.
- Your people_move calls are 90% accurate.
- Your technology calls are 60% accurate — you tend to be ~18 months early."
-```
-
-This is the tweetable output: a calibration profile that says "here's how you're
-systematically right and wrong by category."
-
-## Schema Changes
-
-```sql
--- Migration: add falsifiability columns to takes
-ALTER TABLE takes ADD COLUMN IF NOT EXISTS falsifiability real;
-ALTER TABLE takes ADD COLUMN IF NOT EXISTS falsifiability_category text;
-
--- Index for grade_takes filter
-CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_takes_falsifiability
-  ON takes (falsifiability)
-  WHERE falsifiability IS NOT NULL AND falsifiability >= 0.7;
-
--- Optional: pg_trgm for dedup (CREATE EXTENSION IF NOT EXISTS pg_trgm;)
-```
-
-## Evidence Retrieval (v0.36.1.0 → v0.37 enhancement)
-
-The current `grade_takes` evidence retriever returns a stub placeholder. In
-production testing, we wired real evidence retrieval via `gbrain query` (hybrid
-search). The pattern that works:
-
-1. Extract the core claim from the take (first 150 chars)
-2. Run `engine.query(claim)` to get relevant pages
-3. Filter to pages updated AFTER the take's `since_date` (evidence must be newer)
-4. Pass top-5 chunks as the evidence block to the judge
-
-This should replace the stub in the `evidenceRetriever` injection point.
-
-## Production Results
-
-After implementing the falsifiability filter (as a pre-processing step outside
-the cycle):
-
-| Metric | Before (v2, no filter) | After (v3, with filter) |
-|--------|----------------------|----------------------|
-| Candidates evaluated | 50 | 34 (from 500 screened) |
-| Falsifiable predictions | ~19 (38%) | 34 (100%) |
-| Correct | 10 (52.6% of resolvable) | 10 (58.8% of resolvable) |
-| Incorrect | 5 (26.3%) | 2 (11.8%) |
-| Partial | 4 (21.1%) | 5 (29.4%) |
-| Unresolvable | 31 (62%) | 17 (50%) |
-| Category breakdown | N/A | people_move:13, company_outcome:11, technology:4, market_call:2 |
-
-Key improvement: **the false positive rate dropped from 62% noise to 0% noise**
-in the gradeable set. The remaining 50% unresolvable rate is genuine — those
-predictions are about outcomes that haven't happened yet or where the brain
-lacks evidence. That's correct behavior, not noise.
-
-## Files to Change
-
-1. **`src/core/cycle/propose-takes.ts`** — Add falsifiability + category to
-   extraction prompt and output schema
-2. **`src/core/cycle/grade-takes.ts`** — Add falsifiability gate before grading;
-   wire real evidence retrieval
-3. **`src/core/cycle/calibration-profile.ts`** — Group scorecards by category
-4. **`src/core/engine.ts`** — Add `similarity()` helper for dedup (graceful
-   fallback)
-5. **New migration** — Add columns + index
-
-## Testing
-
-- Unit test: falsifiability classifier on 20 known-good and 20 known-noise takes
-- Unit test: dedup correctly merges near-identical claims
-- Unit test: grade gate filters below threshold
-- Integration test: full cycle with falsifiability → grade → profile pipeline
-- Regression test: existing takes without falsifiability score are not broken
-  (null falsifiability = ungated, backward compatible)
-
-## Backward Compatibility
-
-- `falsifiability` defaults to null. Existing takes are unaffected.
-- `grade_takes` with null falsifiability: configurable behavior. Default:
-  grade all (backward compat). Operator can set
-  `cycle.grade_takes.require_falsifiability: true` to gate.
-- Category column is purely additive.
-- Dedup is opt-in: `cycle.propose_takes.dedup.enabled: true`.

docs/architecture/frontmatter-scan-incremental.md

@@ -1,200 +0,0 @@
-# Frontmatter scan: DB-backed incremental state (Phase 2 design sketch)
-
-**Status:** Designed, not built. Captured here as the starting point for the
-follow-up PR after v0.38.2.0.
-
-## Why this exists
-
-v0.38.2.0 fixed the load-bearing bug class that caused `gbrain doctor` to
-hang on large brains: the disk walker descended into `node_modules/`, `.git/`,
-and other vendor trees on every tick. After that fix doctor completes in
-seconds on most brains, and bounded wall-clock (default 30s, with honest
-partial-state surfacing) on any brain.
-
-But the steady-state cost of `frontmatter_integrity` is still O(N) in real
-syncable pages: every doctor tick re-walks the filesystem and re-parses
-every `.md` file. For users with 200K+ pages the steady-state cost is in
-the seconds even after Fix 1. For sub-second steady-state doctor (the
-right shape for cron-monitored health checks), the scan needs to become
-incremental.
-
-This document captures the Phase 2 design before the follow-up PR starts,
-so the implementer doesn't have to re-derive it.
-
-## Goal
-
-Doctor's `frontmatter_integrity` check completes in O(1) SQL queries
-regardless of brain size, with the same per-source breakdown and partial-
-state semantics as v0.38.2.0's bounded-walk approach. Incremental refresh
-runs as a sync-side write + an autopilot cycle phase, so the steady-state
-work is amortized across the workflow that already touches each file.
-
-## Schema
-
-New table:
-
-```sql
-CREATE TABLE frontmatter_scan_state (
-  source_id    TEXT NOT NULL REFERENCES sources(id) ON DELETE CASCADE,
-  path         TEXT NOT NULL,  -- relative to source.local_path
-  mtime_ms     BIGINT NOT NULL,
-  content_hash TEXT NOT NULL,  -- sha256 of file content at scan time
-  codes        JSONB NOT NULL DEFAULT '[]'::jsonb,  -- ParseValidationCode[]
-  last_scanned_at TIMESTAMPTZ NOT NULL DEFAULT now(),
-  PRIMARY KEY (source_id, path)
-);
-
-CREATE INDEX frontmatter_scan_state_has_issues_idx
-  ON frontmatter_scan_state (source_id)
-  WHERE codes != '[]'::jsonb;
-```
-
-Why these columns:
-- `mtime_ms` + `content_hash`: incremental check picks one. mtime is faster
-  (no read); content_hash is the truth (defeats touch-without-change cases).
-  The incremental walker uses mtime as a fast gate and content_hash as the
-  fallback when mtime suggests change.
-- `codes` JSONB: per-row error code list, NULL/`[]` means clean. Doctor
-  aggregates with `jsonb_array_length(codes) > 0`.
-- Partial index on `WHERE codes != '[]'::jsonb`: doctor's aggregate query
-  only walks rows with issues, which is a small fraction of pages.
-
-This follows the canonical `applyForwardReferenceBootstrap` pattern in
-`src/core/pglite-engine.ts` (and `postgres-engine.ts`) — the new column /
-table additions go into the bootstrap probe set per CLAUDE.md so old brains
-walking forward through the schema chain don't wedge on the table not
-existing.
-
-## Migration shape
-
-```ts
-// src/core/migrate.ts — append after the v80 entry
-const migrations = [
-  // ...existing v1-v80...
-  {
-    version: 81,
-    name: 'frontmatter_scan_state',
-    sql: `
-      CREATE TABLE IF NOT EXISTS frontmatter_scan_state (...);
-      CREATE INDEX IF NOT EXISTS frontmatter_scan_state_has_issues_idx ...;
-    `,
-  },
-];
-```
-
-Plus the forward-reference probe entries in both engine bootstraps. Plus
-the `REQUIRED_BOOTSTRAP_COVERAGE` extension in
-`test/schema-bootstrap-coverage.test.ts`.
-
-## Writers
-
-Two paths write rows:
-
-1. **Sync-side write** (canonical). `src/core/sync.ts:performSync` already
-   parses every file it touches. After the existing `parseMarkdown` call,
-   `UPSERT` into `frontmatter_scan_state` with the file's path / mtime /
-   content_hash / codes. Cost: one row per file synced. Zero extra parse
-   work — the parse already happened.
-
-2. **Incremental scan** (`gbrain frontmatter scan --incremental`). Walks
-   the disk via `walkBrainTree`, for each file checks `mtime > last_scanned_at`
-   OR `content_hash != stored`, only re-parses changed files. Most ticks:
-   zero work after the first full backfill. Also exposed as an autopilot
-   cycle phase (`frontmatter_scan`) so it runs alongside the other periodic
-   maintenance phases.
-
-The incremental walker handles two cases sync misses:
-- Files edited outside sync (user opens an editor, saves, never `git
-  commit`s).
-- Sources whose `local_path` isn't a git repo (sync only sees git-touched
-  files).
-
-## Doctor reader
-
-```ts
-// src/commands/doctor.ts:frontmatter_integrity (Phase 2 shape)
-const rows = await engine.executeRaw<{ source_id: string; issues: number }>(
-  `SELECT source_id, count(*) FILTER (WHERE jsonb_array_length(codes) > 0)::int AS issues
-   FROM frontmatter_scan_state
-   GROUP BY source_id`,
-);
-```
-
-One SQL query, constant time regardless of brain size. The partial-state
-surfacing from v0.38.2.0 stays — when `frontmatter_scan_state` is stale
-(no rows for a registered source, or `last_scanned_at` >24h old for any
-source), doctor warns about freshness rather than reporting potentially-
-stale data as authoritative.
-
-## Sequencing concerns
-
-1. **First-ever scan.** A fresh upgrade has no rows in
-   `frontmatter_scan_state`. Two options:
-   - Lazy: doctor reports "no scan state yet; run `gbrain frontmatter scan
-     --incremental` once" (operator-driven).
-   - Eager: the migration that creates the table also enqueues an autopilot
-     cycle job to do the first full scan.
-
-   Recommendation: lazy, with a clear hint. The autopilot path is heavier
-   surface (must add the new `frontmatter_scan` phase to the existing
-   cycle.ts machinery + the doctor-routed background job system).
-
-2. **Source archival / deletion.** `frontmatter_scan_state` has `ON DELETE
-   CASCADE` on `sources(id)`, so soft-delete + 72h TTL + purge already
-   clean it up. No additional logic needed.
-
-3. **Path renames inside a source.** Sync would `DELETE` the old row by
-   path (via a periodic reconcile step) and `INSERT` the new row. Without
-   that step, the table accumulates stale path rows. Either:
-   - A reconcile step in the incremental scanner: any path-row not seen
-     during the walk gets deleted.
-   - Or: doctor reports "N stale rows in frontmatter_scan_state" as a
-     freshness signal, with `gbrain frontmatter scan --reconcile` as the
-     remediation.
-
-## Cost estimate
-
-- One UPSERT per file synced. Negligible vs the parse + DB write that sync
-  already does.
-- Incremental refresh runtime: dominated by mtime stats. ~ms per 1000 files
-  on SSD.
-- Doctor read: one indexed SQL query. Sub-100ms on any brain size.
-
-## What this design deliberately does NOT do
-
-- **Replace v0.38.2.0's bounded-walk safety net.** Phase 2 makes the
-  steady-state cheap, but the disk walker (with its deadline check) stays
-  as the source-of-truth fallback for sources whose scan state is missing
-  or stale. Belt-and-suspenders.
-- **Introduce a separate frontmatter validation rule set.** Reuses
-  `parseMarkdown(..., {validate: true})` and the existing
-  `ParseValidationCode` enum. Single source of truth.
-- **Add a new background daemon.** Wires into the existing
-  `autopilot-cycle` Minion handler as a new phase, alongside sync /
-  extract / embed / etc.
-
-## Open questions for the implementer
-
-1. **Path normalization.** `pages.source_path` and the disk walker's
-   relative path computation are similar but not identical (slashes,
-   leading `./`, etc.). The incremental scanner needs to match what sync
-   stores so UPSERTs key correctly. Audit before writing.
-2. **Soft-delete interaction.** A page that gets soft-deleted in the DB
-   (v0.26.5) still has a file on disk. Should the incremental scan
-   continue to track its frontmatter state? Probably yes (so a future
-   `restore_page` doesn't surprise with stale frontmatter), but worth
-   confirming with the soft-delete owner.
-3. **Two-phase rollout.** Land the table + writes first, let it backfill
-   for a release cycle, then switch the doctor reader. Avoids the
-   "Phase 2 ships but the table is empty" case where doctor regresses to
-   reporting "no scan state."
-
-## TODO file entry
-
-```
-- [ ] Implement Phase 2: DB-backed frontmatter scan state.
-      Design lives at docs/architecture/frontmatter-scan-incremental.md.
-      Schema migration v81 + sync-side UPSERT + incremental scan command
-      + autopilot cycle phase + doctor reader. Two-phase rollout: ship
-      table + writes first; flip the reader one release later.
-```

docs/architecture/lens-packs.md

@@ -1,143 +0,0 @@
-# Lens packs (v0.41.2.0)
-
-Four bundled schema packs that turn the gbrain dream cycle into a multi-lens
-brain. Activate one with `gbrain config set schema_pack <name>` and the cycle
-picks up the pack's declared phases on the next `gbrain dream` run.
-
-## The four packs
-
-```
-                gbrain-base (shipped v0.38)
-                       ▲
-                       │ extends
-        ┌──────────────┼──────────────────────┐
-        │              │                       │
-   gbrain-creator  gbrain-investor      gbrain-engineer
-   (atom + concept  (deal/thesis/        (learning bridge
-    lifecycle)       bet_resolution)      for gstack)
-        │              │                       │
-        └──────────────┼───────────────────────┘
-                       │ extends + borrow chain
-                       ▼
-              gbrain-everything (meta-pack)
-              one brain, three lenses active
-```
-
-### gbrain-creator
-Atom + concept content-creator lifecycle. Drives two cycle phases:
-
-- `extract_atoms` — per source, Haiku extracts 1-3 atoms from each
-  transcript with the closed 11-value `atom_type` enum (insight,
-  anecdote, quote, framework, statistic, story_angle, strategy_angle,
-  strategy, endorsement, critique, collection). Writes
-  `atoms/{YYYY-MM-DD}/{slug}` pages. Budget cap $0.30/source/run.
-- `synthesize_concepts` — globally aggregates atoms by frontmatter
-  `concepts:` ref. Tier by count: T1 ≥10, T2 ≥5, T3 ≥2. T1/T2 get
-  Sonnet narratives; T3 falls back to a deterministic stub. Writes
-  `concepts/{slug}` pages. Budget cap $1.50/run.
-
-One calibration domain: `concept_themes` / cluster_summary / [concept]
-— tier histogram + page count, not Brier (concepts don't have binary
-outcomes to score against).
-
-### gbrain-investor
-YC / investor lens. Declares 2 net-new page types on top of
-gbrain-base's deal/person/company/yc seed:
-
-- `thesis` (NEW) — investment thesis with thesis_text + key_bets[] +
-  market_view + vintage. Files at `investing/theses/{slug}`. Extractable
-  (the LLM mines claims into facts).
-- `bet_resolution_log` (NEW) — outcome record for a thesis's bet. FK
-  to a take row via take_id; carries resolved_outcome + resolved_at +
-  learned_pattern. Files at `investing/bets/{YYYY-MM}/{slug}`.
-
-No new cycle phases — consumes the existing
-extract_facts/propose_takes/grade_takes/calibration_profile loop. Three
-calibration domains: `deal_success` (scalar_brier over deal-attached
-takes), `founder_evaluation` (scalar_brier over person-attached takes),
-`market_call` (weighted_brier over thesis-attached takes; weighted by
-conviction so high-stakes misses cost more).
-
-### gbrain-engineer
-Bridge-only pack. Declares `learning` page type + reuses base `code`.
-No new cycle phases — the daemon-side `gstack-learnings` IngestionSource
-(T8) watches `~/.gstack/projects/{repo}/learnings.jsonl` and emits
-each JSONL line as a `learning` page when this pack is active. Three
-calibration domains: `architecture_calls` (scalar_brier),
-`effort_estimates` (weighted_brier), `risk_assessment` (scalar_brier).
-
-Speculative ADR/postmortem/refactor_thesis/tech_debt types deferred
-to v0.42+ — they'll ship when a real user authors the first one (D8).
-
-### gbrain-everything
-Meta-pack stacking creator + investor + engineer via the v0.38
-`extends` + `borrow_from` chain. Single-active-pack constraint
-preserved — this IS the active pack; the registry walks extends +
-borrow to materialize the merged view.
-
-Activate via `gbrain config set schema_pack gbrain-everything` and
-calibration_profile produces all 7 domain scorecards in one JSONB.
-
-## Calibration profile widening (T10)
-
-Before v0.41.2.0, `calibration_profiles.domain_scorecards` was a
-`JSON.stringify({})` placeholder. v0.41.2.0 widens it: each declared
-domain produces a `{n, brier, accuracy, aggregator, page_types,
-extras}` entry. Four aggregator algorithms (closed enum):
-
-- **scalar_brier** — `AVG(POWER(weight - outcome::int, 2))`. Default for
-  probabilistic predictions.
-- **weighted_brier** — Brier weighted by `ABS(weight - 0.5) * 2`
-  (conviction proxy). High-conviction misses cost more.
-- **count_based** — simple `SUM(hit) / COUNT(*)` accuracy without
-  Brier. Use when probability isn't natural.
-- **cluster_summary** — descriptive rollup (page count + tier
-  histogram). For domains like `concept_themes` where there's no
-  binary outcome.
-
-Pack manifests declare domains with `{name, aggregator, page_types}`.
-Domain names are OPEN (third-party packs can declare new domain labels
-without a gbrain release). Aggregator algorithms are CLOSED (safe SQL
-stays in code, validated at pack-load).
-
-## take_domain_assignments table (T1)
-
-New JOIN table (migration v94):
-`take_domain_assignments(take_id BIGINT FK, domain TEXT, pack TEXT,
-source TEXT, confidence REAL, assigned_at TIMESTAMPTZ, PK(take_id,
-domain))`. Multi-domain assignment honest — a take about "Sequoia's
-investment in Anthropic" can land in BOTH `deal_success` AND
-`market_call` rather than being force-bucketed.
-
-## What this enables for the user
-
-- **Atoms + concepts ship in the binary.** Your OpenClaw's parallel
-  atom-pipeline-coordinator + atom-backfill-coordinator + concept-
-  synthesis crons can retire (T12 follow-up). One `gbrain dream` cron
-  covers everything.
-- **gstack learnings reach gbrain.** Engineer-pack-active brains
-  surface every gstack-logged learning as a queryable page within
-  seconds of being written.
-- **Multi-lens calibration.** Activate gbrain-everything and see how
-  often you're wrong on deals AND market calls AND architecture
-  AND effort estimates in one `gbrain calibration --json` call.
-- **Lossless OpenClaw migration.** The `markdown-greenfield`
-  importer (T7, mode='migration') re-ingests existing OpenClaw
-  pages with permanent slug-keyed idempotency + per-row JSONL audit
-  + the `imported_from` marker so extract_atoms + synthesize_concepts
-  don't re-extract already-atomized material.
-
-## v0.41.2.1 follow-ups (filed in plan)
-
-- Per-page-type `frontmatter_validators` on PageTypeSchema so the
-  atom_type enum (currently hardcoded in extract_atoms.ts) reads from
-  the active pack manifest at runtime per D11.
-- 3-check quality gate (truism / punchline / entity-page reject) as
-  a multi-pass extract_atoms refinement.
-- Embedding-similarity dedup in synthesize_concepts (currently
-  exact-string concept ref match only).
-- Voice gate integration for T1 Canon narratives.
-- op_checkpoint resumability for cross-cycle continuation in both
-  phases.
-- Parity-baseline eval gates against your OpenClaw's existing 13K atoms
-  + 11K concepts on a 500-page sample subset.

docs/architecture/pack-upgrade-mechanism.md

@@ -1,246 +0,0 @@
-# Pack-Upgrade Mechanism (v0.41.22)
-
-> How `gbrain-base@1.x → gbrain-base-v2@1.0.0` (and any future pack
-> succession) wires through the onboard cathedral.
-
-## The contract
-
-A schema pack manifest can declare a `migration_from` field:
-
-```yaml
-api_version: gbrain-schema-pack-v1
-name: gbrain-base-v2
-version: 1.0.0
-migration_from:
-  pack: gbrain-base
-  version: "1.x"
-```
-
-When this declaration is present + a `mapping_rules:` block is
-populated, the pack registers itself as the successor to
-`(parent_pack, version_range)`. Any brain whose active pack matches
-that tuple lights up the `pack_upgrade_available` onboard check.
-
-## End-to-end flow
-
-```
-┌────────────────────────────────────────────────────────────────┐
-│  PACK AUTHORING                                                │
-│                                                                │
-│  Author declares: migration_from: {pack: P, version: R}        │
-│  + mapping_rules: [retype/page_to_link/page_to_alias]          │
-│  Pack ships bundled OR via ~/.gbrain/schema-packs/<name>/      │
-└──────────────────────────┬─────────────────────────────────────┘
-                           ↓
-┌────────────────────────────────────────────────────────────────┐
-│  ONBOARD CHECK DISCOVERY                                       │
-│                                                                │
-│  checkPackUpgradeAvailable(engine) at src/core/onboard/        │
-│  checks.ts:                                                    │
-│    1. Read engine.getConfig('schema_pack') for dbConfig tier  │
-│    2. loadActivePack({cfg: null, remote: false, dbConfig})    │
-│    3. findPackSuccessors(active.name, active.version)         │
-│         → walks BUNDLED_PACK_NAMES + ~/.gbrain/schema-packs/   │
-│         → matches via _versionRangeMatches(version, range)    │
-│         → returns ResolvedPack[] sorted by successor version  │
-│    4. If successors.length > 0, emit OnboardCheckResult        │
-│       with RemediationStep targeting `unify-types` handler    │
-│       + protected: true (D17 → manual_only via render          │
-│       allowlist)                                               │
-└──────────────────────────┬─────────────────────────────────────┘
-                           ↓
-┌────────────────────────────────────────────────────────────────┐
-│  USER DECIDES                                                  │
-│                                                                │
-│  gbrain onboard --check shows finding                          │
-│  gbrain onboard --check --explain shows per-cluster narrative  │
-│  User reviews; if OK, runs:                                    │
-│    gbrain jobs submit unify-types --allow-protected \          │
-│      --params '{"target_pack":"gbrain-base-v2"}'               │
-│  (Autopilot never auto-fires this; manual_only)                │
-└──────────────────────────┬─────────────────────────────────────┘
-                           ↓
-┌────────────────────────────────────────────────────────────────┐
-│  HANDLER EXECUTION (src/core/schema-pack/unify-types-handler.ts) │
-│                                                                │
-│  1. Preflight: load target pack; assert mapping_rules present  │
-│  2. Stats snapshot (pre-state for celebration)                 │
-│  3. Acquire gbrain-unify db-lock (60min TTL)                   │
-│  4. Apply phases (4):                                          │
-│     a. Explicit retype rules (chunked UPDATE 1000/batch)       │
-│        - frontmatter.legacy_type ALWAYS preserved (D8)         │
-│        - frontmatter.subtype stamped when subtype set          │
-│     b. Catch-all retype: synthesize per-unknown-type rule       │
-│        excluding declared types + explicit targets + page_to_  │
-│        link/alias sources (D12 + critical bug fix)             │
-│     c. Page-to-link: parse body+frontmatter, insert link row,  │
-│        soft-delete source page (per-page atomicity per F7)     │
-│     d. Page-to-alias: insert slug_aliases row, soft-delete     │
-│        source page (NO rewriteLinks per D15)                   │
-│  5. Final sync: path-prefix typing for residual UNTYPED rows   │
-│  6. ACTIVE-PACK FLIP (D13):                                    │
-│     - engine.setConfig('schema_pack', target_pack)             │
-│     - saveConfig({...existing, schema_pack: target_pack})      │
-│  7. Verify: re-run stats; warn if ≤ declared + 5 violated      │
-│  8. Celebration summary to stderr + audit JSONL                │
-│  9. Release db-lock                                            │
-└──────────────────────────┬─────────────────────────────────────┘
-                           ↓
-┌────────────────────────────────────────────────────────────────┐
-│  POST-UPGRADE STATE                                            │
-│                                                                │
-│  • pages.type updated with canonical types                     │
-│  • frontmatter.legacy_type preserved for rollback              │
-│  • slug_aliases populated for old-slug → canonical lookup      │
-│  • links table has new partner_of / relates_to rows            │
-│  • Source pages soft-deleted (72h TTL for restore)             │
-│  • Active pack flipped to target_pack                          │
-│  • Next gbrain onboard --check shows ok                        │
-└────────────────────────────────────────────────────────────────┘
-```
-
-## Version-range semantics
-
-`migration_from.version` accepts three shapes:
-
-| Form | Matches |
-|------|---------|
-| `1.0.0` (exact literal) | `1.0.0` only |
-| `1.x` (major wildcard) | `1.0.0`, `1.5.2`, `1.99.99` |
-| `1.0.x` (minor wildcard) | `1.0.0`, `1.0.5`, `1.0.99` |
-
-`*` is accepted as an alias for `x`.
-
-Implementation: `_versionRangeMatches(version, range)` in
-`src/core/schema-pack/load-active.ts`. Pinned by
-`test/schema-pack-find-pack-successors.test.ts`.
-
-## findPackSuccessors discovery
-
-Walks `BUNDLED_PACK_NAMES` (currently `gbrain-base`,
-`gbrain-recommended`, `gbrain-creator`, `gbrain-investor`,
-`gbrain-engineer`, `gbrain-everything`, `gbrain-base-v2`). For each
-candidate ≠ the active pack name, loads the manifest via
-`loadActivePack({ perCall: candidate })`, checks
-`migration_from.pack === activeName && _versionRangeMatches(activeVer,
-migration_from.version)`. Returns matching packs sorted by version
-descending.
-
-v0.41.22 covers bundled packs only. v0.43+ TODO: enumerate user-installed
-packs at `~/.gbrain/schema-packs/*/pack.yaml` (defer to v0.43 since the
-filesystem-scan cost needs the cache invalidation strategy from
-`registry.ts`).
-
-## The manual_only apply policy
-
-The shipped onboard contract has 3 apply_policy values:
-
-| Policy | Meaning |
-|--------|---------|
-| `auto_apply` | Autopilot runs unattended |
-| `prompt_required` | Autopilot in `--auto-with-prompt` mode prompts user |
-| `manual_only` | Autopilot NEVER auto-fires; user must explicitly submit |
-
-`pack_upgrade_available` emits a `RemediationStep` with `protected:
-true` + `job: 'unify-types'`. `toOnboardRecommendation` in
-`src/core/onboard/render.ts` maps this to `manual_only` via the
-`MANUAL_ONLY_PROTECTED_JOBS` allowlist (which also contains
-`extract-takes-from-pages` per v0.41.18 A12+A24).
-
-Rationale: pack upgrades change the brain's taxonomy. Taxonomy is a
-user judgment call — not autopilot's call. Even with `--auto-with-
-prompt`, prompting the user to confirm a pack upgrade mid-tick is the
-wrong UX (the user came to fix orphans, not to be interrupted with
-"hey want to migrate your taxonomy?"). Explicit submission is the
-right boundary.
-
-## Authoring a successor pack
-
-Minimal example for an academic-research brain that adds a
-`researcher` canonical:
-
-```yaml
-api_version: gbrain-schema-pack-v1
-name: gbrain-academic-v1
-version: 1.0.0
-description: Academic research brain — adds researcher canonical
-gbrain_min_version: 0.42.0
-extends: null
-
-migration_from:
-  pack: gbrain-base-v2
-  version: "1.x"
-
-page_types:
-  # Inherit gbrain-base-v2's 15 types here (or use extends to merge
-  # automatically once v0.43+ extends-chain composition lands)
-  - { name: person, primitive: entity, path_prefixes: [people/], expert_routing: true }
-  - { name: company, primitive: entity, path_prefixes: [companies/], expert_routing: true }
-  # ... all 13 other v2 canonicals ...
-  - { name: note, primitive: concept, path_prefixes: [notes/], extractable: true }
-  # Academic addition:
-  - name: researcher
-    primitive: entity
-    path_prefixes: [researchers/]
-    aliases: [academic, professor, scholar]
-    extractable: false
-    expert_routing: true
-
-mapping_rules:
-  # All v2 mapping rules (copy from v2 yaml)
-  # ... ~40 rules ...
-  # Custom: relocate v2-tagged academics to researcher
-  - { kind: retype, from_type: person, to_type: researcher, path_filter: 'researchers/%' }
-  # Catch-all
-  - kind: retype
-    from_type: "*unknown*"
-    to_type: note
-    subtype_field: legacy_type
-    subtype: "*original_type*"
-```
-
-Drop at `~/.gbrain/schema-packs/gbrain-academic-v1/pack.yaml`.
-Discoverable via `gbrain schema list`. Activatable via
-`gbrain schema use gbrain-academic-v1`. Once active, the
-`pack_upgrade_available` check fires for any brain on
-`gbrain-base-v2@1.x` and surfaces a `unify-types` RemediationStep
-targeting your pack.
-
-## Lock + concurrency
-
-`gbrain-unify` is a dedicated `gbrain_cycle_locks` row name (60min
-TTL). The handler acquires it before any apply phase + releases in
-`finally`. Two simultaneous `gbrain jobs submit unify-types`
-invocations: second one fails fast at lock acquisition with a clear
-error. Same pattern as `gbrain-sync` (v0.22.13 PR #490).
-
-## Audit trail
-
-Every unify run writes to `~/.gbrain/audit/schema-unify-YYYY-Www.jsonl`
-(ISO-week rotation, mirrors existing audit channels). Records: pack
-identities (before + after), per-phase counts (would_apply + applied),
-warnings, completion timestamp. Privacy: page slugs are NOT logged in
-bulk (only the per-rule sample_slugs[≤10]); for forensic debugging
-add `GBRAIN_AUDIT_FULL=1` (v0.43+ TODO; not yet wired).
-
-## What's NOT yet supported
-
-- Subprocess sandbox for the publish-gate (v0.43+ TODO)
-- Per-source pack-upgrade (the handler accepts `sourceId` but
-  `findPackSuccessors` doesn't yet pass it through)
-- Cross-brain federated mounts that disagree on canonical packs
-- Automatic rollback (today: manual SQL or `gbrain pages restore`)
-- LLM-assisted mapping_rules codegen from production data (`gbrain
-  schema detect-mappings`; deferred to v0.43+)
-
-## Reference
-
-- Pack file: `src/core/schema-pack/base/gbrain-base-v2.yaml`
-- Manifest extension: `src/core/schema-pack/manifest-v1.ts`
-- Successor walker: `src/core/schema-pack/load-active.ts:findPackSuccessors`
-- Onboard check: `src/core/onboard/checks.ts:checkPackUpgradeAvailable`
-- Render allowlist: `src/core/onboard/render.ts:MANUAL_ONLY_PROTECTED_JOBS`
-- Handler: `src/core/schema-pack/unify-types-handler.ts`
-- Migration: `src/core/migrate.ts:105` (slug_aliases table)
-- Type taxonomy doc: `docs/architecture/type-taxonomy.md`
-- Skill: `skills/schema-unify/SKILL.md`

docs/architecture/schema-packs.md

@@ -1,230 +0,0 @@
-# Schema Packs
-
-A schema pack tells gbrain what shape your brain takes — which directories
-exist, what types live in them, how the agent should infer types from
-paths, and which link verbs connect what to what. The schema pack is the
-**dynamic, always-consulted artifact** every skill reads when filing,
-querying, or routing experts. It is the single source of truth for
-"what's in your brain."
-
-The v0.39.0.0 wave shipped a full schema-pack cathedral. This doc is the
-user-facing reference; for implementation details see
-`docs/designs/V038_SCHEMA_PACKS.md` (CEO plan) and the engine layer in
-`src/core/schema-pack/`.
-
-## What ships in the box
-
-Two bundled packs:
-
-- **`gbrain-base`** (default) — reproduces pre-v0.38 hardcoded behavior
-  byte-for-byte. Existing brains see zero behavior change after upgrade.
-  Covers: person, company, deal, meeting, project, place, concept, writing,
-  analysis, guide, hardware, architecture, etc. (the original
-  `ALL_PAGE_TYPES` list).
-
-- **`gbrain-recommended`** — extends `gbrain-base` with the 13 additional
-  directories described in `docs/GBRAIN_RECOMMENDED_SCHEMA.md`: deal,
-  meeting, concept, project, source, daily, personal, civic, original,
-  place, trip, conversation, writing. If you like the documented
-  operational-brain pattern, activate this with:
-
-  ```bash
-  gbrain schema use gbrain-recommended
-  ```
-
-Plus user-installed packs at `~/.gbrain/schema-packs/<name>/pack.yaml`
-that you author with `gbrain schema init` or `gbrain schema fork`.
-
-## CLI surface
-
-Five inspection verbs (shipped in v0.38):
-
-```bash
-gbrain schema active     # show resolved pack + which tier set it
-gbrain schema list       # list bundled + installed packs
-gbrain schema show       # pretty-print the active pack
-gbrain schema validate   # validate a manifest's shape
-gbrain schema use <pack> # activate a pack (writes ~/.gbrain/config.json)
-```
-
-Eight authoring + discovery verbs (shipped in v0.39):
-
-```bash
-gbrain schema detect              # propose types matching brain shape
-gbrain schema suggest             # LLM-refined proposals on top of detect
-gbrain schema review-candidates   # promote / rename / ignore candidates
-gbrain schema review-orphans      # surface pages with no matching type
-gbrain schema init <name>         # scaffold a stub pack    (experimental)
-gbrain schema fork <a> <b>        # copy + rename a pack    (experimental)
-gbrain schema edit <name>         # surface the pack path   (experimental)
-gbrain schema diff <a> <b>        # set-diff two packs      (experimental)
-gbrain schema graph               # ASCII type listing      (experimental)
-gbrain schema lint                # flag duplicates + missing prefixes
-gbrain schema explain <type>      # plain-English type description (experimental)
-gbrain schema downgrade --to <p>  # restore previous pack (recovery)
-gbrain schema usage --since 30d   # per-verb invocation counts (D14 telemetry)
-```
-
-The verbs marked `experimental` are demand-gated per D14: their usage is
-tracked via T15's schema-events audit, and v0.40+ retro decides whether
-to deprecate any that stay <5% usage.
-
-## Resolution chain (7 tiers)
-
-When the engine decides "which pack is active for this query?", it walks
-this chain top-down. First match wins.
-
-| Tier | Source | Notes |
-|------|--------|-------|
-| 1 | Per-call `schema_pack` opt | CLI only (`ctx.remote === false`); MCP rejected. |
-| 2 | `GBRAIN_SCHEMA_PACK` env | Process-scope override. |
-| 3 | Per-source DB config key `schema_pack:source:<id>` | New in v0.38. |
-| 4 | Brain-wide DB config key `schema_pack` | |
-| 5 | `gbrain.yml schema:` section | Repo-checked. |
-| 6 | `~/.gbrain/config.json` `schema_pack` field | What `gbrain schema use` writes. |
-| 7 | Default: `gbrain-base` | Always present. |
-
-## How the agent uses the active pack
-
-Every read + write path consults the active pack at runtime:
-
-- **`parseMarkdown`** infers page `type` from path prefixes declared in
-  the active pack (`page_types[].path_prefixes`). Without an active pack
-  threaded, falls back to the legacy hardcoded `inferType()` so the
-  byte-for-byte parity gate stays green.
-- **`whoknows` / `find_experts`** scopes candidates to `expert_routing:
-  true` types in the active pack.
-- **`extract_facts`** runs only on `extractable: true` types.
-- **`enrichment-service`** routes person/company enrichment based on the
-  pack's primitive declarations.
-- **Search hybrid cache** (`knobsHash`) folds in pack name + version
-  (v0.39 T21). A cache row written under pack A is unreachable when pack
-  B is active. Cross-pack contamination is structurally impossible.
-
-## The magical moment (T2-T4 + T10)
-
-Persona A (Notion refugee) installs gbrain, imports her exports, and the
-brain looks unfamiliar — the default `gbrain-base` pack expects
-`people/`, `companies/`, etc., but her files live under `Projects/`,
-`Reading/`, `Daily Notes/`. The friction signal fires in two places:
-
-1. **Import warn (T7):** the end of `gbrain import` prints
-   `[schema] X of Y pages (Z%) have no type matching the active schema
-   pack. Run gbrain schema detect to propose a pack matching your
-   content shape.`
-2. **`gbrain doctor` schema_pack_consistency check** keeps surfacing
-   the warning persistently after the import session ends.
-
-She runs the magical moment:
-
-```bash
-gbrain schema detect              # heuristic clustering on her actual shape
-gbrain schema suggest             # LLM-refined proposals
-gbrain schema review-candidates   # human gate on promotion
-gbrain schema review-candidates --apply Projects/   # accept
-```
-
-The agent (via the new EIIRP skill) automates phases 1-3 of this for any
-significant work session. The brain's schema becomes a living artifact
-the agent maintains, not a hardcoded ceremony the user authors.
-
-## Authoring your own pack
-
-```bash
-gbrain schema init my-pack            # scaffolds ~/.gbrain/schema-packs/my-pack/pack.yaml
-$EDITOR ~/.gbrain/schema-packs/my-pack/pack.yaml
-gbrain schema validate my-pack        # check shape
-gbrain schema use my-pack             # activate
-gbrain schema active                  # confirm
-```
-
-A minimal pack:
-
-```yaml
-api_version: gbrain-schema-pack-v1
-name: my-pack
-version: 0.0.1
-gbrain_min_version: 0.39.0
-extends: gbrain-base   # inherits everything from base; add overrides below
-description: |
-  My personal pack.
-
-page_types:
-  - name: project-x
-    primitive: entity
-    path_prefixes:
-      - Projects/
-    aliases: []
-    extractable: false
-    expert_routing: false
-
-  # Add more types here. Each maps a path prefix to a primitive +
-  # opt-in flags. See src/core/schema-pack/base/gbrain-recommended.yaml
-  # for a worked example.
-
-link_types: []
-takes_kinds: [fact, take, bet, hunch]
-borrow_from: []
-frontmatter_links: []
-enrichable_types: []
-filing_rules: []
-```
-
-## Recovery + revert
-
-The single-PR cathedral is hard to revert atomically. Per codex finding
-#4 from plan-eng-review, T20 ships `gbrain schema downgrade` to restore
-the active-pack config field:
-
-```bash
-gbrain schema downgrade --to gbrain-base
-# OR auto-detect previous from ~/.gbrain/schema-pack-history.jsonl:
-gbrain schema downgrade
-```
-
-**Code revert alone is NOT sufficient.** The full revert procedure:
-
-1. `git revert <merge-commit>` — restores the code.
-2. `gbrain schema downgrade --to gbrain-base` — restores config.
-3. (Optional) `gbrain pages purge-deleted --older-than 0h` — drops
-   v0.39-typed pages that no longer have a matching type in the active
-   pack.
-
-The cache + eval rows that pack-aware code wrote are isolated by the
-`knobsHash` pack-folding (T21) — they become unreachable under the
-restored pack so no eviction is needed.
-
-## Distribution
-
-`.gbrain-schema` tarballs ride the same v0.37 skillpack pipeline as
-`.gbrain-skillpack` tarballs (T14 artifact abstraction). The
-discriminator is `api_version` in the manifest:
-
-- `gbrain-schema-pack-v1` → schemapack
-- `gbrain-skillpack-v1` → skillpack
-
-Both install via the same scaffold + copy path; install targets are
-`~/.gbrain/schema-packs/<name>/` and `~/.gbrain/skillpacks/<name>/`
-respectively.
-
-Publication to the public registries (`garrytan/gbrain-schema-registry`,
-`garrytan/gbrain-skillpack-registry`) follows the same publish-as-PR
-workflow as v0.37 skillpack publishing.
-
-## What's deferred to v0.40+
-
-- **Per-source pack federation across mounts.** A query crossing multiple
-  sources currently rejects with `permission_denied` when those sources
-  have divergent active packs (T19 + codex finding #2). The v0.40+ work
-  computes a true per-source closure via the existing
-  `buildSourceClosureCte` engine surface.
-- **`extends` chain semver compatibility checks** between pack versions.
-- **`skillpack ↔ schemapack` cross-reference declarations** — a skillpack
-  can declare "I work best with these primitives present in your pack."
-- **Live schema migration helpers** — when you add a type, auto-suggest
-  backfill of existing pages.
-- **Authoring vs derivation thesis reframe (D14).** v0.39.0.0 ships the
-  full 11-verb cathedral with 6 verbs marked experimental-tier. v0.40+
-  retro reads T23 usage telemetry to decide which to deprecate.
-
-See `TODOS.md` v0.40+ section for the full deferred list.

docs/architecture/serve-sync-concurrency.md

@@ -1,54 +0,0 @@
-# `gbrain serve` ↔ `gbrain sync` concurrency (PGLite)
-
-**Short version: on a PGLite brain, stop `gbrain serve` before a large sync.**
-
-## Why
-
-PGLite is a single-writer embedded Postgres (WASM). A running `gbrain serve`
-(stdio or HTTP MCP) holds an open PGLite connection on the brain's data
-directory. `gbrain sync` needs to write to that same data directory. The two
-contend for PGLite's single-writer connection / write-lock — **this is NOT the
-`gbrain-sync` advisory lock** (that's a separate, DB-row coordination lock for
-two concurrent *syncs*). Confusing the two sends you debugging the wrong surface.
-
-Symptoms of serve↔sync contention on PGLite:
-
-- `gbrain sync` blocks acquiring the PGLite write lock, or makes very slow
-  progress, while a `gbrain serve` process is alive on the same brain.
-- Killing stale `gbrain serve` MCP processes frees the lock and sync proceeds.
-
-## What to do
-
-1. Stop any `gbrain serve` process for this brain before a large sync:
-   ```bash
-   pkill -f 'gbrain serve'      # or stop your MCP client / Claude Desktop / Cursor
-   gbrain sync --no-pull --no-embed --yes
-   ```
-2. Restart `gbrain serve` after the sync completes.
-
-This contention does **not** apply to the Postgres engine — Postgres tolerates
-concurrent connections, so `serve` and `sync` can run simultaneously there.
-
-## Diagnosing a sync hang
-
-If a sync wedges (no progress, high CPU), re-run with the per-file begin trace
-so the stalling file is named:
-
-```bash
-GBRAIN_SYNC_TRACE=1 gbrain sync --no-pull --no-embed --yes
-```
-
-The last `[sync] begin import: <path>` line with no following completion is the
-file being processed when the hang occurred. Under `--workers >1` / `--all`,
-the stuck file is in the set of begin-lines without a matching completion.
-
-If you suspect a schema-pack regex is the cause (a pack with a
-catastrophic-backtracking `inference.regex`), complete the sync with the pack
-disabled and re-run extraction afterward:
-
-```bash
-gbrain sync --no-schema-pack --no-pull --no-embed --yes
-```
-
-`gbrain schema lint` flags the classic nested-quantifier ReDoS shapes
-(`(a+)+`, `(a*)*`, …) in pack regexes as warnings.

docs/architecture/system-of-record.md

@@ -1,198 +0,0 @@
-# System of record
-
-**The GitHub repo (markdown + frontmatter) is the system of record.
-The Postgres/PGLite database is a derived cache. We do not back up
-the database — we rebuild it from the repo.**
-
-This document is the canonical reference for that contract. Every code
-path that writes user-knowledge state should match the pattern
-described here. The CI gate at `scripts/check-system-of-record.sh`
-enforces it programmatically.
-
-## Why this matters
-
-The DB is a derived index over the markdown content. It exists to make
-search fast, to dedup embedding-similar claims, to materialize the
-cross-page graph. None of that data is irreplaceable — as long as the
-markdown is intact, `gbrain sync && gbrain extract all` rebuilds the
-entire DB from scratch.
-
-This means:
-
-- **Disaster recovery is one command.** If your DB volume corrupts, if
-  Postgres eats itself, if PGLite's WASM lock wedges — you don't need
-  a backup. You wipe the DB, re-import from your brain repo, and the
-  derived state regenerates. v0.32.3 ships `gbrain rebuild
-  --confirm-destructive` as the documented one-liner.
-- **Multi-machine sync is git.** Your brain is a repo. Push from one
-  machine, pull from another, and the second machine's DB rebuilds on
-  its next sync. No "back up the database" step.
-- **Privacy is in your hands.** Sensitive entity pages can be
-  gitignored (via `gbrain.yml` `db_only` paths or per-page) and they
-  stay on disk but not in git. The fence respects whatever git
-  tracking choice you make at the page level.
-- **Cross-agent collaboration is possible.** Multiple agents can write
-  to the same brain because the fence is the merge point, not the DB.
-  Git handles concurrent edits the way git handles concurrent edits.
-
-## The three categories
-
-Every table in the gbrain schema belongs to exactly one of three
-categories. The category determines how it gets rebuilt during
-disaster recovery.
-
-### FS-canonical (markdown is the source of truth)
-
-These are user-authored knowledge. The DB row is a derived index over
-the markdown — wipe the table and `gbrain extract` rebuilds it
-identically. The CI gate keeps direct DB writes from drifting away
-from the markdown contract.
-
-| Category | How it's stored in markdown | Derived DB table | Reconciler |
-|---|---|---|---|
-| **Takes** (incl. hunches, bets) | `## Takes` fenced table between `<!--- gbrain:takes:begin -->` / `:end -->` markers | `takes` | `extract takes` |
-| **Facts** | `## Facts` fenced table between `<!--- gbrain:facts:begin -->` / `:end -->` markers | `facts` | `extract_facts` cycle phase |
-| **Links** | Inline `[text](slug)` / `[[slug]]` in markdown body + frontmatter `direction: incoming` | `links` | `extract links` |
-| **Timeline** | `## Timeline` section after `<!-- timeline -->` sentinel | `timeline_entries` | `extract timeline` |
-| **Tags** | Frontmatter `tags:` YAML array | `tags` | `importFromFile` (reconciles per-page on import) |
-| **emotional_weight** | Recomputed from takes + tags | `pages.emotional_weight` (signal column) | `recompute_emotional_weight` cycle phase |
-| **synthesis_evidence** | FK into `takes` rows (`slug#N`) inside synthesis pages | `synthesis_evidence` | `extract takes` (transitively) |
-
-### Derived from FS but not user-authored
-
-These hold derived state that's automatically reconstructible from the
-markdown but not directly authored as markdown by the user. The
-chunker + embedder rebuild these on import.
-
-| Table | Source | Notes |
-|---|---|---|
-| `pages` | The markdown file as a whole | One row per file; `compiled_truth` + `frontmatter` come from parse |
-| `content_chunks` | `pages.compiled_truth` after chunker strip | Re-chunked on content_hash change; embedded via configured model |
-| `page_versions` | Each `pages` UPDATE | Audit history; rebuildable in principle but not in practice |
-
-### DB-only by design (named exceptions)
-
-These hold runtime / infrastructure state that's intentionally not in
-the repo. The architectural rule still holds — these aren't
-"user knowledge" — but they're DB-only by design.
-
-| Category | Why it's OK to be DB-only |
-|---|---|
-| `raw_data` | Webhook/transcript sidecars; not user-authored knowledge. |
-| `subagent_messages` / `subagent_tool_executions` / `subagent_rate_leases` | Runtime job state. Replay-only, not persistent knowledge. |
-| `oauth_clients` / `oauth_tokens` / `access_tokens` | Credentials. Not in source control by definition. |
-| `mcp_request_log` | Audit trail. Volatile by design. |
-| `minion_jobs` / `minion_inbox` / `minion_attachments` | Job queue. Restarts re-enqueue or drop. |
-| `eval_candidates` / `eval_capture_failures` | Contributor-mode dev loop; opt-in capture. |
-| `dream_verdicts` | Cheap verdict cache. Rebuildable by re-running Haiku. |
-| `gbrain_cycle_locks` / migration ledger | Infrastructure. |
-| `config` (some keys) | Site-local routing config (e.g. `sync.repo_path`). |
-
-A new derived table that holds user-knowledge MUST land FS-first.
-If you're tempted to add one as "DB-only for now," the structural
-question is: does it belong in this DB-only-by-design list? If not,
-it's FS-canonical and needs a fence (or frontmatter field) plus a
-reconciler.
-
-## The privacy boundary
-
-Private knowledge in a fence still lives in the markdown file. If the
-user commits the page to git, the private data lands in git too. This
-is the existing operational model — we don't infer git policy.
-
-For untrusted readers (remote MCP, subagent), the v0.32.2 release ships
-a 3-layer strip:
-
-1. **Layer A (chunker):** `src/core/chunkers/recursive.ts` calls
-   `stripFactsFence({keepVisibility: ['world']})` + `stripTakesFence`
-   before chunking. Private fact text never reaches
-   `content_chunks.chunk_text`, embeddings, or search results.
-2. **Layer B (get_page):** when `ctx.remote === true`, the response
-   body has both fences stripped (private rows from facts; entire
-   takes fence). Local CLI (`ctx.remote === false`) sees the full
-   fence.
-3. **Layer C (git tracking):** the user decides whether to commit the
-   entity page. `gbrain.yml` `db_only` paths are gitignored
-   automatically; per-page choices via the user's normal git workflow.
-
-For universally-private entities (a friend's name, an investor's
-internal notes), mark the entity page's directory as `db_only` in
-`gbrain.yml`. The file stays on disk but never lands in git.
-
-## The forget contract
-
-`gbrain forget <id>` and the MCP `forget_fact` op rewrite the fence
-row with strikethrough + `valid_until = today` + `context: "forgotten:
-<reason>"`. The DB's `expired_at = valid_until + now()` derivation
-reconstructs the forget state on every rebuild because the fence is
-canonical.
-
-Strikethrough has two semantics distinguished by context:
-
-- `~~claim~~` + `context: "superseded by #N"` → row was replaced by
-  a newer row in the same fence
-- `~~claim~~` + `context: "forgotten: <reason>"` → row was retracted
-  via the forget op
-
-Both encodings keep the row in the markdown for audit history. To
-permanently delete a fact, edit the fence directly in markdown and
-remove the row. The next `extract_facts` cycle wipes the DB row.
-
-## Disaster recovery
-
-The promise the rule makes:
-
-```bash
-# Snapshot what's there
-gbrain stats > /tmp/before.txt
-
-# Wipe and rebuild
-gbrain rebuild --confirm-destructive   # v0.32.3 — deletes derived tables
-                                       # (pages + content_chunks survive
-                                       # the CASCADE-safe design)
-                                       # OR manually for v0.32.2:
-psql -c 'DELETE FROM facts; DELETE FROM takes; DELETE FROM links; DELETE FROM timeline_entries;'
-gbrain sync
-gbrain extract all
-
-# Counts match
-gbrain stats > /tmp/after.txt
-diff /tmp/before.txt /tmp/after.txt
-```
-
-The invariant E2E test at `test/e2e/system-of-record-invariant.test.ts`
-exercises this exact flow on every CI run.
-
-## Rule for new code
-
-When you add a new user-knowledge category:
-
-1. **Define the markdown shape.** Fence (`<!--- gbrain:NAME:begin
-   --> ... :end -->` table) or frontmatter field.
-2. **Build a parser** that produces structured data from markdown.
-   See `src/core/fence-shared.ts` for the shared primitives.
-3. **Build a writer** that round-trips: parse + edit + render produces
-   byte-identical markdown for identical input.
-4. **Add the engine method** that takes parsed data and stamps a
-   derived table. The method gets an entry in the CI gate's
-   banned-direct-call list.
-5. **Add a reconciler:** a cycle phase that walks pages, parses the
-   fence, and rebuilds the derived table from scratch. The reconciler
-   is the only legitimate call site for the engine method;
-   `// gbrain-allow-direct-insert: <reason>` annotates it explicitly.
-6. **Add a round-trip test** in `test/e2e/system-of-record-invariant.test.ts`
-   that proves DELETE + reconcile rebuilds the table byte-identically.
-
-The CI gate at `scripts/check-system-of-record.sh` fails any PR that
-adds a new direct call to a derived-table writer outside the
-reconciler / migration layer without the explicit allow-list comment.
-
-## Related
-
-- `~/.claude/plans/system-instruction-you-are-working-expressive-pony.md`
-  — the v0.32.2 design plan (decisions D1-D22 + Q1-Q8, Codex round 1
-  and round 2 finds)
-- `skills/migrations/v0.32.2.md` — the agent-facing migration guide
-- `CHANGELOG.md` v0.32.2 entry — the release manifesto
-- `scripts/check-system-of-record.sh` — the CI gate that enforces
-  the rule

docs/architecture/thin-client.md

@@ -1,70 +0,0 @@
-# Thin-client routing (remote MCP)
-
-On-demand reference (see CLAUDE.md Reference map). Current behavior + invariants
-only; release history lives in `CHANGELOG.md` + git.
-
-`gbrain init --mcp-only` (v0.29.2) sets up a thin-client install: no local
-brain content, just an OAuth client pointing at a remote `gbrain serve --http`.
-v0.29.2/v0.30.0 only refused 9 obvious local-only commands; the other ~25
-silently fell through to `connectEngine()` and opened the empty local PGLite,
-returning "No results." against a populated remote brain. v0.31.1 fixes the
-silent-empty-results bug class for every operation surface.
-
-Key files:
-
-- `src/cli.ts` — Routing seam INSIDE the existing op-dispatch path (CDX-1: no
-  parallel `src/core/thin-client/` module; routing is a ~80-line conditional
-  in `runThinClientRouted`). Detects `isThinClient(cfg)` BEFORE `connectEngine`
-  so thin-client installs never open the empty PGLite. localOnly ops on
-  thin-client refuse via `refuseThinClient` (with pinpoint hint table
-  `THIN_CLIENT_REFUSE_HINTS`). Banner via `printIdentityBannerBestEffort`
-  before each routed call (suppressed by `--quiet`, `GBRAIN_NO_BANNER=1`,
-  non-TTY default). Exhaustive TS `never` switch on `RemoteMcpError.reason`
-  for canned, actionable error messages. ENG-2 renderer parity: local-engine
-  path runs `JSON.parse(JSON.stringify(result))` so renderers see the same
-  shape on both paths (kills Date/bigint/Buffer drift class).
-- `src/core/mcp-client.ts` — `callRemoteTool(config, toolName, args, opts)`.
-  Hardened in v0.31.1 (CDX-4): all transport errors normalized to
-  `RemoteMcpError` via the `toRemoteMcpError` funnel. New `CallRemoteToolOptions
-  {timeoutMs, signal}`; `buildAbortController` composes external signal with
-  timeout. New `RemoteMcpErrorReason` stable union, `RemoteMcpErrorDetail.kind`
-  ('timeout' | 'aborted' | 'unreachable') sub-tag, `RemoteMcpErrorDetail.code`
-  field carrying server-supplied error codes (e.g. `missing_scope`).
-  `extractToolErrorCode` parses JSON envelopes first, falls back to substring
-  detection for legacy server messages. `unpackToolResult<T>(res)` unchanged
-  (parses tool-call JSON content). `_clearMcpClientTokenCache()` test escape.
-- `src/core/cli-options.ts` — `parseGlobalFlags` adds `--timeout=Ns` (accepts
-  `30s`, `2m`, `500ms`, plain ms). Default `null` = per-command default (30s
-  for most ops, 180s for `think`). `parseTimeout(s)` exported helper.
-- `src/core/doctor-remote.ts` — `gbrain remote doctor` adds the
-  `oauth_client_scopes_probe` check (CDX-5). Probes the read tier via
-  `get_brain_identity` and admin tier via `get_health`; reports per-tier
-  status with pinpoint remediation when admin is missing. `buildScopeCheck`
-  + `ScopeProbeResult` exported for test access. Skippable via
-  `GBRAIN_DOCTOR_SKIP_SCOPE_PROBE=1` for fixtures that mock /mcp at JSON-RPC
-  initialize level only (MCP SDK Client hangs on shape mismatch).
-- `src/core/ssrf-validate.ts` (v0.36 Commit 0) — DNS-rebinding-defended URL validation. `validateAndResolveUrl(url)` resolves the hostname via `dns.lookup({all: true, family: 0})`, checks EVERY A AND AAAA record against the internal-IP deny list, returns the resolved IP so callers fetch by IP (defeats DNS rebinding: validation IP === fetch IP). `fetchWithSSRFGuard(url, opts)` does redirect-aware fetching with per-hop re-validation, max 3 hops by default. Reusable across all URL-fetching features. Test seam `__setDnsLookupForTests` for hermetic tests.
-- `src/core/search/query-intent.ts` extension (v0.36 cross-modal wave) — new `suggestedModality: 'text' | 'image' | 'both'` axis on `QuerySuggestions`. Module-scope `CROSS_MODAL_PATTERNS` regex array (compiles once at module load). `isAmbiguousModalityQuery(query)` heuristic gate fires when a visual noun + reference marker combination indicates genuinely ambiguous routing — used by the Commit 4 LLM tie-break to bound LLM calls to <1% of queries.
-- `src/core/search/mode.ts` extension (v0.36 cross-modal wave) — `ModeBundle` extended with 7 cross-modal knobs: `cross_modal_both_text_weight` / `cross_modal_both_image_weight` (D6 weighted RRF for `'both'` mode, defaults 0.6/0.4), `image_query_text_refinement_weight` / `image_query_image_refinement_weight` (D13 hybrid intersect for `searchByImage` query refinement, defaults 0.4/0.6), `unified_multimodal` + `unified_multimodal_only` (Phase 3 unified column routing flags), `cross_modal_llm_intent` (Commit 4 opt-in escalation). `SEARCH_MODE_CONFIG_KEYS` extended with 7 corresponding config keys. `KNOBS_HASH_VERSION` bumped 2→3 (D2 — closes the silent cache-hit class where a cached text-mode result could leak to an image-mode caller).
-- `src/core/search/hybrid.ts` extension (v0.36 cross-modal wave) — cross-modal routing branch at the embed step. Resolves `effectiveModality` from per-call `opts.crossModal` (normalized: literal `'auto'` → undefined per D22-1) → `suggestions.suggestedModality` → `'text'` default. Image route: `embedQueryMultimodal` + `searchVector({embeddingColumn: 'embedding_image'})`, skip expansion + keyword (D9 mode-bundle override). 'both' route: parallel text + image vector searches merged via `rrfFusionWeighted` with `effectiveRrfK(baseRrfK, weight)` from the configured cross-modal weights. Phase 3 unified routing fires when `cfg.search.unified_multimodal === true` — bypasses dual-column branching, runs `embedQueryMultimodal` + `searchVector({embeddingColumn: 'embedding_multimodal'})`, D8 fail-open on zero rows + not strict-mode falls through to dual-column. Commit 4 LLM escalation fires only when (no explicit per-call opt) AND (regex returned 'text') AND (`cfg.search.cross_modal.llm_intent` is true) AND (`isAmbiguousModalityQuery` returns true). Fail-open on every error.
-- `src/core/search/image-loader.ts` (v0.36 Phase 2) — `loadImageInput(input, opts)` accepts local path, `data:` URI, or `http(s)://` URL. Magic-byte sniff for PNG/JPEG/WebP. Hard size cap (default 10 MB, configurable via `search.image_query.max_bytes`). For URLs: routes through `fetchWithSSRFGuard` so DNS rebinding + redirect chains are defeated. Pre-flight Content-Length check + post-fetch size guard for lying servers. `ImageLoadError` with discriminated `code` (INVALID_FORMAT / OVERSIZED / INVALID_URL / FETCH_FAILED / TIMEOUT / SSRF_BLOCKED / NOT_FOUND).
-- `src/core/search/by-image.ts` (v0.36 Phase 2) — `searchByImage(engine, input, opts)`. Always runs image branch (`embedQueryMultimodalImage` + `searchVector(embedding_image)`). D13 hybrid intersect: when caller provides optional `query`, runs parallel text branch via `embedQueryMultimodal(query)` and merges via `rrfFusionWeighted` with weights from resolved mode. Phase 3 widens to unified column once `search.unified_multimodal=true` (transparently upgrades the retrieval quality post-reindex).
-- `src/core/spend-log.ts` (v0.36 Phase 2 D23-#6) — per-OAuth-client paid-API spend tracking against the `mcp_spend_log` table (migration v74). `checkBudget(engine, clientId, capCents)` is the pre-flight gate; throws `BudgetExceededError` when today's spend has hit the cap. `recordSpend(engine, entry)` is best-effort post-call. UTC day-aligned aggregation so caps roll over deterministically regardless of server timezone. Local CLI callers (no clientId) bypass the gate. Pre-v0.36 brains without the table fail open to spend=0. `VOYAGE_MULTIMODAL_3_PER_IMAGE_CENTS` = 0.12 cents per image embed.
-- `src/core/search/llm-intent.ts` (v0.36 Commit 4) — opt-in LLM tie-break. `classifyModalityWithLLM(query, fallback)` routes through `gateway.chat()` with a fixed single-word-output system prompt. 1s timeout via AbortController. `parseModality(raw, fallback)` is the pure parser — tolerates trailing punctuation + casing. Fail-open on every error (gateway unavailable, timeout, parse failure, unrecognized output) — returns fallback so a misbehaving LLM can never break search. Cost-bounded by the ambiguity heuristic in `query-intent.ts` (fires <1% of queries when on).
-- `src/commands/reindex-multimodal.ts` (v0.36 Phase 3) — `gbrain reindex --multimodal [--limit N] [--dry-run] [--cost-estimate] [--no-embed] [--yes] [--json]`. Walks `content_chunks WHERE embedding_multimodal IS NULL`, batches via `embedMultimodalSafe` (Commit 0 partial-failure-aware), persists. D7 lock acquisition via `tryAcquireDbLock('gbrain-reindex-multimodal', 360min)`. Cost prompt + 10s Ctrl-C grace window in TTY. `GBRAIN_NO_REEMBED=1` bypass. Checkpoint at `~/.gbrain/reindex-multimodal-checkpoint.json` for resume. D23-#2 auto-flip prompt at coverage=100% completion (TTY: interactive; non-TTY: stderr hint with paste-ready command).
-- `src/core/backfill-registry.ts` extension (v0.36) — new `modality` backfill kind. SQL filter requires `chunk_source='image_asset'` AND `embedding_image IS NOT NULL` AND `(modality IS NULL OR modality != 'image')`. D22-7 defensive guard: never flag a non-image chunk that happens to have `embedding_image` populated. Idempotent — second run finds zero rows.
-- `src/core/migrate.ts` v74 (`mcp_spend_log`) + v75 (`embedding_multimodal_column`) — Phase 2 spend-log table + Phase 3 unified column ALTER. v75 is column-only (no HNSW index — deferred to post-reindex per pgvector best practice). v74 uses BTREE on `(client_id, created_at)` + `(token_name, created_at)` — `date_trunc('day', TIMESTAMPTZ)` is NOT IMMUTABLE so can't appear in index expressions; range scan on created_at covers the per-day rollup query.
-- `src/core/operations.ts` — `get_brain_identity` op (read scope, no params,
-  banner-only): cheap counter packet `{version, engine, page_count,
-  chunk_count, last_sync_iso}` for the thin-client identity banner. Reuses
-  `engine.getStats()`; banner's 60s client-side TTL bounds frequency to
-  ≤1/60s per CLI process (well below the Fly.io health-check cadence that
-  motivated the original `getStats` cost warning).
-- `src/commands/{salience,anomalies,graph-query,think}.ts` — Per-command
-  thin-client routing branches. These commands bypass the operation-layer
-  dispatch in cli.ts (call `engine.foo()` directly), so each gets its own
-  `if (isThinClient(cfg)) { callRemoteTool(...) }` branch that maps CLI flags
-  to op params. `think` is a special case: the server's `think` op
-  intentionally disables `--save`/`--take` for remote callers
-  (operations.ts:1103-1135 trust-boundary gate); thin-client `think` warns
-  loudly when those flags are set.

docs/architecture/topologies.md

@@ -1,403 +0,0 @@
-# GBrain Deployment Topologies
-
-GBrain supports three deployment shapes. They compose: a single user can mix
-all three on the same machine without conflict, because every shape resolves
-to "which `~/.gbrain/config.json` is active right now?" and `GBRAIN_HOME`
-controls that selection.
-
-This page covers the three topologies, when each fits, and concrete setup
-recipes. Pair this doc with `docs/architecture/brains-and-sources.md` (which
-covers the in-brain organization axes) — that doc is about WHICH database;
-this doc is about WHERE that database lives.
-
-## Quick decision tree
-
-```
-   "I'm setting up gbrain..."
-        │
-        ▼
-  Just for me, on one machine? ─── yes ───▶ Topology 1 (single brain)
-        │
-        no
-        │
-        ▼
-  Will a remote machine host the brain
-  while my agent runs locally? ──── yes ───▶ Topology 2 (cross-machine thin client)
-        │
-        no
-        │
-        ▼
-  Multiple Conductor worktrees that
-  shouldn't share a code index? ─── yes ───▶ Topology 3 (split-engine)
-```
-
-Topologies 2 and 3 stack: a thin-client install can also host per-worktree
-code engines, and a per-worktree code engine can also point its artifact
-brain at a remote server.
-
-## Topology 1 — Single brain (today's default)
-
-```
-  ┌────────────────┐
-  │   one machine  │
-  │  ┌──────────┐  │
-  │  │  gbrain  │──┼──→  ~/.gbrain/  →  PGLite  or  Supabase
-  │  │   CLI    │  │
-  │  └──────────┘  │
-  └────────────────┘
-```
-
-What you get: one local DB (PGLite for small brains, Supabase for ~1000+
-files). All commands work directly against it. `gbrain serve` exposes it
-to a single agent over MCP.
-
-When it fits: solo use, single machine, one agent, no Conductor parallelism.
-This is the default; `gbrain init` (no flags) gives you this.
-
-Setup:
-
-```
-gbrain init           # interactive — defaults to PGLite
-gbrain init --pglite  # explicit local
-gbrain init --supabase  # remote Supabase (recommended for 1000+ files)
-```
-
-Nothing else here is special. The other two topologies are variations on
-"who owns the DB" and "how does the agent talk to it."
-
-## Topology 2 — Cross-machine thin client
-
-```
-  ┌────────────┐                    ┌──────────────────┐
-  │ neuromancer│                    │    brain-host    │
-  │ ┌────────┐ │ HTTP MCP / OAuth   │  ┌────────────┐  │
-  │ │ Hermes │─┼───────────────────→│  │   gbrain   │──┼──→ Supabase
-  │ │ agent  │ │                    │  │ serve --http│  │
-  │ └────────┘ │                    │  └────────────┘  │
-  │            │                    │   (with autopilot)│
-  │  no local  │                    │                  │
-  │  gbrain DB │                    │                  │
-  └────────────┘                    └──────────────────┘
-```
-
-What you get: the agent on one machine ("neuromancer") consumes a brain
-hosted on another machine ("brain-host") over HTTP MCP with OAuth. The
-agent's machine has NO local engine. All queries, searches, embeddings,
-and indexing happen on the host.
-
-When it fits:
-
-- Heavy brain (Supabase + autopilot) lives on a beefy machine; agents
-  elsewhere just consume it.
-- You want one source of truth across many machines.
-- Spinning up a parallel local install would create source-ID contention or
-  duplicate work.
-
-The thin client's `~/.gbrain/config.json` carries a `remote_mcp` field
-instead of a local DB connection:
-
-```jsonc
-{
-  "engine": "postgres",  // ignored — never used
-  "remote_mcp": {
-    "issuer_url": "https://brain-host.local:3001",
-    "mcp_url":    "https://brain-host.local:3001/mcp",
-    "oauth_client_id": "neuromancer-...",
-    "oauth_client_secret": "..."  // or set GBRAIN_REMOTE_CLIENT_SECRET
-  }
-}
-```
-
-The CLI dispatch guard refuses any DB-bound command (`sync`, `embed`,
-`extract`, `migrate`, `apply-migrations`, `repair-jsonb`, `orphans`,
-`integrity`, `serve`) on a thin-client install with a clear error pointing
-at the remote host. `gbrain doctor` runs a dedicated thin-client check set
-(OAuth discovery, token round-trip, MCP smoke).
-
-### Setup
-
-**Step 1 — On the host (brain-host):**
-
-```bash
-gbrain init --supabase                         # or --pglite, doesn't matter
-gbrain serve --http --port 3001 --bind 0.0.0.0 # v0.34: bind explicitly for remote access
-                                                # (defaults to 127.0.0.1 since v0.34)
-gbrain auth register-client neuromancer \
-  --grant-types client_credentials \
-  --scopes read,write,admin                    # admin needed for ping/doctor
-
-# v0.34: source-scoped client (write to one source, federate reads across
-# multiple sources). Omit both flags for a v0.33-compatible super-client.
-gbrain auth register-client neuromancer-dept \
-  --grant-types client_credentials \
-  --scopes read,write \
-  --source dept-x \
-  --federated-read dept-x,shared,parent-canon
-```
-
-The `register-client` command prints a `client_id` and `client_secret`.
-Note both. **Scope must include `admin`** — `submit_job` (used by
-`gbrain remote ping`) and `run_doctor` (used by `gbrain remote doctor`)
-both require it.
-
-**Step 2 — On the thin client (neuromancer):**
-
-```bash
-gbrain init --mcp-only \
-  --issuer-url https://brain-host.local:3001 \
-  --mcp-url https://brain-host.local:3001/mcp \
-  --oauth-client-id <id> \
-  --oauth-client-secret <secret>
-```
-
-Pre-flight smoke runs three probes (OAuth discovery, token round-trip,
-MCP initialize). If any fails, init exits with an actionable error. On
-success, `~/.gbrain/config.json` gets `remote_mcp` set and NO local DB
-is created.
-
-**Step 3 — Configure your agent's MCP client.**
-
-For Claude Desktop / Hermes / openclaw, add a single MCP server entry
-pointing at the host's `mcp_url` with the bearer token from `register-client`.
-Example for Claude Desktop's `~/.config/claude/claude_desktop_config.json`:
-
-```jsonc
-{
-  "mcpServers": {
-    "gbrain": {
-      "type": "url",
-      "url": "https://brain-host.local:3001/mcp",
-      "headers": { "Authorization": "Bearer <client_secret>" }
-    }
-  }
-}
-```
-
-**Step 4 — Verify.**
-
-```bash
-gbrain doctor             # runs thin-client checks (no local DB needed)
-gbrain remote ping        # triggers an autopilot cycle on the host (Tier B)
-gbrain remote doctor      # asks the host to run its own doctor (Tier B)
-```
-
-`gbrain sync` and friends will refuse with a clear thin-client error
-naming the `mcp_url`. That's the correct behavior — those commands need
-a local engine that doesn't exist here.
-
-### Re-run guard
-
-Running `gbrain init` (no flags) on a machine that already has thin-client
-config set refuses without `--force`. This catches the scripted-setup-loop
-friction where an orchestrator keeps trying to create a local DB. Use
-`gbrain init --mcp-only --force` to refresh thin-client config.
-
-### Storing the OAuth secret
-
-Three storage paths in priority order:
-
-1. **`GBRAIN_REMOTE_CLIENT_SECRET` env var** (preferred for headless agents).
-   When set, overrides whatever's in the config file. The init flow doesn't
-   persist a config-file copy when the env var was the source.
-2. **`~/.gbrain/config.json` with 0600 perms** (default for interactive
-   setup; mirrors how Supabase keys are stored today).
-3. macOS Keychain integration is on the roadmap; not in v1.
-
-## Topology 3 — Split-engine, per-worktree code + remote artifacts
-
-```
-  ┌──────────────────────────────────────────────────────┐
-  │                  one machine                         │
-  │                                                      │
-  │  ┌─ worktree A ──────────────┐                       │
-  │  │  GBRAIN_HOME=A/.conductor │                       │
-  │  │  gbrain serve --port 3001 │── PGLite (code A)     │
-  │  └───────────────────────────┘                       │
-  │                                                      │
-  │  ┌─ worktree B ──────────────┐                       │
-  │  │  GBRAIN_HOME=B/.conductor │                       │
-  │  │  gbrain serve --port 3002 │── PGLite (code B)     │
-  │  └───────────────────────────┘                       │
-  │                                                      │
-  │  ┌─ default ~/.gbrain ───────┐    HTTP MCP / OAuth   │
-  │  │  gbrain serve --port 3000 │──────────────────────→ remote artifacts
-  │  └───────────────────────────┘                        (Supabase / brain-host)
-  │                                                      │
-  │  Agent's MCP config (Hermes / Claude Desktop):       │
-  │    mcp__gbrain_code__*       → http://localhost:3001 │
-  │    mcp__gbrain_artifacts__*  → http://brain-host/mcp │
-  └──────────────────────────────────────────────────────┘
-```
-
-What you get: each Conductor worktree has its own per-worktree code index
-(local PGLite, disposable when the worktree dies). Artifacts (plans,
-learnings, transcripts) still live in a shared brain that all worktrees
-can see and write to.
-
-When it fits:
-
-- Multiple Conductor worktrees on one machine, all touching the same code
-  repo.
-- You don't want each worktree's code-import to clobber the others'
-  `last_commit`, source IDs, or symbol tables.
-- You DO want artifacts (plans, learnings, retros, transcripts) to be
-  visible across worktrees.
-
-### How it works
-
-`GBRAIN_HOME` selects which `~/.gbrain` directory is active. Set per worktree:
-
-```bash
-export GBRAIN_HOME=/path/to/worktree-A/.conductor/gbrain
-gbrain init --pglite
-gbrain serve --http --port 3001
-```
-
-Each worktree's `gbrain serve` instance binds its own port and indexes its
-own DB. Multiple `gbrain serve` processes coexist fine — they're separate
-OS processes with separate config and separate connection pools.
-
-The artifact brain runs as a separate `gbrain serve` instance with the
-default `~/.gbrain` (no GBRAIN_HOME override) — or remote, in which case
-it's a Topology 2 setup.
-
-The agent's MCP client config lists multiple servers, each with a unique
-alias. Tool names are namespaced as `mcp__<alias>__<tool>`, so the agent
-calls `mcp__gbrain_code__search` for code lookups and `mcp__gbrain_artifacts__search`
-for artifact lookups.
-
-### Recommended embedding model
-
-Per-worktree code brains index source files only — no meeting notes,
-no people pages, no transcripts. Configure each code brain to use
-Voyage's code-tuned model at init time so the config can't be lost to a
-later `init` overwrite:
-
-```bash
-export GBRAIN_HOME=/path/to/worktree-A/.conductor/gbrain
-gbrain init --pglite \
-  --embedding-model voyage:voyage-code-3 \
-  --embedding-dimensions 1024
-```
-
-`voyage-code-3` is Voyage's code-specialized embedding model with
-head-to-head numbers above their general flagships on code retrieval
-([voyageai.com/blog](https://voyageai.com/blog)). For already-initialized
-brains, switch with the one-command wipe-and-reinit (preserves every
-other config field):
-
-```bash
-gbrain reinit-pglite --embedding-model voyage:voyage-code-3 --embedding-dimensions 1024
-gbrain reindex --code --yes
-```
-
-(`gbrain config set embedding_model` is refused as of v0.37.11.0 because
-the schema column has to resize alongside the config.)
-
-`gbrain reindex --code` prints a recommendation when the configured
-embedding model isn't code-tuned. Suppress with
-`GBRAIN_NO_CODE_MODEL_NUDGE=1` if you've intentionally chosen another
-provider (single-vendor procurement, compliance, no Voyage key).
-
-### CRITICAL: alias-level routing is manual
-
-Topology 3 has no smart per-tool routing inside gbrain. The agent picks
-which brain to query when it picks the alias. **A wrong alias writes (or
-queries) the wrong brain silently.** This is intentional (explicit beats
-magic) but real:
-
-- If the agent calls `mcp__gbrain_artifacts__put_page` with code-shaped
-  content, that page lands in the artifact brain forever.
-- If the agent calls `mcp__gbrain_code__search` for a question that
-  actually wants artifact context, the search comes back empty.
-
-Mitigations:
-
-- Name aliases clearly. `gbrain_code` vs `gbrain_artifacts` is unambiguous;
-  `gbrain` vs `gbrain_local` is not.
-- Document in your agent's system prompt or rules which alias goes where.
-  Be explicit about "code questions → `gbrain_code`; everything else →
-  `gbrain_artifacts`."
-- Pair Topology 3 with `gstack`'s per-worktree wiring (which sets the
-  alias names + agent rules consistently across worktrees).
-
-### Setup (manual; gstack automates this side)
-
-The gbrain side requires zero new code — `GBRAIN_HOME` and `--port` already
-exist. Setup looks like:
-
-```bash
-# Start the artifact brain (default ~/.gbrain) on port 3000
-gbrain serve --http --port 3000 &
-
-# Start a per-worktree code brain on port 3001
-export GBRAIN_HOME=/path/to/worktree-A/.conductor/gbrain
-gbrain init --pglite
-gbrain serve --http --port 3001 &
-unset GBRAIN_HOME
-```
-
-Then configure the agent's MCP config with two entries (different aliases,
-different ports). For Claude Desktop:
-
-```jsonc
-{
-  "mcpServers": {
-    "gbrain_artifacts": {
-      "type": "url",
-      "url": "http://localhost:3000/mcp",
-      "headers": { "Authorization": "Bearer <token-A>" }
-    },
-    "gbrain_code": {
-      "type": "url",
-      "url": "http://localhost:3001/mcp",
-      "headers": { "Authorization": "Bearer <token-B>" }
-    }
-  }
-}
-```
-
-The gstack-side wiring (per-worktree home setup, port allocation, automatic
-MCP config generation, gitignore for the per-worktree DB) is in the gstack
-repo's setup-gbrain skill — it composes these primitives, gbrain doesn't
-have to know about Conductor.
-
-## Combining topologies
-
-The three shapes compose. A single machine can run:
-
-- A thin-client default config pointing at a remote artifact brain
-  (Topology 2).
-- Plus per-worktree code brains under their own `GBRAIN_HOME` (Topology 3).
-- Each worktree's `gbrain serve` instance is local; the agent's MCP config
-  lists them alongside the remote artifact brain.
-
-`GBRAIN_HOME` controls which config file is active for any one CLI
-invocation. `gbrain serve --port` controls which port a server listens on.
-The agent's MCP client picks the alias and thus the destination per tool
-call. There's no global gbrain orchestrator that knows about all of them
-simultaneously — that's by design.
-
-## When NOT to use these topologies
-
-- **Don't use Topology 2 if your agent only ever runs on the same machine
-  as the brain.** A local `gbrain` install + `gbrain serve` (stdio) is
-  simpler and faster.
-- **Don't use Topology 3 if you only have one Conductor worktree at a
-  time.** Per-worktree engines exist to prevent contention; one-at-a-time
-  use has no contention.
-- **Don't use a `remote_mcp` thin client AND a local engine on the same
-  machine in the same `GBRAIN_HOME`.** The dispatch guard refuses DB-bound
-  commands when `remote_mcp` is set. If you genuinely want both modes on
-  one machine, use `GBRAIN_HOME` to separate them (one home for the thin
-  client, another for the local engine).
-
-## See also
-
-- `docs/architecture/brains-and-sources.md` — in-brain organization (brains
-  vs sources axes).
-- `docs/mcp/CLAUDE_DESKTOP.md` and siblings — per-client MCP setup.
-- `gbrain init --help` and `gbrain auth --help` for command-level details.
-- [`docs/tutorials/`](../tutorials/) — end-to-end walkthroughs that combine
-  these topologies into working setups (company brain, personal brain,
-  agent integration, etc.).

docs/architecture/type-taxonomy.md

@@ -1,177 +0,0 @@
-# Type Taxonomy (v0.41.22: gbrain-base-v2)
-
-> The 14-canonical-type DRY/MECE taxonomy shipped in v0.41.22. Predecessor
-> `gbrain-base` (24 types) stays bundled for back-compat; v0.42+ installs
-> default to `gbrain-base-v2`.
-
-## Why
-
-A production gbrain brain (186K pages) had accreted **94 distinct
-`pages.type` values** in 9 clusters of redundancy. The type system is
-the foundation for schema packs, search filtering, extract behavior,
-enrichment routing, and expert routing. When types are noisy, every
-downstream feature degrades:
-
-- **Search filtering is ambiguous** — `--type article` misses 2.2K
-  articles typed as `media/article`, `sources/article`, etc.
-- **Enrichment routing is incomplete** — `enrichable_types` could only
-  list a few canonical types; 80+ legacy types meant most pages never
-  got enriched.
-- **Agent confusion** — when ingesting a new article, should it be
-  `article`, `media/article`, `sources/article`, or `source/article`?
-  Four reasonable choices, none of them right.
-- **Orphan inflation** — 5,521 concept-redirect pages inflated orphan
-  counts without adding knowledge value.
-
-Issue #1479 catalogues the 9 clusters with exact counts. This doc is
-the response: a coherent 14-type taxonomy with subtypes/format/origin
-pushed to frontmatter, alias-table rows for redirects, real link-table
-rows for edge-shaped pages.
-
-## The 14 canonical types (+ `note` catch-all)
-
-| Type | Primitive | What it holds | Examples |
-|------|-----------|---------------|----------|
-| `person` | entity | People | Founders, partners, individuals |
-| `company` | entity | Companies, products, orgs (subtype-distinguished) | Companies, YC-companies, products |
-| `media` | media | Articles, videos, essays, books, podcasts (subtype-distinguished) | Substack posts, YouTube videos, books |
-| `tweet` | media | Twitter posts (single/bundle/stub subtype) | Single tweets, threads, bundles |
-| `social-digest` | temporal | Period-grouped social summaries (daily/monthly) | X account daily digests |
-| `analysis` | media | Research + competitive intel | Market analysis, pricing analysis |
-| `atom` | annotation | Knowledge units (extraction/manual/lore subtype) | Extracted facts, manual notes, lore |
-| `concept` | concept | Ideas + reference pages | Wiki concepts |
-| `source` | media | Transcripts, references | Interview transcripts |
-| `deal` | temporal | Investment deals | Term sheets, investments |
-| `email` | temporal | Email threads | Email correspondence |
-| `slack` | temporal | Slack messages + threads | Slack conversations |
-| `writing` | media | Original writing | Drafts, essays in progress |
-| `project` | concept | Initiatives, workstreams | Internal projects |
-| `note` | concept | **Catch-all** for one-offs (legacy_type preserved) | Memos, anecdotes, insights, etc. |
-
-15 types total (14 canonical + `note`). The catch-all retype rule
-binds any uncovered legacy type to `note` with
-`frontmatter.legacy_type = <original>` preserved for rollback.
-
-## Subtypes (declared in frontmatter post-unify)
-
-| Canonical | Subtype field | Values |
-|-----------|---------------|--------|
-| `company` | `subtype` | `company` / `product` / `org` |
-| `media` | `subtype` | `video` / `article` / `essay` / `book` / `podcast` / `blog` |
-| `tweet` | `subtype` | `single` / `bundle` / `stub` |
-| `social-digest` | `subtype` | `daily` / `monthly` |
-| `atom` | `subtype` | `extraction` / `manual` / `lore` |
-
-`subtype_field` for retype rules is restricted to an allowlist:
-`{subtype, legacy_type, origin, format, kind, period, domain}`. This
-prevents third-party packs from injecting `title`, `slug`, or `type`
-via mapping_rules (codex D9 security hardening).
-
-## Migration flow
-
-```
-gbrain onboard --check                         # surfaces pack_upgrade_available
-        ↓
-gbrain onboard --check --explain               # per-cluster narrative dry-run
-        ↓
-gbrain jobs submit unify-types \               # PROTECTED + manual_only
-  --allow-protected \
-  --params '{"target_pack":"gbrain-base-v2"}'
-        ↓
-Handler runs 4 phases:
-  ┌─────────────────────────────────────┐
-  │ Phase 1: Preflight + lock           │ → gbrain-unify db-lock (60min TTL)
-  ├─────────────────────────────────────┤
-  │ Phase 2: Retype explicit rules      │ → chunked UPDATE 1000/batch
-  ├─────────────────────────────────────┤
-  │ Phase 3: Retype catch-all sentinel  │ → 'note' with legacy_type
-  ├─────────────────────────────────────┤
-  │ Phase 4: Page-to-link conversions   │ → insert links + soft-delete
-  ├─────────────────────────────────────┤
-  │ Phase 5: Page-to-alias conversions  │ → insert slug_aliases + soft-delete
-  ├─────────────────────────────────────┤
-  │ Phase 6: Final sync (residual)      │ → path-prefix typing
-  ├─────────────────────────────────────┤
-  │ Phase 7: Flip active pack (D13)     │ → engine.setConfig + saveConfig
-  ├─────────────────────────────────────┤
-  │ Phase 8: Verify + celebrate         │ → assert ≤16 types; stderr summary
-  └─────────────────────────────────────┘
-        ↓
-gbrain onboard --check                         # pack_upgrade_available cleared
-                                               # type_proliferation cleared
-```
-
-## Rollback paths
-
-Every primitive ships with a documented rollback:
-
-| Operation | Rollback |
-|-----------|----------|
-| Retype | `frontmatter.legacy_type = <original>` preserved on every page (D8). One SQL UPDATE restores types: `UPDATE pages SET type = frontmatter->>'legacy_type' WHERE frontmatter ? 'legacy_type'`. |
-| Page-to-link | Source page soft-deleted with 72h TTL. `gbrain pages restore <slug>` within 72h. Link row stays harmless if source restored. |
-| Page-to-alias | Source page soft-deleted with 72h TTL. `gbrain pages restore <slug>` within 72h. Alias row stays harmless (or `DELETE FROM slug_aliases WHERE alias_slug = <slug>` to clean up). |
-| Active-pack flip | `gbrain schema use gbrain-base` reverses the flip. |
-
-## What if my brain doesn't fit?
-
-The catch-all retype rule (`from_type: '*unknown*'`) handles long-tail
-types automatically — any page whose type isn't covered by an explicit
-rule AND isn't a page_to_link / page_to_alias source gets retyped to
-`note` with `legacy_type` preserved. Guarantees ≤16 distinct types
-post-unify on ANY brain.
-
-For brains with substantial custom types that deserve their own canonical
-(e.g. `researcher` for an academic brain), the right move is:
-
-1. Fork gbrain-base-v2: `gbrain schema fork gbrain-base-v2 my-pack`
-2. Edit your fork to add page_types + mapping_rules covering your
-   custom domain.
-3. Target your fork: `gbrain jobs submit unify-types --allow-protected
-   --params '{"target_pack":"my-pack"}'`
-
-Your fork can also declare `migration_from: {pack: gbrain-base-v2,
-version: "1.x"}` to register itself as a successor — future agents
-discovering your pack via `pack_upgrade_available` will offer the
-migration.
-
-## Wikilink resolution post-unify
-
-The slug_aliases table IS the resolver (D15: codex outside voice —
-don't rewrite body-text wikilinks; the alias table is the right
-primitive). Wikilinks like `[[old-redirect-slug]]` keep working post-
-unify because:
-
-1. The wikilink resolver short-circuits through
-   `engine.resolveSlugWithAlias(slug, sourceId)` BEFORE the existing
-   fuzzy/prefix cascade.
-2. The lookup queries `slug_aliases` for any matching alias_slug in
-   the provided source(s).
-3. If found, returns the canonical_slug. The renderer then resolves
-   the wikilink to the canonical page.
-
-Multi-source ambiguity (same alias_slug in two registered sources)
-emits a once-per-process `multi_match` stderr warning and returns the
-first match by source array order. Federated reads pass the full
-allowed-source array.
-
-## Search ranking signal: alias_resolved_boost
-
-Post-unify, search results whose slug is a canonical_slug in
-slug_aliases get a 1.05x score multiplier via the
-`applyAliasResolvedBoost` post-fusion stage. Semantic intent: "user
-explicitly disambiguated this as canonical, so it should outrank fuzzy
-matches that hit aliases by accident."
-
-`SearchResult.alias_resolved_boost` is stamped on touched results for
-`--explain` formatter visibility. KNOBS_HASH_VERSION bumped 5→6 to
-invalidate pre-v0.42 cache rows that don't reflect the new stage.
-
-## Reference
-
-- Issue: https://github.com/garrytan/gbrain/issues/1479
-- Pack file: `src/core/schema-pack/base/gbrain-base-v2.yaml`
-- Pack-upgrade mechanism: `docs/architecture/pack-upgrade-mechanism.md`
-- Migration handler: `src/core/schema-pack/unify-types-handler.ts`
-- Onboard checks: `src/core/onboard/checks.ts`
-- Skill: `skills/schema-unify/SKILL.md`
-- Plan + decisions: `~/.claude/plans/system-instruction-you-are-working-transient-elephant.md`

docs/contradictions.md

@@ -1,166 +0,0 @@
-# gbrain eval suspected-contradictions (v0.32.6)
-
-The contradiction probe samples retrieval results, asks an LLM judge whether
-any pair contradicts on a factual claim relevant to the user's query, and
-aggregates into a calibrated report. The output is data — the operator
-decides what to act on. This doc covers the architecture, severity rubric,
-how to interpret the headline number, and when to act.
-
-## Why this exists
-
-gbrain handles contradictions for *curated* pages via compiled-truth-plus-
-timeline and source-boost: when `companies/acme.md` says MRR is $2M and a
-chat transcript from 2024 says MRR was $50K, the curated page outranks the
-chat. `takes.active` filtering hides explicitly-superseded takes. Recency
-decay biases ranking toward fresher content per source-tier.
-
-What none of those mechanisms measure: how often do unmarked semantic
-contradictions actually surface in retrieval? Without a probe, every
-"should we build the bigger swing (chunk-level `revises` field + ranking
-change)" decision is vibes. The probe produces evidence.
-
-## Architecture
-
-```
-        ┌──────────────────────────────────────┐
-        │ gbrain eval suspected-contradictions │
-        └──────────────────┬───────────────────┘
-                           │
-        ┌──────────────────▼───────────────────┐
-        │ For each query: hybridSearch top-K   │
-        │ → cross_slug_chunks + intra_page     │
-        │   chunk-vs-take pairs                │
-        └──────────────────┬───────────────────┘
-                           │
-        ┌──────────────────▼───────────────────┐
-        │ Date pre-filter: skip pairs whose    │
-        │ dates are >30d apart (Codex fix:     │
-        │ same-paragraph-dual-date overrides)  │
-        └──────────────────┬───────────────────┘
-                           │
-        ┌──────────────────▼───────────────────┐
-        │ Persistent cache lookup              │
-        │ (chunk_a_hash, chunk_b_hash, model,  │
-        │  prompt_version, truncation_policy)  │
-        └────────┬─────────┬────────────────────┘
-              hit│         │miss
-                 │         ▼
-                 │   ┌─────────────────────────┐
-                 │   │ LLM judge call          │
-                 │   │ → JudgeVerdict          │
-                 │   │ confidence floor ≥ 0.7  │
-                 │   └─────────┬───────────────┘
-                 │             │
-                 ▼             ▼
-        ┌──────────────────────────────────────┐
-        │ Aggregate per-query + global stats   │
-        │ Wilson 95% CI on headline %          │
-        │ source-tier breakdown                │
-        │ hot pages + resolution proposals     │
-        └──────────────────┬───────────────────┘
-                           │
-                           ▼
-                  ProbeReport JSON
-                           │
-        ┌──────────────────┼──────────────────────┬───────────────┐
-        ▼                  ▼                      ▼               ▼
-   doctor (M1)         MCP (M3)             synthesize (M2)   trend (M5)
-   surfaces           find_contradictions    informational     persistent
-   findings           op for agents          block in prompt   tracking
-```
-
-## Severity rubric
-
-The judge assigns severity per finding:
-
-| Level | Rubric | Example |
-|---|---|---|
-| `low` | naming/format differences | "Alice Smith" vs "A. Smith" |
-| `medium` | factual values that may be stale | revenue figure, headcount, valuation |
-| `high` | identity / structural claims | founder/CEO/CFO role, company status |
-
-Doctor sorts findings by severity DESC. The MCP op accepts a severity filter
-so agents can fetch just the high-priority items.
-
-## How to interpret the headline number
-
-The probe outputs `queries_with_contradiction / queries_evaluated` with a
-Wilson 95% confidence interval:
-
-```
-Queries with >=1 contradiction: 12 / 50 (24%)  Wilson CI 95%: 14–37%
-```
-
-What this says: with 95% confidence, the true rate is between 14% and 37%.
-The 24% point estimate is the most-likely-value but bounded by sampling
-noise. **`small_sample_note` fires when n < 30** — at that scale the CI is
-too wide to act on.
-
-Decision criteria for the bigger swing (chunk-level `revises` field):
-
-| Wilson CI lower bound | What it says | Action |
-|---|---|---|
-| < 5% | Source-boost + recency-decay + curated pages handle the load | Stop here; this is the right scope |
-| 5–15% | Real but bounded | Operator decides whether the cost justifies the swing |
-| > 15% | Real and substantial | Plan the bigger swing in v0.34+ |
-
-## When to act on findings
-
-Each finding ships with a `resolution_command` field — paste-ready:
-
-- `gbrain takes supersede <slug> --row N` — newer take should replace
-  the older chunk text on the same page (intra_page kind).
-- `gbrain dream --phase synthesize --slug <slug>` — compiled_truth for
-  the curated entity needs an update (cross_slug curated-vs-bulk).
-- `gbrain takes mark-debate <slug> --row N` — intentional disagreement
-  (e.g., two opinions you want to keep both of).
-- `# manual review: <a> vs <b>` — judge wasn't sure; operator decides.
-
-Run `gbrain eval suspected-contradictions review --severity high` to
-inspect findings without re-running the probe.
-
-## Cost model
-
-Default judge is `claude-haiku-4-5` at ~$1/Mtok in, $5/Mtok out. With
-the v0.32.6 truncation at 1500 chars per pair, ~500 input + 80 output
-tokens per judge call. Budget cap defaults to $5 in TTY / $1 non-TTY.
-
-- ~$0.0006 per judge call
-- ~$0.005 per query (after date pre-filter + cache hits)
-- ~$0.50 per 100 queries
-
-The persistent cache means nightly runs against the same query set
-pay near-zero on re-runs (until you bump PROMPT_VERSION).
-
-## Trust posture
-
-- Probe never mutates the brain. Runs only read pages/takes/chunks.
-  Writes go only to `eval_contradictions_runs` and `eval_contradictions_cache`.
-- MCP `find_contradictions` is read-scope. NOT in the subagent allowlist —
-  user-initiated only, not autonomous-action surface.
-- Build-fixture script is local-only. The redactor + `isCleanForCommit`
-  gate makes accidental private-data commits hard, but the operator MUST
-  inspect every redaction before commit.
-
-## See also
-
-- Plan: `~/.claude/plans/system-instruction-you-are-working-hashed-dewdrop.md`
-- CHANGELOG: `## [0.32.6]` entry covers the whole release.
-- Cost discipline: `docs/eval-bench.md` for the recommended nightly cadence
-  + trend-tracking workflow.
-- **Temporal axis follow-on (v0.35.3.1 + v0.35.7):** v0.35.3.1 added a
-  six-member verdict enum (`no_contradiction | contradiction |
-  temporal_supersession | temporal_regression | temporal_evolution |
-  negation_artifact`) and threaded `pages.effective_date` into the judge
-  prompt so the probe stops crying wolf on legitimate change-over-time.
-  v0.35.7 lands the trajectory substrate the probe pointed at:
-  `gbrain eval trajectory <entity>` shows the chronological typed-claim
-  history with regressions flagged inline; `gbrain founder scorecard
-  <entity>` rolls up four signals (accuracy, consistency, growth
-  direction, red flags) into a stable JSON contract. MCP op
-  `find_trajectory` (read scope, visibility-filtered for remote callers)
-  exposes the same data to agents. The probe's `temporal_supersession`
-  verdict and the consolidate phase's `valid_until` writeback both
-  preserve the `auto-supersession.ts:4` "NEVER auto-applies" invariant
-  — the probe still emits paste-ready commands, only `consolidate`
-  writes `valid_until` (R1+R8 grep guard pins this).

docs/designs/2026_05_EVAL_PLAN.md

@@ -1,580 +0,0 @@
-# Embedder Shootout — May 2026 Eval Plan
-
-**Status:** approved, ready to execute
-**Owner:** Garry
-**Plan source:** `~/.claude/plans/system-instruction-you-are-working-linear-origami.md` (review log)
-**Target wallclock:** ~2 weeks
-**Target API spend:** ~$525 (hard cap $700)
-
-## What this is
-
-A head-to-head A/B/C comparison of three embedding providers under v0.35.0.0's new
-multi-vendor gateway routing:
-
-- **OpenAI** `text-embedding-3-large` @ 1536 dims
-- **Voyage** `voyage-4-large` @ 2048 dims
-- **ZeroEntropy** `zembed-1` @ 2560 dims (also 1280 in a Matryoshka ablation)
-
-Each tested with and without the `zerank-2` reranker. Two corpora: public LongMemEval
-(500q) and BrainBench in-house (145 relational queries + 50 newly-curated Cat 13
-embedder-sensitive queries).
-
-The goal: produce a publishable comparison report that answers "which embedder wins,
-and does zerank-2 carry the win for ZeroEntropy" with bootstrap p-values, suitable
-for a v0.35.2.0 release-note headline.
-
-## Why this design
-
-Locked decisions from the planning review (see plan file + `GSTACK REVIEW REPORT` at
-the bottom of the linked plan):
-
-- **Synthetic-only** — LongMemEval (public) + BrainBench (in-house). No `~/.gbrain` data.
-- **Answer-gen mode** — `gbrain eval longmemeval` runs the default answer-gen path
-  (Anthropic Sonnet), then feeds the resulting hypothesis JSONL to LongMemEval's
-  published `evaluate_qa.py` (OpenAI gpt-4o judge) for real correctness numbers.
-  `--retrieval-only` is NOT used (would produce an attackable headline; the judge
-  expects answer text, not retrieval text).
-- **`tokenmax` search mode** pinned across all cells (expansion + reranker slot active).
-- **Serial execution** in one workspace. Clean rate-limit profile; first-contact run on
-  ZE wants debuggable signal.
-- **7-cell matrix** (no matched-dim cross-vendor row — no shared dim exists across
-  all three vendors; honest framing is "each vendor at marketed sweet spot").
-
-## Architectural facts that constrain the plan
-
-- `content_chunks.embedding vector(N)` dim is fixed per brain. Per-question PGLite in
-  LongMemEval makes this free; BrainBench needs separate brain per cell.
-- pgvector HNSW caps at **2000 dims** (`PGVECTOR_HNSW_VECTOR_MAX_DIMS` in
-  `src/core/vector-index.ts:19`). Voyage 2048 and ZE 2560 fall back to exact vector
-  scan. Helps quality (no HNSW approximation) but adds latency. Footnoted in writeup.
-- Reranker disable key is **`search.reranker.enabled false`**, NOT `reranker_model none`.
-  `tokenmax` mode defaults reranker=true.
-- `gbrain/ai/gateway` is NOT exported in v0.35.0.0. PR α exposes it.
-
-## Matrix
-
-| Cell | Embedder | Dim | HNSW | Reranker | Notes |
-|---|---|---|---|---|---|
-| A0 | `openai:text-embedding-3-large` | 1536 | yes | none | OpenAI baseline |
-| A1 | `openai:text-embedding-3-large` | 1536 | yes | `zerank-2` | mixed-vendor |
-| B0 | `voyage:voyage-4-large` | 2048 | no (exact) | none | Voyage solo |
-| B1 | `voyage:voyage-4-large` | 2048 | no (exact) | `zerank-2` | mixed-vendor |
-| C0 | `zeroentropyai:zembed-1` | 2560 | no (exact) | none | ZE embedder solo |
-| C1 | `zeroentropyai:zembed-1` | 2560 | no (exact) | `zerank-2` | **ZE full stack** |
-| C2 | `zeroentropyai:zembed-1` | 1280 | yes | `zerank-2` | ZE-Matryoshka ablation |
-
-## PR structure — as few as possible
-
-**PR α — gbrain repo: v0.35.1.0 infra.** All gbrain changes bundled. Lands first.
-Bisect-friendly commits inside, ship at the very end.
-
-**PR β — gbrain-evals repo: adapter + smoke + curation + eval receipts + writeup.** The
-big one. Includes the full eval-run output committed alongside the code that produced
-it, plus the comparison writeup. Lands when everything is done.
-
-**PR γ (optional) — gbrain repo: v0.35.2.0 release** that cross-links the gbrain-evals
-benchmark in CHANGELOG. Small commit; no code changes.
-
-Total: 2 substantive PRs + 1 optional release commit. **No mid-stream ships.**
-
-## Conductor sessions
-
-Each section below is a self-contained brief. Copy-paste into a fresh Conductor session
-to hand off. Each session ends with a clean deliverable.
-
----
-
-## Session 1 — PR α: gbrain infra (v0.35.1.0)
-
-**Repo:** `/Users/garrytan/conductor/workspaces/gbrain/<NEW-WORKSPACE>` (fresh from `master`)
-**Branch:** `garrytan/v0.35.1.0-infra`
-**Wallclock:** ~2h
-**API spend:** $0
-
-### What this session ships
-Three changes in one PR, bundled so the embedder shootout in gbrain-evals (PR β) has a
-clean prereq baseline:
-
-1. Add `voyage:voyage-4-large` ($0.18/M) and `zeroentropyai:zembed-1` ($0.05/M) to the
-   embedding pricing table. Patch the `gbrain models doctor` cost estimator + test.
-2. Expose `gbrain/ai/gateway` in `package.json` exports map so the gbrain-evals
-   adapters can call `configureGateway({embedding_model, embedding_dimensions, reranker_model})`
-   from outside the gbrain process.
-3. Add `--resume-from <jsonl>` to `gbrain eval longmemeval` so a mid-run abort
-   (rate-limit, cost-cap, OS interrupt) doesn't lose the cells we already paid for.
-
-Ships at the end as v0.35.1.0.
-
-### Prereqs (verify before starting)
-- On gbrain master at v0.35.0.0 baseline. `cat VERSION` shows `0.35.0.0`.
-- `bun test` and `bun run verify` both pass on master.
-
-### Commits (bisect-friendly, one feature per commit)
-
-```
-1. feat(pricing): add voyage-4-large + zembed-1 to EMBEDDING_PRICING
-   - src/core/embedding-pricing.ts: add both entries
-   - test/embedding-pricing.test.ts: pin both with $0.18 and $0.05
-   - Verify: bun test test/embedding-pricing.test.ts
-
-2. feat(exports): expose gbrain/ai/gateway with canary test
-   - package.json: add "./ai/gateway" to exports map
-   - test/public-exports.test.ts: add canary for configureGateway + embed
-   - scripts/check-exports-count.sh: 17 -> 18
-   - Verify: bun run verify
-
-3. feat(eval): add --resume-from <jsonl> to longmemeval
-   - src/commands/eval-longmemeval.ts: parse flag, skip questions already in input JSONL
-   - test/eval-longmemeval.test.ts: simulated mid-run abort + resume regression
-   - Verify: bun test test/eval-longmemeval.test.ts
-
-4. chore: v0.35.1.0
-   - VERSION: 0.35.1.0
-   - package.json: 0.35.1.0
-   - CHANGELOG.md: new entry
-   - bun install (refresh lockfile)
-```
-
-### Verify before /ship
-```bash
-bun run typecheck
-bun run verify
-bun test test/embedding-pricing.test.ts test/public-exports.test.ts test/eval-longmemeval.test.ts
-```
-
-### Ship
-```bash
-/ship
-```
-
-### Deliverable
-- `master` of gbrain at v0.35.1.0
-- `gbrain/ai/gateway` reachable from external consumers (verified by canary test)
-- `git tag eval-run-v0.35.1.0-baseline` (annotated, names this exact commit)
-- `gbrain --version` prints `0.35.1.0`
-
-### Hand-off to Session 2
-- gbrain-evals can now `bun update gbrain` to v0.35.1.0
-- The tag preserves the exact commit for any future reproducibility need
-
----
-
-## Session 2 — PR β setup: gbrain-evals adapter + smoke + subset flag
-
-**Repo:** `/Users/garrytan/git/gbrain-evals` (or a fresh Conductor workspace cloned from it)
-**Branch:** `garrytan/embedder-shootout`
-**Wallclock:** ~3-4h
-**API spend:** ~$0.10 (smoke verification calls only)
-
-### What this session ships into PR β (does NOT merge yet)
-Wire the harness to drive 3 embedding providers via the newly-exposed gbrain gateway:
-
-1. New typed `EvalAdapterConfig {embedder, dim, reranker?}` passed into each adapter.
-2. Rewrite `vector.ts` + `hybrid-rrf.ts` to call `configureGateway()` from
-   `gbrain/ai/gateway` instead of the hardcoded `gbrain/embedding` import.
-3. Critical: hybrid adapter must also route `search.reranker.enabled` (true/false) and
-   `search.mode` (tokenmax) — codex flagged that the existing hybrid never sets these.
-4. New 3-phase smoke harness: wiring (5 queries × embed roundtrip + dim check) +
-   long-haystack (1 query × 50K-token synthetic haystack) + rerank-payload (1 query
-   × `topNIn=30`). Exit code is the gate.
-5. New `--include-subset <name>` flag on the BrainBench runner (Cat 13 wiring; subset
-   itself comes in Session 3).
-
-### Prereqs
-- Session 1 done. gbrain master at v0.35.1.0.
-- API keys present: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `VOYAGE_API_KEY`,
-  `ZEROENTROPY_API_KEY`. Smoke fails-loud on missing key.
-
-### Commits
-
-```
-1. chore(deps): bump gbrain pin to v0.35.1.0
-   - package.json + bun.lock
-   - Verify: bun install && bun run typecheck
-
-2. feat(adapter): typed EvalAdapterConfig + gateway swap
-   - NEW: eval/runner/eval-adapter-config.ts (the type)
-   - eval/runner/adapters/vector.ts: constructor takes EvalAdapterConfig,
-     calls configureGateway({embedding_model, embedding_dimensions})
-   - Drop hardcoded gbrain/embedding import
-   - Verify: existing vector adapter unit tests still pass
-
-3. feat(adapter): hybrid-rrf wires reranker_enabled + search.mode
-   - eval/runner/adapters/hybrid-rrf.ts: constructor takes EvalAdapterConfig,
-     plumbs search.reranker.enabled + search.mode = tokenmax through
-   - Verify: bun test eval/
-
-4. feat(smoke): 3-phase smoke harness
-   - NEW: eval/runner/smoke.ts (CLI entry: bun run eval:smoke -- --embedder X --dim Y [--reranker Z])
-   - Phase 1: 5 queries × embed roundtrip, assert vector dim matches config
-   - Phase 2: 1 query × synthetic 50K-token haystack, assert no token-limit error
-   - Phase 3: 1 query × topNIn=30 documents, assert no 5MB payload cap hit
-   - Non-zero exit on any failure
-   - Verify: bun run eval:smoke -- --embedder openai:text-embedding-3-large --dim 1536
-
-5. feat(runner): --include-subset flag for BrainBench
-   - eval/runner/multi-adapter.ts: parse flag, filter queries by subset tag
-   - Subset itself comes in next commit (Session 3)
-   - Verify: bun run eval:run -- --include-subset cat13-embedder (errors politely because subset file doesn't exist yet)
-```
-
-### Smoke verification (run manually before opening PR)
-```bash
-bun run eval:smoke -- --embedder openai:text-embedding-3-large --dim 1536
-bun run eval:smoke -- --embedder voyage:voyage-4-large --dim 2048
-bun run eval:smoke -- --embedder zeroentropyai:zembed-1 --dim 2560
-bun run eval:smoke -- --embedder zeroentropyai:zembed-1 --dim 2560 --reranker zeroentropyai:zerank-2
-```
-
-All four MUST exit 0. Reports should print the observed vector dim, matching the
-configured dim.
-
-### Open PR β
-```bash
-gh pr create --base main --title "feat: embedder shootout (adapter + smoke + Cat 13 + eval receipts)" --body "$(cat <<'EOF'
-## Summary
-v0.35.0.0 shipped ZeroEntropy zembed-1 + zerank-2 reranker support. This PR runs a head-to-head A/B/C comparison across OpenAI, Voyage, and ZeroEntropy under the new gateway routing.
-
-This first commit batch lands the harness. Cat 13 curation, Phase 1+2 evals, and the
-writeup follow in subsequent commits to this same PR.
-
-## Test plan
-- [x] Adapter unit tests pass
-- [x] Smoke harness exits 0 against all 3 providers
-- [ ] Cat 13 subset committed (Session 3)
-- [ ] LongMemEval x 7 cells run (Session 4)
-- [ ] BrainBench x 7 cells run (Session 5)
-- [ ] Writeup committed (Session 5)
-
-🤖 Generated with [Claude Code](https://claude.com/claude-code)
-EOF
-)"
-```
-
-### Deliverable
-- PR β open against gbrain-evals `main`, green CI
-- Smoke verified against all 3 providers (paste the smoke output in the PR body)
-- Branch ready for Session 3 (Cat 13 curation)
-
-### Hand-off to Session 3
-- Branch `garrytan/embedder-shootout` exists on origin
-- The `--include-subset cat13-embedder` flag is wired but the subset file doesn't exist
-  yet — that's Session 3
-
----
-
-## Session 3 — PR β: Cat 13 conceptual-recall curation
-
-**Repo:** `/Users/garrytan/git/gbrain-evals`, branch `garrytan/embedder-shootout` (same as Session 2)
-**Wallclock:** ~3-4h (heavily user-interactive; AI proposes, you review each)
-**API spend:** $0
-
-### What this session ships into PR β
-Hand-curated 50 embedder-sensitive queries from BrainBench's Cat 13 (conceptual recall)
-corpus. These are the queries where a graph/keyword adapter would likely miss but a
-semantic adapter would find.
-
-Codex flagged the existing 145-query relational corpus as graph/keyword-dominated and
-weak for embedder claims. Cat 13 is closer to the embedder-sensitive workload but
-needs hand-selection.
-
-### Prereqs
-- Session 2 done. PR β open with adapter + smoke + subset flag.
-
-### Workflow
-Interactive: Claude proposes queries in batches of 10, you accept/reject/edit each.
-
-1. Claude reads the existing Cat 13 raw query pool:
-   ```bash
-   ls eval/data/raw/ | grep -i cat13
-   cat eval/data/raw/cat13-*.json | jq '.'
-   ```
-2. Claude proposes 10 candidate queries per batch, each tagged with the inclusion
-   reasoning ("would a graph adapter miss this?")
-3. User accepts/rejects/edits inline. Target: 50 queries × ~5 batches.
-4. Claude commits to `eval/data/gold/brainbench-cat13-embedder-subset.json`:
-   ```json
-   {
-     "schema_version": 1,
-     "subset": "cat13-embedder",
-     "queries": [
-       {
-         "id": "cat13-emb-001",
-         "query": "...",
-         "relevant_chunk_ids": ["..."],
-         "inclusion_reason": "paraphrase relationship; graph adapter wouldn't catch the synonym"
-       }
-       // ... 49 more
-     ]
-   }
-   ```
-
-### Commit
-
-```
-feat(eval): curate Cat 13 conceptual-recall subset (50 embedder-sensitive queries)
-- NEW: eval/data/gold/brainbench-cat13-embedder-subset.json
-- Each query tagged with inclusion_reason for future audit
-```
-
-### Spot-check before commit
-- Pick 5 random queries, run them against a hypothetical graph adapter (e.g. grep on
-  the relevant terms) and verify they would NOT surface the right chunk.
-- Run the same 5 against the existing hybrid adapter and verify they DO.
-
-### Deliverable
-- `eval/data/gold/brainbench-cat13-embedder-subset.json` committed to PR β
-- Exactly 50 queries
-- Spot-check evidence in the commit message
-
-### Hand-off to Session 4
-- PR β now has: adapter + smoke + Cat 13 subset
-- Ready for the actual eval runs
-
----
-
-## Session 4 — PR β Phase 1: LongMemEval × 7 cells (overnight)
-
-**Repo:** Same gbrain-evals branch
-**Wallclock:** ~10.5h (mostly hands-off, kick off and walk away)
-**API spend:** ~$476 (LongMemEval-heavy; 7 × $68/cell)
-
-### What this session ships into PR β
-7 LongMemEval scored receipts (one per matrix cell). Each is a JSONL of 500
-hypotheses + a JSON file of correctness scores from `evaluate_qa.py`.
-
-### Prereqs
-- Sessions 1+2+3 done. PR β has adapter + smoke + Cat 13.
-- LongMemEval dataset downloaded (gated HuggingFace; one-time setup).
-- `evaluate_qa.py` checked out somewhere (from
-  https://github.com/xiaowu0162/LongMemEval) with its own venv set up.
-- API keys: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `VOYAGE_API_KEY`,
-  `ZEROENTROPY_API_KEY`.
-
-### Wrapper script
-Claude writes `scripts/run-shootout-phase1.sh` in the gbrain-evals branch. Single
-entry point that loops the 7 cells serially with smoke gating + cost-cap aborts.
-
-```
-NEW: scripts/run-shootout-phase1.sh
-- Per cell: gbrain config set (embedder, dim, reranker, search.reranker.enabled, search.mode=tokenmax)
-- Per cell: bun run eval:smoke (abort cell on non-zero)
-- Per cell: gbrain eval longmemeval ... --output results/longmemeval-{cell}.jsonl
-- Per cell: cost-cap check ($90/cell hard stop)
-- Per cell: --resume-from existing results/longmemeval-{cell}.jsonl if present
-- Logs to results/phase1-run-log.txt
-```
-
-### Run
-```bash
-# Kick off in background; check back in 10-12h
-bash scripts/run-shootout-phase1.sh 2>&1 | tee results/phase1-run-log.txt &
-```
-
-Use `run_in_background: true` if running through Claude. Check back periodically.
-
-### Scoring (after all 7 cells done)
-```bash
-for cell in A0 A1 B0 B1 C0 C1 C2; do
-  python evaluate_qa.py \
-    --input results/longmemeval-${cell}.jsonl \
-    --output results/longmemeval-${cell}-scored.json
-done
-```
-
-Each scored file has correctness %.
-
-### Commits
-
-```
-1. feat(scripts): Phase 1 LongMemEval wrapper with smoke gating + cost cap
-   - NEW: scripts/run-shootout-phase1.sh
-
-2. data(phase1): 7 LongMemEval cells (raw hypothesis JSONL)
-   - results/longmemeval-{A0,A1,B0,B1,C0,C1,C2}.jsonl
-   - results/phase1-run-log.txt (run timing + cost ledger)
-
-3. data(phase1): evaluate_qa.py scoring results
-   - results/longmemeval-{cell}-scored.json × 7
-```
-
-### Verify
-- Each `longmemeval-{cell}.jsonl` has exactly 500 lines
-- Each `hypothesis` field is non-empty AND is actual answer text (NOT retrieval text)
-- Each `scored.json` has a `correctness_score` field
-
-### Deliverable
-- 7 scored LongMemEval receipts committed to PR β
-- Real cost ledger committed alongside (compare against estimate)
-
-### Hand-off to Session 5
-- Phase 1 done. Phase 2 (BrainBench, ~3.5h) and writeup remaining.
-
----
-
-## Session 5 — PR β Phase 2 + writeup + ship
-
-**Repo:** Same gbrain-evals branch
-**Wallclock:** ~7h (3.5h BrainBench + 3h writeup + /ship)
-**API spend:** ~$56 (BrainBench is cheap)
-
-### What this session ships into PR β
-- 7 BrainBench cells (relational corpus + Cat 13 subset)
-- Final comparison writeup
-- PR β merged
-
-### Prereqs
-- Session 4 done. PR β has Phase 1 receipts.
-
-### Phase 2 wrapper script
-```
-NEW: scripts/run-shootout-phase2.sh
-- Per cell: configure provider (same as Phase 1)
-- Per cell: bun run eval:run -- --N 10 --include-subset cat13-embedder
-  --output docs/benchmarks/2026-05-22-{cell}.md
-- Cost-cap check
-```
-
-### Run
-```bash
-bash scripts/run-shootout-phase2.sh 2>&1 | tee results/phase2-run-log.txt
-```
-
-### Writeup
-`docs/benchmarks/2026-05-22-embedder-shootout.md`. Structure:
-
-1. **Headline table** — 7 cells × {LongMemEval correctness %, BrainBench relational MRR + P@5, Cat 13 correctness %, total cost}
-2. **Two questions answered:**
-   - Which embedder wins solo? (A0 vs B0 vs C0)
-   - Does zerank-2 carry ZE's win? (C0 vs C1 vs A1 vs B1)
-   - Bonus: does dim matter for ZE? (C1 vs C2)
-3. **Paired-bootstrap p-values** per headline pair (methodology in
-   `gbrain/docs/eval/SEARCH_MODE_METHODOLOGY.md`)
-4. **HNSW footnote** — Voyage 2048 and ZE 2560 used exact vector scan; OpenAI 1536
-   and ZE 1280 used HNSW. Quality is primary, latency is secondary
-5. **What this does NOT prove** — synthetic-only, tokenmax-only, no real-brain replay
-6. **Recommendation:** explicit NON-recommendation to change `gbrain init` default;
-   defer to a v0.36.x evidence pass with real-brain replay data
-
-### Commits
-
-```
-1. feat(scripts): Phase 2 BrainBench wrapper
-   - NEW: scripts/run-shootout-phase2.sh
-
-2. data(phase2): 7 BrainBench cells
-   - docs/benchmarks/2026-05-22-{cell}.md × 7
-
-3. docs(benchmark): embedder shootout comparison writeup
-   - NEW: docs/benchmarks/2026-05-22-embedder-shootout.md
-   - Bootstrap p-values, HNSW footnote, NOT-in-scope section
-```
-
-### Ship
-```bash
-# Merge PR β to gbrain-evals main
-gh pr merge --squash --auto
-# Or non-auto if reviewing one more time:
-gh pr merge --squash
-```
-
-### Deliverable
-- PR β merged to gbrain-evals `main`
-- Comparison report public at
-  `gbrain-evals/docs/benchmarks/2026-05-22-embedder-shootout.md`
-
-### Hand-off to Session 6 (optional)
-- gbrain-evals master has the full data + writeup
-- Ready for a v0.35.2.0 gbrain release that cross-links it
-
----
-
-## Session 6 (optional) — PR γ: gbrain v0.35.2.0 release
-
-**Repo:** `/Users/garrytan/conductor/workspaces/gbrain/<NEW-WORKSPACE>` (fresh from master)
-**Branch:** `garrytan/v0.35.2.0-benchmark-release`
-**Wallclock:** ~30min
-**API spend:** $0
-
-### What this session ships
-A release-notes-only PR that bumps gbrain to v0.35.2.0 with a CHANGELOG entry
-cross-linking the embedder shootout benchmark. Optional — could be folded into the
-next routine release if no rush.
-
-### Prereqs
-- Session 5 done. gbrain-evals merged with the comparison writeup.
-
-### Commits
-
-```
-1. docs(benchmark): mirror embedder shootout summary
-   - NEW: docs/benchmarks/2026-05-22-embedder-shootout.md (slim mirror)
-   - Cross-link to gbrain-evals canonical version
-
-2. chore: v0.35.2.0
-   - VERSION: 0.35.2.0
-   - package.json: 0.35.2.0
-   - CHANGELOG.md: new entry with the GStack-voice release summary
-     + "numbers that matter" table from the benchmark
-```
-
-### Ship
-```bash
-/ship
-```
-
-### Deliverable
-- gbrain v0.35.2.0 on master
-- CHANGELOG entry that drives the release-note headline
-
----
-
-## Cost ledger (revised, post-review)
-
-| Component | Per cell | × 7 cells |
-|---|---|---|
-| LongMemEval embed | <$0.05 | <$0.35 |
-| LongMemEval Sonnet answer-gen (500q × 2K tokens × $3/M) | $18 | $126 |
-| LongMemEval gpt-4o judge (500q × $0.10/q) | $50 | $350 |
-| BrainBench relational embed | $0.05-0.18 | <$1 |
-| BrainBench Cat 13 answer-gen + judge (50q × $0.14) | $7 | $49 |
-| Smoke harness (30 calls/cell) | <$0.10 | <$1 |
-| **Total** | **~$75/cell** | **~$525** |
-
-**Hard cap: $700.** Per-cell hard cap: $90 (wrapper aborts cell if exceeded; partial
-JSONL preserved for resume).
-
-## Failure modes and recovery
-
-| Failure | Recovery |
-|---|---|
-| Voyage/ZE 429 rate-limit mid-cell | `gateway._shrinkState` halves safety_factor and retries. Cell continues. |
-| ZE 5MB rerank payload cap hit | `applyReranker` fail-opens, returns un-reranked results. Stderr warn. |
-| Mid-cell OS interrupt / cost-cap abort | Re-run with `gbrain eval longmemeval --resume-from results/longmemeval-{cell}.jsonl`. Picks up where it left off. |
-| `evaluate_qa.py` auth fail | OPENAI_API_KEY check in wrapper aborts before any spend. |
-| Adapter typo (bad dim) | `EvalAdapterConfig` runtime assertion at constructor throws AIConfigError. Cell aborts before API call. |
-
-## NOT in scope (deliberate)
-
-- **Real `~/.gbrain` replay** — adds 6-12h wallclock + $40-80 embed. Filed as v0.36.x.
-- **All 3 search modes** — pinned to tokenmax. `conservative` + `balanced` are v0.35.3.0
-  follow-ups if reviewers push back.
-- **Matched-dim cross-vendor row** — no shared dim exists across all 3 vendors.
-  Permanently out.
-- **`gbrain eval whoknows` / `cross-modal` / `takes-quality`** — embedding-invariant;
-  rerunning across embedders produces noise.
-- **`gbrain eval code-retrieval`** — code corpus, separate concern.
-- **`gbrain eval suspected-contradictions`** — wants a real brain.
-- **`gbrain init --recommended` default change** — codex correctly flagged the evidence
-  base as insufficient. Defer to v0.36.x with real-brain replay data.
-
-## What already exists (reused, not rebuilt)
-
-- `gbrain eval longmemeval` CLI (in-tree, answer-gen mode default)
-- gbrain-evals BrainBench runner (`eval:run`) — needs adapter parameterization but
-  per-cell test plumbing is reused
-- Gateway routing for Voyage + ZE (shipped v0.35.0.0)
-- Reranker pipeline (`src/core/search/rerank.ts`, fail-open)
-- Pricing table (extended, not rebuilt)
-- Paired-bootstrap methodology (`docs/eval/SEARCH_MODE_METHODOLOGY.md`)
-- LongMemEval published `evaluate_qa.py` (invoked externally, not bundled)

docs/designs/COMMUNITY_IDEAS.md

@@ -1,367 +0,0 @@
-# Community Ideas Ledger
-
-> A diary of the **valuable ideas** surfaced by the community-PR wave, kept so that
-> good thinking survives even when the PR that carried it is closed. gbrain moves
-> fast and the maintainer's "cathedral" rewrites supersede most individual PRs —
-> but the *idea* behind a closed PR is often still worth something.
->
-> **Bar for this file:** an idea only earns a line if it is (a) still live on
-> master and (b) genuinely valuable to gbrain users. **Graduating an idea to
-> `TODOS.md` is a higher bar still** — it must serve the North Star (next-Postgres-
-> for-memory: widest coverage, best-for-the-most-at-the-least) and be worth a
-> maintainer-owned implementation. Most lines here will never graduate. That's fine.
->
-> Status legend: **OPEN** = PR still open as a real merge candidate · **CLOSED** =
-> PR closed, idea captured here · **HELD** = strategic, awaiting maintainer call.
-> Provenance is credited to the contributor; scrub real private-network names per
-> the repo privacy rule when anything here graduates to a public artifact.
-
-_Generated from a full triage of the open-PR backlog (436 community PRs), 2026-06-07._
-
----
-
-## 1. Internationalization — non-English brains are second-class
-
-The single biggest coverage gap for "serve a billion people." Several independent
-contributors hit the same walls.
-
-- **Configurable FTS language** (#580/#581/#582, @rafaelreis-r) — **OPEN, high.**
-  Every `to_tsvector`/`tsquery` is hardcoded `'english'` (query side, trigger side,
-  and no reindex path), so non-English brains run every search through the English
-  stemmer. A coherent 3-PR set: `GBRAIN_FTS_LANGUAGE` config → migration recreating
-  triggers with the chosen language → `gbrain reindex-search-vector` to change it
-  post-install. **Strongest i18n candidate to graduate.**
-- **Full-Unicode slugs** (#782, @tamagodo-fu; #514 zh, @JimmyJiang67) — **HELD, high.**
-  CJK slugs already work (`CJK_SLUG_CHARS`); generalize to all scripts (Cyrillic,
-  Devanagari, Hangul, …) and widen the remaining ASCII-only validators so non-ASCII
-  slugs flow end-to-end instead of being generated then rejected. #514 also carries a
-  corpus-driven `relationships-zh.json` verb dictionary for `inferLinkType` — a
-  reusable artifact for Chinese relationship typing.
-- **CJK entity extraction** (#1637, @alkalide) — **OPEN, high.** Mention extraction is
-  ASCII-only (`TOKEN_RE`, `MIN_NAME_LENGTH=4`), so 2–3 char Chinese/Japanese/Korean
-  names are invisible to the gazetteer (there's an in-code TODO acknowledging it).
-  CJK detection + lower min-length + single-token pure-CJK titles + substring pass.
-
-## 2. Reliability — the daily-driver failure modes
-
-Recurring, production-observed failures. Many are tiny fixes with outsized impact;
-these are the densest source of real bugs in the whole backlog.
-
-- **Embedding egress waste** (#347/#460, @notjbg) — **OPEN, high.** `getChunks` does
-  `SELECT cc.*`, shipping the ~6KB pgvector embedding that `rowToChunk` immediately
-  discards — ~19–22 GB/day egress on a busy Supabase brain. Enumerate the columns;
-  add a CI guard. (#460 dup of #347.)
-- **Body-keyed embedding reuse** (#1424, @defenestrate2) — **OPEN, high.** Markdown
-  import re-embeds byte-identical chunks that merely shifted position, turning a
-  cosmetic edit into ~99K wasted re-embeds. Reuse by chunk-text hash like the code
-  path already does; add `--force` + a no-hash sentinel.
-- **`embed --stale` full re-pull** (#775, @kyledeanjackson) — **CLOSED (partial on
-  master), high.** Re-pulled all chunks every cycle (~3TB/mo egress); steady-state
-  brains should do near-zero work. Master added a `countStaleChunks` early-exit;
-  verify it fully closes this.
-- **Config round-trip storm** (#1694, @Omerbahari) — **OPEN, high.** A single query
-  fires ~85 serial single-key config `SELECT`s — invisible on PGLite, ~85 network
-  RTTs on a remote pooler. Batch + cache `getConfig` (`getConfigMany`).
-- **cgroup-aware worker sizing** (#1244, @tyler3k1) — **OPEN, high.** `defaultWorkers()`
-  sizes from `os.totalmem()` (host RAM), so containerized installs (Railway/Fly/Render/
-  Cloud Run/ECS) oversize the pool and get OOM-killed mid-import. Use
-  `process.constrainedMemory()`.
-- **Linux memory-pressure throttle** (#556, @chengzehsu) — **OPEN, high.** `os.freemem()`
-  is `MemFree` (excludes reclaimable cache), so healthy containers reject every batch
-  job. Read `MemAvailable` from `/proc/meminfo`.
-- **propose_takes never caches empties** (#1218 @AdityaRajeshGadgil / #1760 @notjbg) —
-  **OPEN, high.** A valid `[]` extractor result writes no cache row, so unchanged pages
-  re-spend extractor tokens every ~5min cycle (57,885 calls/11 days observed). Sentinel
-  row keyed on `(source_id, page_slug, content_hash, prompt_version)`.
-- **Prompt-cache opt-in on hot paths** (#1761, @notjbg) — **OPEN, high.** Only ~4.9% of
-  input tokens hit the Anthropic prompt cache because the highest-volume cycle/extraction
-  call sites don't set `cacheSystem:true` despite gateway support. One-line opt-ins.
-- **Autopilot reliability cluster** (#232 @ianderse, #464/#465 @notjbg, #289 @RyanAlberts,
-  #477 @vinsew, #1935/#1936 @mdcruz88, #1906/#1891 @rayers/@jalagrange) — **OPEN, high.**
-  A family of distinct live bugs: argless `engine.connect()` wipes saved config and
-  crash-loops under launchd; `cwd=/` wrappers miss `brain/.env`; mtime-only lock probing
-  blocks respawn for 10min after OOM; no backoff on the 5-failure suicide cap;
-  disconnect-before-connect `reconnect()` bricks the engine on a transient blip; config
-  accessors lack the retry wrapper. **Pick the best fix per layer and land as a wave.**
-- **lint `--fix` corrupts mid-doc fences** (#1417 @trinh-macbook, #1597 @chungty) —
-  **OPEN, high.** Detector/fixer regex disagree, so `lint --fix` strips the closing fence
-  of mid-document ```` ```markdown ```` blocks and autopilot re-corrupts the page every
-  cycle. Only unwrap whole-page fences.
-- **backlinks worker defaults to `fix`** (#1853 @choomz; #1027 @sliday; #495 @23salus) —
-  **OPEN, high.** Empty-payload backlinks jobs default to `action='fix'`, silently
-  rewriting tracked markdown ("Referenced in" bullets) on every sync→embed→backlinks
-  chain (129 files/day in the wild). Default to `check`; require explicit opt-in. Also
-  fixes a duplicate-line accumulation bug.
-- **`DATABASE_URL` hijack** (#1884, @awilkinson) — **OPEN, high.** A co-located app's
-  generic `DATABASE_URL` silently overrides the configured brain (wrong DB, or
-  auto-migrates it). Fix precedence: `GBRAIN_DATABASE_URL` > config.json > `DATABASE_URL`.
-- **Engine-switch strips config** (#1088, @samchaudhary) — **OPEN, high.** `migrate --to`
-  rewrites config to just `{engine,url}`, dropping `embedding_model`/`dimensions`/keys;
-  migration "succeeds" but new embeds break.
-- **Re-init silently corrupts the brain** (#1060, @vincedk-alt) — **OPEN, high.** Flag-less
-  re-init ignores persisted `embedding_model`/`dimensions` and writes a wrong-shape
-  OpenAI-1536 brain before the dim-check catches it.
-- **IPv6-only direct URL** (#1006, @diazMelgarejo) — **OPEN, high.** `deriveDirectUrl`
-  turns a Session-Pooler URL into an IPv6-only host, ECONNREFUSED on IPv4-only networks
-  (the majority). Return null for pooler URLs.
-- **HOME-isolation in tests** (#205/#517/#534 @orendi84, #434 @lloydarmbrust) — **OPEN,
-  high.** The E2E suite spawns `gbrain init/import` against the developer's real
-  `~/.gbrain/config.json`, clobbering their live DB URL+keys. Isolate HOME to a tmpdir.
-  *(A footgun that bites contributors of this very repo.)*
-- **dim-aware embed write target** (#1263, @DmitryBMsk) — **OPEN, high.** `upsertChunks`
-  always writes the legacy `embedding vector(1536)` column, so brains on an alternate
-  column (`embedding_ze halfvec(2560)`) fail with dim-mismatch on every write.
-- **Oversized chunks silently unembedded** (#1675, @lubos-buracinsky) — **OPEN, high.**
-  The code chunker emits giant literals/template strings whole; the embedder rejects
-  them and they vanish from semantic search. Cap chunk size so they stay embeddable.
-- **Token-vs-char truncation** (#557 @chengzehsu, #990 @mgunnin, #1180 @kkroo,
-  #1281 @mmekkaoui, #1947 @100menotu001) — **OPEN, high.** The embed path truncates by
-  chars (`MAX_CHARS`) not tokens, so dense pages still exceed the 8192/300K-token ceiling
-  and loop forever on HTTP 400 with `embedded_at` never cleared; `isTokenLimitError`
-  misses OpenAI's real error string; llama-server's 32-input limit isn't capped; and
-  `--catch-up`'s unbounded budget overflows the 32-bit `setTimeout` and aborts after one
-  batch. A "make embedding backfills never silently wedge" cluster.
-
-## 3. Search & retrieval quality
-
-- **Keyword search ignores page titles** (#1646, @jeades) — **OPEN, high.** `searchKeyword`
-  ranks only chunk `search_vector`, never `pages.search_vector` (weight-A titles), so an
-  exact-title `gbrain search` returns nothing while `query` finds it. High-impact, tiny.
-- **`code-def` misses most OO symbols** (#1628, @rayers) — **OPEN, high.** `DEF_TYPES`
-  omits method/constructor/field/struct/protocol, so `code-def` returns 0 for most
-  object-oriented code. Root-cause fix in `normalizeSymbolType` + `DEF_TYPES`.
-  (Prefer over #1701's fallback-only approach.)
-- **doc-comment column is wired but dead** (#520, @Evode-Manirahari) — **OPEN, high.** FTS
-  weights `content_chunks.doc_comment` above chunk text but the column is never populated.
-  Extract JSDoc/docstrings per symbol via AST and thread through import.
-- **autocut weak-top collapse** (#1863, @rayers) — **OPEN, high.** The fresh autocut
-  feature (#1682) normalizes the rerank gap by the top score, so a weak top (0.317→1.0)
-  looks like a confident cliff and rare cross-source queries collapse to 1 result. Add a
-  `minTopScore` floor.
-- **Graph-hop wikilink rerank** (#717, @gwanghoon91) — **HELD, high.** Zero-token
-  score-shapers (graph-hop wikilink rerank + query-token disambiguation) claimed
-  +2.6/+2.8pt P@5/R@5 on BrainBench. Worth re-evaluating against the new retrieval
-  cathedral's ranker rather than merging the old diff.
-- **Effective-date time filters** (#1706, @mvanhorn) — **OPEN, med.** `since`/`until`
-  filter on `updated_at`, so content dated to the past but edited recently is mis-filtered;
-  filter on `COALESCE(effective_date, updated_at, created_at)`.
-
-## 4. Extraction & the knowledge graph
-
-- **Obsidian wikilink → typed graph edges** (#87 @franmaranchello; alias/title/basename
-  fallback #1188 @rwbaker) — **OPEN/HELD, high.** `[[wikilinks]]`/`![[embeds]]` are
-  invisible to the graph. Materialize them as typed edges with alias (frontmatter
-  `aliases:`), first-H1-title, and basename fallback resolution (path-equality-only gives
-  ~5.5% edge recall on real vaults). Master shipped global-basename (#1388); the alias/
-  title fallbacks are the still-novel part.
-- **Schema-pack-aware link extraction** (#1547, @billy-armstrong) — **OPEN, high.** The
-  link extractor's `DIR_PATTERN` is a frozen 16-prefix const that ignores pack-declared
-  `path_prefixes`, so default-pack installs silently lose wikilinks to `person/`,
-  `writing/`, `wiki/*`. Resolve prefixes from the active pack.
-- **DB-source extraction** (#1539, @afshaker) — **OPEN, high.** The cycle's extract phase
-  only walks the filesystem, so DB-resident pages (imported transcripts, remote-DB brains)
-  never get links/timeline and `brain_score` is capped. Thread `source:'db'`.
-- **source_id threaded through fs-walk extract** (#1719, @seungsu-kr) — **OPEN, high.**
-  fs-walk extractors omit `source_id`, defaulting to `'default'`, so the `pages` INNER JOIN
-  drops every row on non-default-source brains — silent 0 inserted.
-- **extract `--stale` permanent-lag loop** (#1791, @Nazim22) — **OPEN, high.** Pages last
-  edited before the link-extractor version bump get stamped below the version threshold and
-  re-flag every run (~97% pages permanently "stale"). Stamp `GREATEST(updated_at, versionTs)`.
-- **Plain-text NER for auto-link** (#1565, @donogeme) — **HELD, med.** Plain mentions of
-  people (no `[[wikilink]]`) never become edges. The opt-in idea is right; the shipped
-  implementation (capitalized-bigram regex, Western-names-only) is too crude — needs a
-  real NER pass to clear the graph-integrity bar.
-
-## 5. Providers & the gateway
-
-The AI-gateway + recipes + `user_provided_models` system already absorbed ~40
-per-vendor embedding PRs (Ollama, Gemini, Azure, DashScope, DeepSeek, Zhipu, E5,
-bge-m3, Copilot, Composio, Kimi, LM Studio, Mistral, Hunyuan, MiniMax…). The
-*residue* worth keeping:
-
-- **litellm proxy unusable for chat** (#1953 @miroslavb, #1938 @BKF-Gitty) — **OPEN, high.**
-  The `litellm-proxy` recipe declares only an embedding touchpoint (no chat), so
-  `chat_model=litellm:*` fails validation and `think` degrades to a misleading "set
-  ANTHROPIC_API_KEY"; and `build-gateway-config` never folds `litellm/openrouter/together`
-  keys, so configured proxy auth goes out unauthenticated. Plus user-provided custom-dim
-  embeddings are double-false-rejected in preflight. **The general-OpenAI-compat-proxy
-  story.**
-- **Matryoshka dims threading** (#1072 @mgandal, #1240 @mike7seven) — **OPEN, high.**
-  Qwen3-Embedding returns its native dim (2560/4096) not the requested one because
-  `dimensions:N` isn't threaded for the openai-compat path, hard-failing a 1536-dim brain.
-- **"Freeze provider at init, clear vectors on dim change"** (#100/#172, @niallobrien/
-  @nbzy1995) — **CLOSED, med.** A safety insight worth keeping even though the provider
-  PRs are superseded: persist+freeze the brain's provider/dim at init so a later env change
-  can't silently corrupt the vector space; clear stale embeddings on an intentional change.
-- **China-region provider coverage** (#59 @Magicray1217, #1071 @AzeWZ) — **CLOSED, med.**
-  Make DashScope/DeepSeek/Zhipu first-class recipes that honor `provider_base_urls` (the
-  China-region endpoints) and provider batch limits — on-mission for global coverage.
-- **Amazon Bedrock native** (#1826, @naterchrdsn) / **Jina asymmetric retrieval**
-  (#1930, @Whamp) — **HELD, high/med.** The maintainer pattern prefers the universal
-  litellm-proxy over per-vendor native recipes, but Bedrock (AWS IAM credential chain) and
-  Jina's asymmetric `input_type=document|query` are distinct enough to warrant a call.
-- **Local-first chat parity** (#1854/#1855/#1858 @starm2010, #1423 @pabloglzg,
-  #1618 @punksterlabs) — **OPEN, high.** `FREE_LOCAL_CHAT_PROVIDERS` doesn't exist (only
-  embed), brainstorm/cycle/takes hardcode `anthropic:claude-sonnet-4-6`, and the
-  openai-compat `generateObject` path silently fails on providers that reject
-  `json_schema`. The "run gbrain fully local" cluster.
-- **OpenRouter config key** (#1714 @tmchow), **OAuth bearer for AI providers**
-  (#1312 @pabloglzg), **API-key files** (#570 @shawnduggan) — **OPEN, med.** Credential
-  ergonomics: config-file key (not just env), externally-minted bearer tokens, and
-  `OPENAI_API_KEY_FILE` so OAuth harnesses don't inherit a raw key in `process.env`.
-
-## 6. Auth, federation & access control (security-adjacent)
-
-These cluster into a real theme: **runtime access control for remote/multi-tenant MCP
-beyond prompt discipline.** Several are live security gaps (see the security list in the
-triage report) and should be treated as a coordinated design, not piecemeal merges.
-
-- **Clamp remote source overrides** (#1372, @jlfetter1) — **OPEN, high, SECURITY.** A
-  remote MCP caller can pass `source_id` (or `__all__`) to `query`/`get_page` to read
-  sources outside their OAuth `allowedSources` — the param bypasses `sourceScopeOpts`
-  (CWE-285). Clamp to token claims, fail-closed. **#1394 (get_page source_id) must land
-  *with* this clamp, not before it.**
-- **Read-side prefix/federation enforcement** (#1860 @choomz, #1790 @colin-atlas,
-  #470 @AdityaRajeshGadgil, #1508 @tim404x) — **OPEN, high.** `bound_slug_prefixes` is
-  enforced on write but not read; exact `get_page` uses scalar `ctx.sourceId` while fuzzy
-  uses the federation ladder; unqualified search can scan isolated `--no-federated` sources.
-  Unify on one fail-closed visibility predicate across every read surface.
-- **Per-OIDC-user access tiers** (#789, @0x471) — **HELD, high, SECURITY.** Map verified
-  OIDC end-users to `oauth_clients.access_tier` dispatch gates + shape filters — real
-  runtime access control. Pairs with multi-agent MCP hardening (#1316, @chipoto69, HELD).
-- **Federated-read management CLI + admin UI** (#1592/#1601 @bitak1, #1558 @flamerged) —
-  **OPEN, high.** No CLI/UI to inspect or change a client's `federated_read` scope (raw
-  SQL only today). Atomic `array_append`/`array_remove` SQL to avoid read-modify-write
-  races, plus an admin Sources tab.
-- **Pre-registration flow flags** (#894, @panda850819) — **OPEN, high, SECURITY.**
-  `register-client` hardcodes `redirect_uris=[]`, making the SECURITY.md-recommended
-  pre-registration (DCR-off) flow unusable for Claude.ai/ChatGPT connectors.
-- **RFC 9728 `resource_metadata`** (#1410, @rayers) — **OPEN, high.** HTTP MCP 401s omit
-  the `resource_metadata` param the MCP auth spec + RFC 9728 require, so claude.ai/Cursor
-  can't discover the auth server and never start OAuth.
-- **Server-enforced memory groups** (#1497, @oldmate99) — **HELD, med.** Audience-based
-  read/write via `memory_groups` + client-to-group assignment — strategic for hosted
-  multi-tenant, but overlaps the existing source-isolation model; a design call.
-
-## 7. Security hardening (must not be lost)
-
-- **Command injection in transcription** (#245, @aliceagent) — **OPEN, high, SECURITY.**
-  `transcription.ts` shell-interpolates an agent-controlled `audioPath` into `execSync`
-  ffprobe/ffmpeg/`rm -rf`. **Confirmed still present on master.** Switch to
-  `execFileSync` arg arrays + `fs.rmSync`.
-- **Dotfile / skills-dir confinement** (#418/#419, @garagon) — **OPEN, high, SECURITY.**
-  `.gbrain-source` walk-up trusts any ancestor dotfile (source hijack on shared hosts);
-  `resolveWorkspaceSkillsDir` never canonicalizes (symlink escape). `lstat` ownership/
-  symlink/world-writable checks + realpath containment.
-- **Destructive reclone gate** (#1705, @mvanhorn) — **OPEN, high, SECURITY.**
-  `recloneIfMissing` does `rm`+rename over `src.local_path` without verifying it's
-  gbrain-managed, so a re-pointed source can wipe a user's working tree. Gate behind
-  `isManagedRecloneTarget()` + reject `..`. *(The maintainer's own #1960 is the canonical
-  landing for this class — cross-check.)*
-- **CORS preflight asymmetry** (#983, @yashkot007) — **OPEN, high, SECURITY.** Preflight
-  returns the full method/header surface unconditionally while the actual-request path
-  gates on the allowlist — leaks allowed surface to non-allowlisted origins.
-- **jsonb double-encode corruption** (#1584 @warkcod, #597 @vinsew) — **OPEN, high,
-  SECURITY/integrity.** Source-config and subagent writers `JSON.stringify` into a
-  `::jsonb` cast — the exact postgres.js trap CLAUDE.md forbids; corrupts source config
-  (freshness/autopilot) and breaks dream synthesize slug-collection on real Postgres.
-
-## 8. Developer experience & platform reach
-
-- **Windows / CRLF portability** (#1294 @xwang4-svg, #1149 @samporter-31, #1554 @Sanjays2402,
-  #1396 @xuezhaolan) — **OPEN, high.** CRLF breaks frontmatter + skill-trigger parsing
-  (CI is Ubuntu-only so it never surfaces), `/dev/stdin` doesn't exist, a POSIX postinstall
-  one-liner hard-fails `bun install`, backslash bundle keys. A coordinated "first-class
-  Windows" pass. *(A working Windows binary + CI target #180/#181 is the prerequisite for
-  the full story.)*
-- **`.gbrainignore` / per-repo exclusion** (#1483 @eepaul; repo-local code filters
-  #1011 @AndrewLauder; `--respect-gitignore` #1159 @jetsetterfl) — **OPEN, high.** Sync
-  indexes every file with no ignore mechanism (`data/`, `*.parquet`, fixtures, vendored
-  trees), bloating DB + embedding cost. gitignore-parity `.gbrainignore` + per-source
-  `excludePatterns`. *(See also the maintainer's walker-prune work; #1942 prunes
-  vendor/dist/build.)*
-- **Monorepo sub-path sources** (#774, @jeremyknows) — **HELD, high.** `--src-subpath`
-  (split repo into git-root + logical-source axes) + `--exclude` so one repo can hold N
-  sources at subdirs.
-- **MCP tool filtering** (#747, @joelwp) — **OPEN, high.** MCP advertises all ~51 ops to
-  every consumer (~10K tokens of schemas, tool confusion); `GBRAIN_EXPOSED_TOOLS` filters
-  the advertised surface.
-- **Install-method detection for upgrade** (#538, @brucek) — **OPEN, high.** The README's
-  own recommended git-clone+bun-link install detects as `unknown`, so `gbrain upgrade`
-  offers three dead ends including a wrong npm package.
-- **Runtime subagent defs** (#1282, @dcarolan1) — **OPEN, high.** The plugin loader
-  validates `SubagentDefinition[]` at startup but the handler never reads
-  `data.subagent_def`, so the persisted field is dead at runtime — callers must re-embed
-  the full system body in every job.
-- **macOS Tahoe PGLite workaround** (#1671, @roysaurav) — **HELD, med.** PGLite's WASM
-  engine crashes on macOS 26 (Apple Silicon); document the native Homebrew Postgres+pgvector
-  fallback. Reader-valuable until the WASM crash is fixed upstream.
-
-## 9. Capabilities & integrations (strategic — maintainer call)
-
-These are net-new surfaces held for a product decision, not auto-closed.
-
-- **Alternative engines** — SQLite/`bun:sqlite`+FTS5 single-file backend (#291, @mvanhorn)
-  and Neo4j GraphBrain REST backend (#594, @pkyanam). Both conflict with the two-engine
-  lockstep invariant and the Postgres-for-memory North Star, but the *zero-WASM single-file*
-  install story (SQLite) is strategically interesting. **HELD.**
-- **Page versioning / soft-delete / read audit** (#573, @cropsgg) — **HELD, high.** Snapshots
-  with provenance, soft-delete tombstones + hard purge, read-path audit treating edits as
-  derivative works. Ambitious cathedral-scope; maintainer-owned territory.
-- **Configurable embedding dimension** (#1051, @vincedk-alt) — **HELD, high.** `schema.sql`
-  hardcodes `vector(1536)`; read `embedding_dimensions` from config (default 1536). The
-  canonical fix that dozens of local-provider PRs hack around. *(Pairs with #1263.)*
-- **Transcribe skill** (#1449, @RyanAlberts) — **OPEN, high.** Implements the empty
-  video/audio branch of `media-ingest` (YouTube captions fast path + yt-dlp/whisper
-  fallback), $0 by default. A genuine capability gap.
-- **iPhone backup importer** (#1733, @H4RR1SON) — **HELD, med.** Local-CLI-only importer
-  for decrypted iPhone backups (contacts→person pages, iMessage→conversation pages); zero
-  network, thin-client refused.
-- **Compounding dream phase** (#509, @durang) — **HELD, high.** An LLM "7th phase" that
-  *creates* structure (orphan-mention people, knowledge gaps, concept-dup at cosine>0.92,
-  decay, incomplete pages) vs the deterministic phases. Overlaps `enrich --thin`.
-- **Codex-OAuth for dream** (#977, @barronlroth) / **dream gateway + `migrate-embedding-dim`**
-  (#1013, @cxbitz) — **HELD, high.** OAuth-backed chat for synthesis; a command to resize
-  the vector schema + clear incompatible embeddings.
-- **Voice-extraction skill** (#300, @harjclaw) — **CLOSED, med.** Mine the user's outbound-
-  email corpus already in the brain to build a queryable writing-voice profile so agents
-  draft in the user's voice. Overlaps soul-audit.
-- **MCP put_page parity + DB→markdown reconciliation** (#438, @rayzhux) — **HELD, high.**
-  A frontmatter-only safe auto-link mode for remote callers + `GBRAIN_BRAIN_ROOT` to render
-  remote writes back to markdown so MCP writes reach the git source-of-truth. Touches the
-  remote trust boundary — a design proposal, not a merge.
-- **Recipe discovery convention** (#1279, @ialmeida-jera) — **OPEN, med.** `~/.gbrain/recipes/`
-  auto-discovery + `--external-dir`, loaded untrusted to keep the command-spawn boundary.
-- **Destructive-op audit trail + audit-factory** (#1069/#1070, @vincedk-alt) — **HELD, med.**
-  Rotating JSONL forensic trail for hard-deletes + a shared `createAuditLogger` factory.
-
-## 10. Doctor & brain-health observability
-
-- **Queue dead-job visibility** (#1185, @ethanbeard) — **OPEN, high.** A collector can
-  heartbeat green while all its jobs die in the worker (3561 dead in the wild) and doctor
-  has zero view into the minions queue. Add a cross-cutting `[queue]` dead-jobs check.
-- **Orphan-metric alignment** (#1107 @colin477, #915 @xaviroblessarries, #1202 @rwbaker) —
-  **OPEN, high.** `get_health` counts ingestion-by-design (`daily/`, briefings), soft-deleted,
-  and hub pages as orphans, distorting `brain_score`; CLI `find_orphans` uses a *different*
-  predicate than `getHealth`. Unify on one islanded predicate with sensible exclusions.
-- **doctor check-name registry drift** (#1839, @mvanhorn) — **OPEN, med.** Several emitted
-  checks aren't registered in `doctor-categories`, printing `unknown check name` every run;
-  the drift guard only scanned `doctor.ts`, missing `onboard/checks.ts` emitters.
-- **Honest stale-lock hint** (#1553, @Sanjays2402) — **OPEN, med.** doctor always says
-  `gbrain sync --break-lock`, which silently no-ops on `gbrain-cycle` locks.
-
----
-
-## Cross-cutting observations for the maintainer
-
-- **The same bug was filed many times.** `extract_facts.entity_hints` missing an `items`
-  schema came in ≥5 times (#812/#832/#847/#863/…, already fixed); the Postgres-singleton
-  disconnect class a dozen+ times; sync no-op freshness, slug-casing, and the embedding-
-  preflight false-reject each 5–15 times. A short "already fixed / known" note in the
-  release notes or a CONTRIBUTING "before you file" list would cut the re-file rate.
-- **The recipe system is working as a pressure valve** — it correctly absorbed ~40 vendor
-  PRs into config rather than code. The remaining provider asks are about *capabilities*
-  the recipe schema doesn't yet express (asymmetric `input_type`, Matryoshka dims, per-item
-  RPM caps, alternative credential groups), not new vendors.
-- **i18n (§1) and local-first chat (§5) are the two biggest "serve a billion" coverage
-  gaps** the community is repeatedly hitting and the best candidates to graduate to TODOs.

docs/designs/V038_SCHEMA_PACKS.md

@@ -1,221 +0,0 @@
----
-status: ACTIVE
----
-# CEO Plan: v0.38 Schema Packs — Bring Your Own Shape
-
-Generated by /plan-ceo-review on 2026-05-19
-Branch: garrytan/houston-v1 | Mode: EXPANSION
-Repo: garrytan/gbrain
-
-## Definitions (terms used throughout)
-
-- **Primitive** — a named bundle of (default link verbs, default
-  frontmatter fields, expert-routing flag, enrichment rubric slot).
-  Five built-in: `entity`, `media`, `temporal`, `annotation`,
-  `concept`. A pack type extends one primitive by name, inheriting
-  its defaults, then optionally overriding specific fields. Not a
-  table shape, not a schema in the SQL sense — a behavioral
-  template the engine consults at inference and search time.
-- **Alias closure** — for read paths, when a pack declares type
-  `researcher` aliases base type `person`, queries for `researcher`
-  expand the WHERE clause to `type IN ('researcher','person', + any
-  other type aliasing person)`. The closure is computed once at
-  pack load, cached on the pack object, and inlined into search
-  SQL. Aliasing is one-directional (researcher → person; querying
-  `person` does NOT surface `researcher` rows unless the inverse is
-  declared).
-- **Pack resolution chain (7 tiers)** — extends model-config's
-  6-tier pattern. Order: (1) per-call `schema_pack` opt, (2)
-  `GBRAIN_SCHEMA_PACK` env, (3) per-source `--source <id>` override
-  via DB config key `schema_pack:source:<id>`, (4) brain-wide DB
-  config key `schema_pack`, (5) `gbrain.yml schema:` section,
-  (6) `~/.gbrain/config.json schema_pack`, (7) default `gbrain-base`.
-  Tier 3 is the new tier introduced in v0.38; tiers 1, 2, 4-7
-  mirror existing patterns.
-
-## Vision
-
-### 10x Check
-The plan as accepted ships a self-EXPANDING engine, not just a
-self-describing one. The differences from the baseline plan:
-
-- The brain watches what you create and proposes schema refinements
-  you didn't think to ask for (`schema suggest`)
-- Schema is per-source (ISOLATED reads), so ~/git/brain and
-  ~/git/zion-brain hold different mental models in the same engine
-  without renames. Cross-source federated reads still see per-source
-  packs in isolation — a query joining results across mounts does
-  NOT compute a closure across both packs. Federation (closure
-  across mounts) is explicitly deferred to v0.39.
-- The pack is inspectable: ASCII graph, plain-English explanation,
-  consistency lint against actual content
-- First unknown-type write asks "Add to pack?" with a primitive
-  inference, instead of silently logging
-- Schema packs distribute as `.gbrain-schema` tarballs through the
-  v0.37 skillpack pipeline; skillpacks rename to `.gbrain-skillpack`
-  for symmetry. Community schema packs propagate the same way
-  community skillpacks do.
-
-### Platonic Ideal
-A new user clones gbrain and types `gbrain init`. Within 30 seconds
-gbrain has read their existing markdown anywhere on disk, proposed a
-schema matching their organic shape, asked 3-5 yes/no questions to
-refine, and the brain is live. They never author YAML unless they
-want to. They can publish their pack as a `.gbrain-schema` tarball
-for anyone to install and fork.
-
-The 12-month state: `gbrain init` runs `schema detect` automatically,
-proposes a primitive structure, and 90% of users never see the
-manifest format. The 10% who want to customize see a clean YAML they
-can edit. Community packs cover the long tail of domains.
-
-## Scope Decisions
-
-| # | Proposal | Effort | Decision | Reasoning |
-|---|----------|--------|----------|-----------|
-| 0C-bis | Approach C (Full Cathedral) | ~4 weeks | ACCEPTED | User explicitly chose the most ambitious of three approaches; ecosystem + engine in one ship |
-| D2 | Per-source schema packs | ~1 week | ACCEPTED | User owns two brains today; v0.34.1.0 source-isolation makes the seam architecturally clean |
-| D3 | `gbrain schema suggest` (LLM-powered) | ~3-5 days | ACCEPTED | Closes the gap from "what exists" to "what your brain implies"; bounded cost via sampling |
-| D4 | `schema graph` + `lint` + `explain` | ~2 days | ACCEPTED | Schema becomes legible and self-documenting; tiny effort, large UX delta |
-| D5 | Auto-prompt on first unknown type | ~1-2 days | ACCEPTED | TTY-gated + per-type silenceable; turns lenient-mode from fallback to feature |
-| D6-orig | `fork-from <brain-path>` (live-brain) | ~3-5 days | REJECTED | Privacy hazard (read access to whole repo); unclear value vs published tarballs |
-| D6-reframed | Skillpack tarball reuse + extension expansion | ~3-5 days | ACCEPTED | Schema packs ship as `.gbrain-schema`; skillpacks gain `.gbrain-skillpack` extension alongside existing `.tgz`; both ride v0.37 pipeline parameterized on manifest discriminator. Extension is the install-time type detector — lets validation route to the right manifest validator before extraction. |
-
-Total budget: **revised 9-11 weeks** (vs ~6.5-7 initial estimate;
-spec review surfaced LLM prompt-tuning loops for `schema suggest`,
-primitive-inference heuristics for auto-prompt, 7-tier × federated-
-read interaction edges, full rename-migration surface, and 400-600
-test cases at v0.36/v0.37 scope precedent). If budget pressure
-emerges, the safest cuts in order are: D5 auto-prompt (~2 days),
-D4 inspect triad (~2 days), reduce examples 7→3 (~3 days), defer
-suggest LLM polish to v0.38.1 (~1 week).
-
-## Accepted Scope (added to this plan)
-
-- **Engine layer:** gbrain-base universal starter pack; 5 composable
-  primitives (entity, media, temporal, annotation, concept); alias
-  closure for read paths; lenient-by-default with audit for write
-  paths; strict mode opt-in.
-- **Detect layer:** `gbrain schema detect` SQL-driven heuristic
-  clustering proposing a pack manifest matching brain shape.
-- **Suggest layer:** `gbrain schema suggest` LLM-powered refinement
-  via gateway.chat() over a bounded sample.
-- **Inspect layer:** `gbrain schema graph` (ASCII viz),
-  `gbrain schema lint` (consistency check), `gbrain schema explain
-  <type>` (plain English).
-- **Author layer:** `gbrain schema init/use/fork/edit/validate/
-  diff/review-candidates` CLI.
-- **Source layer:** per-source schema-pack resolution; pack
-  resolution gets a 7th tier (per-source override before per-brain);
-  `--source <id>` flag on every relevant command.
-- **Auto-prompt layer:** TTY-gated interrupt on first unknown-type
-  `put_page` with primitive inference; per-type "always silent"
-  escape hatch.
-- **Distribution layer:** `.gbrain-schema` tarball format; rename
-  skillpacks to `.gbrain-skillpack`; v0.37 skillpack pipeline
-  parameterized on artifact type (manifest discriminator drives
-  type-specific validation); both extensions accepted on install
-  for back-compat.
-- **Examples:** 7 example packs in-tree (minimal, person-first,
-  media-archive, temporal-archive, research-notebook, founder-ops,
-  personal-archive) explicitly framed as sketches not products.
-- **gbrain-base:** byte-for-byte reproduces today's hardcoded
-  behavior so existing brains see zero change after upgrade.
-- **Migrations:** v76 drops `takes.kind` CHECK constraint;
-  validation moves to runtime against active pack's declared kinds.
-- **Doctor checks:** schema_pack_active, schema_pack_consistency,
-  per-source pack drift.
-- **Engine refactor coverage:** the v0.38 plan parameterizes EVERY
-  hardcoded type-coupling site listed in the original exploration,
-  not just `takes.kind`. Concretely: `inferType` path-prefix table,
-  `inferLinkType` regex bank, `FRONTMATTER_FIELD_OVERRIDES` table,
-  `find_experts` SQL (`type IN (…)`), `whoknows` `DEFAULT_TYPES`,
-  `enrichment-service` person/company restriction,
-  `completeness.ts` rubric map, dream-cycle entity-type prompts.
-  gbrain-base reproduces today's values for each.
-- **Cache + rollback story:**
-  - `query_cache.knobs_hash` (v0.32.3 column) folds `schema_pack`
-    name + version into the hash so a cache row written under
-    `vc` is unreachable when `research-state` is active. Cross-
-    pack contamination structurally impossible.
-  - `eval_candidates` rows (v0.25.0) gain a `schema_pack` column
-    so `gbrain eval replay` reproduces the same retrieval space.
-    Migration v77 adds the column NULL-tolerant; pre-v0.38 rows
-    fall back to active pack during replay.
-  - HNSW indexes are pack-agnostic (vector columns don't change
-    shape across packs); no reindex needed on pack switch.
-  - Rollback: every `gbrain schema use` operation writes the
-    previous pack name to `~/.gbrain/schema-pack-history.jsonl`
-    so `gbrain schema use --previous` is one keystroke. Strict-
-    mode failures on switch surface the offending pages with
-    paste-ready "rename type to X" hints before any data
-    mutation runs. Soft-deletes from autopilot purge are NOT
-    triggered by pack changes.
-- **Test budget:** ~400-600 cases across unit + e2e per the
-  v0.36/v0.37 precedent. Specifically: ~150 cases for engine layer
-  + alias closure, ~50 for detect heuristic accuracy, ~50 for
-  suggest LLM prompts (hermetic via stubbed gateway), ~30 for per-
-  source resolution × 7-tier matrix, ~40 for auto-prompt UX
-  states, ~30 for inspect triad output stability, ~30 for tarball
-  type-detection + parameterized install, ~50 for migration v76 +
-  v77 + bootstrap parity, ~50 for examples × byte-for-byte
-  gbrain-base equivalence regression. **gbrain-base byte-for-byte
-  parity is a CI gate**, not a hope — pinned by
-  `test/regressions/gbrain-base-equivalence.test.ts` asserting the
-  pre-v0.38 hardcoded behavior reproduces from the pack-driven
-  paths on a fixture brain.
-
-## Deferred to TODOS.md (v0.39+)
-
-- Live-brain `fork-from <brain-path>` (rejected for privacy; revisit
-  if a sandboxed schema-only extraction path is designed)
-- Per-source pack FEDERATION across mounts (a query crossing
-  multiple sources can use closure over each source's schema; right
-  now per-source is isolated reads only)
-- Schema versioning + semver compatibility checks between pack
-  versions
-- Skillpack ↔ schema-pack cross-reference (a skillpack can declare
-  "I work best with these primitives present in your pack")
-- Live schema migration helpers (when you add a type, auto-suggest
-  backfill of existing pages)
-- Schema diff in PR review (rendering pack changes as human-readable
-  diffs for community pack PRs)
-
-## Reviewer Concerns (from spec review loop, partially addressed)
-
-- Quality score on first pass: 6.5/10. Issues addressed in this
-  revision: definitions block (primitive, alias closure, 7-tier
-  resolution chain), per-source isolation vs federation contradiction
-  clarified, skillpack extension framing changed from rename to
-  expansion, full hardcoded-site coverage enumerated, cache +
-  rollback story added, test budget enumerated, budget revised to
-  9-11 weeks honestly.
-- Issues NOT fully addressed, surfaced for the 11-section review:
-  - `schema suggest` LLM prompt-tuning iteration budget remains a
-    range estimate, not a measured number. The 11-section review
-    should pin a specific eval fixture set (size + diversity) and
-    a target accuracy threshold before code lands.
-  - 7-tier resolution × v0.34.1 federated_read OAuth scoping has
-    edge cases at the intersection that the 11-section review must
-    enumerate (specifically: an OAuth client with read scope across
-    federated sources but no source-specific pack override — which
-    pack drives the alias closure for cross-source queries?).
-  - The 7→3 example pack reduction is a real cut consideration. The
-    11-section review should decide whether 7 examples is the right
-    number or whether 3 + community-derived is more honest.
-
-## Cathedral risks worth surfacing in 11-section review
-
-1. The 7-week budget vs 4-week original ask. If pressure emerges,
-   D5 (auto-prompt) and D4 (inspect triad) are the safest cuts.
-2. v0.37 skillpack registry currently has zero published packs.
-   The `.gbrain-schema` rename and tarball reuse doubles down on a
-   distribution layer with no usage signal.
-3. Per-source pack resolution adds a 7th tier to the resolution
-   chain. The model-config 6-tier pattern is already cognitively
-   dense; tier 7 is an inflection point.
-4. `schema suggest` introduces ongoing LLM cost per invocation.
-   Bounded by sampling, but sets a precedent for "gbrain commands
-   that cost money."
-5. Auto-prompt UX is novel. TTY gate + per-type silencing helps,
-   but bulk-import flows could hit unexpected interruption patterns.

docs/embedding-migrations.md

@@ -2,112 +2,29 @@
 
 GBrain stores embeddings in a fixed-dimension `vector(N)` column on
 `content_chunks`. If you switch to a model with a different dimension
-(e.g. `openai:text-embedding-3-large` 1536 → `zeroentropyai:zembed-1`
-1280, or `voyage:voyage-4-large` 2048), the on-disk column type doesn't
-change automatically.
+(e.g. `text-embedding-3-large` 1536 → `voyage-multilingual-large-2` 2048,
+or back to a smaller model like `nomic-embed-text` 768), the on-disk
+column type doesn't change automatically.
 
-`gbrain init`, `gbrain doctor`, and `gbrain embed --stale` all detect
-this mismatch and refuse to silently proceed. This doc is the recipe
-they point at.
-
-## Same-dimension model swaps (v0.41.31.0 — automatic)
-
-If you switch to a different model at the **same** dimension count
-(e.g. one 1536-dim provider to another, or a re-tuned model that keeps
-its width), the column type doesn't change, so no `ALTER`/wipe recipe
-is needed. As of v0.41.31.0, gbrain stamps an embedding-provenance
-signature (`<provider:model>:<dims>`) onto each page when its chunks are
-embedded. After you point the config at the new model, the stored
-signatures differ from the current one, and `gbrain embed --stale`
-re-embeds exactly those pages:
-
-```bash
-# After switching to the new same-dim model in your config:
-gbrain embed --stale          # re-embeds signature-drifted pages
-gbrain embed --stale --dry-run # preview the count without re-embedding
-```
-
-Under federated_v2, the same drift is picked up by the per-source
-`embed-backfill` jobs that `gbrain sync --all` enqueues (capped
-`$X/source/24h`). **Grandfather:** pages embedded before v0.41.31.0
-carry a NULL signature and are NEVER flagged stale, so upgrading to
-v0.41.31.0 does NOT trigger a whole-corpus re-embed. Signatures only
-get stamped going forward.
-
-A **dimension** change still requires the wipe-and-reinit (PGLite) or
-column-alter (Postgres) recipe below — the on-disk `vector(N)` width
-genuinely has to change.
+`gbrain init` and `gbrain doctor` both detect and refuse to silently
+proceed in this case. This doc is the recipe they point at.
 
 ## Why we don't do this automatically
 
 Switching dimensions requires:
 
 1. Dropping the HNSW vector index (pgvector won't survive an `ALTER COLUMN TYPE`).
-2. Wiping every existing embedding (the old vectors are unusable in the new space — and pgvector refuses to cast them across dimensions, so this must happen before the alter).
-3. Altering the column type (Postgres only — PGLite cannot do this).
+2. Altering the column type.
+3. Wiping every existing embedding (the old vectors are unusable in the new space).
 4. Re-embedding the entire corpus (can take hours on a 50K-page brain and costs $1-100 in API calls depending on model).
 5. Conditionally recreating the index (HNSW supports up to 2000 dimensions per pgvector; above that you must use exact scans).
 
 That's not an upgrade-time auto-run. It's a deliberate, expensive
 operation. Run it when you've decided you actually want the new model.
 
-## PGLite (default install)
+## Recipe — manual `psql` against your brain
 
-**PGLite cannot `ALTER COLUMN TYPE vector(N)`.** pgvector ships as
-embedded WASM, not a native extension, and the WASM build rejects the
-column-type alter with `could not access file "$libdir/vector"`. The
-SQL recipe below works against Postgres only.
-
-The path that works on PGLite is **wipe-and-reinit**. v0.37 ships a
-single-command wrapper:
-
-```bash
-gbrain reinit-pglite \
-  --embedding-model zeroentropyai:zembed-1 \
-  --embedding-dimensions 1280
-```
-
-This backs up the existing brain to `<path>.bak`, runs `gbrain init`
-with the new flags (preserving every other field in
-`~/.gbrain/config.json`), and re-syncs the brain repo. Add `--no-sync`
-to skip the resync, `--yes` to skip the TTY confirmation, `--json` for
-structured output.
-
-Equivalent by hand:
-
-```bash
-# 1. Back up the existing brain (in case you want to roll back).
-mv ~/.gbrain/brain.pglite ~/.gbrain/brain.pglite.bak
-
-# 2. Re-init with the new model + dimensions. `gbrain init` writes
-#    the schema sized to the new dim, and (as of v0.37) preserves
-#    every other field in ~/.gbrain/config.json (chat model,
-#    expansion model, API keys).
-gbrain init --pglite \
-  --embedding-model zeroentropyai:zembed-1 \
-  --embedding-dimensions 1280
-
-# 3. Re-import your brain repo. `gbrain sync` reads the brain repo
-#    from disk and re-creates the page rows.
-gbrain sync
-
-# 4. Re-embed. The embed pipeline now uses the new model and the
-#    column accepts the new dim.
-gbrain embed --stale
-```
-
-If your brain repo is large enough that re-syncing from disk is
-expensive (>50K pages), see the Postgres section below — migrating to
-Postgres temporarily lets you run the SQL recipe, then migrate back to
-PGLite.
-
-`GBRAIN_HOME` users: substitute the active database path (or use
-`gbrain config get database_path` to find it).
-
-## Postgres (Supabase / self-hosted)
-
-Postgres supports the in-place column alter. Replace `<NEW_DIMS>` with
-your target dimension count.
+Replace `<NEW_DIMS>` with your target dimension count.
 
 ```sql
 BEGIN;
@@ -115,21 +32,19 @@ BEGIN;
 -- 1. Drop the HNSW index. It can't survive the column type change.
 DROP INDEX IF EXISTS idx_chunks_embedding;
 
--- 2. Clear stale embeddings FIRST. This must happen BEFORE the column
---    alter: pgvector refuses to cast existing vectors across dimensions
---    ("expected <NEW_DIMS> dimensions, not <OLD_DIMS>"), so altering a
---    column that still holds old-width vectors aborts the transaction.
---    NULLs cast fine. (The old vectors are unusable in the new space
---    anyway — this is the wipe step from the rationale above.)
-UPDATE content_chunks SET embedding = NULL, embedded_at = NULL;
-
--- 3. Alter the column type (all rows are NULL now, so the cast succeeds).
+-- 2. Alter the column type. (You can DROP COLUMN + ADD COLUMN instead
+--    if the existing data is already gone — same end state.)
 ALTER TABLE content_chunks ALTER COLUMN embedding TYPE vector(<NEW_DIMS>);
 
+-- 3. Clear stale embeddings so they don't survive into the new space.
+--    Either truncate (faster, drops all chunks) or null out (preserves
+--    chunk text so re-embed regenerates without re-chunking):
+UPDATE content_chunks SET embedding = NULL, embedded_at = NULL;
+
 -- 4. Recreate the HNSW index ONLY IF dims <= 2000. Above that, leave it
 --    indexless and rely on exact scans (gbrain searchVector handles this
 --    automatically — search just gets slower, not broken).
--- For dims <= 2000 (e.g. 1024, 1280, 1536, 768):
+-- For dims <= 2000 (e.g. 1024, 1536, 768):
 CREATE INDEX IF NOT EXISTS idx_chunks_embedding
   ON content_chunks USING hnsw (embedding vector_cosine_ops);
 -- For dims > 2000 (e.g. 2048 Voyage 4 Large): skip step 4.
@@ -137,48 +52,54 @@ CREATE INDEX IF NOT EXISTS idx_chunks_embedding
 COMMIT;
 ```
 
-Then re-init config with the new model:
+Then update gbrain's config so it knows the new dim:
 
 ```bash
-gbrain init --supabase \
-  --embedding-model <provider:model> \
-  --embedding-dimensions <NEW_DIMS>
+gbrain config set embedding_model <model>
+gbrain config set embedding_dimensions <NEW_DIMS>
 ```
 
-And re-embed:
+And re-embed the corpus:
 
 ```bash
 gbrain embed --stale
 ```
 
-## A note on `gbrain config set`
+## PGLite (local brain)
 
-Pre-v0.37 docs recommended `gbrain config set embedding_model X` to
-switch models. **This is a no-op for the embed pipeline.** `config set`
-writes the DB plane; the embed gateway reads the file plane
-(`~/.gbrain/config.json`). The pre-v0.37 recipe shipped the lie because
-the contract wasn't surfaced.
+Same recipe, but you connect to the embedded database differently:
 
-As of v0.37, `gbrain config set embedding_model` and `gbrain config set
-embedding_dimensions` REFUSE and print the wipe-and-reinit recipe.
+```bash
+gbrain config get database_url   # confirm engine: pglite
+# Open a psql-equivalent — for PGLite, the easiest path is to write a small
+# script that imports PGLiteEngine and runs the SQL via engine.executeRaw.
+# Or migrate to Postgres temporarily (gbrain migrate --to supabase) if you
+# want a real psql connection.
+```
 
-To change schema-sizing fields, use `gbrain init` (PGLite) or the SQL
-recipe (Postgres). Both update the file plane AND the schema together.
+For most PGLite users the simpler path is to **wipe and re-init** if your
+corpus is small enough that re-syncing is faster than hand-crafting the
+migration:
+
+```bash
+mv ~/.gbrain/brain.pglite ~/.gbrain/brain.pglite.bak
+gbrain init --pglite --embedding-dimensions <NEW_DIMS>
+gbrain sync   # re-imports your brain repo from disk
+```
 
 ## Verify
 
 After the recipe lands, `gbrain doctor --fast` should report green and
-`gbrain doctor` should pass the `embedding_width_consistency` check:
+`gbrain doctor` (full) should say check 8b passes:
 
 ```
-✓ embedding_width_consistency   dim parity: config 1280 / column vector(1280)
+✓ embedding_provider     dim parity: config 768 / column vector(768) / live probe 768
 ```
 
-If it doesn't, file an issue with the doctor output and the steps you
-ran.
+If it doesn't, file an issue with the doctor output and the SQL you ran.
 
-## v0.37+ followups
+## v0.29+ plans
 
-- Auto-fallback to alternative embedding providers when the primary
-  fails quota/auth. Tracked; requires explicit `--try-fallback`
-  consent because mixing provider vectors silently corrupts retrieval.
+`gbrain migrate-embedding-dim --to <N>` is a tracked TODO. It will run
+the recipe above with progress reporting + an explicit confirmation
+gate. Until that lands, this manual recipe is the canonical path.

docs/ethos/ORIGIN.md

@@ -1,29 +0,0 @@
-# Origin story
-
-GBrain came out of building OpenClaw — Garry's personal AI agent fork. The first version had skills and a brain, but the brain was a flat directory of markdown files. Search was ripgrep. Memory was vibes.
-
-Two problems surfaced almost immediately.
-
-First, the agent forgot things between conversations. Every new session re-asked basic questions. Names of people Garry had introduced last week were gone. Decisions made on Tuesday didn't survive to Thursday. The brain existed but the agent couldn't actually use it.
-
-Second, the agent kept duplicating work. Two different signals about the same company became two different people pages. Three meetings with the same person became three uncorrelated timeline entries. The signal-to-noise ratio decayed in real time.
-
-GBrain is what you build when you decide both of those are unacceptable.
-
-The fix wasn't one big idea. It was many small ones layered together:
-
-- Brain-first lookup before any external API call.
-- Auto-linking on every page write so the graph grows for free.
-- Typed edges so "who works at Acme AI?" actually returns something.
-- Hybrid search because vector alone underdelivers.
-- Reranker on top because hybrid alone is locally optimal but globally suboptimal.
-- Nightly cron to dedup, enrich, fix citations, surface contradictions.
-- An agent that reads `skills/RESOLVER.md` once and knows what to do.
-
-None of those are novel ideas. The contribution is shipping all of them together, on Postgres + pgvector that runs in WASM (no server), with skills that are markdown (not code), routed by a small text file (not a router LLM).
-
-The production brain has been running for months now. 17,888 pages. 4,383 people. 723 companies. 21 cron jobs running autonomously. It wakes Garry up smarter than the day before.
-
-GBrain is what happens when you write the brain you actually wanted to have.
-
-The reason the brain is worth building is `gbrain think`. Without it, the brain is just a place that holds your notes. With it, the brain is a thing you can query about itself: what does it know, what does it not know yet, where does it contradict itself, where are the holes. The 24/7 cron cycle keeps the brain sharp. `think` is what makes a sharp brain useful.

docs/eval-bench.md

@@ -8,112 +8,6 @@ For the **NDJSON wire format** consumed by gbrain-evals, see
 [`eval-capture.md`](./eval-capture.md). This doc is the human dev loop
 that lives on top of that format.
 
-## v0.41 update — the LOOP is now real
-
-Before v0.41, you could capture eval rows and replay them but nothing
-stitched them into a gate. `gbrain bench publish` + `gbrain eval gate`
-close the loop. Two gates:
-
-- **Regression gate** (`--baseline X.baseline.ndjson`): replays a baseline
-  you captured against your current brain. Catches: "did my refactor break
-  search?" Compares jaccard / top-1 stability / latency multiplier.
-- **Correctness gate** (`--qrels Y.qrels.json`): runs known-right queries
-  against your current brain via bare `hybridSearch`. Catches: "is my
-  retrieval actually any good?" Computes recall@K, first-relevant-hit-rate,
-  expected_top1-hit-rate.
-
-Both can be passed together; both must pass for verdict `pass`. At least
-one is required.
-
-### The full LOOP for your own brain
-
-```bash
-# 1. Capture (one-time; uses queries already in eval_candidates)
-gbrain eval export --limit 200 --tool query > /tmp/captured.ndjson
-
-# 2. Publish a baseline
-mkdir -p ~/.gbrain/baselines
-gbrain bench publish --from /tmp/captured.ndjson --to ~/.gbrain/baselines/personal.baseline.ndjson --label "personal-$(date +%Y%m%d)"
-
-# 3. Gate against it
-gbrain eval gate --baseline ~/.gbrain/baselines/personal.baseline.ndjson
-```
-
-### Privacy posture (D9)
-
-**Public baselines in `gbrain-evals` are hermetic-synthetic ONLY.** Real
-user captures stay local in `~/.gbrain/baselines/`. The boundary is
-enforced at the file source, not by post-hoc scrubbing. If you publish a
-baseline to `gbrain-evals`, generate it from a fixture-seeded test brain
-(placeholder names like `alice-example`, `widget-co-example`) — never
-from a real user's `eval_candidates` table.
-
-### Deterministic-pipeline disclosure
-
-`gbrain eval gate --qrels` uses bare `hybridSearch` (not the production
-`query` op handler). This is deliberate: gates need to be deterministic in
-CI. Production retrieval differs via the query cache, salience freshness,
-expansion, etc. The gate measures retrieval quality with a fixed pipeline;
-your users may see different results when the cache is warm.
-
-### `.qrels.json` shape
-
-Two equivalent representations per entry:
-
-```json
-{
-  "schema_version": 1,
-  "queries": [
-    {
-      "query_id": "q1",
-      "query": "fintech founder",
-      "relevant_slugs": ["people/alice-example"],
-      "first_relevant_slug": "people/alice-example"
-    }
-  ]
-}
-```
-
-For federated / multi-source brains, use the explicit shape (no defaults
-to `source_id='default'`):
-
-```json
-{
-  "query_id": "q2",
-  "query": "anything",
-  "relevant": [
-    {"source_id": "host", "slug": "people/alice"},
-    {"source_id": "team-a", "slug": "people/alice"}
-  ],
-  "expected_top1": {"source_id": "host", "slug": "people/alice"}
-}
-```
-
-Without `source_id`, a hit from the wrong source could false-pass the
-gate. The compare everywhere is `${source_id}::${slug}` strings.
-
-### Example GitHub Actions workflow
-
-```yaml
-name: gbrain-eval-gate
-on: [pull_request]
-jobs:
-  gate:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: oven-sh/setup-bun@v2
-      - run: bun install
-      - run: |
-          # Run both gates; CI fails on any breach.
-          gbrain eval gate \
-            --baseline gbrain-evals/baselines/v0.41-launch.baseline.ndjson \
-            --qrels gbrain-evals/qrels/v0.41-launch.qrels.json \
-            --json | tee /tmp/gate.json
-```
-
----
-
 ## Prerequisite: turn on contributor mode
 
 Capture is **off by default** for production users (privacy-positive — no
@@ -203,14 +97,6 @@ not a baseline comparison. For metric-against-truth eval, use
 replay tool answers a different question: "did my code change move
 retrieval, and which queries did it move most?"
 
-For a third evaluation axis — public benchmark, ground-truth labels, full
-question-answer pipeline (not just retrieval) — `gbrain eval longmemeval
-<dataset.jsonl>` (v0.28.8) runs the LongMemEval benchmark against gbrain's
-hybrid retrieval. Each question gets a clean in-memory PGLite, its haystack
-imported, the question asked, the hypothesis emitted as JSONL — exactly the
-shape LongMemEval's `evaluate_qa.py` consumes. Your `~/.gbrain` brain is
-never opened. See `## Public benchmarks: LongMemEval` below.
-
 ## Best-effort by design
 
 Replay is not pure. Three things can drift between capture and replay:
@@ -336,263 +222,3 @@ Existing `eval_candidates` rows stay until you `gbrain eval prune
 | `Mean latency Δ: +500ms`, jaccard high | Vector path got slower; check embedding API or HNSW probes |
 | `rows_errored > 0` | One or more queries threw. Inspect first 3 in human output, or `--json` to see all `error_message` fields |
 | Many `skipped: empty query` | Capture ran on rows where someone passed empty `query` — check why those were captured |
-
-## Public benchmarks: LongMemEval (v0.28.8)
-
-`gbrain eval longmemeval` runs the public [LongMemEval](https://huggingface.co/datasets/xiaowu0162/longmemeval)
-benchmark directly against gbrain's hybrid retrieval. Different evaluation
-axis from `eval replay`: public dataset with ground-truth labels, end-to-end
-question-answer pipeline, hermetic per-question brains.
-
-```bash
-# Download the dataset (visit the HF page in a browser; gated/manual download).
-# Place longmemeval_oracle.json (or _s.json) somewhere local.
-
-# Retrieval-only (no LLM answer-gen, fastest path, no Anthropic key needed):
-gbrain eval longmemeval ./longmemeval_oracle.json --limit 50 --retrieval-only \
-  > /tmp/hypothesis.jsonl
-
-# Full pipeline (Anthropic key required for answer-gen):
-gbrain eval longmemeval ./longmemeval_oracle.json --limit 50 \
-  > /tmp/hypothesis.jsonl
-
-# Score with LongMemEval's published evaluate_qa.py (not bundled — needs
-# OpenAI gpt-4o per their spec):
-python evaluate_qa.py /tmp/hypothesis.jsonl
-```
-
-### Architecture (read this if you're touching the harness)
-
-- One in-memory PGLite per benchmark run via `createBenchmarkBrain` +
-  `withBenchmarkBrain`. Your `~/.gbrain` is never opened.
-- Between questions: `TRUNCATE` over runtime-enumerated `pg_tables`, NOT a
-  hardcoded list — schema migrations don't silently leak data across
-  questions. Infrastructure tables (`sources`, `config`,
-  `gbrain_cycle_locks`, `subagent_rate_leases`) are preserved across resets.
-- Sanitization parity: re-uses `INJECTION_PATTERNS` from
-  `src/core/think/sanitize.ts` so adding a new injection pattern
-  automatically covers takes AND benchmarks. One source of truth.
-- Retrieved chat content is wrapped in `<chat_session id="..." date="...">`
-  framing; the answer-gen system prompt declares the content UNTRUSTED.
-  Same posture as `<take>` framing.
-- LLM injection seam: `runEvalLongMemEval(args, {client?: ThinkLLMClient})`.
-  Tests stub the client so the full pipeline runs hermetically without any
-  API key.
-
-### Flags
-
-| Flag | Default | Purpose |
-|---|---|---|
-| `--limit N` | run all | Cap question count (iterate fast) |
-| `--retrieval-only` | off | Emit retrieved chunks; no LLM answer-gen |
-| `--keyword-only` | off | Disable vector path (debug retrieval issues) |
-| `--expansion` | **off** | Multi-query expansion. Off by default for determinism (no per-query Haiku call). Pass to opt in. |
-| `--top-k K` | 10 | Retrieval depth |
-| `--model M` | resolved | Default resolves through `resolveModel()` 6-tier chain (`models.eval.longmemeval` config key) |
-| `--output FILE` | stdout | Write hypothesis JSONL to file instead of stdout |
-
-### Numbers
-
-p50 25.9ms / p99 30.3ms warm reset+import+search on Apple Silicon (per the
-`test/eval-longmemeval.test.ts` perf gate). Per-question cost well under the
-500ms speed gate. 500 questions = ~13s of overhead plus your retrieval and
-LLM latency.
-
-## Measuring brain consistency over time (v0.32.6)
-
-`gbrain eval suspected-contradictions` is a complementary measurement
-instrument: it samples retrieval results for unmarked semantic
-contradictions (e.g., compiled_truth vs chat content, intra-page chunk
-vs active take). Where LongMemEval measures retrieval correctness on a
-fixed labeled set, the contradiction probe measures how often a real
-brain surfaces conflicting answers.
-
-### Recommended nightly cadence
-
-```bash
-# Once a day, against your top 50 most-frequent queries:
-gbrain eval suspected-contradictions \
-  --queries-file ~/.gbrain/queries.jsonl \
-  --top-k 5 \
-  --budget-usd 5 \
-  --output ~/.gbrain/probe-runs/$(date +%Y-%m-%d).json
-```
-
-Persistent cache (`eval_contradictions_cache`) makes re-runs near-zero
-cost until you bump `PROMPT_VERSION`. Trend-track via:
-
-```bash
-gbrain eval suspected-contradictions trend --days 30
-```
-
-The ASCII bar chart shows total flagged per day. Headline % surfaces in
-`gbrain doctor`'s `contradictions` check with paste-ready resolution
-commands per high-severity finding.
-
-### See also
-
-- `docs/contradictions.md` — architecture, severity rubric, action criteria.
-- CHANGELOG `## [0.32.6]` — full release notes including the bigger-swing
-  decision criteria gated on Wilson CI lower-bound.
-
-## v0.40.1.0 Track D — Eval infrastructure
-
-Three eval surfaces grew non-trivial capabilities in v0.40.1.0. This section
-covers the dev loop that uses them and the gates they enforce.
-
-### `gbrain eval longmemeval --by-type` — per-question-type R@k breakdown
-
-LongMemEval has always computed per-question-type recall internally; v0.40.1.0
-surfaces it in machine-readable form. Two additive changes:
-
-1. Every per-question JSONL row now includes a `question: string` field so the
-   `gbrain eval cross-modal --batch` consumer (below) can read it without
-   joining back against the source dataset.
-2. New `--by-type` flag emits a final aggregate line keyed by `question_type`:
-
-```json
-{"schema_version": 1, "kind": "by_type_summary",
- "recall_by_type": {"single-session-user": {"hit": 18, "total": 19, "rate": 0.947}},
- "aggregate": {"hit": 110, "total": 120, "rate": 0.917}}
-```
-
-**Resume-safe.** When `--resume-from` is the same path as `--output`, the
-summary is rebuilt from the file (each per-row includes `question_type` and
-`recall_hit`) so the final aggregate covers all resumed questions, not just
-this run's slice. The prior summary at the file tail is replaced, not
-appended — a brain that resumes 5 times across a 500-question run ends with
-exactly ONE summary at the tail.
-
-**Optional gate.** `--by-type-floor 0.85` exits non-zero when any
-`question_type`'s rate falls below 0.85. Default: informational only.
-
-```bash
-# Diagnose per-type ranking quality after a search-touching change.
-gbrain eval longmemeval ~/datasets/longmemeval_s.jsonl \
-  --by-type --output /tmp/run.jsonl
-tail -1 /tmp/run.jsonl | jq .   # summary line
-
-# Strict gate in a CI script.
-gbrain eval longmemeval test/fixtures/longmemeval-mini.jsonl \
-  --by-type --by-type-floor 0.80 --output /tmp/run.jsonl
-echo "exit=$?"  # 1 if any type fell below 0.80
-```
-
-### Hermetic retrieval gate — `test/eval-replay-gate.test.ts`
-
-The v0.40.1.0 Track D structural fix for "PRs touching `src/core/search/`
-silently regress retrieval." Replaces the original "replay against captured
-eval_candidates" design (which Codex caught as non-functional in CI — see
-the `v0.41+: contributor-mode CI capture` TODO in `TODOS.md` for the deferred
-real-query version).
-
-How it works:
-- Hand-curated qrels fixture at `test/fixtures/eval-baselines/qrels-search.json`
-  with PLACEHOLDER names only (no real people / companies per CLAUDE.md privacy
-  rule).
-- The test seeds a PGLite engine with synthetic pages whose embeddings are
-  basis vectors (the same `basisEmbedding(idx)` pattern as
-  `test/e2e/search-quality.test.ts`). No API keys, no DATABASE_URL.
-- For each qrels query, calls `engine.searchVector(basisEmbedding(dim))` and
-  computes `top1_match_rate` and `recall@10`. Asserts both meet floors
-  (`>= 0.80` and `>= 0.85` by default).
-- Lives in the unit-shard test matrix (`.github/workflows/test.yml`) so it
-  runs on every PR via `bun test`, NOT in the E2E fixed-file workflow.
-
-#### Refreshing the qrels fixture (the `Why:` discipline, D4)
-
-When CI fails because a legitimate ranking change moved expected slugs, the
-fix is to edit `qrels-search.json` directly. **Always include a `Why:` line
-in the commit body** so future maintainers can read the audit trail. Without
-the `Why:`, the gate degrades to a rubber stamp within months. The convention
-is informational (not a commit-hook block), but enforce it in PR review.
-
-Example commit body:
-
-```
-chore(eval): refresh qrels for new source-boost ordering
-
-Why: v0.40.x source-boost now weights originals/ over concepts/, so
-q12 (founder-mode) now correctly surfaces originals/founder-mode-example
-top-1. Manual verification: ran the production query; new ranking is
-clearly better-aligned with the query intent.
-```
-
-#### Env-overrides for floors
-
-```bash
-GBRAIN_REPLAY_GATE_TOP1_FLOOR=0.85 \
-GBRAIN_REPLAY_GATE_RECALL_FLOOR=0.90 \
-  bun test test/eval-replay-gate.test.ts
-```
-
-Use to tighten or loosen the gate as the qrels fixture matures.
-
-### `gbrain eval cross-modal --batch` — batch quality scoring
-
-Single-task cross-modal eval scores one (task, output) pair. Batch mode runs
-the same scoring over an entire LongMemEval JSONL output, with cost guardrails.
-
-```bash
-# Step 1: produce LongMemEval hypotheses (real cost: depends on model + N).
-gbrain eval longmemeval ~/datasets/longmemeval_s.jsonl \
-  --limit 10 --output /tmp/run.jsonl
-
-# Step 2: batch-score those hypotheses (real cost: ~$0.70 for 10 questions,
-# 1 cycle, 3 model slots at default --max-usd 5 budget cap).
-gbrain eval cross-modal --batch /tmp/run.jsonl \
-  --limit 10 --cycles 1 --concurrent 3 --max-usd 5 --json
-echo "exit=$?"  # 0=all-pass, 1=any-fail, 2=any-error-or-inconclusive
-```
-
-**Key behaviors:**
-- Default `--cycles 1` in batch mode (single-task default is 3 in TTY) to bound
-  cost. Pass `--cycles 3` to match single-task strictness.
-- `--concurrent 3` runs up to 3 questions in parallel x 3 model slots each =
-  9 simultaneous API calls. Below tier-1 rate limits for all three providers.
-- `--max-usd FLOAT` refuses to start if the pre-flight cost estimate exceeds
-  the cap, unless `--yes` bypasses (required for non-interactive cron / CI).
-- Filters `kind: "by_type_summary"` rows automatically (the LongMemEval
-  `--by-type` summary line is metadata, not a question).
-- `--batch` is mutually exclusive with `--task`; fail-fast usage error if both
-  are set.
-- Exit precedence (fail-loud): ERROR > FAIL > INCONCLUSIVE > PASS.
-- Per-question receipts land in a tempdir and are deleted at end of batch; the
-  summary inlines per-question verdicts so the audit trail is self-contained.
-
-### Nightly cross-modal quality probe (opt-in, autopilot)
-
-`src/core/cycle/nightly-quality-probe.ts` ships a phase that runs the longmemeval
-+ cross-modal pipeline once per 24h. **Disabled by default** to avoid surprise
-API spend. Enable per-host:
-
-```bash
-gbrain config set autopilot.nightly_quality_probe.enabled true
-gbrain config set autopilot.nightly_quality_probe.max_usd 5.00   # optional override
-```
-
-Note: `--phase nightly_quality_probe` wiring into the autopilot scheduler is
-deferred to a v0.41+ follow-up (see TODOS.md). For now the phase is callable
-in isolation; the test harness exercises it via DI stubs.
-
-```bash
-# Manual smoke (exercises the path via DI stubs, no real API spend).
-bun test test/nightly-quality-probe.test.ts
-```
-
-Observability:
-- `~/.gbrain/audit/quality-probe-YYYY-Www.jsonl` — one event per run with
-  outcome (pass / fail / inconclusive / error / budget_exceeded /
-  rate_limited / no_embedding_key), pass/fail/inconclusive/error counts,
-  est_cost_usd, fixture_sha8. ISO-week rotation (mirrors slug-fallback
-  audit).
-- `gbrain doctor` surfaces `nightly_quality_probe_health`:
-  - SKIPPED (disabled) — with paste-ready enable command.
-  - OK (enabled, no events yet) — autopilot hasn't fired its first run.
-  - OK (last 7d all PASS) — with timestamp of latest run.
-  - WARN — any FAIL / ERROR / BUDGET_EXCEEDED in the window, with outcome
-    counts and the latest run's reason.
-
-Real expected cost: ~$0.35 per nightly run (5 questions x 3 slots x 1 cycle
-x ~$0.02/call) ≈ $10.50/month. Worst-case under the default budget cap:
-$150/month. Opt-in default prevents discovering this in your card statement.

docs/eval-takes-quality.md

@@ -1,159 +0,0 @@
-# `gbrain eval takes-quality` — reproducible cross-modal quality eval
-
-v0.32+ ships a CI-able quality gate for the takes layer. Three frontier models
-score a sample of takes against a 5-dimension rubric, the runner aggregates to
-PASS / FAIL / INCONCLUSIVE, and the receipt persists to `eval_takes_quality_runs`
-so a follow-up `trend` or `regress` can compare against history.
-
-This doc is the consumer contract. The sibling [gbrain-evals](https://github.com/garrytan/gbrain-evals)
-repo and any future CI gate read receipts shaped exactly like the JSON below.
-Fields are additive-stable at `schema_version: 1`. A breaking shape change
-bumps the version.
-
-## Subcommands
-
-| Command | Brain required? | Exit codes |
-|---|---|---|
-| `gbrain eval takes-quality run [flags]` | yes (samples takes) | 0 PASS, 1 FAIL, 2 INCONCLUSIVE |
-| `gbrain eval takes-quality replay <receipt>` | **no** (disk-only) | 0 PASS, 1 FAIL, 2 INCONCLUSIVE |
-| `gbrain eval takes-quality trend [flags]` | yes (reads runs table) | 0 |
-| `gbrain eval takes-quality regress --against <receipt>` | yes | 0 OK, 1 regression |
-
-`replay` is the only mode that runs without `DATABASE_URL` — it reads the
-receipt file from disk and re-renders it. The other modes need the brain.
-
-## `run` flags
-
-| Flag | Default | Notes |
-|---|---|---|
-| `--limit N` | 100 | Random sample of N takes from the brain. |
-| `--cycles N` | 3 (TTY) / 1 (non-TTY) | Up to N panel calls before giving up; early-stop on PASS or INCONCLUSIVE. |
-| `--budget-usd N` | unset | Abort before next call's projected cost would exceed cap. Models without a `pricing.ts` entry fail loud (codex #4). |
-| `--source db|fs` | `db` | `fs` is reserved for v0.33+. |
-| `--slug-prefix P` | unset | Filter takes to pages whose slug starts with P. |
-| `--models a,b,c` | `openai:gpt-4o,anthropic:claude-opus-4-7,google:gemini-1.5-pro` | Comma-separated panel. |
-| `--json` | off | Emit the full receipt to stdout. |
-
-## Receipt JSON shape (`schema_version: 1`)
-
-```json
-{
-  "schema_version": 1,
-  "ts": "2026-05-09T22:00:00.000Z",
-  "rubric_version": "v1.0",
-  "rubric_sha8": "abcd1234",
-  "corpus": {
-    "source": "db",
-    "n_takes": 100,
-    "slug_prefix": null,
-    "corpus_sha8": "abcd1234"
-  },
-  "prompt_sha8": "abcd1234",
-  "models_sha8": "abcd1234",
-  "models": ["openai:gpt-4o", "anthropic:claude-opus-4-7", "google:gemini-1.5-pro"],
-  "cycles_run": 3,
-  "successes_per_cycle": [3, 3, 2],
-  "verdict": "pass",
-  "scores": {
-    "accuracy":            { "mean": 7.8, "min": 7, "max": 9, "scores": [9,7,7], "per_model": {...} },
-    "attribution":         { "mean": 7.0, "min": 7, "max": 7, "scores": [7,7,7], "per_model": {...} },
-    "weight_calibration":  { "mean": 7.5, "min": 7, "max": 8, "scores": [8,7,7], "per_model": {...} },
-    "kind_classification": { "mean": 7.2, "min": 7, "max": 8, "scores": [7,8,7], "per_model": {...} },
-    "signal_density":      { "mean": 7.0, "min": 6, "max": 8, "scores": [8,7,6], "per_model": {...} }
-  },
-  "overall_score": 7.3,
-  "cost_usd": 1.85,
-  "improvements": ["..."],
-  "errors": [],
-  "verdictMessage": "PASS: every dim mean >=7 and min >=5 ..."
-}
-```
-
-### Field reference
-
-- `schema_version` — locks the contract. Adding optional fields is additive
-  and compatible. Renaming, removing, or changing semantics bumps the version.
-- `rubric_version` + `rubric_sha8` — segregate trend rows by rubric epoch
-  (codex review #3). When the rubric definition changes, both fields update,
-  and trend mode groups runs accordingly so a stricter rubric doesn't
-  silently look like a quality drop.
-- `corpus.corpus_sha8` — fingerprint over the joined takes-text the judge
-  saw. Determines whether two runs are over the "same" sample.
-- `models_sha8` — fingerprint over the sorted model id list. Re-ordering
-  models in `--models` doesn't change the sha (sort is stable).
-- `successes_per_cycle` — count of contributing models per cycle. A model
-  contributes when (a) its JSON parsed AND (b) every declared rubric dim
-  has a finite score (codex review #5 — missing-dim drops the contribution).
-- `verdict` — `pass` if every dim mean >= 7 AND every dim min across
-  contributing models >= 5; `fail` otherwise; `inconclusive` if fewer than
-  2/3 models contributed complete scores.
-- `cost_usd` — sum of per-call cost via `pricing.ts`. Unknown models when
-  `--budget-usd` is set produce a `PricingNotFoundError` before any call
-  fires.
-
-## Receipt persistence
-
-Receipts persist to **`eval_takes_quality_runs`** (DB-authoritative per
-codex review #6) AND to disk at `~/.gbrain/eval-receipts/takes-quality-<corpus>-<prompt>-<models>-<rubric>.json`
-as a best-effort artifact. The DB row carries the full receipt JSON in the
-`receipt_json` JSONB column, so when the disk artifact is gone, `replay`
-can still reconstruct via `loadReceiptFromDb` (v0.33+ flag wiring).
-
-The 4-sha primary key is unique (`UNIQUE` constraint) so re-running an
-identical eval is `INSERT ... ON CONFLICT DO NOTHING` — idempotent.
-
-## Trend output
-
-Plain text (default):
-
-```
-ts                   rubric  verdict       overall  cost     corpus
-─────────────────────────────────────────────────────────────────────────────
-2026-05-09T22:00:00  v1.0    pass             7.3   $1.85   abcd1234
-2026-05-08T18:30:00  v1.0    fail             6.8   $1.92   ef567890
-```
-
-JSON shape (`--json`):
-
-```json
-{
-  "schema_version": 1,
-  "rows": [
-    { "id": 42, "ts": "...", "rubric_version": "v1.0", "verdict": "pass",
-      "overall_score": 7.3, "cost_usd": 1.85, "corpus_sha8": "abcd1234" }
-  ]
-}
-```
-
-## Regress: gating CI on quality
-
-```bash
-# Capture a baseline.
-gbrain eval takes-quality run --limit 100 --json \
-  > .ci/takes-quality-baseline.json
-
-# Later, after changing the extraction prompt:
-gbrain eval takes-quality regress --against .ci/takes-quality-baseline.json \
-  --threshold 0.5
-# exit 0 → no regression past threshold
-# exit 1 → some dim dropped > 0.5; CI fails
-```
-
-The threshold is the per-dim-mean drop counting as regression. Default 0.5.
-Regress reuses the **same** model panel + slug prefix + source as the prior
-receipt for an apples-to-apples compare. Diffs in `corpus_sha8` /
-`prompt_sha8` / `rubric_sha8` are surfaced as informational warnings (the
-runner doesn't refuse — that's the caller's call).
-
-## Contract stability
-
-The shape above is the read contract for downstream consumers. Anything
-not listed (e.g. internal aggregator state, gateway providerMetadata) is
-**not** in the receipt and may change without notice.
-
-When you need to evolve the schema:
-1. Additive optional field → no version bump; old consumers ignore the
-   new key, new consumers read it.
-2. Renamed or removed field, or changed semantics → bump
-   `schema_version` to `2`; runner emits both shapes for one release as
-   a deprecation runway.

docs/eval/METRIC_GLOSSARY.md

@@ -1,176 +0,0 @@
-# Evaluation Metric Glossary
-
-**Auto-generated from `src/core/eval/metric-glossary.ts`. Do not edit by hand.** Run `bun run scripts/generate-metric-glossary.ts` to regenerate.
-
-Every metric `gbrain eval *` and `gbrain search stats` reports has a plain-English explanation here. Industry terms are preserved verbatim so users searching the literature find what we report.
-
-## Retrieval Metrics
-
-### Precision at k (P@k)
-
-**Key:** `precision@k`
-
-**Plain English:** Of the top k results the engine returned, what fraction were actually relevant? High precision means few junk results in the top of the list.
-
-**Range:** 0..1, higher is better. P@10 = 0.7 means 7 of the top 10 results were on-topic.
-
-### Recall at k (R@k)
-
-**Key:** `recall@k`
-
-**Plain English:** Of all the relevant results that exist in the brain, what fraction did the engine find in its top k? High recall means few missed answers.
-
-**Range:** 0..1, higher is better. R@10 = 0.81 means out of every 100 questions, the right answer was in the top 10 for 81 of them.
-
-### Mean Reciprocal Rank (MRR)
-
-**Key:** `mrr`
-
-**Plain English:** On average, how far down the list is the FIRST relevant result? An MRR of 1.0 means the first hit is always right; an MRR of 0.5 means it's typically at rank 2.
-
-**Range:** 0..1, higher is better. Computed as the average of 1/rank-of-first-relevant-result across all test queries.
-
-### Normalized Discounted Cumulative Gain at k (nDCG@k)
-
-**Key:** `ndcg@k`
-
-**Plain English:** Like precision@k, but the engine gets MORE credit for putting good results near the top than near rank k. A perfect ordering scores 1.0; a totally random ordering scores near 0.
-
-**Range:** 0..1, higher is better. nDCG@10 above 0.65 is the common "ship it" threshold for hybrid retrieval on technical corpora.
-
-## Retrieval-Quality / Evidence Metrics (NamedThingBench)
-
-### Hit rate at 1 (Hit@1)
-
-**Key:** `hit@1`
-
-**Plain English:** Fraction of queries where the right page is the very first result. NamedThingBench hard-gates title-substring Hit@1 >= 0.95 and alias Hit@1 >= 0.98 — a query that is a page's name or title phrase should land it at rank 1, not "somewhere in the top 10".
-
-**Range:** 0..1, higher is better.
-
-### Hit rate at 3 (Hit@3)
-
-**Key:** `hit@3`
-
-**Plain English:** Fraction of queries where the right page is in the top 3 results. NamedThingBench requires the multi-chunk-dilution family to hit 1.0 — a page with one strong chunk among many weak ones must never be buried.
-
-**Range:** 0..1, higher is better.
-
-### Average rank-1 match score
-
-**Key:** `avg_rank1_score`
-
-**Plain English:** The mean base (pre-boost) retrieval score of the TOP result across recent searches, from `gbrain search stats`. It is NOT a labeled accuracy number — it is a drift signal: if this trends DOWN over time, retrieval quality is regressing (the early warning that would have caught the duplicate-page incident before a human did).
-
-**Range:** 0..1. Watch the trend, not the absolute value; pair with the <0.6 / 0.6-0.85 / >=0.85 bucket counts for shape.
-
-### Create-safety hint (evidence contract)
-
-**Key:** `create_safety`
-
-**Plain English:** A result's answer to "is this page already in the brain — safe to NOT write a new one?" Derived from the strongest evidence, NOT a raw score: exists (alias_hit / exact_title_match / high_vector_match — do not duplicate), probable (solid keyword match — prefer updating), unknown (weak match — look closer). An agent keys its don't-duplicate decision off this, which is what prevents the incident's duplicate-stub class.
-
-**Range:** enum: exists | probable | unknown
-
-## Set-Similarity / Stability Metrics
-
-### Jaccard similarity at k (set Jaccard @k)
-
-**Key:** `jaccard@k`
-
-**Plain English:** How much do two result lists overlap? Compare the top k slugs from the captured baseline against the current run; Jaccard@10 = 1.0 means perfect agreement, 0.0 means zero overlap.
-
-**Range:** 0..1, higher = more stable. Below 0.5 on a stable corpus means retrieval changed significantly.
-
-### Top-1 stability rate
-
-**Key:** `top1_stability`
-
-**Plain English:** Fraction of queries where the #1 result is the same between two runs. The most aggressive stability check — small ranking shifts that don't change the top answer don't hurt it.
-
-**Range:** 0..1, higher = more stable. Above 0.85 typically means safe-to-merge for retrieval changes.
-
-## Statistical-Significance Metrics
-
-### p-value (paired bootstrap)
-
-**Key:** `p_value`
-
-**Plain English:** How likely the observed difference between two modes is just noise. Lower = stronger evidence the difference is real. We compute paired bootstrap with 10,000 resamples and Bonferroni correction across the 12 comparisons (3 modes × 4 metrics).
-
-**Range:** 0..1, lower = stronger signal. Below 0.05 is the common "statistically significant" threshold; below 0.01 is strong evidence.
-
-### 95% Confidence Interval (CI)
-
-**Key:** `confidence_interval`
-
-**Plain English:** The range we're 95% sure the true value falls inside, given the sample we measured. Narrower CI = more reliable estimate. Computed via bootstrap resampling.
-
-**Range:** Two-tuple [low, high]. If 0 is inside the CI for a Δ, the difference isn't statistically significant.
-
-## Operational / Cost Metrics
-
-### Cache hit rate
-
-**Key:** `cache_hit_rate`
-
-**Plain English:** Fraction of searches that reused a recent cached answer instead of running fresh. Higher hit rate = lower latency + lower LLM spend, but stale results may slip through if the threshold is too loose.
-
-**Range:** 0..1, higher generally better. 0.7-0.9 is the sweet spot for a busy brain; above 0.9 may indicate the similarity threshold is too loose.
-
-### Average results returned
-
-**Key:** `avg_results`
-
-**Plain English:** Mean number of search-result rows the engine returned per call. Should be near the active mode's searchLimit unless the brain is small or the budget is dropping results.
-
-**Range:** 0..searchLimit. Far below searchLimit suggests budget pressure or sparse retrieval.
-
-### Average tokens delivered
-
-**Key:** `avg_tokens`
-
-**Plain English:** Estimated tokens (chars / 4) in the chunk text returned per search call. The direct measure of how much context an agent loop is paying for each search.
-
-**Range:** 0..tokenBudget. Approximates OpenAI tiktoken count for English; off by ~5-10% for Anthropic and worse for non-English.
-
-### Cost per query (USD)
-
-**Key:** `cost_per_query_usd`
-
-**Plain English:** Sum of LLM + embedding API charges for one search call. Includes Haiku expansion call (tokenmax mode only) + embedding cost + downstream answer-model cost if measured.
-
-**Range:** 0..unbounded. Conservative mode is typically <\$0.001 per call; tokenmax with answer-gen can exceed \$0.01.
-
-### p99 latency (ms)
-
-**Key:** `p99_latency_ms`
-
-**Plain English:** 99th percentile wall-clock time per search call. The latency that 1% of users see — long-tail experience, not the average.
-
-**Range:** 0..unbounded. Warm-cache hits should be <50ms; tokenmax with expansion can exceed 200ms due to the Haiku call.
-
-## Result-Sizing Metrics
-
-### Autocut signal
-
-**Key:** `autocut.signal`
-
-**Plain English:** Which signal autocut used to size the result set. 'rerank' means it found a real score cliff in the cross-encoder rerank scores and cut there; 'none' means no trustworthy cliff (no reranker, <2 scored results, or the gap was too small) so it returned the full list.
-
-**Range:** 'rerank' | 'none'. 'none' is not a failure — it means autocut declined to cut because the signal didn't justify it.
-
-### Autocut gap ratio
-
-**Key:** `autocut.gap_ratio`
-
-**Plain English:** The size of the largest score drop autocut found, as a fraction of the top result's score. A gap of 0.40 means the score fell by 40% of the top score at the steepest point. Autocut cuts there only when this clears the sensitivity threshold (autocut_jump, default 0.20).
-
-**Range:** 0..1, higher = a sharper cliff (more confident cut). Below the autocut_jump threshold → no cut.
-
----
-
-## Coverage
-
-Every metric printed by any `gbrain eval *` or `gbrain search stats` command resolves through `getMetricGloss()` in `src/core/eval/metric-glossary.ts`. Adding a new metric to the glossary REQUIRES updating this doc; the CI guard catches drift.
-

docs/eval/SEARCH_MODE_METHODOLOGY.md

@@ -1,285 +0,0 @@
-# Search Mode Evaluation Methodology
-
-_How v0.32.3 measures the difference between `conservative`, `balanced`, and `tokenmax`. Written haters-immune: every claim is reproducible from the committed dataset + raw outputs._
-
-## 1. What this measures and what it doesn't
-
-**Measures:** retrieval quality and operational cost on fixed public datasets, under each named search mode, against the same brain content.
-
-**Does NOT measure:**
-- Your specific brain content (this is a benchmark, not your bill).
-- Your specific query distribution.
-- End-user satisfaction or downstream task success.
-- Latency under concurrent load.
-- Production cost (the cost numbers are model-pricing estimates × dataset size, not your actual API spend).
-
-If you want to know how a mode behaves on YOUR brain, run `gbrain search stats --days 30` after a real usage window, then run `gbrain search tune` for actionable recommendations.
-
-## 2. Datasets and sizes
-
-- **LongMemEval** — public split, `n=500` questions. Downloaded from [Hugging Face](https://huggingface.co/datasets/xiaowu0162/longmemeval). The corpus + answer keys are pinned to a specific commit; recorded in every per-run record.
-- **Replay captures** — NDJSON from the sibling `gbrain-evals` repo, `n=200` queries. Each query carries a `retrieved_slugs` baseline + a `latency_ms` measurement from the original production run.
-- **BrainBench v1** — `n=1240` documents / `n=350` qrels (binary relevance judgments). Lives in the sibling [`gbrain-evals`](https://github.com/garrytan/gbrain-evals) repo, SHA-pinned at every run.
-
-No private brain content is used in any reported result. The committed NDJSON dumps under `<repo>/.gbrain-evals/` contain only the LongMemEval question IDs + the rank-ordered retrieved session IDs.
-
-## 3. Sample selection
-
-- **Random seed:** `42` throughout. Set via `--seed N` on `gbrain eval run-all`; recorded in every per-run record.
-- **No per-question curation.** Splits are taken whole; no question is filtered for reporting.
-- **No mode-specific tuning.** The same dataset + same seed feeds every mode. The mode is the only independent variable.
-- **Stability across re-runs:** with `--seed 42` and the same dataset SHA, two runs of the same (mode, suite) produce identical retrieval orderings (modulo the optional Haiku expansion call, which is non-deterministic). Persisted in `eval_results` so anyone can re-score from the committed dumps.
-
-## 4. Run procedure
-
-The command is the doc. Anyone can reproduce.
-
-```bash
-# Setup: in your gbrain working tree, with OPENAI_API_KEY + ANTHROPIC_API_KEY exported.
-git rev-parse HEAD  # record the commit for the methodology footer
-
-# Sweep all 3 modes × 2 retrieval-focused suites with seed 42.
-gbrain eval run-all \
-  --modes conservative,balanced,tokenmax \
-  --suites longmemeval,replay \
-  --seed 42 \
-  --limit 500 \
-  --budget-usd-retrieval 5 \
-  --budget-usd-answer 20 \
-  --output docs/eval/results/v0.32.3/
-
-# Render the comparison.
-gbrain eval compare --md > docs/eval/results/v0.32.3/README.md
-gbrain eval compare --json > docs/eval/results/v0.32.3/comparison.json
-```
-
-The orchestrator writes per-run records to `<repo>/.gbrain-evals/eval-results.jsonl`. Every record carries: `run_id`, `ran_at`, `suite`, `mode`, `commit`, `seed`, `limit`, `params`, `status`, `duration_ms`. The dumps under `docs/eval/results/v0.32.3/` carry the raw question-level outputs so a reviewer can re-score with their own metric implementation.
-
-## 5. Threats to validity
-
-Honest list. We name what would let a critic dismiss the numbers.
-
-- **LongMemEval skews English + technical.** The questions are software-engineering and consumer-product flavored. Performance on a brain rich in non-English / non-technical content (writing, art history, etc.) may differ.
-- **BrainBench is small** (1240 docs) relative to a production brain (10K-100K pages). Absolute scores aren't predictive of your hit rate; the _delta_ between modes is.
-- **char/4 token heuristic.** Token-budget enforcement and cost estimates use a character-count / 4 heuristic. Accurate within ~5-10% for English with the OpenAI tiktoken family; off worse for Voyage (we don't use Voyage in chat retrieval, so it doesn't bias the reported numbers, but if you do, your budget caps will be approximate).
-- **Expansion's quality lift varies by query distribution.** The eval data shows ~97.6% relative quality with LLM expansion vs without (i.e., barely measurable lift) on the LongMemEval corpus. On rarer-entity / longer-tail queries, the lift can be larger. We report the corpus we measured; YMMV.
-- **Paired bootstrap assumes question-level independence.** Multi-hop questions within the same conversation thread aren't independent; the bootstrap CI is slightly tighter than reality.
-- **Single brain instance per benchmark.** The benchmark spins up an in-memory PGLite per question. Cache hit rate measured here doesn't reflect a long-running production brain's cache state.
-
-## 6. Per-question raw outputs
-
-Every reported metric is reproducible from the NDJSON dumps committed at `docs/eval/results/v0.32.3/`. The commit SHA in the methodology footer pins the code version.
-
-**Examples per mode:** the auto-generated `README.md` next to the dumps includes both winning and losing examples per mode, chosen by the deterministic rule:
-
-- **Wins:** the 3 questions where this mode's score exceeded the next-best mode by the largest margin.
-- **Losses:** the 3 questions where this mode's score fell short of the next-best mode by the largest margin.
-
-Picked by the score delta, NOT cherry-picked by hand. The README documents the rule so a critic can verify.
-
-## 7. Pre-registered expectations
-
-Before running, we expect:
-
-1. **tokenmax wins Recall@10** by 5-15 percentage points over conservative. LLM expansion + 50-result ceiling helps rare-entity surface forms.
-2. **conservative wins cost-per-query** by 5-15× over tokenmax. No Haiku expansion + tight 4K budget cap = single-digit-cent queries.
-3. **balanced lands within 3pp of tokenmax** on Recall@10. Intent weighting (zero-LLM cost) closes most of the expansion gap on common queries.
-4. **No mode breaks nDCG@10 ≥ 0.65** — the published "ship it" threshold for hybrid retrieval on technical corpora.
-
-Then we publish whether the data agrees. **If a hypothesis fails, that's documented honestly** in the release README, not buried. Pre-registration is what makes the comparison defensible — without it, a "we expected X and got X" outcome is observation, not prediction.
-
-## 8. Re-run cadence
-
-This document + the eval results are regenerated on every release that touches retrieval-affecting code. The `gbrain doctor eval_drift` check surfaces changes to the curated watch-list in `src/core/eval/drift-watch.ts`:
-
-- `src/core/search/**`
-- `src/core/embedding.ts`
-- `src/core/chunkers/**`
-- `src/core/ai/recipes/anthropic.ts`
-- `src/core/ai/recipes/openai.ts`
-- `src/core/operations.ts`
-
-Additions to the watch-list require a CHANGELOG line.
-
-## Statistical-significance discipline
-
-When `gbrain eval compare --md` reports a Δ between two modes, it computes:
-
-- **Paired bootstrap** with 10,000 resamples per metric. Each resample draws _question-level_ pairs (same question, mode A vs mode B), so question-level variance is differenced out.
-- **Bonferroni correction** across the 12 comparisons (3 modes × 4 metrics). The reported p-value is the comparison's raw p-value × 12 (clamped at 1.0).
-- **95% confidence intervals** computed from the bootstrap distribution.
-
-If the CI for a Δ includes 0 OR the Bonferroni-adjusted p-value exceeds 0.05, the difference is **not** statistically significant. The MD report says "not significant" verbatim.
-
-## Glossary
-
-Every metric the report prints has a plain-English entry in `docs/eval/METRIC_GLOSSARY.md`, auto-generated from `src/core/eval/metric-glossary.ts`. The CI guard at `scripts/check-eval-glossary-fresh.sh` regenerates and diffs against the committed file on every test run; a stale doc fails the build.
-
-## Cost anchors
-
-The mode-picker prompt at `gbrain init` and the CLAUDE.md `## Search Mode` table both surface these rough cost anchors. Working through the math so they're auditable:
-
-**Variables:**
-- `T` = avg tokens per search-result chunk. The recursive chunker targets 300 words / chunk → ~400 tokens (English, OpenAI tiktoken approx).
-- `N` = chunks delivered per query (capped by the mode's `searchLimit`).
-- `R` = downstream model input rate. Sonnet 4.6 = \$3/M. Opus 4.7 = \$5/M. Haiku 4.5 = \$1/M.
-- `Q` = queries per month.
-
-**Per-query input cost** (downstream agent reads the chunks):
-
-    cost_per_query = T × N × R
-
-| Mode | T (tokens) | N (chunks) | Sonnet (\$3/M) | Opus (\$5/M) | Haiku (\$1/M) |
-|---|---|---|---|---|---|
-| conservative (4K cap, 10 max) | ~400 | 10 (or fewer if budget hits) | \$0.012 | \$0.020 | \$0.004 |
-| balanced (12K cap, 25 max) | ~400 | ~25 | \$0.030 | \$0.050 | \$0.010 |
-| tokenmax (no cap, 50 max) | ~400 | ~50 | \$0.060 | \$0.100 | \$0.020 |
-
-**Monthly cost** (Q × per-query):
-
-| Mode @ Sonnet | 1K Q/mo | 10K Q/mo | 100K Q/mo |
-|---|---|---|---|
-| conservative | \$12 | \$120 | \$1,200 |
-| balanced | \$30 | \$300 | \$3,000 |
-| tokenmax | \$60 | \$600 | \$6,000 |
-
-| Mode @ Opus | 1K Q/mo | 10K Q/mo | 100K Q/mo |
-|---|---|---|---|
-| conservative | \$20 | \$200 | \$2,000 |
-| balanced | \$50 | \$500 | \$5,000 |
-| tokenmax | \$100 | \$1,000 | \$10,000 |
-
-**gbrain's own cost** on top:
-- Query embedding (text-embedding-3-large @ \$0.13/M tokens): ~\$0.00001 per query. Negligible at every scale.
-- Tokenmax Haiku expansion call (\$1/M input, \$5/M output, ~500 input + 200 output per call): ~\$0.0015 per query, or \$150/mo at 100K queries. Cache hits cut this in half.
-- Per-page indexing (one-time): bounded by your import volume, not query volume. Not modeled here.
-
-**Cache hit adjustment.** A warmed brain typically sees 30-50% cache hits on repeat-query traffic. Cache hits skip the downstream input cost entirely (the cached result was already in the agent's context once). So real-world costs run ~50-70% of the table above on a busy brain.
-
-**Why these numbers DRIFT from your actual bill:**
-- Your agent's system prompt + reasoning tokens add input that gbrain doesn't see.
-- Compaction reduces input over a long session.
-- Most agents make 1-5 searches per turn; cost-per-turn is what bills you, not cost-per-query.
-- The model price column drifts as providers reprice; pin the rate via `src/core/model-pricing.ts` (the canonical chat-pricing table) for a current snapshot.
-
-The picker copy + CLAUDE.md table are the canonical user-facing source. Update them in lockstep when the underlying chunker size or default `searchLimit` changes.
-
-## Mode × Model matrix (the 25x spread)
-
-The per-query math above assumes Sonnet 4.6 downstream. In reality, the
-downstream model tier is the BIGGER cost lever. Per-query cost at 10K
-queries/month (typical single-user volume), search payload only (no cache
-savings):
-
-| Mode (search tokens) | Haiku 4.5 (\$1/M) | Sonnet 4.6 (\$3/M) | Opus 4.7 (\$5/M) |
-|---|---|---|---|
-| conservative (~4K) | **\$40/mo** | \$120/mo | \$200/mo |
-| balanced (~10K) | \$100/mo | \$300/mo | \$500/mo |
-| tokenmax (~20K) | \$200/mo | \$600/mo | **\$1,000/mo** |
-
-Scales linearly: multiply by 10 for 100K/mo (heavy power user / multi-user
-fleet); divide by 10 for 1K/mo (light usage).
-
-**Natural pairings span ~4x** (cheap model + tight mode → frontier model + loose
-mode). **Mismatches waste capacity:**
-
-- `tokenmax + Haiku`: Haiku gets 20K of search results stuffed into its
-  context per query. Haiku's reasoning is weaker; more chunks = more noise,
-  not more signal. You pay Haiku rates but get sub-Haiku quality. Wrong
-  direction.
-- `conservative + Opus`: Opus has 200K context window and can synthesize
-  across many chunks. Capping at 10 chunks / 4K tokens leaves Opus
-  reasoning underfed. You pay Opus rates but get conservative-shape
-  retrieval. Wasted spend.
-
-**Right-sizing rule:** match the mode's `searchLimit` to the downstream
-model's "useful context depth":
-
-- Haiku struggles past ~5-10 chunks of cross-referenced content → conservative
-- Sonnet handles ~25-40 chunks well → balanced
-- Opus benefits from 50+ chunks for multi-hop reasoning → tokenmax
-
-## Realistic-scale anchor (single power-user agent loop)
-
-The per-query math above is honest but theoretical: it treats each search as an isolated billable event. Real agent loops amortize a lot of context across turns via Anthropic prompt caching. Here's what one heavy power-user loop actually looks like in production, anonymized + scaled so the numbers represent a representative power user rather than any specific deployment.
-
-**Reference shape — tokenmax in production at a single-user scale:**
-
-| Quantity | Approximate value |
-|---|---|
-| 30-day total agent spend | ~\$700/mo |
-| 30-day total tokens billed | ~800M |
-| Turns per month | ~860 (~29/day; one active agent loop) |
-| Average tokens per turn | ~900K |
-| Average cost per turn | ~\$0.85 |
-| Anthropic prompt-cache hit rate | ~88% |
-
-A "turn" here is one agent loop iteration: read user message, plan, execute tool calls (including gbrain searches), generate response. Each turn typically includes 2-4 gbrain searches.
-
-**Per-mode scaling from the tokenmax anchor:**
-
-The cost difference between modes is concentrated in the search-attributable fraction of per-turn cost. System prompt, tool definitions, conversation history, and reasoning tokens don't change with mode — only the chunks gbrain delivers do. Assume 3 searches per turn at the mode's `searchLimit`:
-
-| Mode | Search tokens/turn | Search cost/turn (at \$3/M effective) | Search-attributable @ 860 turns | Δ vs tokenmax |
-|---|---|---|---|---|
-| tokenmax | ~60K (3 × 20K) | ~\$0.18 | ~\$155/mo | — |
-| balanced | ~30K (3 × 10K) | ~\$0.09 | ~\$77/mo | -\$78 |
-| conservative | ~12K (3 × 4K) | ~\$0.036 | ~\$31/mo | -\$124 |
-
-**Implied total agent spend by NATURAL PAIRING** (mode + matched
-downstream model). Per-turn cost scales with the downstream model's
-per-token rate, since the cached prefix + uncached portion + reasoning
-tokens all bill at that rate:
-
-| Pairing | Per-turn cost | Total @ 860 turns/mo |
-|---|---|---|
-| tokenmax + Opus (frontier, max quality) | ~\$0.85 | ~\$700/mo |
-| balanced + Sonnet (the sweet spot) | ~\$0.50 | ~\$430/mo |
-| conservative + Haiku (cost-sensitive) | ~\$0.20 | ~\$170/mo |
-
-**4x spread across natural pairings.** The model tier dominates because
-the per-token rate applies to the WHOLE per-turn payload (system + tools
-+ history + reasoning + search), not just gbrain's chunks. Mode choice
-contributes ~10-20% on top of that base.
-
-**Mismatched pairings push you off the curve:**
-
-| Pairing | Per-turn estimate | Total @ 860 turns/mo | Compared to natural |
-|---|---|---|---|
-| tokenmax + Haiku | ~\$0.20 | ~\$170/mo | Same cost as conservative+Haiku, worse quality |
-| conservative + Opus | ~\$0.75 | ~\$640/mo | 92% of tokenmax+Opus spend, conservative-shape retrieval |
-
-The mismatch math says: a tokenmax+Haiku user pays the same as
-conservative+Haiku but gets a noisier context (Haiku can't filter signal
-from 50 chunks). A conservative+Opus user pays nearly the same as
-tokenmax+Opus but starves Opus on retrieval depth. Both burn budget for
-no improvement.
-
-**What this anchor tells us that the per-query math doesn't:**
-
-1. **At realistic agent-loop scale with disciplined prompt caching, mode choice saves 10-20% of total agent spend** — meaningful, but smaller than the per-query 5x ratio implies. Disciplined prompt-cache layouts blunt the mode delta because most of the per-turn cost is the cached prefix, not the search payload.
-
-2. **Without that prompt-cache discipline, the per-query framing reasserts itself.** Setups that churn the prompt prefix on every turn (frequent system-prompt edits, untemplated tool defs, no prompt-cache structuring) see search payload contribute a much larger fraction of total cost. Those setups should care about mode choice more, not less.
-
-3. **The cache hit rate quoted here (~88%) is achievable but not automatic.** It requires structuring the prompt so the cached prefix stays stable across turns: system prompt + tool defs first, history compacted but cache-aware, retrieved chunks appended LAST (where their volatility doesn't invalidate the prefix). Agents that interleave search results inside the cached region pay the prefix-rebuild tax on every turn.
-
-**Caveats stacked here:**
-
-- The anchor represents ONE power-user loop. Multi-user fleets aggregate proportionally; the per-user shape doesn't change.
-- The "3 searches per turn" assumption varies wildly. A code-review agent might issue 10+ searches per turn; a chat-only loop might do 0.
-- The 88% cache hit rate is the high end of what's achievable. Half that is closer to a default agent without cache-aware prompt layout.
-- The "Δ vs tokenmax" math assumes the OTHER cost components (system, tools, history, reasoning) stay constant. In practice, conservative's smaller per-turn payload also leaves more room in the context window for history → which can change agent behavior in either direction.
-
-This anchor + the per-query math both live in this doc on purpose. The per-query framing is what an isolated benchmark would measure (and what `gbrain eval run-all` will produce). The realistic-scale anchor is what an operator actually pays. Both are honest; neither is the whole truth.
-
-## Reproducibility footer
-
-Every release that publishes eval numbers includes a footer with:
-
-- Code commit SHA
-- Dataset SHA (LongMemEval, BrainBench, Replay)
-- `--seed N`
-- Run commands verbatim
-- API model identifiers used (Anthropic + OpenAI + judge model)
-
-Without these, the numbers are unfalsifiable. With them, anyone with API keys can re-score.

docs/guardrails.md

@@ -1,102 +0,0 @@
-# Content Guardrail Seams
-
-GBrain exposes **vendor-neutral guardrail seams** at the boundaries where
-external content enters the retrieval layer and where queries/tool-inputs enter
-the LLM gateway. A guardrail is any external classifier — a content firewall, a
-prompt-injection detector, a PII scrubber — that wants to *observe* content at
-those boundaries.
-
-The OSS distribution ships **inert**: zero guardrails are registered by default,
-and every seam is a no-op until an operator registers a provider.
-
-## Design contract (hard invariants)
-
-These hold for every seam and are enforced by `test/guardrails.test.ts`:
-
-- **Observe-only.** `runGuardrails()` returns `void`. Callers never branch on a
-  provider verdict. A guardrail registered through this interface *cannot*
-  block, rewrite, drop, retry, or reorder GBrain behavior. Enforcement, if ever
-  added, will get its own explicitly-named seam and its own RFC — it will not
-  silently reuse this one.
-- **Fail open.** Missing config, provider throw/reject, timeout, and network
-  error are all swallowed. A broken guardrail never breaks an ingest, a query,
-  or a tool call.
-- **Inline await.** Hooks await the provider before proceeding, so the
-  classifier sees content at the exact pre-persist / pre-inference moment.
-- **No verdict persistence.** GBrain writes no guardrail rows. Providers own
-  their own audit trail.
-- **Content boundaries.** Hooks pass only the ingest/user-facing payload — the
-  markdown/code body, the last user message, the expansion query, the tool
-  input. They never pass system prompts, full chat history, tool *output*, LLM
-  output, embeddings, or multimodal/OCR/rerank payloads.
-
-## The five seams
-
-All seams call `runGuardrails({ hook, content, metadata })` from
-`src/core/guardrails.ts`.
-
-| `hook` | Location | Fires |
-| --- | --- | --- |
-| `file_storage.markdown` | `import-file.ts` → `importFromContent` | After `parseMarkdown` + size guard, **before** content-sanity, hashing, chunking, embedding, DB write |
-| `file_storage.code` | `import-file.ts` → `importCodeFile` | After code size guard, **before** hashing, code-chunking, embedding, DB write |
-| `ai_gateway.chat` | `ai/gateway.ts` → `chat` | On the **latest user message only**, before provider inference |
-| `ai_gateway.expand` | `ai/gateway.ts` → `expand` | On the query, before the expansion model call |
-| `ai_gateway.tool_input` | `ai/gateway.ts` → `toolLoop` | On `{toolName, input}`, before pending-persist and before tool execution |
-
-The two `file_storage.*` hooks cover every natural ingest caller that routes
-through `importFromContent` / `importCodeFile`: `gbrain import`, sync, capture,
-`put_page`, subagent `brain_put_page`, trusted-workspace writes,
-`ingest_capture`, inbox daemon dispatch, reindex, code reindex, and the public
-import APIs.
-
-## Writing a guardrail provider
-
-```ts
-import { registerGuardrailProvider, type GuardrailInput } from 'gbrain/core/guardrails';
-
-registerGuardrailProvider({
-  id: 'my-firewall',
-  async classify(input: GuardrailInput) {
-    // input.hook      — which boundary ('file_storage.markdown', etc.)
-    // input.content   — the raw text to classify
-    // input.metadata  — provider-opaque context (slug, source_kind, tool_name, model, ...)
-    //
-    // Do your own timeout/retry/logging here. The return value is IGNORED by
-    // GBrain — return a typed verdict only if your own audit code consumes it.
-    await fetch(MY_API, { method: 'POST', body: JSON.stringify({ text: input.content }) });
-  },
-});
-```
-
-Register once at process init (e.g. from a plugin entry or an operator boot
-hook). Registration is idempotent by `id`, so a re-init won't double-fire.
-
-### Provider responsibilities
-
-GBrain deliberately keeps the seam minimal. The provider owns:
-
-- **Timeout discipline.** GBrain does not impose a timeout in `runGuardrails`
-  so you can tune per-deployment latency. Use an `AbortController`.
-- **Secret handling.** Read API keys from env at call time. Never log the key.
-- **Redacted logging.** Don't log raw classified content (it may itself be the
-  payload you're trying to protect). Log a hash + verdict, not the body.
-- **Async fan-out.** If you don't want to block ingest on your classifier,
-  enqueue inside `classify` and return immediately. The seam awaits *your*
-  function; what it does is up to you.
-
-## Example: shadow-mode firewall provider
-
-A typical "shadow mode" provider (classify, log a redacted verdict, change
-nothing) is ~80 lines and lives entirely in the provider's own package. See
-the reference provider doc shipped to integration partners for a complete
-`classify` implementation that:
-
-1. resolves `<base>/classify` from an env URL,
-2. posts `{ text, hook, metadata }` with an `x-api-key` header,
-3. parses a `{ prediction, blocked, score, threshold }` response,
-4. emits one redacted stderr line (`status=… prediction=… content_sha256=…`),
-5. fails open on every error path.
-
-Because the verdict is ignored by GBrain, "shadow mode" requires *no* special
-GBrain flag — it is the only mode this interface supports. Enforcement would be
-a separate, future, RFC-gated seam.

docs/guides/agent-to-gbrain.md

@@ -1,199 +0,0 @@
-# How a downstream agent should talk to gbrain
-
-This guide is for authors of downstream agents (hermes, openclaw, future
-forks) that need to call gbrain operations from their own runtime. Reading
-this first will save you a debugging cycle: gbrain has **two distinct
-surfaces**, and which one you pick depends on the operation.
-
-## The two surfaces
-
-```
-                       ┌─────────────────────────────────────────────┐
-                       │                gbrain process                │
-                       │                                              │
-   Agent (hermes,      │  ┌──────────────────┐    ┌────────────────┐ │
-   openclaw, fork) ────┼──▶  MCP ops surface  │    │   localOnly    │ │
-                       │  │ (HTTP + OAuth)    │    │   admin ops    │ │
-                       │  │                   │    │                │ │
-                       │  │  search, query,   │    │  sync, embed,  │ │
-                       │  │  put_page,        │    │  dream, doctor,│ │
-                       │  │  get_page,        │    │  autopilot,    │ │
-                       │  │  find_experts,    │    │  init, secrets │ │
-                       │  │  ...              │    │                │ │
-                       │  └──────────────────┘    └────────────────┘ │
-                       │           ▲                       ▲          │
-                       │           │                       │          │
-                       │           │                       │          │
-                       │     thin-client OAuth      shell-job `inherit:`│
-                       │     (preferred for          (only path for   │
-                       │      MCP-equivalent ops)    localOnly ops)   │
-                       └─────────────────────────────────────────────┘
-```
-
-The two surfaces are **not interchangeable**. Pick by op, not by preference.
-
-## Surface 1 — MCP ops over HTTP (thin-client + OAuth)
-
-Use for any operation that has an MCP equivalent: `search`, `query`,
-`put_page`, `get_page`, `find_experts`, `find_orphans`, `find_anomalies`,
-`get_recent_salience`, `find_trajectory`, and so on. The canonical list is
-the set of ops in `src/core/operations.ts` whose `localOnly` flag is unset
-(or `false`).
-
-### Setup
-
-The host runs gbrain as a long-lived HTTP server:
-
-```bash
-GBRAIN_ALLOW_SHELL_JOBS=1 gbrain serve --http --port 3131
-```
-
-The agent registers as an OAuth client (one-time):
-
-```bash
-gbrain auth register-client hermes \
-  --grant-types client_credentials \
-  --scopes read,write
-# Prints client_id + client_secret one-time. Store securely.
-```
-
-The agent's runtime calls `/mcp` with a bearer token from `client_credentials`
-grant. Secrets stay in the gbrain serve process; the agent never sees
-DATABASE_URL or API keys.
-
-Thin-client mode (`gbrain init --mcp-only`) gives the agent the same
-client-credentials wiring, plus the `gbrain` CLI itself routes MCP-eligible
-commands through the configured remote MCP. The agent can call
-`gbrain search` / `gbrain query` directly and the CLI does the OAuth dance.
-
-### Why this is preferred for MCP ops
-
-- Secrets never leave the server process.
-- OAuth scopes give you `read`, `write`, `admin` separation — agent only gets
-  what it needs.
-- Source-scoped tokens (`--source dept-x` on `register-client`) confine the
-  agent to a specific source within a federated brain.
-- One audit surface (`mcp_request_log`) covers every op call uniformly.
-
-## Surface 2 — localOnly admin ops via shell-job `inherit:`
-
-Some operations are flagged `localOnly: true` in `src/core/operations.ts` and
-are **refused** in thin-client mode at `src/cli.ts:isThinClient`. The full
-list (as of v0.36.5.0) includes:
-
-- `sync` (filesystem walks need local FS access)
-- `embed` (orchestrates the embed pipeline)
-- `extract` (walks markdown files)
-- `dream` (synthesis cycle)
-- `doctor` (filesystem hygiene checks)
-- `autopilot` (background daemon orchestration)
-- `init` (creates `~/.gbrain/`)
-- `secrets` (config management)
-
-For these, the agent cannot route through HTTP MCP. The only path is to run
-`gbrain` as a CLI subprocess. The recommended pattern is to submit the
-subprocess as a shell job to the gbrain Minions worker so retry / backoff /
-DLQ / audit trail all come for free.
-
-### Setup
-
-```bash
-gbrain jobs submit shell --params '{
-  "cmd": "gbrain sync --skip-failed && gbrain embed --stale",
-  "cwd": "/data/gbrain",
-  "inherit": ["database_url"]
-}'
-```
-
-The `inherit: ["database_url"]` field tells the worker to look up
-`database_url` from its `loadConfig()` and inject the value into the child
-env as `GBRAIN_DATABASE_URL`. The DB row in `minion_jobs.data` carries the
-names only — `inherit: ["database_url"]` — never the value. See
-[minions-shell-jobs.md#secrets](./minions-shell-jobs.md#secrets) for the
-full validation rules and error catalog.
-
-### Why this is preferred over writing secrets into `env:` per-job
-
-- Pre-v0.36.5.0 callers passed `env: { GBRAIN_DATABASE_URL: "postgresql://..." }`
-  per job. The URL landed plaintext in `minion_jobs.data` and the shell-audit
-  JSONL. Anyone with brain-DB read access (or a brain dump, or a shared brain
-  via mounts) saw the URL. As of v0.36.5.0, this is rejected at pre-enqueue
-  validation. The error message names `inherit: ["database_url"]` as the
-  replacement.
-
-### Worker setup (one-time, per host)
-
-The agent's host needs a worker that processes shell jobs:
-
-```bash
-# One-shot inline execution (PGLite or Postgres):
-gbrain jobs submit shell --params '{...}' --follow
-
-# Persistent worker (Postgres only — PGLite uses --follow inline):
-GBRAIN_ALLOW_SHELL_JOBS=1 gbrain jobs work
-```
-
-`GBRAIN_ALLOW_SHELL_JOBS=1` is the worker-side opt-in. Without it, shell jobs
-sit in `waiting` indefinitely. Set it on the worker process env (or in your
-deploy unit / launchd plist), not per-submission — submitter env is a weak
-proxy for worker env.
-
-## Decision table
-
-| Operation | Surface | Why |
-|---|---|---|
-| `search` / `query` | HTTP MCP via thin-client | Has MCP op; OAuth-scoped. |
-| `get_page` / `list_pages` | HTTP MCP | Same. |
-| `put_page` | HTTP MCP | Same; respects subagent allow-list when applicable. |
-| `find_experts` / `find_orphans` | HTTP MCP | Same. |
-| `sync` / `embed` / `extract` | Shell job + `inherit:` | `localOnly: true`. |
-| `dream` | Shell job + `inherit:` | `localOnly: true`. |
-| `doctor` | Shell job + `inherit:` (or no inherit if no DB) | `localOnly: true`. |
-| `autopilot` | Run as a daemon directly on the host | Long-lived, not job-shaped. |
-| `init` / `secrets` | One-time host setup | Operator action, not agent action. |
-
-## Recommended patterns
-
-- **Prefer `inherit:` for secrets you don't want in the row.** Names land in
-  `minion_jobs.data`; values resolve at child-spawn from the worker's config.
-  If a brain DB ever traverses a trust boundary, secrets stay out.
-- **Free-form names.** `inherit:` accepts any snake_case config-key on your
-  worker — `database_url`, `anthropic_api_key`, `openai_api_key`,
-  `voyage_api_key`, `groq_api_key`, `zeroentropy_api_key`, or any custom
-  field you stuff into `~/.gbrain/config.json`. The agent picks what it
-  needs.
-- **`env:` still works** for non-secret values, or for cases where you
-  WANT the value in the row (e.g. an opaque correlation token your audit
-  flow needs to read back later). The validator doesn't second-guess you.
-- **Never try to route a `localOnly` op through thin-client MCP.** It will
-  fail with `localOnly op refused in thin-client mode`. Use shell-job +
-  `inherit:` (for secrets) or `env:` (for non-secrets).
-
-## Migration: from pre-v0.36.5.0
-
-If your agent submits shell jobs that pass secrets via `env:`:
-
-```jsonc
-// Pre-v0.36.5.0: works but URL persists in minion_jobs.data plaintext.
-{
-  "cmd": "gbrain sync --skip-failed",
-  "cwd": "/data/gbrain",
-  "env": { "GBRAIN_DATABASE_URL": "postgresql://..." }
-}
-```
-
-Switch to (recommended):
-
-```jsonc
-// v0.36.5.0+: name in row, value resolved at child-spawn from worker config.
-{
-  "cmd": "gbrain sync --skip-failed",
-  "cwd": "/data/gbrain",
-  "inherit": ["database_url"]
-}
-```
-
-Make sure the worker host has `database_url` configured (either via
-`gbrain config set database_url <value>` or via `GBRAIN_DATABASE_URL` /
-`DATABASE_URL` env on the worker process). If the worker can't resolve the
-key, the validator rejects the job at submit time with a paste-ready hint.

docs/guides/live-sync.md

@@ -15,20 +15,17 @@ with the brain repo automatically. You never have to remember to run sync.
 
 ## Implementation
 
-### Prerequisite: a reachable direct connection
+### Prerequisite: Session Mode Pooler
 
-GBrain is tuned for the Supabase **Transaction pooler** (port 6543): it
-auto-disables prepared statements there and routes `engine.transaction()`
-(migrations, DDL, sync imports) to a derived **direct** connection
-(`db.<ref>.supabase.co:5432`). That direct host is IPv6-only, so on an
-IPv4-only host, reads work but sync **silently skips most pages**. This is the
-number one cause of "sync ran but nothing happened."
+Sync uses `engine.transaction()` on every import. If `DATABASE_URL` points to
+Supabase's **Transaction mode** pooler, sync will throw `.begin() is not a
+function` and **silently skip most pages**. This is the number one cause of
+"sync ran but nothing happened."
 
-Fix: make the direct connection reachable over IPv4. Either set
-`GBRAIN_DIRECT_DATABASE_URL` to the **Session pooler** string (port 5432 on the
-`pooler.supabase.com` host, IPv4), or enable Supabase's IPv4 add-on. Verify by
-running `gbrain sync` and checking that the page count in `gbrain stats` matches
-the syncable file count in the repo.
+Fix: use the **Session mode** pooler string (port 6543, Session mode) or the
+direct connection (port 5432, IPv6-only). Verify by running `gbrain sync` and
+checking that the page count in `gbrain stats` matches the syncable file count
+in the repo.
 
 ### The Primitives
 
@@ -61,9 +58,8 @@ gbrain sync --repo /data/brain && gbrain embed --stale
 Name: gbrain-auto-sync
 Schedule: */15 * * * *
 Prompt: "Run: gbrain sync --repo /data/brain && gbrain embed --stale
-  Log the result. If sync errors mention an unreachable host or timeout,
-  the direct connection isn't reachable over IPv4 (set
-  GBRAIN_DIRECT_DATABASE_URL to the Session pooler, or enable the IPv4 add-on)."
+  Log the result. If sync fails with .begin() is not a function,
+  the DATABASE_URL is using Transaction mode pooler."
 ```
 
 **Hermes:**
@@ -120,27 +116,6 @@ hashes match. If both a cron and `--watch` fire simultaneously, no conflict.
    server is down when a push happens, that sync is missed. Pair webhooks
    with a cron fallback that catches anything the webhook missed.
 
-4. **A single un-parseable file can't wedge all indexing.** When a file fails
-   to import (malformed YAML frontmatter, an unquoted colon, etc.), sync holds
-   the bookmark and tells you exactly which file broke — a *fresh* failure
-   fails closed so nothing is silently dropped. But a file that fails the same
-   way `GBRAIN_SYNC_AUTOSKIP_AFTER` consecutive syncs (default 3, set `0` to
-   disable) is auto-skipped so the rest of the brain keeps indexing past it.
-   Skipped files don't disappear: `gbrain doctor` keeps warning until you fix
-   or delete them, and fixing the file clears it on the next sync. A repository
-   history rewrite still hard-blocks even with `--skip-failed`. Run
-   `gbrain sync --skip-failed` to acknowledge a known-bad set yourself.
-
-5. **Import checkpoints name the import target, not the caller's CWD.**
-   Interrupted `gbrain import <dir>` runs may leave
-   `~/.gbrain/import-checkpoint.json` so the next import can resume. The
-   checkpoint `dir` is the absolute, resolved import target captured when
-   import starts. It is not a cleanup instruction and it must not be
-   re-derived from the process working directory. Checkpoints written by
-   gbrain include `schema_version: 1`, `owner: "gbrain"`, and
-   `kind: "import"` so downstream tools can validate the contract before
-   deciding whether to resume.
-
 ## How to Verify
 
 1. **Edit a file and search for the change.** Edit a brain markdown file,
@@ -150,8 +125,8 @@ hashes match. If both a cron and `--watch` fire simultaneously, no conflict.
 
 2. **Compare page count to file count.** Run `gbrain stats` and count the
    syncable markdown files in the brain repo. The page count in the database
-   should match. If they diverge, files are being silently skipped (likely an
-   unreachable direct connection on IPv4 — see the prerequisite above).
+   should match. If they diverge, files are being silently skipped (likely
+   a Transaction mode pooler issue).
 
 3. **Check embedded chunk count.** In `gbrain stats`, the embedded chunk
    count should be close to the total chunk count. A large gap means

docs/guides/minions-deployment.md

@@ -54,33 +54,6 @@ gbrain jobs supervisor stop
 An agent seeing exit=2 can safely treat it as "one is already running";
 exit=1 should page a human.
 
-### Lowering scheduling priority (`--nice`)
-
-When the worker pool runs at full concurrency on a machine you also use
-interactively, it can drive the load average high enough to starve your
-shell. Cutting `--concurrency` throws away throughput. Reach for `--nice`
-instead — it lowers the job tree's CPU scheduling priority without touching
-width, so the work runs full-speed when the box is idle and yields when it
-isn't:
-
-```bash
-# Full concurrency, low priority. Propagates to the spawned worker and its
-# children (shell jobs, subagents) via OS niceness inheritance.
-gbrain jobs supervisor --concurrency 4 --nice 10
-
-# Equivalent for a bare worker, or set it durably in the environment.
-GBRAIN_NICE=10 gbrain jobs work --concurrency 4
-```
-
-`--nice` takes a POSIX value from `-20` (highest priority) to `19`
-(nicest/lowest); positive values need no privilege, negative values need
-root. `GBRAIN_NICE` is the env equivalent (the flag wins). Confirm the
-effective value with `gbrain jobs stats`, `gbrain jobs supervisor status
---json`, or the `supervisor_niceness` check in `gbrain doctor` — the doctor
-check warns if what you asked for isn't what's actually running (e.g. a
-negative value denied without privilege, or an OS `RLIMIT_NICE` clamp). This
-is distinct from the concurrency / inflight cap and composes with it.
-
 ### Which supervisor when?
 
 The supervisor solves in-process crash recovery. Platform-level

docs/guides/minions-shell-jobs.md

@@ -46,13 +46,10 @@ pass:
 **What the env allowlist does AND does not do.** Shell jobs run with a minimal
 env: `PATH, HOME, USER, LANG, TZ, NODE_ENV`. Your secrets like `OPENAI_API_KEY`
 and `DATABASE_URL` are NOT passed to the child. You opt-in additional keys per
-job via `env: { ... }` (non-secret values only — see "Secrets" below) or via
-`inherit: ["database_url"]` (recommended for secrets — names only in the row,
-values resolved at child-spawn from `gbrain config set`). This stops accidental
-`$OPENAI_API_KEY` interpolation in a user-authored script. It does **not**
-sandbox filesystem reads: a shell script can `cat ~/.env` or any file the
-worker process can read. The operator picks a safe `cwd`. That is the trust
-boundary.
+job via `env: { ... }`. This stops accidental `$OPENAI_API_KEY` interpolation in
+a user-authored script. It does **not** sandbox filesystem reads: a shell
+script can `cat ~/.env` or any file the worker process can read. The operator
+picks a safe `cwd`. That is the trust boundary.
 
 **Audit trail, not forensic insurance.** Every submission writes a JSONL line
 to `~/.gbrain/audit/shell-jobs-YYYY-Www.jsonl` (ISO-week rotation; override
@@ -109,115 +106,6 @@ Note: `--follow` blocks the crontab slot until the job finishes. If 14 shell
 crons land at the same minute and each takes 30s, they serialize through
 crontab's spawning limits. Postgres + persistent worker scales better.
 
-### Calling `gbrain` itself from a shell job — use `inherit:` for DATABASE_URL {#secrets}
-
-A common pattern is submitting shell jobs that run `gbrain` CLI commands:
-
-```bash
-gbrain jobs submit shell --params '{
-  "cmd": "gbrain sync --skip-failed && gbrain embed --stale",
-  "cwd": "/data/gbrain",
-  "inherit": ["database_url"]
-}'
-```
-
-`inherit: ["database_url"]` tells the worker to look up `database_url` from its
-own `loadConfig()` (file + env merged) and inject the value into the child's
-env as `GBRAIN_DATABASE_URL`. The job row in `minion_jobs.data` stores
-`inherit: ["database_url"]` — **names only, never values**. The shell-audit
-JSONL records the same. Pre-enqueue validation rejects the submission if the
-worker can't resolve the requested key, with a paste-ready
-`gbrain config set database_url <value>` hint.
-
-**Why not just write the URL into `env:` directly?** Pre-v0.36.5.0 callers
-wrote things like:
-
-```jsonc
-// ❌ Deprecated as of v0.36.5.0 — REJECTED at submit time.
-{
-  "cmd": "gbrain stats",
-  "cwd": "/data/gbrain",
-  "env": { "GBRAIN_DATABASE_URL": "postgresql://..." }
-}
-```
-
-This planted plaintext secrets in `minion_jobs.data` (DB row) and in the
-shell-audit JSONL. Anyone with read access to the brain DB (or a brain dump,
-or a shared brain via the mounts feature) saw the URL. v0.36.5.0 doesn't
-forbid that pattern — the validator trusts the agent — but **prefer
-`inherit:`** for any secret you want kept out of the row. Names land in the
-row; values resolve at child-spawn from the worker's config.
-
-**Scope:** v0.36.5.0 `inherit:` is **free-form**. Pass any snake_case
-config-key name and the worker resolves the value from `loadConfig()` at
-child-spawn time:
-
-- `inherit: ["database_url"]` → child env `GBRAIN_DATABASE_URL`
-- `inherit: ["anthropic_api_key"]` → child env `ANTHROPIC_API_KEY`
-- `inherit: ["openai_api_key"]` → child env `OPENAI_API_KEY`
-- `inherit: ["voyage_api_key"]` → child env `VOYAGE_API_KEY`
-- `inherit: ["groq_api_key", "zeroentropy_api_key"]` → both injected
-- Or any arbitrary config-key your worker has (`my_custom_field` →
-  `MY_CUSTOM_FIELD`)
-
-The env-key name is derived by uppercasing the config-key name. The one
-override is `database_url` → `GBRAIN_DATABASE_URL` (plain `DATABASE_URL` is
-ambiguous in most Postgres-app contexts).
-
-Pre-enqueue validation fail-fasts if the worker can't resolve a requested
-name. The validator does NOT police which secrets you choose to inherit —
-the agent submitting the minion is in the same uid as the worker, so it's
-your call.
-
-**Output-side leakage (read this).** The `inherit:` allowlist prevents
-secrets from landing in the JOB ROW INPUT fields (`data.cmd`, `data.argv`,
-`data.env`). By default it does NOT scrub the OUTPUT fields — if your
-script prints the secret to stdout or stderr (`echo "$GBRAIN_DATABASE_URL"`,
-`psql "$GBRAIN_DATABASE_URL"` echoing the URL on error), the value lands
-plaintext in `result.stdout_tail` / `result.stderr_tail` / `error_text`,
-and from there into the brain DB row.
-
-**`redact_secrets: true` opts into output-side scrubbing.** Set it per-job
-(or pass `--redact-secrets` on the CLI):
-
-```bash
-gbrain jobs submit shell --params '{
-  "cmd": "gbrain sync --skip-failed",
-  "cwd": "/data/gbrain",
-  "inherit": ["database_url"],
-  "redact_secrets": true
-}'
-
-# Or, equivalently:
-gbrain jobs submit shell \
-  --params '{"cmd":"gbrain sync --skip-failed","cwd":"/data/gbrain","inherit":["database_url"]}' \
-  --redact-secrets
-```
-
-When `redact_secrets: true`, the worker resolves each name in `inherit:` to
-a value, runs the child, then string-replaces every occurrence of those
-values in `stdout_tail` / `stderr_tail` (and in the `error_text` derived from
-`stderr_tail` on non-zero exit) with `<REDACTED:name>` before persistence.
-Only `inherit:`-resolved values are scrubbed; caller-supplied `env:` values
-are not (those are the "I'm fine with this in the row" channel by design).
-
-**Heuristic, not perfect.** The redactor uses literal string-replace. A
-script that base64-encodes the secret before printing, or that emits it
-one character at a time, will bypass the scrub. Those are adversarial
-shapes — the agent + the script are in the same trust domain, so this
-layer defends against accidental echo (the common case), not deliberate
-exfiltration.
-
-**Three rules for shell-job authors who deal with secrets:**
-
-- **Prefer not to echo secrets at all.** Even with `redact_secrets`, less
-  output means less risk if the redactor ever has an edge-case miss.
-- **Wrap noisy CLI tools to suppress URLs on error.** `psql --quiet`,
-  `pg_dump --quiet`, or pipe through
-  `2>&1 | sed 's|postgresql://[^@]*@|postgresql://REDACTED@|g'`.
-- **Inspect with `gbrain jobs get <id>` after a failure** to verify what
-  actually persisted.
-
 ### Submitting with `argv` (no shell interpolation)
 
 For programmatic callers assembling commands from JSON, use `argv` instead of
@@ -273,11 +161,6 @@ gbrain jobs list --status waiting --name shell
 | `shell: cwd is required and must be an absolute path` | `cwd` must be a string starting with `/`. | Set `cwd` in `--params` to an absolute path. |
 | `shell: argv must be an array of strings` | `argv` has a non-string entry or isn't an array. | Pass `argv: ["bin","arg1","arg2"]`. |
 | `shell: env values must all be strings` | `env` has a number/bool/object value. | Stringify: `"env":{"COUNT":"3"}` not `"env":{"COUNT":3}`. |
-| `shell: inherit must be an array of config-key names` | `inherit` wasn't an array. | Pass `"inherit": ["database_url", ...]`. |
-| `shell: inherit entries must be non-empty strings` | An element of `inherit` was empty, non-string, or null. | Use snake_case config-key names like `database_url`, `anthropic_api_key`. |
-| `shell: inherit name "<X>" must match [a-z][a-z0-9_]*` | Name failed snake_case regex (uppercase, leading digit/underscore, special char). | Use the config-key name verbatim — `database_url`, not `DATABASE_URL`. |
-| `shell: inherit requested "<X>" but worker has no <X> configured` | Worker can't resolve the requested name from `loadConfig()`. | Run `gbrain config set <X> <value>` on the worker host, OR check the config file at `~/.gbrain/config.json`. |
-| `shell: redact_secrets must be a boolean if set` | Caller passed a non-boolean for `redact_secrets`. | Pass `true` or `false` (or omit). The CLI `--redact-secrets` flag sets it automatically. |
 | `permission_denied: shell jobs cannot be submitted over MCP` | An MCP client tried to submit a shell job. By design CLI-only. | Submit from CLI or via a trusted operation handler (`ctx.remote === false`). |
 | `protected job name 'shell' requires CLI or operation-local submitter` | A caller invoked `MinionQueue.add('shell', ...)` without the `trusted` opt-in. | Pass `{ allowProtectedSubmit: true }` as the 4th arg. CLI and `submit_job` do this automatically. |
 | `aborted: timeout` / `aborted: cancel` / `aborted: shutdown` / `aborted: lock-lost` | The worker's abort signal fired mid-execution. Child got SIGTERM, 5s grace, then SIGKILL. | Expected: timeout / user cancel / deploy restart / stall. Inspect `gbrain jobs get` to see which. |

docs/guides/multi-language-fts.md

@@ -1,97 +0,0 @@
-# Multi-language full-text search
-
-GBrain's keyword search arm uses Postgres full-text search (tsvector/tsquery).
-The tokenizer language is configurable via the `GBRAIN_FTS_LANGUAGE`
-environment variable. Default: `english`.
-
-## How it works
-
-Postgres text-search configurations control stemming and stop-word removal.
-`GBRAIN_FTS_LANGUAGE` is read by `src/core/fts-language.ts` and applied on
-both sides of the search:
-
-- **Query side** — `websearch_to_tsquery('<lang>', $query)` in both engines
-  (Postgres and PGLite).
-- **Write side** — the `update_page_search_vector` and
-  `update_chunk_search_vector` trigger functions that populate
-  `pages.search_vector` and `content_chunks.search_vector`.
-
-The value is validated against `/^[a-z][a-z0-9_]*$/` before it is ever
-interpolated into SQL (tsvector functions don't accept parameterized config
-names). Invalid values fall back to `english` with a warning.
-
-## Built-in languages
-
-Set the env var to any configuration your Postgres instance ships:
-
-```bash
-export GBRAIN_FTS_LANGUAGE=portuguese
-export GBRAIN_FTS_LANGUAGE=spanish
-export GBRAIN_FTS_LANGUAGE=german
-```
-
-List what's available:
-
-```sql
-SELECT cfgname FROM pg_ts_config;
-```
-
-PGLite (the embedded default engine) ships the same built-in snowball
-configurations as stock Postgres.
-
-## First install vs. changing language later
-
-On first install (or upgrade), the `configurable_fts_language` schema
-migration reads `GBRAIN_FTS_LANGUAGE` and stamps the trigger functions with
-that language. After the migration has run, changing the env var alone does
-NOT retokenize existing rows — the migration shows as applied and is skipped.
-Use the explicit command:
-
-```bash
-export GBRAIN_FTS_LANGUAGE=portuguese
-gbrain reindex-search-vector --dry-run    # preview: language + row counts
-gbrain reindex-search-vector --yes        # recreate triggers + backfill
-```
-
-The command recreates both trigger functions under the new language and
-backfills every existing `pages` and `content_chunks` row in batches,
-streaming progress to stderr. It is idempotent: re-running with the same
-language produces identical vectors. `--json` prints a machine-readable
-result envelope but still requires `--yes` (or an interactive confirm).
-
-## Recipe: accent-insensitive Portuguese (`pt_br`)
-
-Brazilian Portuguese content often mixes accented and unaccented spellings
-("São Paulo" vs "Sao Paulo"). Build a custom config that folds accents via
-the `unaccent` extension, then stems with the portuguese snowball dictionary:
-
-```sql
-CREATE EXTENSION IF NOT EXISTS unaccent;
-
-CREATE TEXT SEARCH CONFIGURATION pt_br (COPY = portuguese);
-
-ALTER TEXT SEARCH CONFIGURATION pt_br
-  ALTER MAPPING FOR hword, hword_part, word
-  WITH unaccent, portuguese_stem;
-```
-
-Then point GBrain at it:
-
-```bash
-export GBRAIN_FTS_LANGUAGE=pt_br
-gbrain reindex-search-vector --yes
-```
-
-Note: custom configurations require a real Postgres instance (e.g. the
-Supabase engine). The config must exist BEFORE the migration or the reindex
-command runs, or Postgres will reject the trigger recreation with
-`text search configuration "pt_br" does not exist`.
-
-## Caveats
-
-- One language per brain: the setting is global to the database, not
-  per-source. Mixed-language brains should pick the dominant language (the
-  vector-search arm is language-agnostic and covers the rest).
-- Keep `GBRAIN_FTS_LANGUAGE` set consistently in every environment that
-  writes to the brain (CLI shells, MCP server, cron jobs) — a writer without
-  the env var tokenizes new rows in `english` until the next reindex.

docs/guides/multi-source-brains.md

@@ -114,11 +114,8 @@ Flip later with `gbrain sources federate <id>` / `unfederate <id>`.
 Full subcommand reference:
 
 ```
-gbrain sources add <id> --path <p> [--name <n>] [--federated|--no-federated] [--force]
+gbrain sources add <id> --path <p> [--name <n>] [--federated|--no-federated]
                                Register a source. id: [a-z0-9](?:[a-z0-9-]{0,30}[a-z0-9])?
-                               --path must be a git repo (or a subdirectory of one) — see
-                               "The git requirement for --path sources" below. --force
-                               skips that check to register before git-init exists.
 gbrain sources list [--json]   List all sources with page counts + federation state.
 gbrain sources remove <id> [--yes] [--dry-run] [--keep-storage]
                                Cascade-delete a source (pages, chunks, timeline).
@@ -131,47 +128,6 @@ gbrain sources federate <id>
 gbrain sources unfederate <id>
 ```
 
-## The git requirement for --path sources
-
-Every `--path` source must be a git repository (or live inside one — a
-subdirectory of a git repo works too) with at least one committed, tracked
-file under that path. `gbrain sources add` validates this at registration
-time and refuses a directory that doesn't qualify — no `.git` at all, a
-`git init` with no commit yet, or a commit made before `git add` — with an
-actionable error instead of silently registering a source that will fail
-(or worse, "succeed" while importing nothing) on its first `gbrain sync`.
-Fix it with:
-
-```bash
-git -C <path> init
-git -C <path> add -A
-git -C <path> commit -m "initial import"
-gbrain sources add <id> --path <path>
-```
-
-Two details that are easy to miss:
-
-- **Files must actually be committed, not just present.** The sync walker
-  reads files through git objects, so `git init` alone — even followed by an
-  empty commit (`git commit --allow-empty`) — isn't enough. Registration
-  checks for real tracked content (`git ls-tree HEAD` scoped to the path),
-  not just a resolvable `HEAD`, so this footgun is caught immediately
-  instead of surfacing later as a sync that imports nothing.
-- **`--force` registers the source anyway**, skipping the check. Use this if
-  you're registering a path before an automated pipeline gets around to
-  `git init`-ing it. GBrain never auto-`git init`s a `--path` source for
-  you — it's your directory, not a gbrain-managed clone (same consent
-  boundary as sync-time self-heal, which also never mutates a `--path`
-  source without an explicit ask).
-
-**If sync ever reports a problem with the sync anchor** (`last_commit`) —
-after a force-push, a history rewrite, or a from-scratch `git init` on a
-directory that was synced before — you do not need to reset anything by
-hand. `gbrain sync` detects an unreachable or non-ancestor anchor
-automatically and recovers: either a full reimport (anchor object missing)
-or a direct tree-to-tree diff against the orphaned bookmark (anchor present
-but rewritten), advancing the anchor to the new HEAD when it completes.
-
 ## Citation format for agents
 
 When agents receive multi-source results they MUST cite pages in
@@ -199,58 +155,6 @@ Reads span federated sources by default. Writes require a resolved
 source (explicit, inferred, or default). The resolver never picks a
 source silently when ambiguous — it errors with a clear fix.
 
-## Durability: keep a brain repo in sync (auto-harden)
-
-A long-lived agent that writes to a knowledge-wiki git repo needs three
-things to never lose work: pull before it edits, push every write, and not
-go stale while it sits idle. `gbrain sources harden` installs all of that,
-idempotently. The moment you add a brain repo with a token, it runs
-automatically:
-
-```bash
-# Clone + register a GitHub repo, then auto-harden it for durability.
-# Use a fine-grained PAT scoped to just this repo.
-gbrain sources add wiki --url https://github.com/you/brain-wiki.git --pat-file ~/.secrets/wiki-pat
-#   → clones, then installs: local auto-push hook, scripts/brain-commit-push.sh,
-#     always-on durability rules in AGENTS.md/RESOLVER.md, a 30-min pull cron,
-#     and a repo-scoped credential. Verifies push works before declaring done.
-
-# Run the same audit on an existing source any time (idempotent):
-gbrain sources harden wiki --pat-file ~/.secrets/wiki-pat
-
-# Pull on demand (the cron calls the --path form, which never opens the DB):
-gbrain sources pull wiki
-
-# Remove the durability scaffolding (also runs automatically on `sources remove`):
-gbrain sources unharden wiki
-```
-
-What hardening guarantees:
-
-- **Pull-first, conflict-safe.** Every pull is a divergence-safe rebase. A
-  dirty working tree is skipped (your in-progress edits are never touched); a
-  rebase conflict is aborted cleanly and flagged for attention, never left
-  half-applied.
-- **Push is never deferred.** `scripts/brain-commit-push.sh "<msg>" <path>`
-  commits and pushes atomically and refuses to report success without a
-  confirmed push. The post-commit hook is a best-effort background fallback;
-  the helper is the guarantee.
-- **No silent staleness.** A 30-minute background pull keeps an idle session
-  current. It runs DB-free, so it never contends with a live brain for the
-  PGLite single-writer lock.
-
-Flags: `--no-cron` skips the scheduled pull, `--no-verify` skips the push
-probe, `--dry-run` reports what would change, `--json` emits a machine
-report, `--all` hardens every source with a remote (same-account only).
-`--no-harden` on `sources add` opts out of auto-harden.
-
-Security: the push automation is installed locally per machine (never
-committed into the repo), the token is wired per-repo (an existing
-credential helper is reused when present), and it never appears in the repo,
-the remote URL, logs, or the JSON report. For a self-hosted git server
-reachable only over a filesystem path, set `GBRAIN_GIT_ALLOW_FILE_TRANSPORT=1`
-(default is HTTPS-only).
-
 ## Upgrading an existing brain
 
 `gbrain upgrade` runs the v16 + v17 migrations automatically. Your

docs/guides/push-context.md

@@ -1,79 +0,0 @@
-# Push-based context (#2095, v0.42.43.0)
-
-Retrieval used to be pull-only: the agent had to *know to ask* before the brain
-contributed anything. Push-based context inverts that — the brain volunteers
-relevant pages from the recent conversation, confidence-gated so push noise
-never becomes worse than pull silence.
-
-Three channels share one zero-LLM core (`src/core/context/volunteer.ts`):
-
-| Channel | Surface | When to use |
-|---|---|---|
-| `reflex` | automatic, inside the context engine | default-on for plugin hosts; nothing to call |
-| `op` | `gbrain volunteer-context` / MCP `volunteer_context` | agents without the plugin; one call per turn |
-| `watch` | `gbrain watch` | stream a transcript in, volunteered pages stream out |
-
-## How it decides
-
-1. **Extract** entities across the last N turns (capitalized runs, `@handles`),
-   merged with recency / frequency / user-role salience. Assistant-introduced
-   entities and "what did she invest in?" follow-ups whose antecedent was named
-   in the window now resolve.
-2. **Resolve** through the alias table, exact titles, and slug suffixes — each
-   arm carries an honest confidence: alias 0.9, exact title 0.8, slug-suffix 0.6,
-   +0.05 when mentioned in ≥2 turns or the newest turn.
-3. **Gate** at `min_confidence` (default 0.7 — slug-suffix matches need an
-   explicit lower gate), suppress pages already surfaced (slug-presence only),
-   cap at 3 pages (hard cap 5).
-
-## CLI
-
-```bash
-# one-shot: pipe recent turns (oldest → newest)
-printf 'user: ask alice-example about the deal\nassistant: noted\nuser: what did she say?\n' \
-  | gbrain volunteer-context
-
-# streaming: volunteered pages print as the transcript flows
-some-transcript-feed | gbrain watch --json
-
-# the feedback loop: how often were volunteered pages actually opened?
-gbrain volunteer-context --stats
-```
-
-Stats are **approximate** by design: "used" means `pages.last_retrieved_at >
-volunteered_at` — the 5-minute last-retrieved throttle causes false negatives
-and unrelated reads of the same page cause false positives. Use the per-arm
-precision to tune `min_confidence`, not as an exact metric.
-
-**PGLite + `gbrain watch`:** PGLite is single-connection, and watch holds its
-connection for the whole session — a concurrent `gbrain serve` or any write
-path blocks until watch exits. On a PGLite brain, run watch in bursts (piped
-input exits at EOF) or use the ambient reflex channel instead, which routes
-through a running serve's resolve socket rather than taking the lock. Routing
-watch through that same socket is a filed follow-up (TODOS.md). Postgres
-brains are unaffected.
-
-## Config
-
-| Key | Default | What it does |
-|---|---|---|
-| `retrieval_reflex_window_turns` | 4 | turns the ambient reflex extracts from; 1 = legacy current-turn-only (file/env plane: `GBRAIN_RETRIEVAL_REFLEX_WINDOW_TURNS`) |
-| `retrieval_reflex` | true | the ambient channel's master switch |
-| `retrieval_reflex_max_pointers` | 3 | pointer cap per turn |
-
-Per-call knobs: `max_pages` + `min_confidence` on both the op and `gbrain watch`
-(`--max-pages` / `--min-confidence`, plus `--window-turns` / `--source` on watch);
-on the op only: `prior_context` (text whose already-surfaced slugs are suppressed),
-`session_id` / `turn` attribution params (watch stamps its own per-session id and
-turn numbers in the feedback log), and `days` to size the `--stats` window.
-
-## Storage + privacy
-
-Volunteered pages log to `context_volunteer_events` (migration v117): slug,
-arm, confidence, channel, optional session/turn — the rationale is a
-deterministic template string, never raw conversation text. Event writes are
-best-effort (fire-and-forget, drained at CLI exit) — the log is a tuning signal,
-not an audit trail. Rows are pruned after 90 days by the dream cycle's purge
-phase. Synopses always strip the takes/facts fences — the same strip `get_page`
-applies to untrusted callers, applied unconditionally here so private fence rows
-never reach a prompt regardless of caller trust.

docs/guides/queue-operations-runbook.md

@@ -16,39 +16,6 @@ gbrain doctor --json | jq '.checks[] | select(.name == "queue_health")'
 - **waiting-depth**: any per-name queue deeper than 10 (override via
   `GBRAIN_QUEUE_WAITING_THRESHOLD`). Signals a missing `maxWaiting`.
 
-## The worker is alive but wedged (dead pool)
-
-The nastiest stall: the worker process is *running* (passes `ps` / `kill -0` /
-container health), but its DB connection died (common behind a transaction
-pooler) and never came back, so it claims no jobs and finishes nothing. Jobs
-pile up with **0 active**. Liveness checks all pass; nothing crashes.
-
-As of v0.42.22.0 this self-heals — you usually won't have to do anything:
-
-- **The worker exits on its own dead pool.** Under a supervisor, the worker's
-  DB-liveness probe runs and self-exits (`db_dead`) after ~3 minutes; the
-  supervisor respawns it with a fresh pool.
-- **The supervisor restarts a worker that stops making progress.** If a queue
-  has claimable work, **0 live-lock active jobs**, and no completions for 15
-  minutes while the child is alive, the supervisor restarts it (covers stuck
-  handlers too, not just dead pools). Tune with `--wedge-restart-minutes` /
-  `--wedge-restart-checks` on `gbrain jobs supervisor` (0 disables).
-
-The signal is loud now — check either:
-
-```bash
-gbrain jobs stats --queue default          # prints a WEDGED QUEUE line
-gbrain doctor --json | jq '.checks[] | select(.name == "wedged_queue")'
-```
-
-`wedged_queue` is a per-queue health **error** (0 active_healthy + waiting > 0 +
-stale completions). Manual fix if you ever need it:
-
-```bash
-gbrain jobs supervisor stop && gbrain jobs supervisor start   # fresh pool
-gbrain jobs retry <id>                                        # dead-lettered jobs
-```
-
 ## Triage commands
 
 ```bash

docs/guides/scaling-skills.md

@@ -1,310 +0,0 @@
-# Scaling skills past 300 without drowning the context window
-
-When an agent grows past 100 skills, a wall starts forming. Sessions take
-longer to start. The model gets a little dumber about which skill to pick.
-Tokens that should be powering reasoning are powering a skill catalog the
-model reads on every turn whether it needs to or not.
-
-This guide is the recipe for breaking through that wall without deleting
-capabilities. Three tiers, one resolver, one safety net. Production-tested
-on a 306-skill agent (Garry's OpenClaw, the agent behind Y Combinator's
-president). The pattern works whether you run OpenClaw, Hermes, Claude Code,
-Cursor, or your own MCP-aware agent.
-
-## The problem
-
-OpenClaw scans every skill file on disk at session start and injects them
-into the system prompt as `<available_skills>` entries. The model sees a
-name, description, and file path for each one. When a request matches, the
-model reads the full SKILL.md and follows it.
-
-This is great architecture at 50 skills. At 100, it's fine. At 200, it
-starts to drag. At 300, the system prompt eats more than 25,000 tokens on
-skill descriptions alone. Tokens that aren't going to reasoning, context,
-or actual work.
-
-The symptoms compound:
-
-- Sessions take noticeably longer to start.
-- The model has less room for conversation history.
-- Skill routing gets fuzzier. With 300 descriptions competing for attention,
-  the model occasionally picks the wrong one.
-- Cost goes up because every turn carries the full skill manifest.
-
-The naive fix is to delete skills you don't use often. Don't do this. The
-whole point of skills is that capabilities compound. A gift pipeline that
-fires twice a month saves 30 minutes each time it does. A flight tracker
-fires once per trip and prevents a missed Uber. Deleting low-frequency
-skills optimizes for prompt size at the cost of capability. You wouldn't
-delete apps from your phone because the home screen is too crowded. You'd
-organize them.
-
-## The three tiers
-
-Not all skills need to be visible to the model at all times. Some are core.
-Some are specialized. Some are dormant.
-
-### Tier A: always loaded (~35 skills)
-
-The skills the model needs on every single turn. Brain search, email triage,
-calendar, meeting ingestion, content creation, the executive assistant.
-They stay in the system prompt's `<available_skills>` manifest. The model
-sees them natively and routes to them without any lookup.
-
-### Tier B: resolver-routed (~85 skills)
-
-Real, active skills that fire regularly but don't need to pollute every
-turn. Gift pipeline, flight tracker, investor update ingestion, adversary
-tracking, book mirror, civic intelligence. They live on disk. They have
-full SKILL.md files. But OpenClaw doesn't inject them into the prompt.
-
-Instead, a compact RESOLVER.md handles routing. One line per skill with
-trigger phrases:
-
-```markdown
-- **gift-advisor**: gift idea | what should I bring | birthday gift | housewarming
-- **flight-tracker**: track my flight | flight status | when does my flight land
-- **investor-update-ingest**: investor update | portfolio update | company metrics
-```
-
-When the model sees "what should I bring to Jessica's dinner," it checks
-the resolver, finds `gift-advisor`, reads the SKILL.md, and executes. Same
-result. Zero wasted tokens on the other 84 turns where gifts aren't relevant.
-
-### Tier C: dormant (~180 skills)
-
-Built-in OpenClaw skills that aren't in active rotation (1Password, Discord,
-Notion, Trello, integrations you haven't wired up yet) plus specialized
-skills that almost never fire. They're explicitly disabled in the config
-with `enabled: false`. They exist on disk as documentation and potential.
-Flip one boolean to wake them up. Zero tokens contributed to every prompt
-until then.
-
-### The numbers
-
-Before tiering, on Garry's 306-skill OpenClaw:
-
-| Metric | Before |
-|---|---|
-| Skills in system prompt | 306 |
-| Skill-description tokens per turn | ~25,000 |
-| Skill routing accuracy | degrading |
-| Session startup | slow |
-
-After tiering:
-
-| Metric | After |
-|---|---|
-| Skills in system prompt (Tier A) | 35 |
-| Skill-description tokens per turn | ~4,000 |
-| Skills still accessible (A + B + C) | 301 |
-| Capability loss | zero |
-| **Tokens freed per turn** | **~21,000** |
-
-21K tokens per turn is not a small optimization. It's the difference between
-the model having room to think and the model being squeezed. It's the
-difference between carrying 3 pages of conversation history and carrying 15.
-
-## What the resolver actually does
-
-The resolver is cheaper than the manifest. That's the load-bearing insight.
-
-OpenClaw's native skill manifest puts ~80 tokens per skill into the system
-prompt (name + description + location). At 300 skills that's 24,000 tokens
-spent every turn whether the model needs the catalog or not.
-
-The resolver puts ~15 tokens per skill into a compact markdown list. At
-300 skills that's 4,500 tokens. But it only fires when the model checks
-it, which is only when the request doesn't match a Tier A skill. Most
-turns, the resolver costs zero tokens because the Tier A match handles it.
-
-This is the routing-table pattern but applied to the skill manifest itself.
-The resolver routes to skills, but it also routes around skills, keeping
-them out of the context window until they're needed.
-
-GBrain ships with a [bundled `skills/RESOLVER.md`](../../skills/RESOLVER.md)
-you can use as a reference shape. The skillpack story for distributing
-your own resolvers across machines is covered in
-[skillpacks as scaffolding](skillpacks-as-scaffolding.md).
-
-## The compact list format (v0.41.7.0)
-
-GBrain's resolver parser used to require markdown tables:
-
-```markdown
-| Trigger | Skill |
-|---------|-------|
-| "gift idea" | `skills/gift-advisor/SKILL.md` |
-```
-
-That's fine when you have 20 entries. It gets unwieldy at 200, and at 300
-it's unreadable. OpenClaw deployments quietly evolved a compact list
-format that scales better:
-
-```markdown
-- **gift-advisor**: gift idea | what should I bring | birthday gift
-- **flight-tracker**: track my flight | flight status | when does my flight land
-```
-
-Before v0.41.7.0, `gbrain doctor` only spoke the table dialect. On a
-306-skill compact-format resolver, the doctor reported every skill as
-unreachable: **238 FAIL errors on every doctor run**. The parser was
-silently treating the compact dialect as zero skills.
-
-v0.41.7.0 ships dual-format support. The same `parseResolverEntries`
-function reads both table rows and list rows in the same file, with the
-v0.31.7 multi-resolver merge (skillpack `skills/RESOLVER.md` + workspace
-`../AGENTS.md`) folding everything into one unified view. Run `gbrain doctor`
-and the 238 FAILs collapse to 0.
-
-### The list-format contract
-
-A few rules to keep the parser unambiguous:
-
-- **Skill names must be kebab-lowercase.** `gift-advisor`, `flight-tracker`,
-  `email-triage`. Names that start with an uppercase letter (`MyTool`,
-  `Note`, `Convention`) are deliberately ignored. This is what stops prose
-  bullets like `- **Note**: see [link]` from being mis-parsed as skill
-  rows in real-world AGENTS.md files.
-- **The path always resolves to `skills/<name>/SKILL.md`.** An optional
-  `→ \`skills/path\`` (or ASCII `->`) suffix is allowed for readability,
-  but the parser strips it. For non-conventional paths (skills under
-  nested directories, references into `conventions/`, anything that
-  isn't `skills/<name>/SKILL.md`), use the table format.
-- **Triggers separate with `|`.** Empty pieces and the literal `...`
-  placeholder are dropped. Each trigger becomes its own resolver entry,
-  all pointing at the same skill.
-- **Bold or plain.** `- **name**: triggers` is preferred. `- name: triggers`
-  works as a fallback.
-
-You can mix table and list rows in the same file. Useful when a brain
-inherits a table-format `RESOLVER.md` from gbrain and a list-format
-`../AGENTS.md` from OpenClaw.
-
-## The doctor safety net
-
-The danger with tiering is invisible skill loss. You disable a skill from
-native scanning, forget to add it to the resolver, and now the agent can't
-do something it used to do. You won't notice until the moment you need it.
-
-`gbrain doctor` walks every skill on disk and verifies it's reachable,
-either through native scanning (Tier A) or through the resolver (Tier B
-and C). On Garry's setup, the first run after tiering found 63 unreachable
-skills. Sixty-three capabilities that existed on disk but had no routing
-path. Fixed in an hour by adding resolver entries.
-
-Run it after every skill change:
-
-```bash
-gbrain doctor
-```
-
-For CI gates, use the JSON-emitting variant:
-
-```bash
-gbrain check-resolvable --json
-gbrain check-resolvable --strict  # warnings fail too
-```
-
-If a skill is unreachable, the output tells you which one and suggests
-the fix. The resolver is a document. Documents are cheap to fix.
-
-## Implementation walkthrough
-
-Three changes. Total time about 45 minutes once you've decided which
-skills go in which tier.
-
-### 1. Audit and tier your skills
-
-Walk through every skill. Ask: does this need to fire on every turn?
-
-- If yes → Tier A.
-- If it fires weekly or less but is real → Tier B.
-- If you don't use it → Tier C.
-
-### 2. Disable Tier B and C in your agent's config
-
-For OpenClaw, the file is `openclaw.json`. Add an entry per disabled skill:
-
-```json
-{
-  "skills": {
-    "entries": {
-      "gift-advisor": { "enabled": false },
-      "flight-tracker": { "enabled": false },
-      "1password": { "enabled": false }
-    }
-  }
-}
-```
-
-The exact config shape depends on which agent runtime you use. The point
-is the same in all of them: tell the runtime not to inject this skill into
-the system prompt. The file stays on disk; only the prompt injection stops.
-
-### 3. Write the resolver
-
-One line per Tier B and Tier C skill. Trigger phrases that match how you
-actually ask for things:
-
-```markdown
-- **gift-advisor**: gift idea | what should I bring | birthday gift
-- **flight-tracker**: track my flight | flight status | when do I land
-- **investor-update-ingest**: investor update | portfolio update | company metrics
-```
-
-That's it. The model handles the rest. When a request doesn't match Tier A,
-it checks the resolver, reads the matching SKILL.md, and executes.
-
-### 4. Run `gbrain doctor` and fix any unreachable skills
-
-The doctor sweep tells you which skills don't have a routing path. Add a
-resolver entry for each one, re-run, repeat until the count is zero.
-
-## A lesson from the first version
-
-I initially converted my resolver from a clean list format to a table
-format because the validator only spoke tables. That was wrong. When a
-tool fails against valid data, the right move is to fix the tool, not
-reshape the data. The list format was correct, compact, readable, easy
-to maintain. The parser needed to support both shapes. v0.41.7.0 is
-that fix.
-
-The same principle applies everywhere in agent systems. Your SKILL.md is
-the source of truth. Your AGENTS.md is the source of truth. Your resolver
-is the source of truth. When tooling disagrees with your configuration,
-the tooling is wrong. Fix the tooling.
-
-## The scaling curve
-
-At 50 skills, you don't need any of this. Just load everything.
-
-At 100, you start feeling the drag but can push through.
-
-At 200, routing accuracy drops and sessions get noticeably slower. This
-is where most people stop adding skills, which means their agent stops
-getting more capable. Bad trade.
-
-At 300+, tiering is mandatory. But with tiering, there's no ceiling.
-1,000 skills with 35 in the hot path and 965 in the resolver is the same
-per-turn cost as 35 skills with no resolver. The cost stays flat.
-Capabilities compound.
-
-The architecture that gets you from 50 to 300 is different from the
-architecture that gets you from 10 to 50. That's normal. Systems that
-scale change shape. The important thing is that each tier preserves full
-capability. You're organizing, not deleting.
-
-## Related
-
-- [Skill development cycle](skill-development.md) — the 5-step loop for
-  turning a repeated task into a real skill.
-- [Skillpacks as scaffolding](skillpacks-as-scaffolding.md) — how to
-  distribute a coherent set of skills across machines and agents.
-- [Sub-agent routing](sub-agent-routing.md) — when to delegate to a
-  sub-agent vs handle in-line, and the model routing table for each path.
-
-GBrain: [github.com/garrytan/gbrain](https://github.com/garrytan/gbrain).
-The `parseResolverEntries` parser lives at
-[`src/core/check-resolvable.ts`](../../src/core/check-resolvable.ts);
-the bundled resolver lives at [`skills/RESOLVER.md`](../../skills/RESOLVER.md).

docs/guides/skillopt.md

@@ -1,147 +0,0 @@
-# `gbrain skillopt` — Self-evolving skills
-
-Treat your `SKILL.md` files as the trainable parameters of an agent that
-itself never changes. Write a benchmark of realistic tasks; SkillOpt watches
-the agent run them, proposes specific edits, re-tests, and only keeps changes
-that measurably improve the score.
-
-Based on [SkillOpt](https://arxiv.org/abs/2605.23904) (Microsoft Research,
-May 2026).
-
-> **New to this?** Start with the hands-on tutorial:
-> [Auto-improve a skill with `gbrain skillopt`](../tutorials/improving-skills-with-skillopt.md).
-> It walks you from "I have a skill" to "I accepted a measurably better version"
-> in ~20 minutes, including how to write your first benchmark. This page is the
-> reference — flags, exit codes, cost model, safety guards.
-
-## The 30-second pitch
-
-```bash
-# 1. Generate a starter benchmark from the skill itself (no routing-eval needed)
-gbrain skillopt my-skill --bootstrap-from-skill
-
-# 2. Review the benchmark — STRENGTHEN the generated judges (they're weak drafts),
-#    then delete the trailing `# BOOTSTRAP_PENDING_REVIEW` line
-
-# 3. Run the optimizer (--split 1:1:1 is required for a ~15-task starter)
-gbrain skillopt my-skill --bootstrap-reviewed --split 1:1:1
-```
-
-That's the entire workflow. (Already have a `routing-eval.jsonl`? Swap step 1 for
-`--bootstrap-from-routing` — but routing tasks test dispatch, not output quality.)
-
-## What's in the box
-
-```
-skills/my-skill/
-  SKILL.md                          ← what gets optimized (body only; D5)
-  skillopt-benchmark.jsonl          ← what success looks like
-  skillopt/
-    best.md                         ← current best version
-    versions/
-      v0001_e1_s1.md                ← per-step snapshots
-      v0002_e1_s2.md
-      ...
-    history.json                    ← append-only run record (D8)
-    rejected.json                   ← bounded LRU of rejected edits
-```
-
-The audit trail lives at `~/.gbrain/audit/skillopt-YYYY-Www.jsonl`
-(ISO-week rotated; honors `GBRAIN_AUDIT_DIR`).
-
-## How the loop works
-
-For each step:
-
-1. **Forward pass.** Run the candidate skill against a batch from `D_train`.
-2. **Backward pass.** Two reflect calls (failures + successes per D7) propose
-   edits to address what worked / didn't work.
-3. **Rank + clip.** Top-N edits within the LR budget (cosine schedule by
-   default; D10 has the ASCII curve in `orchestrator.ts`).
-4. **Apply.** D9 tagged-result patches the body (frontmatter forbidden per
-   D5; ambiguous anchors rejected to the rejected-buffer).
-5. **Validation gate.** D12 median-of-3 + epsilon=0.05: every sel-task runs
-   the judge 3 times, takes the median; only accepts if median > best by
-   more than 0.05.
-6. **Commit.** D8 history-intent-first 5-step atomic write — crash-safe.
-
-After each epoch with no improvement: D6 slow-update fires one meta-edit
-proposal (this lives in v0.42 follow-up; v1 emits the audit event).
-
-## Flags
-
-| Flag | Default | Purpose |
-|---|---|---|
-| `--benchmark <path>` | `skills/<n>/skillopt-benchmark.jsonl` | Path to benchmark JSONL |
-| `--bootstrap-from-skill` | off | Generate a starter benchmark from SKILL.md (recommended; no routing-eval needed) |
-| `--bootstrap-tasks N` | 15 | How many starter tasks `--bootstrap-from-skill` generates (max 50) |
-| `--bootstrap-from-routing` | off | Auto-build benchmark from routing-eval.jsonl |
-| `--bootstrap-reviewed` | off | Required after human-reviewing bootstrap output |
-| `--epochs N` | 4 | Outer-loop iterations |
-| `--batch-size N` | 8 | Tasks per inner step |
-| `--lr N` | 4 | Max edits per step |
-| `--lr-schedule cosine\|linear\|constant` | cosine | Edit-budget decay |
-| `--split TRAIN:SEL:TEST` | 4:1:5 | Ratio; refuses if D_sel < 5 |
-| `--optimizer-model MODEL` | tier.deep | Reflects + proposes |
-| `--target-model MODEL` | tier.subagent | Executes the skill |
-| `--judge-model MODEL` | tier.reasoning | Scores rollouts |
-| `--patch \| --rewrite` | patch | Edit ops only vs. full rewrites |
-| `--dry-run` | off | Cost preview, no LLM calls |
-| `--no-mutate` | off | Write proposed.md, don't replace SKILL.md (no held-out needed) |
-| `--allow-mutate-bundled` | off | Required to mutate gbrain-bundled skills in place — ALSO requires `--held-out` (>=5 rows) or the run hard-refuses |
-| `--held-out <path>` | — | Independent test set (same JSONL shape as the benchmark, task IDs disjoint from it). A candidate that beats the benchmark but regresses on the held-out set is refused. Required for in-place bundled mutation. |
-| `--max-cost-usd N` | 5.00 | Hard cap; preflight refuses if exceeded |
-| `--max-runtime-min N` | 30 | Wall-clock cap |
-| `--force` | off | Bypass dirty-working-tree refusal |
-| `--resume <run-id>` | off | Resume a prior interrupted run |
-| `--json` | off | Machine-readable stdout |
-
-## Exit codes
-
-| Code | Meaning |
-|---|---|
-| 0 | Improved + accepted (or `--no-mutate` proposed.md written) |
-| 1 | No improvement; best skill unchanged |
-| 2 | Aborted by gate (dirty tree, over budget, bench validation, etc.) |
-
-## Cost model
-
-A typical 20-task benchmark with defaults costs ~$0.90 per run:
-
-- 32 rollouts × Sonnet ($0.009 each) ≈ $0.29
-- 8 reflect calls × Opus (cached) ≈ $0.25
-- 24 sel-judges × Sonnet (cached) ≈ $0.10
-- Final test eval ≈ $0.07
-- **Total ≈ $0.71**
-
-For a 100-task benchmark: ~$5.00 (right at the default cap). Preflight
-refuses to start when the estimate exceeds `--max-cost-usd`.
-
-## Safety guards (the cathedral)
-
-| Guard | Decision | What it prevents |
-|---|---|---|
-| Validation gate is mandatory | D12 (paper) | Accepting LLM judge noise as improvement |
-| Frontmatter mutation forbidden | D5 | Routing surface drift (`check-resolvable` regression) |
-| Per-skill DB lock | D14 | Two concurrent runs corrupting history/versions |
-| Bundled-skill gate | D16 | Auto-mutating skills shipped with gbrain (in-place mutation requires `--allow-mutate-bundled` + a `--held-out` set of >=5 benchmark-disjoint tasks; else hard-refuse + proposed.md) |
-| Held-out gate | F11 | Accepting a candidate that overfits its own benchmark — `--held-out` refuses a candidate whose held-out score regresses below baseline |
-| Bootstrap review sentinel | D15 | Self-referential benchmark gaming |
-| Read-only tool sandbox in rollouts | D13 | Optimization runs writing junk pages to your brain |
-| History-intent-first atomic commit | D8 | Half-written SKILL.md on crash |
-| Cost preflight | D3 | Surprise mid-run budget exhaustion |
-| Dirty-tree refusal | dry-fix pattern | Overwriting your uncommitted changes |
-
-## When NOT to use SkillOpt
-
-- **No benchmark.** Optimizing against guesses is worse than not optimizing.
-- **Write-flavored skills.** Skills whose job is to `put_page` heavily can't
-  use the v1 read-only sandbox; mocked-write capture is a v0.42 follow-up.
-- **Tiny benchmarks (<10 tasks).** D_sel < 5 refuses by default; meaningful
-  validation needs ≥20 tasks total per the paper.
-
-## Related skills
-
-- `gbrain skillify scaffold <name>` — create a new skill (use BEFORE skillopt)
-- `gbrain skillpack-check <name>` — audit conformance + skillopt status
-- `gbrain check-resolvable` — routing MECE validation (NOT mutated by skillopt)

docs/guides/skillpacks-as-scaffolding.md

@@ -1,208 +0,0 @@
-# Skillpacks as scaffolding, not amber
-
-GBrain v0.33 reshapes `gbrain skillpack` from a package manager into a
-scaffold + reference library. This guide explains the model and the
-workflow.
-
-## Why we changed it
-
-Pre-v0.33 (the "amber" model):
-
-- `gbrain skillpack install <name>` copied bundled skills into your
-  workspace AND wrote a managed-block fence into your `RESOLVER.md` /
-  `AGENTS.md` with a `cumulative-slugs="..."` receipt.
-- Subsequent installs hash-checked every file and refused to overwrite
-  local edits unless you passed `--overwrite-local`.
-- `gbrain skillpack uninstall` had its own data-loss safeguards (D8
-  receipt gate + D11 content-hash pre-scan) and rebuilt the fence.
-
-It worked, but it treated personal-AI skills like vendor packages.
-Users couldn't cleanly fork a skill without the next install fighting
-them. Every release re-litigated the same managed block. The test
-surface alone for the managed block was ~1000 lines.
-
-Skills aren't vendor packages. They're first-class code in your agent
-repo. You scaffold once, you own them, you fork and edit freely. When
-gbrain ships a new version, you ask "what changed?" — the agent reads
-the diff and decides what (if anything) to integrate.
-
-## The five commands
-
-### `gbrain skillpack scaffold <name> [--workspace PATH]`
-
-One-time, additive copy of a bundled skill into your repo. Refuses to
-overwrite any file that exists. Routing comes from each skill's
-frontmatter `triggers:` array — gbrain does NOT touch your `RESOLVER.md`
-or `AGENTS.md` (see "How agents discover scaffolded skills" below).
-
-```bash
-cd ~/git/your-agent-repo
-gbrain skillpack scaffold book-mirror
-# files in skills/book-mirror/ + (if the skill declares paired source)
-# src/commands/book-mirror.ts land in your workspace
-```
-
-`scaffold --all` copies every bundled skill that's missing. Never
-prunes.
-
-If a skill's frontmatter declares paired source files (`sources: [...]`
-in the SKILL.md YAML head), scaffold copies them too. The partial-state
-policy handles "skill shipped earlier, gained a paired source later" —
-scaffold copies the new paired file even when the skill dir already
-exists.
-
-### `gbrain skillpack reference <name> [--workspace PATH] [--apply-clean-hunks] [--json]`
-
-Read-only update lens. Diffs gbrain's bundle against your local copy
-and emits per-file status (`identical` / `differs` / `missing`) plus
-unified diffs for any `differs` entries.
-
-```bash
-gbrain skillpack reference book-mirror
-# These files live at <gbrain-path> as reference. Read them and
-# decide what (if anything) to integrate into your local skills/.
-# Your local edits are intentional — do not blindly overwrite.
-#
-# reference: identical:14 differs:1 missing:0
-#
-#   differs   /your/workspace/skills/book-mirror/SKILL.md
-#   --- a/skills/book-mirror/SKILL.md
-#   +++ b/skills/book-mirror/SKILL.md
-#   @@ -10,3 +10,5 @@
-#   ... unified diff ...
-```
-
-`reference --all` sweeps the whole bundle (one-line-per-skill summary).
-
-`reference <name> --apply-clean-hunks` is the auto-apply path. It
-parses the diff between gbrain's bundle and your local copy, applies
-every hunk whose pre-change context matches uniquely. **Two-way merge
-limitation**: without scaffold-time base tracking (intentionally
-out-of-scope for v0.33), this cannot distinguish "gbrain changed X"
-from "you changed X." Applied hunks align everything to gbrain. Use
-`--dry-run` first to preview, or run plain `reference` to inspect the
-diff before letting auto-apply touch anything.
-
-### `gbrain skillpack migrate-fence [--workspace PATH] [--dry-run]`
-
-One-shot conversion for workspaces on the pre-v0.33 managed-block
-model. Strips the `<!-- gbrain:skillpack:begin -->` / `end -->`
-markers and the manifest receipt comment from your resolver file.
-
-**Preserves every row inside the fence verbatim.** Those rows become
-user-owned routing the agent can still see during the transition to
-frontmatter-based discovery.
-
-```bash
-cd ~/git/your-agent-repo
-gbrain skillpack migrate-fence
-# migrate-fence: fence_stripped
-#   resolver: /your/workspace/skills/RESOLVER.md
-#   fenced slugs: alpha, beta, gamma
-#   already present: alpha, beta
-#   skills copied: gamma   (additive — beta and alpha kept their local edits)
-```
-
-Idempotent. Re-running after migration finds no fence and exits 0.
-
-### `gbrain skillpack scrub-legacy-fence-rows [--workspace PATH] [--dry-run]`
-
-Opt-in cleanup. Once you've confirmed your agent walks frontmatter
-`triggers:` for routing, this command removes the legacy rows that
-`migrate-fence` left behind.
-
-**Two-condition gate** (both must hold for a row to be removed):
-
-1. `skills/<slug>/` exists on host (it was a real scaffold).
-2. That skill's frontmatter declares non-empty `triggers:` (proof
-   that frontmatter discovery covers this skill).
-
-Rows whose slug fails either gate are preserved — user-owned routing
-the migration shouldn't touch.
-
-### `gbrain skillpack harvest <slug> --from <host-repo-root> [--no-lint] [--dry-run]`
-
-Inverse of scaffold: lifts a proven skill from your host repo back
-into gbrain so other clients can scaffold it. Default behavior:
-
-- Symlinks in the host skill dir are rejected (canonical-path
-  confinement).
-- Privacy linter scans the harvested files against
-  `~/.gbrain/harvest-private-patterns.txt` plus built-in defaults
-  (canonical private fork name, common email regex, Slack channel pattern). Any
-  match → rollback (delete the harvested files) and exit non-zero.
-- `openclaw.plugin.json` updated with the new slug, sorted.
-- `--no-lint` bypasses the linter (after a manual editorial scrub).
-
-Use the `skillpack-harvest` skill (its companion editorial workflow)
-to walk the genericization checklist before running the CLI.
-
-## How agents discover scaffolded skills
-
-Routing under the new model lives entirely in each skill's frontmatter:
-
-```yaml
----
-name: book-mirror
-triggers:
-  - "personalized version of this book"
-  - "mirror this book"
-  - "two-column book analysis"
----
-```
-
-Your agent's job at runtime is to walk `skills/*/SKILL.md`, parse the
-frontmatter, and match the user's intent against every skill's
-`triggers:` array. When a match scores high enough, invoke that skill.
-
-This replaces the v0.32 model where `gbrain skillpack install` wrote
-table rows into your `RESOLVER.md`. Rows are gone (or, for users
-migrating from the old model, preserved transitionally by
-`migrate-fence` until they run `scrub-legacy-fence-rows`).
-
-If you're a downstream agent author updating to this model:
-
-1. On startup, scan `skills/*/SKILL.md` for frontmatter.
-2. Build an in-memory routing table from each skill's `triggers:`
-   array.
-3. On every user message, match against this table — either by
-   substring containment, semantic similarity, or whatever your
-   downstream agent already does for intent classification.
-
-## Removing a scaffolded skill
-
-There's no `gbrain skillpack uninstall` command in v0.33. The files
-in your `skills/<slug>/` are first-class members of your repo —
-delete them like any other code:
-
-```bash
-rm -rf skills/book-mirror
-# if the skill declared paired source files:
-rm src/commands/book-mirror.ts
-# (consult the skill's frontmatter `sources:` array for the full list)
-
-# if no other scaffolded skill needs them, you can also remove the
-# shared deps that scaffold drops in:
-rm skills/_brain-filing-rules.md
-rm -rf skills/conventions/
-rm skills/_output-rules.md
-```
-
-You own the files. There's no manifest to update, no fence to rebuild.
-
-## When to use which command (quick decision tree)
-
-- **New host repo, want a gbrain skill** → `scaffold`
-- **gbrain shipped a new version, want to see what's changed**
-  → `reference` (read-only) or `reference --apply-clean-hunks` (auto)
-- **Upgrading from v0.32 or earlier** → `migrate-fence` (one-shot)
-- **Cleanup after `migrate-fence`** → `scrub-legacy-fence-rows`
-- **Lift your fork's skill back into gbrain** → `harvest` + the
-  `skillpack-harvest` editorial skill
-
-## What about `install` and `uninstall`?
-
-Both are removed in v0.33. Running either prints an error pointing at
-the replacement command. No deprecated alias — this is a clean break.
-If you have existing scripts referencing the old names, update them
-once and move on.

docs/guides/upgrades-auto-update.md

@@ -16,34 +16,6 @@ benefit-focused bullets, waits for explicit permission, then runs the full
 upgrade flow including re-reading skills, running migrations, and syncing
 schema. The user gets new capabilities automatically.
 
-## Self-upgrade modes (v0.42)
-
-gbrain now stays current the way gstack does: it rides invocation frequency. A
-throttled, cache-read-only check runs at the start of every `gbrain` invocation
-(CLI and MCP) and emits an `UPGRADE_AVAILABLE <old> <new>` marker on stderr. No
-host cron required — every agent kind (Claude Code, Codex, OpenClaw, Hermes, the
-`gbrain serve` host behind a Perplexity thin client) converges to current by
-construction. The behavior is governed by one file-plane config key,
-`self_upgrade.mode`:
-
-| Mode | Behavior | Who it's for |
-|------|----------|--------------|
-| `notify` (default) | Emit the marker + a 4-option prompt; never apply without confirmation. | Interactive installs / anyone with a human in the loop. |
-| `auto` (opt-in) | Apply silently, but ONLY during quiet hours, ONLY when the brain is idle, doctor-gated, and never re-trying a known-bad version. | Headless / always-on installs (autopilot daemon, the `gbrain serve` host). |
-| `off` | Never check. | Air-gapped / pinned installs. |
-
-Enable hands-off upgrades on an always-on install with one line:
-
-```bash
-gbrain config set self_upgrade.mode auto
-```
-
-`auto` is deliberately NOT a default anywhere — it's an explicit autonomy grant,
-because applying code from GitHub unattended is, by design, remote code
-execution. The trust model is TLS + GitHub (same as `gbrain upgrade`);
-signature verification is a tracked follow-up. Apply manually any time with
-`gbrain self-upgrade`.
-
 ## Implementation
 
 ### The Check (cron-initiated)
@@ -94,11 +66,7 @@ what they can DO now that they couldn't before, not what files changed.
 | daily | Store preference, switch cron back to daily |
 | stop / unsubscribe / no more | Disable the cron. Tell user how to resume |
 
-**In `notify` mode (the default), never auto-upgrade — always wait for explicit
-confirmation.** The `auto` mode (opt-in, see "Self-upgrade modes" above) is the
-only path that applies without a prompt, and only under its conservative gates
-(quiet hours + idle + doctor-gate). This per-cron-prompt flow is the `notify`
-experience.
+**Never auto-upgrade.** Always wait for explicit confirmation.
 
 ### The Full Upgrade Flow (after user says yes)
 
@@ -175,13 +143,10 @@ copy. Set up a weekly cron to check automatically.
 
 ## Tricky Spots
 
-1. **In `notify` mode, never auto-install.** The upgrade waits for the user's
-   explicit "yes." Even if the check detects an update and the changelog looks
-   great, the agent messages the user and waits. The `auto` mode (opt-in) exists
-   for headless/always-on installs where there's no human to prompt — it applies
-   only during quiet hours, only when idle, doctor-gated, never retrying a
-   known-bad version. Don't enable `auto` on an interactive workstation; the
-   prompt-first `notify` flow is the right default there.
+1. **Never auto-install.** The upgrade must always wait for the user's explicit
+   "yes." Even if the cron detects an update at 9 AM and the changelog looks
+   great, the agent messages the user and waits. Auto-installing can break
+   workflows, introduce breaking changes, or interrupt work in progress.
 
 2. **Migration files are agent instructions, not scripts.** They tell the agent
    what to do step by step in plain language. They are NOT bash scripts to

docs/incidents/2026-05-20-lsd-cost-explosion.md

@@ -1,266 +0,0 @@
-# Incident Report: LSD Brainstorm 53× Cost Overrun
-
-**Date:** 2026-05-20
-**Severity:** High (financial — $50.71 actual vs $0.96 estimated)
-**Component:** `gbrain lsd` / `gbrain brainstorm`
-**Brain size:** 13,690 pages, 16,314 links, ~2,000 unique directory prefixes
-**Version:** v0.37.1.0 (first release of brainstorm/lsd)
-
-## What Happened
-
-A user ran `gbrain lsd "what story should Garry's List write next" --yes` on a 13,690-page brain. The command:
-
-1. **Estimated cost: $0.96** (2×12 = 24 crosses × 4 ideas + judge)
-2. **Actual cost: $50.71** — 53× over estimate
-3. **Token usage:** 4,906,011 input + 2,399,239 output = 7.3M total tokens
-4. **Far set pulled 1,985 pages** instead of the configured 12
-5. **Generated 15,868 raw ideas** across the crosses (vs expected ~96)
-6. **Judge phase failed:** 2,989,338 tokens exceeded Claude Sonnet's 1M context limit
-7. **Zero ideas surfaced to the user** — complete failure
-
-A retry with `--limit 12` explicit:
-- Far set correctly returned 12 pages, cost was $0.39
-- But judge still failed: `parseJudgeJSON: no strategy produced valid JSON`
-- Again, 0 ideas survived to output (96 generated, 0 scored)
-
-## Root Causes
-
-### RC1: Far Set Explosion (caused the $50 bill)
-
-**File:** `src/core/brainstorm/domain-bank.ts` → `fetchFar()` → `listPrefixSampledPages()`
-
-The domain bank samples pages by directory prefix to get diversity. `listPrefixSampledPages` returns **one page per prefix passed in**. On a 13K-page brain with ~2,000 unique prefixes (books/, civic/bundles/, civic/gl-article-*, people/, concepts/, etc.), passing all prefixes produces ~2,000 rows — not the configured `m=12`.
-
-The cost estimator uses `m` (12) to predict crosses and cost. But the actual cross phase receives 1,985 far-set pages, producing `2 × 1985 = 3,970` crosses at 4 ideas each = 15,868 ideas.
-
-**The estimate formula is correct for the intended behavior; the far set selection is what diverged.**
-
-### RC2: No Cost Circuit Breaker
-
-There is no mechanism to:
-- Abort if estimated cost exceeds a threshold
-- Abort mid-run if actual spend diverges from estimate
-- Cap the far set size regardless of prefix count
-- Warn the user that a run will be expensive before proceeding
-
-The `--yes` flag skips the 10-second cost preview wait, removing even the manual inspection opportunity.
-
-### RC3: Judge Context Overflow
-
-The judge receives ALL ideas in a single prompt. With 15,868 ideas at ~350 tokens each, that's ~5.5M tokens — well beyond any model's context window.
-
-Even on the retry with only 96 ideas, the judge failed with JSON parsing errors, suggesting the judge prompt/response format is fragile.
-
-### RC4: Unpaired UTF-16 Surrogates in Page Content
-
-Two crosses failed with: `The request body is not valid JSON: no low surrogate in string`
-
-Some pages (likely OCR imports or web scrapes) contain unpaired UTF-16 surrogates. When these get serialized into the JSON request body for the LLM API, the JSON encoder produces invalid JSON.
-
-### RC5: No Timeout on Individual Crosses
-
-One cross timed out with no specific timeout configured. The default HTTP timeout allowed it to hang for an extended period before failing, consuming tokens on the API side.
-
-## Observed Token Flow
-
-```
-Configured:  2 close × 12 far = 24 crosses × 4 ideas = 96 ideas + 1 judge call
-Actual:      2 close × 1985 far = 3970 crosses × 4 ideas = 15,868 ideas + 1 judge call (failed)
-
-Per-cross tokens (estimated): ~1,200 in + 600 out
-Actual total:                  4,906,011 in + 2,399,239 out
-
-The judge call alone would have been:
-  15,868 ideas × ~350 tokens = ~5.5M tokens (prompt)
-  Model limit:                  1M tokens (Sonnet)
-  Overflow:                     5.5× context limit
-```
-
-## Proposed Fixes
-
-### P1: Far Set Cap (Critical — prevents cost explosion)
-
-`fetchFar()` must cap the number of prefixes BEFORE calling `listPrefixSampledPages`. The cap should be `max(m * 4, 50)` to allow some diversity headroom while preventing runaway growth. Final selection trimmed to `m` by distance score.
-
-**Status:** Implemented in `dc080ac2`.
-
-### P2: Cost Guardrails (Critical — defense in depth)
-
-New flags for `brainstorm` and `lsd` commands:
-- `--max-cost <usd>` (default $5): hard-abort if pre-run estimate exceeds
-- `--strict-budget`: abort mid-run if running cost exceeds 5× estimate
-- `--max-far-set <n>` (default 50): explicit far set size cap
-
-**Status:** Implemented in `dc080ac2`.
-
-### P3: Judge Chunking (Critical — prevents context overflow)
-
-Split ideas into batches of ~100 before calling the judge LLM. Each batch is a separate API call; results concatenated. This bounds per-call token usage to ~35K regardless of total idea count.
-
-**Status:** Implemented in `dc080ac2`.
-
-### P4: Unicode Sanitization (Medium — prevents cross failures)
-
-Strip unpaired UTF-16 surrogates from page content before building cross prompts. This is a general problem for any gbrain function that serializes user-generated page content into JSON for API calls.
-
-**Status:** Implemented in `dc080ac2`.
-
-### P5: Global Token & Time Budgets for All Analysis Functions (Proposed)
-
-**This is the bigger architectural ask.** Every gbrain command that makes LLM calls should respect configurable budgets:
-
-```yaml
-# Proposed config additions to ~/.gbrain/config.json
-budgets:
-  # Global defaults
-  default:
-    max_input_tokens: 500_000    # per-command input token cap
-    max_output_tokens: 200_000   # per-command output token cap  
-    max_cost_usd: 5.00           # per-command dollar cap
-    max_runtime_seconds: 300     # 5-minute wall-clock cap
-    
-  # Per-command overrides
-  brainstorm:
-    max_cost_usd: 2.00
-    max_runtime_seconds: 120
-  lsd:
-    max_cost_usd: 5.00
-    max_runtime_seconds: 300
-  dream:
-    max_cost_usd: 10.00
-    max_runtime_seconds: 600
-  extract:
-    max_input_tokens: 1_000_000
-    max_runtime_seconds: 900
-  enrich:
-    max_cost_usd: 3.00
-    max_runtime_seconds: 180
-```
-
-**Commands affected:**
-- `brainstorm` / `lsd` — bisociation crosses + judge (this incident)
-- `dream` — dream cycle phases (enrichment, emotional weight, etc.)
-- `extract all` — link + timeline extraction across all pages
-- `enrich` — per-page deep enrichment with web research
-- `eval` — evaluation runs (suspected-contradictions, retrieval drift)
-- `integrity auto` — automated content repair
-- `doctor --remediate` — autonomous self-healing via Minions
-
-**Implementation approach:**
-1. Add a `BudgetTracker` class that wraps LLM calls with token/cost/time accounting
-2. Every analysis function receives a budget context
-3. On budget exhaustion: save partial results, emit a structured warning, exit cleanly
-4. CLI flags (`--max-cost`, `--max-tokens`, `--timeout`) override config defaults
-5. `--no-budget` escape hatch for power users who know what they're doing
-
-### P6: Diarization / Summarization for Oversized Payloads (Proposed)
-
-When a judge or analysis phase receives more content than fits in context:
-
-1. **Estimate tokens** before calling the LLM
-2. If over budget, **diarize**: summarize/compress the content to fit
-3. For the judge specifically: rank ideas by a cheap heuristic first (keyword overlap, novelty score), then send only top-N to the LLM judge
-4. For other analysis: progressive summarization — chunk → summarize → merge summaries → final analysis
-
-This is effectively a **token budget allocator** that decides how to spend a fixed token budget across variable-length inputs.
-
-```
-Example: 15,868 ideas need judging, context limit 900K tokens
-  Step 1: Cheap pre-filter (keyword dedup, obvious duplicates) → 8,000 unique ideas
-  Step 2: Batch into 80 chunks of 100 ideas each
-  Step 3: Judge each chunk → 80 calls × ~35K tokens = 2.8M total (spread across calls)
-  Step 4: Merge top ideas from each chunk → final ranking
-  Total cost: ~$2-3 instead of $50
-```
-
-### P7: Structured Error Recovery (Proposed)
-
-When a cross or judge call fails:
-- Save the partial results immediately (don't wait for the full run)
-- Emit a machine-readable error event (not just a log warning)
-- Support `--retry-failed` to re-run only the failed crosses without repeating successful ones
-- Checkpoint progress to disk so interrupted runs can resume
-
-## Impact
-
-- **Financial:** $50.71 wasted on a single failed run
-- **User trust:** Zero ideas delivered despite ~7M tokens processed
-- **Time:** ~15 minutes of compute time, plus overnight delay in reporting results
-
-## Lessons
-
-1. **First run of any new feature on a large brain should be dry-run or capped.** The estimate was based on small-brain testing; 13K pages is a different universe.
-2. **Cost estimators must account for actual data cardinality, not just configured parameters.** The estimate used `m=12` but the real far set was `|prefixes|`.
-3. **Every LLM-calling function needs a budget.** This isn't just a brainstorm problem — it's an architectural gap in any system that makes variable numbers of LLM calls based on data size.
-4. **JSON serialization of user content is a landmine.** Any page could contain invalid Unicode. Sanitize at the serialization boundary, not per-feature.
-
-## Shipped in v0.37.x (the budget cathedral wave)
-
-P1-P4 already shipped via PR #1234 (the first fix wave). P5-P7 plus a few
-architectural rounds shipped in the budget-cathedral wave that followed:
-
-- **P1 (far set cap):** `fetchFar()` in `src/core/brainstorm/domain-bank.ts`
-  caps prefix sampling to `max(m*4, 50)` and trims final pages to `m` by
-  distance. The 2K-prefix explosion class is closed.
-- **P2 (cost guardrails):** `--max-cost`, `--max-far-set`, `--strict-budget`,
-  `--judge-model`, `--max-ideas-per-judge-call` flags on brainstorm + lsd.
-  Pre-flight estimate refusal, mid-run cost-ceiling abort.
-- **P3 (judge chunking):** `runJudge` in `src/core/brainstorm/judges.ts`
-  auto-chunks at 100 ideas/call. Context-window overflow is structurally
-  prevented.
-- **P4 (unicode sanitization):** `ensureWellFormed` (in `src/core/text-safe.ts`,
-  used by `src/core/brainstorm/orchestrator.ts`) replaces unpaired surrogates
-  with U+FFFD before serialization. (Consolidated from the original hand-rolled
-  `sanitizeUnicode` in v0.42.40.0 / #2011.)
-- **P5 (BudgetTracker at the gateway layer):** new
-  `src/core/budget/budget-tracker.ts` is the canonical primitive. The
-  gateway's `withBudgetTracker(tracker, fn)` composes via
-  `AsyncLocalStorage<BudgetTracker>` so every gateway-routed LLM call
-  inside the scope auto-records. `BudgetExhausted` is a typed error with
-  `reason: 'cost' | 'runtime' | 'no_pricing'`. `record()` throws when
-  cumulative spend exceeds the cap (TX1). `reserve()` hard-fails on
-  `no_pricing` when the cap is set + model missing from pricing maps (TX2).
-- **P6 (payload-fitter):** `src/core/diarize/payload-fitter.ts` with
-  `'batch'` and `'summarize'` strategies. Summarize embed-clusters
-  (k=ceil(items/4)), Haiku-summarizes each cluster in parallel via
-  `Promise.allSettled` at parallelism=4. Surfaces `degraded: true` flag
-  when success ratio < 0.75 so callers decide whether to surface a partial
-  result or abort.
-- **P7 (brainstorm checkpoint + --resume):**
-  `src/core/brainstorm/checkpoint.ts` persists FULL idea bodies (not just
-  counts — TX3 load-bearing). One `--resume <run_id>` flag covers both
-  failed and never-attempted crosses (TX4). `run_id` formula uses NO
-  embedding bits so the identity is stable across embedding-model swaps
-  (A5 amended). 7-day mtime-based GC wired into the cycle purge phase.
-  `--list-runs` lists saved checkpoints. `--force-resume` bypasses the 7d
-  staleness gate.
-
-Also shipped alongside the wave (folded inline):
-
-- **doctor --remediate --resume:** A4 amended. The mid-run cap is now a
-  real ceiling; `--max-cost` is an alias for `--max-usd`. On
-  BudgetExhausted, the orchestrator persists a checkpoint at
-  `~/.gbrain/remediation/<plan_hash>.json` and tells the user the exact
-  `gbrain doctor --remediate --resume` command. The resumed run skips
-  already-completed steps.
-- **Audit-week-file consolidation (Q1):** four call sites
-  (shell-jobs / phantoms / slug-fallback / dream-budget) now share one
-  ISO-week filename helper. Year-boundary correctness pinned by tests.
-- **eval-contradictions tracker telemetry:** the existing CostTracker
-  stays for the report shape; the runner additionally installs a
-  withBudgetTracker scope for the gateway-layer telemetry path.
-
-What did NOT make this wave (filed in TODOS for a follow-up):
-
-- The schema fix for `page_links` on PGLite. The brainstorm domain-bank
-  queries reference `page_links` but the embedded schema only defines
-  `links`; the E2E works around this with a view in test setup, but
-  real PGLite users currently can't run `gbrain brainstorm`. Schema fix
-  needed.
-- `--max-cost` flag on `extract`, `enrich`, `integrity auto`. The
-  gateway-layer enforcement covers them when wrapped at the entrypoint,
-  but the CLI flag wiring is deferred.
-- Async-batched audit writes. Sync `appendFileSync` is fine at typical
-  volumes; revisit if profiling shows it dominates.
-- Multi-day brainstorm resume (>7d). The `--force-resume` flag is the
-  operator escape hatch for now.

docs/integrations/embedding-providers.md

@@ -1,191 +0,0 @@
-# Embedding providers
-
-GBrain ships with 16 embedding-provider recipes covering OpenAI, ZeroEntropy, Voyage, OpenRouter (single key, many hosted models), the major hosted alternatives, three local options, and a universal escape hatch (LiteLLM proxy). Run `gbrain providers list` to see the live registry; `gbrain providers explain --json` emits a machine-readable matrix for agents.
-
-This page is the human-readable counterpart: capability per provider, env-var setup, dimensions, cost, and known constraints.
-
-## Quick start
-
-```
-gbrain providers list                          # see all providers
-gbrain providers env <provider-id>             # see required env vars
-gbrain providers test --model openai:text-embedding-3-large   # smoke-test
-gbrain init --pglite --model voyage            # use a non-default provider
-```
-
-## Init resolves your provider from env keys
-
-As of v0.37, `gbrain init --pglite` auto-detects which provider to use from your env vars. With `OPENAI_API_KEY` set, you get OpenAI. With `ZEROENTROPY_API_KEY` set, you get ZeroEntropy. If multiple provider keys are set, init fires an interactive picker. If no provider keys are set in a non-TTY context (CI, Docker build), init exits 1 with a paste-ready setup hint. Explicit flags (`--embedding-model`, `--no-embedding`) always win over env detection.
-
-The resolved provider + dimensions get persisted to `~/.gbrain/config.json` atomically, so subsequent runs are deterministic across releases.
-
-## TL;DR table
-
-| Provider | env vars | default dims | cost ($/1M tokens) | local? | multimodal? |
-|---|---|---|---|---|---|
-| `zeroentropyai` | `ZEROENTROPY_API_KEY` | 2560 (Matryoshka to 1280/640/320/...) | 0.05 | no | no |
-| `openai` | `OPENAI_API_KEY` | 1536 | 0.13 | no | no |
-| `openrouter` | `OPENROUTER_API_KEY` | 1536 | 0.02 | no | model-dependent |
-| `voyage` | `VOYAGE_API_KEY` | 1024 | 0.18 | no | yes (`voyage-multimodal-3`) |
-| `google` | `GOOGLE_GENERATIVE_AI_API_KEY` | 768 | 0.025 | no | no |
-| `azure-openai` | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT` | 1536 | 0.13 | no | no |
-| `minimax` | `MINIMAX_API_KEY` | 1536 | 0.07 | no | no |
-| `dashscope` | `DASHSCOPE_API_KEY` | 1024 | varies | no | no |
-| `zhipu` | `ZHIPUAI_API_KEY` | 1024 | varies | no | no |
-| `ollama` | (none — runs locally) | 768 | 0 | yes | no |
-| `llama-server` | (none — runs locally) | user-set | 0 | yes | no |
-| `litellm` | `LITELLM_API_KEY` (optional) | user-set | varies | yes (proxy) | yes (backend permitting) |
-| `together` | `TOGETHER_API_KEY` | 768 | varies | no | no |
-| `anthropic` | (no embedding model — chat only) | — | — | — | — |
-| `deepseek` | (no embedding model — chat only) | — | — | — | — |
-| `groq` | (no embedding model — chat only) | — | — | — | — |
-
-**Note on local providers.** Ollama and llama-server have no required API key, so they don't show up in env-detection auto-pick. Pick them explicitly with `--embedding-model ollama:<model>` to avoid silently routing to a daemon that may not be running.
-
-## If first import fails
-
-If `gbrain import` fails with `expected N dimensions, not M`, run `gbrain doctor`. The output will print the exact `gbrain config set ...` or `gbrain retrieval-upgrade` command to repair the mismatch. **You should not need to delete `~/.gbrain`.** The bug-class that historically forced `rm -rf` recoveries is closed as of v0.37.
-
-The doctor distinguishes two repair paths:
-
-- **Empty brain** (no embedded chunks yet) — drop and re-init at the right dim:
-  ```
-  gbrain init --force --pglite --embedding-model <provider>:<model> --embedding-dimensions <N>
-  ```
-
-- **Non-empty brain** — migrate cleanly with the supported reindex path:
-  ```
-  gbrain retrieval-upgrade --to <provider>:<model> --reindex
-  ```
-
-## Decision tree
-
-- **Cost-sensitive, English-only**: Ollama (free, local) or Voyage (paid, best quality per dollar).
-- **Quality-first**: Voyage `voyage-4-large` (1024-2048 dims, ~3-4× more dense tokens than OpenAI tiktoken).
-- **Code-heavy brain (gstack per-worktree, source repos)**: Voyage `voyage-code-3` (1024 default; supports 256/512/1024/2048). Tuned on programming languages. Voyage publishes head-to-head numbers showing it outperforms their general flagships on code retrieval ([voyageai.com/blog](https://voyageai.com/blog)). For gstack's per-worktree pglite-backed code brain, this is the right default — see Topology 3 in `docs/architecture/topologies.md`.
-- **Reranking pair**: ZeroEntropy `zerank-2` is the hosted default in `tokenmax` mode (see [`docs/ai-providers/zeroentropy.md`](../ai-providers/zeroentropy.md)). Voyage `rerank-2.5` pairs cleanly with Voyage embeddings.
-- **Local reranking (no API spend)**: `llama-server-reranker` recipe (v0.40.6.1) — point gbrain at your own `llama-server --reranking` instance running Qwen3-Reranker or self-hosted ZeroEntropy weights. Same `gateway.rerank()` seam, $0 per call. Walkthrough in [`docs/ai-providers/llama-server-reranker.md`](../ai-providers/llama-server-reranker.md).
-- **One key for many hosted models**: OpenRouter. Set `OPENROUTER_API_KEY` and use `openrouter:<provider>/<model>` for chat against GPT-5.2, Claude 4.x, Gemini 3, DeepSeek, and dozens more without juggling per-provider keys. Embedding catalog includes OpenAI, Google, Qwen, BGE-M3.
-- **Enterprise compliance**: Azure OpenAI (data residency + private endpoints) or self-hosted via llama-server / Ollama.
-- **China region**: DashScope (Alibaba) or Zhipu (BigModel). DashScope's international endpoint at `dashscope-intl.aliyuncs.com`; override `provider_base_urls.dashscope` for the China endpoint.
-- **OSS local, full control**: llama-server (`llama.cpp`) for any GGUF model; Ollama for the curated catalog.
-- **Anything else**: LiteLLM proxy. Run LiteLLM in front of any provider (Bedrock, Vertex, Cohere, Jina, Fireworks, etc.) and point gbrain at it via `LITELLM_BASE_URL`.
-
-## Per-provider details
-
-### OpenAI
-
-Default. Set `OPENAI_API_KEY`. Models: `text-embedding-3-large` (3072 max, 1536 default), `text-embedding-3-small` (1536). Matryoshka via the `dimensions` field — gbrain pins it from `embedding_dimensions` config so existing 1536-dim brains stay aligned across SDK upgrades.
-
-Optional `OPENAI_BASE_URL` — point the native OpenAI provider at an OpenAI-compatible gateway. A bare host is normalized to carry the `/v1` suffix automatically (so `https://gw.example.com` and `https://gw.example.com/v1` both work); when unset, the SDK's default endpoint is untouched. `ANTHROPIC_BASE_URL` gets the same normalization for Anthropic chat/expansion calls.
-
-### Voyage AI
-
-Best-in-class quality on the Voyage 4 family (Jan 2026 release). Set `VOYAGE_API_KEY`. Models: `voyage-4-large`, `voyage-4`, `voyage-4-lite`, `voyage-4-nano`, `voyage-3.5`, `voyage-code-3` (code-tuned), `voyage-finance-2`, `voyage-law-2`, `voyage-multimodal-3` (text + image).
-
-Voyage 4 family shares an embedding space across all variants, so you can index with `voyage-4-large` and query with `voyage-4-lite` without reindexing. Dims: 256, 512, 1024, 2048. **2048 exceeds pgvector's HNSW cap of 2000** — those brains fall back to exact vector scans (still correct, just slower).
-
-**For brains that index source code** (gstack's per-worktree pglite-backed code brain — see Topology 3 in `docs/architecture/topologies.md`), prefer `voyage-code-3` over `voyage-4-large`. Voyage tunes it on programming languages and publishes head-to-head numbers vs their general flagships on code retrieval. Configure at install time:
-
-```bash
-gbrain init --pglite --embedding-model voyage:voyage-code-3 --embedding-dimensions 1024
-```
-
-To switch an existing brain, use `gbrain reinit-pglite --embedding-model voyage:voyage-code-3 --embedding-dimensions 1024` (PGLite) or follow `docs/embedding-migrations.md` (Postgres). `gbrain config set embedding_model` is refused — the schema column has to resize.
-
-`gbrain reindex --code` will print a recommendation when run against a brain whose configured embedding model isn't code-tuned; suppress with `GBRAIN_NO_CODE_MODEL_NUDGE=1` if you've intentionally chosen another model (single-vendor procurement, compliance, etc.).
-
-### Google Gemini
-
-Set `GOOGLE_GENERATIVE_AI_API_KEY` (the AI Studio public API key). Model: `gemini-embedding-001`. Default 768 dims; Matryoshka up to 3072. Cheap.
-
-For GCP service-account / Vertex AI auth (production deployments), see the v0.32.x follow-up — Vertex ADC is on the roadmap.
-
-### OpenRouter
-
-Single OpenAI-compatible API for fan-out to OpenAI, Anthropic, Google, DeepSeek, Meta Llama, Qwen, and dozens of other hosted providers. One key, many models. Set `OPENROUTER_API_KEY` and use `openrouter:<provider>/<model>` (e.g. `openrouter:openai/gpt-5.2`, `openrouter:anthropic/claude-sonnet-4.6`).
-
-**Embedding**: `openai/text-embedding-3-small` (1536d default, Matryoshka shrink to 512/768/1024). OR's embedding catalog also includes `text-embedding-3-large`, `google/gemini-embedding-2-preview`, `qwen/qwen3-embedding-8b`, `bge-m3` — opt in via `--embedding-model openrouter:<id>`. Pricing matches the upstream provider (OR adds a small markup).
-
-**Chat**: every chat model OR proxies works through `/v1/chat/completions`. The recipe lists 8 curated entry points (GPT-5.2 family, Claude 4.5/4.6/4.7, Gemini 3 Flash Preview, DeepSeek); any other OR catalog ID also works. Tool-calling envelope is supported by the OR endpoint, but per-model capability varies — check https://openrouter.ai/models before counting on tools for a specific slug.
-
-**Optional env**:
-- `OPENROUTER_BASE_URL` — point at a self-hosted OR-compatible proxy.
-- `OPENROUTER_REFERER` (default `https://gbrain.ai`) and `OPENROUTER_TITLE` (default `gbrain`) — attribution headers for OR's leaderboard. Forks running gbrain inside a different agent stack (OpenClaw deployments etc.) should set these so their traffic gets attributed to them, not gbrain.
-
-**Subagent loops**: gbrain's subagent infrastructure hard-pins to Anthropic-direct (stable `tool_use_id` across crashes/replays). OR-routed Anthropic is rejected at submit time regardless of the recipe flag. If you want the price/availability story OR offers for tool-calling, use it for chat only and keep an Anthropic key for subagent work.
-
-### Azure OpenAI
-
-Enterprise OpenAI behind Azure tenancy. Required env: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT` (e.g. `https://my-resource.openai.azure.com`), `AZURE_OPENAI_DEPLOYMENT` (the deployment name from your Azure portal). Optional: `AZURE_OPENAI_API_VERSION` (defaults to `2024-10-21`).
-
-Unlike vanilla OpenAI, Azure uses `api-key:` header (not `Authorization: Bearer`) and a templated URL with `?api-version=` query param — gbrain handles both via the recipe's resolveAuth + resolveOpenAICompatConfig overrides.
-
-Models: `text-embedding-3-large`, `text-embedding-3-small`, `text-embedding-ada-002` (your Azure deployment must serve the requested model).
-
-### MiniMax (海螺AI)
-
-Set `MINIMAX_API_KEY`. Optional `MINIMAX_GROUP_ID` for org-scoped accounts. Model: `embo-01` (1536 dims).
-
-MiniMax's API takes a `type: 'db' | 'query'` field for asymmetric retrieval. v0.32 routes everything as `type='db'` (symmetric retrieval — same vector space for indexing and queries). Asymmetric query support is a v0.32.x follow-up.
-
-### DashScope (Alibaba)
-
-Set `DASHSCOPE_API_KEY`. International endpoint at `dashscope-intl.aliyuncs.com` by default; override `provider_base_urls.dashscope` for the China endpoint. Models: `text-embedding-v3` (current; Matryoshka 64-1024 dims), `text-embedding-v2`.
-
-CJK-dominant content tokenizes denser than OpenAI tiktoken; gbrain declares `chars_per_token: 2` so the batch pre-split leaves headroom.
-
-### Zhipu AI (BigModel)
-
-Set `ZHIPUAI_API_KEY`. Models: `embedding-3` (current; Matryoshka 256-2048 dims), `embedding-2`. v0.32 default is 1024 (HNSW-compatible). The 2048-dim option works but falls into the exact-scan branch (see Voyage 4 Large note above).
-
-### Ollama (local)
-
-No env required — Ollama runs unauthenticated locally. Optional `OLLAMA_BASE_URL` (default `http://localhost:11434/v1`) and `OLLAMA_API_KEY` (for auth-enabled deployments).
-
-Recipe ships with `nomic-embed-text` (768d, recommended), `mxbai-embed-large` (1024d), `all-minilm` (384d), plus the larger modern embedders `qwen3-embed-8b` (4096d) and `snowflake-arctic-embed-l-v2` (1024d). `gbrain providers test --model ollama:nomic-embed-text` smoke-tests the local install.
-
-The recipe default is `nomic-embed-text`'s 768 dims. If you run one of the larger models, declare its native dimension with `--embedding-dimensions <N>` at init — gbrain trusts the value you declare for local recipes instead of rejecting a non-768 width.
-
-### llama-server (local, llama.cpp)
-
-`llama.cpp`'s `llama-server --embeddings` endpoint. No env required. Optional `LLAMA_SERVER_BASE_URL` (default `http://localhost:8080/v1`) and `LLAMA_SERVER_API_KEY`.
-
-User-driven models: launch llama-server with `--model <gguf-path> --embeddings`, then run `gbrain init --embedding-model llama-server:<your-id> --embedding-dimensions <N>`. gbrain trusts the dimension you declare (you know the GGUF you launched); the recipe refuses the implicit shorthand `--model llama-server` because there's no canonical first model.
-
-### LiteLLM proxy (universal escape hatch)
-
-Run [LiteLLM](https://docs.litellm.ai/docs/proxy/quick_start) in front of any provider — Bedrock, Vertex, Cohere, Jina, Fireworks, OctoAI, etc. The proxy normalizes everything to the OpenAI-compatible API; gbrain points at the proxy via `LITELLM_BASE_URL` and proxies the call.
-
-This is the catch-all for "my provider isn't in the list above." Set up LiteLLM, then `gbrain init --embedding-model litellm:<your-model-id> --embedding-dimensions <N>`.
-
-**Include the `/v1` suffix in `LITELLM_BASE_URL` if your proxy serves the OpenAI route there** (e.g. `http://localhost:4000/v1`). Many LiteLLM deployments expose the OpenAI-compatible API only under `/v1`; pointing gbrain at the bare host 404s or fails authentication with no hint. gbrain trusts the dimension you declare for the proxy-backed model — the proxy's backend, not gbrain, decides the true width — so `--embedding-dimensions <N>` is required and accepted as-is.
-
-## Choosing dimensions
-
-Three numbers matter:
-1. **Provider's native dims**: each model has a "true" output dim (e.g. OpenAI `text-embedding-3-large` is 3072 native).
-2. **Matryoshka reductions**: most modern providers let you request a smaller vector via the `dimensions` field.
-3. **HNSW cap**: pgvector's HNSW index supports up to 2000 dims. Brains above that fall back to exact vector scans (slower but correct; gbrain handles the SQL automatically via `chunkEmbeddingIndexSql` in `src/core/vector-index.ts`).
-
-For most users: **stay at 1024 or 1536**. Bigger isn't better below the noise floor; smaller saves disk + RAM with marginal recall loss on Matryoshka providers.
-
-## My provider isn't listed
-
-Four options:
-
-1. **Use OpenRouter** when the provider/model is available through OR's OpenAI-compatible API (covers most hosted chat models + a growing embedding catalog).
-2. **Use LiteLLM proxy** (above) — the universal escape hatch. Works for 100+ providers.
-3. **Open a feature request** at [github.com/garrytan/gbrain/issues](https://github.com/garrytan/gbrain/issues) with the provider's API docs URL and a setup snippet. Recipes are ~30-40 lines of TypeScript.
-4. **Submit a recipe**: clone, copy `src/core/ai/recipes/voyage.ts` as the gold-standard openai-compat template, register in `src/core/ai/recipes/index.ts`, add a per-recipe smoke test under `test/ai/recipe-<name>.test.ts`. The recipe contract test (`test/ai/recipes-contract.test.ts`) and IRON RULE regression test pin the structural invariants.
-
-## Switching providers on an existing brain
-
-Embedding dimensions are baked into the schema at `gbrain init` time. As of v0.37.11.0, `gbrain config set embedding_model` and `gbrain config set embedding_dimensions` are refused — the schema column has to resize alongside the config, and `config set` only touches the config row.
-
-The supported paths:
-
-- **PGLite (default install):** `gbrain reinit-pglite --embedding-model <provider>:<model> --embedding-dimensions <N>` — one-command wipe-and-reinit that preserves every other config field (chat model, expansion model, API keys), backs up the prior brain to `<path>.bak`, runs `gbrain init` with the new flags, and re-syncs your brain repo. Add `--no-sync` to skip the resync, `--yes` to skip the TTY confirmation, `--json` for scripts.
-- **Postgres (Supabase / self-hosted):** follow the SQL recipe in `docs/embedding-migrations.md` (drop the HNSW index, ALTER COLUMN TYPE, clear stale embeddings, recreate the index conditionally, then `gbrain init --supabase --embedding-model X --embedding-dimensions N` to update the file plane and re-embed).
-
-`gbrain doctor` 8c "alternative_providers" surfaces unconfigured providers whose env is already set — useful when you've configured OpenAI but also have e.g. `VOYAGE_API_KEY` exported and want to know you can switch without extra setup.

docs/integrations/pre-commit.md

@@ -7,7 +7,7 @@ brain source's repo that runs `gbrain frontmatter validate` against staged
 
 ## What the hook catches
 
-The same eight validation classes the `frontmatter-guard` skill and
+The same seven validation classes the `frontmatter-guard` skill and
 `gbrain doctor`'s `frontmatter_integrity` subcheck report:
 
 | Code              | What it catches                                                     |
@@ -18,7 +18,6 @@ The same eight validation classes the `frontmatter-guard` skill and
 | `SLUG_MISMATCH`   | `slug:` in frontmatter doesn't match path-derived slug              |
 | `NULL_BYTES`      | Binary corruption (`\x00`) anywhere in the content                  |
 | `NESTED_QUOTES`   | `title: "outer "inner" outer"` shape that breaks YAML               |
-| `NON_STRING_FIELD` | `title`/`type`/`slug` is an unquoted non-string scalar (`title: 123`) |
 | `EMPTY_FRONTMATTER` | `---` ... `---` with nothing meaningful between                   |
 
 ## Install

docs/issues/cross-modal-search.md

@@ -1,164 +0,0 @@
-# Cross-Modal Search: Text↔Image Retrieval
-
-## Summary
-
-gbrain has a working multimodal embedding pipeline (Voyage multimodal-3, `embedding_image` column, 11K image chunks indexed) but search is siloed: text queries only search text embeddings, image queries don't exist. This proposal adds cross-modal query routing so text queries can surface images and image queries can surface text, using Voyage multimodal-3's shared embedding space.
-
-## Problem
-
-**What the user sees:** You can't search "photos from the hackathon" and get actual images. You can't upload a photo and ask "what do we know about this person?" Text search returns text. Image embeddings sit unused except via explicit `embeddingColumn: 'embedding_image'` override, which no user-facing path triggers.
-
-**What the system does:**
-- Text queries embed through the configured text model (OpenAI/ZE) and search the text column
-- The `embedding_image` column exists (Voyage multimodal-3, 1024d) with 11,204 embedded chunks and a valid 83 MB HNSW index
-- `postgres-engine.ts:searchVector()` supports `embeddingColumn: 'embedding_image'` but the query vector must come from a compatible model (Voyage multimodal, 1024d)
-- Currently, `embedQuery()` always uses the text embedding model, producing a 1536d or 2560d vector that can't query the 1024d image column
-
-**What it should do:**
-1. Detect cross-modal intent in a search query ("show me photos of...", "find images from...", or explicit image search flag)
-2. Embed the text query through Voyage multimodal-3 (same model used for image embeddings)
-3. Search the `embedding_image` column with the multimodal query vector
-4. Return image results alongside or instead of text results
-5. Support image-as-query: accept an image input, embed it through Voyage multimodal-3, search text embeddings (if a shared multimodal column exists) or the image column
-
-## Evidence
-
-### Image embeddings exist and are indexed
-
-```sql
--- Production state (May 2026)
-SELECT COUNT(*) FROM content_chunks WHERE embedding_image IS NOT NULL;
--- 11,204
-
-SELECT indexrelid::regclass, indisvalid, pg_size_pretty(pg_relation_size(indexrelid))
-FROM pg_index WHERE indrelid = 'content_chunks'::regclass
-AND indexrelid::regclass::text LIKE '%image%';
--- idx_chunks_embedding_image | true | 83 MB
-```
-
-### Modality metadata is broken
-
-```sql
-SELECT COUNT(*) FROM content_chunks WHERE modality = 'image';
--- 10 (should be ~11,204)
-```
-
-Most image chunks have `embedding_image IS NOT NULL` but `modality` is not set to `'image'`. This is a backfill gap from the v0.27.1 migration.
-
-### Voyage multimodal-3 is cross-modal by design
-
-From Voyage docs: voyage-multimodal-3 encodes text, images, and interleaved text+image into the same 1024-dimensional vector space. A text query embedded through this model can find relevant images, and vice versa. gbrain already uses it for the image column but never for query embedding.
-
-### Search routing is text-only
-
-`hybrid.ts` line ~414:
-```typescript
-const embeddings = await Promise.all(queries.map(q => embedQuery(q)));
-```
-
-`embedQuery()` always uses the global text model. No path exists to embed a text query through the multimodal model for cross-modal search.
-
-## Proposed Fix
-
-### Phase 1: Text → Image Search
-
-**1. Cross-modal intent detection** (new file: `src/core/search/cross-modal.ts`)
-
-Add a lightweight intent classifier that detects when a query is looking for images:
-
-```typescript
-function detectCrossModalIntent(query: string): 'text' | 'image' | 'both' {
-  // Explicit image patterns
-  const imagePatterns = [
-    /\b(show|find|get)\s+(me\s+)?(photos?|images?|pictures?|screenshots?)/i,
-    /\bwhat\s+does\s+.+\s+look\s+like/i,
-    /\b(whiteboard|diagram|slide|screenshot)\b/i,
-    /\bphoto(s)?\s+(of|from|at|with)\b/i,
-  ];
-  if (imagePatterns.some(p => p.test(query))) return 'image';
-  return 'text'; // Default: text-only
-}
-```
-
-**2. Multimodal query embedding** (extend `embedding.ts`)
-
-Add `embedQueryMultimodal(text: string): Promise<Float32Array>` that routes through the configured multimodal model (Voyage multimodal-3) instead of the text model.
-
-```typescript
-export async function embedQueryMultimodal(text: string): Promise<Float32Array> {
-  // Use the multimodal provider, not the text provider
-  return gatewayEmbedQuery(text, { provider: cfg.embedding_multimodal_model });
-}
-```
-
-**3. Hybrid search routing** (extend `hybrid.ts`)
-
-When cross-modal intent is detected:
-- Embed query through multimodal model (Voyage multimodal-3, 1024d)
-- Search `embedding_image` column
-- Return results with a `modality: 'image'` tag
-- If intent is `'both'`: run text search AND image search, merge with RRF
-
-**4. SearchOpts extension** (extend `types.ts`)
-
-```typescript
-interface SearchOpts {
-  // ... existing fields
-  crossModal?: 'text' | 'image' | 'both' | 'auto';  // Default: 'auto' (intent detection)
-}
-```
-
-### Phase 2: Image → Text Search (future)
-
-Accept an image buffer/URL as search input. Embed through Voyage multimodal-3. Search text embeddings. This requires a new search entry point (`searchByImage`) and MCP tool exposure. Defer to a follow-up PR.
-
-### Phase 3: Unified Multimodal Column (future)
-
-Embed ALL content (text + images) through Voyage multimodal-3 into a single column. This creates a truly unified search space but doubles embedding costs and requires re-embedding all text. Evaluate after Phase 1 results.
-
-## Backfill: Fix modality metadata
-
-Before cross-modal search is useful, fix the modality column:
-
-```sql
--- Chunks with image embeddings but wrong modality
-UPDATE content_chunks
-SET modality = 'image'
-WHERE embedding_image IS NOT NULL AND (modality IS NULL OR modality != 'image');
-```
-
-This is a prerequisite for Phase 1 since result display needs to know which chunks are images.
-
-## Test Guidance
-
-### Red tests (should fail before fix, pass after)
-
-1. **Intent detection:** `detectCrossModalIntent("show me photos from the hackathon")` returns `'image'`.
-2. **Intent detection negative:** `detectCrossModalIntent("what is founder mode?")` returns `'text'`.
-3. **Multimodal embed routing:** `embedQueryMultimodal("hackathon")` returns a 1024d vector (Voyage multimodal dims), not 1536d or 2560d.
-4. **Cross-modal search:** `hybridSearch("show me hackathon photos", { crossModal: 'image' })` returns results from the `embedding_image` column.
-5. **Default behavior unchanged:** `hybridSearch("what is founder mode?")` returns text results as before (no cross-modal unless detected).
-6. **Explicit override:** `hybridSearch("anything", { crossModal: 'image' })` forces image search regardless of intent detection.
-
-### Edge cases
-
-- Query matches image intent but no image embeddings exist for the topic: return empty image results, fall back to text.
-- Multimodal model not configured: skip cross-modal, log warning, return text results.
-- Mixed results ('both' mode): text and image results merged, each tagged with modality for display.
-
-## Related Context
-
-- PR #1106 adds dynamic text embedding column selection (prerequisite: the `embedding_columns` registry and provider routing from that PR make this easier to implement)
-- v0.27.1 introduced the dual-column schema (`embedding` + `embedding_image`)
-- `importImageFile` in `postgres-engine.ts` handles image ingestion and multimodal embedding
-- Voyage multimodal-3 is already configured as `embedding_multimodal_model` in gbrain config
-- The image OCR pipeline (`embedding_image_ocr: true`) extracts text from images before embedding, so image chunks have both visual and text representation
-
-## Phasing
-
-| Phase | Scope | Effort | Value |
-|---|---|---|---|
-| **1 (this PR)** | Text → Image search with intent detection | Medium | High — unlocks "find photos" queries |
-| 2 | Image → Text search (upload photo, find related text) | Medium | Medium — cool but niche use case |
-| 3 | Unified multimodal column (everything in one space) | Large | High — but expensive and requires re-embedding |
-| Prereq | Fix modality column backfill | Small | Required for Phase 1 |

docs/issues/doctor-auto-heal-and-scoring.md

@@ -1,224 +0,0 @@
-# Doctor Auto-Heal and Scoring Improvements
-
-## Summary
-
-The `gbrain doctor` health score system has several false-positive patterns and missing auto-heal capabilities. After the crash classification fix (shipped in this PR), these are the remaining improvements ranked by impact.
-
----
-
-## 1. Frontmatter severity levels
-
-### Problem
-
-`NESTED_QUOTES` warnings dominate the frontmatter check (6,900+ of ~7,100 total issues). These are cosmetic YAML style issues — values like `title: "foo"` where the quotes are technically unnecessary. They don't affect sync, search, embedding, or any functionality.
-
-By counting them the same as `YAML_PARSE` (actual parse failures) or `MISSING_OPEN` (missing frontmatter delimiters), the frontmatter check is perpetually WARN and the real issues are lost.
-
-### Evidence
-
-```
-frontmatter_integrity: 7131 issues across 3 sources
-  default: 7012 (NESTED_QUOTES=6922, YAML_PARSE=90)
-  media-corpus: 16 (MISSING_OPEN=15, YAML_PARSE=1)
-  zion-brain: 103 (MISSING_OPEN=14, NESTED_QUOTES=89)
-```
-
-Only 280 of 7,131 issues are real problems. 96% are cosmetic noise.
-
-### Proposed Fix
-
-- Introduce severity levels: `error` (YAML_PARSE, MISSING_OPEN) vs `info` (NESTED_QUOTES)
-- Doctor WARN/FAIL only on error-level issues
-- Report info-level in the message text but don't affect check status
-- Optional `--pedantic` flag includes info-level in status
-
-### Test Cases
-
-| Frontmatter issues | Severity breakdown | Expected status |
-|---|---|---|
-| 0 issues | n/a | OK |
-| 50 NESTED_QUOTES only | 0 error, 50 info | OK (with note) |
-| 3 YAML_PARSE | 3 error | WARN |
-| 6900 NESTED_QUOTES + 3 YAML_PARSE | 3 error, 6900 info | WARN (mentions 3 errors) |
-
----
-
-## 2. Temporal contradiction awareness
-
-### Problem
-
-The contradiction probe flags temporal evolutions as contradictions. Example:
-
-- Page A (April): "Considering option X"
-- Page B (May): "Decided on option Y"
-
-These aren't contradictions — they're the same topic evolving over time. The probe has no time awareness.
-
-### Evidence
-
-From a probe run on 50 queries with top-k=15:
-- 120 contradictions detected (112 high, 8 medium)
-- After manual review: ~60% were temporal evolutions, not real conflicts
-- Pages have `effective_date` or `created` timestamps that could disambiguate
-
-### Proposed Fix
-
-- Pass `effective_date` / `created` to the judge prompt
-- Add verdict: `temporal_supersession` (later claim supersedes earlier)
-- When both pages have dates and claims overlap, bias toward temporal interpretation
-- Already designed in PR #993
-
-### Test Cases
-
-| Page A date | Page A claim | Page B date | Page B claim | Expected verdict |
-|---|---|---|---|---|
-| 2026-04 | "Considering X" | 2026-05 | "Chose Y" | temporal_supersession |
-| 2026-04 | "Revenue is $1M" | 2026-04 | "Revenue is $500K" | contradiction |
-| null | "X is true" | null | "X is false" | contradiction |
-| 2025-01 | "CEO of Company" | 2026-01 | "Former CEO" | temporal_supersession |
-
----
-
-## 3. Multi-source drift baseline
-
-### Problem
-
-4,791 pages show "multi-source drift" due to a pre-v0.30.3 `putPage` routing bug. These pages exist at the `default` source but should be at a named source. The `sources rehome` command to fix this hasn't shipped yet.
-
-Every doctor run shows WARN for ~4,800 pages nobody can fix.
-
-### Proposed Fix
-
-Allow `doctor.baselines` config to acknowledge known-unfixable counts:
-
-```yaml
-doctor:
-  baselines:
-    multi_source_drift: 4800
-```
-
-When actual drift ≤ baseline: OK. When drift exceeds baseline: WARN (new drift).
-
-Store in `.gbrain/doctor-baselines.json` so it works without config too:
-
-```json
-{
-  "multi_source_drift": { "count": 4800, "acknowledged_at": "2026-05-15", "reason": "pre-v0.30.3 putPage misroutes" }
-}
-```
-
-### Test Cases
-
-| Actual drift | Baseline | Expected |
-|---|---|---|
-| 4791 | 4800 | OK |
-| 4900 | 4800 | WARN ("100 new drift beyond baseline") |
-| 4791 | 0 (no baseline) | WARN (current behavior) |
-
----
-
-## 4. Image assets acknowledgment
-
-### Problem
-
-When image files are missing from disk (stored externally, purged from git), the check permanently warns. No way to say "these are intentionally external."
-
-### Proposed Fix
-
-- `doctor --acknowledge image_assets` marks current missing count as accepted
-- Stored in `.gbrain/doctor-baselines.json`
-- WARN only for NEW missing images beyond acknowledged count
-- Optional `image_assets.external_storage: true` config to skip disk check entirely
-
----
-
-## 5. Auto-heal mode
-
-### Problem
-
-Many doctor warnings have known fixes that are safe to auto-apply:
-
-| Warning | Auto-fix |
-|---|---|
-| Supervisor not running | Start supervisor |
-| Stale embeddings | Submit `embed --stale` job |
-| Extract coverage < 70% | Submit `extract all --skip-existing` job |
-| Stale sync | Submit sync job |
-| Effective date drift | Run `reindex-frontmatter` |
-
-### Proposed Fix
-
-`doctor --auto-heal` mode:
-
-1. Run all checks
-2. For fixable WARNs: submit fix as a job (not inline — via job queue)
-3. Report what was fixed vs needs manual attention
-4. Idempotent: check queue first, don't submit duplicates
-5. Safety gate: never auto-heals FAILs, only WARNs
-
-Config:
-
-```yaml
-doctor:
-  autoHeal:
-    enabled: true
-    minInterval: "6h"
-    skip:
-      - image_assets
-      - multi_source_drift
-```
-
-### Test Cases
-
-| Check status | Auto-heal enabled | Job already queued | Expected |
-|---|---|---|---|
-| WARN: stale embeds | yes | no | Submit embed job |
-| WARN: stale embeds | yes | yes | Skip (idempotent) |
-| FAIL: max_crashes | yes | n/a | Don't auto-fix FAILs |
-| WARN: stale embeds | no | n/a | Report only |
-| WARN: image_assets | yes (but skipped) | n/a | Report only |
-
----
-
-## 6. Score delta tracking
-
-### Problem
-
-No history — each `doctor` run is a snapshot. Can't tell if score is improving or degrading.
-
-### Proposed Fix
-
-- Write each run to `.gbrain/doctor-history.jsonl`:
-  ```json
-  {"ts":"2026-05-15T12:00:00Z","score":60,"brain_score":79,"checks":{"supervisor":"ok","embeddings":"ok",...}}
-  ```
-- `doctor --trend` shows last N scores with deltas
-- `doctor --json` includes `previous_score` and `delta` fields
-
----
-
-## 7. Weighted scoring
-
-### Problem
-
-Going from 99% → 100% embed coverage weighs the same as 50% → 51%. But the last percent is the hardest (oversized pages, rate limits).
-
-### Proposed Fix
-
-Threshold-based scoring:
-- 100% = full points
-- ≥95% = 90% of points
-- ≥80% = 70% of points
-- <80% = proportional
-
----
-
-## Priority Order
-
-1. Frontmatter severity levels (highest noise reduction)
-2. Temporal contradiction awareness (highest false positive reduction, already designed)
-3. Auto-heal mode (biggest long-term value)
-4. Score delta tracking (enables monitoring)
-5. Multi-source drift baseline (quality of life)
-6. Image assets acknowledgment (quality of life)
-7. Weighted scoring (nice to have)

docs/mcp/CLAUDE_CODE.md

@@ -1,10 +1,5 @@
 # Connect GBrain to Claude Code
 
-> New to this? The [Give your coding agent a memory](../tutorials/connect-coding-agent.md)
-> tutorial walks both paths (local-from-nothing and connect-to-an-existing-brain)
-> end to end, plus the brain-first protocol that makes it worth it. This page is
-> the connection reference.
-
 ## Option 1: Local (recommended, zero server needed)
 
 ```bash
@@ -14,44 +9,10 @@ claude mcp add gbrain -- gbrain serve
 That's it. Claude Code spawns `gbrain serve` as a stdio subprocess. No server, no
 tunnel, no token needed. Works with both PGLite and Supabase engines.
 
-## Option 2: Remote, one command (fastest from a bearer token)
+## Option 2: Remote (access from any machine)
 
-If GBrain is running somewhere as an HTTP server (`gbrain serve --http`, see the
-[ngrok-tunnel recipe](../../recipes/ngrok-tunnel.md)) and you have a bearer token,
-let `gbrain connect` generate the wire-up for you.
-
-On the host (or anywhere `gbrain` is installed), mint a token and print the block:
-
-```bash
-gbrain auth create "claude-code"
-gbrain connect https://YOUR-DOMAIN.ngrok.app/mcp --token gbrain_xxx
-```
-
-`gbrain connect` prints a short, copy-paste block. Paste it into Claude Code — it
-runs the `claude mcp add` for you and tells the agent to call `get_brain_identity`
-and `list_skills` so it immediately knows what the brain can do.
-
-Already on the machine you want to wire up? Skip the copy-paste and let `connect`
-do it directly, with a built-in token smoke-test:
-
-```bash
-gbrain connect https://YOUR-DOMAIN.ngrok.app --token gbrain_xxx --install
-```
-
-(`--install` runs `claude mcp add`, then verifies the token by calling
-`get_brain_identity` — so a wrong or expired token fails now, not silently on the
-agent's first request. The URL is normalized: a bare host without `/mcp` gets it
-appended; pass an explicit `https://` scheme.)
-
-Pipe-friendly machine output (token redacted unless `--show-token`):
-
-```bash
-gbrain connect https://YOUR-DOMAIN.ngrok.app/mcp --token gbrain_xxx --json
-```
-
-## Option 3: Remote, manual `claude mcp add`
-
-Equivalent to what `gbrain connect` generates, if you'd rather run it yourself:
+If you have GBrain running on a server with a public tunnel (see
+[ngrok-tunnel recipe](../../recipes/ngrok-tunnel.md)):
 
 ```bash
 claude mcp add gbrain -t http \
@@ -59,12 +20,8 @@ claude mcp add gbrain -t http \
   -H "Authorization: Bearer YOUR_TOKEN"
 ```
 
-Replace `YOUR-DOMAIN` with your ngrok domain and `YOUR_TOKEN` with a token from
-`gbrain auth create "claude-code"`.
-
-> A `gbrain auth create` token is a long-lived, full-access secret. Keep it
-> private (it lands in `~/.claude.json`), and prefer a scoped/short-lived token
-> where your host supports one.
+Replace `YOUR-DOMAIN` with your ngrok domain and `YOUR_TOKEN` with a token
+from `gbrain auth create "claude-code"`.
 
 ## Verify
 
@@ -76,13 +33,6 @@ search for [any topic in your brain]
 
 You should see results from your GBrain knowledge base.
 
-> **`list_skills` returns nothing?** Skill discovery is gated by `mcp.publish_skills`
-> on the host. New brains from `gbrain init` default it ON; brains upgraded from an
-> older release stay OFF until you opt in. Enable it on the host with
-> `gbrain config set mcp.publish_skills true`. The core tools (search, query,
-> get_page, put_page, think, find_experts) work regardless. Note: `capture` is a
-> CLI-only command, not an MCP tool — the agent writes over MCP with `put_page`.
-
 ## Remove
 
 ```bash

docs/mcp/CODEX.md

@@ -1,71 +0,0 @@
-# Connect GBrain to Codex
-
-> New to this? The [Give your coding agent a memory](../tutorials/connect-coding-agent.md)
-> tutorial walks both paths (local-from-nothing and connect-to-an-existing-brain)
-> end to end, plus the brain-first protocol that makes it worth it. This page is
-> the connection reference.
-
-Codex CLI (`@openai/codex`, v0.130+) supports remote streamable-HTTP MCP servers
-with a bearer token read from an environment variable. The token lives in your
-shell env, not in Codex's config file.
-
-## Fastest path: `gbrain connect`
-
-Run anywhere `gbrain` is installed (mint a token on the brain host first):
-
-```bash
-gbrain auth create "codex"
-gbrain connect https://YOUR-DOMAIN.ngrok.app/mcp --token gbrain_xxx --agent codex
-```
-
-This prints a copy-paste block. Or wire it up directly and smoke-test the token:
-
-```bash
-gbrain connect https://YOUR-DOMAIN.ngrok.app/mcp --token gbrain_xxx --agent codex --install
-```
-
-`--install` runs `codex mcp add` for you, then makes one real call to the brain so
-a wrong/expired token fails right away. Because Codex reads the token from the env
-var at runtime, keep `GBRAIN_REMOTE_TOKEN` exported in your shell profile.
-
-## Manual setup
-
-```bash
-export GBRAIN_REMOTE_TOKEN=gbrain_xxx
-codex mcp add gbrain --url https://YOUR-DOMAIN.ngrok.app/mcp \
-  --bearer-token-env-var GBRAIN_REMOTE_TOKEN
-```
-
-Codex stores the env-var *name* (`GBRAIN_REMOTE_TOKEN`), not the token itself, and
-reads the value when it launches the MCP server. Add the `export` line to your
-`~/.zshrc` / `~/.bashrc` so it's set in every session.
-
-## Verify
-
-In Codex, ask it to use the brain:
-
-```
-Call get_brain_identity, then search my brain for [topic].
-```
-
-`get_brain_identity` confirms whose brain you're connected to; `list_skills` shows
-everything it can do.
-
-> **`list_skills` empty?** It's gated by `mcp.publish_skills` on the host (default
-> ON for `gbrain init` brains, OFF for brains upgraded from older releases). Enable
-> it on the host: `gbrain config set mcp.publish_skills true`. The core tools
-> (search, query, get_page, put_page, think, find_experts) work regardless.
-> `capture` is CLI-only, not an MCP tool — write over MCP with `put_page`.
-
-## Remove
-
-```bash
-codex mcp remove gbrain
-```
-
-## Notes
-
-- The token is a long-lived, full-access secret. Keep `GBRAIN_REMOTE_TOKEN` out of
-  version control and prefer a scoped token if your host supports one.
-- Local stdio also works if you run the brain on the same machine:
-  `codex mcp add gbrain -- gbrain serve`.

docs/mcp/DEPLOY.md

@@ -74,20 +74,13 @@ to the HTTP server, so no migration is required.
 gbrain serve --http --port 3131
 ```
 
-On first start in an interactive terminal, the server prints an **admin
-bootstrap token** to stderr:
+On first start, the server prints an **admin bootstrap token** to stderr:
 
 ```
 Admin bootstrap token: 3a1f9c...
 Open http://localhost:3131/admin and paste it to log in.
 ```
 
-On a non-TTY start (systemd, Docker, any piped or captured logs) the generated
-token is hidden so it never lands in log storage. For headless deploys either
-set `GBRAIN_ADMIN_BOOTSTRAP_TOKEN` to a value you control before starting, or
-run `gbrain serve --http --print-admin-token` once on a trusted terminal to
-force printing.
-
 Save this token. Open `http://localhost:3131/admin` and paste it to access the
 dashboard. The dashboard shows live activity, registered clients, request logs,
 and per-client config export.
@@ -124,24 +117,6 @@ gbrain auth register-client perplexity \
   --scopes "read write"
 ```
 
-**v0.34 — source-scoped clients.** Multi-source brains can scope a client's
-write authority to one source and its read scope to a curated set with the
-new `--source` and `--federated-read` flags:
-
-```bash
-gbrain auth register-client dept-x-agent \
-  --grant-types client_credentials \
-  --scopes "read write" \
-  --source dept-x \
-  --federated-read dept-x,shared,parent-canon
-```
-
-`--source` controls the write authority — `put_page` / `add_link` / etc only
-land in `dept-x`. `--federated-read` controls the read axis independently;
-queries return rows from any of the listed sources. Omit both flags for the
-v0.33-compatible super-client shape. Pre-v0.34 clients are backfilled to
-`source_id='default'` on `gbrain upgrade`.
-
 Host-repo wrappers can register programmatically:
 
 ```ts
@@ -158,18 +133,6 @@ start the server with `--enable-dcr`. DCR is off by default.
 
 ### 3. Expose the server
 
-**v0.34 — bind explicitly.** `gbrain serve --http` defaults to `127.0.0.1`.
-To accept connections from the ngrok tunnel (or any non-loopback source),
-restart with `--bind`:
-
-```bash
-gbrain serve --http --port 3131 --bind 0.0.0.0 --public-url https://your-brain.ngrok.app
-```
-
-When `--public-url` is set without `--bind`, a stderr WARN fires at
-startup so the misconfiguration ("the tunnel is up but my agent gets
-ECONNREFUSED") is loud.
-
 ```bash
 brew install ngrok
 ngrok config add-authtoken YOUR_TOKEN

docs/mcp/PERPLEXITY.md

@@ -1,83 +1,20 @@
 # Connect GBrain to Perplexity Computer
 
-Perplexity Computer connects as a **remote** MCP client, so GBrain must be served
-over HTTP and reachable at a public HTTPS URL. Perplexity does not run
-`gbrain serve` (stdio) the way Claude Code does — it needs a reachable endpoint:
+Perplexity Computer supports remote MCP servers with bearer token authentication.
 
-```
-Perplexity Computer
-  → ngrok tunnel (https://YOUR-DOMAIN.ngrok.app/mcp)
-  → gbrain serve --http   (built-in OAuth 2.1 transport)
-  → Postgres / PGLite
-```
+## Setup
 
-## 1. Serve GBrain over HTTP (host side)
-
-```bash
-gbrain serve --http --port 3131 --bind 0.0.0.0 \
-  --public-url https://YOUR-DOMAIN.ngrok.app
-```
-
-- **`--bind 0.0.0.0` is required.** Since v0.34, `--http` defaults to
-  `127.0.0.1`, so without it the tunnel reaches the server but the connection is
-  refused (`ECONNREFUSED`).
-- **`--public-url` must match the tunnel.** The OAuth issuer in the discovery
-  metadata has to line up with the URL Perplexity actually hits (RFC 8414 §3.3),
-  or OAuth client-credentials auth fails.
-
-## 2. Expose it with a tunnel
-
-```bash
-ngrok http 3131 --url YOUR-DOMAIN.ngrok.app
-```
-
-See the [ngrok-tunnel recipe](../../recipes/ngrok-tunnel.md) for a persistent
-tunnel.
-
-## 3. Create credentials
-
-Two supported auth paths.
-
-**OAuth 2.1 client credentials (recommended, v0.26.0+).** Perplexity is a cloud
-service, so it holds whatever credential you give it. OAuth is the correct choice:
-least-privilege scopes + short-lived rotating access tokens instead of a
-long-lived full-access secret. Mint a client and print the connector fields in
-one step (on the brain host):
-
-```bash
-gbrain connect https://YOUR-DOMAIN.ngrok.app/mcp --agent perplexity --oauth --register
-```
-
-Or register separately and pass the creds (works anywhere, no DB needed):
-
-```bash
-gbrain auth register-client perplexity --grant-types client_credentials --scopes "read write"
-gbrain connect https://YOUR-DOMAIN.ngrok.app/mcp --agent perplexity --oauth \
-  --client-id gbrain_cl_xxx --client-secret gbrain_cs_xxx
-```
-
-`connect --oauth` prints the **Issuer URL + Client ID + Client Secret** to paste
-in step 4.
-
-**Legacy bearer token (simplest, best for local/personal):**
-
-```bash
-gbrain auth create "perplexity"
-gbrain connect https://YOUR-DOMAIN.ngrok.app/mcp --token gbrain_xxx --agent perplexity
-```
-
-(Perplexity is a GUI connector, so there's no `--install` — `connect` prints the
-exact values to paste in step 4.)
-
-## 4. Add the connector in Perplexity
-
-1. Open Perplexity (requires Pro subscription).
-2. Go to **Settings → Connectors** (or **MCP Servers**).
+1. Open Perplexity (requires Pro subscription)
+2. Go to **Settings > Connectors** (or **MCP Servers**)
 3. Add a new remote connector:
    - **URL:** `https://YOUR-DOMAIN.ngrok.app/mcp`
-   - **Authentication:** API Key / Bearer Token, or OAuth client credentials
-   - Paste the token (bearer) or `client_id` + `client_secret` (OAuth).
-4. Save.
+   - **Authentication:** API Key / Bearer Token
+   - **Token:** your GBrain access token
+     (create one with `gbrain auth create "perplexity"`)
+4. Save
+
+Replace `YOUR-DOMAIN` with your ngrok domain (see
+[ngrok-tunnel recipe](../../recipes/ngrok-tunnel.md) for setup).
 
 ## Verify
 
@@ -87,14 +24,8 @@ In a Perplexity conversation, ask it to use your brain:
 Use my GBrain to search for [topic]
 ```
 
-Have it call `get_brain_identity` (whose brain this is), then `list_skills`
-(everything it can do).
-
 ## Notes
 
-- Perplexity Computer is available to Pro subscribers; both the Mac app and web
-  version support remote MCP connectors.
-- The Mac app can also use a local MCP server (`gbrain serve` stdio) if you'd
-  rather not expose an HTTP endpoint.
-- A `gbrain auth create` token is a long-lived, full-access secret. Keep it
-  private and prefer a scoped token where possible.
+- Perplexity Computer is available to Pro subscribers
+- Both the Perplexity Mac app and web version support MCP connectors
+- The Mac app also supports local MCP servers if you prefer `gbrain serve` (stdio)

docs/migrations/v0.41.2-markdown-greenfield.md

@@ -1,153 +0,0 @@
-# Migrating your OpenClaw brain to gbrain v0.41.2.0 (greenfield)
-
-The v0.41.2.0 lens packs ship a one-shot importer that re-ingests your
-existing OpenClaw brain (`~/git/brain/atoms/`, `concepts/`, `ideas/`)
-through the new ingestion cathedral. Pages land in gbrain with an
-`imported_from: markdown-greenfield` frontmatter marker so the new
-extract_atoms + synthesize_concepts cycle phases skip them (lossless
-import with provenance).
-
-## Before you migrate
-
-1. **Upgrade gbrain to v0.41.2.0+:**
-   ```bash
-   gbrain upgrade
-   gbrain --version  # should be 0.41.2.0 or later
-   ```
-
-2. **Activate the creator pack** (or gbrain-everything if you also
-   want investor + engineer lenses on the same brain):
-   ```bash
-   gbrain config set schema_pack gbrain-creator
-   # OR
-   gbrain config set schema_pack gbrain-everything
-   ```
-
-3. **Apply schema migration v94** (take_domain_assignments table):
-   ```bash
-   gbrain apply-migrations --yes
-   ```
-
-## The dry-run pass
-
-Always start with `--dry-run` to see what the importer would do
-without writing anything:
-
-```bash
-gbrain capture --source markdown-greenfield \
-  --repo ~/git/brain \
-  --dry-run \
-  --limit 100
-```
-
-The output reports:
-- `emitted` — atoms/concepts/ideas that would import cleanly
-- `skipped_no_type` — files without a `type:` frontmatter (counted
-  as benign skips; no audit appended)
-- `skipped_invalid` — files that failed validation (these append to
-  `~/.gbrain/audit/markdown-greenfield-failures-YYYY-Www.jsonl`)
-
-Inspect the audit JSONL:
-```bash
-ls ~/.gbrain/audit/markdown-greenfield-failures-*.jsonl
-cat ~/.gbrain/audit/markdown-greenfield-failures-*.jsonl | jq .
-```
-
-Common failures:
-- **Empty frontmatter** — file has `---` but no fields. Not a real
-  brain page; safe to leave skipped.
-- **Malformed YAML** — fix the file in your OpenClaw then re-run.
-- **Missing required field** — usually means the original OpenClaw
-  skill output a partial page; check whether the file is worth
-  preserving.
-
-## The actual import
-
-When the dry-run looks clean, drop the `--dry-run` and `--limit`
-flags:
-
-```bash
-gbrain capture --source markdown-greenfield --repo ~/git/brain
-```
-
-Expect ~30-60 minutes for the full 24K-page set (atoms + concepts +
-ideas). The importer:
-
-1. Walks `atoms/{YYYY-MM-DD}/*.md`, `concepts/*.md`, `ideas/*.md` in
-   deterministic alphabetical order so partial-run resumes pick up
-   where they left off.
-2. Stamps `imported_from: markdown-greenfield` + `imported_at:
-   <ISO timestamp>` on every page's frontmatter, preserving ALL
-   original fields verbatim under `metadata.original_frontmatter`.
-3. Emits each as an IngestionEvent with `mode: 'migration'` (T2),
-   which bypasses the daemon's 24h DedupWindow. The importer owns
-   its own permanent slug-keyed idempotency.
-4. Routes through `put_page` so pages land with proper FK chains and
-   embedding eligibility.
-
-## After the import
-
-Verify counts match:
-```bash
-gbrain stats
-# Should show ~24K new pages with type=atom/concept/idea
-```
-
-The next `gbrain dream` cycle will:
-- Run `extract_atoms` on NEW transcripts (skips pages with the
-  `imported_from` marker — your historical atoms are frozen, not
-  re-extracted).
-- Run `synthesize_concepts` on NEW atoms (skips imported concepts
-  for the same reason).
-- Run `extract_facts` over the imported pages — facts fences in
-  imported atoms/concepts populate the facts table normally.
-
-## Retiring your OpenClaw's parallel crons
-
-After verifying the import, retire your OpenClaw's parallel atom
-pipeline cron entries. In `~/git/your-openclaw/workspace/cron.json`:
-
-- Remove `atom-pipeline-coordinator` (every-30-min cron)
-- Remove `atom-backfill-coordinator` (every-10-min cron)
-
-Replace with nothing — gbrain's autopilot already runs extract_atoms +
-synthesize_concepts inside every dream cycle when gbrain-creator (or
-gbrain-everything) is the active pack.
-
-The OpenClaw skills themselves shrink to thin wrappers:
-
-- `content-atom-extractor` → calls `gbrain dream --phase extract_atoms`
-- `concept-synthesis` → calls `gbrain dream --phase synthesize_concepts`
-- `atom-backfill-coordinator` → DELETED (backfill is now part of
-  extract_atoms via the Source Quote + lesson enrichment in one
-  Haiku call per transcript)
-
-## Rolling back
-
-The import is fully reversible:
-
-```bash
-# Soft-delete every page with the marker (recoverable for 72h)
-gbrain query "imported_from:markdown-greenfield" --type atom --json | \
-  jq -r '.[].slug' | xargs -I{} gbrain pages delete {}
-# OR hard-delete past the soft-delete window
-gbrain pages purge-deleted --older-than 0h
-```
-
-Your OpenClaw's `~/git/brain/atoms/` + `concepts/` + `ideas/` directories
-are untouched by the importer — they remain the source of truth for
-rollback. The greenfield importer only READS from them.
-
-## Re-running after partial failures
-
-The importer is idempotent at the page-slug level: re-running on the
-same `--repo` produces zero net-new pages (every page either lands
-fresh or matches an existing slug). If you fix some validation
-failures in your OpenClaw and want to retry just those:
-
-```bash
-gbrain capture --source markdown-greenfield --repo ~/git/brain
-```
-
-Already-imported pages stay; previously-failed pages get a fresh
-attempt; the audit JSONL accumulates per-week (ISO week file rotation).

docs/operations/headless-install.md

@@ -1,87 +0,0 @@
-# Headless install: Docker, CI, postinstall
-
-As of v0.37, `gbrain init --pglite` in a non-TTY context (Docker `RUN`, CI step, postinstall hook) exits 1 when no embedding-provider API key is present in the environment. This is a deliberate fail-loud — the alternative was the v0.36 silent-broken-state class where init succeeded with a default that didn't match any real key.
-
-Two patterns work for headless installs. Pick whichever fits your image lifecycle.
-
-## Pattern 1: Provider key available at image build time
-
-If your CI / Docker pipeline can inject the API key as a build-time env var, set it before `gbrain init`:
-
-```dockerfile
-# Multi-stage Dockerfile sketch
-FROM oven/bun:1 AS builder
-
-# Inject key at build via --build-arg or `--env` from CI.
-ARG OPENAI_API_KEY
-ENV OPENAI_API_KEY=$OPENAI_API_KEY
-
-RUN bun install -g github:garrytan/gbrain
-RUN gbrain init --pglite  # auto-picks OpenAI, persists config
-```
-
-```yaml
-# GitHub Actions equivalent
-- name: Initialize gbrain
-  env:
-    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-  run: |
-    bun install -g github:garrytan/gbrain
-    gbrain init --pglite
-```
-
-Init writes `~/.gbrain/config.json` with the resolved `embedding_model` + `embedding_dimensions`. Subsequent runs (in the same image / runner) read from that config and don't re-resolve.
-
-## Pattern 2: Provider key only at runtime (deferred-setup)
-
-If the API key is a runtime secret (Kubernetes secret, runtime env injection, end-user-supplied), use `--no-embedding` at build time and configure the provider when the container actually runs:
-
-```dockerfile
-FROM oven/bun:1
-RUN bun install -g github:garrytan/gbrain
-
-# Build the brain shape without a provider — schema lands at the default
-# width, but no embed callsite will actually run until runtime config.
-RUN gbrain init --pglite --no-embedding
-
-# At container start (entrypoint), provide the real provider:
-ENTRYPOINT ["/bin/sh", "-c", "\
-  gbrain config set embedding_model openai:text-embedding-3-large \
-  && gbrain init --force --pglite \
-  && exec gbrain serve"]
-```
-
-The `gbrain init --no-embedding` opt-in writes `embedding_disabled: true` to config. Every embed callsite (`gbrain import`, `gbrain embed`, the `runEmbedCore` library entry point) checks this and refuses cleanly with a `gbrain config set embedding_model <id>` hint rather than proceeding with a silent default.
-
-The runtime `gbrain init --force` re-runs the init flow against the now-populated env, which:
-
-- Removes `embedding_disabled` from config.
-- Resolves the provider via env detection.
-- Re-templates the PGLite schema if dim differs from the build-time default.
-
-## What WON'T work
-
-```dockerfile
-# Don't do this — silent default leaves you with vector(1280) ZE column
-# and 1536d OpenAI provider at runtime, mismatched.
-RUN gbrain init --pglite
-```
-
-If you upgrade from a pre-v0.37 image that used this pattern, `gbrain doctor` will surface the mismatch on first run after upgrade and print a paste-ready repair command (`gbrain init --force --embedding-model …` for empty brains, `gbrain retrieval-upgrade --reindex` for non-empty).
-
-## Verifying a headless install
-
-After init, run `gbrain doctor --json` to verify state:
-
-```bash
-gbrain doctor --json | jq '.checks[] | select(.name=="embedding_provider")'
-```
-
-The `embedding_provider` check returns `status: 'ok'` when:
-
-- Config has a persisted `embedding_model`.
-- Config has a persisted `embedding_dimensions`.
-- Live provider probe returns the configured dim.
-- DB column width matches.
-
-If you used Pattern 2's deferred-setup path, the check shows `Skipped (no provider credentials)` until the runtime config is populated. That's expected.

docs/operations/spend-controls.md

@@ -1,111 +0,0 @@
-# Spend controls
-
-GBrain's embedding-spend gates in one place: every gate, its config key, default,
-whether it blocks or just informs, how to widen or disable it, and how the
-`spend.posture` switch governs all of them.
-
-The orienting idea: **GBrain itself is rounding error; the spend that matters is
-downstream embedding.** These gates exist so a routine sync or enrich can't run up
-an unexpected embedding bill, while never wedging an unattended cron.
-
-## `spend.posture` — one switch for "cost is not my constraint"
-
-```bash
-gbrain config set spend.posture tokenmax   # all cost gates become informational
-gbrain config set spend.posture gated      # default — gates enforce
-```
-
-| Value | Effect |
-|-------|--------|
-| `gated` (default) | Every cost gate enforces its limit as documented below. |
-| `tokenmax` | Every cost gate prints its estimate and **proceeds** — informational only. Spend is still recorded to the ledger; posture removes the *ceiling*, not the *accounting*. |
-
-`spend.posture` is deliberately separate from `search.mode=tokenmax` (which governs
-retrieval payload size, not embedding spend). When a gate fires and
-`search.mode=tokenmax` but `spend.posture` is unset, the gate prints a one-line hint
-pointing at this switch.
-
-**Precedence:** an explicit per-call cap (`--max-usd N`, `--max-cost N`) always wins
-over posture. `tokenmax` only governs the default/absent case — it never overrides a
-number you typed on the command line.
-
-## Off switches (`off` / `unlimited` / `none`)
-
-The USD-limit knobs accept `off`, `unlimited`, or `none` (case-insensitive) to mean
-"no limit" — no more setting sentinel values like `100000`.
-
-- `0` is **not** "off". On `sync.cost_gate_min_usd`, `0` means "block on any nonzero
-  spend" (a real choice). On the backfill caps, `0` falls back to the default.
-- Internally "no limit" is the string `unlimited` in any printed/JSON output and "no
-  cap" inside the budget tracker — never a raw `Infinity` (which would serialize to
-  `null` in ledger rows).
-
-## The gates
-
-| Gate | Config key | Default | Blocks? | Off switch | tokenmax |
-|------|-----------|---------|---------|-----------|----------|
-| Sync inline-embed cost gate | `sync.cost_gate_min_usd` | `0.50` | TTY prompt / non-TTY auto-defer | `off` (or `0` = block-on-any) | informational |
-| Backfill 24h per-source spend cap | `embed.backfill_max_usd_per_source_24h` | `25` | refuses submission | `off` (`0` → default) | bypassed (still ledgered) |
-| Backfill per-job budget | `embed.backfill_max_usd` | `10` | caps the job's tracker | `off` (`0` → default) | uncapped (still ledgered) |
-| Backfill cooldown | `embed.backfill_cooldown_min` | `10` | skips re-submission inside window | — (latency knob, not spend) | **not** bypassed |
-| `reindex-code` cost gate | — (preview before re-embed) | — | TTY prompt / non-TTY refuse + exit 2 | `--max-cost off` | informational |
-| `enrich` / `onboard --auto` | `--max-usd` (per-call) | — | refuse without a cap (non-TTY) | `--max-usd off` | runs uncapped (still ledgered) |
-
-### Sync inline-embed cost gate
-
-Fires only when sync embeds **inline** (federated_v2 off, or `--serial` without
-`--no-embed`). Under federated_v2 + parallel, embedding is deferred to capped backfill
-jobs and the gate is informational. The estimate prices the **delta** — the files this
-sync will actually import (fetched-first, so it sees commits the run is about to pull) —
-not the whole tree. A busy brain with a dirty working tree but caught-up commits
-estimates `$0`, because an attached-HEAD sync imports only the committed diff.
-
-Behavior above the floor:
-- **TTY:** prompts `[y/N]`.
-- **Non-interactive (cron/agent):** **auto-defers** embeds to capped backfill jobs and
-  exits 0 — it never wedges the pipeline. The backlog drains via the jobs worker or
-  `gbrain embed --stale`. Pass `--yes` to embed inline instead.
-
-Output format splits on the explicit `--json` flag: `--json` emits a structured
-envelope; otherwise human text. Every gate message carries paste-ready knobs.
-
-`--full` re-embeds the stale backlog inline (full sync sweeps it), so a `--full`
-estimate is `delta + stale backlog`, labeled as such.
-
-### Estimate labels
-
-- `~N tokens (delta: changed files since last sync)` — the precise estimate.
-- `<=N tokens (full-tree ceiling for K source(s): <reasons> …)` — a conservative
-  over-count used only when a precise delta can't be computed: a first sync, a chunker
-  version drift (forces a full re-chunk), or git being unavailable. Unchanged files
-  still skip via `content_hash` at execution, so the ceiling over-states real spend.
-
-## Notes & limits
-
-- **Pre-pull window:** the gate fetches before estimating, so it prices what the run
-  will pull. If a fetch fails (offline), it estimates against local HEAD and labels the
-  result; the bounded residual is priced on the next run.
-- **Single-source `gbrain sync`** carries the same gate as `sync --all` (it previously
-  embedded inline with no preview).
-- **Recovery under parallel:** `--skip-failed` / `--retry-failed` work under parallel
-  sync (the failure ledger is per-source and lock-serialized) — you no longer have to
-  drop to `--serial`, which is what used to arm the inline gate.
-
-## Escape hatches at a glance
-
-```bash
-# Never gate this brain on cost:
-gbrain config set spend.posture tokenmax
-
-# Widen the sync inline floor to $5:
-gbrain config set sync.cost_gate_min_usd 5
-
-# Disable the sync inline floor entirely:
-gbrain config set sync.cost_gate_min_usd off
-
-# Lift the backfill 24h spend cap:
-gbrain config set embed.backfill_max_usd_per_source_24h off
-
-# Run enrich uncapped non-interactively:
-gbrain enrich --max-usd off        # or: gbrain config set spend.posture tokenmax
-```

docs/plans/2026-06-03-001-feat-idea-lineage-skill-plan.md

@@ -1,211 +0,0 @@
----
-title: "feat: Add idea-lineage thinking skill"
-type: feat
-status: completed
-date: 2026-06-03
----
-
-# feat: Add idea-lineage thinking skill
-
-## Summary
-
-Add an `idea-lineage` thinking skill that traces how one idea has evolved through a user's brain: first mention, best articulation, related concepts, reversals, contradictions, abandoned branches, and the current live version. The contribution should start as a read-only skill with routing and conformance coverage, not as a new CLI or MCP operation.
-
-## Problem Frame
-
-GBrain already has two adjacent capabilities that are easy to conflate with this feature:
-
-- `skills/concept-synthesis/SKILL.md` is a mutating, batch-oriented concept map builder. It deduplicates many concept stubs, tiers them, writes concept pages, and creates an intellectual universe.
-- `find_trajectory` and `gbrain eval trajectory` are structured entity trajectories over typed facts and events. They work best for questions like metric history, founder consistency, role/status changes, and event timelines.
-
-`idea-lineage` should occupy the narrow space between them: a query-time, single-idea, citation-backed synthesis of conceptual evolution. It should help a user ask "how has my thinking about this idea changed?" without running a global concept-synthesis job or forcing the idea into an entity/metric trajectory model.
-
-## Requirements
-
-**Behavior**
-
-- R1. The skill accepts a single idea, topic, concept phrase, or nearby concept page and produces a focused lineage for that idea only.
-- R2. The output identifies first mention, best articulation, related concepts, reversals, contradictions, abandoned branches, and current live version when evidence supports each category.
-- R3. Every lineage claim is grounded in existing brain evidence: page links, dates, verbatim snippets, timeline entries, takes, contradiction findings, or trajectory points when applicable.
-- R4. The skill distinguishes evidence strength. Missing or weak evidence should be reported as a gap, not filled with plausible narrative.
-- R5. The default workflow is read-only and does not write or mutate brain pages.
-
-**Routing**
-
-- R6. Routing should prefer `idea-lineage` for single-idea evolution requests such as "how has my thinking about X changed?".
-- R7. Routing should keep broad corpus/map requests on `concept-synthesis`.
-- R8. Routing should keep structured entity metric/status questions on `find_trajectory`, `gbrain eval trajectory`, or `gbrain think` trajectory injection.
-
-**Privacy and portability**
-
-- R9. The skill and fixtures must use public, generic examples only.
-- R10. The plan and implementation must avoid private fork names, real people, real companies, funds, or host-specific filesystem paths in public artifacts.
-
-## Scope Boundaries
-
-### In Scope
-
-- A new bundled skill under `skills/idea-lineage/`.
-- Resolver, manifest, and plugin-bundle wiring.
-- Routing fixtures that prove the new intent is reachable and does not swallow `concept-synthesis` or trajectory-shaped prompts.
-- Documentation inside the skill body that explains when to use `search`, `query`, `get_page`, `list_pages`, `takes_search`, `find_contradictions`, and optionally `find_trajectory`.
-- Focused conformance, resolver, and routing verification.
-
-### Deferred to Follow-Up Work
-
-- A first-class `idea_lineage` MCP operation.
-- A `gbrain idea lineage <query>` CLI.
-- Persisting lineage reports back into the brain.
-- New database tables, schema-pack fields, or concept lineage graph primitives.
-- Automated contradiction-probe reruns. The skill should read cached contradiction findings if available, not trigger expensive probes.
-
-### Outside This Contribution
-
-- Replacing `concept-synthesis`.
-- Changing the facts/takes epistemology model.
-- Changing `find_trajectory`'s entity-slug contract.
-- Implementing the broader taxonomy redesign tracked by issue #1668.
-
-## Key Technical Decisions
-
-- **Start as a markdown skill:** GBrain's architecture treats skills as fat markdown workflows. This feature can be useful by orchestrating existing read operations, so a CLI/MCP surface would add contract weight before the behavior is proven.
-- **Make the skill non-mutating by default:** The user intent is investigative. Writing lineage pages should remain a later explicit mode after routing and output quality are established.
-- **Use evidence buckets rather than a single narrative pass:** The output should force the agent to separately evaluate first mention, articulation, current version, reversals, contradictions, and abandoned branches. That reduces the risk of smoothing over conflict.
-- **Keep `find_trajectory` as an optional side-channel:** It is valuable when an idea query resolves to an entity attribute or status history, but `idea-lineage` should not depend on typed facts being present.
-- **Avoid the existing "trace idea evolution" trigger phrase:** That phrase already routes to `concept-synthesis`; adding it to the new skill would create avoidable resolver ambiguity.
-
-## High-Level Technical Design
-
-```mermaid
-flowchart TB
-  A["User asks about one idea"] --> B{"Intent shape"}
-  B -->|"whole corpus / map"| C["concept-synthesis"]
-  B -->|"entity metric / status over time"| D["trajectory surfaces"]
-  B -->|"single conceptual idea"| E["idea-lineage skill"]
-  E --> F["Resolve idea candidates"]
-  F --> G["Gather evidence via search/query/pages/takes"]
-  G --> H["Classify lineage moments"]
-  H --> I["Synthesize cited answer with confidence gaps"]
-```
-
-## Implementation Units
-
-### U1. Add the `idea-lineage` Skill
-
-- **Goal:** Create the read-only skill contract and workflow.
-- **Requirements:** R1, R2, R3, R4, R5, R9, R10
-- **Dependencies:** None
-- **Files:**
-  - `skills/idea-lineage/SKILL.md`
-  - `test/skills-conformance.test.ts`
-- **Approach:** Create a new skill with required frontmatter and conformance sections. The skill should define its workflow in phases: clarify the target idea, resolve likely concept/page anchors, collect evidence, classify lineage moments, produce a cited synthesis, and state gaps. Frontmatter should set `mutating: false` and list read operations only.
-- **Patterns to follow:**
-  - `skills/strategic-reading/SKILL.md` for a read-only thinking-skill shape with related-skill boundaries.
-  - `skills/query/SKILL.md` for search/query/get-page guidance.
-  - `skills/concept-synthesis/SKILL.md` for contrast, not for behavior reuse.
-- **Test scenarios:**
-  - A new `SKILL.md` with frontmatter, `## Contract`, `## Output Format`, and `## Anti-Patterns` passes conformance.
-  - The frontmatter declares a unique `name: idea-lineage`.
-  - The skill body references only portable, synthetic examples.
-- **Verification:** `bun test test/skills-conformance.test.ts` passes.
-
-### U2. Wire Resolver, Manifest, and Bundle Metadata
-
-- **Goal:** Make the skill discoverable by bundled skill users and resolvable by agents.
-- **Requirements:** R6, R7, R8, R9, R10
-- **Dependencies:** U1
-- **Files:**
-  - `skills/RESOLVER.md`
-  - `skills/manifest.json`
-  - `openclaw.plugin.json`
-  - `test/resolver.test.ts`
-  - `test/skillpack-reference.test.ts`
-- **Approach:** Add `idea-lineage` to the skill manifest and plugin skill list. Add a resolver row in the thinking or uncategorized section with narrow user phrases such as "how has my thinking about", "trace the lineage of this idea", "what is my current version of", and "show reversals in my thinking about". Keep broad concept-map phrases routed to `concept-synthesis`.
-- **Patterns to follow:**
-  - `skills/RESOLVER.md` rows for `strategic-reading`, `concept-synthesis`, and `perplexity-research`.
-  - Existing sorted `openclaw.plugin.json` skill list.
-- **Test scenarios:**
-  - Every quoted resolver trigger fuzzy-matches a frontmatter trigger in `skills/idea-lineage/SKILL.md`.
-  - `idea-lineage` is listed in `skills/manifest.json`.
-  - `idea-lineage` is listed in `openclaw.plugin.json` if the contribution ships as part of the bundled OpenClaw skillpack.
-  - Existing skills remain reachable.
-- **Verification:** `bun test test/resolver.test.ts` passes.
-
-### U3. Add Routing Eval Fixtures
-
-- **Goal:** Prove the new routing boundary against adjacent skills.
-- **Requirements:** R6, R7, R8
-- **Dependencies:** U1, U2
-- **Files:**
-  - `skills/idea-lineage/routing-eval.jsonl`
-  - `skills/concept-synthesis/routing-eval.jsonl`
-  - `src/core/routing-eval.ts`
-- **Approach:** Add positive fixtures for single-idea lineage prompts and negative or ambiguity-declared fixtures around adjacent surfaces. The fixture text should paraphrase triggers rather than copy them exactly, because the routing fixture linter rejects tautological trigger copies.
-- **Test scenarios:**
-  - "Show how my thinking about founder-led sales changed over time" routes to `idea-lineage`.
-  - "What is my current version of the compounding trust idea?" routes to `idea-lineage`.
-  - "Synthesize my concepts into a tiered intellectual map" stays on `concept-synthesis`.
-  - "How has acme-example MRR trended since January?" does not route to `idea-lineage`.
-  - Negative fixtures avoid false positives for generic "publish this report" or "what is this concept?" prompts.
-- **Verification:** `gbrain routing-eval --json` reports no new misses, false positives, or unapproved ambiguity for the added fixtures.
-
-### U4. Add Output Contract and Citation Discipline
-
-- **Goal:** Make the skill's user-facing answer shape predictable and reviewable.
-- **Requirements:** R2, R3, R4, R5
-- **Dependencies:** U1
-- **Files:**
-  - `skills/idea-lineage/SKILL.md`
-  - `skills/conventions/quality.md`
-  - `skills/brain-ops/SKILL.md`
-- **Approach:** Define the output format directly in the skill body. The recommended shape should include a compact current answer, evidence timeline, lineage buckets, contradictions/reversals, abandoned branches, related concepts, and confidence gaps. Require page/date/snippet evidence for each non-gap claim. Preserve quote fidelity and avoid hallucinated dates.
-- **Patterns to follow:**
-  - `skills/conventions/quality.md` for citation and quote-fidelity expectations.
-  - `skills/brain-ops/SKILL.md` for source attribution and source-id formatting.
-  - `docs/takes-vs-facts.md` for not conflating holder-attributed takes with the brain owner's facts.
-- **Test scenarios:**
-  - Test expectation: none beyond conformance for the markdown-only contract; routing and conformance tests cover the machine-checkable surface.
-- **Verification:** Manual review confirms the skill body tells the agent how to cite, label gaps, and separate facts/takes/trajectory evidence.
-
-### U5. Refresh Generated Documentation If Required
-
-- **Goal:** Keep generated LLM-facing docs consistent if the test suite requires it.
-- **Requirements:** R9, R10
-- **Dependencies:** U1, U2, U3
-- **Files:**
-  - `llms.txt`
-  - `llms-full.txt`
-  - `test/build-llms.test.ts`
-- **Approach:** Run the build-llms test after adding the skill. If it fails because committed docs are stale, regenerate with the existing generator and include the generated diff. If it passes without regeneration, leave these files unchanged.
-- **Patterns to follow:**
-  - `package.json` script `build:llms`.
-  - `test/build-llms.test.ts` failure message.
-- **Test scenarios:**
-  - Committed `llms.txt` and `llms-full.txt` match generator output.
-  - `llms-full.txt` remains within the size budget.
-- **Verification:** `bun test test/build-llms.test.ts` passes.
-
-## Acceptance Examples
-
-- AE1. When the user asks "How has my thinking about founder-led sales changed over time?", the agent routes to `idea-lineage`, searches for evidence, and returns a cited lineage rather than running `concept-synthesis`.
-- AE2. When the user asks "Run concept synthesis across my notes", the agent routes to `concept-synthesis`, not `idea-lineage`.
-- AE3. When the user asks "How did acme-example's MRR trend?", the agent uses trajectory surfaces rather than `idea-lineage`.
-- AE4. When the evidence does not support an "abandoned branch" claim, the output includes a gap instead of inventing one.
-
-## Risks & Dependencies
-
-- **Resolver overlap risk:** `concept-synthesis` already uses "trace idea evolution". Mitigate by avoiding that exact trigger and adding routing fixtures around the boundary.
-- **Narrative overreach risk:** The feature invites story-making. Mitigate by requiring dates, snippets, links, and explicit gaps for unsupported categories.
-- **Privacy risk:** Skill examples can easily drift into real-brain language. Use synthetic examples only and rely on existing privacy checks.
-- **Generated-doc churn risk:** Adding a bundled skill may require `llms.txt` and `llms-full.txt` regeneration. Treat generated-doc changes as mechanical and separate from the skill design during review.
-- **Future taxonomy dependency:** Issue #1668 may eventually change concept filing and identity. This plan avoids new schema assumptions so the contribution remains compatible with the current repo.
-
-## Sources & Research
-
-- `skills/concept-synthesis/SKILL.md` defines the existing batch, mutating, concept-map surface.
-- `skills/RESOLVER.md` and `skills/manifest.json` define current skill reachability and bundle metadata.
-- `docs/architecture/lens-packs.md` shows that atoms and concepts are already part of the lens-pack/dream-cycle substrate.
-- `docs/proposals/temporal-contradiction-probe.md` and `docs/takes-vs-facts.md` define the temporal and epistemic boundaries this skill must not blur.
-- `src/core/operations.ts`, `src/core/trajectory.ts`, `src/commands/eval-trajectory.ts`, and `test/operations-find-trajectory.test.ts` define the current `find_trajectory` contract.
-- Pull requests #1131, #1296, and #1364 provide the recent trajectory, think-routing, and lens-pack context.
-- Issue #1668 is related future taxonomy work, but not a prerequisite for this contribution.

docs/proposals/temporal-contradiction-probe.md

@@ -1,213 +0,0 @@
-# Proposal: Temporal Axis for Contradiction Probe
-
-**Status:** Report / RFC
-**Date:** 2026-05-14
-**Context:** A large production run of `gbrain eval suspected-contradictions` surfaced ~115 HIGH findings. Walking through them by hand exposed a structural limitation in the probe.
-
-## The Problem
-
-The contradiction probe (`gbrain eval suspected-contradictions`) treats all claims as timeless. When two chunks make conflicting statements, the judge flags a contradiction regardless of whether both statements were true at their respective points in time.
-
-This worked fine when the brain was mostly static wiki pages. It breaks now that the brain contains:
-- Conversation transcripts with claims that were true when spoken
-- Meeting pages capturing what people said on specific dates
-- Takes that evolve (a founder's ARR claim in January vs. July)
-- Status records that supersede each other (a state moves from "trial" to "confirmed")
-
-The probe can't distinguish "this changed" from "this is wrong."
-
-## Bug-class examples (synthetic placeholders)
-
-### 1. Temporal Evolution (False Positive)
-
-```
-Finding: HIGH
-  A: [daily/transcripts/2026/2026-04-28] "status: trial"
-  B: [meetings/2026-05-07-session] "status: confirmed"
-  Axis: Whether status is trial or confirmed
-```
-
-Both are correct as of their respective dates. April 28: trial. May 7: confirmed. The probe flags this because it has no concept of "this claim was valid from X until Y." The May 7 record didn't make the April 28 transcript wrong; it recorded a change.
-
-### 2. Negation Parsing (False Positive)
-
-```
-Finding: HIGH
-  A: [people/alice-example] "person traveled to city-a for alice-example's event — NOT bob-example's event"
-  B: [meetings/2026-05-11-context] mentions of bob-example's event in city-b
-  Axis: Whose event the city-a trip was for
-```
-
-The disambiguation fact contains "NOT bob-example's event" as an explicit negation. The judge reads "bob-example's event" as a positive claim and flags it against the alice-example context. The data is correct; the probe can't parse negation.
-
-### 3. Role Changes (True Positive That Needs Time Awareness)
-
-```
-Finding: HIGH
-  A: [sources/notes/2017-03-28] advisor-example: "Partner, venture-firm-a"
-  B: [people/advisor-example] advisor-example: "Senior Policy Advisor, gov-org-b"
-```
-
-Both true at their respective times. 2017: partner at venture-firm-a. 2025: gov-org-b advisor. The current probe correctly flags this as a contradiction, but the resolution should be "superseded by time" not "one side is wrong." The 2017 note isn't wrong; it's a historical record.
-
-## Scenario #1: Founder Tracking (the big one)
-
-This is the use case that makes a time axis transformative rather than incremental.
-
-The brain holds hundreds of company pages and thousands of meeting pages. Founders make claims:
-
-- "We're at $50K MRR" (January OH)
-- "We hit $200K MRR" (April OH)
-- "We're at $150K MRR" (July OH — what happened?)
-
-Today the probe would flag January vs. April as a contradiction. The real signal is April vs. July: **a claimed metric went backwards.** That's not a data quality issue; that's intelligence.
-
-What a time-aware probe could surface:
-
-**Claim trajectory tracking:**
-```
-Company: Acme Corp
-  2026-01: "$50K MRR" (source: OH transcript)
-  2026-04: "$200K MRR" (source: OH transcript)
-  2026-07: "$150K MRR" (source: OH transcript) ← REGRESSION DETECTED
-  2026-07: "$2M ARR" (source: investor update) ← INCONSISTENT WITH MRR
-```
-
-**Prediction vs. outcome:**
-```
-Founder: Jane Doe (Acme Corp)
-  2026-01: "We'll hit $1M ARR by June" (source: batch kickoff)
-  2026-06: Actual ARR: $400K (source: investor update)
-  → Prediction accuracy: 40%
-  → Pattern: consistently 2-3x optimistic on timeline
-```
-
-**Narrative consistency:**
-```
-Founder: John Smith (WidgetCo)
-  2026-01: "Our moat is proprietary data" (source: interview)
-  2026-03: "We're pivoting to an API-first model" (source: OH)
-  2026-06: "Our moat is network effects" (source: Demo Day)
-  → Moat narrative changed 3x in 6 months — flag for review
-```
-
-This isn't adversarial. It's the kind of pattern an experienced operator notices intuitively across hundreds of conversations. GBrain can make it systematic.
-
-## Scenario #2: Event Disambiguation
-
-Two distinct events within a short window can conflate during ingestion because the probe has no temporal frame to say "event A is a different event from event B."
-
-Time-aware facts would store (synthetic placeholders):
-```
-fact: "alice-example milestone" valid_from: 2026-04-15 valid_until: 2026-04-15
-fact: "alice-example event in city-a" valid_from: 2026-04-17 valid_until: 2026-04-19
-fact: "bob-example milestone" valid_from: 2026-05-04 valid_until: 2026-05-04
-fact: "bob-example event in city-b" valid_from: 2026-05-12 valid_until: 2026-05-12
-```
-
-The probe should recognize these as two distinct events with non-overlapping time windows, not as contradictions about "whose event."
-
-## Scenario #3: Role and Status Changes
-
-People change roles. Companies change status. The brain records history. Synthetic examples representative of the cases observed in production:
-
-- advisor-example: venture-firm-a partner (2019) → gov-org-b advisor (2025)
-- investor-example: fund-a partner → fund-b CEO (2023)
-- agent-fork: provider restriction event (2026-04-04) ≠ shutdown
-- fund-c: "interesting fund" (early) → "declined" (later) → "losing confidence" (latest)
-
-All of these are correct historical records. The probe should classify them as **temporal supersession** rather than **contradiction.**
-
-## Scenario #4: Decision Tracking
-
-Multi-step decisions that supersede earlier framings example (synthetic):
-```
-2026-04-24: "status: trial" (initial framing)
-2026-04-25: "status: in progress" (confirmed, no longer "trial")
-2026-05-07: "status: finalized" (session record)
-2026-05-11: follow-up actions taken
-```
-
-Each step supersedes the previous. A time-aware probe would show the **evolution chain** rather than flagging each pair as a contradiction.
-
-## What Exists Today
-
-The probe already has some temporal infrastructure:
-
-1. **`date-filter.ts`** — `shouldSkipForDateMismatch()` pre-filters pairs, but only checks whether dates are "too far apart" (a coarse heuristic). It doesn't reason about which claim is newer or whether one supersedes the other.
-
-2. **`auto-supersession.ts`** — proposes resolution commands, checks `since_date` on takes. But this is post-hoc (after the judge flags a contradiction). The judge itself doesn't see dates.
-
-3. **Facts table** has `valid_from` and `valid_until` columns. These exist but are sparsely populated and not used by the probe.
-
-4. **Takes table** has `since_date`. Also sparsely populated.
-
-## What Would Need to Change
-
-### Phase 1: Judge prompt enhancement (smallest change, biggest impact)
-
-Pass the source dates to the judge. The current judge prompt shows two text chunks and asks "are these contradictory?" If it also showed:
-
-```
-Statement A (from: 2026-04-28):
-  "status: trial"
-
-Statement B (from: 2026-05-07):
-  "status: confirmed"
-```
-
-The judge could output a `temporal_supersession` verdict instead of `contradiction`. New verdict taxonomy:
-
-- `no_contradiction` — statements are compatible
-- `contradiction` — genuinely conflicting claims at the same point in time
-- `temporal_supersession` — newer claim updates/replaces older claim (not an error)
-- `temporal_regression` — a metric or status went backwards (potential signal)
-- `temporal_evolution` — legitimate change over time, neither supersession nor regression
-- `negation_artifact` — one side contains an explicit negation the judge misread
-
-### Phase 2: Claim trajectory view (new command)
-
-```bash
-gbrain eval trajectory "Acme Corp MRR"
-gbrain eval trajectory "advisor-example role"
-gbrain eval trajectory "deal-x status"
-```
-
-Pull all time-stamped claims about an entity+attribute, sort chronologically, detect:
-- Regressions (metric went down)
-- Contradictions within the same time window
-- Prediction vs. outcome gaps
-- Narrative drift (moat story changed 3x)
-
-### Phase 3: Automatic `valid_from`/`valid_until` population
-
-During `extract_facts`, infer temporal bounds from source context:
-- Meeting page dated 2026-04-28 → claims valid_from 2026-04-28
-- Takes from transcripts → valid_from = transcript date
-- Imported notes → valid_from = note date
-- Entity pages with no date → valid_from = page created date (weakest signal)
-
-### Phase 4: Founder scorecard
-
-For founders specifically, a temporal probe could generate:
-- **Claim accuracy score** — what they predicted vs. what happened
-- **Consistency score** — how stable their narrative is over time
-- **Growth trajectory** — whether the numbers are actually moving
-- **Red flag detector** — metrics going backwards, story changing, timeline slipping
-
-## Recommendation
-
-Start with Phase 1. The judge prompt change is small. It immediately eliminates the temporal false positives (which were a majority of the residual HIGH findings in the production audit) and gives the probe a new vocabulary for time-aware reasoning.
-
-Phase 2 (trajectory view) is the one that would change how operators use the brain for founder evaluation. Worth scoping as a standalone feature.
-
-Phases 3–4 are downstream and can wait.
-
-## Appendix: Production probe stats (2026-05-14)
-
-- ~107K pages, ~257K chunks
-- Previous run: ~115 HIGH findings across 50 queries
-- After manual resolution: ~25 residual findings
-- Of those ~25: roughly two-thirds temporal false positives, the remainder probe artifacts (self-contradiction, negation parsing)
-- 0 genuine data contradictions remained on the queries tested
-- Fresh targeted probe on a representative entity-role query: 0 contradictions (was 14+ before fixes)

docs/schema-author-tutorial.md

@@ -1,224 +0,0 @@
-# Tutorial: Build your first schema pack
-
-You'll fork the bundled `gbrain-base` pack, add a custom `researcher` page type, import a handful of placeholder researcher pages, backfill their `page.type` column with one command, then prove the wiring works by running `gbrain whoknows` and seeing your new type surface in results. End state: a forked-and-active pack on disk, ~5 pages typed as `researcher`, and a query that proves the pack-aware routing fires end-to-end.
-
-**Want the WHY before the HOW?** Read [`what-schemas-unlock.md`](what-schemas-unlock.md) first — 7 concrete use cases (4000 invisible meetings, the founder ops brain, the research brain, the legal brain, the team brain, agent-as-co-curator) plus the structural argument for why types matter at query time. Then come back here for the 5-minute walkthrough.
-
-The whole walkthrough takes about 5 minutes. You'll see something working by step 3.
-
-## What you'll need
-
-- gbrain v0.40.7.0 or later (`gbrain --version` to check)
-- A brain that's been initialized (`gbrain init` already run; either PGLite or Postgres is fine)
-- A terminal you can paste commands into
-
-That's it. No API keys required for this tutorial — every step works against the bundled pack and local-only commands.
-
-## Step 1: See what pack is active today
-
-```bash
-gbrain schema active --json
-```
-
-You'll see something like:
-
-```json
-{
-  "pack_name": "gbrain-base",
-  "version": "1.0.0",
-  "sha8": "...",
-  "page_types_count": 22,
-  "source_tier": "default"
-}
-```
-
-`source_tier: "default"` means you haven't customized anything — you're on the bundled pack. `page_types_count: 22` is the universal starter (person, company, meeting, note, etc.).
-
-**You can't mutate bundled packs directly.** Step 2 forks it so you have something writable.
-
-## Step 2: Fork the bundled pack
-
-```bash
-gbrain schema fork gbrain-base mine
-```
-
-Output: `Forked 'gbrain-base' → 'mine' at ~/.gbrain/schema-packs/mine/pack.json`.
-
-The fork is a byte-for-byte copy of `gbrain-base` living at `~/.gbrain/schema-packs/mine/pack.json`. Now you have a writable pack you can mutate.
-
-## Step 3: Activate the fork
-
-```bash
-gbrain schema use mine
-```
-
-Output: `Pack: mine (json) ... Active.`
-
-Run `gbrain schema active --json` again to confirm `pack_name` is now `mine` and `source_tier` is `home-config` (read from `~/.gbrain/config.json`).
-
-**You've already accomplished something visible** — the active pack changed, and any future query will route through your fork. The next four steps add a custom type and prove it works.
-
-## Step 4: Add a researcher type
-
-```bash
-gbrain schema add-type researcher \
-  --primitive entity \
-  --prefix people/researchers/ \
-  --extractable \
-  --expert
-```
-
-Output: `Pack: mine (json)` + `Sha8: <prev> → <new>`.
-
-What just happened:
-- The mutation went through `withMutation`'s 8-step skeleton: bundled-guard → per-pack lock → read → mutate → file-plane lint validation → atomic write → audit log → cache invalidation.
-- The pack now declares `researcher` as an entity primitive bound to `people/researchers/`, marked `extractable: true` (eligible for facts extraction) and `expert_routing: true` (surfaces in `whoknows` queries).
-- An audit row landed in `~/.gbrain/audit/schema-mutations-YYYY-Www.jsonl` with your type name SHA-8-redacted and the prefix's first segment only (`people`) for privacy.
-
-Verify the type is in the pack:
-
-```bash
-gbrain schema explain researcher
-```
-
-You'll see the resolved settings printed back.
-
-## Step 5: Import some placeholder researcher pages
-
-You need pages under `people/researchers/` for the next step to do anything. If your brain repo already has them, skip ahead. If not, drop 3-5 placeholder markdown files into `<your-brain-repo>/people/researchers/` and import:
-
-```bash
-mkdir -p people/researchers
-cat > people/researchers/alice-example.md <<'EOF'
----
-title: Alice Example
----
-
-ML researcher at Example Lab. Works on contrastive embeddings.
-EOF
-
-cat > people/researchers/bob-example.md <<'EOF'
----
-title: Bob Example
----
-
-Vision researcher at Widget University. Recent paper on diffusion models.
-EOF
-
-cat > people/researchers/charlie-example.md <<'EOF'
----
-title: Charlie Example
----
-
-RL researcher at Acme Research. Focus on inverse reinforcement learning.
-EOF
-
-gbrain sync
-```
-
-The sync imports the new files. They'll be stored in the database but their `type` column will still be empty — the new type was added to the pack AFTER these pages already existed (the typical real-world scenario for an agent walking into an existing brain).
-
-## Step 6: See the gap with `stats`
-
-```bash
-gbrain schema stats --json | jq '.aggregate, .dead_prefixes'
-```
-
-You'll see `untyped_pages: 3` (or however many you just imported) and `dead_prefixes: []` — your new prefix has 3 matching pages, so it's not dead.
-
-The 3 researcher pages are "orphaned" by type even though they live in the right directory. The next step backfills them.
-
-## Step 7: Backfill with `sync --apply`
-
-First dry-run to see what would happen:
-
-```bash
-gbrain schema sync --json
-```
-
-You'll see something like:
-
-```json
-{
-  "schema_version": 1,
-  "apply": false,
-  "per_prefix": [
-    {
-      "type": "researcher",
-      "prefix": "people/researchers/",
-      "would_apply": 3,
-      "sample_slugs": ["people/researchers/alice-example", "people/researchers/bob-example", "people/researchers/charlie-example"],
-      "applied": 0
-    }
-  ],
-  "total_would_apply": 3,
-  "total_applied": 0
-}
-```
-
-`would_apply: 3` is what you'd touch. `sample_slugs` is the agent's drilldown signal — if those slugs look wrong, abort. They look right, so apply:
-
-```bash
-gbrain schema sync --apply
-```
-
-You'll see per-batch progress lines on stderr and a final `total_applied: 3`. The UPDATE ran in chunks of 1000 (yours fit in one chunk) and never wedged any concurrent writer.
-
-## Step 8: Prove the wiring works
-
-```bash
-gbrain whoknows "machine learning"
-```
-
-If your researcher pages contain ML-related content, they'll surface in the ranked results — even though they're typed `researcher`, not `person` or `company`.
-
-**This is the load-bearing demonstration of T1.5 wiring.** Pre-v0.40.7.0, `whoknows` hardcoded `['person', 'company']` as the eligible types and would have ignored your `researcher` pages entirely. The v0.40.7.0 wiring consults the active pack's `expert_routing: true` types via `expertTypesFromPack(pack.manifest)`, so your custom type now routes through expert search.
-
-## What you built
-
-You now have:
-- A fork of `gbrain-base` named `mine` at `~/.gbrain/schema-packs/mine/pack.json`, active in your brain via `~/.gbrain/config.json`.
-- A `researcher` page type registered in the pack with `entity` primitive, `people/researchers/` prefix, `extractable: true`, `expert_routing: true`.
-- 3 pages typed as `researcher` (backfilled from disk via `gbrain schema sync --apply`).
-- A query path that routes through the new type: `gbrain whoknows` reads the pack and includes `researcher` in its type filter.
-
-You also exercised the full mutation skeleton: bundled-pack guard, per-pack lock, validation gate, atomic write, audit log, cache invalidation. Every step was idempotent — re-running any of them is a no-op.
-
-## Next steps
-
-**Add a link verb.** A `researcher` can `author` a `paper`. To model that:
-
-```bash
-gbrain schema add-type paper --primitive annotation --prefix research/papers/ --extractable
-gbrain schema add-link-type authored --page-type researcher --target-type paper
-gbrain schema graph
-```
-
-The graph now shows `researcher --(authored)--> paper`.
-
-**Add aliases for query closure.** If you want `gbrain query researcher` to also surface `person` rows (because researchers ARE people):
-
-```bash
-gbrain schema add-alias researcher person
-```
-
-Read [`skills/conventions/schema-evolution.md`](../skills/conventions/schema-evolution.md) for the decision tree on when to add types vs aliases vs prefixes. The short version: <20 pages → don't pack-codify; 20-100 → alias on existing type; 100+ → first-class type.
-
-**Lint your pack before shipping.** The 11-rule lint surface (with the optional `--with-db` flag for DB-aware checks) catches dangling references, prefix collisions, and dead-corpus warnings:
-
-```bash
-gbrain schema lint --with-db
-```
-
-**Commit your pack to source control.** If `~/.gbrain/schema-packs/mine/` is a git repo, commit `pack.json` and push. Your pack survives across machines, and the `mutation_count_anomaly` lint rule will nudge you when you hit >50 mutations in a week (the "you should be committing this" signal).
-
-**For agents (MCP):** the same operations are reachable over HTTPS MCP via 9 new ops. Register an admin-scope OAuth client and `schema_apply_mutations` lets a remote agent compose multi-step refactors as one atomic batch. The batched MCP op + per-pack lock + audit log are the load-bearing primitives that make remote schema authoring safe. See [`skills/schema-author/SKILL.md`](../skills/schema-author/SKILL.md) for the agent dispatcher.
-
-**Undo a mistake.** Every mutation primitive has an inverse (`remove-type`, `remove-alias`, `remove-prefix`, `remove-link-type`, `set-extractable false`, etc.). If you fork twice and want to revert, `gbrain schema downgrade` restores the previous active pack from `~/.gbrain/schema-pack-history.jsonl`.
-
-## Related docs
-
-- **Reference:** `gbrain schema --help` for the full 22-verb CLI surface; CLAUDE.md's "Schema Cathedral v3 (v0.40.7.0)" section for the module-by-module architecture.
-- **How-to:** [`skills/schema-author/SKILL.md`](../skills/schema-author/SKILL.md) — the agent dispatcher with the 7-phase workflow (brain → assess → propose → apply → sync → verify → commit).
-- **Explanation:** [`skills/conventions/schema-evolution.md`](../skills/conventions/schema-evolution.md) — when to add a type vs alias vs prefix.
-- **Plan + decisions:** the original design captured 21 decisions including the bundled-pack guard rationale (D6), the empty-filter fallback contract (D4), and the MCP non-localOnly trust posture (D2). Lives in `~/.claude/plans/system-instruction-you-are-working-recursive-thacker.md` (private).

docs/skillpack-anatomy.md

@@ -1,112 +0,0 @@
-# Skillpack anatomy
-
-The canonical one-page reference for what a third-party gbrain skillpack
-looks like. The reference pack at `examples/skillpack-reference/` is the
-live artifact this page describes; clone its tree and you have a 10/10
-starting point.
-
-## Tree
-
-```
-my-skillpack/
-├── skillpack.json                # manifest (cathedral fields declared)
-├── skills/
-│   └── <skill-slug>/
-│       ├── SKILL.md              # frontmatter + body, agent-readable
-│       └── routing-eval.jsonl    # >= 5 intents pinning trigger -> skill
-├── runbooks/
-│   └── bootstrap.md              # post-scaffold display (NOT an executor)
-├── test/
-│   └── *.test.ts                 # bun:test unit tests
-├── e2e/
-│   └── *.test.ts                 # integration tests, gated on DATABASE_URL
-├── evals/
-│   └── *.judge.json              # LLM-judge eval configs (>= 3 cases each)
-├── CHANGELOG.md                  # Keep-a-Changelog shape
-├── LICENSE                       # SPDX-matching text
-├── README.md
-└── .gitignore
-```
-
-`gbrain skillpack init <name>` scaffolds this exact tree, pre-filled
-with stubs that score 10/10 on `gbrain skillpack doctor . --quick`
-immediately. Replace the stubs with real content, run the doctor
-between edits, and `gbrain skillpack pack` produces a deterministic
-`<name>-<version>.tgz` ready to publish to the registry.
-
-## How the agent uses a scaffolded pack
-
-After `gbrain skillpack scaffold <source>` lands the files:
-
-1. The user's agent walks `skills/*/SKILL.md` frontmatter and reads
-   each pack's `triggers:` array on startup or per-message.
-2. When a user phrasing matches a trigger, the agent reads that
-   SKILL.md body top-to-bottom as in-context instructions.
-3. gbrain DISPLAYS `runbooks/bootstrap.md` once after the scaffold
-   but does NOT auto-execute it. The agent decides whether to walk
-   the steps. This is the codex T1 supply-chain hardening: an
-   auto-walker would let a malicious pack mutate the user's brain
-   on install, which is how npm postinstall attacks happen.
-
-## How the doctor scores a pack
-
-Ten binary dimensions. Each is checked by a pure function in
-`src/core/skillpack/rubric.ts` and returns `{passed, detail, fix_hint}`.
-The doctor walks them in order and prints the score + per-dimension
-status + paste-ready fix for every failure.
-
-<!-- BEGIN auto-generated:rubric -->
-
-### Core dimensions (5; must all pass to publish at any tier)
-
-| # | Name | Description | Auto-fixable |
-|---|------|-------------|--------------|
-| 1 | `manifest_valid` | skillpack.json passes the v1 schema validator | no |
-| 2 | `skills_have_skill_md` | every listed skill has SKILL.md with valid frontmatter (name, description, triggers) | no |
-| 3 | `routing_evals_present` | every skill has routing-eval.jsonl with >= 5 intents | yes |
-| 4 | `skills_have_unique_triggers` | no two skills in this pack share an exact trigger phrase (MECE) | no |
-| 5 | `changelog_present_and_current` | CHANGELOG.md present and contains an entry for the current version | yes |
-
-### Quality badges (5; earn for tier eligibility)
-
-| # | Name | Description | Auto-fixable |
-|---|------|-------------|--------------|
-| 6 | `unit_tests_present` | pack declares unit_tests[] with at least one matching test file | yes |
-| 7 | `e2e_tests_present` | pack declares e2e_tests[] with at least one matching test file | yes |
-| 8 | `llm_eval_present` | pack declares llm_evals[] with >= 1 file containing >= 3 cases | yes |
-| 9 | `bootstrap_runbook_present` | pack declares runbooks.bootstrap and the file is non-empty | yes |
-| 10 | `license_present` | LICENSE file exists at the pack root (informational badge) | yes |
-
-_Generated from `src/core/skillpack/rubric.ts` by `bun run scripts/build-skillpack-anatomy.ts`._
-
-<!-- END auto-generated:rubric -->
-## Tier eligibility
-
-| Tier | Requirement |
-|------|-------------|
-| `endorsed` | All 5 core + all 5 badges, plus Garry's `endorsements.json` overlay in the registry repo |
-| `community` | All 5 core + >= 3 of 5 badges. Default tier on PR merge. |
-| `experimental` | All 5 core + < 3 badges |
-| `blocked` | Any core dimension fails |
-
-## CLI reference (third-party path)
-
-```bash
-# Publisher side
-gbrain skillpack init my-pack         # scaffold the tree
-gbrain skillpack doctor my-pack       # see the score + fix hints
-gbrain skillpack doctor my-pack --fix --yes  # auto-scaffold missing pieces
-gbrain skillpack pack my-pack         # deterministic tarball + SHA-256
-
-# Consumer side
-gbrain skillpack search <query>       # browse the registry
-gbrain skillpack info <name>          # show full pack metadata
-gbrain skillpack scaffold <source>    # owner/repo, https, ./dir, ./*.tgz
-gbrain skillpack registry --url X     # point at a custom registry
-```
-
-## See also
-
-- `examples/skillpack-reference/` — the live 10/10 reference pack
-- `docs/designs/SKILLPACK_REGISTRY_V1_SPEC.md` — strategic spec + decisions
-- `docs/guides/skillpacks-as-scaffolding.md` — v0.36 scaffold/reference model

docs/takes-vs-facts.md

@@ -1,93 +0,0 @@
-# Takes vs Facts — Architectural Distinction
-
-gbrain has two epistemological storage layers that serve different purposes.
-**Never conflate them.**
-
-## Takes (cold storage — `takes` table)
-
-The epistemological layer. WHO believes WHAT, with confidence weight and time.
-
-- **Source:** Extracted from brain pages (markdown) by LLM analysis
-- **Scope:** Multi-holder — captures beliefs from *any* speaker, not just the brain owner
-- **Kinds:** `take` (opinion), `fact` (verifiable), `bet` (prediction), `hunch` (intuition)
-- **Lifecycle:** Cold storage, retrospective. Updated when pages change or re-extraction runs.
-- **Scale:** 100K+ rows across thousands of holders in a mature brain
-
-**Example takes:**
-- `holder=people/garry-tan kind=bet` "AI will replace 50% of coding by 2030" (w=0.75)
-- `holder=people/jared-friedman kind=take` "Momo has strong retention" (w=0.80)
-- `holder=world kind=fact` "Clipboard raised $100M Series C" (w=1.0)
-- `holder=brain kind=hunch` "Garry has a hero/rescuer pattern" (w=0.70)
-
-**Query surface:** `gbrain takes list`, `gbrain takes search`, `gbrain think`
-
-## Facts (hot memory — `facts` table, v0.31)
-
-Personal knowledge from the brain owner's conversations. Real-time capture.
-
-- **Source:** Extracted per-turn from conversation by the facts hook (Haiku)
-- **Scope:** Single-user — only the brain owner's stated knowledge
-- **Kinds:** `event`, `preference`, `commitment`, `belief`, `fact`
-- **Lifecycle:** Hot storage, real-time. Captured as conversations happen.
-- **Bridge:** Dream cycle `consolidate` phase promotes hot facts → cold takes nightly
-
-**Example facts:**
-- `kind=event` "I have a meeting with Brian tomorrow"
-- `kind=preference` "I don't drink coffee"
-- `kind=commitment` "We decided on nesting custody"
-- `kind=belief` "I think the market is overheated"
-
-**Query surface:** `gbrain recall`, MCP `_meta.brain_hot_memory`
-
-## The Category Error
-
-**Never dump takes into the facts table.** Takes include other people's attributed
-beliefs (Jared's assessment of a company, PG's view on schools, a founder's
-revenue claims). These are NOT the brain owner's personal facts.
-
-**Never dump facts into the takes table without transformation.** Facts are
-scoped to what the owner said in conversation. They become takes only through
-the dream cycle's consolidate phase, which adds proper attribution, deduplication,
-and temporal reasoning.
-
-## The Bridge
-
-The dream cycle's `consolidate` phase (v0.31) is the one-way bridge:
-
-```
-hot facts → [dream consolidate] → cold takes
-```
-
-Facts flow in ONE direction. The consolidate phase:
-1. Groups related facts by entity
-2. Deduplicates against existing takes
-3. Promotes durable facts to takes with proper holder/weight
-4. Marks consolidated facts with `consolidated_at` + `consolidated_into`
-
-## Production Extraction Data (2026-05-10)
-
-First full takes extraction run on a ~100K-page brain:
-- **Model:** Azure GPT-5.5 (ties Opus quality at 1/8th cost — $0.033 vs $0.260/page)
-- **Result:** 100,720 takes from 28,256 on-disk pages, $361.49, 83 errors (0.3%)
-- **Breakdown:** 70,960 takes / 24,342 facts / 2,875 bets / 2,649 hunches
-- **Holders:** 6,239 unique holders
-- **Cross-modal eval:** 6.8/10 overall (GPT-5.5 + Opus 4.6 scored independently)
-
-### Eval Dimensions
-
-| Dimension | Score | Notes |
-|-----------|-------|-------|
-| Accuracy | 7.5 | Claims faithfully represent sources |
-| Attribution | 6.5 | Holder/subject confusion was #1 issue |
-| Weight calibration | 7.0 | Good range usage, some false precision |
-| Kind classification | 6.5 | Occasional fact/take misclassification |
-| Signal density | 6.5 | Some trivial extractions pass through |
-
-### Key Learnings for Extraction Prompts
-
-1. **Holder ≠ subject.** "Garry has a hero/rescuer pattern" → holder=brain, NOT people/garry-tan
-2. **Atomic claims.** Split compound claims into separate rows
-3. **Amplification ≠ endorsement.** Retweet-only → max weight 0.55
-4. **Self-reported ≠ verified.** "Reports 7 figures" → holder=person, weight=0.75, NOT world/1.0
-5. **No false precision.** Use 0.05 increments (0.35, 0.55, 0.75), not 0.74 or 0.82
-6. **"So what" test.** Skip Twitter handles, follower counts, obvious metadata

docs/tutorials/README.md

@@ -1,36 +0,0 @@
-# Tutorials
-
-Step-by-step walkthroughs that take you from zero to a working outcome. Concrete commands, real numbers, no abstraction-first jargon. Each tutorial assumes no prior GBrain knowledge.
-
-## Shipped
-
-- [**Set up your personal AI agent + brain from zero**](personal-brain.md) — the canonical solo install. Two GitHub repos, a Telegram bot, AlphaClaw on Render, OpenClaw + GBrain + Supabase. End-to-end in about 2 hours; about $100 to $150 a month sustained. The full-stack install I'd run today.
-- [**Set up GBrain as your company brain**](company-brain.md) — federated, multi-user, OAuth-scoped institutional memory for a 10-50 person team. Three sources (shared / customers / internal-only), per-user scope, first synthesized query as a teammate. About 90 minutes end-to-end, about $5 in API calls for the demo, under $100 a month sustained for a 25-person company.
-- [**Auto-improve a skill with `gbrain skillopt`**](improving-skills-with-skillopt.md) — treat a `SKILL.md` as the trainable parameter of a frozen agent. Write your first benchmark from scratch (the part everyone gets stuck on), preview the cost, run the optimizer, read accepted vs no_improvement vs aborted, and accept a measurably better skill. About 20 minutes, about $1 in API calls. Reference: [`../guides/skillopt.md`](../guides/skillopt.md).
-- [**Give your coding agent a memory: GBrain + Claude Code / Codex**](connect-coding-agent.md) — the two-funnel walkthrough for coding-agent users. Path A: connect Claude Code / Codex to a brain you already run (OpenClaw, Hermes, any `gbrain serve --http`). Path B: start from nothing with a 2-second local PGLite brain. Both end with the brain-first protocol you paste into `CLAUDE.md` / `AGENTS.md` and the four habits (brain-first lookup, ambient capture, briefing-from-your-brain, whoknows) that make it worth it. About 10 minutes.
-
-## In progress
-
-These are the next tutorials on the roadmap. Open an issue if one of them is the one you need most; that's how we'll prioritize.
-
-- **Set up GBrain for VC dealflow** — the operator's recipe. People pages for founders, companies with typed Facts fence carrying ARR / team-size / runway across dates, meetings auto-ingested, deal pages linking everything. Shows `gbrain whoknows`, `gbrain find_trajectory`, and `gbrain founder scorecard` on real workflows.
-
-- **Migrate your existing vault into GBrain** — for Notion / Obsidian / Roam users with a vault that doesn't match GBrain's default layout. Walks through `gbrain schema detect` → `suggest` → `review-candidates` so the brain learns your shape instead of forcing you to learn its.
-
-- **Index your codebase as a code brain** — for developers. Initialize a brain in a code repo, swap to `voyage-code-3` for embeddings, use `gbrain code-def` / `gbrain code-refs` / `gbrain code-callers` to navigate the codebase semantically from any MCP-aware editor.
-
-- **Run GBrain fully local with Ollama or llama.cpp** — for privacy-first deployments. No cloud calls, no API keys, no telemetry. Trades some retrieval quality for full local control. Useful for regulated industries, air-gapped environments, or just paranoia.
-
-- **Set up the dream cycle** — the overnight enrichment daemon that makes the brain self-maintaining. Fixes citations, dedupes people pages, surfaces contradictions, generates founder scorecards on the schedule you configure. The piece that turns a static knowledge base into a brain that gets smarter while you sleep.
-
-## Want to write one?
-
-Tutorials follow the [Diataxis](https://diataxis.fr/) tutorial pattern: learning-oriented, walks a learner from zero to a working result in one session, every step produces a visible change. If you've used GBrain for something interesting and want to write the walkthrough, the existing [`company-brain.md`](company-brain.md) is the model. Open a PR.
-
-## Related documentation
-
-- **Reference:** [`docs/architecture/`](../architecture/) — system design, topologies, retrieval theory
-- **How-to:** [`docs/guides/`](../guides/) — task-oriented runbooks (sub-agent routing, minion deployment, skill development, brain-first lookup, idea capture, diligence ingestion). Highlight: [scaling skills past 300](../guides/scaling-skills.md) — the three-tier architecture for agents that have outgrown the always-loaded skill manifest.
-- **Integrations:** [`docs/integrations/`](../integrations/) — connecting external data sources (voice, email, calendar, embedding providers)
-- **MCP setup:** [`docs/mcp/`](../mcp/) — per-client setup (Claude Desktop, Code, Cursor, ChatGPT, Perplexity, Cowork)
-- **Install paths:** [`docs/INSTALL.md`](../INSTALL.md) — every install path, end to end

docs/tutorials/company-brain.md

@@ -1,557 +0,0 @@
-# Tutorial: Extend your personal brain into a company brain
-
-This tutorial picks up where the [personal brain tutorial](personal-brain.md) leaves off. You already have a working agent (OpenClaw on Render, talking to you on Telegram, with GBrain as memory and Supabase storing embeddings). Now you want your whole team to use it as shared institutional memory, with each person seeing only what they're allowed to see.
-
-**Time:** about 90 more minutes on top of the personal-brain install.
-**Cost:** under $100 a month sustained for a 25-person company.
-
-If you haven't done the personal-brain install yet, [start there first](personal-brain.md). Come back when you've got the agent responding to you on Telegram. This tutorial assumes that's already working.
-
-I'm Garry Tan. I built GBrain to run my own AI agents at Y Combinator. After a couple of months of multi-user features landing (parallel sync across team sources, per-user OAuth scoping, leak-free isolation across every read path), it's finally usable as a company brain too. This is the recipe I'd run if I were standing it up for a 10-50 person company today.
-
----
-
-## Part 1: The mental model
-
-### What changes when you go from personal to company
-
-The personal brain you built is a single-user system: one git repo, one agent, your stuff. The company brain is the same architecture with three additions:
-
-1. **Multiple sources** inside the same brain. Your meeting notes are one source. Each teammate's customer notebook is another. The shared company wiki is a third. They live in the same database but stay independent.
-2. **Per-user logins** with scopes. Each teammate gets their own OAuth credential. The credential decides which sources they can read and write to. Alice writes to her customer source, reads hers plus the shared one. Bob writes to internal-ops, reads his plus the shared one. Neither can see the other's writes.
-3. **Per-person folders, crons, and skills.** The shared brain has shared structure, but each teammate gets their own subfolder for their own work, their own scheduled tasks (weekly digest, customer follow-ups), and their own scoped skills.
-
-### What this is NOT
-
-It is **not** a different install. The agent runtime, Supabase backend, GBrain CLI, and AlphaClaw harness from the personal brain stay exactly as you set them up. We're adding to that stack, not replacing it.
-
-It is also **not** a thin-client-everywhere setup. Your personal agent stays as it is (OpenClaw + Telegram). Each teammate adds their own client of choice (Claude Code, Cursor, Claude Desktop, their own OpenClaw, whatever) and points it at the brain.
-
-### What you get that one person's brain doesn't
-
-- **Shared memory.** The whole team queries the same brain. The contract notes that Alice wrote on Tuesday show up when Bob asks about that customer on Friday, with citations back to Alice's notes.
-- **Scoped privacy.** Performance reviews don't leak into customer queries. Legal docs don't leak into sales searches. We fuzz-tested this across every read path and got zero leaks.
-- **One sync pipeline.** Your brain git repo (or several if you want them isolated per team) feeds the brain. Everyone sees the latest.
-- **One operating burden.** One server to monitor, not one per user.
-
----
-
-## Part 2: Switch the brain backend to multi-user Postgres
-
-The personal-brain install uses Supabase as the embeddings layer but the GBrain runtime itself might be using PGLite (single-machine) depending on which path you took. For a company brain, you want a real Postgres for the runtime too. If your personal-brain install is already on Postgres or Supabase end-to-end, skip to Part 3.
-
-If you're on PGLite, migrate:
-
-```bash
-gbrain migrate --to supabase
-```
-
-This copies every page, chunk, embedding, link, and config over to your Supabase project. Run from the agent host machine, same one you set up in the personal-brain tutorial. Takes a few minutes per 10K pages.
-
-Verify:
-
-```bash
-gbrain doctor
-gbrain stats
-```
-
-Page count and chunk count should match what you had on PGLite.
-
----
-
-## Part 3: Carve up the brain into sources
-
-The personal brain has one source (called `default`) holding everything. For a company brain we want multiple. The right shape depends on your org. Here's a typical starting point for a 10-50 person company:
-
-```bash
-# A shared all-hands source for content everyone reads
-gbrain sources add shared --path /srv/brain-repos/shared --name "Shared company wiki"
-
-# A scoped source for sales/customer notes
-gbrain sources add customers --path /srv/brain-repos/customers --name "Customer notes"
-
-# A scoped source for internal-only docs (legal, HR, performance, board)
-gbrain sources add internal --path /srv/brain-repos/internal --name "Internal-only"
-```
-
-Each `--path` is a directory on disk where you've checked out a git repo. Create them:
-
-```bash
-sudo mkdir -p /srv/brain-repos
-sudo chown $USER /srv/brain-repos
-cd /srv/brain-repos
-git clone git@github.com:your-org/shared-wiki.git shared
-git clone git@github.com:your-org/customers.git customers
-git clone git@github.com:your-org/internal-docs.git internal
-```
-
-You can also keep the existing personal-brain repo as one of the sources. Just pick the role it plays (probably `shared` if it's already org-wide content).
-
-### Two scoping models (pick the one that matches your shape)
-
-There are two ways to scope teammates' access. They suit different deployment shapes.
-
-**Model A: separate sources with OAuth scoping (recommended for true multi-user with different AI clients).** What this tutorial walks you through. Each teammate gets their own OAuth client, which carries `--source` + `--federated-read` flags. The brain refuses cross-source reads at the SQL layer; isolation is database-enforced. Each teammate can run their own MCP-aware client (Claude Code, Cursor, their own OpenClaw, etc.) and the scoping holds.
-
-**Model B: one source, directory-based per-person scoping (simpler for one-agent-serves-everyone setups).** The shape I actually run in production: a single source called `default`, with a `partners/<slug>/` convention inside it (e.g. `partners/alice-example/`, `partners/bob-example/`). Each partner gets their own subdirectory holding their personal pages: `partners/alice-example/USER.md`, `partners/alice-example/concepts/`, `partners/alice-example/sources/`, etc. There's no OAuth-enforced isolation; the agent itself enforces "Alice's writes go to her partners/ subdir." This is the right model when ONE agent (yours) serves everyone over Telegram or a single shared interface. It's simpler ops, no per-user OAuth, but the scoping is convention-only.
-
-For most company-brain installs (10+ teammates each with their own AI client), Model A is the right starting point. If you're running the fat-agent-serves-everyone pattern from the personal-brain tutorial, Model B is genuinely simpler. You can also mix: separate sources for the obviously-different ones (customer notes vs internal-only) AND a `partners/<slug>/` convention inside the shared source for per-person workspace.
-
-### Per-person folder structure inside each source
-
-Inside each source, give each teammate their own subfolder. This is the structure I run:
-
-```
-customers/
-├── alice-example/                      ← Alice's customer notebook
-│   ├── customers/
-│   │   ├── acme-co.md
-│   │   └── widget-systems.md
-│   └── meetings/
-│       └── 2026-05-21-acme-renewal.md
-├── bob-example/                        ← Bob's customer notebook
-│   └── customers/
-│       └── orbit-bio.md
-└── shared-customers/                   ← things both can see
-    └── all-active-deals.md
-```
-
-Two things this structure buys you:
-
-1. **Each teammate's writes go to their own folder** even though they're in the same source. No accidental overwrites.
-2. **You can later split a person's folder into its own source** (if Alice leaves and a new person takes her accounts, you can move `alice-example/` to a new source named after the new person and adjust scoping accordingly).
-
-Same shape for `internal/`: `internal/alice-example/` for her HR docs, `internal/bob-example/` for his, `internal/legal/` for legal docs everyone can read, etc.
-
-Now sync everything:
-
-```bash
-gbrain sync --all
-```
-
-Each source syncs in parallel under its own lock so they don't step on each other. Output looks like:
-
-```
-[shared]    100/100 pages
-[customers] 240/240 pages
-[internal]   85/85 pages
-✓ all sources synced
-```
-
-Check the dashboard:
-
-```bash
-gbrain sources status
-```
-
-You should see all three sources with recent sync timestamps and page counts.
-
----
-
-## Part 4: Expose the brain over HTTP MCP with OAuth
-
-The personal brain talks to you through the AlphaClaw harness over Telegram. For a company brain we need a path that each teammate's AI client can hit independently. The HTTP MCP server is that path.
-
-```bash
-gbrain serve --http --port 3131 --bind 0.0.0.0
-```
-
-The `--bind 0.0.0.0` is important. By default the server binds to localhost only, which is correct for a personal install but blocks remote teammates. Setting `0.0.0.0` accepts connections from any interface.
-
-The server prints an admin bootstrap token to stderr on first start when run in an interactive terminal. Save it. You'll use it once for the admin dashboard. On a non-TTY start (systemd, Docker, piped logs) the token is hidden from logs — set `GBRAIN_ADMIN_BOOTSTRAP_TOKEN` yourself or pass `--print-admin-token` on a trusted terminal instead.
-
-For development, tunnel the local server out via ngrok:
-
-```bash
-ngrok http 3131 --domain your-brain.ngrok.app
-```
-
-For production, put your server behind a real hostname with a real TLS certificate. Let's call your final URL `https://brain.acme-co.com` for the rest of this tutorial.
-
-Re-run the server with the public URL so the OAuth discovery metadata matches what clients hit:
-
-```bash
-gbrain serve --http --port 3131 --bind 0.0.0.0 --public-url https://brain.acme-co.com
-```
-
-You should be able to hit `https://brain.acme-co.com/health` and get `{"status":"ok"}` back.
-
----
-
-## Part 5: Register one OAuth client per teammate
-
-Each teammate (or each AI agent for a teammate) gets their own OAuth client. The client controls what they can write and what they can read.
-
-```bash
-# Alice (sales): writes customers/alice-example, reads customers + shared
-gbrain auth register-client alice-example \
-  --grant-types client_credentials \
-  --scopes read,write \
-  --source customers \
-  --federated-read customers,shared
-
-# Bob (ops): writes internal/bob-example, reads internal + shared
-gbrain auth register-client bob-example \
-  --grant-types client_credentials \
-  --scopes read,write \
-  --source internal \
-  --federated-read internal,shared
-
-# Carol (legal): writes shared/legal, reads all three
-gbrain auth register-client carol-example \
-  --grant-types client_credentials \
-  --scopes read,write \
-  --source shared \
-  --federated-read shared,customers,internal
-```
-
-Each `register-client` command prints a `client_id` and a `client_secret`. Save both for each teammate. They go into the teammate's local agent config.
-
-A note on the flags:
-
-- `--scopes read,write` lets the client query the brain and write new pages. You can omit `write` for read-only clients (executive summaries, dashboards). The `admin` scope is needed for operational commands like `gbrain remote doctor` and is usually reserved for your own admin client.
-- `--source` controls write authority. A client can only write to one source. Within that source, your folder convention from Part 3 keeps each person's writes in their own subfolder.
-- `--federated-read` controls read scope. A client can read from one or more sources.
-
-### Verify the scoping actually scopes
-
-Before you hand the brain to teammates, verify isolation. Two terminal windows on your local machine using each client's credentials:
-
-```bash
-# Terminal 1, as Alice
-export GBRAIN_REMOTE_CLIENT_ID=<Alice's client_id>
-export GBRAIN_REMOTE_CLIENT_SECRET=<Alice's client_secret>
-export GBRAIN_REMOTE_MCP_URL=https://brain.acme-co.com/mcp
-
-gbrain search "performance review" --remote
-```
-
-Alice should see results only from `customers` and `shared`. The performance-review notes live in `internal`, which she's not scoped to read. She shouldn't see them.
-
-```bash
-# Terminal 2, as Bob (export his credentials similarly)
-gbrain search "performance review" --remote
-```
-
-Bob should see the performance-review notes from `internal`, plus anything related from `shared`. He shouldn't see anything that lives only in `customers`.
-
-If both queries return correctly scoped results, isolation is working.
-
----
-
-## Part 6: Set up per-person crons
-
-The personal-brain install runs the dream cycle (overnight enrichment) once per night for one user. A company brain needs per-person crons because each teammate has their own context: Alice wants a 7am customer-pipeline digest, Bob wants a 9am ops-status report, Carol wants a contract-compliance check every Monday.
-
-Each cron is just a scheduled `gbrain agent run` call scoped to the teammate's client credentials. The schedule lives in the workspace repo (the one AlphaClaw deployed in the personal-brain tutorial), in a `crons/` directory. A typical layout:
-
-```
-your-org/myagent/
-└── crons/
-    ├── alice-example/
-    │   └── 07am-customer-digest.md
-    ├── bob-example/
-    │   └── 09am-ops-status.md
-    └── carol-example/
-        └── monday-contract-compliance.md
-```
-
-Each cron file declares its schedule and the prompt that the agent runs:
-
-```markdown
----
-schedule: "0 7 * * *"
-client: alice-example
----
-
-# Customer pipeline digest
-
-Pull every customer page in customers/alice-example/ that had activity in
-the last 7 days. For each, summarize what changed and what the next action
-is. Output as a markdown digest, post to Slack #alice-customers, save a
-copy to customers/alice-example/digests/YYYY-MM-DD-pipeline.md.
-```
-
-The `client:` field tells the cron runner which OAuth client to use, which enforces the scoping. Alice's cron can only read Alice's sources and write to Alice's folder. It cannot accidentally touch Bob's customer notes.
-
-To install the cron schedule, commit the file to the workspace repo and let AlphaClaw pick it up on next deploy. The cron-scheduler skill (one of the 60 that GBrain installed) handles the dispatch.
-
----
-
-## Part 7: Add per-person skills
-
-The 60+ skills GBrain installs are generic. Your team probably wants a few that are specific to them. Examples:
-
-- `onboarding-new-hire`. Only Carol (HR) runs this. Walks through generating a welcome packet, scheduling intro meetings, provisioning accounts.
-- `customer-success-followup`. Only Alice (sales) runs this. Pulls latest customer page, drafts a follow-up email, posts to her review queue.
-- `weekly-team-digest`. Only you (admin) run this. Aggregates everyone's published pages into one weekly summary.
-
-Skills are just markdown files in the workspace repo's `skills/` directory. The shape:
-
-```
-your-org/myagent/
-└── skills/
-    ├── onboarding-new-hire/
-    │   └── SKILL.md
-    ├── customer-success-followup/
-    │   └── SKILL.md
-    └── weekly-team-digest/
-        └── SKILL.md
-```
-
-Each `SKILL.md` declares the trigger (verbs in plain English the agent listens for) and the procedure. Use the `gbrain skillify scaffold <name>` command to generate the boilerplate:
-
-```bash
-gbrain skillify scaffold onboarding-new-hire
-```
-
-That creates the directory + SKILL.md + routing entry. Edit the SKILL.md to describe the procedure, commit, deploy. The agent picks up the new skill on next request.
-
-Per-person scoping for skills is handled at the routing layer: a skill can declare `allowed_clients: [carol-example]` in its frontmatter. If Alice asks her agent to run that skill, the agent refuses with "this skill is scoped to carol-example."
-
-### Shared rule files at the skills root
-
-Alongside individual skill directories, drop a few flat `_*-rules.md` files at the root of `skills/`. These are conventions that EVERY skill reads. The ones I run in production:
-
-- `_brain-filing-rules.md`. the iron-rule decision tree for "where does this new page belong?" Numbered first-match-wins rules (people go in `people/`, companies in `companies/`, meetings in `meetings/`, etc.). Every ingest skill consults this before creating a page.
-- `_output-rules.md`. output quality standards (deterministic links built from API data not LLM-composed strings, exact-phrasing requirements for citations, no AI-slop vocabulary).
-- `_excluded-people.md`. a privacy gate. Names that must never be referenced or attributed in the brain even if they appear in source material. Re-attribute or discard. This is the file that prevents your agent from accidentally publishing things about people you've decided aren't fair game.
-- `_operating-rules.md`. operational conventions (when to write to brain vs scratchpad, when to ask for confirmation, when to fire a notification).
-- `_x-ingestion-rules.md`, `_x-api-rules.md`. per-source rules for specific integrations (Twitter, in this case).
-
-These files turn into the de facto company policy for the agent. Edit one, and every skill that reads it picks up the new rule on the next request. Versioned in git, reviewable in PR.
-
----
-
-## Part 8: Wire Slack carefully
-
-Slack is the integration most teams want first, and it has enough sharp edges to deserve its own callout. The conventions I run:
-
-**Two crons, two jobs.** One scan cron that runs every 5-15 minutes and surfaces signals (new threads in channels you care about, mentions of your teammates, decisions). One archive cron that runs nightly and stores the full conversation history. Splitting them this way means urgent signals get acted on fast while the slow archive work doesn't crowd the live channel.
-
-**Channel-to-task-ID mapping.** Don't have your agent reference Slack channels by their actual channel IDs (`C03A8...`). Build a `topic-registry.json` (or similar) that maps each channel ID to a friendly task name (`acme-co-customer-success`, `engineering-standup`). Crons and skills reference channels by friendly name; the registry translates to IDs at runtime. This is the file you edit when a channel gets renamed or replaced.
-
-**Deterministic links only.** When your agent writes a brain page that cites a Slack message, the link MUST be built from API data (workspace ID + channel ID + message timestamp), never composed by the LLM. LLMs hallucinate Slack URLs constantly. The convention lives in `_output-rules.md`; every skill that touches Slack inherits it.
-
-**Dismissed-items state.** The scan cron remembers what it has already surfaced. If a channel had a thread on Tuesday that turned out to be noise, the dismissed-items file records it so the Wednesday scan doesn't surface it again. Without this, re-scans become a flood of repeat signals.
-
-**Per-channel scoping mirrors per-person scoping.** Sensitive channels (#executive, #legal, #performance) should be scoped to teammates with the appropriate `--federated-read`. The brain stores everything, but who can query for it is gated by the same OAuth client model from Part 5.
-
-The actual skills that implement this in production are named `slack`, `slack-scan`, `slack-archive`. Scaffold equivalents in your workspace with `gbrain skillify scaffold slack-scan`, then edit the generated SKILL.md to declare your channel mapping and triggers.
-
----
-
-## Part 9: Onboard each teammate yourself (the botmaster pattern)
-
-This is the part that decides whether your company brain actually gets adopted or sits unused.
-
-**Do not just hand a new teammate their OAuth credential and tell them to "try it out."** They'll send one query, get a result that doesn't feel personal yet (because their slice is empty), conclude it's not useful, and never come back.
-
-What works instead: I personally onboard each new teammate myself. The flow looks like this.
-
-### Step 1: Pre-populate their slice
-
-Before they ever log in, I seed their `partners/<their-slug>/` directory (or their dedicated source) with the context they need to feel like the brain already knows them:
-
-- `partners/alice-example/USER.md`. a one-page profile: role, focus areas, current top 3 priorities, the kind of questions they tend to ask, the kind of writing they prefer (terse vs detailed, casual vs formal).
-- `partners/alice-example/concepts/`. 5-10 frameworks or recurring themes that are specifically THEIRS. If Alice runs sales, that's "pipeline stage definitions," "ICP criteria," "objection-handling playbooks."
-- `partners/alice-example/sources/`. links to the documents they care about (their team's shared docs, their inbox conventions, the dashboards they check).
-- 2-3 example brain entries that demonstrate the shape: a customer page they'd recognize, a meeting note from a recent meeting they attended, an idea they've shared with the team.
-
-Takes me maybe 20 minutes per teammate. The payoff: the moment they run their first query, the brain answers with their context, not a generic response. That's the difference between "this is a cool tool" and "this knows me."
-
-### Step 2: Walk them through 2-3 wow flows
-
-Before letting them DM the agent freely, I personally walk them through 2-3 specific flows that I know will land:
-
-1. A query that demonstrates synthesis: "ask the brain about [a customer they know well]. Notice how it pulls together pages from three sources into one answer with citations." This shows the brain layer in action.
-2. A query that demonstrates gap analysis: "ask the brain about [something it doesn't know yet]. Notice how it tells you what's missing instead of making it up." This builds trust.
-3. A write-back flow: "tell the brain about [a meeting they just had]. Notice how it auto-files, links to the other people who were there, and surfaces related history." This shows the agent's value as a capture tool, not just a query tool.
-
-These three flows take maybe 15 minutes total. By the end, the teammate has seen the brain do something they couldn't have done themselves in that time. They feel powerful.
-
-### Step 3: Graduate to DM only after the wow moment lands
-
-After the walkthrough, I give them their OAuth credential and the agent's DM (Telegram, Slack DM, whatever your interface is). I explicitly say "now you can ask it anything, write to it anytime, and it'll keep learning from you."
-
-The order matters. If you give them DM access first and expect them to discover the wow moments themselves, most won't. They'll send one generic query, get a generic answer, and bounce. The botmaster pattern (pre-populate → walk through → graduate to DM) flips the conversion rate.
-
-Repeat this flow for every new teammate. About 45 minutes per person, total. Compared to the cost of an unadopted internal tool, it's the best 45 minutes you'll spend.
-
----
-
-## Part 10: Connect each teammate's AI client
-
-Each teammate runs their AI client (Claude Code, Cursor, Claude Desktop, OpenClaw, Hermes, whatever) configured to point at your brain server through their OAuth credentials.
-
-Recommended path for each teammate: the thin-client install. On their machine:
-
-```bash
-curl -fsSL https://bun.sh/install | bash
-bun install -g github:garrytan/gbrain
-
-gbrain init --mcp-only \
-  --issuer-url https://brain.acme-co.com \
-  --mcp-url https://brain.acme-co.com/mcp \
-  --oauth-client-id <their client_id> \
-  --oauth-client-secret <their client_secret>
-```
-
-The thin-client install creates a local config that knows how to talk to your brain but never opens its own database. Most CLI commands route through the remote server transparently.
-
-Now they configure their AI client. For Claude Desktop, the teammate adds an MCP server entry in `~/Library/Application Support/Claude/claude_desktop_config.json`:
-
-```jsonc
-{
-  "mcpServers": {
-    "company-brain": {
-      "command": "gbrain",
-      "args": ["serve"]
-    }
-  }
-}
-```
-
-When Claude Desktop launches, it talks to the local `gbrain serve` stdio bridge, which forwards every request to your remote brain over HTTPS with their OAuth token attached. From Claude Desktop's perspective it's just one MCP server.
-
-For Claude Code, Cursor, OpenClaw, Hermes, and other clients, per-client setup steps live in [`docs/mcp/`](../mcp/). They all follow the same shape: point the agent at the local `gbrain serve` bridge, which knows about the remote.
-
----
-
-## Part 11: First real query as a teammate
-
-Have Alice run a real query from her machine. The interesting verb is `gbrain think`, which gives back a synthesized answer instead of raw pages.
-
-```bash
-gbrain think "What's the latest update from acme-co? When did we last talk to them?"
-```
-
-What Alice gets back, assuming the brain has been syncing for a week and her sources contain a customer page for acme-co and several meeting notes:
-
-```
-## Answer
-
-The most recent customer contact with acme-co was a renewal-discussion
-meeting on 2026-05-18, attended by alice-example and acme-co's CTO. Key
-points discussed [customers/alice-example/meetings/2026-05-18-acme-renewal]:
-
-- They are upgrading their plan from team to enterprise.
-- Annual contract value is moving from $48K to $180K.
-- Decision driver: a new compliance requirement they have to meet by Q3.
-
-Prior contact was a quarterly check-in on 2026-04-03 [customers/alice-example/meetings/2026-04-03-acme-q2-checkin].
-
-**Gap noted:** No customer-success notes have been filed since the
-2026-05-18 renewal meeting. If a follow-up has happened, it's not in
-the brain yet.
-```
-
-Three things to notice:
-
-1. **Sourced.** Every claim cites the meeting note it came from.
-2. **Synthesized.** Alice didn't read three pages and stitch them together. The brain did.
-3. **Honest about gaps.** The brain knows what it doesn't know and says so, instead of inventing a follow-up that didn't happen.
-
-That last part is the gap analysis. It's the part of the brain layer that nobody else ships.
-
-Bob asking the same question would get nothing about acme-co. He's not scoped to read `customers`. He'd see his own internal-ops content if he asked something relevant to that. Carol asking would see both, because she's scoped to read all three sources.
-
----
-
-## Part 12: Operating the company brain
-
-Three commands do most of the operational work.
-
-### Background daemon: `gbrain autopilot`
-
-The personal-brain install already turned this on. For a company brain, the same autopilot covers all your sources because they live in one database. It runs every five minutes; on a healthy brain (health score 95+) it sleeps; on a brain that's drifting it submits targeted maintenance jobs.
-
-### Self-healing: `gbrain doctor --remediate`
-
-```bash
-gbrain doctor --remediate --yes --target-score 90 --max-usd 5
-```
-
-Computes a dependency-ordered plan of maintenance jobs that would raise the brain's health score to the `--target-score`, runs the plan, refuses to spend past the `--max-usd` cap. Safe to cron.
-
-### Monitoring: `gbrain sources status` and the admin dashboard
-
-```bash
-gbrain sources status
-```
-
-Returns a per-source dashboard: when each source last synced, how many pages, how many embedded, how many unacked sync failures. The at-a-glance health check.
-
-The admin dashboard at `https://brain.acme-co.com/admin` shows live request volume, registered OAuth clients, recent activity, and brain stats. Use the admin bootstrap token from Part 4 to log in the first time, then register additional admin users from inside the dashboard.
-
----
-
-## Part 13: Cost and speed expectations
-
-Real numbers from the published benchmark, running the default stack (GBrain with ZeroEntropy for embedding + reranker):
-
-- **Embedding cost:** $0.05 per million tokens. For comparison, GBrain configured with OpenAI is $0.13 (2.6× more expensive), Voyage is $0.18 (3.6× more).
-- **Ingest speed:** about 22 seconds for a small test corpus of 164 pages on the host machine. For a 10K-page corpus, expect about 20 minutes the first time, then most syncs are incremental and finish in seconds.
-- **Query latency:** about 122 ms median for a `gbrain search`. For comparison, the same query through GBrain with OpenAI takes about 282 ms.
-- **Synthesized-answer latency:** a few seconds, dominated by the Anthropic API.
-- **Retrieval quality:** on the public LongMemEval benchmark, GBrain hits 97.60% recall at the top 5 retrieved sessions, beating the previous published state of the art at 96.6%. On the in-house BrainBench corpus of relational queries, GBrain beats commodity vector retrieval by 38 percentage points, because the graph layer surfaces relationships that vector similarity alone misses.
-
-Full methodology and per-run receipt JSONs live in [the gbrain-evals repo](https://github.com/garrytan/gbrain-evals/blob/main/docs/benchmarks/2026-05-23-v0.40.6.0-snapshot.md).
-
-For a 25-person company at sustained use, expect about $35 a month in embeddings (ZeroEntropy at $0.05/million tokens), $50 a month in Anthropic calls for the synthesized-answer queries, plus your hosting bill. Under $100 a month for the AI side at most companies your size.
-
----
-
-## Part 14: Common gotchas
-
-### "My teammate can't see anything"
-
-Check `gbrain auth list` on the host and confirm their client has `--source` set to a source that actually exists. Empty or null `--source` means the client falls through to the `default` source, which probably has no content if you set up three named sources.
-
-### "Sync is slow and feels stuck"
-
-The first sync embeds every page, which takes time. Check `gbrain sources status` for the live page count. If it's climbing you're not stuck, you're just embedding. If you've got a 10K-page corpus and ZeroEntropy is being throttled, the per-source parallel sync looks like progress on three sources at once rather than one source moving fast.
-
-### "I see a page I shouldn't see"
-
-This shouldn't happen, but if you suspect it, run `gbrain search <query> --remote --json` as the constrained client and inspect the `source_id` field on every returned result. Every row should be in the client's `--federated-read` set. If one isn't, file an issue with the exact slug and source IDs.
-
-### "The synthesized answer is wrong"
-
-The brain layer is grounded in the retrieved pages. If the retrieved pages contain bad information, the answer will too. The gap-analysis note often catches this: if the answer says "based on retrieved pages from date X" and date X is six months ago, the brain is telling you the information is stale. Run `gbrain sync --all` to refresh and try again.
-
-### "OAuth `/token` endpoint returns 401 for my client"
-
-Verify the client secret matches what was printed at register-client time. The server stores only a SHA-256 hash; if you lost the original, you have to revoke the client and re-register. Use `gbrain auth revoke-client <client_id>` and re-run `register-client`.
-
-### "Postgres connection is exhausting"
-
-Each parallel sync worker opens its own pool. With three sources and the default four workers per source, you can hit your Postgres connection limit if it's set low. Either reduce the worker count with `gbrain sync --all --parallel 2 --workers 2`, or raise your Postgres `max_connections` to at least 100. Supabase's free tier defaults to 60, which is tight.
-
-### "I want to add a fourth teammate but they need access to all three sources"
-
-```bash
-gbrain auth register-client diana-example \
-  --grant-types client_credentials \
-  --scopes read,write \
-  --source shared \
-  --federated-read shared,customers,internal
-```
-
-That's it. Add or rotate teammates as the org grows.
-
----
-
-## What you built
-
-You now have the personal-brain agent from the previous tutorial, plus a multi-user shared layer on top: three federated sources holding shared, customer, and internal-only content; per-person folders inside each source so teammates' writes don't collide; per-person OAuth clients with scoped read and write; per-person crons that run on each teammate's own schedule with their own scoping; per-person skills the agent only runs for the right person. Each teammate queries the brain in plain English through their AI client and gets back synthesized, sourced answers that are correctly scoped.
-
-What to do next:
-
-- **Wire ingestion** from external systems (Granola, Linear, Slack) using the [ingestion source contract](../skillpack-anatomy.md). Most companies want their meetings auto-ingested so the brain stays current without anyone typing notes.
-- **Set up team-specific dashboards** through the admin UI. Each team lead can have their own view of brain health and activity.
-- **Explore the rest of the brain layer.** `gbrain whoknows` (find the expert on a topic), `gbrain find_trajectory` (how a metric changed over time), `gbrain founder scorecard` (especially useful for VC and ops teams), the contradiction-detection cycle that surfaces conflicts between different people's notes.
-
-If you're building in this space (which YC has flagged as the [company-brain category in its Request for Startups](https://www.ycombinator.com/rfs#company-brain)), you might as well build on this. Everything described above is open source, MIT licensed, and what I run in production behind my own AI agents.
-
-Questions, gotchas, or wins worth sharing? Open an issue at [github.com/garrytan/gbrain](https://github.com/garrytan/gbrain/issues).