gbrain v0.46.11.0 codex/claude plugin dist (history-less; source: the release commit)

2026-08-16 18:02:30 +00:00 · 2026-08-16 17:24:01 +00:00
1373 changed files with 12725 additions and 310702 deletions
@@ -0,0 +1,84 @@
+#!/bin/sh
+# .agents/gbrain-launcher — MCP-server launcher for the gbrain Codex and
+# Claude Code plugins. Unix-only (macOS/Linux): needs /bin/sh, executable
+# bits, and `command -v`. Windows support is a filed follow-up.
+#
+# Resolves the gbrain binary (the plugin snapshot cannot ship it — the CLI
+# installs separately), then execs it with the argv the plugin manifest
+# pinned. Resolution order:
+#   1. $GBRAIN_BIN         explicit override (must be executable)
+#   2. `gbrain` on PATH
+#   3. ~/.bun/bin/gbrain   the sanctioned global-install location
+#
+# GBRAIN_SURFACE: when set and argv[0] is `serve`, replaces the value of an
+# existing `--surface <x>` pair, or appends `--surface $GBRAIN_SURFACE` if
+# the pair is absent — so a user can widen (full) or narrow (verbs) this
+# machine's plugin surface without editing the plugin snapshot.
+#
+# No auto-install by design: an MCP server start must never run a network
+# install. On a miss this exits 127 with the recovery path on stderr; the
+# bundled `setup` skill walks the install interactively.
+
+set -eu
+
+resolve_bin() {
+  if [ -n "${GBRAIN_BIN:-}" ]; then
+    if [ ! -x "$GBRAIN_BIN" ]; then
+      echo "gbrain-launcher: GBRAIN_BIN='$GBRAIN_BIN' is not an executable file" >&2
+      exit 127
+    fi
+    printf '%s' "$GBRAIN_BIN"
+    return
+  fi
+  # ~/.bun/bin (the sanctioned global-install location) is preferred OVER a
+  # bare PATH lookup: a hostile repo that prepends node_modules/.bin with a
+  # fake `gbrain` must not win over the real install. GBRAIN_BIN (above) is
+  # the explicit escape hatch for a gbrain living elsewhere.
+  if [ -x "${HOME:-}/.bun/bin/gbrain" ]; then
+    printf '%s' "$HOME/.bun/bin/gbrain"
+    return
+  fi
+  if command -v gbrain >/dev/null 2>&1; then
+    command -v gbrain
+    return
+  fi
+  echo "gbrain-launcher: gbrain binary not found." >&2
+  echo "  install: bun install -g github:garrytan/gbrain#latest-stable" >&2
+  echo "  (the npm package named 'gbrain' is unrelated - do not npm install it)" >&2
+  echo "  then run the bundled 'setup' skill to initialize your brain," >&2
+  echo "  or set GBRAIN_BIN to an absolute gbrain binary path." >&2
+  exit 127
+}
+
+BIN="$(resolve_bin)"
+echo "gbrain-launcher: using $BIN" >&2
+
+# Surface override — only for `serve` invocations. Rebuilds the positional
+# params in place (rotate-through-sentinel idiom; no eval, no word-splitting
+# hazards): replace the value of an existing `--surface <x>` pair, or append
+# the pair when absent.
+if [ -n "${GBRAIN_SURFACE:-}" ] && [ "${1:-}" = "serve" ]; then
+  replaced=0
+  expect_value=0
+  set -- "$@" "__gbrain_end__"
+  while [ "$1" != "__gbrain_end__" ]; do
+    a="$1"
+    shift
+    if [ "$expect_value" = 1 ]; then
+      expect_value=0
+      replaced=1
+      set -- "$@" "$GBRAIN_SURFACE"
+      continue
+    fi
+    if [ "$a" = "--surface" ]; then
+      expect_value=1
+    fi
+    set -- "$@" "$a"
+  done
+  shift
+  if [ "$replaced" = 0 ]; then
+    set -- "$@" "--surface" "$GBRAIN_SURFACE"
+  fi
+fi
+
+exec "$BIN" "$@"
@@ -0,0 +1,12 @@
+{
+  "name": "gbrain",
+  "interface": { "displayName": "GBrain" },
+  "plugins": [
+    {
+      "name": "gbrain",
+      "source": { "source": "local", "path": "./" },
+      "policy": { "installation": "AVAILABLE", "authentication": "ON_USE" },
+      "category": "Productivity"
+    }
+  ]
+}
@@ -0,0 +1,13 @@
+{
+  "name": "gbrain",
+  "description": "GBrain — a persistent knowledge brain for your coding agent: hybrid search, synthesis, and cross-session memory.",
+  "owner": { "name": "Garry Tan" },
+  "plugins": [
+    {
+      "name": "gbrain",
+      "source": "./",
+      "description": "Personal knowledge brain for your coding agent — hybrid search, synthesis, and durable cross-session memory, plus a curated brain-first skill set.",
+      "category": "productivity"
+    }
+  ]
+}
@@ -0,0 +1,34 @@
+{
+  "name": "gbrain",
+  "version": "0.46.11.0",
+  "description": "Personal knowledge brain for your coding agent — hybrid search, synthesis, graph traversal, and durable cross-session memory over Postgres/PGLite with pgvector, plus a curated brain-first skill set.",
+  "author": {
+    "name": "Garry Tan",
+    "url": "https://github.com/garrytan"
+  },
+  "homepage": "https://github.com/garrytan/gbrain",
+  "repository": "https://github.com/garrytan/gbrain",
+  "license": "MIT",
+  "keywords": [
+    "memory",
+    "knowledge-base",
+    "mcp",
+    "search",
+    "agent",
+    "brain",
+    "pgvector"
+  ],
+  "skills": "./plugin/skills/",
+  "mcpServers": {
+    "gbrain": {
+      "command": "${CLAUDE_PLUGIN_ROOT}/.agents/gbrain-launcher",
+      "args": [
+        "serve",
+        "--surface",
+        "starter",
+        "--source-guard"
+      ],
+      "cwd": "${CLAUDE_PLUGIN_ROOT}"
+    }
+  }
+}
@@ -0,0 +1,75 @@
+{
+  "mcpServers": {
+    "gbrain": {
+      "command": "./.agents/gbrain-launcher",
+      "args": [
+        "serve",
+        "--surface",
+        "starter",
+        "--source-guard"
+      ],
+      "cwd": ".",
+      "env_vars": [
+        "ANTHROPIC_API_KEY",
+        "ANTHROPIC_BASE_URL",
+        "AZURE_OPENAI_API_KEY",
+        "DASHSCOPE_API_KEY",
+        "DATABASE_URL",
+        "DEEPGRAM_API_KEY",
+        "DEEPSEEK_API_KEY",
+        "GBRAIN_BIN",
+        "GBRAIN_BRAIN_ID",
+        "GBRAIN_CHAT_FALLBACK_CHAIN",
+        "GBRAIN_CHAT_MODEL",
+        "GBRAIN_DATABASE_URL",
+        "GBRAIN_EMBEDDING_DIMENSIONS",
+        "GBRAIN_EMBEDDING_IMAGE_OCR",
+        "GBRAIN_EMBEDDING_IMAGE_OCR_MODEL",
+        "GBRAIN_EMBEDDING_MODEL",
+        "GBRAIN_EMBEDDING_MULTIMODAL",
+        "GBRAIN_EMBEDDING_MULTIMODAL_MODEL",
+        "GBRAIN_EXPANSION_MODEL",
+        "GBRAIN_HOME",
+        "GBRAIN_MAX_MARKUP_RATIO",
+        "GBRAIN_MCP_FORCE_SURFACE",
+        "GBRAIN_NO_JUNK_PATTERNS",
+        "GBRAIN_NO_SANITY",
+        "GBRAIN_PAGE_BLOCK_BYTES",
+        "GBRAIN_PAGE_WARN_BYTES",
+        "GBRAIN_REMOTE_CLIENT_SECRET",
+        "GBRAIN_RETRIEVAL_REFLEX",
+        "GBRAIN_RETRIEVAL_REFLEX_WINDOW_TURNS",
+        "GBRAIN_SOURCE",
+        "GBRAIN_SURFACE",
+        "GEMINI_API_KEY",
+        "GOOGLE_GENERATIVE_AI_API_KEY",
+        "GROQ_API_KEY",
+        "HOME",
+        "LITELLM_API_KEY",
+        "LITELLM_BASE_URL",
+        "LLAMA_SERVER_API_KEY",
+        "LLAMA_SERVER_BASE_URL",
+        "LLAMA_SERVER_RERANKER_API_KEY",
+        "LLAMA_SERVER_RERANKER_BASE_URL",
+        "LMSTUDIO_BASE_URL",
+        "MINIMAX_API_KEY",
+        "MISTRAL_API_KEY",
+        "MOONSHOT_API_KEY",
+        "NVIDIA_API_KEY",
+        "OLLAMA_API_KEY",
+        "OLLAMA_BASE_URL",
+        "OPENAI_API_KEY",
+        "OPENAI_BASE_URL",
+        "OPENROUTER_API_KEY",
+        "OPENROUTER_BASE_URL",
+        "PATH",
+        "PERPLEXITY_API_KEY",
+        "PPLX_API_KEY",
+        "TOGETHER_API_KEY",
+        "VOYAGE_API_KEY",
+        "ZEROENTROPY_API_KEY",
+        "ZHIPUAI_API_KEY"
+      ]
+    }
+  }
+}
@@ -0,0 +1,39 @@
+{
+  "name": "gbrain",
+  "version": "0.46.11.0",
+  "description": "Personal knowledge brain for your coding agent — hybrid search, synthesis, graph traversal, and durable cross-session memory over Postgres/PGLite with pgvector, plus a curated brain-first skill set.",
+  "author": {
+    "name": "Garry Tan",
+    "url": "https://github.com/garrytan"
+  },
+  "homepage": "https://github.com/garrytan/gbrain",
+  "repository": "https://github.com/garrytan/gbrain",
+  "license": "MIT",
+  "keywords": [
+    "memory",
+    "knowledge-base",
+    "mcp",
+    "search",
+    "agent",
+    "brain",
+    "pgvector"
+  ],
+  "skills": "./plugin/skills/",
+  "mcpServers": "./.codex-plugin/mcp.json",
+  "interface": {
+    "displayName": "GBrain",
+    "shortDescription": "Give your agent a persistent brain: search, synthesis, memory",
+    "longDescription": "GBrain wires a personal knowledge brain into every session: hybrid keyword+vector search, entity graph traversal, synthesis, and memory your agent writes itself — served on the starter MCP surface (the seven memory verbs plus the daily-driver brain ops). Bundles the curated brain-first skill set: setup (walks install + gbrain init), cold-start day-one brain filling, ingest, query, briefing, upgrade, and more. Requires the gbrain CLI (bun install -g github:garrytan/gbrain#latest-stable) and a brain (gbrain init); the bundled setup skill walks the rest. Unix (macOS/Linux) only.",
+    "developerName": "Garry Tan",
+    "category": "Productivity",
+    "capabilities": [
+      "Interactive",
+      "Write"
+    ],
+    "websiteURL": "https://github.com/garrytan/gbrain",
+    "defaultPrompt": [
+      "Search my brain, recall context across sessions, and write new memory as we work"
+    ],
+    "brandColor": "#1F6F5C"
+  }
+}
@@ -1,12 +0,0 @@
-# GBrain E2E Test Configuration
-# Copy to .env.testing and fill in real values
-#
-# Tier 1 (required for E2E tests)
-# Option A: Local Docker Postgres (default)
-DATABASE_URL=postgresql://postgres:postgres@localhost:5433/gbrain_test
-# Option B: Real Supabase instance (tests the actual production path)
-# DATABASE_URL=postgresql://postgres.[project-ref]:[password]@aws-0-us-east-1.pooler.supabase.com:6543/postgres
-
-# Tier 2 (required for skill tests, optional for mechanical tests)
-OPENAI_API_KEY=sk-...
-ANTHROPIC_API_KEY=sk-ant-...
@@ -1,27 +0,0 @@
---
-name: Bug Report
-about: Something isn't working
-labels: bug
---
-
-**What happened?**
-
-
-**What did you expect?**
-
-
-**Steps to reproduce**
-1.
-2.
-3.
-
-**Environment**
- gbrain version: (`gbrain version`)
- OS:
- Bun version: (`bun --version`)
- Database: Supabase / self-hosted Postgres
-
-**`gbrain doctor --json` output**
-```json
-(paste output here)
-```
@@ -1,14 +0,0 @@
---
-name: Feature Request
-about: Suggest an improvement
-labels: enhancement
---
-
-**What problem does this solve?**
-
-
-**What does the solution look like?**
-
-
-**Alternatives considered**
-
@@ -1,39 +0,0 @@
-<!--
-Tier 5.5 Externally-Authored Query Submission template
-See eval/CONTRIBUTING.md for the full workflow.
-->
-
-## Summary
-
-Submitting **N** Tier 5.5 queries for BrainBench.
-
- Author handle: `@your-handle`
- File location: `eval/external-authors/your-handle/queries.json`
- Queries authored fresh (not copy-pasted from a model output)
- Slugs verified against `eval/data/world-v1/` (via `bun run eval:world:view`)
-
-## Checklist
-
- [ ] `bun run eval:query:validate eval/external-authors/your-handle/queries.json` passes
- [ ] At least 20 queries
- [ ] Each query has either `gold.relevant` (with real slugs) or `gold.expected_abstention: true`
- [ ] Temporal queries have `as_of_date` set (`corpus-end` | `per-source` | ISO-8601)
- [ ] Phrasing is varied (not all the same template)
- [ ] `author` field matches my handle
-
-## Phrasing variety (optional self-audit)
-
-Tick the styles represented in your batch:
-
- [ ] Full sentence questions
- [ ] Fragment-style ("crypto founder Goldman Sachs background")
- [ ] Comparison ("X vs Y")
- [ ] Follow-up ("And who else...")
- [ ] Imperative ("Pull up Alice Davis")
- [ ] Trait-based ("the demanding engineering leader")
- [ ] Abstention bait (answer is "not in corpus")
-
-## Notes to reviewer
-
-Anything worth flagging — ambiguous cases, corpus gaps you found, specific
-phrasings you were uncertain about.
@@ -1,100 +0,0 @@
-name: E2E Tests
-
-on:
-  push:
-    branches: [master]
-  pull_request:
-    branches: [master]
-  schedule:
-    - cron: '0 6 * * *'  # Nightly at 6am UTC
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  tier1:
-    name: Tier 1 (Mechanical)
-    runs-on: ubuntu-latest
-    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: latest
-      - run: bun install
-      - name: Run Tier 1 E2E tests
-        run: bun test test/e2e/mechanical.test.ts test/e2e/mcp.test.ts
-        env:
-          DATABASE_URL: postgresql://postgres:postgres@localhost:5432/gbrain_test
-
-  tier2:
-    name: Tier 2 (LLM Skills)
-    runs-on: ubuntu-latest
-    # Runs on every push/PR now (promoted from schedule-only in v0.19.0).
-    # Tier 1 must pass first; Tier 2 uses OPENAI_API_KEY + ANTHROPIC_API_KEY
-    # from repo/org secrets. Nightly + manual triggers still supported via
-    # the workflow-level `on:` list.
-    needs: tier1
-    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: latest
-      - run: bun install
-      - name: Install OpenClaw
-        run: npm install -g openclaw@2026.4.9
-      - name: Configure OpenClaw MCP
-        run: |
-          mkdir -p ~/.openclaw
-          cat > ~/.openclaw/config.json << 'EOF'
-          {
-            "mcpServers": {
-              "gbrain": {
-                "command": "bun",
-                "args": ["run", "src/cli.ts", "serve"],
-                "env": {
-                  "DATABASE_URL": "${{ env.DATABASE_URL }}"
-                }
-              }
-            }
-          }
-          EOF
-      - name: Run Tier 2 skill tests
-        run: bun test test/e2e/skills.test.ts test/e2e/zeroentropy-live.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 }}
@@ -1,48 +0,0 @@
-name: Release
-
-on:
-  push:
-    tags: ['v*']
-
-permissions:
-  contents: write
-
-jobs:
-  build:
-    strategy:
-      matrix:
-        include:
-          - os: macos-latest
-            target: bun-darwin-arm64
-            artifact: gbrain-darwin-arm64
-          - os: ubuntu-latest
-            target: bun-linux-x64
-            artifact: gbrain-linux-x64
-    runs-on: ${{ matrix.os }}
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: latest
-      - run: bun install
-      - run: bun test
-      - run: bun build --compile --target=${{ matrix.target }} --outfile bin/${{ matrix.artifact }} src/cli.ts
-      - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02  # v4
-        with:
-          name: ${{ matrix.artifact }}
-          path: bin/${{ matrix.artifact }}
-
-  release:
-    needs: build
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093  # v4
-        with:
-          path: artifacts
-      - name: Create release
-        uses: softprops/action-gh-release@153bb8e04406b158c6c84fc1615b65b24149a1fe  # v2
-        with:
-          files: |
-            artifacts/gbrain-darwin-arm64/gbrain-darwin-arm64
-            artifacts/gbrain-linux-x64/gbrain-linux-x64
-          generate_release_notes: true
@@ -1,48 +0,0 @@
-name: Test
-
-on:
-  push:
-    branches: [master]
-  pull_request:
-    branches: [master]
-
-permissions:
-  contents: read
-
-jobs:
-  gitleaks:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-        with:
-          fetch-depth: 0
-      - uses: gitleaks/gitleaks-action@dcedce43c6f43de0b836d1fe38946645c9c638dc  # v2
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-  test:
-    # 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
-    strategy:
-      fail-fast: false
-      matrix:
-        shard: [1, 2, 3, 4]
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-      - uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6  # v2
-        with:
-          bun-version: latest
-      - run: bun install
-      - 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
-      - name: Run *.serial.test.ts at --max-concurrency=1 (shard 1 only)
-        # Serial files share file-wide state (top-level mock.module, module
-        # singletons) that leaks across files in the same bun-test process.
-        # test-shard.sh excludes them; this step runs them at concurrency=1.
-        if: matrix.shard == 1
-        run: bun run test:serial
@@ -1,40 +0,0 @@
-node_modules/
-bin/
-.DS_Store
-*.log
-.env
-.env.*
-!.env.*.example
-# Bun --compile temp artifacts. Each build emits a new hash-named .bun-build
-# file in cwd; glob catches all of them.
-*.bun-build
-.gstack/
-supabase/.temp/
-.claude/skills/
-# admin/dist/ is the React SPA bundle. CLAUDE.md says it's committed for
-# self-contained binaries (the bun --compile path embeds it via
-# `import path from 'admin/dist/index.html' with { type: 'file' }`).
-# Build via: cd admin && bun install && bun run build.
-admin/node_modules/
-.idea
-eval/reports/
-eval/data/world-v1/world.html
-
-# BrainBench amara-life-v1 Opus cache (regenerate via eval:generate-amara-life)
-eval/data/amara-life-v1/_cache/
-
-# claw-test E2E build cache (shim + scratch outputs)
-test/.cache/
-
-.claude/
-export/
-
-# Conductor workspace-local agent artifacts: plans, todos, run-unit-parallel
-# failure logs and per-shard test output. v0.26.4 (run-unit-parallel.sh)
-# writes .context/test-failures.log + .context/test-summary.txt +
-# .context/test-shards/. Workspace-local by design — never committed.
-.context/
-
-# Tier 3 PGLite snapshot fixture (built on demand by build:pglite-snapshot)
-test/fixtures/pglite-snapshot.tar
-test/fixtures/pglite-snapshot.version
@@ -1,11 +0,0 @@
-title = "GBrain gitleaks config"
-
-[allowlist]
-  paths = [
-    '''.env\.testing\.example''',
-    '''.env\.example''',
-    '''test/''',
-    '''skills/''',
-    '''.claude/skills/''',
-    '''GBRAIN_SKILLPACK\.md''',
-  ]
@@ -1,88 +0,0 @@
-# Agents working on GBrain
-
-This is your install + operating protocol. Claude Code reads `./CLAUDE.md` automatically.
-Everyone else (Codex, Cursor, OpenClaw, Aider, Continue, or an LLM fetching via URL):
-start here.
-
-## Install (5 min)
-
-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.
-4. **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).
-5. 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) — 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.
-4. [`./skills/conventions/brain-routing.md`](./skills/conventions/brain-routing.md) —
-   agent-facing decision table: when to switch brain, when to switch source, how
-   cross-brain federation works (latent-space only; the agent decides).
-5. [`./skills/RESOLVER.md`](./skills/RESOLVER.md) — skill dispatcher. Read before any task.
-
-## Trust boundary (critical)
-
-GBrain distinguishes **trusted local CLI callers** (`OperationContext.remote = false`,
-set by `src/cli.ts`) from **untrusted agent-facing callers** (`remote = true`, set by
-`src/mcp/server.ts`). Security-sensitive operations like `file_upload` tighten filesystem
-confinement when `remote = true` and default to strict behavior when unset. If you are
-writing or reviewing an operation, consult `src/core/operations.ts` for the contract.
-
-## Common tasks
-
- **Configure:** [`docs/ENGINES.md`](./docs/ENGINES.md),
-  [`docs/guides/live-sync.md`](./docs/guides/live-sync.md),
-  [`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:** [`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:
-  [`docs/eval-bench.md`](./docs/eval-bench.md).
- **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.
-
-## Before shipping
-
-Easiest path: `bun run ci:local` runs the full CI gate inside Docker (gitleaks,
-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.
-
-## Privacy
-
-Never commit real names of people, companies, or funds into public artifacts. See the
-Privacy rule in `./CLAUDE.md`. GBrain pages reference real contacts; public docs must
-use generic placeholders (`alice-example`, `acme-example`, `fund-a`).
-
-## Forks
-
-If you are a fork, regenerate `llms.txt` + `llms-full.txt` with your own URL base before
-publishing: `LLMS_REPO_BASE=https://raw.githubusercontent.com/your-org/your-fork/main bun run build:llms`.
@@ -1,289 +0,0 @@
-# Contributing to GBrain
-
-## Setup
-
-```bash
-git clone https://github.com/garrytan/gbrain.git
-cd gbrain
-bun install
-bun test
-```
-
-Requires Bun 1.0+.
-
-## Project structure
-
-```
-src/
-  cli.ts                  CLI entry point
-  commands/               CLI-only commands (init, upgrade, import, export, etc.)
-  core/
-    operations.ts         Contract-first operation definitions (the foundation)
-    engine.ts             BrainEngine interface
-    postgres-engine.ts    Postgres implementation
-    db.ts                 Connection management + schema loader
-    import-file.ts        Import pipeline (chunk + embed + tags)
-    types.ts              TypeScript types
-    markdown.ts           Frontmatter parsing
-    config.ts             Config file management
-    storage.ts            Pluggable storage interface
-    storage/              Storage backends (S3, Supabase, local)
-    supabase-admin.ts     Supabase admin API
-    file-resolver.ts      MIME detection + content hashing
-    migrate.ts            Migration helpers
-    yaml-lite.ts          Lightweight YAML parser
-    chunkers/             3-tier chunking (recursive, semantic, llm)
-    search/               Hybrid search (vector, keyword, hybrid, expansion, dedup)
-    embedding.ts          OpenAI embedding service
-  mcp/
-    server.ts             MCP stdio server (generated from operations)
-  schema.sql              Postgres DDL
-skills/                   Fat markdown skills for AI agents
-test/                     Unit tests (bun test, no DB required)
-test/e2e/                 E2E tests (requires DATABASE_URL, real Postgres+pgvector)
-  fixtures/               Miniature realistic brain corpus (16 files)
-  helpers.ts              DB lifecycle, fixture import, timing
-  mechanical.test.ts      All operations against real DB
-  mcp.test.ts             MCP tool generation verification
-  skills.test.ts          Tier 2 skill tests (requires OpenClaw + API keys)
-docs/                     Architecture docs
-```
-
-## Running tests
-
-```bash
-# Inner edit loop (~85s on a Mac dev box, 3700+ unit tests)
-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 + typecheck
-
-# Pre-merge sanity (everything CI runs)
-bun run test:full                 # verify + parallel unit + slow + smart e2e
-
-# Slow / serial / e2e in isolation
-bun run test:slow                 # *.slow.test.ts only (cold-path correctness)
-bun run test:serial               # *.serial.test.ts only (--max-concurrency=1)
-bun run test:e2e                  # real-Postgres E2E (requires DATABASE_URL)
-
-# E2E setup (Postgres with pgvector)
-docker compose -f docker-compose.test.yml up -d
-DATABASE_URL=postgresql://postgres:postgres@localhost:5434/gbrain_test bun run test:e2e
-
-# Or use your own Postgres / Supabase
-DATABASE_URL=postgresql://... bun run test:e2e
-```
-
-Use `bun run verify` before pushing. The guard chain catches: banned fork-name
-leaks (`scripts/check-privacy.sh`), `JSON.stringify(x)::jsonb` interpolation
-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`), 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
-
-`bun run test` shards 92+ unit-test files across 8 worker processes. Files in the
-same shard share a process, so process-global state leaks between them. Four
-lint rules (`scripts/check-test-isolation.sh`, R1-R4) enforce isolation:
-
-| Rule | What it bans | Fix |
-|---|---|---|
-| **R1** | Direct `process.env.X = ...` mutation | Use `withEnv()` from `test/helpers/with-env.ts`, or rename to `*.serial.test.ts` |
-| **R2** | `mock.module(...)` anywhere in the file | Rename to `*.serial.test.ts` |
-| **R3** | `new PGLiteEngine(` outside ~50 lines after `beforeAll(` | Use the canonical PGLite block (see below) |
-| **R4** | `new PGLiteEngine(` without paired `afterAll(disconnect)` | Add the `afterAll(() => engine.disconnect())` |
-
-Canonical PGLite block (R3 + R4 compliant — paste this verbatim):
-
-```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); });
-```
-
-Env-touching tests:
-
-```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');
-  });
-});
-```
-
-`withEnv` saves and restores keys via try/finally including when the callback
-throws. Cross-test safe; **NOT** intra-file concurrent-safe (`process.env` is
-process-global). Files using `withEnv` stay outside the future
-`test.concurrent()` codemod's eligibility filter.
-
-When to quarantine instead of fix: rename to `*.serial.test.ts` if the file
-uses `mock.module(...)`, is genuinely env-coupled (module-load env readers +
-ESM caching defeat dynamic-import-after-env tricks), or intentionally shares
-state across `it()` boundaries. Quarantine count cap: 10 (informational).
-
-Files that violated these rules at the v0.26.7 baseline are listed in
-`scripts/check-test-isolation.allowlist`. **The allow-list MUST shrink over
-time** ... never add new entries. v0.26.8 (env sweep) and v0.26.9 (PGLite sweep
-+ codemod) remove entries as files get fixed.
-
-### Local CI gate (recommended before pushing, v0.23.1+)
-
-```bash
-bun run ci:local         # full gate: gitleaks + unit + ALL 29 E2E files (sequential)
-bun run ci:local:diff    # gate with diff-aware E2E selector
-bun run ci:select-e2e    # print which E2E files the selector would run
-```
-
-`ci:local` spins up `pgvector/pgvector:pg16` + `oven/bun:1` via
-`docker-compose.ci.yml`, runs everything PR CI runs plus the full E2E suite, then
-tears down. Named volumes keep the install warm across runs (~16-20 min sequential
-E2E after the first cold pull). Requires Docker (Docker Desktop, OrbStack, or
-Colima) and `gitleaks` on host (`brew install gitleaks`). Override the postgres
-host port with `GBRAIN_CI_PG_PORT=5435 bun run ci:local` if 5434 collides.
-
-Fail-closed selector: an unmapped `src/` change runs all 29 E2E files. Hand-tune
-narrower mappings via `scripts/e2e-test-map.ts`.
-
-## Building
-
-```bash
-bun build --compile --outfile bin/gbrain src/cli.ts
-```
-
-## Adding a new operation
-
-GBrain uses a contract-first architecture. Add your operation to one file and it
-automatically appears in the CLI, MCP server, and tools-json:
-
-1. Add your operation to `src/core/operations.ts` (define params, handler, cliHints)
-2. Add tests
-3. That's it. The CLI, MCP server, and tools-json are generated from operations.
-
-For CLI-only commands (init, upgrade, import, export, files, embed, doctor, sync):
-1. Create `src/commands/mycommand.ts`
-2. Add the case to `src/cli.ts`
-
-Parity tests (`test/parity.test.ts`) verify CLI/MCP/tools-json stay in sync.
-
-## Adding a new engine
-
-See `docs/ENGINES.md` for the full guide. In short:
-
-1. Create `src/core/myengine-engine.ts` implementing `BrainEngine`
-2. Add to engine factory in `src/core/engine.ts`
-3. Run the test suite against your engine
-4. Document in `docs/`
-
-The SQLite engine is designed and ready for implementation. See `docs/SQLITE_ENGINE.md`.
-
-## CONTRIBUTOR_MODE — turn on the dev loop
-
-gbrain captures retrieval traffic so you can replay real queries against
-your code changes before merging. **This is off by default** (production
-users get a quiet brain, no surprise data accumulation). Contributors turn
-it on with one shell rc line:
-
-```bash
-# In ~/.zshrc or ~/.bashrc:
-export GBRAIN_CONTRIBUTOR_MODE=1
-```
-
-That's it. Every `query` / `search` you (or agents pointed at your dev
-brain) run from that shell now writes a row to `eval_candidates`, and the
-[replay tool](#running-real-world-eval-benchmarks-touching-retrieval-code)
-has data to work against.
-
-What CONTRIBUTOR_MODE actually does:
-
- Turns on `query`/`search` capture into the local `eval_candidates` table.
-  Without it the gate is closed and capture is a no-op.
- That's all. PII scrubbing, retention, and replay are independent.
-
-Resolution order (most explicit wins):
-
-1. `eval.capture: true` in `~/.gbrain/config.json` → on
-2. `eval.capture: false` in `~/.gbrain/config.json` → off
-3. `GBRAIN_CONTRIBUTOR_MODE=1` → on
-4. otherwise → off
-
-Quick check that capture is actually running:
-
-```bash
-gbrain query "anything" >/dev/null
-psql $DATABASE_URL -c 'SELECT count(*) FROM eval_candidates'
-# (or `gbrain doctor` — surfaces silent capture failures cross-process)
-```
-
-To disable capture even with the env var set, write
-`{"eval": {"capture": false}}` to `~/.gbrain/config.json` — explicit config
-beats the env var both directions.
-
-## Running real-world eval benchmarks (touching retrieval code)
-
-If your PR touches retrieval — search ranking, RRF fusion, embeddings,
-intent classification, query expansion, source boost, or the `query` /
-`search` op handlers — run `gbrain eval replay` against a snapshot of
-real traffic before merging. Requires `CONTRIBUTOR_MODE` (above) so you
-have captured rows to replay against.
-
-Quick loop:
-
-```bash
-gbrain eval export --since 7d > baseline.ndjson    # snapshot before your change
-# ... make your change ...
-gbrain eval replay --against baseline.ndjson       # diff retrieval, get Jaccard@k
-```
-
-Three numbers come back: mean Jaccard@k between captured and current slug
-sets, top-1 stability, and mean latency Δ. The replay tool flags the worst
-regressions so you can eyeball whether the change is hurting real queries.
-
-Trigger paths (rerun if your diff touches any of these):
-
- `src/core/search/hybrid.ts`
- `src/core/search/source-boost.ts`, `sql-ranking.ts`
- `src/core/search/intent.ts`, `expansion.ts`, `dedup.ts`
- `src/core/embedding.ts`
- `src/core/operations.ts` (query / search handlers)
- `src/core/postgres-engine.ts` / `pglite-engine.ts` (searchKeyword /
-  searchVector SQL)
-
-See [`docs/eval-bench.md`](./docs/eval-bench.md) for the full guide
-including CI integration, hand-crafted NDJSON corpora (so a fresh checkout
-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
- Docker Compose for self-hosted Postgres
- Additional migration sources
- New enrichment API integrations
- Performance optimizations
@@ -1,237 +0,0 @@
-# GBrain Installation Guide for AI Agents
-
-Read this entire file, then follow the steps. Ask the user for API keys when needed.
-Target: ~30 minutes to a fully working brain.
-
-## Step 0: If you are not Claude Code
-
-Read `AGENTS.md` at the repo root first. It's the non-Claude-agent operating
-protocol (install, read order, trust boundary, common tasks). Claude Code reads
-`CLAUDE.md` automatically and can skip ahead.
-
-If you fetched this file by URL without cloning yet, the companion files live at:
- `https://raw.githubusercontent.com/garrytan/gbrain/master/AGENTS.md` — start here
- `https://raw.githubusercontent.com/garrytan/gbrain/master/llms.txt` — full doc map
- `https://raw.githubusercontent.com/garrytan/gbrain/master/llms-full.txt` — same map, inlined
-
-## Step 1: Install GBrain
-
-```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 && 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.
-
-> **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:
-
-```bash
-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`. Without OpenAI, keyword search still works.
-Without Anthropic, search works but skips query expansion.
-
-## Step 3: Create the Brain
-
-```bash
-gbrain init                           # PGLite, no server needed
-gbrain doctor --json                  # verify all checks pass
-```
-
-The user's markdown files (notes, docs, brain repo) are SEPARATE from this tool repo.
-Ask the user where their files are, or create a new brain repo:
-
-```bash
-mkdir -p ~/brain && cd ~/brain && git init
-```
-
-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
-gbrain import ~/brain/ --no-embed     # import markdown files
-gbrain embed --stale                  # generate vector embeddings
-gbrain query "key themes across these documents?"
-```
-
-## Step 4.5: Wire the Knowledge Graph
-
-If the user already had a brain repo (Step 3 imported existing markdown), backfill
-the typed-link graph and structured timeline. This populates the `links` and
-`timeline_entries` tables that future writes will maintain automatically.
-
-```bash
-gbrain extract links --source db --dry-run | head -20    # preview
-gbrain extract links --source db                         # commit
-gbrain extract timeline --source db                      # dated events
-gbrain stats                                             # verify links > 0
-```
-
-For brand-new empty brains, skip this step — auto-link populates the graph as the
-agent writes pages going forward. There is nothing to backfill yet.
-
-After this step:
- `gbrain graph-query <slug> --depth 2` works (relationship traversal)
- Search ranks well-connected entities higher (backlink boost)
- Every future `put_page` auto-creates typed links and reconciles stale ones
-
-If a user has a very large brain (>10K pages), `extract --source db` is idempotent
-and supports `--since YYYY-MM-DD` for incremental runs.
-
-## Step 5: Load Skills
-
-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:
-
-1. **Signal detector** (`skills/signal-detector/SKILL.md`) — fire this on EVERY
-   inbound message. It captures ideas and entities in parallel. The brain compounds.
-
-2. **Brain-ops** (`skills/brain-ops/SKILL.md`) — brain-first lookup on every response.
-   Check the brain before any external API call.
-
-3. **Conventions** (`skills/conventions/quality.md`) — citation format, back-linking
-   iron law, source attribution. These are non-negotiable quality rules.
-
-## Step 6: Identity (optional)
-
-Run the soul-audit skill to customize the agent's identity:
-
-```
-Read skills/soul-audit/SKILL.md and follow it.
-```
-
-This generates SOUL.md (agent identity), USER.md (user profile), ACCESS_POLICY.md
-(who sees what), and HEARTBEAT.md (operational cadence) from the user's answers.
-
-If skipped, minimal defaults are installed automatically.
-
-## Step 7: Recurring Jobs
-
-Set up using your platform's scheduler (OpenClaw cron, Railway cron, crontab):
-
- **Live sync** (every 15 min): `gbrain sync --repo ~/brain && gbrain embed --stale`
- **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. 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
-
-Run `gbrain integrations list`. Each recipe in `~/gbrain/recipes/` is a self-contained
-installer. It tells you what credentials to ask for, how to validate, and what cron
-to register. Ask the user which integrations they want (email, calendar, voice, Twitter).
-
-Verify: `gbrain integrations doctor` (after at least one is configured)
-
-## Step 9: Verify
-
-Read `docs/GBRAIN_VERIFY.md` and run all 7 verification checks. Check #4 (live sync
-actually works) is the most important.
-
-## Upgrade
-
-```bash
-cd ~/gbrain && git pull origin master && bun install
-gbrain init                           # apply schema migrations (idempotent)
-gbrain post-upgrade                   # show migration notes for the version range
-```
-
-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).
-
-For v0.12.2+ specifically: if your brain is Postgres- or Supabase-backed and
-predates v0.12.2, the `v0_12_2` migration runs `gbrain repair-jsonb`
-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.
@@ -1,884 +0,0 @@
-# GBrain
-
-Your AI agent is smart but forgetful. GBrain gives it a brain.
-
-Built by the President and CEO of Y Combinator to run his actual AI agents. The production brain powering his OpenClaw and Hermes deployments: **17,888 pages, 4,383 people, 723 companies**, 21 cron jobs running autonomously, built in 12 days. The agent ingests meetings, emails, tweets, voice calls, and original ideas while you sleep. It enriches every person and company it encounters. It fixes its own citations and consolidates memory overnight. You wake up and the brain is smarter than when you went to bed.
-
-The brain wires itself. Every page write extracts entity references and creates typed links (`attended`, `works_at`, `invested_in`, `founded`, `advises`) with zero LLM calls. Hybrid search. Self-wiring knowledge graph. Structured timeline. Backlink-boosted ranking. Ask "who works at Acme AI?" or "what did Bob invest in this quarter?" and get answers vector search alone can't reach. Benchmarked side-by-side against the category: gbrain lands **P@5 49.1%, R@5 97.9%** on a 240-page Opus-generated rich-prose corpus, beating its own graph-disabled variant by **+31.4 points P@5** and ripgrep-BM25 + vector-only RAG by a similar margin. The graph layer plus v0.12 extract quality together carry the gap. Full BrainBench scorecards + corpus live in the sibling [gbrain-evals](https://github.com/garrytan/gbrain-evals) repo.
-
-GBrain is those patterns, generalized. 34 skills. Install in 30 minutes. Your agent does the work. As Garry's personal agent gets smarter, so does yours.
-
-**New in v0.25.0 — BrainBench-Real (session capture, contributor opt-in):** with `GBRAIN_CONTRIBUTOR_MODE=1` set in your shell, every real `query` + `search` call through MCP, CLI, or the subagent tool-bridge gets captured (PII-scrubbed) into an `eval_candidates` table. Snapshot with `gbrain eval export`, replay against your code change with `gbrain eval replay`. Three numbers come back: mean Jaccard@k between captured and current retrieved slugs, top-1 stability, and latency Δ. **Off by default** for production users — no surprise data accumulation. Walkthrough: [docs/eval-bench.md](docs/eval-bench.md). NDJSON wire format: [docs/eval-capture.md](docs/eval-capture.md).
-
-**New in v0.28.8 — LongMemEval in the box:** `gbrain eval longmemeval <dataset.jsonl>` runs the public [LongMemEval](https://huggingface.co/datasets/xiaowu0162/longmemeval) benchmark against gbrain's hybrid retrieval. One in-memory PGLite per run, `TRUNCATE` between questions (runtime-enumerated tables, schema-migration-safe), 25.9ms p50 per question on Apple Silicon. Your `~/.gbrain` brain is never touched. Retrieved chat content is sanitized with the same `INJECTION_PATTERNS` that protect takes — one source of truth for prompt-injection defense. Hand the JSONL output to LongMemEval's `evaluate_qa.py` to score.
-
-> **~30 minutes to a fully working brain.** Database ready in 2 seconds (PGLite, no server). You just answer questions about API keys.
-
-> **LLMs:** fetch [`llms.txt`](llms.txt) for the documentation map, or [`llms-full.txt`](llms-full.txt) for the same map with core docs inlined in one fetch. **Agents:** start with [`AGENTS.md`](AGENTS.md) (or [`CLAUDE.md`](CLAUDE.md) if you're Claude Code).
-
-> **Embedding providers:** OpenAI is the default, but gbrain ships with **14 recipes** covering Voyage, Google Gemini, Azure OpenAI, MiniMax, Alibaba DashScope, Zhipu, Ollama (local), llama.cpp llama-server (local), LiteLLM proxy (universal), and 5 more. Run `gbrain providers list` to see them, or read [`docs/integrations/embedding-providers.md`](docs/integrations/embedding-providers.md) for setup, pricing, and a decision tree. `gbrain doctor` will surface alternative providers whose env vars you already have set.
-
-> **New in v0.32.3.0 — compress your AGENTS.md without losing accuracy:** if your downstream agent fork has grown a 25KB+ `AGENTS.md` / `RESOLVER.md`, the new [`functional-area-resolver`](skills/functional-area-resolver/SKILL.md) skill ships a two-layer dispatch pattern that compresses 25KB → 13KB (48% the size) while **beating** the verbose baseline by +13 to +17pp across Opus 4.7, Sonnet 4.6, and Haiku 4.5. A/B eval harness, cross-model receipts, and reproduction instructions live at [`evals/functional-area-resolver/`](evals/functional-area-resolver/). The static-prompt analog of AnyTool / RAG-MCP / Anthropic Agent Skills progressive disclosure — single-LLM-pass dispatch, no second routing call.
-
-## Install
-
-### On an agent platform (recommended)
-
-GBrain is designed to be installed and operated by an AI agent. If you don't have one running yet:
-
- **[OpenClaw](https://openclaw.ai)** ... Deploy [AlphaClaw on Render](https://render.com/deploy?repo=https://github.com/chrysb/alphaclaw) (one click, 8GB+ RAM)
- **[Hermes Agent](https://github.com/NousResearch/hermes-agent)** ... Deploy on [Railway](https://github.com/praveen-ks-2001/hermes-agent-template) (one click)
-
-Paste this into your agent:
-
-```
-Retrieve and follow the instructions at:
-https://raw.githubusercontent.com/garrytan/gbrain/master/INSTALL_FOR_AGENTS.md
-```
-
-That's it. The agent clones the repo, installs GBrain, sets up the brain, loads 34 skills, and configures recurring jobs. You answer a few questions about API keys. ~30 minutes.
-
-If your agent doesn't auto-read `AGENTS.md`, point it at that file first:
-`https://raw.githubusercontent.com/garrytan/gbrain/master/AGENTS.md` is the non-Claude
-agent operating protocol (install, read order, trust boundary, common tasks). For
-the full doc map, use `llms.txt` at the same URL root.
-
-### Standalone CLI (no agent)
-
-```bash
-git clone https://github.com/garrytan/gbrain.git && cd gbrain && bun install && bun link
-gbrain init                     # local brain, ready in 2 seconds
-                                # picks a search mode (conservative / balanced / tokenmax)
-gbrain import ~/notes/          # index your markdown
-gbrain query "what themes show up across my notes?"
-gbrain search modes             # see the active search mode + per-knob attribution
-gbrain search stats             # cache hit rate + intent mix after some real usage
-```
-
-**v0.32.3 — named search modes.** `gbrain init` asks once which mode fits
-your workload. The cost spread depends on BOTH the mode AND your downstream
-model — 25x corner-to-corner. Per-query cost @ 10K queries/month (typical
-single-user volume; multiply by 10 for heavy / multi-user fleets):
-
-| Mode \ Downstream | 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** |
-
-Natural pairings (corner-diagonal) span ~4x at realistic single-user
-volume. Auto-suggests based on your configured `models.tier.subagent`.
-Non-TTY installs auto-pick `balanced` and print a hint pointing at
-`gbrain config set search.mode <m>`. After some real usage, run
-`gbrain search stats` for observability and `gbrain search tune` for
-data-driven recommendations. Methodology + eval results live at
-[docs/eval/SEARCH_MODE_METHODOLOGY.md](docs/eval/SEARCH_MODE_METHODOLOGY.md).
-
-**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()` the first time it opens PGLite. Use `git clone + bun install
-&& bun link` as shown above. See [#218](https://github.com/garrytan/gbrain/issues/218).
-
-**Do NOT use `bun add -g gbrain` or `npm install -g gbrain`.** The npm registry
-has an unrelated package squatting that name (`gbrain@1.3.x`) — you'd silently
-install the wrong binary and overwrite the canonical one. v0.28.5+ detects this
-and prints a recovery message on `gbrain upgrade`, but the `git clone + bun link`
-path above is the only reliable install method until we publish under
-`@garrytan/gbrain` (tracked v0.29 follow-up). See
-[#658](https://github.com/garrytan/gbrain/issues/658).
-
-```
-3 results (hybrid search, 0.12s):
-
-1. concepts/do-things-that-dont-scale (score: 0.94)
-   PG's argument that unscalable effort teaches you what users want.
-   [Source: paulgraham.com, 2013-07-01]
-
-2. originals/founder-mode-observation (score: 0.87)
-   Deep involvement isn't micromanagement if it expands the team's thinking.
-
-3. concepts/build-something-people-want (score: 0.81)
-   The YC motto. Connected to 12 other brain pages.
-```
-
-### MCP server (Claude Code, Cursor, Windsurf)
-
-GBrain exposes 30+ MCP tools via stdio:
-
-```json
-{
-  "mcpServers": {
-    "gbrain": { "command": "gbrain", "args": ["serve"] }
-  }
-}
-```
-
-Add to `~/.claude/server.json` (Claude Code), Settings > MCP Servers (Cursor), or your client's MCP config.
-
-### Remote MCP with OAuth 2.1 (ChatGPT, Claude Desktop, Cowork, Perplexity)
-
-`gbrain serve --http` starts a production-grade OAuth 2.1 server with an embedded admin dashboard. Zero external infrastructure. Every major AI client connects, every request is scoped, every action is logged.
-
-```bash
-# Start the HTTP server (prints admin bootstrap token on first start)
-gbrain serve --http --port 3131
-
-# Open the admin dashboard, paste the bootstrap token, register a client
-open http://localhost:3131/admin
-
-# Expose publicly (set --public-url so the OAuth issuer matches)
-ngrok http 3131 --url your-brain.ngrok.app
-gbrain serve --http --port 3131 --public-url https://your-brain.ngrok.app
-
-# ChatGPT and other OAuth-aware clients can also connect:
-claude mcp add gbrain -t http https://your-brain.ngrok.app/mcp -H "Authorization: Bearer TOKEN"
-```
-
-Register OAuth clients from the `/admin` dashboard — click **Register client**,
-pick scopes, save the credentials shown once in the reveal modal. Programmatic
-registration via `oauthProvider.registerClientManual(...)` and the
-`gbrain auth register-client` CLI are also available.
-
- **OAuth 2.1 via the MCP SDK** — client credentials (machine-to-machine: Perplexity, Claude), authorization code + PKCE (browser-based: ChatGPT), refresh token rotation, revocation, protected resource metadata. PKCE-only public clients (`token_endpoint_auth_method: "none"`) register without a secret per RFC 7591 §3.2.1 (v0.34). Optional Dynamic Client Registration behind `--enable-dcr` (DCR redirect_uris must be `https://` or loopback per RFC 6749 §3.1.2.1).
- **Source-scoped OAuth clients (v0.34)** — `gbrain auth register-client my-agent --source dept-x` ties the client's write authority to one source; read paths only return rows matching that source. `--federated-read S1,S2,S3` adds an orthogonal read-scope axis for shared brains (departments writing to one canon while reading the union). Pre-v0.34 clients are backfilled to `source_id='default'` on upgrade.
- **Loopback default for `serve --http` (v0.34)** — listens on `127.0.0.1` unless `--bind 0.0.0.0` (or a specific interface IP). Personal-laptop installs no longer publish the brain to the LAN by accident. A stderr WARN fires when `--public-url` is set without `--bind` so the operator sees the binding before the first request.
- **Scoped operations** — 30 operations tagged `read | write | admin`. `sync_brain` and `file_upload` are `localOnly`, rejected over HTTP.
- **React admin dashboard** — 7 screens baked into the binary (~65KB gzip). Live SSE activity feed, agents table, credential reveal, filterable request log, per-client config export.
- **Legacy bearer tokens still work** — pre-v0.26 `gbrain auth create` tokens continue to authenticate as `read+write+admin`. v0.22.7's simpler `src/mcp/http-transport.ts` path stays compiled in for backward compat callers; v0.26+ deployments use the OAuth-aware `serve-http.ts`.
-
-Per-client guides: [`docs/mcp/`](docs/mcp/DEPLOY.md). Hardening defaults, env vars, and threat model: [SECURITY.md](SECURITY.md).
-
-### Using gbrain with GStack
-
-If your engineering agent runs on [GStack](https://github.com/garrytan/gstack), point it at gbrain for code lookup instead of grep+read. Cathedral II (v0.21.0) ships call-graph edges and two-pass retrieval — `/investigate`, `/review`, `/plan-eng-review`, and `/office-hours` all benefit when the agent walks the symbol graph instead of scanning files line by line.
-
-The five magical-moment commands:
-
-```bash
-gbrain code-callers searchKeyword           # who calls this symbol?
-gbrain code-callees searchKeyword           # what does this symbol call?
-gbrain code-def BrainEngine                 # where is X defined?
-gbrain code-refs BrainEngine                # all reference sites
-gbrain query "how does N+1 handling work" --near-symbol BrainEngine.searchKeyword --walk-depth 2
-```
-
-All five auto-emit JSON on non-TTY (gh-CLI convention) so a GStack subagent shelling out via bash gets a clean parseable response. Run `gbrain sources add <repo> --strategy code` to index a repo, then your agent's brain-first lookup covers code, not just markdown. ([Cathedral II release notes](CHANGELOG.md#0210---2026-04-25))
-
-## The 34 Skills
-
-GBrain ships 34 skills organized by `skills/RESOLVER.md` (or your OpenClaw's `AGENTS.md` — both filenames are supported as of v0.19). The resolver tells your agent which skill to read for any task. v0.25.1 added 9 research-flavored skills (`book-mirror` flagship plus 8 pairings); see the new "Research and synthesis" section below.
-
-[Skill files are code.](https://x.com/garrytan/status/2042925773300908103) They're the most powerful way to get knowledge work done. A skill file is a fat markdown document that encodes an entire workflow: when to fire, what to check, how to chain with other skills, what quality bar to enforce. The agent reads the skill and executes it. Skills can also call deterministic TypeScript code bundled in GBrain (search, import, embed, sync) for the parts that shouldn't be left to LLM judgment. [Thin harness, fat skills](docs/ethos/THIN_HARNESS_FAT_SKILLS.md): the intelligence lives in the skills, not the runtime.
-
-### Always-on
-
-| Skill | What it does |
-|-------|-------------|
-| **signal-detector** | Fires on every message. Spawns a cheap model in parallel to capture original thinking and entity mentions. The brain compounds on autopilot. |
-| **brain-ops** | Brain-first lookup before any external API. The read-enrich-write loop that makes every response smarter. |
-
-### Content ingestion
-
-| Skill | What it does |
-|-------|-------------|
-| **ingest** | Thin router. Detects input type and delegates to the right ingestion skill. |
-| **idea-ingest** | Links, articles, tweets become brain pages with analysis, author people pages, and cross-linking. |
-| **media-ingest** | Video, audio, PDF, books, screenshots, GitHub repos. Transcripts, entity extraction, backlink propagation. |
-| **meeting-ingestion** | Transcripts become brain pages. Every attendee gets enriched. Every company gets a timeline entry. |
-| **voice-note-ingest** | Voice notes captured verbatim — exact phrasing preserved, never paraphrased. Routes to originals/concepts/people/companies/ideas/personal/voice-notes based on content. |
-| **article-enrichment** | Raw article dumps become structured pages with executive summary, verbatim quotes, key insights, and why-it-matters. |
-
-### Research and synthesis (v0.25.1)
-
-| Skill | What it does |
-|-------|-------------|
-| **book-mirror** | Flagship. Hand the agent a book, get a personalized two-column chapter-by-chapter analysis. Left column preserves the chapter's actual content; right column maps every idea to your life using your words from the brain. ~$6 for a 20-chapter book at Opus. Pairs with `gbrain book-mirror` CLI for the trusted runtime. |
-| **strategic-reading** | Read a book / article / case study through ONE specific problem-lens. Output: applied playbook with do / avoid / watch-for and short / medium / long-term recommendations. |
-| **concept-synthesis** | Deduplicate thousands of concept stubs into a tiered intellectual map (T1 Canon to T4 Riff). Trace how ideas evolved across years of notes. |
-| **perplexity-research** | Brain-augmented web research. Sends brain context to Perplexity so the search focuses on what's NEW vs already-known. Output: Executive Summary + Key New Developments + Confirming Signals + Contradictions or Updates + Recommended Brain Updates + Citations. |
-| **archive-crawler** | Universal archivist for personal file archives (Dropbox / Backblaze / Gmail-takeout / hard-drive dumps). REFUSES to run unless `archive-crawler.scan_paths:` is set in `gbrain.yml`. Safe-by-default safety fence. |
-| **academic-verify** | Trace a research claim through publication → methodology → raw data → independent replication. Routes through perplexity-research; produces a verdict (verified / partial / unverifiable / misattributed / retracted). |
-| **brain-pdf** | Render any brain page to publication-quality PDF via the gstack `make-pdf` binary. Strips frontmatter, sanitizes emoji, applies running headers. |
-
-### Brain operations
-
-| Skill | What it does |
-|-------|-------------|
-| **enrich** | Tiered enrichment (Tier 1/2/3). Creates and updates person/company pages with compiled truth and timelines. |
-| **query** | 3-layer search with synthesis and citations. Says "the brain doesn't have info on X" instead of hallucinating. |
-| **maintain** | Periodic health: stale pages, orphans, dead links, citation audit, back-link enforcement, tag consistency. v0.23 adds the dream cycle's synthesize + patterns phases ... overnight conversation transcripts become reflections, originals, and 25-year patterns. |
-| **citation-fixer** | Scans pages for missing or malformed citations. Fixes format to match the standard. |
-| **repo-architecture** | Where new brain files go. Decision protocol: primary subject determines directory, not format. |
-| **publish** | Share brain pages as password-protected HTML. Zero LLM calls. |
-| **data-research** | Structured data research with parameterized YAML recipes. Extract investor updates, expenses, company metrics from email. |
-
-### Operational
-
-| Skill | What it does |
-|-------|-------------|
-| **daily-task-manager** | Task lifecycle with priority levels (P0-P3). Stored as searchable brain pages. |
-| **daily-task-prep** | Morning prep: calendar lookahead with brain context per attendee, open threads, task review. |
-| **cron-scheduler** | Schedule staggering (5-min offsets), quiet hours (timezone-aware with wake-up override), idempotency. |
-| **reports** | Timestamped reports with keyword routing. "What's the latest briefing?" finds it instantly. |
-| **cross-modal-review** | Quality gate via second model. Refusal routing: if one model refuses, silently switch. |
-| **webhook-transforms** | External events (SMS, meetings, social mentions) converted into brain pages with entity extraction. |
-| **testing** | Validates every skill has SKILL.md with frontmatter, manifest coverage, resolver coverage. |
-| **skill-creator** | Create new skills following the conformance standard. MECE check against existing skills. |
-| **skillify** | The "skillify it!" meta-skill. Orchestrates the 10-step loop so failures become durable skills: scaffold the stubs via `gbrain skillify scaffold`, write the real logic, gate with `gbrain skillify check` + `gbrain check-resolvable`. |
-| **skillpack-check** | Agent-readable gbrain health report. Exit code for CI; JSON for debugging. Cron-friendly. |
-| **smoke-test** | 8 post-restart health checks with auto-fix (Bun, CLI, DB, worker, Zod CJS, gateway, API key, brain repo). Drop-in user tests at `~/.gbrain/smoke-tests.d/*.sh`. |
-| **minion-orchestrator** | Background work in one skill. Shell jobs via `gbrain jobs submit shell` (operator/CLI, MCP blocks protected names) and LLM subagents via `gbrain agent run`. Parent-child DAGs, `child_done` inbox, durability across worker restarts. |
-
-### Identity and setup
-
-| Skill | What it does |
-|-------|-------------|
-| **soul-audit** | 6-phase interview generating SOUL.md (agent identity), USER.md (user profile), ACCESS_POLICY.md (4-tier privacy), HEARTBEAT.md (operational cadence). |
-| **setup** | Auto-provision PGLite or Supabase. First import. GStack detection. |
-| **migrate** | Universal migration from Obsidian, Notion, Logseq, markdown, CSV, JSON, Roam. |
-| **briefing** | Daily briefing with meeting context, active deals, and citation tracking. |
-
-### Conventions
-
-Cross-cutting rules in `skills/conventions/`:
- **quality.md** ... citations, back-links, notability gate, source attribution
- **brain-first.md** ... 5-step lookup before any external API call
- **model-routing.md** ... which model for which task
- **test-before-bulk.md** ... test 3-5 items before any batch operation
- **cross-modal.yaml** ... review pairs and refusal routing chain
-
-## How It Works
-
-```
-Signal arrives (meeting, email, tweet, link)
-  -> Signal detector captures ideas + entities (parallel, never blocks)
-  -> Brain-ops: check the brain first (gbrain search, gbrain get)
-  -> Respond with full context
-  -> Write: update brain pages with new information + citations
-  -> Auto-link: typed relationships extracted on every write (zero LLM calls)
-  -> Sync: gbrain indexes changes for next query
-```
-
-Every cycle adds knowledge. The agent enriches a person page after a meeting. Next time that person comes up, the agent already has context. The difference compounds daily.
-
-The system gets smarter on its own. Entity enrichment auto-escalates: a person mentioned once gets a stub page (Tier 3). After 3 mentions across different sources, they get web + social enrichment (Tier 2). After a meeting or 8+ mentions, full pipeline (Tier 1). The brain learns who matters without being told. Deterministic classifiers improve over time via a fail-improve loop that logs every LLM fallback and generates better regex patterns from the failures. `gbrain doctor` shows the trajectory: "intent classifier: 87% deterministic, up from 40% in week 1."
-
-> "Prep me for my meeting with Jordan in 30 minutes"
-> ... pulls dossier, shared history, recent activity, open threads
-
-> "What have I said about the relationship between shame and founder performance?"
-> ... searches YOUR thinking, not the internet
-
-## Minions: your sub-agents won't drop work anymore
-
-A durable, Postgres-native job queue built into the brain. Every long-running agent task is now a job that survives gateway restarts, streams progress, gets paused / resumed / steered mid-flight, and shows up in `gbrain jobs list`. Zero infra beyond your existing brain.
-
-### The production numbers that matter
-
-Here's my personal OpenClaw deployment: one Render container. Supabase Postgres holding a 45,000-page brain. 19 cron jobs firing on schedule. Real gateway load from real daily work. The task: pull a month of my social posts from an external API and ingest them end-to-end into the brain as a structured page.
-
-|              | Minions   | `sessions_spawn`               |
-|---           |---        |---                             |
-| Wall time    | **753ms** | **>10,000ms** (gateway timeout) |
-| Token cost   | **$0.00** | ~$0.03 per run                 |
-| Success rate | **100%**  | **0%** (couldn't even spawn)   |
-| Memory/job   | ~2 MB     | ~80 MB                         |
-
-Under that 19-cron load, sub-agent spawn couldn't clear the 10-second gateway wall. Minions landed it in under a second for zero tokens. **Scaling:** 19,240 posts across 36 months, single bash loop, ~15 min total, $0.00. Sub-agents: ~9 min best case, ~$1.08 in tokens, ~40% spawn failure. **Lab:** durability ∞ (SIGKILL mid-flight, 10/10 rescued), throughput ~10× faster, fan-out ~21× with no failure wall, memory ~400× less.
-
-Full benchmarks live in [gbrain-evals](https://github.com/garrytan/gbrain-evals/tree/main/docs/benchmarks).
-
-### The routing rule
-
-> **Deterministic** (same input → same steps → same output) → **Minions**
-> **Judgment** (input requires assessment or decision) → **Sub-agents**
-
-Pull posts, parse JSON, write a brain page, run a sync — deterministic. $0 tokens, survives restart, millisecond runtime. Triage the inbox, assess meeting priority, decide if a cold email deserves a reply — judgment. What sub-agents are actually good at. `minion_mode: pain_triggered` (the default) automates the routing.
-
-### What's fixed
-
-The six daily pains — spawn storms, agents that stop responding, forgotten dispatches, gateway crashes mid-run, runaway grandchildren, debugging soup — all belonged to the "deterministic work through a reasoning model" mistake. Minions fixes them by not making that mistake: `max_children` cap, `timeout_ms` + AbortSignal, `child_done` inbox, full `parent_job_id`/`depth`/transcript per job, Postgres durability with stall detection, cascade cancel via recursive CTE. Plus idempotency keys, attachment validation, `removeOnComplete`, and `gbrain jobs smoke` that proves the install in half a second.
-
-```bash
-gbrain jobs smoke                        # verify install
-gbrain jobs submit sync --params '{}'    # fire a background job
-gbrain jobs stats                        # health dashboard
-gbrain jobs supervisor --concurrency 4   # canonical: auto-restarting worker (Postgres only)
-gbrain jobs work --concurrency 4         # raw worker (no crash recovery — prefer `supervisor`)
-```
-
-`gbrain jobs supervisor` keeps the worker alive across crashes with exponential backoff, atomic PID locking, structured audit events at `~/.gbrain/audit/supervisor-*.jsonl`, and a `start --detach` / `status --json` / `stop` subcommand surface for agents. In containers it runs as PID 1; on systemd hosts it's the child of `gbrain-worker.service`. Full deployment guide: [`docs/guides/minions-deployment.md`](docs/guides/minions-deployment.md).
-
-Read [`skills/minion-orchestrator/SKILL.md`](skills/minion-orchestrator/SKILL.md) for parent-child DAGs, fan-in collection, steering via inbox.
-
-**Minions is not incrementally better than sub-agents for background work. It's categorically different.** 753ms vs gateway timeout. $0 vs tokens. 100% vs couldn't-spawn. If your agent does deterministic work on a schedule, it runs on Minions now.
-
-### Health check and self-heal
-
-Minions is canonical as of v0.11.1 — every `gbrain upgrade` runs the migration automatically (schema → smoke → prefs → host rewrites → env-aware autopilot install). If you ever want to verify manually or wire a cron into your morning briefing:
-
-```bash
-gbrain doctor                    # half-migrated state? prints loud banner + exits non-zero
-gbrain skillpack-check --quiet    # exit 0/1/2 for pipeline gating
-gbrain skillpack-check | jq       # full JSON: {healthy, summary, actions[], doctor, migrations}
-```
-
-If anything's off, `actions[]` tells you the exact command to run. For deeper troubleshooting: [`docs/guides/minions-fix.md`](docs/guides/minions-fix.md).
-
-Moving gateway crons to Minions (deterministic scripts, zero LLM tokens per fire): [`docs/guides/minions-shell-jobs.md`](docs/guides/minions-shell-jobs.md).
-
-## Durable agents: `gbrain agent` (v0.15)
-
-Your subagent runs survive crashes now. OpenClaw died mid-run? The worker re-claims on restart and replays from the last committed turn. Fan-out across 50 shards, one shard crashes — the aggregator still claims after every child reaches a terminal state and writes a mixed-outcome summary. Tool calls persist as a two-phase ledger (`pending` → `complete | failed`) so replay is safe by construction, not by hope.
-
-```bash
-# Submit a single-subagent run
-gbrain agent run "summarize my last 10 journal pages"
-
-# Fan out N prompts across N subagent children + 1 aggregator
-gbrain agent run "analyze every page" \
-  --fanout-manifest manifests/pages.json \
-  --subagent-def analyzer
-
-# Tail a running job (heartbeat per turn + full transcript on completion)
-gbrain agent logs 1247 --follow --since 5m
-```
-
-Durability is the point: every Anthropic turn commits to `subagent_messages`, every tool call to `subagent_tool_executions`. Worker kills, OpenClaw crashes, timeouts — all resumable. Host repos (your OpenClaw, etc.) ship their own subagent definitions via `GBRAIN_PLUGIN_PATH` + a `gbrain.plugin.json` manifest: see [`docs/guides/plugin-authors.md`](docs/guides/plugin-authors.md). Requires `ANTHROPIC_API_KEY` on the worker.
-
-## Skillify: say "skillify it!" and the bug becomes structurally impossible to repeat
-
-Your OpenClaw hit a new failure. You fix it once in conversation. You say "skillify it!"
-And now the fix is permanent: a SKILL.md with triggers, a deterministic script with tests, a
-routing fixture the agent re-evaluates daily, a filing audit that keeps the output from
-drifting. Ten items. Every one required. The bug can't recur.
-
-Hermes and similar agent frameworks auto-create skills as a background behavior. Fine until
-you don't know what the agent shipped. Checklists decay. Tests drift. Resolver entries get
-stale. Six months later it's an opaque pile nobody has read, nobody has tested, and nobody
-is sure still works. GBrain ships the same capability except the human stays in the loop
-and every step is a command you can run.
-
-### The four verbs you need (v0.19)
-
-```bash
-# 1. Scaffold all 5 stub files for a new skill in one shot.
-gbrain skillify scaffold webhook-verify \
-  --description "verify ngrok webhooks" \
-  --triggers "verify the webhook,check tunnel" \
-  --writes-pages --writes-to people/,companies/
-
-# 2. Replace the SKILLIFY_STUB sentinels with real logic + real tests.
-$EDITOR skills/webhook-verify/scripts/webhook-verify.mjs
-$EDITOR test/webhook-verify.test.ts
-
-# 3. Run the 10-item audit: SKILL.md exists, script exists, unit + E2E tests,
-#    LLM evals, resolver entry, trigger eval, check-resolvable gate, brain filing.
-gbrain skillify check skills/webhook-verify/scripts/webhook-verify.mjs
-
-# 4. Verify the whole tree: reachability, MECE overlap, DRY, routing gaps,
-#    filing audit, SKILLIFY_STUB sentinels (fails if any skill still has one).
-gbrain check-resolvable              # warnings advisory, errors block
-gbrain check-resolvable --strict     # warnings block too (CI opt-in)
-```
-
-Idempotent re-runs. `--force` regenerates stub files but NEVER duplicates a resolver row.
-Scaffold completes in under 2 seconds. The real work (your rule, your script, your tests)
-is what you spend time on. Everything else is boilerplate the CLI writes for you.
-
-### `gbrain routing-eval` — catch the routing gaps your users actually hit
-
-Drop a `routing-eval.jsonl` fixture next to any skill. Each line is `{intent, expected_skill,
-ambiguous_with?}`. `gbrain check-resolvable` runs the structural layer by default; `gbrain
-routing-eval` runs the same structural layer as a dedicated CI verb. The `--llm` flag is
-accepted as a placeholder for a future LLM tie-break layer; in this release it emits a stderr
-notice and runs structural only. False positives (wrong skill matched), missed routes (no
-skill matched), and tautological fixtures (intent copies trigger verbatim) all surface as
-specific advisories with the exact file:line to fix.
-
-### Works on your OpenClaw, not just gbrain's repo
-
-v0.19 teaches `gbrain check-resolvable` to accept `AGENTS.md` as a resolver file alongside
-`RESOLVER.md`, at either the skills directory OR one level up (OpenClaw-native workspace-root
-layout). The skill manifest auto-derives from walking `skills/*/SKILL.md` when `manifest.json`
-is missing. Set `OPENCLAW_WORKSPACE=~/your-openclaw/workspace` and everything just works:
-
-```bash
-export OPENCLAW_WORKSPACE=~/your-openclaw/workspace
-gbrain check-resolvable --verbose
-# Auto-detects: AGENTS.md at workspace root, 107 skills derived from SKILL.md walk,
-# 15 unreachable errors surfaced, 108 advisory warnings for overlaps and gaps.
-```
-
-First run on a real OpenClaw deployment found 15 unreachable skills out of 102 — about 15%
-of the tree was dark. The essay's "skills the agent can never reach" footgun, now visible.
-
-### `gbrain skillpack install` — drop 25 curated skills into your OpenClaw
-
-The skills gbrain ships are a curated bundle. Install them into your workspace with
-dependency closure (shared conventions come along), per-file diff protection (your local
-edits are never clobbered without `--overwrite-local`), a file lock that serializes
-concurrent installers, and an atomic managed-block update to your AGENTS.md so you can
-see exactly what gbrain wrote.
-
-```bash
-gbrain skillpack list                          # 25 curated skills
-gbrain skillpack install brain-ops             # one skill + its shared conventions
-gbrain skillpack install --all                 # the full bundle
-gbrain skillpack install brain-ops --dry-run   # preview; no writes
-gbrain skillpack diff brain-ops                # compare bundle vs your local copy
-```
-
-Re-running is safe. The managed-block markers in your AGENTS.md let `skillpack install`
-accumulate rows across separate single-skill installs instead of overwriting each other.
-A receipt comment inside the fence (`<!-- gbrain:skillpack:manifest cumulative-slugs="..." -->`)
-tracks what gbrain has installed across runs. `install --all` is the only path that prunes;
-per-skill install never deletes what it didn't install. If you hand-add a row inside the fence,
-gbrain preserves it on reinstall and emits a stderr notice telling your agent to investigate.
-
-**Skillify is the piece that makes the skills tree survive six months of compounding work.**
-Read [`skills/skillify/SKILL.md`](skills/skillify/SKILL.md) for the full 10-item checklist
-and the anti-patterns it catches.
-
-## Storage tiering: keep bulk content out of git (v0.22.11)
-
-When your brain crosses 100K files and bulk machine-generated content (tweets, articles, transcripts)
-becomes the size driver, declare which directories belong in git and which live in the database only.
-
-```yaml
-# gbrain.yml at the brain repo root
-storage:
-  db_tracked:
-    - people/
-    - companies/
-    - deals/
-  db_only:
-    - media/x/
-    - media/articles/
-    - meetings/transcripts/
-```
-
-`gbrain sync` auto-manages your `.gitignore` for `db_only` paths. `gbrain export --restore-only --repo .`
-repopulates missing files from the database (container restart, fresh clone, accidental rm).
-`gbrain storage status` shows the tier breakdown.
-
-Full guide: [docs/storage-tiering.md](docs/storage-tiering.md).
-
-## Getting Data In
-
-GBrain ships integration recipes that your agent sets up for you. Each recipe tells the agent what credentials to ask for, how to validate, and what cron to register.
-
-| Recipe | Requires | What It Does |
-|--------|----------|-------------|
-| [Public Tunnel](recipes/ngrok-tunnel.md) | — | Fixed URL for MCP + voice (ngrok Hobby $8/mo) |
-| [Credential Gateway](recipes/credential-gateway.md) | — | Gmail + Calendar access |
-| [Voice-to-Brain](recipes/twilio-voice-brain.md) | ngrok-tunnel | Phone calls to brain pages (Twilio + OpenAI Realtime) |
-| [Email-to-Brain](recipes/email-to-brain.md) | credential-gateway | Gmail to entity pages |
-| [X-to-Brain](recipes/x-to-brain.md) | — | Twitter timeline + mentions + deletions |
-| [Calendar-to-Brain](recipes/calendar-to-brain.md) | credential-gateway | Google Calendar to searchable daily pages |
-| [Meeting Sync](recipes/meeting-sync.md) | — | Circleback transcripts to brain pages with attendees |
-| [Restart Sweep](recipes/restart-sweep.md) | OpenClaw + Telegram | Detect dropped Telegram messages after OpenClaw gateway restarts |
-
-**Data research recipes** extract structured data from email into tracked brain pages. Built-in recipes for investor updates (MRR, ARR, runway, headcount), expense tracking, and company metrics. Create your own with `gbrain research init`.
-
-Run `gbrain integrations` to see status.
-
-## GBrain + GStack
-
-[GStack](https://github.com/garrytan/gstack) is the engine. GBrain is the mod.
-
- **[GStack](https://github.com/garrytan/gstack)** = coding skills (ship, review, QA, investigate, office-hours, retro). 70,000+ stars, 30,000 developers per day. When your agent codes on itself, it uses GStack.
- **GBrain** = everything-else skills (brain ops, signal detection, ingestion, enrichment, cron, reports, identity). When your agent remembers, thinks, and operates, it uses GBrain.
- **`hosts/gbrain.ts`** = the bridge. Tells GStack's coding skills to check the brain before coding.
-
-`gbrain init` detects if GStack is installed and reports mod status. If GStack isn't there, it tells you how to get it.
-
-## Architecture
-
-```
-┌──────────────────┐    ┌───────────────┐    ┌──────────────────┐
-│   Brain Repo     │    │    GBrain     │    │    AI Agent      │
-│   (git)          │    │  (retrieval)  │    │  (read/write)    │
-│                  │    │               │    │                  │
-│  markdown files  │───>│  Postgres +   │<──>│  29 skills       │
-│  = source of     │    │  pgvector     │    │  define HOW to   │
-│    truth         │    │               │    │  use the brain   │
-│                  │<───│  hybrid       │    │                  │
-│  human can       │    │  search       │    │  RESOLVER.md     │
-│  always read     │    │  (vector +    │    │  routes intent   │
-│  & edit          │    │   keyword +   │    │  to skill        │
-│                  │    │   RRF)        │    │                  │
-└──────────────────┘    └───────────────┘    └──────────────────┘
-```
-
-The repo is the system of record. GBrain is the retrieval layer. The agent reads and writes through both. Human always wins... edit any markdown file and `gbrain sync` picks up the changes.
-
-For multi-machine setups (cross-machine thin client) and multi-worktree setups (per-worktree code engine + shared remote artifacts), see [`docs/architecture/topologies.md`](docs/architecture/topologies.md).
-
-## The Knowledge Model
-
-Every page follows the compiled truth + timeline pattern:
-
-```markdown
---
-type: concept
-title: Do Things That Don't Scale
-tags: [startups, growth, pg-essay]
---
-
-Paul Graham's argument that startups should do unscalable things early on.
-The key insight: the unscalable effort teaches you what users actually
-want, which you can't learn any other way.
-
---
-
- 2013-07-01: Published on paulgraham.com
- 2024-11-15: Referenced in batch W25 kickoff talk
-```
-
-Above the `---`: **compiled truth**. Your current best understanding. Gets rewritten when new evidence changes the picture. Below: **timeline**. Append-only evidence trail. Never edited, only added to.
-
-## Knowledge Graph
-
-Pages aren't just text. Every mention of a person, company, or concept becomes a typed link in a structured graph. The brain wires itself.
-
-```
-Write a meeting page mentioning Alice and Acme AI
-  -> Auto-link extracts entity refs from content (zero LLM calls)
-  -> Infers types: meeting page + person ref => `attended`
-                   "CEO of X" pattern        => `works_at`
-                   "invested in"             => `invested_in`
-                   "advises", "advisor"      => `advises`
-                   "founded", "co-founded"   => `founded`
-  -> Reconciles stale links: edits remove links no longer in content
-  -> Backlinks rank well-connected entities higher in search
-```
-
-```bash
-gbrain graph-query people/alice --type attended --depth 2
-# returns who Alice met with, transitively
-```
-
-The graph powers questions vector search can't: "who works at Acme AI?", "what has Bob invested in?", "find the connection between Alice and Carol". Backfill an existing brain in one command:
-
-```bash
-gbrain extract links --source db        # wire up the existing 29K pages
-gbrain extract timeline --source db     # extract dated events from markdown timelines
-```
-
-Then ask graph questions or watch the search ranking improve. Benchmarked side-by-side against ripgrep-BM25, vector-only RAG (same embedder), and gbrain-with-graph-disabled: gbrain lands **P@5 49.1%, R@5 97.9%** on a 240-page Opus-generated rich-prose corpus, beating hybrid-nograph by **+31.4 points P@5**. Isolate the contribution: v0.11→v0.12 moved the same gbrain codebase from P@5 22.1% → 49.1% on identical inputs, so typed-link extract quality is load-bearing. Full scorecards + reproducible corpus: [gbrain-evals](https://github.com/garrytan/gbrain-evals).
-
-## Search
-
-Hybrid search: vector + keyword + RRF fusion + multi-query expansion + 4-layer dedup.
-
-```
-Query
-  -> Intent classifier (entity? temporal? event? general?)
-  -> Multi-query expansion (Claude Haiku)
-  -> Vector search (HNSW cosine) + Keyword search (tsvector)
-  -> RRF fusion: score = sum(1/(60 + rank))
-  -> Cosine re-scoring + compiled truth boost
-  -> 4-layer dedup + compiled truth guarantee
-  -> Results
-```
-
-Keyword alone misses conceptual matches. Vector alone misses exact phrases. RRF gets both. Search quality is benchmarked and reproducible: `gbrain eval --qrels queries.json` measures P@k, Recall@k, MRR, and nDCG@k. A/B test config changes before deploying them.
-
-## Why it works: many strategies in concert
-
-The brain isn't one trick. Every retrieval question goes through ~20 deterministic
-techniques layered together. No single one is magic; the win comes from stacking
-them so each layer covers what the others miss.
-
-```
-Question
-  │
-  ├─ INGESTION (every put_page)
-  │    ├─ Recursive markdown chunking (or semantic / LLM-guided)
-  │    ├─ Embedding cache invalidation on edit
-  │    └─ Idempotent imports (content-hash dedup)
-  │
-  ├─ GRAPH EXTRACTION (auto-link post-hook, zero LLM)
-  │    ├─ Entity-ref regex (markdown links + bare slugs)
-  │    ├─ Code-fence stripping (no false-positive slugs in code blocks)
-  │    ├─ Typed inference cascade (FOUNDED → INVESTED → ADVISES → WORKS_AT)
-  │    ├─ Page-role priors (partner-bio language → invested_in)
-  │    ├─ Within-page dedup (same target collapses to one link)
-  │    ├─ Stale-link reconciliation (edits remove dropped refs)
-  │    └─ Multi-type link constraint (same person can works_at AND advises)
-  │
-  ├─ SEARCH PIPELINE (every query)
-  │    ├─ Intent classifier (entity / temporal / event / general — auto-routes)
-  │    ├─ Multi-query expansion (Haiku rephrases the question 3 ways)
-  │    ├─ Vector search (HNSW cosine over OpenAI embeddings)
-  │    ├─ Keyword search (Postgres tsvector + websearch_to_tsquery)
-  │    ├─ Source-aware ranking (curated dirs outrank chat/daily swamp at SQL layer)
-  │    ├─ Hard-exclude (test/ archive/ attachments/ .raw/ filtered before retrieval)
-  │    ├─ Reciprocal Rank Fusion (score = sum 1/(60+rank) across both)
-  │    ├─ Cosine re-scoring (re-rank chunks against actual query embedding)
-  │    ├─ Compiled-truth boost (assessments outrank timeline noise)
-  │    ├─ Backlink boost (well-connected entities rank higher)
-  │    └─ Source-aware dedup (one CT chunk per page guaranteed)
-  │
-  ├─ GRAPH TRAVERSAL (relational queries)
-  │    ├─ Recursive CTE with cycle prevention (visited-array check)
-  │    ├─ Type-filtered edges (--type works_at, attended, etc.)
-  │    ├─ Direction control (in / out / both)
-  │    └─ Depth-capped (≤10 for remote MCP; DoS prevention)
-  │
-  └─ AGENT WORKFLOW (graph-confident hybrid)
-       ├─ Graph-query first (high-precision typed answers)
-       ├─ Grep fallback when graph returns nothing
-       └─ Graph hits ranked first in top-K (better P@K and R@K)
-```
-
-End-to-end on the BrainBench v1 corpus (240 rich-prose pages, before/after PR #188):
-
-| Metric                  | BEFORE PR #188 | AFTER PR #188 | Δ           |
-|-------------------------|----------------|---------------|-------------|
-| **Precision@5**         | 39.2%          | **44.7%**     | **+5.4 pts**|
-| **Recall@5**            | 83.1%          | **94.6%**     | **+11.5 pts**|
-| Correct in top-5        | 217            | 247           | **+30**     |
-| Graph-only F1 (ablation)| 57.8% (grep)   | **86.6%**     | **+28.8 pts**|
-
-Plus 5 orthogonal capability checks (identity resolution, temporal queries,
-performance at 10K-page scale, robustness to malformed input, MCP operation
-contract). All pass. Full report: [gbrain-evals](https://github.com/garrytan/gbrain-evals).
-
-The point: each technique handles a class of inputs the others miss. Vector
-search misses exact slug refs; keyword catches them. Keyword misses conceptual
-matches; vector catches them. RRF picks the best of both. Compiled-truth boost
-keeps assessments above timeline noise. Auto-link extraction wires the graph
-that lets backlink boost rank well-connected entities higher. Graph traversal
-answers questions search alone can't reach. The agent picks graph-first for
-precision and falls back to keyword for recall. **All deterministic, all in
-concert, all measured.**
-
-## Voice
-
-Call a phone number. Your AI answers. It knows who's calling, pulls their full context from the brain, and responds like someone who actually knows your world. When the call ends, a brain page appears with the transcript, entity detection, and cross-references.
-
-<p align="center">
-  <img src="docs/images/voice-client.png" alt="Voice client connected" width="300" />
-</p>
-
-> [See it in action](https://x.com/garrytan/status/2043022208512172263)
-
-The voice recipe ships with GBrain: [Voice-to-Brain](recipes/twilio-voice-brain.md). WebRTC works in a browser tab with zero setup. A real phone number is optional.
-
-## Engine Architecture
-
-```
-CLI / MCP Server
-     (thin wrappers, identical operations)
-              |
-      BrainEngine interface (pluggable)
-              |
-     +--------+--------+
-     |                  |
-PGLiteEngine       PostgresEngine
-  (default)          (Supabase)
-     |                  |
-~/.gbrain/           Supabase Pro ($25/mo)
-brain.pglite         Postgres + pgvector
-embedded PG 17.5
-
-     gbrain migrate --to supabase|pglite
-         (bidirectional migration)
-```
-
-PGLite: embedded Postgres, no server, zero config. When your brain outgrows local (1000+ files, multi-device), `gbrain migrate --to supabase` moves everything.
-
-## File Storage
-
-Brain repos accumulate binaries. GBrain has a three-stage migration:
-
-```bash
-gbrain files mirror <dir>       # copy to cloud, local untouched
-gbrain files redirect <dir>     # replace local with .redirect pointers
-gbrain files clean <dir>        # remove pointers, cloud only
-gbrain files restore <dir>      # download everything back (undo)
-```
-
-Storage backends: S3-compatible (AWS, R2, MinIO), Supabase Storage, or local.
-
-## Commands
-
-```
-SETUP
-  gbrain init [--supabase|--url]        Create brain (PGLite default)
-  gbrain migrate --to supabase|pglite   Bidirectional engine migration
-  gbrain upgrade                        Self-update with feature discovery
-
-PAGES
-  gbrain get <slug>                     Read a page (fuzzy slug matching)
-  gbrain put <slug> [< file.md]         Write/update (auto-versions)
-  gbrain delete <slug>                  Delete a page
-  gbrain list [--type T] [--tag T]      List with filters
-
-SEARCH
-  gbrain search <query>                 Keyword search (tsvector)
-  gbrain query <question>              Hybrid search (vector + keyword + RRF)
-
-IMPORT
-  gbrain import <dir> [--no-embed] [--workers N]
-                                        Import markdown (idempotent)
-  gbrain sync [--repo <path>] [--workers N]
-                                        Git-to-brain incremental sync
-                                        (>100-file diffs auto-parallelize 4 workers on Postgres)
-  gbrain export [--dir ./out/]          Export to markdown
-
-FILES
-  gbrain files list|upload|sync|verify  File storage operations
-
-EMBEDDINGS
-  gbrain embed [<slug>|--all|--stale]   Generate/refresh embeddings
-
-LINKS + GRAPH
-  gbrain link|unlink|backlinks          Cross-reference management
-  gbrain extract links|timeline|all     Batch backfill from existing pages
-                                        (--source db|fs, --type, --since, --dry-run)
-  gbrain graph-query <slug>             Typed traversal (--type T --depth N
-                                        --direction in|out|both)
-
-JOBS (Minions)
-  gbrain jobs submit <name> [--params JSON] [--follow]  Submit a background job
-  gbrain jobs list [--status S] [--queue Q]             List jobs with filters
-  gbrain jobs get|cancel|retry|delete <id>              Manage job lifecycle
-  gbrain jobs prune [--older-than 30d]                  Clean completed/dead jobs
-  gbrain jobs stats                                     Job health dashboard
-  gbrain jobs smoke                                     One-command health check
-  gbrain jobs work [--queue Q] [--concurrency N]        Start worker daemon
-
-SKILLS (v0.19)
-  gbrain skillify scaffold <name>       Create 5 stub files + idempotent resolver row
-  gbrain skillify check [path]          10-item audit of a skill
-  gbrain skillpack list                 Print the 25 curated skills in the bundle
-  gbrain skillpack install <name>       Copy one skill + its shared conventions into target
-  gbrain skillpack install --all        Install the full curated bundle
-  gbrain skillpack diff <name>          Per-file diff: bundle vs target workspace
-  gbrain check-resolvable [--strict]    Resolver audit (reachability, MECE, DRY, routing, filing,
-                                        SKILLIFY_STUB). Accepts RESOLVER.md OR AGENTS.md.
-  gbrain routing-eval [--llm] [--json]  Intent→skill routing accuracy on fixtures
-
-EVAL
-  gbrain eval --qrels <path>            Legacy IR-eval (P@k, R@k, MRR, nDCG@k against ground truth)
-  gbrain eval export [--since DUR]      Stream captured eval_candidates as NDJSON (BrainBench-Real)
-  gbrain eval prune --older-than DUR    Retention cleanup for eval_candidates (requires window)
-  gbrain eval replay --against FILE     Replay captured queries vs current build (Jaccard@k, top-1, latency Δ)
-  gbrain eval longmemeval <dataset>     Run public LongMemEval against gbrain hybrid retrieval (v0.28.8)
-                                        [--limit N] [--retrieval-only] [--keyword-only] [--expansion]
-                                        [--top-k K] [--model M] [--output FILE]
-
-ADMIN
-  gbrain doctor [--json] [--fast]       Health checks (resolver, skills, DB, embeddings)
-  gbrain doctor --fix [--dry-run]       Auto-fix DRY violations (delegate inlined rules to conventions)
-  gbrain doctor --locks                 List idle-in-tx backends (57014 diagnostic, Postgres only)
-  gbrain stats                          Brain statistics
-  gbrain models                         Show live model routing (tier defaults,
-                                        per-task overrides, alias map, source-of-truth).
-                                        v0.31.12: tier system + recipe-models merge.
-                                        Power-user override:
-                                          gbrain config set models.default opus
-                                          gbrain config set models.tier.deep opus
-  gbrain models doctor                  1-token reachability probe for each configured
-                                        chat/expansion model + a zero-token embedding_config
-                                        probe (catches Voyage flexible-dim misconfigs before
-                                        first embed). Catches `model_not_found` before the
-                                        next agent run silently degrades.
-                                        [--skip=<provider>] [--json]
-  gbrain serve                          MCP server (stdio)
-  gbrain serve --http [--port 3131]     HTTP MCP server with OAuth 2.1 + admin dashboard
-                                        [--bind HOST] (v0.34: default 127.0.0.1; pass
-                                        --bind 0.0.0.0 for LAN/remote access)
-                                        [--token-ttl 3600] [--enable-dcr]
-                                        [--public-url URL] [--log-full-params]
-  gbrain auth create|list|revoke|test   Legacy bearer token management
-  gbrain auth register-client <name>    Register an OAuth 2.1 client
-        --grant-types client_credentials,authorization_code
-        --scopes "read write admin"
-        --source <id>                   v0.34: write authority for source-scoped clients
-        --federated-read <S1,S2,...>    v0.34: read scope across multiple sources
-  gbrain auth revoke-client <client_id> Revoke an OAuth 2.1 client (cascade purges
-                                        active tokens + auth codes via FK CASCADE)
-  # OAuth 2.1 clients can also be registered from the /admin dashboard or
-  # programmatically via oauthProvider.registerClientManual() for host-repo wrappers.
-  gbrain integrations                   Integration recipe dashboard
-  gbrain sources list|add|remove|...    Multi-source brain management (v0.18)
-                                        v0.28.2: --url <https://...> registers a federated
-                                        remote git repo; clone is auto-managed under
-                                        $GBRAIN_HOME/clones/<id>/ and re-cloned on sync if
-                                        it goes missing. Also exposed via MCP for remote
-                                        agent setup (whoami + sources_{add,list,remove,status}).
-  gbrain dream [--dry-run] [--phase N]  11-phase maintenance cycle (lint→backlinks→sync→synthesize
-                                        →extract→patterns→recompute_emotional_weight→consolidate
-                                        →embed→orphans→purge). v0.23 added synthesize + patterns.
-                                        v0.29 added emotional-weight recompute. v0.30.2: synthesize
-                                        chunks fat transcripts. v0.31: consolidate promotes hot facts
-                                        into takes overnight.
-  gbrain dream --input <file>           Ad-hoc transcript synthesis (implies --phase synthesize)
-  gbrain dream --date YYYY-MM-DD        Synthesize a single day; --from/--to for backfill ranges
-
-  # v0.31 Hot Memory: cross-session facts queryable in real time.
-  gbrain recall <entity>                List active facts for an entity (newest first)
-  gbrain recall --since "1h ago"        Recency-filtered recall
-  gbrain recall --session <id>          Facts captured in a session id
-  gbrain recall --today                 Markdown render with kind icons (📅🎯🤝💭📌)
-  gbrain recall --supersessions         Audit log of auto-overwritten facts
-  gbrain recall --grep <text>           Substring filter (case-insensitive)
-  gbrain recall --as-context            Prompt-injection-ready markdown for headless agents
-  gbrain recall --json                  Structured output with effective_confidence per row
-  gbrain forget <fact-id>               Expire a fact (soft delete; never hard-DELETE)
-
-  gbrain check-backlinks check|fix      Back-link enforcement
-  gbrain lint [--fix]                   LLM artifact detection
-  gbrain repair-jsonb [--dry-run]       Repair v0.12.0 double-encoded JSONB (Postgres)
-  gbrain orphans [--json] [--count]     Find pages with zero inbound wikilinks
-  gbrain transcribe <audio>             Transcribe audio (Groq Whisper)
-  gbrain research init <name>           Scaffold a data-research recipe
-  gbrain research list                  Show available recipes
-```
-
-Run `gbrain --help` for the full reference.
-
-## Origin Story
-
-I was setting up my [OpenClaw](https://openclaw.ai) agent and started a markdown brain repo. One page per person, one page per company, compiled truth on top, timeline on the bottom. Within a week: 10,000+ files, 3,000+ people, 13 years of calendar data, 280+ meeting transcripts, 300+ captured ideas.
-
-The agent runs while I sleep. The dream cycle scans every conversation, enriches missing entities, fixes broken citations, consolidates memory. I wake up and the brain is smarter than when I went to sleep.
-
-The skills in this repo are those patterns, generalized. What took 11 days to build by hand ships as a mod you install in 30 minutes.
-
-## Docs
-
-**For agents:**
- **[skills/RESOLVER.md](skills/RESOLVER.md)** ... Start here. The skill dispatcher.
- [Individual skill files](skills/) ... 28 standalone instruction sets (25 ship in the curated `gbrain skillpack install` bundle)
- [GBRAIN_SKILLPACK.md](docs/GBRAIN_SKILLPACK.md) ... Legacy reference architecture
- [Getting Data In](docs/integrations/README.md) ... Integration recipes and data flow
- [GBRAIN_VERIFY.md](docs/GBRAIN_VERIFY.md) ... Installation verification
-
-**For humans:**
- [GBRAIN_RECOMMENDED_SCHEMA.md](docs/GBRAIN_RECOMMENDED_SCHEMA.md) ... Brain repo directory structure
- [Thin Harness, Fat Skills](docs/ethos/THIN_HARNESS_FAT_SKILLS.md) ... Architecture philosophy
- [ENGINES.md](docs/ENGINES.md) ... Pluggable engine interface
-
-**Reference:**
- [GBRAIN_V0.md](docs/GBRAIN_V0.md) ... Full product spec
- [CHANGELOG.md](CHANGELOG.md) ... Version history
-
-**Benchmarks:**
- [gbrain-evals](https://github.com/garrytan/gbrain-evals) ... BrainBench, the sibling repo that holds the eval harness, corpus, scorecards, and 4-adapter comparisons. Depends on gbrain; not installed alongside gbrain.
-
-## Contributing
-
-See [CONTRIBUTING.md](CONTRIBUTING.md). Run `bun run test` for the parallel unit-test fast loop (~85s on a Mac dev box, 3700+ tests) or `bun run verify` for the pre-push gate (privacy + jsonb + progress + test-isolation + wasm + admin-build + typecheck). For the full local CI gate (gitleaks + unit + all 29 E2E files in Docker, the same checks GH Actions runs), use `bun run ci:local` ... or `bun run ci:local:diff` for the diff-aware subset during fast iteration.
-
-If you're working on retrieval or any of the search/embedding/ranking surface, set `GBRAIN_CONTRIBUTOR_MODE=1` in your shell rc and use `gbrain eval replay` to gate your changes against a snapshot of real captured queries — the dev loop is documented in [`docs/eval-bench.md`](docs/eval-bench.md). Capture is **off by default** for production users (no surprise data accumulation); the env var is the contributor opt-in.
-
-PRs welcome for: new enrichment APIs, performance optimizations, additional engine backends, new skills following the conformance standard in `skills/skill-creator/SKILL.md`.
-
-## License
-
-MIT
@@ -1,191 +0,0 @@
-# Security
-
-## Reporting Vulnerabilities
-
-If you discover a security issue in GBrain, please report it privately by opening
-a [private security advisory](https://github.com/garrytan/gbrain/security/advisories/new)
-on GitHub.
-
-Do not open a public issue for security vulnerabilities.
-
-## Remote MCP Security
-
-### ⚠️ Do NOT use open OAuth client registration for remote MCP
-
-If you deploy GBrain's MCP server behind an HTTP wrapper with OAuth 2.1
-support, **never allow unauthenticated client registration**. An attacker
-who discovers your server URL can:
-
-1. Register a new OAuth client via `POST /register`
-2. Use `client_credentials` grant to obtain a bearer token
-3. Access all brain data via the MCP tools
-
-### Recommended: `gbrain serve --http`
-
-As of v0.22.7, GBrain ships a built-in HTTP transport that uses the
-existing `access_tokens` table for authentication:
-
-```bash
-# Create a token
-gbrain auth create "my-client"
-
-# Start the HTTP server
-gbrain serve --http --port 8787
-
-# Connect via ngrok, Tailscale, or any tunnel
-ngrok http 8787 --url your-brain.ngrok.app
-```
-
-This is the recommended way to expose GBrain remotely. No OAuth, no
-registration endpoint, no self-service tokens. Tokens are managed
-exclusively via `gbrain auth create/list/revoke`.
-
-### If you must use a custom HTTP wrapper
-
-1. **Require a secret for client registration** — check a header or body
-   parameter before creating new OAuth clients
-2. **Disable `client_credentials` grant** — only allow `authorization_code`
-   with browser-based approval
-3. **Restrict scopes** — never issue tokens with unlimited scope
-4. **Log all token issuance** — alert on unexpected registrations
-5. **Rate-limit registration and token endpoints**
-
-### Token Management
-
-```bash
-gbrain auth create "claude-desktop"   # Create a new token
-gbrain auth list                       # List all tokens
-gbrain auth revoke "claude-desktop"    # Revoke a token
-gbrain auth test <url> --token <tok>   # Smoke-test a remote server
-```
-
-Tokens are stored as SHA-256 hashes in the `access_tokens` table. The
-plaintext token is shown once at creation and never stored.
-
-## `gbrain serve --http` hardening (v0.22.7+)
-
-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
-design and the `access_tokens` / `mcp_request_log` tables don't exist in
-the PGLite schema. Local agents continue to use stdio (`gbrain serve`).
-Running `--http` against a PGLite-backed install fails fast with a clear
-error message at startup.
-
-### CORS
-
-Default-deny: no `Access-Control-Allow-Origin` header is sent unless an
-allowlist is configured. To allow browser-based MCP clients:
-
-```bash
-GBRAIN_HTTP_CORS_ORIGIN=https://claude.ai gbrain serve --http --port 8787
-# Multiple origins: comma-separated
-GBRAIN_HTTP_CORS_ORIGIN=https://claude.ai,https://your.app gbrain serve --http
-```
-
-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.
-
-### Rate limiting
-
-Two buckets, both stored in a bounded LRU map (default 10K keys, evicts
-least-recently-used on overflow, prunes entries older than 2× the
-window):
-
-| Bucket | When it fires | Default | Env var |
-|---|---|---|---|
-| Pre-auth IP | Before the DB lookup, on every `/mcp` request | 30 req / 60s | `GBRAIN_HTTP_RATE_LIMIT_IP` |
-| Post-auth token | After a valid token is resolved | 60 req / 60s | `GBRAIN_HTTP_RATE_LIMIT_TOKEN` |
-| LRU cap | Maximum distinct keys across both buckets | 10000 | `GBRAIN_HTTP_RATE_LIMIT_LRU` |
-
-On exhaustion the server returns `429 Too Many Requests` with a
-`Retry-After` header.
-
-**Caveat for tunneled deployments (ngrok, Tailscale Funnel, Cloudflare
-Tunnel):** all requests share one egress IP, so the pre-auth IP bucket
-becomes effectively shared by all clients on that tunnel. The
-post-auth token-id bucket is the load-bearing limiter for tunnel-fronted
-deployments.
-
-### Reverse-proxy trust
-
-Disabled by default. To honor `X-Forwarded-For` (or `X-Real-IP`) when
-gbrain runs behind a trusted reverse proxy:
-
-```bash
-GBRAIN_HTTP_TRUST_PROXY=1 gbrain serve --http --port 8787
-```
-
-**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.
-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 $remote_addr` does this; Cloudflare and most cloud
-   load balancers handle it automatically.)
-
-If gbrain is reachable directly AND `GBRAIN_HTTP_TRUST_PROXY=1` is set,
-clients can spoof their IP by sending arbitrary `X-Forwarded-For`
-headers, defeating the pre-auth IP rate limit. Without the flag, gbrain
-ignores all forwarded-for headers and uses the socket peer address,
-which is the safe default for direct-exposure deployments.
-
-### Body size cap
-
-Default 1 MiB, stream-counted (chunked transfers without
-`Content-Length` are still capped). Override:
-
-```bash
-GBRAIN_HTTP_MAX_BODY_BYTES=2097152 gbrain serve --http   # 2 MiB
-```
-
-Over-cap requests get `413 Payload Too Large` immediately, before any
-body is materialized in memory.
-
-### Audit log
-
-Every `/mcp` request writes one row to `mcp_request_log`:
-
-```bash
-psql "$DATABASE_URL" -c \
-  "SELECT created_at, token_name, operation, status, latency_ms
-   FROM mcp_request_log
-   ORDER BY created_at DESC LIMIT 100"
-```
-
-`status` is one of: `success`, `error`, `auth_failed`, `rate_limited`,
-`body_too_large`, `parse_error`, `unknown_method`. Failed-auth rows have
-`token_name = NULL`. Inserts are fire-and-forget so audit failures
-never block requests.
-
-**v0.26.9 redaction default.** The `params` column now stores
-`{redacted, kind, declared_keys, unknown_key_count, approx_bytes}` instead
-of raw JSON-RPC payloads. Declared keys (intersected against the operation's
-spec) preserve for debug visibility; unknown keys are counted but never
-named so attackers can't probe key existence; byte sizes bucket to 1KB so
-content sizes can't be binary-searched. The same shape is broadcast on the
-admin SSE feed at `/admin/events`. Operators on a personal laptop who want
-raw payloads back can pass `gbrain serve --http --log-full-params` (loud
-stderr warning at startup). Multi-tenant deployments should leave it
-on the redacted default.
@@ -1 +0,0 @@
-0.35.3.1
@@ -1,158 +0,0 @@
-# Design System — GBrain Admin Dashboard
-
-## Product Context
- **What this is:** Admin dashboard for GBrain MCP server — manage OAuth agents, API keys, monitor requests
- **Who it's for:** GBrain operators managing multi-agent access to their brain
- **Space/industry:** Developer infrastructure (peers: Supabase dashboard, Vercel, Railway)
- **Project type:** Dense utilitarian admin panel — Steve Krug "Don't Make Me Think"
-
-## Aesthetic Direction
- **Direction:** Industrial/Utilitarian — function-first, data-dense, zero decoration
- **Decoration level:** None — every pixel earns its place with information
- **Mood:** Ops dashboard for someone who builds. Not a marketing site. Not a consumer app. A cockpit.
- **Reference:** Supabase dashboard (dark + dense), Linear (restrained), Grafana (data-forward)
-
-## Alignment
- **Text alignment:** Left-align everything. No centered text in tables, cards, forms, or labels.
- **Headings:** Left-aligned
- **Table data:** Left-aligned (including numbers — contextual readability over columnar alignment)
- **Form labels:** Left-aligned above inputs
- **Buttons in forms:** Right-aligned (action flows left-to-right: Cancel → Submit)
- **Modal titles:** Left-aligned
- **Page titles:** Left-aligned
- **Only exception:** Empty states and the login page lock icon can center for visual weight
-
-## Typography
- **Display/Headings:** Inter (Semibold 600) — clean, neutral, disappears into the content
- **Body/UI:** Inter (Regular 400 / Medium 500)
- **Data/Tables/Code:** JetBrains Mono (Regular 400 / Medium 500) — monospace for anything the user might copy, any ID, any token, any technical value
- **Loading:** Google Fonts. `display=swap`.
- **Scale:**
-  - Page title: 24px / Inter Semibold
-  - Section title: 14px / Inter Semibold, uppercase, letter-spacing 0.5px
-  - Table header: 12px / Inter Medium, uppercase, letter-spacing 1px, muted color
-  - Body: 14px / Inter Regular
-  - Small/Caption: 13px
-  - Micro: 12px (badges, timestamps)
-  - Code/Data: 13px / JetBrains Mono
-
-## Color
- **Approach:** Monochrome base + semantic color only. No primary brand color. Color means something.
- **Background:**
-  - Base: #0a0a0f (near-black with blue undertone)
-  - Surface/cards: #12121a
-  - Hover: #1a1a2a
-  - Input/code blocks: #0f0f1a
- **Borders:** #1e1e2e (default), #3a3a5a (hover/active)
- **Text:**
-  - Primary: #e0e0e0
-  - Secondary: #888888
-  - Muted: #555555
-  - Link: #88aaff
- **Semantic (badges only):**
-  - Success/active: #34a853
-  - Error/danger: #ff6b6b
-  - Warning: #f5a623
-  - Read scope: #3b82f6
-  - Write scope: #f59e0b
-  - Admin scope: #ef4444
- **No accent color.** The data IS the interface. Badges carry all the color.
-
-## Spacing
- **Base unit:** 4px
- **Density:** Dense — this is an ops tool, not a landing page
- **Scale:** 4px, 8px, 12px, 16px, 20px, 24px, 32px, 48px
- **Table row padding:** 10px 16px
- **Card padding:** 24px
- **Modal padding:** 24px
- **Section gaps:** 24px between sections, 12px between related elements
-
-## Layout
- **Sidebar:** Fixed left, 200px wide, dark (#0a0a0f)
- **Main content:** Fluid, max-width none (fills available space)
- **Grid:** Single column for tables (full width), 2-column for stats cards
- **Border radius:**
-  - Cards/panels: 16px
-  - Buttons/inputs: 8px
-  - Badges: 9999px (pill)
-  - Tables: 0 (sharp edges — data is rectangular)
-
-## Components
-
-### Tables
- Full-width, no outer border
- Header row: uppercase, letter-spaced, muted color, no background
- Data rows: subtle hover (#1a1a2a), pointer cursor when clickable
- All text left-aligned
- Monospace for IDs, tokens, latency values
-
-### Badges
- Pill shape (border-radius: 9999px)
- Padding: 2px 8px
- Font: 12px
- Scoped to semantic meaning: `success`, `danger`, `read`, `write`, `admin`
-
-### Buttons
- Primary: white text on #3a3a5a, hover brightens
- Secondary: muted text on transparent, border #1e1e2e
- Danger: white text on #ff6b6b background
- Size: 13px font, 6px 14px padding
-
-### Modals
- Overlay: rgba(0,0,0,0.7)
- Card: #12121a, border #1e1e2e, border-radius 16px, max-width 480px
- Title: 18px Semibold, left-aligned
- Close: top-right ✕ button
-
-### Drawers
- Right-side panel, 400px wide
- Slide in from right
- Dark overlay behind
- Close button top-right
- Sections separated by section titles (uppercase, muted)
-
-### Tabs
- Inline horizontal, wrapping allowed
- Active: white text, bottom border
- Inactive: muted text, no border
- No background color on tabs
-
-### Code blocks
- Background: rgba(0,0,0,0.3)
- Border-radius: 8px
- Padding: 10px 14px
- Font: JetBrains Mono 12px
- Copy button: right-aligned, subtle
-
-### Empty states
- Centered text (only exception to left-align rule)
- Muted color
- Suggest next action
-
-## Motion
- **Approach:** Minimal — transitions for hover states only
- **Duration:** 150ms for hovers, 200ms for drawer slide
- **No loading spinners** — show stale data until fresh arrives
- **SSE live feed:** Real-time, no animation on new entries (just prepend)
-
-## Anti-Patterns (do NOT do these)
- ❌ Center-aligned table data
- ❌ Center-aligned headings or labels (except empty states)
- ❌ Gradient backgrounds
- ❌ Shadows (the dark theme IS the depth model)
- ❌ Rounded table corners
- ❌ Icons as navigation (use text labels)
- ❌ Loading skeletons (show real data or nothing)
- ❌ Confirmation toasts (action → result is immediate and visible)
- ❌ Color for decoration (every color means something)
-
-## Decisions Log
-| Date | Decision | Rationale |
-|------|----------|-----------|
-| 2026-05-01 | Dark theme only | Ops dashboard. No light mode needed. |
-| 2026-05-01 | Steve Krug lens | Zero happy talk, mindless choices, scannable tables, billboard-speed comprehension. |
-| 2026-05-01 | JetBrains Mono for data | Anything copyable or technical should be monospace. |
-| 2026-05-03 | Left-align everything | Garry preference. Centered text is a design crutch. Left-align forces hierarchy through typography weight and spacing, not position. |
-| 2026-05-03 | Incorporate GStack design DNA | Same family: Inter + JetBrains Mono, dark base, semantic-only color. Diverges on accent (GStack: amber; GBrain: none — data is the color). |
-| 2026-05-03 | Per-client config export tabs | Claude Code, ChatGPT, Claude.ai, Cursor, Perplexity, JSON. Every agent has a copy-paste setup path. |
-| 2026-05-03 | Magic link auth | Login page tells you to ask your agent. No pasting hex strings into forms. |
@@ -1,257 +0,0 @@
-{
-  "lockfileVersion": 1,
-  "configVersion": 1,
-  "workspaces": {
-    "": {
-      "name": "gbrain-admin",
-      "dependencies": {
-        "react": "^19.1.0",
-        "react-dom": "^19.1.0",
-      },
-      "devDependencies": {
-        "@types/react": "^19.1.2",
-        "@types/react-dom": "^19.1.2",
-        "@vitejs/plugin-react": "^4.4.1",
-        "typescript": "^5.8.3",
-        "vite": "^6.3.3",
-      },
-    },
-  },
-  "packages": {
-    "@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=="],
-
-    "@babel/compat-data": ["@babel/compat-data@7.29.0", "", {}, "sha512-T1NCJqT/j9+cn8fvkt7jtwbLBfLC/1y1c7NtCeXFRgzGTsafi68MRv8yzkYSapBnFA6L3U2VSc02ciDzoAJhJg=="],
-
-    "@babel/core": ["@babel/core@7.29.0", "", { "dependencies": { "@babel/code-frame": "^7.29.0", "@babel/generator": "^7.29.0", "@babel/helper-compilation-targets": "^7.28.6", "@babel/helper-module-transforms": "^7.28.6", "@babel/helpers": "^7.28.6", "@babel/parser": "^7.29.0", "@babel/template": "^7.28.6", "@babel/traverse": "^7.29.0", "@babel/types": "^7.29.0", "@jridgewell/remapping": "^2.3.5", "convert-source-map": "^2.0.0", "debug": "^4.1.0", "gensync": "^1.0.0-beta.2", "json5": "^2.2.3", "semver": "^6.3.1" } }, "sha512-CGOfOJqWjg2qW/Mb6zNsDm+u5vFQ8DxXfbM09z69p5Z6+mE1ikP2jUXw+j42Pf1XTYED2Rni5f95npYeuwMDQA=="],
-
-    "@babel/generator": ["@babel/generator@7.29.1", "", { "dependencies": { "@babel/parser": "^7.29.0", "@babel/types": "^7.29.0", "@jridgewell/gen-mapping": "^0.3.12", "@jridgewell/trace-mapping": "^0.3.28", "jsesc": "^3.0.2" } }, "sha512-qsaF+9Qcm2Qv8SRIMMscAvG4O3lJ0F1GuMo5HR/Bp02LopNgnZBC/EkbevHFeGs4ls/oPz9v+Bsmzbkbe+0dUw=="],
-
-    "@babel/helper-compilation-targets": ["@babel/helper-compilation-targets@7.28.6", "", { "dependencies": { "@babel/compat-data": "^7.28.6", "@babel/helper-validator-option": "^7.27.1", "browserslist": "^4.24.0", "lru-cache": "^5.1.1", "semver": "^6.3.1" } }, "sha512-JYtls3hqi15fcx5GaSNL7SCTJ2MNmjrkHXg4FSpOA/grxK8KwyZ5bubHsCq8FXCkua6xhuaaBit+3b7+VZRfcA=="],
-
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-
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-  }
-}
@@ -1,16 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>GBrain Admin</title>
-  <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-CDv6_ml5.js"></script>
-  <link rel="stylesheet" crossorigin href="/admin/assets/index-BOifXQpQ.css">
-</head>
-<body>
-  <div id="root"></div>
-</body>
-</html>
@@ -1,15 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>GBrain Admin</title>
-  <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" />
-</head>
-<body>
-  <div id="root"></div>
-  <script type="module" src="/src/main.tsx"></script>
-</body>
-</html>
@@ -1,21 +0,0 @@
-{
-  "name": "gbrain-admin",
-  "private": true,
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "react": "^19.1.0",
-    "react-dom": "^19.1.0"
-  },
-  "devDependencies": {
-    "@types/react": "^19.1.2",
-    "@types/react-dom": "^19.1.2",
-    "@vitejs/plugin-react": "^4.4.1",
-    "vite": "^6.3.3",
-    "typescript": "^5.8.3"
-  }
-}
@@ -1,84 +0,0 @@
-import React, { useState, useEffect } from 'react';
-import { LoginPage } from './pages/Login';
-import { DashboardPage } from './pages/Dashboard';
-import { AgentsPage } from './pages/Agents';
-import { RequestLogPage } from './pages/RequestLog';
-import { api } from './api';
-
-type Page = 'login' | 'dashboard' | 'agents' | 'log';
-
-function getPage(): Page {
-  const hash = window.location.hash.replace('#', '') || 'dashboard';
-  if (['login', 'dashboard', 'agents', 'log'].includes(hash)) return hash as Page;
-  return 'dashboard';
-}
-
-export function App() {
-  const [page, setPage] = useState<Page>(getPage);
-
-  useEffect(() => {
-    const onHash = () => setPage(getPage());
-    window.addEventListener('hashchange', onHash);
-    return () => window.removeEventListener('hashchange', onHash);
-  }, []);
-
-  const navigate = (p: Page) => {
-    window.location.hash = p;
-    setPage(p);
-  };
-
-  if (page === 'login') {
-    return <LoginPage onLogin={() => navigate('dashboard')} />;
-  }
-
-  const handleSignOutEverywhere = async () => {
-    if (!confirm('Sign out every active admin session, including other browsers and tabs? Each one will need to re-authenticate via a fresh magic link.')) {
-      return;
-    }
-    try {
-      await api.signOutEverywhere();
-    } catch {
-      // Even if the call fails, push to login — cookie is likely already invalid.
-    }
-    navigate('login');
-  };
-
-  return (
-    <div className="app">
-      <nav className="sidebar">
-        <div className="sidebar-logo">GBrain</div>
-        <div className="sidebar-nav">
-          <a className={`nav-item ${page === 'dashboard' ? 'active' : ''}`}
-             onClick={() => navigate('dashboard')}>Dashboard</a>
-          <a className={`nav-item ${page === 'agents' ? 'active' : ''}`}
-             onClick={() => navigate('agents')}>Agents</a>
-          <a className={`nav-item ${page === 'log' ? 'active' : ''}`}
-             onClick={() => navigate('log')}>Request Log</a>
-        </div>
-        <div style={{ marginTop: 'auto', padding: '16px 12px', borderTop: '1px solid var(--border)' }}>
-          <button
-            onClick={handleSignOutEverywhere}
-            style={{
-              background: 'transparent',
-              border: '1px solid var(--border)',
-              color: 'var(--text-secondary)',
-              padding: '6px 10px',
-              borderRadius: 6,
-              fontSize: 12,
-              cursor: 'pointer',
-              width: '100%',
-            }}
-            title="Revoke every active admin session — every browser, every tab"
-          >
-            Sign out everywhere
-          </button>
-        </div>
-      </nav>
-      <main className="main">
-        {page === 'dashboard' && <DashboardPage />}
-        {page === 'agents' && <AgentsPage />}
-        {page === 'log' && <RequestLogPage />}
-      </main>
-    </div>
-  );
-}
@@ -1,37 +0,0 @@
-const BASE = '';
-
-// v0.26.3 trust model (D11 + D12): the admin UI does NOT cache the
-// bootstrap token in browser JS state. On 401, redirect to login —
-// no auto-reauth via saved token, no localStorage/sessionStorage read.
-// The HttpOnly cookie set by /admin/login is the only session credential.
-async function apiFetch(path: string, options?: RequestInit) {
-  const res = await fetch(`${BASE}${path}`, {
-    ...options,
-    credentials: 'same-origin',
-    headers: { 'Content-Type': 'application/json', ...options?.headers },
-  });
-  if (res.status === 401) {
-    // No token cache to retry from. Redirect to login.
-    window.location.hash = '#login';
-    throw new Error('Unauthorized');
-  }
-  if (!res.ok) {
-    const body = await res.json().catch(() => ({}));
-    throw new Error(body.error || `HTTP ${res.status}`);
-  }
-  return res.json();
-}
-
-export const api = {
-  login: (token: string) => apiFetch('/admin/login', { method: 'POST', body: JSON.stringify({ token }) }),
-  signOutEverywhere: () => apiFetch('/admin/api/sign-out-everywhere', { method: 'POST' }),
-  stats: () => apiFetch('/admin/api/stats'),
-  health: () => apiFetch('/admin/api/health-indicators'),
-  agents: () => apiFetch('/admin/api/agents'),
-  requests: (page = 1, qs = '') => apiFetch(`/admin/api/requests?page=${page}${qs}`),
-  apiKeys: () => apiFetch('/admin/api/api-keys'),
-  createApiKey: (name: string) => apiFetch('/admin/api/api-keys', { method: 'POST', body: JSON.stringify({ name }) }),
-  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 }) }),
-};
@@ -1,356 +0,0 @@
-:root {
-  --bg-primary: #0a0a0f;
-  --bg-secondary: #14141f;
-  --bg-tertiary: #1e1e2e;
-  --text-primary: #e0e0e0;
-  --text-secondary: #888;
-  --text-muted: #555;
-  --accent: #3b82f6;
-  --success: #22c55e;
-  --warning: #f59e0b;
-  --error: #ef4444;
-  --font-mono: 'JetBrains Mono', monospace;
-  --font-sans: 'Inter', system-ui, sans-serif;
-}
-
-* { margin: 0; padding: 0; box-sizing: border-box; }
-
-body {
-  font-family: var(--font-sans);
-  background: var(--bg-primary);
-  color: var(--text-primary);
-  font-size: 14px;
-  line-height: 1.5;
-}
-
-/* Layout */
-.app { display: flex; min-height: 100vh; }
-
-.sidebar {
-  width: 200px;
-  background: var(--bg-secondary);
-  border-right: 1px solid #1e1e2e;
-  padding: 16px 0;
-  flex-shrink: 0;
-  display: flex;
-  flex-direction: column;
-}
-
-.sidebar-logo {
-  font-size: 18px;
-  font-weight: 600;
-  padding: 0 16px 24px;
-  color: var(--text-primary);
-}
-
-.sidebar-nav { display: flex; flex-direction: column; gap: 2px; }
-
-.nav-item {
-  display: flex;
-  align-items: center;
-  gap: 8px;
-  padding: 8px 16px;
-  color: var(--text-secondary);
-  text-decoration: none;
-  font-size: 13px;
-  cursor: pointer;
-  border-left: 3px solid transparent;
-  transition: all 0.15s;
-}
-
-.nav-item:hover { background: var(--bg-tertiary); color: var(--text-primary); }
-.nav-item.active {
-  border-left-color: var(--accent);
-  background: var(--bg-tertiary);
-  color: var(--text-primary);
-}
-
-.main { flex: 1; padding: 24px 32px; overflow-y: auto; }
-
-.page-title {
-  font-size: 24px;
-  font-weight: 600;
-  margin-bottom: 24px;
-}
-
-/* Metrics bar */
-.metrics { display: flex; gap: 16px; margin-bottom: 24px; }
-.metric {
-  background: var(--bg-secondary);
-  padding: 16px 20px;
-  border-radius: 6px;
-  min-width: 140px;
-}
-.metric-value {
-  font-family: var(--font-mono);
-  font-size: 28px;
-  font-weight: 500;
-}
-.metric-label { font-size: 12px; color: var(--text-secondary); margin-top: 4px; }
-
-/* Tables */
-table { width: 100%; border-collapse: collapse; }
-th {
-  text-align: left;
-  font-size: 11px;
-  text-transform: uppercase;
-  color: var(--text-muted);
-  padding: 8px 12px;
-  font-weight: 500;
-  letter-spacing: 0.5px;
-}
-td {
-  padding: 10px 12px;
-  font-size: 13px;
-  border-top: 1px solid #1a1a2a;
-}
-tr:hover td { background: var(--bg-tertiary); }
-
-/* Badges */
-.badge {
-  display: inline-block;
-  padding: 2px 8px;
-  border-radius: 10px;
-  font-size: 11px;
-  font-weight: 500;
-}
-.badge-read { background: rgba(59,130,246,0.15); color: var(--accent); }
-.badge-write { background: rgba(245,158,11,0.15); color: var(--warning); }
-.badge-admin { background: rgba(239,68,68,0.15); color: var(--error); }
-.badge-success { background: rgba(34,197,94,0.15); color: var(--success); }
-.badge-error { background: rgba(239,68,68,0.15); color: var(--error); }
-
-/* Status dots */
-.status-dot {
-  display: inline-block;
-  width: 8px;
-  height: 8px;
-  border-radius: 50%;
-  margin-right: 6px;
-}
-.status-active { background: var(--success); }
-.status-warning { background: var(--warning); }
-.status-inactive { background: var(--text-muted); }
-
-/* Buttons */
-.btn {
-  padding: 8px 16px;
-  border-radius: 6px;
-  font-size: 13px;
-  font-weight: 500;
-  cursor: pointer;
-  border: none;
-  transition: all 0.15s;
-}
-.btn-primary { background: var(--accent); color: white; }
-.btn-primary:hover { background: #2563eb; }
-.btn-secondary { background: transparent; color: var(--text-secondary); border: 1px solid #333; }
-.btn-secondary:hover { border-color: var(--text-secondary); color: var(--text-primary); }
-.btn-danger { background: transparent; color: var(--error); border: 1px solid var(--error); }
-.btn-danger:hover { background: rgba(239,68,68,0.1); }
-
-/* Forms */
-input, select {
-  background: var(--bg-primary);
-  border: 1px solid #333;
-  color: var(--text-primary);
-  padding: 8px 12px;
-  border-radius: 6px;
-  font-size: 13px;
-  font-family: var(--font-sans);
-  width: 100%;
-}
-input:focus, select:focus {
-  outline: none;
-  border-color: var(--accent);
-  box-shadow: 0 0 0 2px rgba(59,130,246,0.2);
-}
-input::placeholder { color: var(--text-muted); }
-label { display: block; font-size: 13px; font-weight: 500; margin-bottom: 6px; }
-
-/* Modal */
-.modal-overlay {
-  position: fixed;
-  inset: 0;
-  background: rgba(0,0,0,0.7);
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  z-index: 100;
-}
-.modal {
-  background: var(--bg-secondary);
-  border-radius: 8px;
-  padding: 24px;
-  min-width: 420px;
-  max-width: 520px;
-}
-.modal-title { font-size: 18px; font-weight: 600; margin-bottom: 20px; }
-
-/* Drawer */
-.drawer-overlay {
-  position: fixed;
-  inset: 0;
-  background: rgba(0,0,0,0.5);
-  z-index: 90;
-}
-.drawer {
-  position: fixed;
-  right: 0;
-  top: 0;
-  bottom: 0;
-  width: 420px;
-  background: var(--bg-secondary);
-  border-left: 1px solid var(--accent);
-  padding: 24px;
-  z-index: 91;
-  overflow-y: auto;
-}
-.drawer-close {
-  position: absolute;
-  top: 16px;
-  right: 16px;
-  background: none;
-  border: none;
-  color: var(--text-muted);
-  font-size: 18px;
-  cursor: pointer;
-}
-
-/* Section headers */
-.section-title {
-  font-size: 11px;
-  text-transform: uppercase;
-  color: var(--text-muted);
-  letter-spacing: 0.5px;
-  margin: 20px 0 12px;
-  font-weight: 500;
-}
-
-/* Health panel */
-.health-panel {
-  background: var(--bg-secondary);
-  border-radius: 6px;
-  padding: 16px;
-}
-.health-row {
-  display: flex;
-  justify-content: space-between;
-  padding: 6px 0;
-  font-size: 13px;
-}
-
-/* Code block */
-.code-block {
-  background: var(--bg-primary);
-  border-radius: 6px;
-  padding: 12px;
-  font-family: var(--font-mono);
-  font-size: 12px;
-  overflow-x: auto;
-  position: relative;
-}
-.code-block .copy-btn {
-  position: absolute;
-  top: 8px;
-  right: 8px;
-  background: var(--accent);
-  color: white;
-  border: none;
-  padding: 4px 10px;
-  border-radius: 4px;
-  font-size: 11px;
-  cursor: pointer;
-}
-
-/* Activity feed */
-.feed { max-height: 400px; overflow-y: auto; }
-.feed-empty {
-  color: var(--text-muted);
-  text-align: center;
-  padding: 32px;
-  font-size: 13px;
-}
-
-/* Sparkline */
-.sparkline { display: inline-block; vertical-align: middle; }
-
-/* Filter bar */
-.filter-bar { display: flex; gap: 12px; margin-bottom: 16px; align-items: center; }
-.filter-bar select { width: auto; min-width: 140px; }
-
-/* Pagination */
-.pagination {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 12px 0;
-  font-size: 13px;
-  color: var(--text-secondary);
-}
-.pagination button {
-  background: var(--bg-secondary);
-  border: 1px solid #333;
-  color: var(--text-primary);
-  padding: 6px 12px;
-  border-radius: 4px;
-  cursor: pointer;
-  font-size: 12px;
-}
-.pagination button:disabled { opacity: 0.3; cursor: default; }
-
-/* Warning bar */
-.warning-bar {
-  background: rgba(245,158,11,0.15);
-  border: 1px solid var(--warning);
-  color: var(--warning);
-  padding: 10px 16px;
-  border-radius: 6px;
-  font-size: 13px;
-  margin: 12px 0;
-}
-
-/* Checkbox */
-.checkbox-group { display: flex; gap: 16px; flex-wrap: wrap; }
-.checkbox-label {
-  display: flex;
-  align-items: center;
-  gap: 6px;
-  font-size: 13px;
-  cursor: pointer;
-}
-
-/* Tabs */
-.tabs { display: flex; gap: 0; margin-bottom: 12px; }
-.tab {
-  padding: 6px 12px;
-  font-size: 13px;
-  color: var(--text-secondary);
-  cursor: pointer;
-  border-bottom: 2px solid transparent;
-}
-.tab.active { color: var(--accent); border-bottom-color: var(--accent); }
-
-/* Login page */
-.login-page {
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  min-height: 100vh;
-  background: var(--bg-primary);
-}
-.login-box { text-align: left; width: 340px; }
-.login-logo { font-size: 32px; font-weight: 600; margin-bottom: 32px; }
-.login-hint { color: var(--text-muted); font-size: 12px; margin-top: 12px; }
-.login-error { color: var(--error); font-size: 13px; margin-top: 8px; }
-
-/* Monospace data */
-.mono { font-family: var(--font-mono); font-size: 12px; }
-
-/* Responsive */
-@media (max-width: 768px) {
-  .sidebar { display: none; }
-  .main { padding: 16px; }
-  .metrics { flex-wrap: wrap; }
-  .drawer { width: 100%; }
-}
@@ -1,22 +0,0 @@
-/**
- * Admin SPA scope constants — HAND-MAINTAINED MIRROR of src/core/scope.ts.
- *
- * The admin tsconfig.json scopes `include: ['src']` to admin/src/, so we
- * cannot directly import from ../../src/core/scope.ts without breaking the
- * SPA's compile boundary. Instead, this file is a hand-maintained duplicate;
- * scripts/check-admin-scope-drift.sh fails the build if the two lists drift.
- *
- * If you change ALLOWED_SCOPES in src/core/scope.ts, update this file too,
- * or `bun run verify` will reject the change.
- */
-
-export type Scope = 'read' | 'write' | 'admin' | 'sources_admin' | 'users_admin';
-
-// MIRROR OF src/core/scope.ts ALLOWED_SCOPES_LIST — keep alphabetically sorted.
-export const ALLOWED_SCOPES_LIST: ReadonlyArray<Scope> = [
-  'admin',
-  'read',
-  'sources_admin',
-  'users_admin',
-  'write',
-];
@@ -1,10 +0,0 @@
-import React from 'react';
-import ReactDOM from 'react-dom/client';
-import { App } from './App';
-import './index.css';
-
-ReactDOM.createRoot(document.getElementById('root')!).render(
-  <React.StrictMode>
-    <App />
-  </React.StrictMode>,
-);
@@ -1,633 +0,0 @@
-import React, { useState, useEffect } from 'react';
-import { api } from '../api';
-import { ALLOWED_SCOPES_LIST, type Scope } from '../lib/scope-constants';
-
-function timeAgo(date: Date): string {
-  const s = Math.floor((Date.now() - date.getTime()) / 1000);
-  if (s < 60) return 'just now';
-  if (s < 3600) return `${Math.floor(s / 60)}m ago`;
-  if (s < 86400) return `${Math.floor(s / 3600)}h ago`;
-  return `${Math.floor(s / 86400)}d ago`;
-}
-
-interface Agent {
-  id: string;
-  name: string;
-  auth_type: 'oauth' | 'api_key';
-  client_id?: string;  // compat
-  client_name?: string; // compat
-  grant_types: string[];
-  scope: string;
-  created_at: string;
-  last_used_at: string | null;
-  total_requests: number;
-  requests_today: number;
-  token_ttl: number | null;
-  status: 'active' | 'revoked';
-}
-
-interface ApiKey {
-  id: string;
-  name: string;
-  created_at: string;
-  last_used_at: string | null;
-  status: 'active' | 'revoked';
-}
-
-export function AgentsPage() {
-  const [agents, setAgents] = useState<Agent[]>([]);
-  const [hideRevoked, setHideRevoked] = useState(true);
-  const [showRegister, setShowRegister] = useState(false);
-  const [showCredentials, setShowCredentials] = useState<{ clientId: string; clientSecret: string; name: string } | null>(null);
-  const [showApiKeyCreate, setShowApiKeyCreate] = useState(false);
-  const [showApiKeyToken, setShowApiKeyToken] = useState<{ name: string; token: string } | null>(null);
-  const [selectedAgent, setSelectedAgent] = useState<Agent | null>(null);
-
-  useEffect(() => { loadAgents(); }, []);
-
-  const loadAgents = () => { api.agents().then(setAgents).catch(() => {}); };
-
-  return (
-    <>
-      <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: 24 }}>
-        <h1 className="page-title" style={{ marginBottom: 0 }}>Agents</h1>
-        <div style={{ display: 'flex', gap: 8, alignItems: 'center' }}>
-          <label style={{ fontSize: 13, color: 'var(--text-secondary)', display: 'flex', alignItems: 'center', gap: 6, cursor: 'pointer' }}>
-            <input type="checkbox" checked={hideRevoked} onChange={e => setHideRevoked(e.target.checked)} /> Hide revoked
-          </label>
-          <button className="btn btn-secondary" onClick={() => setShowApiKeyCreate(true)}>+ API Key</button>
-          <button className="btn btn-primary" onClick={() => setShowRegister(true)}>+ OAuth Client</button>
-        </div>
-      </div>
-
-      {(() => {
-        // Filter once and reuse, so the empty-state guard sees the same
-        // rows the table renders. Pre-fix: agents.length === 0 used the
-        // unfiltered array, so an all-revoked dataset with hideRevoked=on
-        // showed a header-only table with no placeholder.
-        const visibleAgents = agents.filter(a => !hideRevoked || a.status !== 'revoked');
-        if (agents.length === 0) {
-          return (
-            <div style={{ textAlign: 'center', padding: 48, color: 'var(--text-muted)' }}>
-              No agents registered. Register your first agent to get started.
-            </div>
-          );
-        }
-        if (visibleAgents.length === 0) {
-          return (
-            <div style={{ textAlign: 'center', padding: 48, color: 'var(--text-muted)' }}>
-              All agents are revoked. Uncheck "Hide revoked" to view them.
-            </div>
-          );
-        }
-        return (
-        <>
-          <table>
-            <thead>
-              <tr>
-                <th>Name</th>
-                <th>Type</th>
-                <th>Scopes</th>
-                <th>Status</th>
-                <th>Requests</th>
-                <th>Last Used</th>
-              </tr>
-            </thead>
-            <tbody>
-              {visibleAgents.map(a => (
-                <tr key={a.id} onClick={() => setSelectedAgent(a)}
-                    style={{ cursor: 'pointer' }}>
-                  <td style={{ fontWeight: 500 }}>{a.name || a.client_name}</td>
-                  <td>
-                    <span className={`badge ${a.auth_type === 'oauth' ? 'badge-read' : 'badge-write'}`} style={{ fontSize: 11 }}>
-                      {a.auth_type === 'oauth' ? 'OAuth' : 'API Key'}
-                    </span>
-                  </td>
-                  <td>
-                    {(a.scope || '').split(' ').filter(Boolean).map(s => (
-                      <span key={s} className={`badge badge-${s}`} style={{ marginRight: 4 }}>{s}</span>
-                    ))}
-                  </td>
-                  <td>
-                    <span className={`badge ${a.status === 'active' ? 'badge-success' : 'badge-danger'}`}>{a.status}</span>
-                  </td>
-                  <td>
-                    <span style={{ fontWeight: 500 }}>{a.requests_today || 0}</span>
-                    <span style={{ color: 'var(--text-muted)', fontSize: 12 }}> / {a.total_requests || 0}</span>
-                  </td>
-                  <td style={{ color: 'var(--text-secondary)' }}>
-                    {a.last_used_at ? timeAgo(new Date(a.last_used_at)) : 'Never'}
-                  </td>
-                </tr>
-              ))}
-            </tbody>
-          </table>
-          <div style={{ color: 'var(--text-muted)', fontSize: 13, marginTop: 12 }}>
-            {agents.filter(a => a.status === 'active').length} active / {agents.length} total
-          </div>
-        </>
-        );
-      })()}
-
-      {showRegister && (
-        <RegisterModal
-          onClose={() => setShowRegister(false)}
-          onRegistered={(creds) => { setShowRegister(false); setShowCredentials(creds); loadAgents(); }}
-        />
-      )}
-
-      {showCredentials && (
-        <CredentialsModal
-          credentials={showCredentials}
-          onClose={() => setShowCredentials(null)}
-        />
-      )}
-
-      {selectedAgent && (
-        <AgentDrawer agent={selectedAgent} onClose={() => setSelectedAgent(null)} onRevoked={loadAgents} />
-      )}
-
-      {showApiKeyCreate && (
-        <ApiKeyCreateModal
-          onClose={() => setShowApiKeyCreate(false)}
-          onCreated={(result) => { setShowApiKeyCreate(false); setShowApiKeyToken(result); loadAgents(); }}
-        />
-      )}
-
-      {showApiKeyToken && (
-        <ApiKeyTokenModal token={showApiKeyToken} onClose={() => setShowApiKeyToken(null)} />
-      )}
-    </>
-  );
-}
-
-function ApiKeyCreateModal({ onClose, onCreated }: {
-  onClose: () => void;
-  onCreated: (result: { name: string; token: string }) => void;
-}) {
-  const [name, setName] = useState('');
-  const [loading, setLoading] = useState(false);
-  const [error, setError] = useState('');
-
-  const handleSubmit = async (e: React.FormEvent) => {
-    e.preventDefault();
-    if (!name.trim()) { setError('Name required'); return; }
-    setLoading(true);
-    try {
-      const data = await api.createApiKey(name.trim());
-      onCreated({ name: data.name, token: data.token });
-    } catch (err) {
-      setError(err instanceof Error ? err.message : 'Failed');
-    } finally { setLoading(false); }
-  };
-
-  return (
-    <div className="modal-overlay" onClick={onClose}>
-      <form className="modal" onClick={e => e.stopPropagation()} onSubmit={handleSubmit}>
-        <div className="modal-title">Create API Key</div>
-        <p style={{ color: 'var(--text-secondary)', fontSize: 13, marginBottom: 16 }}>
-          API keys use simple bearer token auth. They grant full read+write+admin access.
-          For scoped access, use OAuth clients instead.
-        </p>
-        <div style={{ marginBottom: 16 }}>
-          <label>Key Name</label>
-          <input placeholder="e.g. claude-code-local" value={name} onChange={e => setName(e.target.value)} autoFocus />
-        </div>
-        {error && <div style={{ color: 'var(--error)', fontSize: 13, marginBottom: 12 }}>{error}</div>}
-        <div style={{ display: 'flex', gap: 12, justifyContent: 'flex-end' }}>
-          <button type="button" className="btn btn-secondary" onClick={onClose}>Cancel</button>
-          <button type="submit" className="btn btn-primary" disabled={loading}>
-            {loading ? 'Creating...' : 'Create Key'}
-          </button>
-        </div>
-      </form>
-    </div>
-  );
-}
-
-function ApiKeyTokenModal({ token, onClose }: {
-  token: { name: string; token: string };
-  onClose: () => void;
-}) {
-  const copy = (text: string) => navigator.clipboard.writeText(text);
-
-  return (
-    <div className="modal-overlay">
-      <div className="modal" style={{ maxWidth: 560 }}>
-        <div style={{ textAlign: 'center', marginBottom: 16 }}>
-          <div style={{ fontSize: 36, color: 'var(--success)', marginBottom: 8 }}>&#10003;</div>
-          <div style={{ fontSize: 20, fontWeight: 600 }}>API Key Created</div>
-        </div>
-        <div style={{ marginBottom: 12 }}>
-          <label style={{ fontSize: 12 }}>Name</label>
-          <div className="code-block"><span>{token.name}</span></div>
-        </div>
-        <div style={{ marginBottom: 12 }}>
-          <label style={{ fontSize: 12 }}>Bearer Token</label>
-          <div className="code-block">
-            <span>{token.token}</span>
-            <button className="copy-btn" onClick={() => copy(token.token)}>Copy</button>
-          </div>
-        </div>
-        <div style={{ marginBottom: 12 }}>
-          <label style={{ fontSize: 12 }}>Usage</label>
-          <div className="code-block">
-            <pre style={{ whiteSpace: 'pre-wrap', margin: 0, fontSize: 12 }}>{`Authorization: Bearer ${token.token}`}</pre>
-            <button className="copy-btn" onClick={() => copy(`Authorization: Bearer ${token.token}`)}>Copy</button>
-          </div>
-        </div>
-        <div className="warning-bar">Save this token now. It will not be shown again.</div>
-        <div style={{ display: 'flex', gap: 12, justifyContent: 'flex-end', marginTop: 20 }}>
-          <button className="btn btn-primary" onClick={onClose}>Done</button>
-        </div>
-      </div>
-    </div>
-  );
-}
-
-function RegisterModal({ onClose, onRegistered }: {
-  onClose: () => void;
-  onRegistered: (creds: { clientId: string; clientSecret: string; name: string }) => void;
-}) {
-  const [name, setName] = useState('');
-  // v0.28: scope set sourced from admin/src/lib/scope-constants.ts (mirror
-  // of src/core/scope.ts). CI drift check at scripts/check-admin-scope-drift.sh
-  // fails the build if these diverge.
-  const [scopes, setScopes] = useState<Record<Scope, boolean>>(() =>
-    Object.fromEntries(ALLOWED_SCOPES_LIST.map(s => [s, s === 'read'])) as Record<Scope, boolean>,
-  );
-  const [ttl, setTtl] = useState('86400'); // 24h default
-  const [loading, setLoading] = useState(false);
-  const [error, setError] = useState('');
-
-  const ttlOptions = [
-    { label: '1 hour', value: '3600' },
-    { label: '24 hours', value: '86400' },
-    { label: '7 days', value: '604800' },
-    { label: '30 days', value: '2592000' },
-    { label: '1 year', value: '31536000' },
-    { label: 'No expiry', value: '0' },
-  ];
-
-  const handleSubmit = async (e: React.FormEvent) => {
-    e.preventDefault();
-    if (!name.trim()) { setError('Name required'); return; }
-    setLoading(true);
-    setError('');
-    try {
-      // Use the CLI registration endpoint (POST to admin API)
-      const selectedScopes = Object.entries(scopes).filter(([, v]) => v).map(([k]) => k).join(' ');
-      const res = await fetch('/admin/api/register-client', {
-        method: 'POST',
-        credentials: 'same-origin',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ name: name.trim(), scopes: selectedScopes, tokenTtl: ttl === '0' ? 315360000 : Number(ttl) }),
-      });
-      if (!res.ok) throw new Error('Registration failed');
-      const data = await res.json();
-      onRegistered({ clientId: data.clientId, clientSecret: data.clientSecret, name: name.trim() });
-    } catch (err) {
-      setError(err instanceof Error ? err.message : 'Registration failed');
-    } finally {
-      setLoading(false);
-    }
-  };
-
-  return (
-    <div className="modal-overlay" onClick={onClose}>
-      <form className="modal" onClick={e => e.stopPropagation()} onSubmit={handleSubmit}>
-        <div className="modal-title">Register Agent</div>
-        <div style={{ marginBottom: 16 }}>
-          <label>Agent Name</label>
-          <input placeholder="e.g. perplexity-production" value={name} onChange={e => setName(e.target.value)} autoFocus />
-        </div>
-        <div style={{ marginBottom: 16 }}>
-          <label>Scopes</label>
-          <div className="checkbox-group">
-            {ALLOWED_SCOPES_LIST.map(s => (
-              <label key={s} className="checkbox-label">
-                <input type="checkbox" checked={scopes[s]} onChange={e => setScopes(p => ({ ...p, [s]: e.target.checked }))} />
-                {s}
-              </label>
-            ))}
-          </div>
-        </div>
-        <div style={{ marginBottom: 20 }}>
-          <label>Token Lifetime</label>
-          <select value={ttl} onChange={e => setTtl(e.target.value)}
-            style={{ width: '100%', background: 'var(--bg-secondary)', color: 'var(--text-primary)', border: '1px solid var(--border)', borderRadius: 6, padding: '6px 10px', fontSize: 14 }}>
-            {ttlOptions.map(o => <option key={o.value} value={o.value}>{o.label}</option>)}
-          </select>
-        </div>
-        {error && <div style={{ color: 'var(--error)', fontSize: 13, marginBottom: 12 }}>{error}</div>}
-        <div style={{ display: 'flex', gap: 12, justifyContent: 'flex-end' }}>
-          <button type="button" className="btn btn-secondary" onClick={onClose}>Cancel</button>
-          <button type="submit" className="btn btn-primary" disabled={loading}>
-            {loading ? 'Registering...' : 'Register'}
-          </button>
-        </div>
-      </form>
-    </div>
-  );
-}
-
-function CredentialsModal({ credentials, onClose }: {
-  credentials: { clientId: string; clientSecret: string; name: string };
-  onClose: () => void;
-}) {
-  const copy = (text: string) => navigator.clipboard.writeText(text);
-  const downloadJson = () => {
-    const blob = new Blob([JSON.stringify(credentials, null, 2)], { type: 'application/json' });
-    const url = URL.createObjectURL(blob);
-    const a = document.createElement('a');
-    a.href = url; a.download = `${credentials.name}-credentials.json`; a.click();
-    URL.revokeObjectURL(url);
-  };
-
-  return (
-    <div className="modal-overlay">
-      <div className="modal" style={{ maxWidth: 560 }}>
-        <div style={{ textAlign: 'center', marginBottom: 16 }}>
-          <div style={{ fontSize: 36, color: 'var(--success)', marginBottom: 8 }}>&#10003;</div>
-          <div style={{ fontSize: 20, fontWeight: 600 }}>Agent Registered</div>
-        </div>
-
-        <div style={{ marginBottom: 12 }}>
-          <label style={{ fontSize: 12 }}>Client ID</label>
-          <div className="code-block">
-            <span>{credentials.clientId}</span>
-            <button className="copy-btn" onClick={() => copy(credentials.clientId)}>Copy</button>
-          </div>
-        </div>
-
-        <div style={{ marginBottom: 12 }}>
-          <label style={{ fontSize: 12 }}>Client Secret</label>
-          <div className="code-block">
-            <span>{credentials.clientSecret}</span>
-            <button className="copy-btn" onClick={() => copy(credentials.clientSecret)}>Copy</button>
-          </div>
-        </div>
-
-        <div className="warning-bar">
-          Save this secret now. It will not be shown again.
-        </div>
-
-        <div style={{ display: 'flex', gap: 12, justifyContent: 'flex-end', marginTop: 20 }}>
-          <button className="btn btn-secondary" onClick={downloadJson}>Download as JSON</button>
-          <button className="btn btn-primary" onClick={onClose}>Done</button>
-        </div>
-      </div>
-    </div>
-  );
-}
-
-function AgentDrawer({ agent, onClose, onRevoked }: { agent: Agent; onClose: () => void; onRevoked: () => void }) {
-  const [tab, setTab] = useState<'claude-code' | 'chatgpt' | 'claude-cowork' | 'perplexity' | 'cursor' | 'json'>('claude-code');
-  const copy = (text: string) => navigator.clipboard.writeText(text);
-  const serverUrl = window.location.origin;
-
-  const cid = agent.id || agent.client_id || '';
-  const isOAuth = agent.auth_type === 'oauth';
-  const agentName = agent.name || agent.client_name || 'unknown';
-
-  // For API keys, we can't show the actual token (it was shown once at creation).
-  // For OAuth, we show the client_id and tell them to use their secret.
-
-  const configSnippets: Record<string, string> = {
-    'claude-code': isOAuth
-      ? [
-        `The user wants to connect GBrain MCP to your context. Here's how.`,
-        ``,
-        `# Step 1 — read the client secret from the operator without putting it in shell history.`,
-        `# read -s suppresses the echo; export keeps it scoped to this shell session.`,
-        `read -rs -p "Client Secret: " GBRAIN_CS && echo`,
-        ``,
-        `# Step 2 — mint a token and register GBrain in one go.`,
-        `claude mcp add --transport http gbrain ${serverUrl}/mcp \\`,
-        `  --header "Authorization: Bearer $(curl -s -X POST ${serverUrl}/token \\`,
-        `    -d 'grant_type=client_credentials' \\`,
-        `    -d 'client_id=${cid}' \\`,
-        `    --data-urlencode "client_secret=$GBRAIN_CS" \\`,
-        `    -d 'scope=${agent.scope || 'read write'}' | jq -r .access_token)"`,
-        ``,
-        `# Step 3 — clear the secret from this shell.`,
-        `unset GBRAIN_CS`,
-        ``,
-        `The token will last ${agent.token_ttl ? (agent.token_ttl >= 86400 ? Math.floor(agent.token_ttl / 86400) + ' days' : Math.floor(agent.token_ttl / 3600) + ' hours') : '1 hour (default)'}.`,
-        ``,
-        `─── Fallback: 2-step curl + paste ───`,
-        ``,
-        `If your shell doesn't support read -s, mint the token first, then paste:`,
-        ``,
-        `curl -s -X POST ${serverUrl}/token \\`,
-        `  -d 'grant_type=client_credentials' \\`,
-        `  -d 'client_id=${cid}' \\`,
-        `  -d 'client_secret=PASTE_YOUR_CLIENT_SECRET_HERE' \\`,
-        `  -d 'scope=${agent.scope || 'read write'}' | jq -r .access_token`,
-        ``,
-        `claude mcp add --transport http gbrain ${serverUrl}/mcp \\`,
-        `  --header "Authorization: Bearer PASTE_TOKEN_FROM_ABOVE"`,
-        ``,
-        `# Then run: history -d $((HISTCMD-1))   # zsh: print -s -- '' && fc -p`,
-      ].join('\n')
-      : [
-        `The user wants to connect GBrain MCP to your context. Here's how.`,
-        ``,
-        `Run this command in terminal to register GBrain:`,
-        ``,
-        `claude mcp add --transport http gbrain ${serverUrl}/mcp \\`,
-        `  --header "Authorization: Bearer PASTE_YOUR_API_KEY_HERE"`,
-        ``,
-        `Replace PASTE_YOUR_API_KEY_HERE with the API key shown when "${agentName}" was created.`,
-        `API keys never expire.`,
-      ].join('\n'),
-
-    'chatgpt': [
-      `The user wants to connect GBrain MCP to ChatGPT.`,
-      ``,
-      `1. Open ChatGPT > Settings > Tools & Integrations > Add MCP Server`,
-      `2. Enter this URL — ChatGPT will auto-discover the OAuth configuration:`,
-      ``,
-      `   ${serverUrl}/.well-known/oauth-authorization-server`,
-      ``,
-      `3. When prompted for credentials:`,
-      `   Client ID: ${cid}`,
-      `   Client Secret: (the secret from agent registration)`,
-      `   Grant Type: client_credentials`,
-      `   Scope: ${agent.scope || 'read write'}`,
-    ].join('\n'),
-
-    'claude-cowork': [
-      `The user wants to connect GBrain MCP to Claude.ai.`,
-      ``,
-      `1. Open claude.ai > Settings > Connected Apps > Add MCP Server`,
-      `2. Server URL: ${serverUrl}/mcp`,
-      `3. When prompted for auth:`,
-      `   Token endpoint: ${serverUrl}/token`,
-      `   Client ID: ${cid}`,
-      `   Client Secret: (the secret from agent registration)`,
-      `   Scope: ${agent.scope || 'read write'}`,
-      ``,
-      `Discovery URL: ${serverUrl}/.well-known/oauth-authorization-server`,
-    ].join('\n'),
-
-    cursor: isOAuth
-      ? [
-        `The user wants to connect GBrain MCP to Cursor.`,
-        ``,
-        `Cursor supports OAuth for remote MCP. Add to .cursor/mcp.json:`,
-        ``,
-        `{`,
-        `  "mcpServers": {`,
-        `    "gbrain": {`,
-        `      "url": "${serverUrl}/mcp",`,
-        `      "transport": "sse"`,
-        `    }`,
-        `  }`,
-        `}`,
-        ``,
-        `Cursor will auto-discover OAuth via:`,
-        `${serverUrl}/.well-known/oauth-authorization-server`,
-        ``,
-        `When prompted: Client ID ${cid}, use the secret from registration.`,
-      ].join('\n')
-      : [
-        `The user wants to connect GBrain MCP to Cursor.`,
-        ``,
-        `Add to .cursor/mcp.json:`,
-        ``,
-        `{`,
-        `  "mcpServers": {`,
-        `    "gbrain": {`,
-        `      "url": "${serverUrl}/mcp",`,
-        `      "transport": "sse",`,
-        `      "headers": {`,
-        `        "Authorization": "Bearer PASTE_YOUR_API_KEY_HERE"`,
-        `      }`,
-        `    }`,
-        `  }`,
-        `}`,
-        ``,
-        `Replace PASTE_YOUR_API_KEY_HERE with the API key shown when "${agentName}" was created.`,
-      ].join('\n'),
-
-    perplexity: [
-      `The user wants to connect GBrain MCP to Perplexity.`,
-      ``,
-      `1. Go to Settings > Connectors > Add MCP`,
-      `2. Server URL: ${serverUrl}/mcp`,
-      `3. Client ID: ${cid}`,
-      `4. Client Secret: (the secret from agent registration)`,
-    ].join('\n'),
-
-    json: JSON.stringify({
-      server_url: serverUrl + '/mcp',
-      token_url: serverUrl + '/token',
-      discovery_url: serverUrl + '/.well-known/oauth-authorization-server',
-      client_id: cid,
-      client_name: agentName,
-      auth_type: agent.auth_type,
-      scope: agent.scope,
-    }, null, 2),
-  };
-
-  return (
-    <>
-      <div className="drawer-overlay" onClick={onClose} />
-      <div className="drawer">
-        <button className="drawer-close" onClick={onClose}>&#10005;</button>
-        <div style={{ fontSize: 18, fontWeight: 600, marginBottom: 4 }}>{agent.name || agent.client_name}</div>
-        <span className={`badge ${agent.status === 'active' ? 'badge-success' : 'badge-danger'}`}>{agent.status}</span>
-
-        <div className="section-title">Details</div>
-        <div style={{ display: 'grid', gridTemplateColumns: '100px 1fr', gap: '6px 12px', fontSize: 13 }}>
-          <span style={{ color: 'var(--text-secondary)' }}>Client ID</span>
-          <span className="mono">{(agent.id || agent.id || agent.client_id || '').substring(0, 24)}...</span>
-          <span style={{ color: 'var(--text-secondary)' }}>Scopes</span>
-          <span>{(agent.scope || '').split(' ').filter(Boolean).map(s => (
-            <span key={s} className={`badge badge-${s}`} style={{ marginRight: 4 }}>{s}</span>
-          ))}</span>
-          <span style={{ color: 'var(--text-secondary)' }}>Registered</span>
-          <span>{new Date(agent.created_at).toLocaleDateString()}</span>
-          <span style={{ color: 'var(--text-secondary)' }}>Token TTL</span>
-          <span>{agent.token_ttl ? (agent.token_ttl >= 31536000 ? 'No expiry' : agent.token_ttl >= 86400 ? `${Math.floor(agent.token_ttl / 86400)}d` : agent.token_ttl >= 3600 ? `${Math.floor(agent.token_ttl / 3600)}h` : `${agent.token_ttl}s`) : '1h (default)'}</span>
-        </div>
-
-        {/*
-          Config Export visible for both auth_type=oauth AND auth_type=api_key.
-          Claude Code + Cursor + JSON tabs render real snippets regardless
-          (commit 15's snippets are auth-type-aware for those two clients;
-          JSON is just structured metadata). ChatGPT, Claude.ai, and
-          Perplexity tabs render an "OAuth client required" message on
-          api_key agents — those MCP clients only speak OAuth 2.0
-          client_credentials, not raw bearer tokens.
-
-          Pre-fix (Wintermute commit 16): the entire Config Export
-          section was hidden for api_key agents, dropping the working
-          Claude Code + Cursor snippets along with the broken ones.
-          (D5=C in the eng review.)
-        */}
-        <div className="section-title">Config Export</div>
-        <div className="tabs" style={{ flexWrap: 'wrap' }}>
-          <div className={`tab ${tab === 'claude-code' ? 'active' : ''}`} onClick={() => setTab('claude-code')}>Claude Code</div>
-          <div className={`tab ${tab === 'chatgpt' ? 'active' : ''}`} onClick={() => setTab('chatgpt')}>ChatGPT</div>
-          <div className={`tab ${tab === 'claude-cowork' ? 'active' : ''}`} onClick={() => setTab('claude-cowork')}>Claude.ai</div>
-          <div className={`tab ${tab === 'cursor' ? 'active' : ''}`} onClick={() => setTab('cursor')}>Cursor</div>
-          <div className={`tab ${tab === 'perplexity' ? 'active' : ''}`} onClick={() => setTab('perplexity')}>Perplexity</div>
-          <div className={`tab ${tab === 'json' ? 'active' : ''}`} onClick={() => setTab('json')}>JSON</div>
-        </div>
-        {(() => {
-          const oauthOnlyTabs = new Set(['chatgpt', 'claude-cowork', 'perplexity']);
-          if (!isOAuth && oauthOnlyTabs.has(tab)) {
-            const clientName = { chatgpt: 'ChatGPT', 'claude-cowork': 'Claude.ai', perplexity: 'Perplexity' }[tab] || tab;
-            return (
-              <div style={{
-                background: 'rgba(255, 200, 100, 0.08)',
-                border: '1px solid rgba(255, 200, 100, 0.2)',
-                borderRadius: 8,
-                padding: '14px 16px',
-                marginTop: 12,
-                fontSize: 13,
-                lineHeight: 1.6,
-                color: 'var(--text-secondary)',
-              }}>
-                <div style={{ fontWeight: 600, color: 'var(--text-primary)', marginBottom: 6 }}>
-                  {clientName} requires an OAuth client
-                </div>
-                {clientName} only supports OAuth 2.0 (client_credentials). API keys use raw bearer tokens, which {clientName} does not accept. Register a separate OAuth client and use that to connect this AI.
-              </div>
-            );
-          }
-          return (
-            <div className="code-block">
-              <pre style={{ whiteSpace: 'pre-wrap', margin: 0 }}>{configSnippets[tab]}</pre>
-              <button className="copy-btn" onClick={() => copy(configSnippets[tab])}>Copy</button>
-            </div>
-          );
-        })()}
-
-        <div style={{ marginTop: 32 }}>
-          {agent.status === 'active' && (
-            <button className="btn btn-danger" onClick={async () => {
-              if (!confirm(`Revoke ${agent.name || agent.client_name}? All active tokens will be invalidated.`)) return;
-              try {
-                if (agent.auth_type === 'oauth') {
-                  await api.revokeClient(agent.id || agent.client_id || '');
-                } else {
-                  await api.revokeApiKey(agent.name || '');
-                }
-                onRevoked();
-                onClose();
-              } catch (e) {
-                alert('Revoke failed: ' + (e instanceof Error ? e.message : 'unknown error'));
-              }
-            }}>Revoke Agent</button>
-          )}
-          {agent.status === 'revoked' && (
-            <span style={{ color: 'var(--text-muted)', fontSize: 13 }}>This agent has been revoked.</span>
-          )}
-        </div>
-      </div>
-    </>
-  );
-}
@@ -1,137 +0,0 @@
-import React, { useState, useEffect, useRef } from 'react';
-import { api } from '../api';
-
-interface FeedEvent {
-  agent: string;
-  operation: string;
-  scopes: string;
-  latency_ms: number;
-  status: string;
-  timestamp: string;
-}
-
-export function DashboardPage() {
-  const [stats, setStats] = useState({ connected_agents: 0, requests_today: 0, active_tokens: 0 });
-  const [health, setHealth] = useState({ expiring_soon: 0, error_rate: '0%' });
-  const [events, setEvents] = useState<FeedEvent[]>([]);
-  const [sseStatus, setSseStatus] = useState<'connecting' | 'connected' | 'disconnected'>('connecting');
-  const eventSourceRef = useRef<EventSource | null>(null);
-
-  useEffect(() => {
-    api.stats().then(setStats).catch(() => {});
-    api.health().then(setHealth).catch(() => {});
-
-    const es = new EventSource('/admin/events');
-    eventSourceRef.current = es;
-    es.onopen = () => setSseStatus('connected');
-    es.onmessage = (e) => {
-      try {
-        const event = JSON.parse(e.data) as FeedEvent;
-        setEvents(prev => [event, ...prev].slice(0, 50));
-      } catch {}
-    };
-    es.onerror = () => {
-      setSseStatus('disconnected');
-      setTimeout(() => {
-        setSseStatus('connecting');
-        es.close();
-        // Reconnect handled by browser EventSource auto-retry
-      }, 3000);
-    };
-
-    const interval = setInterval(() => {
-      api.stats().then(setStats).catch(() => {});
-      api.health().then(setHealth).catch(() => {});
-    }, 30000);
-
-    return () => { es.close(); clearInterval(interval); };
-  }, []);
-
-  const timeAgo = (ts: string) => {
-    const diff = Date.now() - new Date(ts).getTime();
-    if (diff < 60000) return `${Math.floor(diff / 1000)}s ago`;
-    if (diff < 3600000) return `${Math.floor(diff / 60000)} min ago`;
-    return `${Math.floor(diff / 3600000)}h ago`;
-  };
-
-  return (
-    <>
-      <h1 className="page-title">Dashboard</h1>
-
-      <div style={{ display: 'flex', gap: 24 }}>
-        <div style={{ flex: 1 }}>
-          <div className="metrics">
-            <div className="metric">
-              <div className="metric-value">{stats.connected_agents}</div>
-              <div className="metric-label">Connected Agents</div>
-            </div>
-            <div className="metric">
-              <div className="metric-value">{stats.requests_today}</div>
-              <div className="metric-label">Requests Today</div>
-            </div>
-            <div className="metric">
-              <div className="metric-value">{stats.active_tokens}</div>
-              <div className="metric-label">Active Tokens</div>
-            </div>
-          </div>
-
-          <h2 className="section-title">
-            Live Activity
-            <span style={{ marginLeft: 8, fontSize: 10, color: sseStatus === 'connected' ? 'var(--success)' : sseStatus === 'connecting' ? 'var(--warning)' : 'var(--error)' }}>
-              {sseStatus === 'connected' ? '● connected' : sseStatus === 'connecting' ? '● connecting...' : '● disconnected'}
-            </span>
-          </h2>
-
-          <div className="feed">
-            {events.length === 0 ? (
-              <div className="feed-empty">
-                {sseStatus === 'connected' ? 'No requests yet. Agents will appear when they connect.' : 'Connecting...'}
-              </div>
-            ) : (
-              <table>
-                <thead>
-                  <tr>
-                    <th>Agent</th>
-                    <th>Operation</th>
-                    <th>Scopes</th>
-                    <th>Latency</th>
-                    <th>Status</th>
-                    <th>Time</th>
-                  </tr>
-                </thead>
-                <tbody>
-                  {events.map((e, i) => (
-                    <tr key={i}>
-                      <td className="mono">{e.agent}</td>
-                      <td className="mono">{e.operation}</td>
-                      <td>{e.scopes.split(',').map(s => (
-                        <span key={s} className={`badge badge-${s.trim()}`} style={{ marginRight: 4 }}>{s.trim()}</span>
-                      ))}</td>
-                      <td className="mono">{e.latency_ms} ms</td>
-                      <td><span className={`badge badge-${e.status}`}>{e.status}</span></td>
-                      <td style={{ color: 'var(--text-secondary)' }}>{timeAgo(e.timestamp)}</td>
-                    </tr>
-                  ))}
-                </tbody>
-              </table>
-            )}
-          </div>
-        </div>
-
-        <div style={{ width: 220 }}>
-          <h2 className="section-title">Token Health</h2>
-          <div className="health-panel">
-            <div className="health-row">
-              <span style={{ color: 'var(--warning)' }}>Expiring Soon</span>
-              <span className="mono">{health.expiring_soon}</span>
-            </div>
-            <div className="health-row">
-              <span style={{ color: 'var(--error)' }}>Error Rate</span>
-              <span className="mono">{health.error_rate}</span>
-            </div>
-          </div>
-        </div>
-      </div>
-    </>
-  );
-}
@@ -1,96 +0,0 @@
-import React, { useState } from 'react';
-import { api } from '../api';
-
-// v0.26.3 trust model (D11 + D12):
-// - The bootstrap token is NEVER stored in browser JS state. No
-//   localStorage, no sessionStorage, no React state beyond the form
-//   submit cycle. After successful POST /admin/login the operator's
-//   token only lives in the HttpOnly cookie that the server set.
-// - Magic-link URLs use single-use server-issued nonces, not the
-//   bootstrap token itself (see /admin/api/issue-magic-link). The
-//   bootstrap token never appears in a URL.
-// - Closing the tab ends the session client-side. Reopening the
-//   dashboard 401s and shows this page again. Operator asks the agent
-//   for a fresh magic link or pastes the bootstrap token from the
-//   server's terminal scrollback.
-export function LoginPage({ onLogin }: { onLogin: () => void }) {
-  const [token, setToken] = useState('');
-  const [error, setError] = useState('');
-  const [loading, setLoading] = useState(false);
-
-  const handleSubmit = async (e: React.FormEvent) => {
-    e.preventDefault();
-    setError('');
-    setLoading(true);
-    try {
-      await api.login(token);
-      // Don't persist the token. The HttpOnly cookie is the only
-      // session credential after this point.
-      setToken('');
-      onLogin();
-    } catch (err) {
-      setError('Invalid token.');
-    } finally {
-      setLoading(false);
-    }
-  };
-
-  return (
-    <div className="login-page">
-      <div className="login-box">
-        <div className="login-logo">GBrain</div>
-
-        <div style={{
-          background: 'rgba(136, 170, 255, 0.08)',
-          border: '1px solid rgba(136, 170, 255, 0.2)',
-          borderRadius: 8,
-          padding: '14px 16px',
-          marginBottom: 20,
-          fontSize: 13,
-          lineHeight: 1.5,
-          color: 'var(--text-secondary)',
-        }}>
-          <div style={{ fontWeight: 600, color: 'var(--text-primary)', marginBottom: 6 }}>
-            🔒 This is a protected dashboard
-          </div>
-          Ask your AI agent for the admin login link:
-          <div style={{
-            background: 'rgba(0,0,0,0.3)',
-            borderRadius: 6,
-            padding: '8px 12px',
-            marginTop: 8,
-            fontFamily: 'var(--font-mono)',
-            fontSize: 12,
-            color: '#88aaff',
-            wordBreak: 'break-all',
-          }}>
-            "Give me the GBrain admin login link"
-          </div>
-          <div style={{ marginTop: 8, fontSize: 12, color: 'var(--text-muted)' }}>
-            Each link is single-use. Your agent generates a fresh one each time.
-          </div>
-        </div>
-
-        <details style={{ marginBottom: 16 }}>
-          <summary style={{ cursor: 'pointer', fontSize: 13, color: 'var(--text-muted)' }}>
-            Or paste bootstrap token manually
-          </summary>
-          <form onSubmit={handleSubmit} style={{ marginTop: 12 }}>
-            <div style={{ marginBottom: 12 }}>
-              <input
-                type="password"
-                placeholder="Admin Token"
-                value={token}
-                onChange={e => setToken(e.target.value)}
-              />
-            </div>
-            <button className="btn btn-primary" style={{ width: '100%' }} disabled={loading}>
-              {loading ? 'Authenticating...' : 'Submit'}
-            </button>
-            {error && <div className="login-error">{error}</div>}
-          </form>
-        </details>
-      </div>
-    </div>
-  );
-}
@@ -1,150 +0,0 @@
-import React, { useState, useEffect } from 'react';
-import { api } from '../api';
-
-interface LogEntry {
-  id: number;
-  token_name: string;
-  agent_name: string;
-  operation: string;
-  latency_ms: number;
-  status: string;
-  params: Record<string, unknown> | null;
-  error_message: string | null;
-  created_at: string;
-}
-
-export function RequestLogPage() {
-  const [data, setData] = useState<{ rows: LogEntry[]; total: number; page: number; pages: number }>({
-    rows: [], total: 0, page: 1, pages: 1,
-  });
-  const [page, setPage] = useState(1);
-  const [agentFilter, setAgentFilter] = useState('all');
-  const [expandedRow, setExpandedRow] = useState<number | null>(null);
-
-  useEffect(() => { loadPage(page); }, [page, agentFilter]);
-
-  const loadPage = (p: number) => {
-    const qs = agentFilter !== 'all' ? `&agent=${encodeURIComponent(agentFilter)}` : '';
-    api.requests(p, qs).then(setData).catch(() => {});
-  };
-
-  const timeAgo = (ts: string) => {
-    const diff = Date.now() - new Date(ts).getTime();
-    if (diff < 60000) return `${Math.floor(diff / 1000)}s ago`;
-    if (diff < 3600000) return `${Math.floor(diff / 60000)} min ago`;
-    if (diff < 86400000) return `${Math.floor(diff / 3600000)}h ago`;
-    return new Date(ts).toLocaleDateString();
-  };
-
-
-
-  const formatParams = (params: Record<string, unknown> | null) => {
-    if (!params) return null;
-    const { query, slug, partial, limit, ...rest } = params as any;
-    const parts: string[] = [];
-    if (query) parts.push(`"${query}"`);
-    if (slug) parts.push(slug);
-    if (partial) parts.push(`~${partial}`);
-    if (limit) parts.push(`limit=${limit}`);
-    if (Object.keys(rest).length > 0) parts.push(`+${Object.keys(rest).length} params`);
-    return parts.join(' ');
-  };
-
-  // Collect unique agents for filter (use name for display, token_name for value)
-  const agentMap = new Map<string, string>();
-  data.rows.forEach(r => { if (r.token_name) agentMap.set(r.token_name, r.agent_name || r.token_name); });
-
-  return (
-    <>
-      <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: 24 }}>
-        <h1 className="page-title" style={{ marginBottom: 0 }}>Request Log</h1>
-        <select value={agentFilter} onChange={e => { setAgentFilter(e.target.value); setPage(1); }}
-          style={{ background: 'var(--bg-secondary)', color: 'var(--text-primary)', border: '1px solid var(--border)', borderRadius: 6, padding: '4px 8px', fontSize: 13 }}>
-          <option value="all">All agents</option>
-          {[...agentMap.entries()].map(([id, name]) => <option key={id} value={id}>{name}</option>)}
-        </select>
-      </div>
-
-      {data.rows.length === 0 ? (
-        <div style={{ textAlign: 'center', padding: 48, color: 'var(--text-muted)' }}>
-          No requests yet.
-        </div>
-      ) : (
-        <>
-          <table>
-            <thead>
-              <tr>
-                <th>Time</th>
-                <th>Agent</th>
-                <th>Operation</th>
-                <th>Params</th>
-                <th>Latency</th>
-                <th>Status</th>
-              </tr>
-            </thead>
-            <tbody>
-              {data.rows.map(r => (
-                <React.Fragment key={r.id}>
-                  <tr onClick={() => setExpandedRow(expandedRow === r.id ? null : r.id)}
-                      style={{ cursor: 'pointer' }}>
-                    <td style={{ color: 'var(--text-secondary)', whiteSpace: 'nowrap' }}>{timeAgo(r.created_at)}</td>
-                    <td>
-                      <a style={{ color: 'var(--text-link, #88aaff)', cursor: 'pointer', textDecoration: 'none', fontWeight: 500 }}
-                         onClick={(e) => { e.stopPropagation(); setAgentFilter(r.token_name); setPage(1); }}>
-                        {r.agent_name || r.token_name}
-                      </a>
-                    </td>
-                    <td className="mono">{r.operation}</td>
-                    <td style={{ color: 'var(--text-secondary)', fontSize: 12, maxWidth: 200, overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap' }}>
-                      {formatParams(r.params)}
-                    </td>
-                    <td className="mono">{r.latency_ms}ms</td>
-                    <td><span className={`badge badge-${r.status}`}>{r.status}</span></td>
-                  </tr>
-                  {expandedRow === r.id && (
-                    <tr>
-                      <td colSpan={6} style={{ background: 'var(--bg-secondary, #0f0f1a)', padding: 16 }}>
-                        <div style={{ display: 'grid', gridTemplateColumns: '100px 1fr', gap: '6px 12px', fontSize: 13 }}>
-                          <span style={{ color: 'var(--text-muted)' }}>Time</span>
-                          <span>{new Date(r.created_at).toLocaleString()}</span>
-                          <span style={{ color: 'var(--text-muted)' }}>Agent</span>
-                          <span className="mono">{r.token_name}</span>
-                          <span style={{ color: 'var(--text-muted)' }}>Operation</span>
-                          <span className="mono">{r.operation}</span>
-                          <span style={{ color: 'var(--text-muted)' }}>Latency</span>
-                          <span>{r.latency_ms}ms</span>
-                          {r.params && (
-                            <>
-                              <span style={{ color: 'var(--text-muted)' }}>Params</span>
-                              <pre className="mono" style={{ margin: 0, whiteSpace: 'pre-wrap', fontSize: 12 }}>
-                                {JSON.stringify(r.params, null, 2)}
-                              </pre>
-                            </>
-                          )}
-                          {r.error_message && (
-                            <>
-                              <span style={{ color: 'var(--error, #ff6b6b)' }}>Error</span>
-                              <span style={{ color: 'var(--error, #ff6b6b)' }}>{r.error_message}</span>
-                            </>
-                          )}
-                        </div>
-                      </td>
-                    </tr>
-                  )}
-                </React.Fragment>
-              ))}
-            </tbody>
-          </table>
-
-          <div className="pagination">
-            <span>Page {data.page} of {data.pages} ({data.total} total)</span>
-            <div style={{ display: 'flex', gap: 8 }}>
-              <button disabled={data.page <= 1} onClick={() => setPage(p => p - 1)}>Previous</button>
-              <button disabled={data.page >= data.pages} onClick={() => setPage(p => p + 1)}>Next</button>
-            </div>
-          </div>
-        </>
-      )}
-    </>
-  );
-}
@@ -1 +0,0 @@
-/// <reference types="vite/client" />
@@ -1,17 +0,0 @@
-{
-  "compilerOptions": {
-    "target": "ES2020",
-    "useDefineForClassFields": true,
-    "lib": ["ES2020", "DOM", "DOM.Iterable"],
-    "module": "ESNext",
-    "skipLibCheck": true,
-    "moduleResolution": "bundler",
-    "allowImportingTsExtensions": true,
-    "isolatedModules": true,
-    "moduleDetection": "force",
-    "noEmit": true,
-    "jsx": "react-jsx",
-    "strict": true
-  },
-  "include": ["src"]
-}
@@ -1,11 +0,0 @@
-import { defineConfig } from 'vite';
-import react from '@vitejs/plugin-react';
-
-export default defineConfig({
-  plugins: [react()],
-  base: '/admin/',
-  build: {
-    outDir: 'dist',
-    emptyOutDir: true,
-  },
-});
@@ -1,610 +0,0 @@
-{
-  "lockfileVersion": 1,
-  "configVersion": 1,
-  "workspaces": {
-    "": {
-      "name": "gbrain",
-      "dependencies": {
-        "@ai-sdk/anthropic": "^3.0.71",
-        "@ai-sdk/google": "^3.0.64",
-        "@ai-sdk/openai": "^3.0.53",
-        "@ai-sdk/openai-compatible": "^2.0.41",
-        "@anthropic-ai/sdk": "^0.30.0",
-        "@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",
-        "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",
-        "marked": "^18.0.0",
-        "openai": "^4.0.0",
-        "pgvector": "^0.2.0",
-        "postgres": "^3.4.0",
-        "tree-sitter-wasms": "0.1.13",
-        "web-tree-sitter": "0.22.6",
-        "zod": "^4.3.6",
-      },
-      "devDependencies": {
-        "@types/bun": "latest",
-        "@types/cookie-parser": "^1.4.7",
-        "@types/cors": "^2.8.19",
-        "@types/express": "^5.0.6",
-        "bun-types": "^1.3.13",
-        "typescript": "^5.6.0",
-      },
-    },
-  },
-  "trustedDependencies": [
-    "@electric-sql/pglite",
-  ],
-  "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=="],
-
-    "@ai-sdk/gateway": ["@ai-sdk/gateway@3.0.109", "", { "dependencies": { "@ai-sdk/provider": "3.0.10", "@ai-sdk/provider-utils": "4.0.26", "@vercel/oidc": "3.2.0" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-r6dOqThjODp1vOhGRJg2OCmyB/ZOQtGx1esZ2SDvwDX5XoX8dBqYaYjLg8MPXTzMGJSgOkJyCxWgUcZtAl16pw=="],
-
-    "@ai-sdk/google": ["@ai-sdk/google@3.0.67", "", { "dependencies": { "@ai-sdk/provider": "3.0.10", "@ai-sdk/provider-utils": "4.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-Qeq+SidYtzMrcf0fdw3L0QLmtXK+ErwdBzbxS4+0Q/2UP85Ges8RJJcbAj7SO8e2JbeJoM35BLqkeNy1o3wJvQ=="],
-
-    "@ai-sdk/openai": ["@ai-sdk/openai@3.0.58", "", { "dependencies": { "@ai-sdk/provider": "3.0.10", "@ai-sdk/provider-utils": "4.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-2+5xGMROmrBboJuoOwqLL3b/o3i56+NRdxXDNVAiTyYjLiBj6KzembeuyuBT217be1X+zkEfAqD1H0irJlGIyw=="],
-
-    "@ai-sdk/openai-compatible": ["@ai-sdk/openai-compatible@2.0.45", "", { "dependencies": { "@ai-sdk/provider": "3.0.10", "@ai-sdk/provider-utils": "4.0.26" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-5YBvurNL7Oj7mT3srws4Rh4cQidoorfEGObAOb5jV40eld8IC7EkXWARZjnWYqgYzabUs6Sn6muiXfQVkgOyOQ=="],
-
-    "@ai-sdk/provider": ["@ai-sdk/provider@3.0.10", "", { "dependencies": { "json-schema": "^0.4.0" } }, "sha512-Q3BZ27qfpYqnCYGvE3vt+Qi6LGOF9R5Nmzn+9JoM1lCRsD9mYaIhfJLkSunN48nfGXJ6n+XNV0J/XVpqGQl7Dw=="],
-
-    "@ai-sdk/provider-utils": ["@ai-sdk/provider-utils@4.0.26", "", { "dependencies": { "@ai-sdk/provider": "3.0.10", "@standard-schema/spec": "^1.1.0", "eventsource-parser": "^3.0.8" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-CsKNLKsOpvPujRlIYvoz+Ybw+kGn7J4/fIZa/58+R7iWLLfwn6ifE2G6Yq8K9XvH/I/3bzaDAJ3NhRwEMsLBKQ=="],
-
-    "@anthropic-ai/sdk": ["@anthropic-ai/sdk@0.30.1", "", { "dependencies": { "@types/node": "^18.11.18", "@types/node-fetch": "^2.6.4", "abort-controller": "^3.0.0", "agentkeepalive": "^4.2.1", "form-data-encoder": "1.7.2", "formdata-node": "^4.3.2", "node-fetch": "^2.6.7" } }, "sha512-nuKvp7wOIz6BFei8WrTdhmSsx5mwnArYyJgh4+vYu3V4J0Ltb8Xm3odPm51n1aSI0XxNCrDl7O88cxCtUdAkaw=="],
-
-    "@aws-crypto/crc32": ["@aws-crypto/crc32@5.2.0", "", { "dependencies": { "@aws-crypto/util": "^5.2.0", "@aws-sdk/types": "^3.222.0", "tslib": "^2.6.2" } }, "sha512-nLbCWqQNgUiwwtFsen1AdzAtvuLRsQS8rYgMuxCrdKf9kOssamGLuPwyTY9wyYblNr9+1XM8v6zoDTPPSIeANg=="],
-
-    "@aws-crypto/crc32c": ["@aws-crypto/crc32c@5.2.0", "", { "dependencies": { "@aws-crypto/util": "^5.2.0", "@aws-sdk/types": "^3.222.0", "tslib": "^2.6.2" } }, "sha512-+iWb8qaHLYKrNvGRbiYRHSdKRWhto5XlZUEBwDjYNf+ly5SVYG6zEoYIdxvf5R3zyeP16w4PLBn3rH1xc74Rag=="],
-
-    "@aws-crypto/sha1-browser": ["@aws-crypto/sha1-browser@5.2.0", "", { "dependencies": { "@aws-crypto/supports-web-crypto": "^5.2.0", "@aws-crypto/util": "^5.2.0", "@aws-sdk/types": "^3.222.0", "@aws-sdk/util-locate-window": "^3.0.0", "@smithy/util-utf8": "^2.0.0", "tslib": "^2.6.2" } }, "sha512-OH6lveCFfcDjX4dbAvCFSYUjJZjDr/3XJ3xHtjn3Oj5b9RjojQo8npoLeA/bNwkOkrSQ0wgrHzXk4tDRxGKJeg=="],
-
-    "@aws-crypto/sha256-browser": ["@aws-crypto/sha256-browser@5.2.0", "", { "dependencies": { "@aws-crypto/sha256-js": "^5.2.0", "@aws-crypto/supports-web-crypto": "^5.2.0", "@aws-crypto/util": "^5.2.0", "@aws-sdk/types": "^3.222.0", "@aws-sdk/util-locate-window": "^3.0.0", "@smithy/util-utf8": "^2.0.0", "tslib": "^2.6.2" } }, "sha512-AXfN/lGotSQwu6HNcEsIASo7kWXZ5HYWvfOmSNKDsEqC4OashTp8alTmaz+F7TC2L083SFv5RdB+qU3Vs1kZqw=="],
-
-    "@aws-crypto/sha256-js": ["@aws-crypto/sha256-js@5.2.0", "", { "dependencies": { "@aws-crypto/util": "^5.2.0", "@aws-sdk/types": "^3.222.0", "tslib": "^2.6.2" } }, "sha512-FFQQyu7edu4ufvIZ+OadFpHHOt+eSTBaYaki44c+akjg7qZg9oOQeLlk77F6tSYqjDAFClrHJk9tMf0HdVyOvA=="],
-
-    "@aws-crypto/supports-web-crypto": ["@aws-crypto/supports-web-crypto@5.2.0", "", { "dependencies": { "tslib": "^2.6.2" } }, "sha512-iAvUotm021kM33eCdNfwIN//F77/IADDSs58i+MDaOqFrVjZo9bAal0NK7HurRuWLLpF1iLX7gbWrjHjeo+YFg=="],
-
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-    "section-matter": ["section-matter@1.0.0", "", { "dependencies": { "extend-shallow": "^2.0.1", "kind-of": "^6.0.0" } }, "sha512-vfD3pmTzGpufjScBh50YHKzEu2lxBWhVEHsNGoEXmCmn2hKGfeNLYMzCJpe8cD7gqX7TJluOVpBkAequ6dgMmA=="],
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-    "send": ["send@1.2.1", "", { "dependencies": { "debug": "^4.4.3", "encodeurl": "^2.0.0", "escape-html": "^1.0.3", "etag": "^1.8.1", "fresh": "^2.0.0", "http-errors": "^2.0.1", "mime-types": "^3.0.2", "ms": "^2.1.3", "on-finished": "^2.4.1", "range-parser": "^1.2.1", "statuses": "^2.0.2" } }, "sha512-1gnZf7DFcoIcajTjTwjwuDjzuz4PPcY2StKPlsGAQ1+YH20IRVrBaXSWmdjowTJ6u8Rc01PoYOGHXfP1mYcZNQ=="],
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-    "serve-static": ["serve-static@2.2.1", "", { "dependencies": { "encodeurl": "^2.0.0", "escape-html": "^1.0.3", "parseurl": "^1.3.3", "send": "^1.2.0" } }, "sha512-xRXBn0pPqQTVQiC8wyQrKs2MOlX24zQ0POGaj0kultvoOCstBQM5yvOhAVSUwOMjQtTvsPWoNCHfPGwaaQJhTw=="],
-
-    "setprototypeof": ["setprototypeof@1.2.0", "", {}, "sha512-E5LDX7Wrp85Kil5bhZv46j8jOeboKq5JMmYM3gVGdGH8xFpPWXUMsNrlODCrkoxMEeNi/XZIwuRvY4XNwYMJpw=="],
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-    "shebang-command": ["shebang-command@2.0.0", "", { "dependencies": { "shebang-regex": "^3.0.0" } }, "sha512-kHxr2zZpYtdmrN1qDjrrX/Z1rR1kG8Dx+gkpK1G4eXmvXswmcE1hTWBWYUzlraYw1/yZp6YuDY77YtvbN0dmDA=="],
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-    "shebang-regex": ["shebang-regex@3.0.0", "", {}, "sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A=="],
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-    "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=="],
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-    "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=="],
-
-    "side-channel-weakmap": ["side-channel-weakmap@1.0.2", "", { "dependencies": { "call-bound": "^1.0.2", "es-errors": "^1.3.0", "get-intrinsic": "^1.2.5", "object-inspect": "^1.13.3", "side-channel-map": "^1.0.1" } }, "sha512-WPS/HvHQTYnHisLo9McqBHOJk2FkHO/tlpvldyrnem4aeQp4hai3gythswg6p01oSoTl58rcpiFAjF2br2Ak2A=="],
-
-    "sprintf-js": ["sprintf-js@1.0.3", "", {}, "sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g=="],
-
-    "statuses": ["statuses@2.0.2", "", {}, "sha512-DvEy55V3DB7uknRo+4iOGT5fP1slR8wQohVdknigZPMpMstaKJQWhwiYBACJE3Ul2pTnATihhBYnRhZQHGBiRw=="],
-
-    "strip-bom-string": ["strip-bom-string@1.0.0", "", {}, "sha512-uCC2VHvQRYu+lMh4My/sFNmF2klFymLX1wHJeXnbEJERpV/ZsVuonzerjfrGpIGF7LBVa1O7i9kjiWvJiFck8g=="],
-
-    "strnum": ["strnum@2.2.3", "", {}, "sha512-oKx6RUCuHfT3oyVjtnrmn19H1SiCqgJSg+54XqURKp5aCMbrXrhLjRN9TjuwMjiYstZ0MzDrHqkGZ5dFTKd+zg=="],
-
-    "toidentifier": ["toidentifier@1.0.1", "", {}, "sha512-o5sSPKEkg/DIQNmH43V0/uerLrpzVedkUh8tGNvaeXpfpuwjKenlSox/2O/BTlZUtEe+JG7s5YhEz608PlAHRA=="],
-
-    "tr46": ["tr46@0.0.3", "", {}, "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw=="],
-
-    "tree-sitter-wasms": ["tree-sitter-wasms@0.1.13", "", { "dependencies": { "tree-sitter-wasms": "^0.1.11" } }, "sha512-wT+cR6DwaIz80/vho3AvSF0N4txuNx/5bcRKoXouOfClpxh/qqrF4URNLQXbbt8MaAxeksZcZd1j8gcGjc+QxQ=="],
-
-    "tslib": ["tslib@2.8.1", "", {}, "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w=="],
-
-    "type-is": ["type-is@2.0.1", "", { "dependencies": { "content-type": "^1.0.5", "media-typer": "^1.1.0", "mime-types": "^3.0.0" } }, "sha512-OZs6gsjF4vMp32qrCbiVSkrFmXtG/AZhY3t0iAMrMBiAZyV9oALtXO8hsrHbMXF9x6L3grlFuwW2oAz7cav+Gw=="],
-
-    "typescript": ["typescript@5.9.3", "", { "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" } }, "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw=="],
-
-    "undici-types": ["undici-types@7.18.2", "", {}, "sha512-AsuCzffGHJybSaRrmr5eHr81mwJU3kjw6M+uprWvCXiNeN9SOGwQ3Jn8jb8m3Z6izVgknn1R0FTCEAP2QrLY/w=="],
-
-    "unpipe": ["unpipe@1.0.0", "", {}, "sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ=="],
-
-    "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=="],
-
-    "webidl-conversions": ["webidl-conversions@3.0.1", "", {}, "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ=="],
-
-    "whatwg-url": ["whatwg-url@5.0.0", "", { "dependencies": { "tr46": "~0.0.3", "webidl-conversions": "^3.0.0" } }, "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw=="],
-
-    "which": ["which@2.0.2", "", { "dependencies": { "isexe": "^2.0.0" }, "bin": { "node-which": "./bin/node-which" } }, "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA=="],
-
-    "wrappy": ["wrappy@1.0.2", "", {}, "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="],
-
-    "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=="],
-
-    "@anthropic-ai/sdk/@types/node": ["@types/node@18.19.130", "", { "dependencies": { "undici-types": "~5.26.4" } }, "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg=="],
-
-    "@aws-crypto/sha1-browser/@smithy/util-utf8": ["@smithy/util-utf8@2.3.0", "", { "dependencies": { "@smithy/util-buffer-from": "^2.2.0", "tslib": "^2.6.2" } }, "sha512-R8Rdn8Hy72KKcebgLiv8jQcQkXoLMOGGv5uI1/k0l+snqkOzQ1R0ChUBCxWMlBsFMekWjq0wRudIweFs7sKT5A=="],
-
-    "@aws-crypto/sha256-browser/@smithy/util-utf8": ["@smithy/util-utf8@2.3.0", "", { "dependencies": { "@smithy/util-buffer-from": "^2.2.0", "tslib": "^2.6.2" } }, "sha512-R8Rdn8Hy72KKcebgLiv8jQcQkXoLMOGGv5uI1/k0l+snqkOzQ1R0ChUBCxWMlBsFMekWjq0wRudIweFs7sKT5A=="],
-
-    "@aws-crypto/util/@smithy/util-utf8": ["@smithy/util-utf8@2.3.0", "", { "dependencies": { "@smithy/util-buffer-from": "^2.2.0", "tslib": "^2.6.2" } }, "sha512-R8Rdn8Hy72KKcebgLiv8jQcQkXoLMOGGv5uI1/k0l+snqkOzQ1R0ChUBCxWMlBsFMekWjq0wRudIweFs7sKT5A=="],
-
-    "@modelcontextprotocol/sdk/eventsource-parser": ["eventsource-parser@3.0.6", "", {}, "sha512-Vo1ab+QXPzZ4tCa8SwIHJFaSzy4R6SHf7BY79rFBDf0idraZWAkYrDjDj8uWaSm3S2TK+hJ7/t1CEmZ7jXw+pg=="],
-
-    "@modelcontextprotocol/sdk/express-rate-limit": ["express-rate-limit@8.3.2", "", { "dependencies": { "ip-address": "10.1.0" }, "peerDependencies": { "express": ">= 4.11" } }, "sha512-77VmFeJkO0/rvimEDuUC5H30oqUC4EyOhyGccfqoLebB0oiEYfM7nwPrsDsBL1gsTpwfzX8SFy2MT3TDyRq+bg=="],
-
-    "@types/bun/bun-types": ["bun-types@1.3.11", "", { "dependencies": { "@types/node": "*" } }, "sha512-1KGPpoxQWl9f6wcZh57LvrPIInQMn2TQ7jsgxqpRzg+l0QPOFvJVH7HmvHo/AiPgwXy+/Thf6Ov3EdVn1vOabg=="],
-
-    "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=="],
-
-    "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=="],
-
-    "@aws-crypto/sha1-browser/@smithy/util-utf8/@smithy/util-buffer-from": ["@smithy/util-buffer-from@2.2.0", "", { "dependencies": { "@smithy/is-array-buffer": "^2.2.0", "tslib": "^2.6.2" } }, "sha512-IJdWBbTcMQ6DA0gdNhh/BwrLkDR+ADW5Kr1aZmd4k3DIF6ezMV4R2NIAmT08wQJ3yUK82thHWmC/TnK/wpMMIA=="],
-
-    "@aws-crypto/sha256-browser/@smithy/util-utf8/@smithy/util-buffer-from": ["@smithy/util-buffer-from@2.2.0", "", { "dependencies": { "@smithy/is-array-buffer": "^2.2.0", "tslib": "^2.6.2" } }, "sha512-IJdWBbTcMQ6DA0gdNhh/BwrLkDR+ADW5Kr1aZmd4k3DIF6ezMV4R2NIAmT08wQJ3yUK82thHWmC/TnK/wpMMIA=="],
-
-    "@aws-crypto/util/@smithy/util-utf8/@smithy/util-buffer-from": ["@smithy/util-buffer-from@2.2.0", "", { "dependencies": { "@smithy/is-array-buffer": "^2.2.0", "tslib": "^2.6.2" } }, "sha512-IJdWBbTcMQ6DA0gdNhh/BwrLkDR+ADW5Kr1aZmd4k3DIF6ezMV4R2NIAmT08wQJ3yUK82thHWmC/TnK/wpMMIA=="],
-
-    "form-data/mime-types/mime-db": ["mime-db@1.52.0", "", {}, "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg=="],
-
-    "openai/@types/node/undici-types": ["undici-types@5.26.5", "", {}, "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="],
-
-    "@aws-crypto/sha1-browser/@smithy/util-utf8/@smithy/util-buffer-from/@smithy/is-array-buffer": ["@smithy/is-array-buffer@2.2.0", "", { "dependencies": { "tslib": "^2.6.2" } }, "sha512-GGP3O9QFD24uGeAXYUjwSTXARoqpZykHadOmA8G5vfJPK0/DC67qa//0qvqrJzL1xc8WQWX7/yc7fwudjPHPhA=="],
-
-    "@aws-crypto/sha256-browser/@smithy/util-utf8/@smithy/util-buffer-from/@smithy/is-array-buffer": ["@smithy/is-array-buffer@2.2.0", "", { "dependencies": { "tslib": "^2.6.2" } }, "sha512-GGP3O9QFD24uGeAXYUjwSTXARoqpZykHadOmA8G5vfJPK0/DC67qa//0qvqrJzL1xc8WQWX7/yc7fwudjPHPhA=="],
-
-    "@aws-crypto/util/@smithy/util-utf8/@smithy/util-buffer-from/@smithy/is-array-buffer": ["@smithy/is-array-buffer@2.2.0", "", { "dependencies": { "tslib": "^2.6.2" } }, "sha512-GGP3O9QFD24uGeAXYUjwSTXARoqpZykHadOmA8G5vfJPK0/DC67qa//0qvqrJzL1xc8WQWX7/yc7fwudjPHPhA=="],
-  }
-}
@@ -1,9 +0,0 @@
-[test]
-# PGLite WASM cold start + initSchema() runs ~5–20s on loaded machines.
-# Default 5s is too short for those tests' beforeAll hooks. 60s is the
-# empirical ceiling we observed for the slowest cold-init paths.
-#
-# v0.26.4: scripts/run-unit-parallel.sh and scripts/run-unit-shard.sh
-# 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
@@ -1,117 +0,0 @@
-# docker-compose.ci.yml
-#
-# Local CI gate with 4-way E2E sharding. Spins up 4 pgvector services + a bun
-# runner that bind-mounts the repo. Used by `bun run ci:local` and
-# `bun run ci:local:diff` (see scripts/ci-local.sh).
-#
-# All services are pulled as `image:` (no build) so `docker compose pull`
-# refreshes everything. The bun version floats with `oven/bun:1` to track CI's
-# `bun-version: latest`. Named volumes isolate the Linux container's deps from
-# the host's darwin-arm64 deps and keep bun + postgres data warm across runs.
-#
-# Why 4 postgres services: bun's E2E suite shares one DB across 36 files and
-# uses TRUNCATE CASCADE in setupDB(). Running files in parallel against ONE DB
-# races (file A's TRUNCATE clobbers file B's fixture import). 4 separate DBs
-# remove the race; we shard the file list 1/4..4/4 and run shards in parallel.
-# Within a shard, files still run sequentially. Total wall-time on a 16-core
-# host: ~6 min sequential -> ~1.5-2 min sharded.
-#
-# Postgres host ports default to 5434-5437 (avoid 5432 manual `gbrain-test-pg`
-# and 5433 sibling-project conflicts). Override BASE port with GBRAIN_CI_PG_PORT;
-# shards take BASE..BASE+3.
-
-services:
-  postgres-1:
-    image: pgvector/pgvector:pg16
-    environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: gbrain_test
-    ports:
-      - "${GBRAIN_CI_PG_PORT:-5434}:5432"
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U postgres -d gbrain_test"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-    volumes:
-      - gbrain-ci-pg-data-1:/var/lib/postgresql/data
-
-  postgres-2:
-    image: pgvector/pgvector:pg16
-    environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: gbrain_test
-    ports:
-      - "${GBRAIN_CI_PG_PORT_2:-5435}:5432"
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U postgres -d gbrain_test"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-    volumes:
-      - gbrain-ci-pg-data-2:/var/lib/postgresql/data
-
-  postgres-3:
-    image: pgvector/pgvector:pg16
-    environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: gbrain_test
-    ports:
-      - "${GBRAIN_CI_PG_PORT_3:-5436}:5432"
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U postgres -d gbrain_test"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-    volumes:
-      - gbrain-ci-pg-data-3:/var/lib/postgresql/data
-
-  postgres-4:
-    image: pgvector/pgvector:pg16
-    environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: gbrain_test
-    ports:
-      - "${GBRAIN_CI_PG_PORT_4:-5437}:5432"
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U postgres -d gbrain_test"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-    volumes:
-      - gbrain-ci-pg-data-4:/var/lib/postgresql/data
-
-  runner:
-    image: oven/bun:1
-    working_dir: /app
-    depends_on:
-      postgres-1:
-        condition: service_healthy
-      postgres-2:
-        condition: service_healthy
-      postgres-3:
-        condition: service_healthy
-      postgres-4:
-        condition: service_healthy
-    # 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:
-      - .:/app
-      # Linux container's node_modules MUST be isolated from host darwin-arm64.
-      # Without this, container `bun install` stomps host node_modules and
-      # subsequent `bun test` on host fails with binary-incompat errors.
-      - gbrain-ci-node-modules:/app/node_modules
-      # Warm install cache across runs.
-      - gbrain-ci-bun-cache:/root/.bun/install/cache
-
-volumes:
-  gbrain-ci-pg-data-1:
-  gbrain-ci-pg-data-2:
-  gbrain-ci-pg-data-3:
-  gbrain-ci-pg-data-4:
-  gbrain-ci-node-modules:
-  gbrain-ci-bun-cache:
@@ -1,14 +0,0 @@
-services:
-  postgres:
-    image: pgvector/pgvector:pg16
-    environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: gbrain_test
-    ports:
-      - "5434:5432"
-    healthcheck:
-      test: pg_isready -U postgres
-      interval: 5s
-      timeout: 3s
-      retries: 5
@@ -1,234 +0,0 @@
-# Pluggable Engine Architecture
-
-## The idea
-
-Every GBrain operation goes through `BrainEngine`. The engine is the contract between "what the brain can do" and "how it's stored." Swap the engine, keep everything else.
-
-v0 shipped `PostgresEngine` backed by Supabase. v0.7 adds `PGLiteEngine` -- embedded Postgres 17.5 via WASM (@electric-sql/pglite), zero-config default. The interface is designed so a `DuckDBEngine`, `TursoEngine`, or any custom backend could slot in without touching the CLI, MCP server, skills, or any consumer code.
-
-## Why this matters
-
-Different users have different constraints:
-
-| User | Needs | Best engine |
-|------|-------|-------------|
-| Getting started | Zero-config, no accounts, no server | PGLiteEngine (default since v0.7) |
-| Power user (you) | World-class search, 7K+ pages, zero-ops | PostgresEngine + Supabase |
-| Open source hacker | Single file, no server, git-friendly | PGLiteEngine |
-| Team/enterprise | Multi-user, RLS, audit trail | PostgresEngine + self-hosted |
-| Researcher | Analytics, bulk exports, embeddings | DuckDBEngine (someday) |
-| Edge/mobile | Offline-first, sync later | PGLiteEngine + sync (someday) |
-
-The engine interface means we don't have to choose. PGLite is the zero-friction default. Supabase is the production scale path. `gbrain migrate --to supabase/pglite` moves between them.
-
-## The interface
-
-```typescript
-// src/core/engine.ts
-
-export interface BrainEngine {
-  // Lifecycle
-  connect(config: EngineConfig): Promise<void>;
-  disconnect(): Promise<void>;
-  initSchema(): Promise<void>;
-  transaction<T>(fn: (engine: BrainEngine) => Promise<T>): Promise<T>;
-
-  // Pages CRUD
-  getPage(slug: string): Promise<Page | null>;
-  putPage(slug: string, page: PageInput): Promise<Page>;
-  deletePage(slug: string): Promise<void>;
-  listPages(filters: PageFilters): Promise<Page[]>;
-
-  // Search
-  searchKeyword(query: string, opts?: SearchOpts): Promise<SearchResult[]>;
-  searchVector(embedding: Float32Array, opts?: SearchOpts): Promise<SearchResult[]>;
-
-  // Chunks
-  upsertChunks(slug: string, chunks: ChunkInput[]): Promise<void>;
-  getChunks(slug: string): Promise<Chunk[]>;
-
-  // Links
-  addLink(from: string, to: string, context?: string, linkType?: string): Promise<void>;
-  removeLink(from: string, to: string): Promise<void>;
-  getLinks(slug: string): Promise<Link[]>;
-  getBacklinks(slug: string): Promise<Link[]>;
-  traverseGraph(slug: string, depth?: number): Promise<GraphNode[]>;
-
-  // Tags
-  addTag(slug: string, tag: string): Promise<void>;
-  removeTag(slug: string, tag: string): Promise<void>;
-  getTags(slug: string): Promise<string[]>;
-
-  // Timeline
-  addTimelineEntry(slug: string, entry: TimelineInput): Promise<void>;
-  getTimeline(slug: string, opts?: TimelineOpts): Promise<TimelineEntry[]>;
-
-  // Raw data
-  putRawData(slug: string, source: string, data: object): Promise<void>;
-  getRawData(slug: string, source?: string): Promise<RawData[]>;
-
-  // Versions
-  createVersion(slug: string): Promise<PageVersion>;
-  getVersions(slug: string): Promise<PageVersion[]>;
-  revertToVersion(slug: string, versionId: number): Promise<void>;
-
-  // Stats + health
-  getStats(): Promise<BrainStats>;
-  getHealth(): Promise<BrainHealth>;
-
-  // Ingest log
-  logIngest(entry: IngestLogInput): Promise<void>;
-  getIngestLog(opts?: IngestLogOpts): Promise<IngestLogEntry[]>;
-
-  // Config
-  getConfig(key: string): Promise<string | null>;
-  setConfig(key: string, value: string): Promise<void>;
-
-  // Migration + advanced (added v0.7)
-  runMigration(sql: string): Promise<void>;
-  getChunksWithEmbeddings(slug: string): Promise<ChunkWithEmbedding[]>;
-}
-```
-
-### Key design choices
-
-**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. 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.
-
-**Search returns `SearchResult[]`, not raw rows.** The engine is responsible for its own search implementation (tsvector vs FTS5, pgvector vs sqlite-vss) but must return a uniform result type. RRF fusion and dedup happen above the engine, in `src/core/search/hybrid.ts`.
-
-**`traverseGraph` exists but is engine-specific.** Postgres uses recursive CTEs. SQLite would use a loop with depth tracking. The interface is the same: give me a slug and max depth, return the graph.
-
-## How search works across engines
-
-```
-                        +-------------------+
-                        |  hybrid.ts        |
-                        |  (RRF fusion +    |
-                        |   dedup, shared)  |
-                        +--------+----------+
-                                 |
-                    +------------+------------+
-                    |                         |
-           +--------v--------+       +--------v--------+
-           | engine.search   |       | engine.search   |
-           |   Keyword()     |       |   Vector()      |
-           +-----------------+       +-----------------+
-                    |                         |
-        +-----------+-----------+   +---------+---------+
-        |                       |   |                   |
-+-------v-------+  +-------v---+   +-------v---+  +----v--------+
-| Postgres:     |  | PGLite:   |   | Postgres: |  | PGLite:     |
-| tsvector +    |  | tsvector +|   | pgvector  |  | pgvector    |
-| ts_rank +     |  | ts_rank   |   | HNSW      |  | HNSW        |
-| websearch_to_ |  | (same SQL)|   | cosine    |  | cosine      |
-| tsquery       |  |           |   |           |  | (same SQL)  |
-+---------------+  +-----------+   +-----------+  +-------------+
-```
-
-RRF fusion, multi-query expansion, and 4-layer dedup are engine-agnostic. They operate on `SearchResult[]` arrays. Only the raw keyword and vector searches are engine-specific.
-
-## PostgresEngine (v0, ships)
-
-**Dependencies:** `postgres` (porsager/postgres), `pgvector`
-
-**Postgres-specific features used:**
- `tsvector` + `GIN` index for full-text search with `ts_rank` weighting
- `pgvector` HNSW index for cosine similarity vector search
- `pg_trgm` + `GIN` for fuzzy slug resolution
- Recursive CTEs for graph traversal
- Trigger-based search_vector (spans pages + timeline_entries)
- JSONB for frontmatter with GIN index
- Connection pooling via Supabase Supavisor (port 6543)
-
-**Hosting:** Supabase Pro ($25/mo). Zero-ops. Managed Postgres with pgvector built in.
-
-**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.
-
-## PGLiteEngine (v0.7, ships)
-
-**Dependencies:** `@electric-sql/pglite` (v0.4.4+)
-
-**What it is:** Embedded Postgres 17.5 compiled to WASM via ElectricSQL's PGLite. Runs in-process, no server, no Docker, no accounts. Same SQL as PostgresEngine -- not a separate dialect. All 37 BrainEngine methods implemented.
-
-**PGLite-specific details:**
- Uses `pglite-schema.ts` for DDL (pgvector extension, pg_trgm, triggers, indexes)
- Parameterized queries throughout (shared utilities in `src/core/utils.ts`)
- `hybridSearch` keyword-only fallback when `OPENAI_API_KEY` is not set
- Data stored at `~/.gbrain/brain.db` (configurable)
- pgvector HNSW index for cosine similarity vector search (same as Postgres)
- tsvector + ts_rank for full-text search (same as Postgres)
- pg_trgm for fuzzy slug resolution (same as Postgres)
-
-**When to use PGLite vs Postgres:**
-
-| Factor | PGLite | PostgresEngine + Supabase |
-|--------|--------|--------------------------|
-| Setup | `gbrain init` (zero-config) | Account + connection string |
-| Scale | Good for < 1,000 files | Production-proven at 10K+ |
-| Multi-device | Single machine only | Any device via remote MCP |
-| Cost | Free | Supabase Pro ($25/mo) |
-| Concurrency | Single process | Connection pooling |
-| Backups | Manual (file copy) | Managed by Supabase |
-
-**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.
-
-## Adding a new engine
-
-1. Create `src/core/<name>-engine.ts` implementing `BrainEngine`
-2. Add to engine factory in `src/core/engine-factory.ts`:
-   ```typescript
-   export function createEngine(type: string): BrainEngine {
-     switch (type) {
-       case 'pglite': return new PGLiteEngine();
-       case 'postgres': return new PostgresEngine();
-       case 'myengine': return new MyEngine();
-       default: throw new Error(`Unknown engine: ${type}`);
-     }
-   }
-   ```
-   The factory uses dynamic imports so engines are only loaded when selected.
-3. Store engine type in `~/.gbrain/config.json`: `{ "engine": "myengine", ... }`
-4. Add tests. The test suite should be engine-agnostic where possible... same test cases, different engine constructor.
-5. Document in this file + add a design doc in `docs/`
-
-### What you DON'T need to touch
-
- `src/cli.ts` (dispatches to engine, doesn't know which one)
- `src/mcp/server.ts` (same)
- `src/core/chunkers/*` (shared across engines)
- `src/core/embedding.ts` (shared across engines)
- `src/core/search/hybrid.ts`, `expansion.ts`, `dedup.ts` (shared, operate on SearchResult[])
- `skills/*` (fat markdown, engine-agnostic)
-
-### What you DO need to implement
-
-Every method in `BrainEngine`. The full interface. No optional methods, no feature flags. If your engine can't do vector search (e.g., a pure-text engine), implement `searchVector` to return `[]` and document the limitation.
-
-## Capability matrix
-
-| Capability | PostgresEngine | PGLiteEngine | Notes |
-|-----------|---------------|-------------|-------|
-| CRUD | Full | Full | Same SQL |
-| Keyword search | tsvector + ts_rank | tsvector + ts_rank | Identical (real Postgres) |
-| Vector search | pgvector HNSW | pgvector HNSW | Identical (real Postgres) |
-| Fuzzy slug | pg_trgm | pg_trgm | Identical (real Postgres) |
-| Graph traversal | Recursive CTE | Recursive CTE | Same SQL |
-| Transactions | Full ACID | Full ACID | Both support this |
-| JSONB queries | GIN index | GIN index | Identical |
-| Concurrent access | Connection pooling | Single process | PGLite limitation |
-| Hosting | Supabase, self-hosted, Docker | Local file | |
-| Migration methods | runMigration, getChunksWithEmbeddings | Same | Added v0.7 |
-
-## Future engine ideas
-
-**TursoEngine.** libSQL (SQLite fork) with embedded replicas and HTTP edge access. Would give SQLite's simplicity with cloud sync. Interesting for mobile/edge use cases.
-
-**DuckDBEngine.** Analytical workloads. Bulk exports, embedding analysis, brain-wide statistics. Not for OLTP. Could be a secondary engine for analytics alongside Postgres for operations.
-
-**Custom/Remote.** The interface is clean enough that someone could build an engine backed by any storage: Firestore, DynamoDB, a REST API, even a flat file system. The interface doesn't assume SQL.
-
-Note: The original SQLite engine plan (`docs/SQLITE_ENGINE.md`) was superseded by PGLite. PGLite uses the same SQL as Postgres, eliminating the need for a separate SQLite dialect with FTS5/sqlite-vss translation.
@@ -1,143 +0,0 @@
-<!-- skillpack-version: 0.7.0 -->
-<!-- source: https://raw.githubusercontent.com/garrytan/gbrain/master/docs/GBRAIN_SKILLPACK.md -->
-# GBrain Skillpack: Reference Architecture for AI Agents
-
-This is a reference architecture for how a production AI agent uses gbrain as its
-knowledge backbone. Based on patterns from a real deployment with 14,700+ brain
-files, 40+ skills, and 20+ cron jobs running continuously.
-
-**The memex vision, realized.** Vannevar Bush imagined a device where an individual
-stores everything, mechanized so it may be consulted with exceeding speed. GBrain is
-that device, except the memex builds itself. The agent detects entities, enriches
-pages, creates cross-references, and maintains compiled truth automatically.
-
-Each section below is a standalone guide. Click through to the full content.
-
---
-
-## Core Patterns
-
-The foundational read-write loop and data model.
-
-| Guide | What It Covers |
-|-------|---------------|
-| [The Brain-Agent Loop](guides/brain-agent-loop.md) | The read-write cycle that makes the brain compound over time |
-| [Entity Detection](guides/entity-detection.md) | Run it on every message. Capture original thinking + entity mentions |
-| [The Originals Folder](guides/originals-folder.md) | Capturing WHAT YOU THINK, not just what you found |
-| [Brain-First Lookup](guides/brain-first-lookup.md) | Check the brain before calling any external API |
-| [Compiled Truth + Timeline](guides/compiled-truth.md) | Above the line: current synthesis. Below: append-only evidence |
-| [Source Attribution](guides/source-attribution.md) | Every fact needs a citation. Format and hierarchy |
-
-## Data Pipelines
-
-Getting data in and keeping it current.
-
-| Guide | What It Covers |
-|-------|---------------|
-| [Enrichment Pipeline](guides/enrichment-pipeline.md) | 7-step protocol, tier system (Tier 1/2/3 by importance) |
-| [Meeting Ingestion](guides/meeting-ingestion.md) | Always pull complete transcript, propagate to all entity pages |
-| [Content & Media Ingestion](guides/content-media.md) | YouTube, social media bundles, PDFs/documents |
-| [Diligence Ingestion](guides/diligence-ingestion.md) | Data room materials: pitch decks, financial models, cap tables |
-| [Deterministic Collectors](guides/deterministic-collectors.md) | Code for data, LLMs for judgment. The collector pattern |
-| [Idea Capture & Originals](guides/idea-capture.md) | Depth test, originality distribution, deep cross-linking |
-| [Getting Data In](integrations/README.md) | Integration recipes: voice, email, X, calendar |
-
-## Operations
-
-Running a production brain.
-
-| Guide | What It Covers |
-|-------|---------------|
-| [Reference Cron Schedule](guides/cron-schedule.md) | 20+ recurring jobs, quiet hours, dream cycle |
-| [Cron via Minions](../skills/conventions/cron-via-minions.md) | Why scheduled work runs as Minion jobs, not `agentTurn`. Auto-applied by v0.11.0 migration for built-in handlers; host-specific handlers use the plugin contract below. |
-| [Plugin Handlers](guides/plugin-handlers.md) | Registering host-specific Minion handlers via code (no data-file exec surface). |
-| [Minions fix](guides/minions-fix.md) | Repairing a half-migrated v0.11.0 install. |
-| [Shell jobs (v0.14.0+)](guides/minions-shell-jobs.md) | Move deterministic crons (API fetch, token refresh, scrape+write) off the LLM gateway. Zero tokens per fire, ~60% gateway headroom. Follow `skills/migrations/v0.14.0.md` for the adoption playbook. |
-| [Quiet Hours & Timezone](guides/quiet-hours.md) | Hold notifications during sleep, timezone-aware delivery |
-| [Executive Assistant Pattern](guides/executive-assistant.md) | Email triage, meeting prep, scheduling |
-| [Operational Disciplines](guides/operational-disciplines.md) | Signal detection, brain-first, sync-after-write, heartbeat, dream cycle |
-| [Skill Development Cycle](guides/skill-development.md) | 5-step cycle: concept, prototype, evaluate, codify, cron |
-
-**Subagent routing (v0.11.0+):** agents that dispatch background work should route through
-`skills/conventions/subagent-routing.md` — it reads `~/.gbrain/preferences.json#minion_mode`
-and branches between native subagents and Minion jobs. The v0.11.0 migration auto-injects
-a marker into AGENTS.md pointing at this convention.
-
-**Cron routing (v0.11.0+):** scheduled work goes through Minions, not OpenClaw's `agentTurn`.
-See `skills/conventions/cron-via-minions.md` for the rewrite pattern. The v0.11.0 migration
-auto-rewrites entries whose handler is a gbrain builtin; host-specific handlers (e.g.
-`ea-inbox-sweep`) need a code-level registration per `docs/guides/plugin-handlers.md`.
-
-## Architecture
-
-How to structure your system.
-
-| Guide | What It Covers |
-|-------|---------------|
-| [Two-Repo Architecture](guides/repo-architecture.md) | Agent repo vs brain repo, boundary rules, decision tree |
-| [Sub-Agent Model Routing](guides/sub-agent-routing.md) | Which model for which task, signal detector pattern, cost optimization |
-| [The Three Search Modes](guides/search-modes.md) | Keyword, hybrid, direct. When to use each |
-| [Brain vs Agent Memory](guides/brain-vs-memory.md) | 3 layers: GBrain (world knowledge), agent memory, session |
-
-## Integrations
-
-Wiring up your life.
-
-| Guide | What It Covers |
-|-------|---------------|
-| [Credential Gateway](integrations/credential-gateway.md) | ClawVisor / Hermes for Gmail, Calendar, Contacts |
-| [Meeting & Call Webhooks](integrations/meeting-webhooks.md) | Circleback transcripts + Quo/OpenPhone SMS/calls |
-| [Voice-to-Brain](../recipes/twilio-voice-brain.md) | Phone calls + WebRTC browser calls create brain pages. 25 production patterns: identity separation, bid system, conversation timing, proactive advisor, prompt compression, caller routing, dynamic VAD, real-time logging, belt-and-suspenders post-call |
-| [Email-to-Brain](../recipes/email-to-brain.md) | Gmail messages flow into entity pages via deterministic collector |
-| [X-to-Brain](../recipes/x-to-brain.md) | Twitter monitoring with deletion detection + engagement velocity |
-| [Calendar-to-Brain](../recipes/calendar-to-brain.md) | Google Calendar events become searchable daily brain pages |
-| [Meeting Sync](../recipes/meeting-sync.md) | Circleback transcripts auto-import with attendee propagation |
-
-## Administration
-
-Keeping it running and up to date.
-
-| Guide | What It Covers |
-|-------|---------------|
-| [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
-
-| Command | Purpose |
-|---------|---------|
-| `gbrain search "term"` | Keyword search across all brain pages |
-| `gbrain query "question"` | Hybrid search (vector + keyword + RRF) |
-| `gbrain get <slug>` | Read a specific brain page by slug |
-| `gbrain sync` | Sync local markdown repo to gbrain index |
-| `gbrain import <path>` | Import files into the brain |
-| `gbrain embed --stale` | Re-embed pages with stale or missing embeddings |
-| `gbrain integrations` | Manage integration recipes (senses + reflexes) |
-| `gbrain stats` | Show brain statistics (page count, last sync, etc.) |
-| `gbrain doctor` | Diagnose brain health issues |
-| `gbrain check-update` | Check for new versions and integration recipes |
-
-Run `gbrain --help` for the full command reference.
-
---
-
-## Architecture & Philosophy
-
- [Infrastructure Layer](architecture/infra-layer.md) — Import pipeline, chunking, embedding, search
- [Thin Harness, Fat Skills](ethos/THIN_HARNESS_FAT_SKILLS.md) — Architecture philosophy
- [Markdown Skills as Recipes](ethos/MARKDOWN_SKILLS_AS_RECIPES.md) — Why markdown is code and your agent is a package manager
- [Homebrew for Personal AI](designs/HOMEBREW_FOR_PERSONAL_AI.md) — The 10-star vision
- [Recommended Schema](GBRAIN_RECOMMENDED_SCHEMA.md) — Directory structure for your brain repo
- [Verification Runbook](GBRAIN_VERIFY.md) — End-to-end installation verification
@@ -1,544 +0,0 @@
-# GBrain v0: Postgres-Native Personal Knowledge Brain
-
-## What this is
-
-GBrain is a compiled intelligence system. Not a note-taking app. Not "chat with your notes."
-
-Every page is an intelligence assessment. Above the line: compiled truth (your current best understanding, rewritten when evidence changes). Below the line: timeline (append-only evidence trail). AI agents maintain the brain. MCP clients query it. The intelligence lives in fat markdown skills, not application code.
-
-The core insight: personal knowledge at scale is an intelligence problem, not a storage problem.
-
-## Why it exists
-
-A 7,471-file / 2.3GB markdown wiki is choking git. Git doesn't scale past ~5K files for wiki-style use. The compiled truth + timeline model (Karpathy-style knowledge pages) is right, but it needs a real database underneath.
-
-There's already a production-grade RAG system (Ruby on Rails, Postgres + pgvector) with 3-tier chunking, hybrid search with RRF, multi-query expansion, and 4-layer dedup. GBrain ports these proven patterns to a standalone Bun + TypeScript tool.
-
-## The knowledge model
-
-```
-+--------------------------------------------------+
-|  Page: concepts/do-things-that-dont-scale         |
-|                                                   |
-|  --- frontmatter (YAML) ---                       |
-|  type: concept                                    |
-|  tags: [startups, growth, pg-essay]               |
-|                                                   |
-|  === COMPILED TRUTH ===                           |
-|  Current best understanding.                      |
-|  Rewritten on new evidence.                       |
-|  This is the "what we know now" section.          |
-|                                                   |
-|  ---                                              |
-|                                                   |
-|  === TIMELINE ===                                 |
-|  Append-only evidence trail.                      |
-|  - 2013-07-01: Published on paulgraham.com        |
-|  - 2024-11-15: Referenced in batch kickoff talk   |
-|  Never edited, only appended.                     |
-+--------------------------------------------------+
-          |                    |
-          v                    v
-  [Semantic chunks]     [Recursive chunks]
-  (best quality for     (predictable format
-   compiled truth)       for timeline)
-          |                    |
-          v                    v
-     [Embeddings: text-embedding-3-large, 1536 dims]
-          |
-          v
-  [HNSW index + tsvector + pg_trgm]
-          |
-          v
-  [Hybrid search: vector + keyword + RRF fusion]
-```
-
-## Architecture decisions
-
-### v0 stack
-
-| Layer | Choice | Why |
-|-------|--------|-----|
-| Database | Postgres + pgvector | Proven RAG patterns, production-tested. World-class hybrid search. |
-| Hosting | Supabase Pro ($25/mo) | Zero-ops. Managed Postgres, pgvector, connection pooling. 8GB storage. |
-| Runtime | Bun + TypeScript | Consistent with GStack ecosystem. Fast. Compiles to single binary. |
-| Embeddings | OpenAI text-embedding-3-large | 1536 dims (reduced from 3072 via dimensions API). ~$0.13/1M tokens. |
-| LLM (chunking/expansion) | Claude Haiku | Cheapest model for topic boundary detection and query expansion. |
-| Background jobs | Trigger.dev | Serverless. Embed backfill, stale detection, orphan audit, tag consistency. |
-| Distribution | npm package + compiled binary + MCP server | Library for OpenClaw, CLI for humans, MCP for agents. |
-
-### What we chose and why
-
-**Postgres over SQLite.** We have 3+ years of proven RAG patterns running on Postgres. tsvector for full-text search, pgvector HNSW for semantic search, pg_trgm for fuzzy slug matching. Porting these to SQLite would mean reimplementing search from scratch. SQLite is a future pluggable engine for lightweight open source users (see `docs/ENGINES.md`).
-
-**Supabase over self-hosted.** Zero maintenance. The brain should be infrastructure that AI agents use, not something you administer. Free tier has pgvector but only 500MB (not enough for 7K+ pages with embeddings, which need ~750MB). Pro tier at $25/mo gives 8GB. No Docker, no self-hosted Postgres in v1.
-
-**Full port over minimal viable.** The patterns are proven. The port is mechanical. Shipping the full 3-tier chunking + hybrid search + 4-layer dedup means world-class RAG from day one. "We'll add that later" means rebuilding everything later.
-
-**Library-first distribution.** gbrain is an npm package. OpenClaw installs it as a dependency (`bun add gbrain`), imports the engine directly. Zero-overhead function calls, shared connection pool, TypeScript types. The CLI and MCP server are thin wrappers over the same engine.
-
-**Trigger-based tsvector (not generated column).** To include timeline_entries content in full-text search, the tsvector needs to span multiple tables. Generated columns can't do cross-table references. A trigger on pages + timeline_entries updates the search_vector.
-
-**Auto-embed during import.** No separate embed step. `gbrain import` chunks and embeds in one pass. Progress bar shows status. `--no-embed` flag for users who want to defer. `embedded_at` column enables `gbrain embed --stale` for backfill.
-
-## Distribution model
-
-```
-+-------------------+     +-------------------+     +-------------------+
-|   npm package     |     |  Compiled binary  |     |   MCP server      |
-|   (library)       |     |  (CLI)            |     |   (stdio)         |
-+-------------------+     +-------------------+     +-------------------+
-|                   |     |                   |     |                   |
-| bun add gbrain    |     | GitHub Releases   |     | gbrain serve      |
-| import { Postgres |     | npx gbrain        |     | in mcp.json       |
-|   Engine }        |     |                   |     |                   |
-|                   |     |                   |     |                   |
-| WHO: OpenClaw,    |     | WHO: Humans       |     | WHO: Claude Code,  |
-| AlphaClaw         |     |                   |     | Cursor, etc.      |
-+-------------------+     +-------------------+     +-------------------+
-         |                         |                         |
-         +-------------------------+-------------------------+
-                                   |
-                          +--------v--------+
-                          |  BrainEngine    |
-                          |  (pluggable     |
-                          |   interface)    |
-                          +-----------------+
-                                   |
-                     +-------------+-------------+
-                     |                           |
-              +------v------+            +-------v-------+
-              | Postgres    |            | SQLite        |
-              | Engine      |            | Engine        |
-              | (v0, ships) |            | (future, see  |
-              +-------------+            | ENGINES.md)   |
-                                         +---------------+
-```
-
-package.json exports:
- Library: `src/core/index.ts` (BrainEngine interface, PostgresEngine, types)
- CLI binary: `src/cli.ts`
-
-## First-time experience
-
-### Path 1: OpenClaw user (primary)
-
-OpenClaw is the AI orchestrator that uses gbrain as its knowledge backend. This is the most common install path.
-
-```bash
-# 1. Install gbrain as a ClawHub skill
-clawhub install gbrain
-
-# 2. The skill runs guided setup on first use:
-#    - Detects if Supabase CLI is available
-#    - If yes: auto-provisions a new Supabase project
-#    - If no: prompts for connection URL
-#    - Runs schema migration
-#    - Scans for markdown repos and imports user's content
-#    - Shows live entity/edge extraction animation
-#    - Brain is ready
-
-# 3. From OpenClaw, brain tools are now available:
-#    "Search the brain for [topic from your data]"
-#    "Ingest my meeting notes from today"
-#    "How many pages are in the brain?"
-```
-
-Behind the scenes, `clawhub install gbrain`:
-1. Installs the `gbrain` npm package
-2. Ships SKILL.md files (ingest, query, maintain, enrich, briefing, migrate)
-3. Registers brain tools with the orchestrator
-4. Runs `gbrain init --supabase` on first use (guided wizard)
-
-### Path 2: CLI user (standalone)
-
-```bash
-# 1. Install
-npm install -g gbrain
-# or: download binary from GitHub Releases
-
-# 2. Initialize with Supabase
-gbrain init --supabase
-# Guided wizard:
-#   Try 1: Supabase CLI auto-provision (npx supabase)
-#   Try 2: If CLI not installed or not logged in, fallback:
-#          "Enter your Supabase connection URL:"
-#   Then: runs schema migration, verifies pgvector extension
-#   Then: verifies database is ready for import
-#   Output: "Brain ready. Run: gbrain import <your-repo>"
-
-# 3. Import your data
-gbrain import /path/to/markdown/wiki/
-# Progress bar: 7,471 files, auto-chunk, auto-embed
-# ~30s for text import, ~10-15 min for embedding
-
-# 4. Query
-gbrain query "what does PG say about doing things that don't scale?"
-```
-
-### Path 3: MCP user (Claude Code, Cursor)
-
-```json
-// ~/.config/claude/mcp.json
-{
-  "mcpServers": {
-    "gbrain": {
-      "command": "gbrain",
-      "args": ["serve"]
-    }
-  }
-}
-```
-
-Then in Claude Code: "Search my brain for people who know about robotics"
-
-### The init wizard in detail
-
-`gbrain init --supabase` runs through these steps:
-
-```
-Step 1: Database Setup
-  ├── Check for Supabase CLI (npx supabase --version)
-  │   ├── Found + logged in → auto-create project
-  │   │   ├── Create project via supabase CLI
-  │   │   ├── Wait for project to be ready
-  │   │   └── Extract connection string
-  │   ├── Found + not logged in →
-  │   │   └── Error: "Supabase CLI found but not logged in."
-  │   │         Cause: "You need to authenticate first."
-  │   │         Fix: "Run: npx supabase login"
-  │   │         Docs: "https://supabase.com/docs/guides/cli"
-  │   └── Not found → fallback to manual
-  │       └── Prompt: "Enter your Supabase connection URL:"
-  │
-Step 2: Schema Migration
-  ├── Connect to database
-  ├── CREATE EXTENSION IF NOT EXISTS vector
-  ├── CREATE EXTENSION IF NOT EXISTS pg_trgm
-  ├── Run src/schema.sql (all tables, indexes, triggers)
-  └── Verify: test insert + vector query
-
-Step 3: Config
-  ├── Write ~/.gbrain/config.json (0600 permissions)
-  │   { "database_url": "...", "service_role_key": "..." }
-  └── Verify connection
-
-Step 4: Kindling Import
-  ├── Import 10 bundled PG essays as demo data
-  ├── Chunk + embed each essay
-  ├── Show live entity/edge extraction animation:
-  │   "Extracting entities... Paul Graham (person), Y Combinator (company)..."
-  │   "Creating links... Paul Graham → Y Combinator (founded)..."
-  └── Output: "Brain ready. 10 pages imported."
-
-Step 5: First Query
-  └── "Try: gbrain query 'what does PG say about doing things that don't scale?'"
-```
-
-Every error follows the style guide: problem + cause + fix + docs link.
-
-## CLI commands
-
-```
-gbrain init [--supabase|--url <conn>]     # create brain
-gbrain get <slug>                          # read a page
-gbrain put <slug> [< file.md]             # write/update a page
-gbrain search <query>                      # keyword search (tsvector)
-gbrain query <question>                    # hybrid search (RRF + expansion)
-gbrain ingest <file> [--type ...]         # ingest a source document
-gbrain link <from> <to> [--type <type>]   # create typed link
-gbrain unlink <from> <to>                 # remove link
-gbrain graph <slug> [--depth 5]           # traverse link graph (recursive CTE)
-gbrain backlinks <slug>                    # incoming links
-gbrain tags <slug>                         # list tags
-gbrain tag <slug> <tag>                    # add tag
-gbrain untag <slug> <tag>                  # remove tag
-gbrain timeline [<slug>]                   # view timeline
-gbrain timeline-add <slug> <date> <text>  # add timeline entry
-gbrain list [--type] [--tag] [--limit]    # list with filters
-gbrain stats                               # brain statistics
-gbrain health                              # brain health dashboard
-gbrain import <dir> [--no-embed]          # import from markdown directory
-gbrain export [--dir ./export/]           # export to markdown (round-trip)
-gbrain embed [<slug>|--all|--stale]       # generate/refresh embeddings
-gbrain serve                               # MCP server (stdio)
-gbrain call <tool> '<json>'               # raw tool invocation
-gbrain upgrade                             # self-update (npm, binary, ClawHub)
-gbrain version                             # version info
-gbrain config [get|set] <key> [value]     # brain config
-```
-
-CLI and MCP expose identical operations. Drift tests assert identical results for all operations across both interfaces.
-
-## Database schema
-
-9 tables in Postgres + pgvector:
-
-```
-+------------------+     +-------------------+     +------------------+
-|     pages        |---->|  content_chunks   |     |     links        |
-|------------------|     |-------------------|     |------------------|
-| id (PK)          |     | id (PK)           |     | id (PK)          |
-| slug (UNIQUE)    |     | page_id (FK)      |     | from_page_id(FK) |
-| type             |     | chunk_index       |     | to_page_id (FK)  |
-| title            |     | chunk_text        |     | link_type        |
-| compiled_truth   |     | chunk_source      |     | context          |
-| timeline         |     | embedding (1536)  |     +------------------+
-| frontmatter(JSONB)|    | model             |
-| search_vector    |     | token_count       |     +------------------+
-| created_at       |     | embedded_at       |     |     tags         |
-| updated_at       |     +-------------------+     |------------------|
-+------------------+                                | id (PK)          |
-       |                                            | page_id (FK)     |
-       +-----> +--------------------+               | tag              |
-       |       | timeline_entries   |               +------------------+
-       |       |--------------------|
-       |       | id (PK)            |               +------------------+
-       |       | page_id (FK)       |               |   page_versions  |
-       |       | date               |               |------------------|
-       |       | source             |               | id (PK)          |
-       |       | summary            |               | page_id (FK)     |
-       |       | detail (markdown)  |               | compiled_truth   |
-       |       +--------------------+               | frontmatter      |
-       |                                            | snapshot_at      |
-       +-----> +--------------------+               +------------------+
-       |       |    raw_data        |
-       |       |--------------------|               +------------------+
-       |       | id (PK)            |               |    config        |
-       |       | page_id (FK)       |               |------------------|
-       |       | source             |               | key (PK)         |
-       |       | data (JSONB)       |               | value            |
-       |       +--------------------+               +------------------+
-       |
-       +-----> +--------------------+
-               |   ingest_log       |
-               |--------------------|
-               | id (PK)            |
-               | source_type        |
-               | source_ref         |
-               | pages_updated      |
-               | summary            |
-               +--------------------+
-```
-
-Indexes:
- `pages.slug`: UNIQUE constraint (implicit B-tree)
- `pages.type`: B-tree
- `pages.search_vector`: GIN (full-text search)
- `pages.frontmatter`: GIN (JSONB queries)
- `pages.title`: GIN with pg_trgm (fuzzy slug resolution)
- `content_chunks.embedding`: HNSW with cosine ops (vector search)
- `content_chunks.page_id`: B-tree
- `links.from_page_id`, `links.to_page_id`: B-tree
- `tags.tag`, `tags.page_id`: B-tree
- `timeline_entries.page_id`, `timeline_entries.date`: B-tree
-
-## Search architecture
-
-```
-Query: "when should you ignore conventional wisdom?"
-           |
-           v
-+---------------------+
-| Multi-query expansion|
-| (Claude Haiku)       |
-| "contrarian thinking"
-| "going against the crowd"
-+---------------------+
-     |   |   |
-     v   v   v
-  [embed all 3 queries]
-     |   |   |
-     +---+---+
-         |
-    +----+----+
-    |         |
-    v         v
-+--------+ +--------+
-| Vector | | Keyword|
-| Search | | Search |
-| (HNSW  | | (tsv + |
-| cosine)| | ts_rank)|
-+--------+ +--------+
-    |         |
-    +----+----+
-         |
-         v
-+------------------+
-| RRF Fusion       |
-| score = sum(     |
-|   1/(60 + rank)) |
-+------------------+
-         |
-         v
-+------------------+
-| 4-Layer Dedup    |
-| 1. By source     |
-| 2. Cosine > 0.85 |
-| 3. Type cap 60%  |
-| 4. Per-page max  |
-+------------------+
-         |
-         v
-+------------------+
-| Stale alerts     |
-| (compiled_truth  |
-|  older than      |
-|  latest timeline)|
-+------------------+
-         |
-         v
-     [Results]
-```
-
-## Chunking strategies
-
-| Strategy | Input | Algorithm | When to use |
-|----------|-------|-----------|-------------|
-| Recursive | Any text | 5-level delimiter hierarchy (paragraphs > lines > sentences > clauses > whitespace). 300-word chunks, 50-word overlap. | Timeline (predictable format), bulk import |
-| Semantic | Quality text | Embed each sentence, Savitzky-Golay filter for topic boundaries, cosine similarity minima. Falls back to recursive. | Compiled truth (intelligence assessments) |
-| LLM-guided | High-value text | Pre-split to 128-word candidates, Claude Haiku finds topic shifts in sliding windows. 3 retries per window. | Explicitly requested via `--chunker llm` |
-
-Dispatch: compiled_truth gets semantic chunker. Timeline gets recursive chunker. Override with `--chunker` flag or `chunk_strategy` in frontmatter.
-
-## Skills (fat markdown, no code)
-
-Each skill is a markdown file that AI agents (Claude Code, OpenClaw) read and follow. The skill contains the workflow, heuristics, and quality rules. No skill logic is in the binary.
-
-| Skill | What it does |
-|-------|-------------|
-| `skills/ingest/SKILL.md` | Ingest meetings, docs, articles. Update compiled truth, append timeline, create links. |
-| `skills/query/SKILL.md` | 3-layer search (FTS + vector + structured). Synthesize answer with citations. |
-| `skills/maintain/SKILL.md` | Find contradictions, stale info, orphans, dead links, tag inconsistency. |
-| `skills/enrich/SKILL.md` | Enrich from external APIs (Crustdata, Happenstance, Exa). Store raw data, distill to compiled truth. |
-| `skills/briefing/SKILL.md` | Daily briefing: meetings with context, active deals, open threads. |
-| `skills/migrate/SKILL.md` | Universal migration from Obsidian, Notion, Logseq, plain markdown, CSV, JSON, Roam. |
-
-## CEO scope expansions (accepted for v0)
-
-1. **CLI/MCP parity with drift tests.** Both interfaces are thin wrappers over the engine. Tests assert identical output.
-2. **Smart slug resolution.** Fuzzy matching via pg_trgm for reads. Writes require exact slugs. `gbrain get "dont scale"` resolves to `concepts/do-things-that-dont-scale`.
-3. **Brain health dashboard.** `gbrain health` shows page count, embed coverage, stale pages, orphans, dead links.
-4. **Normalized timeline.** `timeline_entries` table only (no TEXT column). `detail` field supports markdown.
-5. **Page version control.** `page_versions` table stores full snapshots (compiled_truth + frontmatter + links + tags). `gbrain history`, `gbrain diff`, `gbrain revert` commands. Revert re-chunks and re-embeds.
-6. **Typed links + graph traversal.** `link_type` column (knows, invested_in, works_at, etc.). `gbrain graph` uses recursive CTE with max depth (default 5, configurable via `--depth`).
-7. **Trigger.dev data cleanup jobs.** Daily embed backfill, weekly stale detection + orphan audit + tag consistency.
-8. **Stale alert annotations.** Search results flag pages where compiled_truth is older than latest timeline entry.
-9. **Timeline merge on ingest.** Same event created across all mentioned entities.
-
-## Security model (v0)
-
-Single-user, local-only:
- Supabase service role key in `~/.gbrain/config.json` (0600 permissions)
- MCP stdio transport is inherently local (client spawns `gbrain serve` as subprocess)
- No multi-user, no RLS, no OAuth in v0
- Multi-user path (future): Supabase RLS + per-user API keys
-
-## Upgrade mechanism
-
-`gbrain upgrade` detects the installation method and updates accordingly:
-
-| Path | How |
-|------|-----|
-| npm | `bun update gbrain` (or npm equivalent) |
-| Compiled binary | Download new binary to temp dir, atomic rename swap, exec new process |
-| ClawHub | `clawhub update gbrain` |
-
-Version check: compare local version against latest GitHub release tag.
-
-## Storage and cost estimates
-
-### Storage (~750MB for 7,471 pages)
-
-| Component | Size |
-|-----------|------|
-| Page text (compiled_truth + timeline) | ~150MB |
-| JSONB frontmatter | ~20MB |
-| tsvector + GIN indexes | ~50MB |
-| Content chunks (~22K, text) | ~80MB |
-| Embeddings (22K x 1536 floats x 4 bytes) | ~134MB |
-| HNSW index overhead (~2x embeddings) | ~270MB |
-| Links, tags, timeline, raw_data, versions | ~50MB |
-| **Total** | **~750MB** |
-
-Supabase free tier (500MB) won't fit. Supabase Pro ($25/mo, 8GB) is the starting point.
-
-### Embedding cost (~$4-5 for initial import)
-
-| Step | Cost |
-|------|------|
-| Semantic chunker sentence embeddings (~374K sentences) | ~$1 |
-| Chunk embeddings (~22K chunks) | ~$0.30 |
-| Query expansion (per query, ~3 embeds) | negligible |
-| **Total initial import** | **~$4-5** |
-
-Budget alternative: `gbrain import --chunker recursive` skips sentence-level embeddings, then `gbrain embed --rechunk --chunker semantic` upgrades later.
-
-## Serverless operations stack
-
-```
-+------------------+     +------------------+     +------------------+
-|    Supabase      |     |    Vercel         |     |   Trigger.dev    |
-|  (Postgres +     |     |  (web/API,        |     |  (background     |
-|   pgvector)      |     |   optional)       |     |   jobs)          |
-+------------------+     +------------------+     +------------------+
-| Database         |     | Future web UI     |     | Embed backfill   |
-| Connection pool  |     | API endpoints     |     | Stale detection  |
-| pgvector HNSW    |     | Edge functions    |     | Orphan audit     |
-| tsvector FTS     |     |                   |     | Tag consistency  |
-| pg_trgm fuzzy    |     |                   |     | Daily briefing   |
-+------------------+     +------------------+     +------------------+
-```
-
-The CLI connects directly to Supabase Postgres. Trigger.dev and Vercel are for async/scheduled work. The CLI works without them.
-
-## Verification checklist
-
-1. `gbrain import /data/brain/` migrates all 7,471 files losslessly
-2. `gbrain export` round-trips to semantically identical markdown
-3. `gbrain query "what does PG say about doing things that don't scale?"` returns relevant hybrid search results
-4. `gbrain serve` starts MCP server connectable by Claude Code
-5. All 3 chunkers produce correct output with test fixtures
-6. `gbrain init --supabase` works end-to-end
-7. `bun test` passes all tests
-8. `clawhub install gbrain` installs the skill and runs guided setup
-9. `bun add gbrain` + `import { PostgresEngine } from 'gbrain'` works in external project
-10. Drift tests pass: CLI and MCP produce identical results
-11. `gbrain health` outputs accurate brain health metrics
-12. Migration skill successfully imports an Obsidian vault
-
-## Future plans
-
-See `docs/ENGINES.md` for the pluggable engine architecture and future backend plans.
-
-### v1 candidates (deferred from v0)
-
- **`gbrain ask` natural language CLI alias.** Trivial to add. P1 TODO.
- **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.** 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.
-
-### Interface abstraction principle
-
-All operations go through `BrainEngine`. The engine interface is the contract. Postgres-specific features (tsvector, pgvector HNSW, pg_trgm, recursive CTEs) are implementation details inside `PostgresEngine`. The interface exposes capabilities, not SQL.
-
-This means:
- A SQLite engine can implement `searchKeyword` using FTS5 instead of tsvector
- A SQLite engine can implement `searchVector` using sqlite-vss instead of pgvector
- A future DuckDB engine could implement analytics-heavy workloads
- The CLI, MCP server, and library consumers never know which engine runs underneath
-
-See `docs/ENGINES.md` for the full interface spec and `docs/SQLITE_ENGINE.md` for the SQLite implementation plan.
-
-## Review history
-
-| Review | Runs | Status | Key findings |
-|--------|------|--------|-------------|
-| /office-hours | 1 | APPROVED | Builder mode. Full port approach chosen. |
-| /plan-ceo-review | 1 | CLEAR | 11 proposals, 10 accepted, 1 deferred. SCOPE EXPANSION mode. |
-| /codex review | 1 | issues_found | 24 points challenged, 3 accepted (fuzzy slug, revert spec, tsvector). |
-| /plan-eng-review | 2 | CLEAR | 3 issues (upgrade paths, import guardrails, init wizard), 0 critical gaps. |
-| /plan-devex-review | 1 | CLEAR | DX score 5/10 to 7/10. TTHW 25min to 90s. Champion tier. |
@@ -1,293 +0,0 @@
-# GBrain Installation Verification Runbook
-
-Run these checks after install to confirm every part of GBrain is working.
-Each check includes the command, expected output, and what to do if it fails.
-
-The most important check is #4 (live sync). "Sync ran" is not the same as
-"sync worked." A sync that silently skips pages because of a pooler bug is
-worse than no sync at all, because you think it's working.
-
---
-
-## 1. Schema Verification
-
-**Command:**
-
-```bash
-gbrain doctor --json
-```
-
-**Expected:** All checks return `"ok"`:
- `connection`: connected, N pages
- `pgvector`: extension installed
- `rls`: enabled on all tables
- `schema_version`: current
- `embeddings`: coverage percentage
-
-**If it fails:** The doctor output includes specific fix instructions for each
-check. See `skills/setup/SKILL.md` Error Recovery table.
-
---
-
-## 2. Skillpack Loaded
-
-**Check:** Ask the agent: "What is the brain-agent loop?"
-
-**Expected:** The agent references GBRAIN_SKILLPACK.md Section 2 and describes
-the read-write cycle: detect entities, read brain, respond with context, write
-brain, sync.
-
-**If it fails:** The agent hasn't loaded the skillpack. Run step 6 from the
-install paste (read `docs/GBRAIN_SKILLPACK.md`).
-
---
-
-## 3. Auto-Update Configured
-
-**Command:**
-
-```bash
-gbrain check-update --json
-```
-
-**Expected:** Returns JSON with `current_version`, `latest_version`,
-`update_available` (boolean). The cron `gbrain-update-check` is registered.
-
-**If it fails:** Run step 7 from the install paste. See GBRAIN_SKILLPACK.md
-Section 17.
-
---
-
-## 4. Live Sync Actually Works
-
-This is the most important check. Three parts.
-
-### 4a. Coverage Check
-
-Compare page count in the DB against syncable file count in the repo:
-
-```bash
-gbrain stats
-```
-
-Then count syncable files:
-
-```bash
-find /data/brain -name '*.md' \
-  -not -path '*/.*' \
-  -not -path '*/.raw/*' \
-  -not -path '*/ops/*' \
-  -not -name 'README.md' \
-  -not -name 'index.md' \
-  -not -name 'schema.md' \
-  -not -name 'log.md' \
-  | wc -l
-```
-
-**Expected:** Page count in `gbrain stats` should be close to the file count.
-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 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
-
-```bash
-gbrain stats
-```
-
-**Expected:** Embedded chunk count should be close to total chunk count.
-
-**If embedded is much lower than total:**
-
-```bash
-gbrain embed --stale
-```
-
-If `OPENAI_API_KEY` is not set, embeddings can't be generated. Keyword search
-still works without embeddings, but hybrid/semantic search won't.
-
-### 4c. End-to-End Test
-
-This is the real test. Edit a brain page, push, wait, search.
-
-1. Edit a page in the brain repo (e.g., correct a fact on a person's page):
-
-```bash
-# Example: fix a line in Gustaf's page
-cd /data/brain
-# Make a small edit to any .md file
-git add -A && git commit -m "test: verify live sync" && git push
-```
-
-2. Wait for the next sync cycle (cron interval or `--watch` poll).
-
-3. Search for the corrected text:
-
-```bash
-gbrain search "<text from the correction>"
-```
-
-**Expected:** The search returns the **corrected** text, not the old version.
-
-**If it returns old text:** Sync failed silently. Check:
- Is the sync cron registered and running?
- 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 you see `.begin() is not a function`, fix the pooler (see 4a above).
-
---
-
-## 5. Embedding Coverage
-
-**Command:**
-
-```bash
-gbrain stats
-```
-
-**Expected:** Embedded chunk count matches (or is close to) total chunk count.
-
-**If zero or very low:** `OPENAI_API_KEY` may be missing or invalid. Check:
-
-```bash
-echo $OPENAI_API_KEY | head -c 10
-```
-
-If blank, set the key. Then:
-
-```bash
-gbrain embed --stale
-```
-
---
-
-## 6. Brain-First Lookup Protocol
-
-**Check:** Ask the agent about a person or concept that exists in the brain.
-
-**Expected:** The agent uses `gbrain search` or `gbrain query` FIRST, not grep
-or external APIs. The response includes brain-sourced context with source
-attribution.
-
-**If it fails:** The brain-first lookup protocol isn't injected into the agent's
-system context. See `skills/setup/SKILL.md` Phase D.
-
---
-
-## 7. Knowledge Graph Wired
-
-The v0.12.0 graph layer needs to be populated for existing brains. New writes are
-auto-linked, but historical pages need a one-time backfill.
-
-**Command:**
-
-```bash
-gbrain stats | grep -E 'links|timeline'
-```
-
-**Expected:** Both `links` and `timeline_entries` are non-zero (assuming the brain
-has content with entity references and dated markdown).
-
-**If it's zero on a brain with imported content:** Run the backfill.
-
-```bash
-gbrain extract links --source db --dry-run | head -5    # preview
-gbrain extract links --source db                         # commit
-gbrain extract timeline --source db
-gbrain stats                                             # confirm > 0
-```
-
-**Bonus check** — graph traversal works:
-
-```bash
-# Pick any well-connected slug from your brain
-gbrain graph-query people/<some-person-slug> --depth 2
-```
-
-**Expected:** Indented tree of typed edges (`--attended-->`, `--works_at-->`, etc.).
-If the slug has no inbound or outbound links, try a different one or run extract
-again.
-
-**If extract finds nothing:** Your pages may not use entity-reference syntax. The
-extractor matches `[Name](people/slug)`, `[Name](../people/slug.md)`, and bare
-`people/slug` references. If your brain uses a different format, the auto-link
-heuristics won't find them — file an issue with a sample page.
-
---
-
-## 8. JSONB Frontmatter Integrity (v0.12.2)
-
-Postgres-backed brains created before v0.12.2 had double-encoded JSONB columns
-(`frontmatter->>'key'` returned NULL, GIN indexes were inert). `gbrain upgrade`
-runs `gbrain repair-jsonb` automatically via the `v0_12_2` orchestrator.
-Verify the repair succeeded.
-
-**Command:**
-
-```bash
-gbrain repair-jsonb --dry-run --json
-```
-
-**Expected:** `totalRepaired: 0` across all 5 columns (`pages.frontmatter`,
-`raw_data.data`, `ingest_log.pages_updated`, `files.metadata`,
-`page_versions.frontmatter`). A zero count means every row is properly-typed
-JSON objects, not string-encoded JSON.
-
-**If the count is > 0:** The repair didn't run or was interrupted. Re-run
-without `--dry-run`:
-
-```bash
-gbrain repair-jsonb
-```
-
-Idempotent. PGLite brains always report 0 (unaffected by the original bug).
-
-**Bonus check** — frontmatter-keyed queries actually resolve:
-
-```bash
-gbrain call list_pages '{"frontmatterKey": "type", "frontmatterValue": "person"}'
-```
-
-If this returns rows on a brain with person pages, the JSONB path is healthy.
-
---
-
-## Quick Verification (all checks in one pass)
-
-```bash
-# 1. Schema
-gbrain doctor --json
-
-# 2. Sync recency
-gbrain config get sync.last_run
-
-# 3. Page count + embed coverage
-gbrain stats
-
-# 4. Search works
-gbrain search "test query from your brain content"
-
-# 5. Catch any unembedded chunks
-gbrain embed --stale
-
-# 6. Auto-update
-gbrain check-update --json
-
-# 7. Knowledge graph populated (links + timeline > 0)
-gbrain stats | grep -E 'links|timeline'
-
-# 8. JSONB integrity (v0.12.2 — Postgres only, PGLite always 0)
-gbrain repair-jsonb --dry-run --json
-```
-
-If all eight return successfully, the installation is healthy. For the full
-end-to-end sync test (4c), push a real change and verify it appears in search.
@@ -1,540 +0,0 @@
-# Upgrading Downstream Agents
-
-GBrain ships skills in `skills/`. Downstream agents (custom OpenClaw deployments,
-agent forks of any kind) often **copy** these skill files into their own workspace and
-diverge over time — adding agent-specific phases, removing irrelevant ones, tightening
-language. Once that happens, gbrain can't push updates to those forks. The agent has
-to apply the diffs by hand.
-
-This doc lists the exact diffs each downstream agent needs to apply when upgrading.
-Cross-reference against your fork's local skill files.
-
-## Why this exists
-
-`gbrain upgrade` ships the new binary. `gbrain post-upgrade [--execute --yes]` runs
-the schema migrations and backfills the data. But the **skill files themselves**
-that tell the agent how to behave — those are user-owned. If your `~/git/<your-agent>/workspace/skills/brain-ops/SKILL.md`
-says `# Based on gbrain v0.10.0` at the top, it doesn't know about v0.12.0 features.
-
-The agent will keep manually calling `gbrain link` after every `put_page` (now redundant —
-auto-link does it), miss out on `gbrain graph-query` for relationship questions, and
-not know to backfill the structured timeline.
-
-## How to apply
-
-1. Identify your forked skill files. Typically at `~/git/<your-agent>/workspace/skills/` or wherever your agent's skill directory lives.
-2. For each skill listed below, find the matching phase/section in your fork.
-3. Apply the diff (paste the new block in the indicated location).
-4. Update the version banner at the top of your fork (`# Based on gbrain v0.12.0`).
-5. Verify: ask the agent to write a test page and confirm the response includes
-   `auto_links: { created, removed, errors }`.
-
-Total time: ~10 minutes for all four skills.
-
---
-
-## 1. brain-ops/SKILL.md
-
-**Where:** Insert a new `### Phase 2.5` section immediately after `### Phase 2: On Every Inbound Signal`.
-
-**Why:** Phase 2.5 declares that auto-link runs automatically. Without this, the
-agent's mental model says it must call `gbrain link` after every `put_page`, which
-is now redundant and can cause double-add warnings.
-
-```markdown
-### Phase 2.5: Structured Graph Updates (automatic)
-
-Every `put_page` call automatically extracts entity references and writes them
-to the graph (`links` table) with inferred relationship types. Stale links
-(refs no longer in the page text) are removed in the same call. This is
-"auto-link" reconciliation.
-
- No manual `add_link` calls needed for ordinary page writes.
- Inferred link types: `attended` (meeting -> person), `works_at`, `invested_in`,
-  `founded`, `advises`, `source` (frontmatter), `mentions` (default).
- The `put_page` MCP response includes `auto_links: { created, removed, errors }`
-  so the agent can verify outcomes.
- To disable: `gbrain config set auto_link false`. Default is on.
- Timeline entries with specific dates still need explicit `gbrain timeline-add`
-  (or batch via `gbrain extract timeline --source db`).
-```
-
-**Also update the Iron Law section.** If your fork still says "Back-links maintained
-on every brain write (Iron Law)" without qualification, append:
-
-```markdown
-**v0.12.0 update:** Auto-link satisfies the Iron Law for entity-reference links
-on every `put_page`. The agent's Iron Law obligation is now: include the
-entity reference in the page content (e.g., `[Alice](people/alice)`); auto-link
-handles the structured row. Manual `add_link` calls are reserved for
-relationships you can't express in markdown content.
-```
-
---
-
-## 2. meeting-ingestion/SKILL.md
-
-**Where:** Append to the end of `### Phase 3: Attendee enrichment`.
-
-**Why:** Eliminates redundant `gbrain link` calls per attendee (auto-link handles them
-when the meeting page references attendees as `[Name](people/slug)`).
-
-```markdown
-**Note (v0.12.0):** Once the meeting page is written via `gbrain put`, the
-auto-link post-hook automatically creates `attended` links from the meeting
-to each attendee whose page is referenced as `[Name](people/slug)`. You don't
-need to call `gbrain link` for attendees. You DO still need `gbrain timeline-add`
-for dated events (auto-link only handles links, not timeline entries).
-```
-
-**Where:** In `### Phase 4: Entity propagation`, the line "Back-link from entity page
-to meeting page" can be replaced with:
-
-```markdown
-4. Entity references in the meeting page body auto-create the link via auto-link.
-   For incoming references on the entity page (entity page → meeting page), edit
-   the entity page to mention the meeting and `put_page` it — auto-link handles
-   the rest.
-```
-
---
-
-## 3. signal-detector/SKILL.md
-
-**Where:** Append to the end of `### Phase 2: Entity Detection`.
-
-**Why:** Same logic as brain-ops — eliminates manual `gbrain link` after writing
-originals/ideas pages that reference people or companies.
-
-```markdown
-**Auto-link (v0.12.0):** When you write/update an originals or ideas page that
-references a person or company, the auto-link post-hook on `put_page`
-automatically creates the link from the new page to that entity. You don't
-need to call `gbrain link` manually. Timeline entries still need explicit calls.
-```
-
---
-
-## 4. enrich/SKILL.md
-
-**Where:** Replace `### Step 7: Cross-reference` with the v0.12.0 version.
-
-**Why:** Step 7 used to be primarily about creating links between related entity
-pages. With auto-link, that's automatic. Step 7 is now about content updates,
-not link creation.
-
-Old (delete):
-```markdown
-### Step 7: Cross-reference
-
- Update company pages from person enrichment (and vice versa)
- Update related project/deal pages if relevant context surfaced
- Check index files if the brain uses them
- Add back-links manually via `gbrain link` for any new entity references
-```
-
-New (paste):
-```markdown
-### Step 7: Cross-reference
-
- Update company pages from person enrichment (and vice versa)
- Update related project/deal pages if relevant context surfaced
- Check index files if the brain uses them
-
-**Note (v0.12.0):** Links between brain pages are auto-created on every
-`put_page` call (auto-link post-hook). Step 7 focuses on content
-cross-references (updating related pages' compiled truth with new signal
-from this enrichment), not on creating links. Verify via the `auto_links`
-field in the put_page response (`{ created, removed, errors }`).
-Timeline entries still need explicit `gbrain timeline-add` calls.
-```
-
---
-
-## After all four diffs are applied
-
-1. **Bump the version banner** at the top of each forked file:
-   ```
-   # Based on gbrain v0.12.0 skills/<skill-name>, extended with <your-agent>-specific config
-   ```
-
-2. **Run the v0.12.0 backfill** (this populates the graph for your existing brain):
-   ```bash
-   gbrain post-upgrade
-   ```
-   The v0.12.0 release wires post-upgrade to call `apply-migrations --yes`
-   automatically, which runs the v0_12_0 orchestrator (schema → config check →
-   `extract links --source db` → `extract timeline --source db` → verify).
-   Idempotent; cheap when nothing is pending.
-
-3. **Verify auto-link works:** ask the agent to write a test page that references
-   `[Some Person](people/some-person)`. Confirm the put_page response includes
-   `auto_links: { created: 1, removed: 0, errors: 0 }`.
-
-4. **Verify graph traversal works:**
-   ```bash
-   gbrain graph-query people/some-well-connected-person --depth 2
-   ```
-   Should return an indented tree of typed edges.
-
---
-
-## v0.12.2 hotfix (data-correctness, no skill edits)
-
-v0.12.2 is a Postgres data-correctness hotfix. No forked skill files need to
-change — the skill contracts are unchanged. But you DO need to run the migration,
-and you should know about one behavior change in markdown parsing.
-
-### 1. Run the migration (Postgres-backed brains)
-
-```bash
-gbrain upgrade
-```
-
-The `v0_12_2` orchestrator runs `gbrain repair-jsonb` automatically. It rewrites
-rows where `jsonb_typeof = 'string'` across `pages.frontmatter`, `raw_data.data`,
-`ingest_log.pages_updated`, `files.metadata`, and `page_versions.frontmatter`.
-Idempotent, safe to re-run. PGLite brains no-op cleanly.
-
-Verify after upgrade:
-
-```bash
-gbrain repair-jsonb --dry-run --json    # expect totalRepaired: 0
-```
-
-### 2. Recover any truncated wiki articles
-
-If your brain imported wiki-style markdown before v0.12.2, some pages were
-silently truncated (any standalone `---` in body content was treated as a
-timeline separator). Re-import from source:
-
-```bash
-gbrain sync --full
-```
-
-The new `splitBody` rebuilds `compiled_truth` correctly.
-
-### 3. Know the splitBody contract going forward
-
-`splitBody` now requires an explicit timeline sentinel. Recognized markers
-(priority order):
-
-1. `<!-- timeline -->` (preferred — what `serializeMarkdown` emits)
-2. `--- timeline ---` (decorated separator)
-3. `---` directly before `## Timeline` or `## History` heading (backward-compat)
-
-A bare `---` in body text is now a markdown horizontal rule, not a timeline
-separator. If your agent writes pages with a bare `---` delimiter, migrate to
-`<!-- timeline -->` — the `serializeMarkdown` helper already does this.
-
-### 4. Wiki subtypes now auto-typed
-
-`inferType` now auto-detects five additional directory patterns as their own
-page types (previously they all defaulted to `concept`):
-
-| Path pattern           | New type       |
-|------------------------|----------------|
-| `/wiki/analysis/`      | `analysis`     |
-| `/wiki/guides/`        | `guide`        |
-| `/wiki/hardware/`      | `hardware`     |
-| `/wiki/architecture/`  | `architecture` |
-| `/writing/`            | `writing`      |
-
-If your skills or queries filter by `type=concept` and expect wiki content in
-that bucket, update them to include the new types.
-
---
-
-## v0.13.0 — Frontmatter Relationship Indexing
-
-**Verdict: no action required for most skills.** v0.13 projects YAML frontmatter fields into the graph as typed edges. The ingestion API is unchanged — keep calling `put_page` with frontmatter the way you do today; the graph auto-populates behind the scenes.
-
-Three skills get an optional new phase if you want to consume the new `auto_links.unresolved` response field. Without this, unresolvable frontmatter names silently skip (same as v0.12 behavior).
-
-### 1. meeting-ingestion/SKILL.md (optional)
-
-**Where:** Add a new section after "Phase 3: Write Meeting Page".
-
-```markdown
-### Phase 3.5: Check for unresolved attendees (v0.13+)
-
-After `put_page`, inspect `response.auto_links.unresolved` — an array of frontmatter
-references that did not resolve to existing pages. For meetings, this usually means
-attendees you haven't created a person page for yet.
-
-If `unresolved.length > 0`:
- Option 1 (create pages now): trigger an enrichment pass to build the missing people pages.
- Option 2 (defer): log the unresolved names to the enrichment queue for later.
- Option 3 (accept the gap): the attendee edge will not be created until a page exists.
-  Re-running `gbrain extract links --source db --include-frontmatter` after creating
-  the page fills in the missing edges.
-```
-
-### 2. enrich/SKILL.md (optional)
-
-**Where:** Add to the enrichment trigger list.
-
-```markdown
-### Drain unresolved frontmatter names (v0.13+)
-
-If any `put_page` response includes `auto_links.unresolved` entries, the enrichment
-tier should pick up those (field, name) pairs and try to create the missing entity
-pages. Example flow:
-
-1. signal-detector captures a meeting with `attendees: [Alice Known, Unknown Person]`
-2. put_page returns `auto_links.unresolved = [{field: 'attendees', name: 'Unknown Person'}]`
-3. enrichment tier consumes `Unknown Person` → web search → creates `people/unknown-person.md`
-4. The next put_page (or a backfill run) wires up the `attended` edge automatically
-```
-
-### 3. idea-ingest/SKILL.md (optional)
-
-**Where:** Same pattern as meeting-ingestion — check `auto_links.unresolved` after `put_page`, route names to enrichment.
-
-### Unchanged skills (no diffs needed)
-
- **brain-ops/SKILL.md** — auto-link mechanics are internal; the write path stays the same.
- **signal-detector/SKILL.md** — signal capture path unchanged.
- **query/SKILL.md** — `traverse_graph` now returns richer results automatically.
- **daily-task-manager/SKILL.md**, **briefing/SKILL.md**, **citation-fixer/SKILL.md**, **media-ingest/SKILL.md** — unchanged.
-
-### New edge types you can filter in graph queries
-
-v0.13 edges carry new `link_type` values. If your fork has graph-query skills that filter by type, these are now available:
-
- `works_at` (person → company) — from `company:`, `companies:`, or `key_people:`
- `founded` (person → company) — from `founded:`
- `invested_in` (investor → deal/company) — from `investors:` or `lead:`
- `led_round` (lead → deal) — from `lead:`
- `yc_partner` (partner → company) — from `partner:`
- `attended` (person → meeting) — from `attendees:`
- `discussed_in` (source → page) — from `sources:`
- `source` (page → source) — from `source:`
- `related_to` (page → target) — from `related:` or `see_also:`
-
-### Migration timing
-
-`gbrain upgrade` takes 2-5 min on a 46K-page brain (one-time). Runs out-of-process via `gbrain post-upgrade`. If your agent holds a DB connection during the upgrade, reconnect after; otherwise keep serving.
-
-### Type normalization NOT in v0.13
-
-Legacy rows with `link_type='attendee'` or `link_type='mention'` coexist with new `'attended'` / `'mentions'` rows. Your queries filtering on old type names keep working. A separate opt-in `gbrain normalize-types` command in v0.14 handles the rename.
-## v0.14.0 shell jobs (optional adoption, no skill edits)
-
-Adds a `shell` job type to Minions so deterministic cron scripts (API fetch, token
-refresh, scrape + write) move off the LLM gateway. Zero tokens per fire. ~60%
-gateway CPU headroom at typical scale. Feature is **off by default**, existing
-installs keep running exactly as they did before. Nothing breaks.
-
-To adopt, follow `skills/migrations/v0.14.0.md`. The short version:
-
-1. Set `GBRAIN_ALLOW_SHELL_JOBS=1` on the worker process, then `gbrain jobs work`
-   (Postgres). On PGLite, every crontab invocation uses `--follow` for inline
-   execution; no persistent worker.
-2. Classify each of your host's cron entries: LLM-requiring (keep on gateway) vs
-   deterministic (candidate for shell). Typical splits:
-   - **Deterministic → shell:** `ycli-token-refresh`, `x-oauth2-refresh`,
-     `x-garrytan-unified`, `calendar-sync-to-brain`, `github-pulse`,
-     `frameio-scan`, `flight-tracker`, `x-raw-json-backfill`.
-   - **LLM-requiring → stay:** `social-radar`, `content-ideas`, `adversary-vacuum`,
-     `ea-inbox-sweep`, `morning-briefing`, `brain-maintenance`.
-3. For each deterministic cron, rewrite as:
-   ```cron
-   3 13,16,19,22,1,4,7,10 * * * \
-     gbrain jobs submit shell \
-       --params '{"cmd":"node scripts/your-script.mjs","cwd":"/data/.openclaw/workspace"}' \
-       --max-attempts 3 --timeout-ms 300000
-   ```
-4. Watch `gbrain jobs get <id>` for exit_code / stdout_tail / stderr_tail on each
-   fire. Compare against pre-migration behavior before approving the next batch.
-
-**No skill edits required.** The handler runs worker-side; skill files don't
-change. If your host exposed custom handlers via the plugin contract (v0.11.0),
-they still work the same way.
-
-Iron rule: **never auto-rewrite the operator's crontab.** Every rewrite is
-per-cron, human-approved, with a diff. If you want automation later, the
-upcoming `gbrain crontab-to-minions <file>` helper is P1 in TODOS.
-
---
-
-## v0.16.0: durable agent runtime
-
-v0.15 ships `gbrain agent run` / `gbrain agent logs`, a new `subagent` handler
-type in Minions, and a plugin contract for host-repo subagent defs. None of the
-existing skills need surgery. The question for downstream agents is *how* to
-adopt the new runtime, not how to patch around a breaking change.
-
-### 1. Run a worker with an Anthropic key
-
-The subagent handlers (`subagent` and `subagent_aggregator`) are always
-registered on the worker. No separate opt-in flag — `ANTHROPIC_API_KEY` is
-the natural cost gate (no key, the SDK call fails on the first turn), and
-who-can-submit is already protected (`PROTECTED_JOB_NAMES` + trusted-submit:
-MCP callers get `permission_denied`; only `gbrain agent run` can insert
-these rows).
-
-```bash
-ANTHROPIC_API_KEY=sk-ant-... gbrain jobs work
-```
-
-Worker startup prints:
-
-```
-[minion worker] subagent handlers enabled
-```
-
-### 2. Ship your subagents as a plugin (OpenClaw + similar)
-
-Move your custom subagent definitions out of your gbrain fork and into your own
-repo as a plugin. Concretely:
-
-```
-~/<your-agent>/gbrain-plugin/
-├── gbrain.plugin.json
-└── subagents/
-    ├── meeting-ingestion.md
-    ├── signal-detector.md
-    └── daily-task-prep.md
-```
-
-`gbrain.plugin.json`:
-
-```json
-{
-  "name": "your-openclaw",
-  "version": "2026.4.20",
-  "plugin_version": "gbrain-plugin-v1"
-}
-```
-
-Each `subagents/*.md` is a plain-text agent definition — YAML frontmatter +
-body-as-system-prompt. Recognized frontmatter fields: `name`, `model`,
-`max_turns`, `allowed_tools` (must subset the derived brain-tool registry).
-
-Turn it on:
-
-```bash
-export GBRAIN_PLUGIN_PATH="$HOME/<your-agent>/gbrain-plugin"
-```
-
-Worker startup prints `[plugin-loader] loaded '<name>' v<ver> (N subagents)`
-per plugin; any rejection (bad manifest, unknown tool in `allowed_tools`,
-version mismatch) shows up as a loud warning at startup, not a silent dispatch-
-time failure. See `docs/guides/plugin-authors.md` for the full contract.
-
-### 3. Replace ephemeral subagent runs with durable ones
-
-If your agent currently spawns ephemeral subagents (OpenClaw `Agent()`, ad-hoc
-Anthropic API calls, etc.) for work that should survive crashes, sleeps, or
-worker restarts, migrate those to `gbrain agent run`. The durability is free:
-
-```bash
-gbrain agent run "analyze my last 50 journal pages for recurring themes" \
-  --subagent-def analyzer --fanout-manifest manifests/journal-pages.json
-```
-
-Every turn persists to `subagent_messages`, every tool call is a two-phase
-ledger, and `gbrain agent logs <job>` shows where it died + what the last
-successful call returned. No more "re-run from scratch because the session
-context evaporated."
-
-### 4. `put_page` from subagents writes under an agent namespace
-
-If you adopted the v0.15 subagent runtime, note that `put_page` calls
-originating from a subagent's tool dispatch MUST target
-`wiki/agents/<subagent_id>/...`. The schema shown to the model enforces this
-on first try; a server-side fail-closed check rejects anything else. This
-does NOT affect your skill files, CLI put_page calls, or MCP put_page —
-only tool-dispatched writes from inside an LLM loop.
-
-Aggregation output (the final "here's what all N children found" brain page)
-goes via a separate trusted CLI path, not through a subagent tool call, so
-it can write anywhere you want.
-
-Iron rule: **never grant an agent write access beyond its namespace**. The
-server-side check exists because dispatcher bugs happen; treat it as defense
-in depth, not the primary boundary.
-
---
-
-## v0.22.4 — frontmatter-guard adoption
-
-### 1. Stop hand-rolling frontmatter validators
-
-If your fork has scripts that call `js-yaml` directly to validate brain page
-frontmatter, replace them with `gbrain frontmatter validate` calls. The CLI
-covers the seven canonical error classes and ships a `--json` envelope that's
-stable across releases.
-
-```diff
- # Custom validator script
- node scripts/validate-frontmatter.mjs <path>
-+ gbrain frontmatter validate <path> --json
-```
-
-For consumers that need the validator inside another script, import from
-gbrain's `markdown` export instead of duplicating logic:
-
-```ts
-import { parseMarkdown } from 'gbrain/markdown';
-
-const parsed = parseMarkdown(content, filePath, { validate: true, expectedSlug });
-for (const err of parsed.errors ?? []) {
-  // err.code: MISSING_OPEN | MISSING_CLOSE | YAML_PARSE | SLUG_MISMATCH |
-  //           NULL_BYTES | NESTED_QUOTES | EMPTY_FRONTMATTER
-}
-```
-
-### 2. Drop any references to `lib/brain-writer.mjs`
-
-If your fork's skills or scripts referenced an aspirational
-`lib/brain-writer.mjs` (it never shipped — the spec was in PR #392 and never
-landed), replace those references with the gbrain CLI. The `frontmatter-guard`
-skill lives at `skills/frontmatter-guard/SKILL.md` and points at
-`gbrain frontmatter validate` / `audit` / `install-hook`.
-
-### 3. Wire the doctor subcheck into your health pipeline
-
-`gbrain doctor` now reports `frontmatter_integrity` automatically. If your
-fork has a custom health pipeline (e.g. a daily Slack post about brain
-health), pull from `gbrain doctor --json` and surface the
-`frontmatter_integrity` row counts.
-
-### 4. (Optional) Install the pre-commit hook on brain repos
-
-For sources backed by git, the v0.22.4 install-hook helper drops a
-pre-commit script that blocks commits with malformed frontmatter:
-
-```bash
-gbrain frontmatter install-hook
-```
-
-Skip this if your brain isn't a git repo or if your downstream agent already
-enforces validation at write time. See `docs/integrations/pre-commit.md` for
-the full recipe.
-
-### 5. Migration ergonomics — read pending-host-work.jsonl
-
-After `gbrain apply-migrations --yes` runs the v0.22.4 audit, your agent
-should read `~/.gbrain/migrations/pending-host-work.jsonl` (filter to
-`migration === "0.22.4"`) and walk each entry's `command` field. Each entry
-points to a per-source `gbrain frontmatter validate <source_path> --fix`
-command — surface counts to the user, get explicit consent, then run.
-
-The migration is **audit-only**. It never mutates brain content during
-`apply-migrations`. Your agent runs the fix command with user consent.
-
---
-
-## Future versions
-
-When gbrain ships a new version, this doc will be updated with the diffs for that
-version. Each new version appends a section; old sections stay so you can catch up
-multiple versions at once.
-
-To check what your fork is missing:
-```bash
-diff <(grep -A3 "Based on gbrain" ~/<your-fork>/skills/brain-ops/SKILL.md) \
-     <(grep "v[0-9]" ~/gbrain/skills/migrations/ | tail -3)
-```
@@ -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) |
@@ -1,242 +0,0 @@
-# Brains and Sources — the mental model
-
-GBrain has two orthogonal axes for organizing knowledge. Users and agents both
-need to understand both of them, or queries misroute silently.
-
-**TL;DR:**
- A **brain** is a database. You can have many.
- A **source** is a named repo of content *inside* a brain. One brain can hold many.
- `--brain <id>` picks WHICH DATABASE.
- `--source <id>` picks WHICH REPO WITHIN that database.
- They're independent. You can target any combination.
-
---
-
-## The two axes
-
-### Brains (the DB axis)
-
-A **brain** is one database — PGLite file, self-hosted Postgres, or Supabase.
-Each brain has:
- Its own `pages` table, `chunks` table, `embeddings`, etc.
- Its own OAuth surface if served over HTTP MCP (v0.19+, PR 2).
- Its own separate lifecycle, backup, access control.
-
-Brains are enumerated by:
- **host** — your default brain, configured in `~/.gbrain/config.json`.
- **mounts** — additional brains registered in `~/.gbrain/mounts.json` via
-  `gbrain mounts add <id>` (v0.19+).
-
-Routing: `--brain <id>`, `GBRAIN_BRAIN_ID`, `.gbrain-mount` dotfile, or
-longest-path match against registered mount paths. Falls back to `host`.
-
-### Sources (the repo axis, v0.18.0+)
-
-A **source** is a named content repo *inside* one brain. Every `pages` row
-carries a `source_id`. Slugs are unique per source, not globally.
-
-Example: in one brain, the slug `topics/ai` can exist under `source=wiki`
-AND under `source=gstack` — they're different pages.
-
-Routing: `--source <id>`, `GBRAIN_SOURCE`, `.gbrain-source` dotfile, or
-registered `local_path` match in the `sources` table.
-
-### When does each axis move?
-
-| You want to | Adjust |
-|---|---|
-| Work in a different repo within the same brain (wiki → gstack notes) | `--source` |
-| Query a team-published brain that isn't yours | `--brain` |
-| Isolate a topic so it never leaks into personal search | `--source` with `federated=false` |
-| Share a brain with teammates | `--brain` (mount the team brain) |
-| Add a new repo to your personal brain | `--source` via `gbrain sources add` |
-| Add a team brain | `--brain` via `gbrain mounts add` |
-
-**Rule of thumb:** if the data owner changes, it's a brain boundary. If the
-data owner stays the same but the topic/repo changes, it's a source boundary.
-
---
-
-## Topology: a single-person developer
-
-Simplest case. One brain, one source.
-
-```
-┌─────────────────────────────────────────┐
-│  host brain (~/.gbrain)                 │
-│  ├── source: default (federated=true)   │
-│  │   └── all pages                      │
-└─────────────────────────────────────────┘
-```
-
-`gbrain query "retry budgets"` finds everything. No `--brain`, no `--source`
-needed.
-
---
-
-## Topology: a personal brain with multiple repos
-
-You maintain several codebases or writing streams. Each is its own source
-inside one brain. Cross-source search is on by default so a query about
-"caching" returns hits from every repo.
-
-```
-┌──────────────────────────────────────────────┐
-│  host brain (~/.gbrain)                      │
-│  ├── source: wiki      (federated=true)      │
-│  │   └── personal notes, people, companies   │
-│  ├── source: gstack    (federated=true)      │
-│  │   └── gstack plans, learnings             │
-│  ├── source: openclaw  (federated=true)      │
-│  │   └── openclaw docs, memos                │
-│  └── source: essays    (federated=false)     │
-│      └── draft essays, isolated on purpose   │
-└──────────────────────────────────────────────┘
-```
-
-Inside `~/openclaw/` the `.gbrain-source` dotfile pins every command to
-`source=openclaw`. Inside `~/gstack/` the dotfile pins to `source=gstack`.
-Everything still targets one DB.
-
-Use this topology when:
- You own all the content.
- You want cross-repo search to just work.
- You don't need to share any of it with someone who isn't you.
-
---
-
-## Topology: personal brain + one team brain
-
-You're on a team that publishes a shared brain. Your personal brain stays
-as-is; you mount the team brain alongside it.
-
-```
-┌──────────────────────────────────────────────┐
-│  host brain (~/.gbrain)  — YOUR personal DB  │
-│  ├── source: wiki                            │
-│  ├── source: gstack                          │
-│  └── ...                                     │
-└──────────────────────────────────────────────┘
-
-┌──────────────────────────────────────────────┐
-│  mount: media-team                           │
-│  path:   ~/team-brains/media                 │
-│  engine: postgres (team's Supabase)          │
-│  └── sources: wiki, raw, enriched            │
-└──────────────────────────────────────────────┘
-```
-
-`gbrain query "X"` (no flags) → runs against host (your personal brain).
-`gbrain query "X" --brain media-team` → runs against the team's DB.
-Inside `~/team-brains/media/` a `.gbrain-mount` dotfile pins brain to
-`media-team` automatically.
-
-Use this topology when:
- You're on a team and someone publishes a brain the team subscribes to.
- You need data isolation between work and personal.
- Different teams/orgs own different brains.
-
---
-
-## Topology: a CEO-class user with multiple team memberships
-
-You're senior enough to sit across multiple teams. You maintain your personal
-brain (with N sources inside) AND mount several work team brains. Each team
-brain is itself a multi-source brain in the v0.18.0 sense — organized
-internally however the team owner chose.
-
-```
-┌──────────────────────────────────────────────┐
-│  host brain — YOUR personal DB               │
-│  ├── source: wiki                            │
-│  ├── source: essays                          │
-│  ├── source: gstack                          │
-│  └── source: openclaw                        │
-└──────────────────────────────────────────────┘
-
-┌──────────────────────────────────────────────┐
-│  mount: media-team (your media team's brain) │
-│  └── sources: wiki, pipeline, enriched       │
-└──────────────────────────────────────────────┘
-
-┌──────────────────────────────────────────────┐
-│  mount: policy-team (your policy team's)     │
-│  └── sources: wiki, research, letters        │
-└──────────────────────────────────────────────┘
-
-┌──────────────────────────────────────────────┐
-│  mount: portfolio (another team's)           │
-│  └── sources: companies, deals, diligence    │
-└──────────────────────────────────────────────┘
-```
-
-Inside each team's checkout, a `.gbrain-mount` dotfile pins the brain. Inside
-a specific subdirectory, a `.gbrain-source` dotfile pins the source. So `cd
-~/team-brains/policy/research && gbrain query "X"` targets
-`brain=policy-team, source=research` with zero flags.
-
-Use this topology when:
- You cross-cut multiple teams.
- Each team owns its own brain with its own access policy.
- You need latent-space federation (agent decides when to query across
-  brains), not SQL federation.
-
-Cross-brain queries are **not deterministic** in v0.19. The agent sees the
-brain list and re-queries as needed. That's the feature — it keeps debugging
-sane and access control clean.
-
---
-
-## Resolution precedence (one page to remember)
-
-```
-WHICH BRAIN (DB)?                    WHICH SOURCE (repo in DB)?
- 1. --brain <id>                      1. --source <id>
- 2. GBRAIN_BRAIN_ID env               2. GBRAIN_SOURCE env
- 3. .gbrain-mount dotfile             3. .gbrain-source dotfile
- 4. longest-prefix mount path match   4. longest-prefix source path match
- 5. (reserved: brains.default v2)     5. sources.default config
- 6. fallback: 'host'                  6. fallback: 'default'
-```
-
-Both axes follow the same layered pattern on purpose. If you know one, you
-know the other.
-
---
-
-## For agents reading this
-
- Default assumption when the user asks a question: start in the current
-  brain (resolved via the precedence above). Don't jump brains without a
-  reason.
- If the user asks a question that crosses topic areas a team might own
-  (e.g. "what did Team X decide last week?"), the right move is to *query
-  the team's brain explicitly* rather than searching host with "team x".
- Cross-brain federation is YOUR JOB, not the DB's. You have the brain list
-  (`gbrain mounts list`). You decide when to fan out. You synthesize
-  findings. You cite `brain:source:slug`.
- When writing a page, respect the brain boundary. A fact about a team's
-  work belongs in the team's brain, not in the user's personal brain. Ask
-  before writing cross-brain.
- See `skills/conventions/brain-routing.md` for the full decision table.
-
-## For users reading this
-
- **Default path:** set up your personal brain (`gbrain init`), add a source
-  per repo you care about (`gbrain sources add gstack --path ~/gstack`).
-  You'll almost never need `--brain`.
- **When a team publishes a brain:** `gbrain mounts add <team-id> --path
-  <clone> --db-url <url>` and the `.gbrain-mount` dotfile in that checkout
-  routes queries there automatically.
- **When you are the CEO-class user with multiple team memberships:** mount
-  each team brain. Trust the resolver — inside a team's directory the
-  dotfile picks the brain, inside a subdirectory the dotfile picks the
-  source. The flags are for when you want to query across the boundary
-  deliberately.
-
-## Further reading
-
- v0.18.0 CHANGELOG — introduced `sources` primitive.
- v0.19.0 CHANGELOG (TBD after PR 0+1+2 ship) — introduces `mounts`.
- `docs/mounts/publishing-a-team-brain.md` (PR 2) — how to be the brain
-  publisher, not just the subscriber.
@@ -1,105 +0,0 @@
-# GBrain Infrastructure Layer
-
-The shared foundation that all skills, recipes, and integrations build on.
-
-## Data Pipeline
-
-```
-INPUT (markdown files, git repo)
-  ↓
-FILE RESOLUTION (local → .redirect → .supabase → error)
-  ↓
-MARKDOWN PARSER (gray-matter frontmatter + body)
-  → compiled_truth + timeline separation
-  ↓
-CONTENT HASH (SHA-256 idempotency check — skip if unchanged)
-  ↓
-CHUNKING (3 strategies, configurable)
-  ├── Recursive: 300-word chunks, 50-word overlap, 5-level delimiter hierarchy
-  ├── Semantic: embed sentences, cosine similarity, Savitzky-Golay smoothing
-  └── LLM-guided: Claude Haiku identifies topic shifts in 128-word candidates
-  ↓
-EMBEDDING (OpenAI text-embedding-3-large, 1536 dimensions)
-  → batch 100, exponential backoff, non-fatal if fails
-  ↓
-DATABASE TRANSACTION (atomic: page + chunks + tags + version)
-  ↓
-SEARCH (hybrid, available immediately)
-```
-
-## Search Architecture
-
-GBrain uses Reciprocal Rank Fusion (RRF) to merge vector and keyword search:
-
-```
-User Query
-  ↓
-EXPANSION (optional: Claude Haiku generates 2 alternative phrasings)
-  ↓
-  ├── VECTOR SEARCH (pgvector HNSW, cosine distance)
-  │     → 2x limit results per query variant
-  │
-  └── KEYWORD SEARCH (PostgreSQL tsvector, ts_rank)
-        → 2x limit results
-  ↓
-RRF MERGE (score = Σ(1/(60 + rank)), balances both fairly)
-  ↓
-4-LAYER DEDUP
-  ├── Best 3 chunks per page (source dedup)
-  ├── Jaccard similarity > 0.85 (text dedup)
-  ├── No type exceeds 60% (diversity)
-  └── Max 2 chunks per page (page cap)
-  ↓
-TOP N RESULTS (default 20)
-```
-
-## Key Components
-
-| File | Purpose |
-|------|---------|
-| `src/core/engine.ts` | Pluggable engine interface (BrainEngine) |
-| `src/core/postgres-engine.ts` | Postgres + pgvector implementation |
-| `src/core/import-file.ts` | importFromFile + importFromContent pipeline |
-| `src/core/sync.ts` | Git-based incremental change detection |
-| `src/core/markdown.ts` | YAML frontmatter + compiled_truth/timeline parsing |
-| `src/core/embedding.ts` | OpenAI embedding with batch, retry, backoff |
-| `src/core/chunkers/recursive.ts` | Base chunker (300w, 5-level delimiters) |
-| `src/core/chunkers/semantic.ts` | Embedding-based topic boundary detection |
-| `src/core/chunkers/llm.ts` | Claude Haiku guided chunking |
-| `src/core/search/hybrid.ts` | RRF merge of vector + keyword |
-| `src/core/search/dedup.ts` | 4-layer result deduplication |
-| `src/core/search/expansion.ts` | Multi-query expansion via Claude Haiku |
-| `src/core/storage.ts` | Pluggable storage (S3, Supabase, local) |
-| `src/core/operations.ts` | Contract-first operation definitions (31 ops) |
-| `src/schema.sql` | Full DDL (10 tables, RLS, tsvector, HNSW) |
-
-## Schema Overview
-
-10 tables in Postgres:
-
- **pages** — slug (unique), type, title, compiled_truth, timeline, frontmatter (JSONB)
- **content_chunks** — pgvector 1536-dim embedding, chunk_source (compiled_truth|timeline)
- **links** — typed edges (knows, works_at, invested_in, founded, etc.)
- **tags** — many-to-many page tagging
- **timeline_entries** — structured events (date, source, summary, detail)
- **page_versions** — snapshot history for diff/revert
- **raw_data** — sidecar JSON from external APIs (preserves provenance)
- **files** — binary attachments in storage backend
- **ingest_log** — audit trail of import operations
- **config** — brain-level settings (version, embedding model, chunk strategy)
-
-Full-text search uses weighted tsvector: title (A), compiled_truth (B), timeline (C).
-Vector search uses HNSW index with cosine distance on content_chunks.embedding.
-
-## The Thin Harness Principle
-
-GBrain is the deterministic layer. Skills and recipes are the latent space layer.
-
-See [Thin Harness, Fat Skills](../ethos/THIN_HARNESS_FAT_SKILLS.md) for the full
-architecture philosophy.
-
- **GBrain CLI** = thin harness (same input → same output)
- **Skills** (ingest, query, maintain, enrich, briefing, migrate, setup) = fat skills
- **Recipes** (voice-to-brain, email-to-brain) = fat skills that install infrastructure
-
-The agent reads the skill/recipe and uses GBrain's deterministic tools to do the work.
@@ -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
@@ -1,367 +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.
-
-### 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.
@@ -1,150 +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.
@@ -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)
@@ -1,162 +0,0 @@
-# Code Cathedral II — v0.20.0 Design
-
-**Status:** Accepted. CEO + Eng + 2 codex passes CLEARED (2026-04-24). 16 cross-model findings absorbed total: 7 codex pass 1 (structural prereqs) + 6 codex pass 2 (absorption errors including the CHUNKER_VERSION silent-no-op gate and inbound-edge invalidation) + 3 eng-review architectural decisions. DX review recommended post-Layer 8 (new CLI surfaces) before ship.
-**Supersedes:** Cathedral I (planned v0.18.0–v0.19.0 code indexing, shipped v0.19.0).
-**Mode:** SCOPE EXPANSION (user explicit: "I want the best code search in the world").
-**Scale:** 14 bisectable layers, ~20–25 CC hours, 3–5 human-weeks. One schema migration with split edge tables (`code_edges_chunk` + `code_edges_symbol`). Backfill via `CHUNKER_VERSION` bump (automatic on next sync) + explicit `gbrain reindex-code` command.
-
-## Why v0.20.0
-
-v0.19.0 shipped code indexing: tree-sitter chunker, 29 active languages, symbol columns, forward doc↔impl linking, incremental embed cache, BrainBench code category. Four cathedral-I items got deferred during shipping: `query --lang` filter, `sync --all` cost preview, markdown fence extraction, reverse-scan doc↔impl backfill.
-
-Cathedral II is a promise-keeping release for those four, bundled with the leap that makes gbrain *the* code search: structural edges (call graph + references + imports + inheritance), parent-scope capture, doc-comment FTS binding, and two-pass retrieval. No more grep-class retrieval on code.
-
-## The 10x leap
-
-Today: agent asks "how does hybrid search handle N+1?" → gets 3 prose chunks of `hybrid.ts`.
-
-Cathedral II: same query returns the anchor function + its 3 callers + its 2 callees + its JSDoc + the guide in `/docs` that cites it + the test file exercising it + parent scope chain. One walk. Code-aware brain.
-
-## Scope (5 tiers + Layer 0 prerequisites, 14 bisectable layer commits)
-
-### Tier 0 — Prerequisites (surfaced by codex outside voice)
-
-**0a. File-classification widening.** `sync.ts:35` currently classifies only 9 extensions as code (TS, JS, Python, Go, Rust, Ruby, Java, C, C++). Cathedral II's B1 ships 165 lazy-loadable grammars, so the classifier needs to accept any extension the chunker can handle. Also reorders `detectCodeLanguage` so Magika (B2) runs as a fallback for extension-less files, not after a null-return gate.
-
-**0b. Chunk-grain FTS.** Current keyword search lives on `pages.search_vector`. Adding doc-comments or two-pass anchoring at the chunk level has zero ranking effect against a page-grain primitive. Layer 0b adds `content_chunks.search_vector` with a trigger building from qualified symbol name + doc-comment (weight A) and chunk_text (weight B), plus rewrites `searchKeyword` to rank chunks directly. Page-level search_vector stays for title-heavy searches.
-
-Both Layer 0 items are prerequisites for the 10x leap to actually move retrieval metrics.
-
-### Tier A — Structural edges (the 10x leap)
-
-**A1. Call-graph + reference extraction with qualified symbol identity.** Per-language tree-sitter queries at `importCodeFile` time capture:
-
- `calls` — function call-sites
- `imports` — module deps
- `extends` / `implements` — type hierarchies
- `mixes_in` — Ruby `include`/`extend`/`prepend`
- `type_refs` — parameter + return type usage
- `declares` — chunk owns a symbol definition
-
-**Qualified symbol identity across all 8 langs.** `parent_symbol_path` (A3) is the source of truth for scope; edges use qualified names built from it. Examples: `Admin::UsersController#render` (Ruby instance), `Admin::UsersController.find_all` (Ruby singleton), `admin.users_controller.UsersController.render` (Python), `(*UsersController).Render` (Go), `users::UsersController::render` (Rust), `com.acme.admin.UsersController.render` (Java). Per-lang delimiter + method/class-method distinction. Ruby ships fully in ranker (CLI + A2 two-pass) — no deferral.
-
-**Split schema (two tables, not one polymorphic):**
-```sql
-CREATE TABLE code_edges_chunk (
-  from_chunk_id INTEGER NOT NULL REFERENCES content_chunks(id) ON DELETE CASCADE,
-  to_chunk_id   INTEGER NOT NULL REFERENCES content_chunks(id) ON DELETE CASCADE,
-  from_symbol_qualified TEXT NOT NULL,
-  to_symbol_qualified   TEXT NOT NULL,
-  edge_type     TEXT NOT NULL,
-  source_id     TEXT REFERENCES sources(id) ON DELETE CASCADE,
-  UNIQUE (from_chunk_id, to_chunk_id, edge_type)
-);
-CREATE TABLE code_edges_symbol (
-  from_chunk_id INTEGER NOT NULL REFERENCES content_chunks(id) ON DELETE CASCADE,
-  from_symbol_qualified TEXT NOT NULL,
-  to_symbol_qualified   TEXT NOT NULL,
-  edge_type     TEXT NOT NULL,
-  source_id     TEXT REFERENCES sources(id) ON DELETE CASCADE,
-  UNIQUE (from_chunk_id, to_symbol_qualified, edge_type)
-);
-```
-`code_edges_chunk` = resolved (both endpoints known). `code_edges_symbol` = unresolved (target symbol exists by qualified name, definition chunk not yet seen). Promotion from symbol→chunk table happens on later import. `source_id` is TEXT matching actual `sources.id` type.
-
-**Shipped languages:** TypeScript, TSX, JavaScript, Ruby, Python, Go, Rust, Java (8 langs, ~85% of real brain code). Other languages chunk normally (via B1 lazy-load) but don't emit edges in v0.20.0 — extension is one query file + delimiter config per language, shippable as small follow-up PRs.
-
-**A2. Two-pass retrieval.** Current: keyword + vector → RRF → dedup. New: keyword + vector → anchor set → expand 1–2 hops on `code_edges_chunk` with structural-distance decay → blend into RRF.
-
-**Default OFF in all cases.** Opt-in only via `--walk-depth N` or `--near-symbol <name>`. Exact-symbol-match auto-on was unsafe (symbol names collide across files). Neighbor cap 50 per hop, depth cap 2. Dedup's per-page cap (currently 2) lifts to `min(10, walkDepth × 5)` when walking so structural neighbors from one file aren't clipped. Distance decay: `1/(1 + hop)` on expanded-neighbor RRF contributions.
-
-**A3. Parent-scope capture + nested-chunk emission.** Two parts:
-
-*Part 1:* Nested symbols get `parent_symbol_path text[]` on `content_chunks`. Embedded into chunk header: `[TypeScript] src/foo.ts:42-58 function formatResult (in BrainEngine.searchKeyword)`. Scope flows into embedding. Dual-use: drives A1's qualified symbol identity.
-
-*Part 2:* Extend `splitLargeNode` to emit nested functions/methods/inner-classes as their own chunks. The current chunker is top-level-node oriented — a `class Foo { method1() {} method2() {} }` emits one chunk. Parent_symbol_path on top-level nodes is empty (no parent above top level), so A3 contributes nothing without sub-top-level chunks. Part 2 makes the scope annotation load-bearing.
-
-**A4. Doc-comment → symbol binding.** Leading AST comment extracted to `doc_comment text`. Lands on **chunk-grain** search_vector (Layer 0b prerequisite) with FTS weight `'A'`. Natural-language queries rank docstring matches above body text and below title. `'A' > 'B' > 'C' > 'D'` per Postgres FTS weight convention.
-
-### Tier B — Coverage (honest Chonkie parity)
-
-**B1.** Lazy-load tree-sitter-language-pack (~165 languages). Replace 36 committed WASMs with a manifest + per-process parser cache. Cathedral I promised this and didn't deliver — Cathedral II does.
-
-**B2.** Magika auto-detect for extension-less files (Dockerfile, Makefile, `.envrc`). ~1MB bundled asset. Falls back to null → recursive chunker if classifier fails to load.
-
-### Tier C — Agent CLI surfaces
-
- `query --lang <lang>` — filter by `content_chunks.language`
- `query --symbol-kind function|class|method|type|interface|enum` — filter by `symbol_type`
- `query --near-symbol <name> --depth 1..2` — two-pass retrieval anchored at a known symbol
- `code-callers <symbol>` — uses A1 `calls` edges, reversed
- `code-callees <symbol>` — uses A1 `calls` edges, forward
-
-All auto-JSON on non-TTY. `StructuredAgentError` envelopes on failure. `code-signature` deferred to v0.20.1 (needs per-language type captures).
-
-### Tier D — Bridge items (cathedral I promises)
-
-**D1.** `sync --all` cost preview. `estimateTokens` extracted from `chunkers/code.ts` to new `tokens.ts` module. Before per-source loop: walk sync-diff set, sum tokens, compute $ estimate. TTY + !json + !yes → interactive `[y/N]`. Non-TTY or `--json` or piped → emit `ConfirmationRequired` envelope, exit 2. `--yes` skips. `--dry-run` previews + exit 0. Preview on `--all` only, not single-source (DX review pain is first-time large-sync surprise bills).
-
-**D2.** Markdown fence extraction in `importFromContent`. After `parseMarkdown`, iterate marked lexer tokens for `{type:'code', lang, text}`. Map fence tag → language. Chunk each fence through `chunkCodeText`. Persist as `chunk_source='fenced_code'`. Cap 100 fences per markdown page (DOS defense). Per-fence try/catch — one bad fence doesn't break the page import.
-
-**D3.** `reconcile-links` batch command. Walks markdown pages, calls existing v0.19.0 `extractCodeRefs` per page, emits `addLink(md, code, ..., 'documents')` + reverse. `ON CONFLICT DO NOTHING` handles idempotency. Statement-timeout scoped via `sql.begin` + `SET LOCAL`. Progress reporter + final summary (edges added / existed / missing-target). Respects `auto_link` config.
-
-### Tier E — Eval, backfill, honesty
-
-**E1.** BrainBench code sub-categories: `call_graph_recall` (callers of X → expected set), `parent_scope_coverage` (nested-symbol queries return correct scope), `doc_comment_matching` (NL queries rank doc-comments above prose). Regression gates against A1/A3/A4 drift.
-
-**E2.** Backfill: schema migrates automatically (zero cost). **`CHUNKER_VERSION` bumps 3 → 4** — that constant is folded into each code page's `content_hash`, so every code page's hash changes on upgrade. Next `gbrain sync` won't short-circuit on "git HEAD unchanged"; it re-chunks every code file. New `gbrain reindex-code [--source <id>] [--dry-run] [--yes] [--force]` provides explicit full backfill with cost preview (reuses D1 infra) and `--force` bypasses content_hash skip entirely. Users control when to pay; silent no-op path closed.
-
-**E3.** Honest CHANGELOG. Retire "Chonkie superset" framing. Run BrainBench before/after for real numbers: 150+ languages loaded (after B1), MRR on NL→code queries, P@1 call-graph precision, P@k on symbol_name queries, sync cost preview on 5K-file repo. Back every claim with a runnable command.
-
-## Implementation ordering (14 layers, post-codex)
-
-1. **0a** — File-classification widening (sync.ts:35) + Magika reordered as fallback
-2. **0b** — Chunk-grain FTS (content_chunks.search_vector + trigger + searchKeyword chunk-level rewrite)
-3. **Foundation** — schema migration (split edge tables, qualified name columns on content_chunks) + engine method stubs + types
-4. **B1** — lazy-load grammar manifest + bun --compile guard
-5. **A1** — edge-extractor + 8 per-lang query files + qualified symbol identity + tests
-6. **A3** — parent-scope column + doc-comment column + splitLargeNode nested-chunk emission
-7. **A4** — doc-comment FTS weight A on chunk-grain search_vector
-8. **A2** — two-pass retrieval, default OFF, opt-in only; dedup cap lifts when walking
-9. **D tier bundled** — cost preview + fence extraction + reconcile-links
-10. **B2** — Magika auto-detect
-11. **C tier** — 5 CLI surfaces
-12. **E1** — BrainBench sub-categories + CHUNKER_VERSION 3→4 bump
-13. **E2** — `reindex-code` with `--force` + migration orchestrator with backfill-prompt phase
-14. **E3 + release** — honest CHANGELOG + docs + migration skill + `/ship`
-
-## Size and cost
-
- Diff: ~5500–6500 lines (~2.5x v0.19.0 post-codex expansion)
- Tests: ~2000 lines (8 langs × qualified-name + edge-extraction fixtures + Layer 0b FTS migration tests)
- Files: ~36 new, ~25 modified
- CC time: ~20–25 hours focused (was 14–18 pre-codex; +6h for Layer 0a/0b + qualified identity across 8 langs + nested-chunk emission + CHUNKER_VERSION bump layer)
- Human-equivalent: 3–5 weeks
- First-sync cost bump for upgraded v0.19.0 users: every code page re-chunks on first sync after upgrade (CHUNKER_VERSION bump forces invalidation). Users run `gbrain reindex-code --dry-run` for cost preview, then `--yes` or accept gradual backfill over time as files change.
- Daily autopilot cost post-backfill: unchanged (edges extracted at chunk time, no per-query LLM)
-
-## Risks and mitigations
-
-1. **Schema migration on live Postgres.** Test against production-shape DB before ship. v0.12.0 JSONB incident is the canary.
-2. **Per-language tree-sitter queries are fiddly.** Hand-verified edge-set fixtures per language. Ruby gets extra coverage for dynamic-dispatch false negatives.
-3. **Two-pass retrieval regression.** Default off for prose. BrainBench Cat 1 MUST show no regression before shipping.
-4. **Backfill shape (G1 resolved).** Three composable layers: schema-auto migrates columns empty (zero cost). Lazy on-touch catches 80% over time (zero cost). Explicit `reindex-code` with cost preview for users wanting immediate full benefit. No surprise bills.
-5. **Magika bundle (G2 resolved).** +1MB asset, `bun --compile` guard extension. If bundling surfaces bugs late in implementation, B2 is the only tier that can fall back to v0.20.1 without blocking the cathedral — it's self-contained at Layer 8.
-6. **High-fan-out symbols.** `console.log`-style symbols have 100K callers. Neighbor cap 50, depth cap 2. Chaos test fixture required.
-
-## Review gates
-
- CEO review (cathedral II) — CLEARED 2026-04-24
- Outside voice (codex) — run during cathedral II CEO review
- `/plan-devex-review` — up next (per user request, 5 new CLI surfaces + reindex-code need DX polish review before eng)
- `/plan-eng-review` — required before implementation begins
- `/review` + `/codex review` — required before `/ship`
-
-## What's deferred to later cathedrals
-
- **C6** `code-signature "(A, B) => C"` — per-language type captures. v0.20.1.
- **Call-graph langs beyond 8 shipped** — PHP, Swift, Kotlin, Scala, C#, C++, Elixir, etc. One small PR per language.
- **LSP integration** for live precision. v0.22+ cathedral.
- **Code-tour generator** (cathedral I T1).
- **Private-code redaction pre-embed** (cathedral I T3).
- **`gbrain doctor --chunker-debug`** AST dump.
@@ -1,154 +0,0 @@
-# Homebrew for Personal AI Infrastructure
-
-The 10-star vision for GBrain's integration system. Ship Approach B (v0.7.0),
-build toward this over subsequent releases.
-
-## The Vision
-
-GBrain becomes a personal infrastructure operating system where every signal in
-your life flows through the brain automatically. Integrations are **senses**
-(data inputs) and **reflexes** (automated responses to patterns). Users subscribe
-to the creator's actual operating system, then customize it.
-
-```
-$ gbrain integrations
-
-  SENSES (data inputs)                          STATUS
-  -------------------------------------------------------
-  voice-to-brain    Phone calls -> brain pages  ACTIVE    last call: 2h ago
-  email-to-brain    Gmail -> entity updates     ACTIVE    47 emails today
-  x-to-brain        Twitter -> media pages      ACTIVE    312 tweets tracked
-  calendar-to-brain Google Cal -> meeting prep  ACTIVE    3 meetings tomorrow
-  photos-to-brain   Camera roll -> visual mem   AVAILABLE
-  slack-to-brain    Slack -> conversation index  AVAILABLE
-  rss-to-brain      RSS feeds -> media pages     AVAILABLE
-
-  REFLEXES (automated responses)                STATUS
-  -------------------------------------------------------
-  meeting-prep      Brief me before meetings    ACTIVE    next: 9am tomorrow
-  entity-enrich     Auto-enrich new contacts    ACTIVE    12 enriched today
-  dream-cycle       Overnight brain maintenance ACTIVE    last run: 3am
-  deal-tracker      Alert on deal changes       AVAILABLE
-  follow-up-nudge   Remind on stale threads     AVAILABLE
-
-  This week: 1,247 signals ingested. Top: email (47%), voice (23%), X (18%).
-  34 new entity pages created. 7 calls transcribed.
-
-  Run 'gbrain integrations show <id>' for setup details.
-```
-
-The user feels: "My brain is alive. It's watching everything I care about, and
-it's getting smarter every day. I didn't have to write any code. I just said yes
-when the agent asked."
-
-## Architecture: Senses & Reflexes
-
-### Recipe Format (YAML frontmatter + markdown body)
-
-```yaml
---
-id: voice-to-brain
-name: Voice-to-Brain
-version: 0.7.0
-description: Phone calls create brain pages via Twilio + OpenAI Realtime + GBrain MCP
-category: sense
-requires: [credential-gateway]
-secrets:
-  - name: TWILIO_ACCOUNT_SID
-    description: Twilio account SID
-    where: https://console.twilio.com
-  - name: OPENAI_API_KEY
-    description: OpenAI API key (for Realtime voice)
-    where: https://platform.openai.com/api-keys
-health_checks:
-  - curl -s https://api.twilio.com/2010-04-01 > /dev/null
-  - curl -s https://api.openai.com/v1/models > /dev/null
-setup_time: 30 min
---
-
-[Opinionated setup instructions the agent executes...]
-```
-
-### Dependency Graph
-
-Recipes declare `requires` in frontmatter. The CLI resolves dependencies before
-setup. If voice-to-brain requires credential-gateway, the agent sets up
-credential-gateway first.
-
-```
-credential-gateway
-  ├── voice-to-brain (requires credentials for Twilio)
-  ├── email-to-brain (requires credentials for Gmail)
-  └── calendar-to-brain (requires credentials for Google Calendar)
-
-x-to-brain (standalone, uses X API directly)
-```
-
-### Health Dashboard
-
-`gbrain integrations doctor` runs health_checks from every configured recipe:
-```
-$ gbrain integrations doctor
-  voice-to-brain:   ✓ Twilio reachable  ✓ OpenAI key valid  ✓ ngrok tunnel up
-  email-to-brain:   ✓ Gmail auth valid   ✗ No emails in 48h (check cron)
-  OVERALL: 1 warning
-```
-
-### Sense Analytics
-
-`gbrain integrations stats` aggregates heartbeat data:
-```
-$ gbrain integrations stats
-  This week: 1,247 signals ingested
-  Top sources: email (47%), voice (23%), X (18%), calendar (12%)
-  34 new entity pages created
-  7 calls transcribed
-  Brain growth: 12,400 → 12,834 pages (+434)
-```
-
-### Reflex Rules Engine (future)
-
-Reflexes are recipes that trigger on brain state changes:
-
-```yaml
---
-id: deal-tracker
-category: reflex
-triggers:
-  - type: page_updated
-    filter: {type: deal, field: status}
-  - type: timeline_entry
-    filter: {source: email, mentions: deal}
-action: alert
---
-
-When a deal page's status changes or a new email mentions a deal,
-alert the user with context from the brain.
-```
-
-## Roadmap
-
-| Version | What Ships | Key Recipe |
-|---------|-----------|------------|
-| v0.7.0 | Recipe format, CLI, SKILLPACK breakout | voice-to-brain |
-| v0.8.0 | 3 more senses, reflex format | email, X, calendar |
-| v0.9.0 | Community recipes, install executor | community submissions |
-| v1.0.0 | Full senses/reflexes, health dashboard | meeting-prep, dream-cycle |
-
-## Key Design Decisions
-
-1. **GBrain is deterministic infrastructure.** Cross-sense correlation, pattern
-   detection, and intelligent responses are the agent's job (OpenClaw/Hermes).
-   GBrain provides the plumbing.
-
-2. **Agents ARE the runtime.** No npm packages, Docker images, or deterministic
-   scripts. The recipe markdown IS the installer. The agent reads it and does
-   the work.
-
-3. **Very opinionated defaults.** Ship the creator's exact production setup as
-   the default. Users customize from there. Unknown callers get screened. Quiet
-   hours are enforced. Brain-first lookup happens on every call.
-
-4. **Agent-readable outputs.** All CLI output must be parseable by agents (--json
-   flag). Migration files include agent instructions. The agent is the primary
-   consumer, not the human.
@@ -1,717 +0,0 @@
-# GBrain Knowledge Runtime — Design Doc
-
-**Status:** DRAFT for CEO review.
-**Date:** 2026-04-18.
-**Supersedes:** The earlier "Feynman Ideas Assessment + Phase A/B" plan.
-
---
-
-## 0. Context
-
-During a CEO review of a narrow two-feature plan (bare-tweet citation repair + completeness score, borrowed from Feynman), the scope was reframed. The narrow plan duplicated work Garry's OpenClaw already does and missed the real leverage point: **the bespoke abstractions hiding inside OpenClaw — resolvers, enrichment orchestration, scheduling, deterministic output — should live in GBrain as first-class primitives.**
-
-North star: *"When Garry's OpenClaw's Claw upgrades to this version of GBrain, it should immediately recognize brilliance and completeness and say 'It's time to switch to these abstractions.'"*
-
-That is the test this document is designed against. Everything else is downstream.
-
---
-
-## 1. The Four Layers
-
-The design is four layered abstractions. Each is independently useful; together they are the Knowledge Runtime.
-
-```
-  ┌───────────────────────────────────────────────────────────────────┐
-  │                   KNOWLEDGE RUNTIME (new)                         │
-  ├───────────────────────────────────────────────────────────────────┤
-  │  Layer 4: Deterministic Output Builder                            │
-  │     BrainWriter · Scaffolds · Back-link enforcer · Slug registry  │
-  │     Rule: LLM picks WHAT to write. Code guarantees WHERE and HOW. │
-  ├───────────────────────────────────────────────────────────────────┤
-  │  Layer 3: Scheduler                                               │
-  │     ScheduledResolver · TZ-aware quiet hours (enforced) ·         │
-  │     Auto-stagger · Durable state · Retry/circuit-break            │
-  ├───────────────────────────────────────────────────────────────────┤
-  │  Layer 2: Enrichment Orchestrator                                 │
-  │     Trigger convergence · Tier routing · Budget · Cascade ·       │
-  │     Evidence-weighted completeness · Fail-safe transactions       │
-  ├───────────────────────────────────────────────────────────────────┤
-  │  Layer 1: Resolver SDK                                            │
-  │     Resolver<I,O> interface · Registry · Factory · Plugin recipes │
-  │     Ported reference impls: X-API, Perplexity, Mistral, brain     │
-  └───────────────────────────────────────────────────────────────────┘
-          │                                                │
-          ▼                                                ▼
-     REUSES (polished primitives already in GBrain)  REPLACES (ad-hoc code)
-     FailImproveLoop · backoff · storage factory ·   enrichment-service ·
-     check-resolvable · operations validators ·      embedding · transcription ·
-     engine interface · publish · backlinks          2 recipe formats
-```
-
---
-
-## 2. Why This Order (L1 → L4)
-
-Every higher layer depends on the lower one. **L1 must land first or the rest leaks abstractions.**
-
- **L1 (Resolvers)** is the substrate. Without a uniform lookup interface, every orchestrator + writer has bespoke callers.
- **L2 (Orchestrator)** uses L1 to fetch; without L1 it's still ad-hoc.
- **L3 (Scheduler)** runs L2 periodically; without L2 it's scheduling nothing structured.
- **L4 (Output Builder)** is what every layer ultimately writes through; without it we have 14 call sites doing `fs.writeFile` with hand-rolled citation discipline.
-
-An earlier implementation could ship L1 + L4 first (the two "purest" layers) and have the most immediate integrity impact, then add L2 + L3. But the end-state must include all four.
-
---
-
-## 3. Layer 1 — Resolver SDK
-
-### 3.1 What's broken today
-
-Garry's OpenClaw has **69 distinct external-lookup patterns** across X API (14 shapes), Perplexity, Mistral OCR, Gmail, Calendar, Slack, GitHub, YouTube, Diarize.io, YC tools, OSINT collectors, and brain-local lookups. Each one is a bespoke script under `scripts/` with its own error handling, retry logic, and output shape. GBrain has 3 ad-hoc wrappers (`embedding.ts`, `transcription.ts`, `enrichment-service.ts`) that don't share an interface.
-
-Common consequences:
- No uniform retry/backoff strategy (some scripts retry, most don't)
- No cost tracking (Perplexity bills eaten silently when calls return no-substance results)
- No confidence/provenance propagation (callers can't tell if an answer is verified or inferred)
- Users can't add a resolver without forking GBrain
-
-### 3.2 Interface
-
-```typescript
-// src/core/resolvers/interface.ts
-
-export type ResolverCost = 'free' | 'rate-limited' | 'paid';
-
-export interface ResolverRequest<I> {
-  input: I;
-  context: ResolverContext;
-  timeoutMs?: number;
-}
-
-export interface ResolverResult<O> {
-  value: O;
-  confidence: number;      // 0.0–1.0; 1.0 = deterministic from ground-truth API
-  source: string;          // e.g. "x-api-v2", "perplexity-sonar", "brain-local"
-  fetchedAt: Date;
-  costEstimate?: number;   // dollars; 0 if free
-  raw?: unknown;           // for sidecar preservation via put_raw_data
-}
-
-export interface Resolver<I, O> {
-  readonly id: string;           // stable, slug-like: "x_handle_to_tweet"
-  readonly cost: ResolverCost;
-  readonly backend: string;      // "x-api-v2", "perplexity", "brain-local"
-  readonly inputSchema: JSONSchema;
-  readonly outputSchema: JSONSchema;
-
-  available(ctx: ResolverContext): Promise<boolean>;
-  resolve(req: ResolverRequest<I>): Promise<ResolverResult<O>>;
-}
-```
-
-### 3.3 Context
-
-```typescript
-export interface ResolverContext {
-  engine: BrainEngine;
-  storage: StorageBackend;
-  config: GBrainConfig;
-  logger: Logger;
-  metrics: MetricsRecorder;
-  budget: BudgetLedger;       // hard spend caps, queried pre-resolve
-  requestId: string;
-  remote: boolean;            // trust boundary — untrusted callers get stricter validation
-  deadline?: Date;
-}
-```
-
-### 3.4 Registry + Factory (mirrors `src/core/storage.ts`)
-
-```typescript
-// src/core/resolvers/registry.ts
-export class ResolverRegistry {
-  register<I, O>(r: Resolver<I, O>): void;
-  get(id: string): Resolver<unknown, unknown>;
-  list(filter?: { cost?: ResolverCost; backend?: string }): Resolver[];
-  async resolve<I, O>(id: string, input: I, ctx: ResolverContext): Promise<ResolverResult<O>>;
-}
-
-// src/core/resolvers/factory.ts (dynamic import like engine-factory)
-export async function createResolver(
-  type: 'x-api' | 'perplexity' | 'mistral-ocr' | 'brain-local' | 'plugin',
-  config: ResolverConfig,
-): Promise<Resolver>;
-```
-
-### 3.5 Plugin format (unifies `recipes/` + `data-research` formats)
-
-A plugin is YAML + JS module, discovered via filesystem scan of `~/.gbrain/resolvers/` and `recipes/`.
-
-```yaml
-# Example: resolvers/x-api/handle-to-tweet.yaml
-id: x_handle_to_tweet
-version: 1
-category: lookup
-cost: rate-limited
-backend: x-api-v2
-module: ./handle-to-tweet.ts
-input_schema:
-  type: object
-  properties:
-    handle:   { type: string, pattern: "^[A-Za-z0-9_]{1,15}$" }
-    keywords: { type: string }
-  required: [handle]
-output_schema:
-  type: object
-  properties:
-    url:        { type: string, format: uri }
-    tweet_id:   { type: string }
-    text:       { type: string }
-    created_at: { type: string, format: date-time }
-requires:
-  env: [X_API_BEARER_TOKEN]
-health_check:
-  kind: http
-  url: https://api.twitter.com/2/tweets/1
-  expect: { status: [200, 401] }   # 401 = auth failure but endpoint reachable
-tests:
-  - input:  { handle: "garrytan" }
-    expect: { url: { pattern: "^https://x\\.com/garrytan/status/\\d+$" } }
-```
-
-Trust flagging follows the existing `src/commands/integrations.ts` pattern: only package-bundled resolvers are `embedded=true` and may run arbitrary commands; user-provided resolvers are restricted to `http` and validated schemas.
-
-### 3.6 Wraps every resolver with `FailImproveLoop`
-
-Existing `src/core/fail-improve.ts` is the deterministic-first/LLM-fallback pattern. Every resolver automatically gets wrapped: if the deterministic path (e.g. X API) returns a valid result, use it; if it fails, optionally fall back to an LLM-based resolver; log both paths for future pattern analysis and auto-test generation.
-
-### 3.7 Reference implementations to ship
-
-The OpenClaw survey inventoried 69 resolver shapes. Shipping all of them is wrong (over-scoped); shipping zero is under-scoped. The dogfood set:
-
-| # | Resolver | Purpose | Used by |
-|---|---|---|---|
-| 1 | `x_handle_to_tweet` | Bare-tweet citation repair (original Phase A) | `gbrain integrity` |
-| 2 | `url_reachable` | Dead-link detection | `gbrain integrity` |
-| 3 | `brain_slug_lookup` | Name/email → slug (wraps existing `resolveSlugs`) | Output Builder |
-| 4 | `openai_embedding` | Refactor of `src/core/embedding.ts` into Resolver | Import pipeline |
-| 5 | `perplexity_query` | Query → synthesis + citations | Enrichment Orchestrator |
-| 6 | `text_to_entities` | LLM entity extraction (structured JSON) | Enrichment Orchestrator |
-
-The remaining 63 OpenClaw patterns port incrementally, driven by user need. Each port is a new YAML + module under `recipes/` or `~/.gbrain/resolvers/` with no framework changes.
-
---
-
-## 4. Layer 2 — Enrichment Orchestrator
-
-### 4.1 What's broken today
-
-Garry's OpenClaw's enrichment is **polished at the data layer, hacky at the control layer**:
-
- **Completeness = "length > 500 chars + no `needs-enrichment` tag"** (`lib/enrich.mjs:351-355`). Naïve. A rich page of repetitive Perplexity summaries (see `brain/people/0interestrates.md` — 38 repeating blocks) passes this check.
- **30-day auto-re-enrichment** runs forever. No "done" state. A person met once in 2023 still gets re-researched monthly.
- **Cascade is convention-only.** Person→company stubs are created automatically; company→investors, company→employees traversals are documented but never implemented.
- **No hard budget cap.** Cost is estimated per batch, never enforced across batches or per day.
- **Failure is silent.** A bad Perplexity response logs and continues; partial writes can leave a page with a timeline entry but no raw-data sidecar.
-
-### 4.2 The orchestrator
-
-```typescript
-// src/core/enrichment/orchestrator.ts
-
-export interface EnrichmentRequest {
-  entitySlug: string;
-  trigger: 'mention' | 'stub-creation' | 'cron-sweep' | 'manual' | 'cascade';
-  tier?: 1 | 2 | 3;                // optional override; auto-computed if absent
-  cascadeDepth?: number;           // 0 = no cascade; default 1
-}
-
-export interface EnrichmentResult {
-  entitySlug: string;
-  completenessBefore: number;
-  completenessAfter: number;
-  resolversUsed: string[];         // e.g. ["perplexity_query", "x_handle_to_tweet"]
-  costSpent: number;
-  writtenTo: string[];             // page paths touched, for transaction audit
-  cascadedTo: string[];            // related entities enriched
-  status: 'enriched' | 'skipped' | 'failed' | 'budget-exhausted';
-  reason?: string;
-}
-
-export class EnrichmentOrchestrator {
-  constructor(
-    private registry: ResolverRegistry,
-    private writer: BrainWriter,
-    private budget: BudgetLedger,
-    private scorer: CompletenessScorer,
-    private graph: EntityGraph,
-  ) {}
-
-  async enrich(req: EnrichmentRequest): Promise<EnrichmentResult>;
-  async enrichBatch(reqs: EnrichmentRequest[]): Promise<EnrichmentResult[]>;
-}
-```
-
-### 4.3 Evidence-weighted completeness (replaces length heuristic)
-
-Completeness is a per-entity-type rubric, stored in frontmatter on write and recomputed on demand.
-
-```typescript
-// src/core/enrichment/completeness.ts
-export interface CompletenessRubric<Page> {
-  entityType: PageType;
-  dimensions: {
-    name: string;
-    weight: number;                // sum must = 1.0
-    check: (page: Page) => number; // 0.0–1.0
-  }[];
-}
-
-// Example rubric for persons:
-//   - has_role_and_company   0.20
-//   - has_source_urls        0.20  (≥1 URL with resolver-verified reachability)
-//   - has_timeline_entries   0.15  (≥1)
-//   - has_citations          0.15  (every claim has [Source: ...])
-//   - has_backlinks          0.10  (every linked page links back)
-//   - recency_score          0.10  (last_verified within 90 days)
-//   - non_redundancy         0.10  (no repeated blocks; distinct-lines/total-lines > 0.8)
-```
-
-**Key property:** `non_redundancy` + `recency_score` explicitly kill the two brain pathologies observed in the audit (Wilco-style repeating blocks; stale pages without `last_verified`).
-
-The `completeness` field goes in frontmatter as `0.0–1.0`. It becomes queryable via `list_pages(where: completeness < 0.5)`.
-
-### 4.4 Tier routing with hard budget
-
-Two-dimensional routing: **importance** (tier 1/2/3 from person-score) × **budget state**.
-
-```typescript
-// src/core/enrichment/tiers.ts
-export const TIER_CONFIG = {
-  1: { models: ['opus', 'sonar-deep'], maxCostUsd: 0.10, cascadeDepth: 2 },
-  2: { models: ['sonar'],              maxCostUsd: 0.02, cascadeDepth: 1 },
-  3: { models: ['sonar'],              maxCostUsd: 0.005, cascadeDepth: 0 },
-};
-
-// src/core/enrichment/budget.ts
-export class BudgetLedger {
-  // Hard caps. Queryable pre-resolve.
-  dailyCapUsd: number;
-  perEntityCapUsd: number;
-  perResolverCapUsd: Map<string, number>;
-
-  async reserve(resolverId: string, estimateUsd: number): Promise<Reservation | 'exhausted'>;
-  async commit(reservation: Reservation, actualUsd: number): Promise<void>;
-  async rollback(reservation: Reservation): Promise<void>;
-  async state(): Promise<{ spent: number; remaining: number; perResolver: Record<string, number> }>;
-}
-```
-
-**Property:** if the daily cap is reached, `orchestrator.enrich()` returns `status: 'budget-exhausted'` immediately. No silent overages. Circuit-breaker resets at midnight in the user's configured TZ.
-
-### 4.5 Cascade (entity graph traversal)
-
-```typescript
-// src/core/enrichment/cascade.ts
-export class EntityGraph {
-  // Deterministic, no LLM. Uses engine.getLinks() + engine.getBacklinks().
-  async neighbors(slug: string, depth: number): Promise<string[]>;
-  async cascadeFrom(trigger: string, depth: number): Promise<EnrichmentRequest[]>;
-}
-```
-
-If person X is enriched and gains a new `company: Acme` field, cascade checks: does `companies/acme` exist? If not, create stub + enqueue at tier 2. Does `companies/acme` link back to X? If not, write the back-link. **Iron Law is machine-enforced, not skill-enforced.**
-
-### 4.6 Fail-safe transactions
-
-Every enrichment is wrapped in a BrainWriter transaction (Layer 4). Partial writes are rolled back. No asymmetric state like timeline-entry-without-raw-sidecar.
-
-```typescript
-await writer.transaction(async (tx) => {
-  const research = await registry.resolve('perplexity_query', {...}, ctx);
-  await tx.appendTimeline(slug, {...});
-  await tx.putRawData(slug, 'perplexity', research.raw);
-  await tx.setFrontmatterField(slug, 'completeness', score);
-  // All-or-nothing commit on exit.
-});
-```
-
---
-
-## 5. Layer 3 — Scheduler
-
-### 5.1 What's broken today
-
-Garry's OpenClaw's cron is **externally-driven JSON** (`cron/jobs.json`) with ~30 jobs manually stagger-offset at different minutes. GBrain has **zero native scheduling** — `src/commands/autopilot.ts` is a single daemon loop, and `docs/guides/cron-schedule.md` is architectural guidance, not code.
-
-Failures observed in Garry's OpenClaw's actual state:
- `X OAuth2 Token Refresh`: 11 consecutive timeouts (critical-path silent failure)
- `flight-tracker daily scan`: 5 consecutive timeouts
- `morning-briefing`: 4 consecutive timeouts
- Quiet hours are checked at runtime in skills, so a skill that forgets to check will DM at 3 a.m.
- Staggering is manual convention; no protection against two jobs colliding after a config edit.
-
-### 5.2 ScheduledResolver interface
-
-```typescript
-// src/core/scheduling/scheduler.ts
-export interface Schedule {
-  kind: 'cron' | 'interval';
-  expr?: string;                    // cron string
-  intervalMs?: number;
-  tz: string;                       // IANA: "America/Los_Angeles"
-  quietHours?: {
-    startHour: number;              // 22 = 10 PM local
-    endHour: number;                // 7 = 7 AM local
-    policy: 'skip' | 'defer' | 'silent-run';
-  };
-  staggerKey?: string;              // jobs with same key auto-offset
-  maxConcurrent?: number;           // global concurrency cap
-  maxDurationMs?: number;           // timeout
-}
-
-export interface ScheduledResolver extends Resolver<void, ScheduledResult> {
-  schedule: Schedule;
-  retryPolicy: { maxRetries: number; backoffMs: number };
-  circuitBreaker: { failureThreshold: number; cooldownMs: number };
-  state: DurableState;              // watermark, content-hash, idempotency key
-}
-```
-
-### 5.3 Enforcement vs convention (the key delta from Garry's OpenClaw)
-
-| Concern | Garry's OpenClaw today | Knowledge Runtime |
-|---|---|---|
-| Quiet hours | Checked inside each skill (trust-based) | Enforced at scheduler, skill cannot override |
-| Staggering | Manual minute-offset in `jobs.json` | Scheduler assigns slots via hashed staggerKey |
-| Concurrency | `MAX_BATCH_PROCESSES=2` in backoff, ignored by cron | Global semaphore in scheduler |
-| Timeout | Per-job string in JSON, not always respected | Enforced via `AbortController`, timeout raises `TimeoutError` caught by orchestrator |
-| Retry | None at cron level | `retryPolicy` with exponential backoff |
-| Silent failure | "11 consecutive timeouts" unnoticed | Circuit breaker opens at threshold → escalation to user |
-| Idempotency | State files per job, no framework | `DurableState` primitive: watermark/ID/content-hash |
-
-### 5.4 Native engine + OS cron adapter
-
-The scheduler runs as either:
-1. **Embedded** (default for `gbrain autopilot`): native event loop inside the daemon process. One process, many ScheduledResolvers.
-2. **OS-driven** (for Railway/launchd/systemd): `gbrain schedule run <id>` invoked by OS cron, scheduler state is durable so cross-invocation dedup still works.
-
-Both modes share the same `Schedule` config + state.
-
-### 5.5 Observability
-
-Every scheduled run emits structured events: `started`, `skipped-quiet-hours`, `deferred-to-active-hours`, `failed-retrying`, `circuit-opened`, `completed`. Events go to:
- `~/.gbrain/scheduler/events.jsonl` (local, always)
- `engine.logIngest` (audit trail in brain DB)
- Optional webhook (Slack/Telegram for the user)
-
-`gbrain doctor` reads the event log and reports: current circuit-breaker state, any resolver with > 3 consecutive failures, any resolver that hasn't fired within 3× its interval (freshness SLA like Garry's OpenClaw's `freshness-check.mjs` but built-in).
-
---
-
-## 6. Layer 4 — Deterministic Output Builder
-
-### 6.1 The anti-hallucination invariant
-
-**Iron Law: LLM picks WHAT. Code guarantees WHERE and HOW.**
-
-Garry's OpenClaw's existing `lib/enrich.mjs:buildTweetEntry` is close to this — tweet URLs are built from `tweet.id` returned by the X API, never from LLM memory. But:
-
- A past incident: *"Sub-agent test #2 FAILED — hallucinated 'Philip Leung' entity links across all daily files. LLM rewriting of daily files is too error-prone."* (Garry's OpenClaw memory log, 2026-04-13.)
- Back-links depend on `appendTimeline` being called everywhere; skips are silent.
- Slug collisions are unchecked (no conflict detection on `slugify`).
- Citation format is post-hoc linted weekly, not pre-write enforced.
-
-### 6.2 BrainWriter
-
-```typescript
-// src/core/output/writer.ts
-export class BrainWriter {
-  constructor(
-    private engine: BrainEngine,
-    private slugRegistry: SlugRegistry,
-    private scaffolder: Scaffolder,
-  ) {}
-
-  async transaction<T>(fn: (tx: WriteTx) => Promise<T>): Promise<T>;
-}
-
-export interface WriteTx {
-  // High-level typed operations; never raw string writes.
-  createEntity(input: EntityInput): Promise<string>;          // returns slug, conflict-checked
-  appendTimeline(slug: string, entry: TimelineInput): Promise<void>;
-  setCompiledTruth(slug: string, body: CompiledTruthInput): Promise<void>;
-  setFrontmatterField(slug: string, key: string, value: unknown): Promise<void>;
-  putRawData(slug: string, source: string, data: object): Promise<void>;
-  addLink(from: string, to: string, context: string): Promise<void>;  // auto-creates reverse back-link
-
-  // Validators (called implicitly on commit)
-  validate(): Promise<ValidationReport>;
-}
-```
-
-### 6.3 Scaffolder — deterministic link + citation construction
-
-Every user-visible URL/link/citation is built by code from resolver outputs, not from LLM text.
-
-```typescript
-// src/core/output/scaffold.ts
-export class Scaffolder {
-  tweetCitation(handle: string, tweetId: string, dateISO: string): string {
-    // "[Source: [X/garrytan, 2026-04-18](https://x.com/garrytan/status/123456)]"
-  }
-  emailCitation(account: string, messageId: string, subject: string): string {
-    // deterministic Gmail URL per OpenClaw pattern
-  }
-  sourceCitation(resolverResult: ResolverResult<unknown>): string {
-    // pulls .source, .fetchedAt, .raw from the result
-  }
-  entityLink(slug: string): string {
-    // slugRegistry checks existence; returns resolvable wikilink
-  }
-}
-```
-
-### 6.4 SlugRegistry — conflict detection
-
-```typescript
-// src/core/output/slug-registry.ts
-export class SlugRegistry {
-  async create(desiredSlug: string, displayName: string, type: PageType): Promise<CreatedSlug>;
-  // Throws SlugCollision if another entity already occupies desiredSlug and isn't
-  // confirmed as the same person (via email / x_handle / disambiguator).
-  // Auto-resolves near-collisions by appending disambiguator.
-
-  async confirmSame(slugA: string, slugB: string, confidence: number): Promise<void>;
-  async merge(canonical: string, duplicate: string): Promise<void>;
-}
-```
-
-### 6.5 Pre-write validators (fail-closed for integrity)
-
-On `WriteTx.validate()` before commit:
-
-1. **Citation validator.** Every factual sentence in `compiled_truth` must have an inline `[Source: ...]` within N lines. Non-compliant paragraphs are flagged. Configurable: strict-mode rejects the transaction, lint-mode warns.
-2. **Link validator.** Every `[text](path)` must point to a page that exists OR to a URL the Scaffolder built (so it's guaranteed-valid). No raw LLM-composed URLs.
-3. **Back-link validator.** Every outbound link must have a reverse link written in the same transaction.
-4. **Triple-HR validator.** Compiled truth / timeline split enforced at the schema level.
-
-**Fails closed**: the default is strict-mode. Loosening requires explicit `writer.transaction({ strictMode: false }, ...)` and logs a warning to the ingest log.
-
-### 6.6 LLM output sanitization
-
-Any LLM output destined for a brain page passes through a JSON-Schema-validated parser first. No free-form markdown goes to disk.
-
- Entity extraction: JSON array of `{ name, type, context }` per existing `extractEntities` pattern — strict validation.
- Compiled-truth synthesis: LLM emits structured `{ sections: [{heading, paragraphs: [{text, sources: [...]}]}]}`, scaffolder renders to markdown.
- Timeline entries: LLM emits `{ date, summary, detail, sources }`, scaffolder renders.
-
-LLM never sees file paths, never writes files, never emits finished markdown.
-
---
-
-## 7. Integration with existing GBrain
-
-### 7.1 Reuse (already polished)
-
-| Existing | Used by | Change |
-|---|---|---|
-| `src/core/fail-improve.ts` (9/10) | Wraps every Resolver in L1 | None; becomes default wrapper |
-| `src/core/backoff.ts` (9/10) | ResolverContext.backoff | None |
-| `src/core/storage.ts` (9/10) | Template for Resolver factory pattern | None; serves as pattern reference |
-| `src/core/check-resolvable.ts` (9/10) | Extend to validate Resolver plugins | Add `checkResolvers()` mode |
-| `src/commands/publish.ts` (9/10) | Uses BrainWriter under the hood | Minor: route through L4 |
-| `src/commands/backlinks.ts` (8/10) | Folded into L4 validator | Keep as CLI-facing lint entry point |
-| `src/core/operations.ts` validators | Reused in ResolverContext trust enforcement | None |
-| `src/core/engine.ts` BrainEngine (35 methods) | ResolverContext.engine | Extend with `getResolverRegistry()` |
-
-### 7.2 Replace (ad-hoc today)
-
-| Existing | Replace with |
-|---|---|
-| `src/core/enrichment-service.ts` (5/10) | `src/core/enrichment/orchestrator.ts` (L2) |
-| `src/core/embedding.ts` (monolithic) | `src/core/resolvers/builtin/embedding/openai.ts` |
-| `src/core/transcription.ts` (monolithic) | `src/core/resolvers/builtin/transcription/{groq,openai}.ts` |
-| `src/commands/integrations.ts` recipe format | Unified Resolver plugin format (§3.5) |
-| `src/core/data-research.ts` recipe format | Same unified format |
-| `src/commands/autopilot.ts` hard-coded daemon loop | Wraps a set of ScheduledResolvers |
-
-### 7.3 Extend
-
- `src/core/engine.ts`: add `getResolverRegistry()`, `getWriter()`, `getScheduler()`. Engine becomes the runtime's root container.
- `src/core/operations.ts`: `OperationContext` inherits from `ResolverContext` (or vice-versa). Trust flags unified.
- `src/core/types.ts`: add `completeness: number` to `Page`, `sourcedBy: string[]` for provenance.
-
---
-
-## 8. Migration Path (phased, shippable)
-
-Each phase ships independently, passes full E2E, is feature-flagged, and is reversible. No big-bang.
-
-### Phase 0 — Foundation (human: ~1 wk / CC: ~4 h)
- Define `Resolver<I,O>`, `ResolverContext`, `ResolverRegistry`, `ResolverResult` (§3.2–3.4).
- Add `src/core/resolvers/index.ts` wiring + tests for registry (register/get/list).
- No behavioral change; ship as `v0.11.0-alpha` with feature flag.
-
-### Phase 1 — Three reference resolvers (human: ~1 wk / CC: ~4 h)
- Port `src/core/embedding.ts` → `resolvers/builtin/embedding/openai.ts`.
- Implement `resolvers/builtin/brain-local/slug-lookup.ts` (wraps `engine.resolveSlugs`).
- Implement `resolvers/builtin/url-reachable.ts` (HEAD-check).
- Prove the interface: old callers swap to `registry.resolve('openai_embedding', ...)`.
-
-### Phase 2 — BrainWriter + Slug Registry (human: ~1.5 wk / CC: ~6 h)
- L4 core: `BrainWriter.transaction`, `Scaffolder`, `SlugRegistry` with conflict detection.
- Pre-write validators: citation, link, back-link, triple-HR.
- Migrate `src/commands/publish.ts` + `src/commands/backlinks.ts` to route through BrainWriter.
- **Now** Garry's OpenClaw's "Philip Leung" hallucination is structurally impossible — LLM output passes through JSON-Schema validator before reaching Scaffolder.
-
-### Phase 3 — `gbrain integrity` command (human: ~0.5 wk / CC: ~2 h)
- Ship the originally-scoped user-facing feature on top of the new foundation.
- Uses Resolver SDK: `x_handle_to_tweet` + `url_reachable`.
- Uses BrainWriter: all auto-repairs go through validated writes.
- `--auto --confidence 0.8` mode as user approved in cherry-pick #1.
- **User-visible value ships in Phase 3, not Phase 7.**
-
-### Phase 4 — Enrichment Orchestrator (human: ~2 wk / CC: ~8 h)
- L2 core: `EnrichmentOrchestrator`, `BudgetLedger`, `CompletenessScorer`, `EntityGraph.cascadeFrom`.
- Migrate `src/core/enrichment-service.ts` callers (deprecate the old file after).
- Completeness score in frontmatter on every write (dogfooding cascades).
-
-### Phase 5 — Scheduler (human: ~2 wk / CC: ~8 h)
- L3 core: `Scheduler`, `ScheduledResolver`, `DurableState`, circuit breaker, quiet-hours enforcer.
- Migrate `src/commands/autopilot.ts` to a ScheduledResolver set.
- Ship `gbrain schedule list|run|pause|tail` CLI for observability.
-
-### Phase 6 — Port 5–8 OpenClaw resolvers (human: ~1.5 wk / CC: ~6 h)
- `perplexity_query`, `text_to_entities`, `mistral_ocr_pdf`, `x_search_all`, `x_user_to_tweets`, `gmail_query_to_threads`, `calendar_date_to_events`.
- Each ships as YAML + TS module under `resolvers/builtin/` — **proof of the plugin format.**
-
-### Phase 7 — OpenClaw Adoption Integration (human: ~1 wk / CC: ~4 h)
- Write `docs/openclaw/ADOPTION.md` showing your OpenClaw how to replace its 69 bespoke scripts with calls to `gbrain registry.resolve(...)`.
- Ship a `gbrain claw-bridge` subcommand that proxies Garry's OpenClaw's current script invocations to the resolver registry — zero-edit adoption path.
- **This is the test of the north star.** If your OpenClaw can stand up a 1-line shim and drop `scripts/x-api-client.mjs`, the abstraction succeeded.
-
-Total: human: ~10 weeks / CC: ~42 hours / calendar with single implementer: ~3–4 weeks.
-
---
-
-## 9. Critical Files
-
-### New directories / files
-
-```
-src/core/
-  runtime/
-    index.ts                       # RuntimeContext (engine, storage, config, logger, metrics, budget)
-    registry.ts                    # ResolverRegistry
-    factory.ts                     # createResolver()
-  resolvers/
-    interface.ts                   # Resolver<I, O>
-    fail-improve-wrapper.ts        # auto-wraps every resolver in FailImproveLoop
-    builtin/
-      x-api/
-        handle-to-tweet.ts
-        handle-to-tweet.yaml
-      perplexity/
-        query.ts
-        query.yaml
-      brain-local/
-        slug-lookup.ts
-        url-reachable.ts
-      embedding/
-        openai.ts                  # refactored from src/core/embedding.ts
-      transcription/
-        groq.ts
-        openai.ts
-  enrichment/
-    orchestrator.ts                # EnrichmentOrchestrator
-    tiers.ts                       # TIER_CONFIG
-    budget.ts                      # BudgetLedger
-    completeness.ts                # CompletenessScorer + per-type rubrics
-    cascade.ts                     # EntityGraph
-  scheduling/
-    scheduler.ts                   # Scheduler + ScheduledResolver
-    schedule.ts                    # Schedule type, cron expr parser
-    state.ts                       # DurableState primitives
-    quiet-hours.ts                 # TZ-aware enforcement
-    stagger.ts                     # deterministic slot assignment
-  output/
-    writer.ts                      # BrainWriter
-    scaffold.ts                    # Scaffolder (typed URL builders)
-    slug-registry.ts               # SlugRegistry (conflict detection)
-    validators/
-      citation.ts
-      link.ts
-      back-link.ts
-      triple-hr.ts
-
-src/commands/
-  integrity.ts                     # ships in Phase 3, replaces Feynman Phase A/B
-  schedule.ts                      # gbrain schedule list|run|pause|tail (Phase 5)
-
-docs/openclaw/
-  ADOPTION.md                      # written in Phase 7
-```
-
-### Replaced / removed
- `src/core/enrichment-service.ts` — folded into `enrichment/orchestrator.ts`
- `src/core/embedding.ts` — moved into `resolvers/builtin/embedding/openai.ts`
- `src/core/transcription.ts` — moved into `resolvers/builtin/transcription/`
-
-### Extended
- `src/core/engine.ts` — add `getResolverRegistry()`, `getWriter()`, `getScheduler()`
- `src/core/operations.ts` — unify with ResolverContext; every operation validator reusable by resolvers
- `src/core/types.ts` — add `completeness: number`, `sourcedBy: string[]`, `lastVerified: Date`
-
---
-
-## 10. Testing Strategy
-
-### Contract tests
-Every Resolver implementation tested against the interface spec. Table-driven: run the same suite against `openai_embedding`, `x_handle_to_tweet`, etc. Ensures plugin authors can't ship broken resolvers.
-
-### Property tests
- **Idempotency:** running a ScheduledResolver twice with the same state produces the same output and doesn't double-write.
- **Atomicity:** a BrainWriter transaction that throws mid-flight leaves the brain bit-for-bit identical to pre-transaction.
- **Deterministic scaffolds:** given the same resolver outputs, the Scaffolder produces byte-identical citations/links.
-
-### Integration tests
- `EnrichmentOrchestrator` end-to-end against PGLite (in-memory, no API keys) with mocked resolver registry.
- `Scheduler` with fake clock + quiet-hours scenarios.
- BrainWriter transaction rollback on validator failure.
-
-### Chaos tests
- Kill the process mid-enrichment; next run must resume cleanly.
- Simulate API timeout mid-transaction; transaction must roll back completely.
- Corrupted state file; scheduler must escalate, not silently skip.
-
-### Regression tests vs. Garry's OpenClaw behavior
-For each OpenClaw pattern we port (e.g. X-handle → tweet URL), a regression test proves the new resolver produces the same answer on real-world inputs from the brain audit. This is the "your OpenClaw would adopt" proof.
-
---
-
-## 11. Open Questions (flagged for CEO re-review)
-
-1. **Scope shape.** Is this the right four-layer decomposition, or are some layers better left to OpenClaw (e.g. Scheduling lives above GBrain, not in it)?
-2. **Phase 3 user-value break.** Does Phase 3 (user-visible `gbrain integrity`) ship early enough, or do we need an even smaller MVP?
-3. **LLM-as-resolver.** Should `text_to_entities` be a Resolver, or does that blur the "code vs LLM" line the invariant relies on?
-4. **Plugin format.** YAML + TS module (§3.5) vs. pure TS module with decorator-style metadata. Latter is more type-safe; former is more discoverable.
-5. **Cross-resolver transactions.** Do we support "atomic fetch-from-Perplexity + write-to-brain" at the L2 layer? Current design says yes; implementation is tricky (Perplexity call isn't rollbackable).
-6. **OpenClaw bridge scope.** Phase 7 `gbrain claw-bridge` — is that worth a phase of its own, or should adoption be documentation-only?
-7. **Completeness rubric coverage.** Do we define rubrics for all 9 PageTypes upfront, or ship people/company/meeting first and extend incrementally?
-8. **Budget config UX.** Hard daily cap is strict; should we also expose a soft-cap warning mode, and how is the cap set (env var? config file? prompt on first use?)
-9. **Backwards compat.** `src/commands/publish.ts` and `src/commands/backlinks.ts` have been running cleanly for weeks. Refactoring through BrainWriter carries migration risk. Acceptable?
-10. **Existing TODOS alignment.** `TODOS.md` has P0 "Runtime MCP access control" and P2 security hardening. The new RuntimeContext.remote flag interacts with both — do we fold MCP access control into Phase 0 or keep separate?
-
---
-
-## 12. Verification (the "your OpenClaw would adopt" test)
-
-The design succeeds iff:
-
- [ ] A user can add a new resolver by dropping a YAML + TS module in `~/.gbrain/resolvers/` without editing GBrain source.
- [ ] Your OpenClaw can delete `scripts/x-api-client.mjs` and replace all callers with 1-line `await registry.resolve('x_handle_to_tweet', ...)`.
- [ ] No brain page can be written with a bare tweet reference, a missing back-link, or an unverified URL (validators catch it pre-commit).
- [ ] Running `gbrain integrity --auto --confidence 0.8` over a real brain fixes ≥1,000 of the 1,424 known bare-tweet citations without human review.
- [ ] Full E2E test suite passes on both PGLite + Postgres engines.
- [ ] The Knowledge Runtime ships across 7 phases with each phase individually shippable and reversible.
@@ -1,448 +0,0 @@
---
-status: ACTIVE
---
-# CEO Plan: Minions as Universal Agent Orchestration Protocol
-Generated by /plan-ceo-review on 2026-04-15
-Branch: garrytan/minions-jobs | Mode: SCOPE EXPANSION
-Repo: garrytan/gbrain
-
-## Vision
-
-### 10x Check
-Instead of "GBrain has a queue, OpenClaw uses it," make Minions a universal agent
-orchestration protocol. Any platform (OpenClaw, Hermes, Claude Code, Codex, custom
-scripts) submits, monitors, steers, and composes agents through the same Postgres-native
-protocol. GBrain IS the agent control plane.
-
-### Platonic Ideal (aspirational North Star, NOT in v1 scope)
-Open a terminal, type `gbrain jobs dashboard`. See every agent across every platform.
-Their progress, tool calls, token spend. Click any agent for full execution trace.
-Type a message to redirect a running agent mid-flight. See the governor's decisions
-visualized. Run A/B tests between agent configurations. The feeling: complete
-situational awareness of your AI workforce.
-
-**Note:** The dashboard, A/B testing, and visual governor are future phases. This plan
-builds the primitives they would sit on top of: real-time events, structured progress,
-token accounting, inbox with ack, and session transcripts.
-
-## Scope Decisions
-
-| # | Proposal | Effort | Decision | Reasoning |
-|---|----------|--------|----------|-----------|
-| 1 | pg LISTEN/NOTIFY real-time events | S | ACCEPTED | Sub-second event delivery vs 5s polling. Every platform benefits. |
-| 2 | Structured progress protocol | S | ACCEPTED | Standard progress makes unified dashboard possible. |
-| 3 | Job cost tracking (token accounting) | M | ACCEPTED | Token cost is #1 thing users want to know about agent work. |
-| 4 | Job replay | S | ACCEPTED | Small surface area, high utility for debugging failures. |
-| 5 | Job groups / waves | M | DEFERRED | Parent-child already provides grouping. Overlap concern. |
-| 6 | Inbox acknowledgment (read receipts) | S | ACCEPTED | Without it, inbox is fire-and-forget — same problem we're fixing. |
-| 7 | Universal agent protocol | S | ACCEPTED | Design framing, not extra code. Platform-agnostic naming/docs. |
-| 8 | Session transcript capture | M | ACCEPTED | Full audit trail of every agent run. |
-
-## Accepted Scope — Implementation Detail
-
-### 0a. Pause/resume (from base plan)
-
-**Schema:** Add `'paused'` to `MinionJobStatus` (already in migration v6 constraint).
-
-**New methods:**
- `MinionQueue.pauseJob(id): MinionJob | null`
-  Transitions `waiting` or `active` → `paused`. For `active` jobs, clears `lock_token`
-  and `lock_until` (worker will detect lock loss and stop). Returns null if job not
-  in pausable state.
- `MinionQueue.resumeJob(id): MinionJob | null`
-  Transitions `paused` → `waiting`. Resets for claiming. Returns null if not paused.
-
-**Worker integration:** Worker's lock renewal loop checks `isActive()`. When a job
-is paused, the lock is cleared, so `renewLock()` returns false and the worker stops
-execution gracefully (same path as stall detection). The job's progress and state
-are preserved in the DB for when it resumes.
-
-**MCP operations:** `pause_job`, `resume_job` (added in Step 3 of implementation plan).
-
-**PGLite compatibility:** Full.
-
-### 0b. Resource governor (from base plan)
-
-**New file:** `src/core/minions/governor.ts`
-
-```typescript
-interface GovernorConfig {
-  maxConcurrency: number;       // ceiling
-  minConcurrency: number;       // floor (default 1)
-  checkIntervalMs: number;      // default 10000
-  cpuThreshold: number;         // default 0.80 (80%)
-  memoryThreshold: number;      // default 0.85 (85%)
-  circuitBreakerMemory: number; // default 0.90 (90%)
-}
-
-class ResourceGovernor {
-  getEffectiveConcurrency(): number;  // current allowed concurrency
-  start(): void;                       // begin polling system metrics
-  stop(): void;                        // stop polling
-  onCircuitBreak(cb: (jobId) => void): void; // kill callback
-}
-```
-
-**System metrics:** Reuse `getSystemLoad()` from `src/core/backoff.ts` (already
-implements CPU and memory checks). Add event loop lag measurement via
-`perf_hooks.monitorEventLoopDelay()`.
-
-**Worker integration:** `MinionWorker.start()` consults `governor.getEffectiveConcurrency()`
-before claiming new jobs. If current in-flight count >= effective concurrency, skip claim.
-
-**Circuit breaker:** If memory > 90%, governor calls `onCircuitBreak` with the
-lowest-priority active job ID. Worker cancels that job via `failJob()` with
-`UnrecoverableError("circuit breaker: memory pressure")`.
-
-**Prerequisite:** Concurrent job processing must be implemented first (see
-Concurrency Note below).
-
-**PGLite compatibility:** Full (governor is app-level, not DB-level).
-
-### 1. pg LISTEN/NOTIFY (real-time events)
-
-**Schema:** No new columns. Add NOTIFY triggers to state transitions.
-
-**SQL trigger:**
-```sql
-CREATE OR REPLACE FUNCTION notify_minion_job_change() RETURNS trigger AS $$
-BEGIN
-  PERFORM pg_notify('minion_jobs', json_build_object(
-    'id', NEW.id, 'status', NEW.status, 'name', NEW.name,
-    'queue', NEW.queue, 'prev_status', COALESCE(OLD.status, 'new')
-  )::text);
-  RETURN NEW;
-END;
-$$ LANGUAGE plpgsql;
-
-CREATE TRIGGER minion_job_notify AFTER INSERT OR UPDATE OF status ON minion_jobs
-  FOR EACH ROW EXECUTE FUNCTION notify_minion_job_change();
-```
-
-**New method:** `MinionQueue.subscribe(callback: (event) => void): () => void`
-Returns unsubscribe function. Requires direct Postgres connection (NOT pooled).
-
-**PGLite compatibility:** PGLite does NOT support LISTEN/NOTIFY. Fallback: polling
-via `getJob()` at configurable interval (default 2s). The `subscribe()` method
-detects engine type and uses polling fallback automatically.
-
-**Supabase constraint:** Requires direct connection (port 5432), not pgBouncer
-pooler (port 6543). Document in skill file and setup guide.
-
-### 2. Structured progress protocol
-
-**TypeScript interface (convention, not enforced at DB level):**
-```typescript
-interface AgentProgress {
-  step: number;           // current step (1-based)
-  total: number;          // total expected steps (0 = unknown)
-  message: string;        // human-readable status
-  tokens_in: number;      // cumulative input tokens
-  tokens_out: number;     // cumulative output tokens
-  last_tool: string;      // name of last tool called
-  started_at: string;     // ISO 8601 when this step started
-}
-```
-
-**Storage:** Existing `progress JSONB` column. No schema change needed.
-Handlers use `ctx.updateProgress(agentProgress)`. Non-agent jobs can use
-any JSONB shape (backward compatible).
-
-**Validation:** `updateProgress()` accepts any JSONB. The `AgentProgress`
-interface is a convention enforced by the agent handler, not by the queue.
-
-### 3. Job cost tracking (token accounting)
-
-**Schema changes (migration v6):**
-```sql
-ALTER TABLE minion_jobs ADD COLUMN tokens_input INTEGER DEFAULT 0;
-ALTER TABLE minion_jobs ADD COLUMN tokens_output INTEGER DEFAULT 0;
-ALTER TABLE minion_jobs ADD COLUMN tokens_cache_read INTEGER DEFAULT 0;
-ALTER TABLE minion_jobs ADD COLUMN cost_usd NUMERIC(10,6) DEFAULT 0;
-```
-
-**New method:** `MinionQueue.updateTokens(id, lockToken, { input, output, cache_read, cost_usd })`
-Accumulates (adds to existing values, does not replace).
-
-**Parent rollup:** When `completeJob()` is called, if `parent_job_id` is set,
-add this job's token counts to the parent's via:
-```sql
-UPDATE minion_jobs SET
-  tokens_input = tokens_input + $child_input,
-  tokens_output = tokens_output + $child_output,
-  tokens_cache_read = tokens_cache_read + $child_cache,
-  cost_usd = cost_usd + $child_cost
-WHERE id = $parent_id;
-```
-
-**PGLite compatibility:** Full support (standard columns).
-
-### 4. Job replay
-
-**New method:** `MinionQueue.replayJob(id, dataOverrides?: Record<string, unknown>): MinionJob`
-
-Implementation: Read the completed/failed/dead job. Create a NEW job with:
- Same `name`, `queue`, `priority`, `max_attempts`, `backoff_type`, `backoff_delay`
- `data` = deep merge of original data + overrides
- Fresh `attempts_made: 0`, `status: 'waiting'`
- `parent_job_id` = null (replay is a new top-level job, not a child)
- Does NOT clone children (replay is a single job, not a DAG)
-
-**Constraint:** Only works on terminal statuses (completed/failed/dead).
-Returns the new job record.
-
-**Idempotency:** Each replay creates a distinct new job. No deduplication.
-If the original had side effects, the replay may repeat them. Document this
-in the skill file as a user responsibility.
-
-### 5. Inbox (sidechannel messaging)
-
-**Schema changes (migration v6):**
-```sql
-ALTER TABLE minion_jobs ADD COLUMN inbox JSONB DEFAULT '[]';
-```
-
-**Inbox message format:**
-```typescript
-interface InboxMessage {
-  id: string;          // UUIDv4
-  sent_at: string;     // ISO 8601
-  read_at: string | null;  // null until worker reads it
-  sender: string;      // 'parent' | 'user' | job ID
-  payload: unknown;    // arbitrary directive
-}
-```
-
-**New methods:**
- `MinionQueue.sendMessage(jobId, payload, sender?): InboxMessage`
-  Appends message to inbox array via atomic JSONB append
-  (`inbox = inbox || $1::jsonb`), not read-modify-write. Returns the message with id + sent_at.
- `MinionQueue.readInbox(jobId, lockToken): InboxMessage[]`
-  Returns unread messages (read_at = null). Marks them as read (sets read_at).
-  Token-fenced: only the worker holding the lock can read.
-
-**Worker integration:** Agent handler calls `readInbox()` on each iteration.
-If messages exist, injects them into the agent's context as system messages.
-
-**PGLite compatibility:** Full support (standard JSONB column).
-
-### 6. Inbox acknowledgment (read receipts)
-
-Built into the inbox design above. The `read_at` field on each `InboxMessage`
-provides the receipt. `sendMessage()` returns the message ID; the sender can
-later check `getJob(id)` and inspect `inbox` to see which messages have been
-read.
-
-No additional schema or methods needed beyond what's in #5.
-
-### 7. Universal agent protocol (platform-agnostic framing)
-
-**This is a design decision, not code.** It means:
-
-1. The skill file (`skills/minion-orchestrator/SKILL.md`) is written for ANY
-   agent platform, not just OpenClaw. Examples show MCP tool calls, not
-   OpenClaw-specific commands.
-
-2. The agent handler (`agent-handler.ts`) accepts a generic interface:
-   ```typescript
-   interface AgentJobData {
-     prompt: string;
-     tools?: string[];        // MCP tool names
-     model?: string;          // e.g., 'claude-opus-4-6', 'gpt-4o'
-     context?: string;        // additional context
-     platform?: string;       // 'openclaw' | 'hermes' | 'claude-code' | 'custom'
-     max_iterations?: number; // agent loop budget
-   }
-   ```
-
-3. The OpenClaw plugin is ONE consumer. Hermes, Claude Code extensions,
-   or custom scripts can submit `agent` jobs through the same MCP operations.
-
-4. **NOT in v1 scope:** Multi-tenant auth, cross-network connectivity,
-   protocol versioning, API key isolation. These are Phase 2 concerns when
-   actual multi-platform usage materializes. v1 is single-user, single-brain.
-
-### Agent Handler Architecture (critical design decision)
-
-The agent handler does NOT live in GBrain. GBrain provides the queue infrastructure
-and a clean handler contract. The actual agent execution lives in the platform plugin.
-
-```
-GBrain (this repo):
-  MinionQueue  — queue/claim/complete/inbox/tokens/NOTIFY
-  MinionWorker — poll/lock/stall/governor framework
-  Handler contract — AgentJobData interface + MinionJobContext
-
-OpenClaw plugin (separate repo):
-  Registers "agent" handler with MinionWorker
-  Handler calls OpenClaw's PI agent core (the actual LLM loop)
-  Each iteration: readInbox → inject as system message, updateProgress, updateTokens
-  Completion: store result + session transcript in job.result + job.stacktrace
-
-GBrain ships a test/echo handler for unit testing only.
-```
-
-**Handler contract (GBrain side):**
-```typescript
-// The handler receives this context (already exists in worker.ts)
-interface MinionJobContext {
-  id: number;
-  name: string;
-  data: Record<string, unknown>;  // AgentJobData when name="agent"
-  attempts_made: number;
-  updateProgress(progress: unknown): Promise<void>;
-  updateTokens(tokens: TokenUpdate): Promise<void>;  // NEW
-  log(message: string | TranscriptEntry): Promise<void>;
-  isActive(): Promise<boolean>;
-  readInbox(): Promise<InboxMessage[]>;  // NEW
-}
-```
-
-**Why this is right:** GBrain is orchestration, not execution. OpenClaw has the
-PI agent core. Hermes has AIAgent. Claude Code has its own loop. Each platform
-brings its own engine and registers a handler. GBrain manages lifecycle, progress,
-steering, cost tracking, and persistence around it.
-
-### 8. Session transcript capture
-
-**Extends existing stacktrace mechanism.** The `stacktrace` field (JSONB array
-of strings) already captures log messages. Session transcripts use the same
-field with structured entries:
-
-```typescript
-type TranscriptEntry =
-  | { type: 'log'; message: string; ts: string }
-  | { type: 'tool_call'; tool: string; args_size: number; result_size: number; ts: string }
-  | { type: 'llm_turn'; model: string; tokens_in: number; tokens_out: number; ts: string }
-  | { type: 'error'; message: string; stack?: string; ts: string };
-```
-
-**Storage:** Existing `stacktrace JSONB` column. No schema change.
-The agent handler appends `TranscriptEntry` objects instead of plain strings.
-Backward compatible: non-agent jobs continue appending strings.
-
-**Size concern:** Long agent runs could generate large transcripts. Add a
-`max_transcript_entries` option (default 1000) that rotates oldest entries
-when exceeded (FIFO). The full transcript for forensic analysis can be
-stored as a brain file via `gbrain files upload-raw`.
-
-## Schema Migration v6
-
-All schema changes are additive (ALTER TABLE ADD COLUMN). No backfill needed.
-Existing jobs continue to work with default values.
-
-```sql
-- Migration v6: Agent orchestration primitives
-ALTER TABLE minion_jobs ADD COLUMN IF NOT EXISTS tokens_input INTEGER DEFAULT 0;
-ALTER TABLE minion_jobs ADD COLUMN IF NOT EXISTS tokens_output INTEGER DEFAULT 0;
-ALTER TABLE minion_jobs ADD COLUMN IF NOT EXISTS tokens_cache_read INTEGER DEFAULT 0;
-
-- Separate inbox table (not JSONB on job row)
-CREATE TABLE IF NOT EXISTS minion_inbox (
-  id SERIAL PRIMARY KEY,
-  job_id INTEGER NOT NULL REFERENCES minion_jobs(id) ON DELETE CASCADE,
-  sender TEXT NOT NULL,
-  payload JSONB NOT NULL,
-  sent_at TIMESTAMPTZ NOT NULL DEFAULT now(),
-  read_at TIMESTAMPTZ
-);
-CREATE INDEX IF NOT EXISTS idx_minion_inbox_unread
-  ON minion_inbox (job_id) WHERE read_at IS NULL;
-
-- Status constraint update: add 'paused'
-ALTER TABLE minion_jobs DROP CONSTRAINT IF EXISTS minion_jobs_status_check;
-ALTER TABLE minion_jobs ADD CONSTRAINT minion_jobs_status_check
-  CHECK (status IN ('waiting','active','completed','failed','delayed','dead','cancelled','waiting-children','paused'));
-
-- NOTIFY trigger for real-time events (Postgres only, not PGLite)
-CREATE OR REPLACE FUNCTION notify_minion_job_change() RETURNS trigger AS $$
-BEGIN
-  PERFORM pg_notify('minion_jobs', json_build_object(
-    'id', NEW.id, 'status', NEW.status, 'name', NEW.name,
-    'queue', NEW.queue, 'prev_status', COALESCE(OLD.status, 'new')
-  )::text);
-  RETURN NEW;
-END;
-$$ LANGUAGE plpgsql;
-
-CREATE TRIGGER minion_job_notify AFTER INSERT OR UPDATE OF status ON minion_jobs
-  FOR EACH ROW EXECUTE FUNCTION notify_minion_job_change();
-```
-
-## PGLite Compatibility Matrix
-
-| Feature | Postgres | PGLite | Fallback |
-|---|---|---|---|
-| Pause/resume | Full | Full | — |
-| Inbox + ack | Full | Full | — |
-| Token accounting | Full | Full | — |
-| Job replay | Full | Full | — |
-| LISTEN/NOTIFY | Full | NO | Polling (2s interval) |
-| NOTIFY trigger | Full | NO | Skipped in PGLite schema |
-| Structured progress | Full | Full | — |
-| Session transcripts | Full | Full | — |
-| Resource governor | Full | Full | — |
-| Worker daemon | Full | NO (existing limitation) | — |
-
-## Concurrency Note
-
-The current `MinionWorker.start()` processes jobs sequentially (one at a time)
-despite `concurrency` being declared in `MinionWorkerOpts`. Implementing actual
-concurrent job processing (Promise pool) is a prerequisite for the resource
-governor to be meaningful. The governor adjusts effective concurrency, which
-requires actual concurrent processing to exist.
-
-**Action:** Implement concurrent job processing in `worker.ts` before or as
-part of the governor step. Use a semaphore pattern: maintain up to N in-flight
-promises, claim new jobs as slots free up.
-
-## Outside Voice Decisions (from adversarial review)
-
-1. **AbortController for pause/resume** — Handler contract gets `signal: AbortSignal`.
-   Pause clears lock AND signals abort. Handler must check `signal.aborted` on each
-   iteration. Without this, pausing active jobs creates duplicate execution.
-
-2. **Drop cost_usd column** — Token counts (input/output/cache_read) are stable facts.
-   USD pricing is volatile. Compute cost at display/read time from a pricing table,
-   not at write time. Removes `cost_usd NUMERIC(10,6)` from migration v6.
-
-3. **Separate minion_inbox table** — Instead of JSONB array on job row, use a dedicated
-   table for inbox messages. Avoids row bloat from rewriting entire inbox on every send.
-   Properly concurrent-safe with standard INSERT (no JSONB append concerns).
-   ```sql
-   CREATE TABLE minion_inbox (
-     id SERIAL PRIMARY KEY,
-     job_id INTEGER NOT NULL REFERENCES minion_jobs(id) ON DELETE CASCADE,
-     sender TEXT NOT NULL,
-     payload JSONB NOT NULL,
-     sent_at TIMESTAMPTZ NOT NULL DEFAULT now(),
-     read_at TIMESTAMPTZ
-   );
-   CREATE INDEX idx_minion_inbox_unread ON minion_inbox (job_id) WHERE read_at IS NULL;
-   ```
-
-4. **One release, not two** — Ship all features in one migration (v6). User prefers
-   cohesive release over incremental delivery for this feature set.
-
-5. **Selective column projection** — Fix SELECT * queries in getJobs(), claim(),
-   handleStalled() to exclude stacktrace column. Include stacktrace only in getJob()
-   detail view. Prevents transcript bloat from affecting query performance.
-
-## Future Phases (accepted trajectory)
-
- **Phase 2: Dashboard CLI** — `gbrain jobs dashboard` live TUI showing all agents.
-  Enabled by: LISTEN/NOTIFY, structured progress, token accounting.
- **Phase 3: Multi-tenant auth** — Runtime MCP access control, per-platform API keys.
-  Enabled by: platform-agnostic framing, sender validation on inbox.
- **Phase 4: Agent composition patterns** — Map-reduce, pipeline, approval gates as
-  first-class primitives. Enabled by: parent-child DAGs, inbox sidechannel.
-
-## Deferred to TODOS.md
- Job groups / waves (parent-child covers this; revisit if real grouping need emerges)
- cost_usd column (compute from pricing table at read time when pricing API exists)
-
-## Key Premises Confirmed
-1. GBrain is intentionally evolving from knowledge brain to agent infrastructure (user confirmed)
-2. Coupling between OpenClaw and GBrain's Postgres is acceptable (OpenClaw already depends on GBrain)
-3. Full Infrastructure approach (all 8+ steps) selected over Minimal Viable or Sidecar Tracking
-4. Prior learning [agent-dx-instruction-layer] validates that the teaching layer (skill + evals) is mandatory
@@ -1,105 +0,0 @@
-# Switching embedding models or dimensions on an existing brain
-
-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. `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` 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. 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.
-
-## Recipe — manual `psql` against your brain
-
-Replace `<NEW_DIMS>` with your target dimension count.
-
-```sql
-BEGIN;
-
-- 1. Drop the HNSW index. It can't survive the column type change.
-DROP INDEX IF EXISTS idx_chunks_embedding;
-
-- 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, 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.
-
-COMMIT;
-```
-
-Then update gbrain's config so it knows the new dim:
-
-```bash
-gbrain config set embedding_model <model>
-gbrain config set embedding_dimensions <NEW_DIMS>
-```
-
-And re-embed the corpus:
-
-```bash
-gbrain embed --stale
-```
-
-## PGLite (local brain)
-
-Same recipe, but you connect to the embedded database differently:
-
-```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.
-```
-
-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` (full) should say check 8b passes:
-
-```
-✓ 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 SQL you ran.
-
-## v0.29+ plans
-
-`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.
@@ -1,188 +0,0 @@
---
-type: essay
-title: "Homebrew for Personal AI"
-subtitle: "Why Markdown is Code and Your Agent is a Package Manager"
-author: Garry Tan
-created: 2026-04-11
-updated: 2026-04-11
-tags: [ai, gbrain, gstack, markdown-is-code, open-source, software-distribution, agents, openclaw]
-status: draft-v2
-prior: "Thin Harness, Fat Skills"
---
-
-# Homebrew for Personal AI
-
-`brew install` gives you someone else's binary. `npm install` gives you someone else's source code. Both require you to understand the tool, configure it, integrate it, maintain it.
-
-What if software distribution worked differently? What if you could describe a capability in plain English, hand that description to an AI agent, and the agent built a native implementation tailored to your setup?
-
-That's what happens when markdown is code.
-
-## Markdown is code
-
-Here's a real skill file. This one teaches an AI agent to screen phone calls:
-
-```markdown
-# Voice Agent — Your Phone Number
-
-Caller → Twilio → <Stream> WebSocket → Voice Server (port 8765)
-                                            ↕ audio
-                                      OpenAI Realtime API
-                                            ↓ tool calls
-                                      Brain / Calendar / Telegram
-
-## Call Routing
-
-Every inbound call routes based on caller phone number + brain lookup:
-
-### Owner → Authenticated Mode
- Send crypto-random 6-digit code to secure channel
- Caller reads it back
- Match → full assistant mode (brain, calendar, scheduling)
- No match → treated as unknown caller
-
-### Known Person, Inner Circle (brain score ≥ 4) → Forward
- Greet by name with brain context
- Transfer to cell
- If no answer (30s timeout), take message
- Text Telegram with who called and context
-
-### Unknown Caller → Screen
- Get their name, look them up in brain
- If inner circle → offer to transfer
- Otherwise → take message
- Create brain entry with phone number (marked UNVERIFIED)
-```
-
-That's not pseudocode. That's not documentation. That's a working specification that a model like Claude Opus 4.6 with a million-token context window can read and implement. The architecture diagram tells it the components. The routing table tells it the logic. The security model tells it the constraints. The agent reads this file, understands it, and builds the Twilio integration, the WebSocket server, the Telegram bot hooks, the brain lookup, all of it, shaped to whatever infrastructure the user already has.
-
-A skill file is a method call. It takes parameters (your phone number, your brain, your preferred messaging app). Same skill, different arguments, different implementation. The procedure is the package. The model is the runtime.
-
-## The distribution mechanism
-
-Traditional package managers distribute artifacts: compiled binaries, source tarballs, container images. The consumer runs someone else's code.
-
-GBrain distributes recipes: markdown files that describe capabilities with enough specificity that an AI agent can implement them from scratch. The consumer gets a native implementation. No dependency hell. No version conflicts. No transitive vulnerability chains. Because there is no upstream code. There's a description of what to build and why.
-
-Here's how it works:
-
-1. **Build a feature.** Implement a voice agent, meeting ingestion pipeline, email triage system, investment diligence workflow, whatever.
-
-2. **GBrain captures the recipe.** Not just the code. The architecture, the integration points, the failure modes, the judgment calls. A markdown file that encodes the full capability.
-
-3. **Push to the repo.** Open source. Anyone can read it.
-
-4. **Someone else's agent pulls the recipe.** Reads the markdown. Says: "New recipe available: AI voice agent with caller screening. Want it?" User says yes. The agent reads the spec and builds it.
-
-No installation. No configuration wizard. No README. The agent read a document and figured it out.
-
-## Why this works now
-
-This didn't work two years ago. Two things changed.
-
-**Context windows hit a million tokens.** A real skill file for meeting ingestion is 200+ lines. The enrichment skill that calls it references a brain schema, a resolver, a citation standard, five external APIs, and a cross-linking protocol. An agent implementing this recipe needs to hold all of that in working memory simultaneously while also understanding the user's existing setup. At 8K tokens, impossible. At 128K, marginal. At 1M, comfortable.
-
-**Models crossed the judgment threshold.** Here's a snippet from a real enrichment recipe:
-
-```markdown
-## Philosophy
-
-A brain page should read like an intelligence dossier crossed
-with a therapist's notes, not a LinkedIn scrape. We want:
-
- What they believe — ideology, worldview, first principles
- What they're building — current projects, what's next
- What motivates them — ambition drivers, career arc
- What makes them emotional — angry, excited, defensive, proud
- Their trajectory — ascending, plateauing, pivoting, declining?
- Hard facts — role, company, funding, location, contact info
-
-Facts are table stakes. Texture is the value.
-```
-
-A model implementing this recipe has to understand the difference between a LinkedIn scrape and an intelligence dossier. That's a judgment call about what information is worth capturing and how to weight it. GPT-3 couldn't do this. GPT-4 could sort of do it. Opus 4.6 does it well. The enabling technology is models that are smart enough to interpret intent, not just follow instructions.
-
-## What a recipe actually contains
-
-A good recipe has five sections:
-
-**Architecture.** The component diagram. What talks to what, over what protocol, with what data flow. This is the skeleton the agent builds first.
-
-**Routing logic.** The decision tree. When X happens, do Y. When Z fails, fall back to W. This is where domain knowledge lives. A voice agent recipe encodes call routing. A diligence recipe encodes how to process pitch decks vs. financial models vs. cap tables. A meeting ingestion recipe encodes how to turn a raw transcript into actionable intelligence.
-
-**Integration points.** What external systems does this touch? Twilio, Telegram, Gmail, Circleback, Slack, GitHub, Supabase, whatever. The recipe names the integrations; the agent figures out how to connect them given what the user already has configured.
-
-**Judgment calls.** The hard part. Not "send an email" but "decide whether this email is worth surfacing to the user based on sender importance, time sensitivity, and whether it requires a decision." Recipes that skip the judgment calls produce shallow implementations. The judgment calls are the actual value.
-
-**Failure modes.** What goes wrong and what to do about it. "If Circleback token expires, message the user and ask them to reconnect. Don't silently skip." "If caller ID is spoofed, never trust it for authentication. Use a challenge-response code via a separate channel." Recipes without failure modes produce brittle systems.
-
-Here's a real example. This is the diligence recipe's detection logic:
-
-```markdown
-## Detection
-
-Recognize data room materials by:
- PDF filenames: "Data Deck", "Intro Deck", "Cap Table",
-  "Financial Model", "Pitch Deck", "Series [A-D]"
- Spreadsheets with tabs: Revenue, Retention, Cohorts,
-  CAC, Gross Margin, Unit Economics, ARR
- User saying: "data room", "diligence", "deck", "pitch"
- Context: shared in the Diligence topic
-```
-
-That's a pattern matcher expressed in English. An agent reads this and knows how to classify incoming documents. No regex. No file type configuration. Just a description of the pattern and the model's judgment about whether a given document matches.
-
-## Pick and choose
-
-GBrain is not monolithic. Recipes are independent. Take what you want:
-
- **Voice agent** — phone screening, caller ID, brain lookup, message routing
- **Meeting ingestion** — transcript processing, entity extraction, action item capture, timeline updates
- **Email triage** — inbox sweep, priority classification, draft replies, scheduling extraction
- **Enrichment pipeline** — people and company research from multiple data sources, diarized into brain pages
- **Diligence processing** — data room ingestion, PDF extraction, financial model analysis
- **Social monitoring** — X/Twitter timeline analysis, mention tracking, narrative detection
- **Content pipeline** — idea capture, link ingestion, article summarization
-
-Each recipe is self-contained. Your agent knows what you already have. GBrain pings daily: "Three new recipes since last sync. Want any?" You pick. It builds.
-
-And because the source code is English, forking is trivial. Don't like how the voice agent handles unknown callers? Edit the markdown. Change "take a message" to "ask three screening questions first." The behavior changes because the spec changed.
-
-## The thin harness, fat skills connection
-
-This essay is a sequel. The prequel was "Thin Harness, Fat Skills," which argued that the secret to 100x AI productivity isn't better models but better context management. Keep the harness thin (the program running the model). Make the skills fat (markdown procedures encoding judgment and process).
-
-"Markdown is code" is the distribution corollary. If the skills are fat markdown files, and if models are smart enough to implement from markdown, then the skills are distributable software. The skill file is simultaneously:
-
- **Documentation** for humans reading it
- **Specification** for the implementing agent
- **Package** for the distribution system
- **Source code** for the resulting capability
-
-Four artifacts collapsed into one. That's why this is different from every previous package manager. `brew install` separates the formula from the binary from the docs from the source. GBrain collapses them. The markdown is all four.
-
-## The architecture underneath
-
-Three layers, same as the talk:
-
-**Fat skills** on top. Markdown recipes encoding judgment, process, failure modes, and domain knowledge. This is where 90% of the value lives. This is what gets distributed.
-
-**Thin harness** in the middle. The program running the model. File operations, tool dispatch, context management, safety enforcement. About 200 lines. OpenClaw or any equivalent. The less the harness constrains, the more the recipes can express.
-
-**Deterministic foundation** on the bottom. Databases, APIs, CLIs. Same input, same output, every time. SQL queries, HTTP calls, file reads. The skills describe WHEN to call these; the harness executes them.
-
-Push intelligence UP into skills. Push execution DOWN into deterministic tooling. Distribute the skills. That's the whole system.
-
-## What this means
-
-When implementation cost approaches zero, the bottleneck shifts. It's no longer "can we build this?" It's "should we build this?" and "what exactly should it do?"
-
-Taste, vision, and domain knowledge become the scarce resources. The person who deeply understands call screening and writes a precise recipe creates more value than the person who can implement a Twilio integration from scratch. The recipe IS the implementation.
-
-This also means the best AI agent setups will be open source by default. Closed, proprietary agent configurations are competing against a world where someone publishes a recipe and a thousand agents implement it overnight. The recipe propagates at the speed of a git push. The moat is taste, not code.
-
-Software distribution reimagined: the package is a markdown file, the runtime is a sufficiently smart model, the package manager is your AI agent, and the app store is a git repo.
-
-`gbrain install voice-agent`
-
-That's it.
@@ -1,209 +0,0 @@
---
-type: essay
-title: "Thin Harness, Fat Skills"
-subtitle: "How to Make AI Agents Actually Understand Your Data"
-author: Garry Tan
-created: 2026-04-09
-updated: 2026-04-11
-tags: [ai, agents, gstack, harness-engineering, skills, architecture]
-status: draft-v4
-talk: "YC Spring 2026 -- Thin Harness, Fat Skills"
-thread: https://x.com/garrytan/status/2042925773300908103
---
-
-# Thin Harness, Fat Skills
-
-Steve Yegge says people using AI coding agents are "10x to 100x as productive as engineers using Cursor and chat today, and roughly 1000x as productive as Googlers were back in 2005."
-
-That's a real number. I've seen it. I've lived it. But when people hear 100x, they think: better models. Smarter Claude. More parameters.
-
-That's the wrong frame entirely. The 2x people and the 100x people are using the same models. The difference is five concepts that fit on an index card.
-
-## The harness is the secret sauce
-
-On March 31, 2026, Anthropic accidentally shipped the entire source code for Claude Code to the npm registry. 512,000 lines. When I read it, it confirmed everything I'd been teaching at YC. The secret sauce isn't the model. It's the thing wrapping the model: the harness. Live repo context. Prompt caching. Purpose-built tools. Context bloat minimization. Structured session memory. Parallel sub-agents.
-
-None of that is about making the model smarter. All of it is about giving the model the right context, at the right time, without drowning it in noise.
-
-That's the only question that matters. And the answer has a specific shape. I call it **thin harness, fat skills**.
-
-## Five definitions
-
-The bottleneck is never the model's intelligence. The bottleneck is whether the model understands your schema. Models already know how to reason, synthesize, and write code. They fail because they don't know your data. Five definitions fix this.
-
-### Definition 1: Skill File
-
-A skill file is a reusable markdown procedure that teaches the model HOW to do something. Not WHAT to do. The user supplies the specifics. The skill supplies the process.
-
-**Markdown is actually code.** A skill file is a more perfect encapsulation of capability than rigid source code, because it describes process, judgment, and context in the language the model already thinks in.
-
-On the left is a skill called `/investigate`. Seven steps: scope the dataset, build a timeline, diarize every document, synthesize, argue both sides, cite sources. It takes three parameters: TARGET, QUESTION, and DATASET.
-
-On the right are two completely different invocations of the same skill. One points at Dr. Sarah Chen and 2.1 million discovery emails, asking whether a safety scientist was silenced. The other points at Pacific Corporate Services and FEC filings, asking whether shell companies are coordinating campaign donations.
-
-Same skill. Same seven steps. Same markdown file. In one case it's a medical research analyst. In the other it's a forensic investigator. The skill describes a process of judgment. The invocation supplies the world.
-
-**This is the key insight most people miss: a skill file works like a method call.** It takes parameters. You invoke it with different arguments. The same procedure produces radically different capabilities depending on what you pass in. This is not prompt engineering. This is software design, using markdown as the programming language and human judgment as the runtime.
-
-### Definition 2: Harness
-
-The harness is the program that runs the LLM. It does four things: runs the model in a loop, reads and writes your files, manages context, and enforces safety. That's the "thin."
-
-The anti-pattern is a fat harness with thin skills: 40+ tool definitions eating half the context window. God tools with 2 to 5 second MCP round-trips. REST API wrappers that turn every endpoint into a tool. 3x the tokens, 3x the latency, 3x the failure rate.
-
-What you should build instead: a Playwright CLI that does each browser operation in 100 milliseconds. Compare: Chrome MCP takes 15 seconds for screenshot + find + click + wait + read. Playwright CLI takes 200 milliseconds for screenshot + assert. 75x faster. Software doesn't have to be precious anymore. Build exactly what you need.
-
-### Definition 3: Resolver
-
-A resolver is a routing table for context. When task type X appears, load document Y first.
-
-Skills say HOW. Resolvers say WHAT to load WHEN. A developer changes a prompt. Without the resolver, they ship it. With the resolver, the model reads `docs/EVALS.md` first, which says: run the eval suite, compare scores, if accuracy drops more than 2%, revert and investigate. The developer didn't know the eval suite existed. The resolver loaded the right context at the right moment.
-
-Claude Code has a built-in resolver. Every skill has a description field, and the model matches user intent to skill descriptions automatically. You never have to remember `/ship` exists. The description IS the resolver. It's like Clippy. Except it actually works.
-
-A confession: my CLAUDE.md was 20,000 lines. Every single thing I ran across went in there. Every quirk, every pattern, every lesson. Completely ridiculous. The model's attention degraded. Claude Code literally told me to cut it back. The fix: about 200 lines. Just pointers to documents. The resolver loads the right one when it matters.
-
-### Definition 4: Latent vs. Deterministic
-
-Every step in your system is one or the other.
-
-**Latent space** is where intelligence lives. The model reads, interprets, decides. Judgment. Synthesis. Pattern recognition.
-
-**Deterministic** is where trust lives. Same input, same output. Every time. SQL. Code. Numbers.
-
-An LLM can seat 8 people at a dinner table. Ask it to seat 800 and it will hallucinate a seating chart that looks plausible but is completely wrong. That's a deterministic problem forced into latent space. The worst systems put the wrong work on the wrong side.
-
-### Definition 5: Diarization
-
-The model reads everything about a subject and writes a structured profile. Read 50 documents, produce 1 page of judgment.
-
-No SQL query produces this. No RAG pipeline produces this. The model has to actually read, hold contradictions in mind, notice what changed and when, and write structured intelligence. This is what makes AI useful for real knowledge work.
-
-## The architecture
-
-Three layers:
-
-**Fat skills** on top. Markdown procedures that encode judgment, process, and domain knowledge. This is where 90% of the value lives.
-
-**Thin CLI harness** in the middle. About 200 lines. JSON in, text out. Read-only by default. CLI first, add MCP later.
-
-**Your app** on the bottom. QueryDB. ReadDoc. Search. Timeline. The deterministic foundation.
-
-Push intelligence UP into skills. Push execution DOWN into deterministic tooling. Keep the harness THIN.
-
-## The system that learns: YC Startup School
-
-Let me show you all five definitions working together. Not in theory. In an actual system we're building at YC.
-
-Chase Center. July 2026. 6,000 founders. Each one has a structured application, questionnaire answers, transcripts from 1:1 advisor chats, and public signals: X posts, GitHub commits, Claude Code transcripts showing how fast they ship.
-
-The traditional approach: a program team of 15 reads applications, makes gut calls, updates a spreadsheet. It works at 200 founders. It breaks at 6,000.
-
-No human can hold 6,000 profiles in working memory and notice that the three best candidates for the infrastructure-for-AI-agents cohort are a dev tools founder in Lagos, a compliance founder in Singapore, and a CLI-tooling founder in Brooklyn who all described the same pain point in different words during their 1:1 chats.
-
-The model can.
-
-**Step 1: Enrich every founder.**
-
-The `/enrich-founder` skill: pull all sources, run enrichments, diarize, highlight what they SAY vs what they're ACTUALLY BUILDING. On the right, the deterministic calls: SQL to find stale profiles, GitHub stats, browser test on the demo URL, social signal pulls, CrustData for company intel.
-
-Cron runs nightly at 2am. 6,000 profiles, every night, always fresh.
-
-The diarization output catches things no keyword search would find:
-
-```
-FOUNDER: Maria Santos
-COMPANY: Contrail (contrail.dev)
-SAYS: "Datadog for AI agents"
-ACTUALLY BUILDING: 80% of commits are in billing module.
-  She's building a FinOps tool disguised as observability.
-```
-
-"SAYS" vs "ACTUALLY BUILDING." That requires reading the GitHub commit history, the application, and the advisor transcript and holding all three in mind at once.
-
-**Step 2: Match 6,000 founders. Make judgment calls.**
-
-This is where skill-as-method-call really shines. Three invocations:
-
-`/match-breakout`: 1,200 founders, cluster by sector affinity, 30 per room. Embed + deterministic assign.
-
-`/match-lunch`: 600 founders, serendipity matching (cross-sector), 8 per table, no repeats. The LLM invents the themes, then assigns.
-
-`/match-live`: whoever is in the zone, nearest-neighbor embedding, real-time at 200ms, 1:1 pairs, not already met.
-
-Same skill. Three invocations. Three completely different matching strategies. Different parameters, different strategies, different group sizes. The skill describes the process. The arguments shape the output.
-
-And the model's judgment calls: "Santos and Oram are both AI infra, but they're not competitors. Santos is cost attribution, Oram is orchestration. Put them in the same group." And: "Kim applied as 'developer tools' but his 1:1 transcript reveals he's building compliance automation for SOC2. Move him to FinTech/RegTech."
-
-No embedding captures the Kim reclassification. No algorithm can do it. The model has to read the entire profile.
-
-**Step 3: The self-learning loop.**
-
-After the event, the `/improve` skill reads NPS surveys, diarizes the "OK" responses (not the bad ones, the mediocre ones), and extracts patterns. Then it proposes new rules and writes them back into the matching skills:
-
-```
-When attendee says "AI infrastructure"
-    but startup is 80%+ billing code:
-    -> Classify as FinTech, not AI Infra.
-
-When two attendees in same group
-    already know each other:
-    -> Penalize proximity.
-       Prioritize novel introductions.
-```
-
-These rules get written back into the skill file. Next run uses them automatically. The skill rewrites itself.
-
-July event: 12% "OK" ratings. Next event: 4%. The skill file learned what "OK" actually meant.
-
-Same pattern as every other domain: retrieve, read, diarize, count, synthesize. Then: survey, investigate, diarize, rewrite the skill. It transfers everywhere.
-
-## OpenClaw: where the skills live
-
-I want to tell you about one more harness. Not for coding. For everything else.
-
-I run a personal AI agent on OpenClaw. It has a persona, knows who I am, and maintains a knowledge base of thousands of interconnected files. But the thing that makes it work is the exact same principle. Thin harness, fat skills.
-
-I tweeted about this a few days ago:
-
-> *You are not allowed to do one-off work. If I ask you to do something and it's the kind of thing that will need to happen again, you must: do it manually the first time on 3 to 10 items. Show me the output. If I approve, codify it into a skill file. If it should run automatically, put it on a cron.*
-
-> *The test: if I have to ask you for something twice, you failed.*
-
-That resonated: a thousand likes, twenty-five hundred bookmarks. People thought it was a prompt engineering trick. It's not. It's the same architecture.
-
-Claude Code is the best harness for coding. OpenClaw is the best harness for everything else: email, calendar, meetings, people, research, alerts. Same principle. Thin harness. Fat skills. The skills are method calls. The parameters change. The process stays.
-
-Every skill I write is a permanent upgrade. It never degrades. It never forgets. It runs at 3 AM while I sleep. And when the next model drops, every skill instantly gets better, because the judgment in the latent steps improves while the deterministic steps stay perfectly reliable.
-
-That's how you get Yegge's 10x to 100x. Not a smarter model. Fat skills, thin harness, and the discipline to codify everything.
-
-The system compounds. Build it once. It runs forever.
-
-Five definitions. Three layers. One principle. Thin harness, fat skills.
-
---
-
-## Agent Decision Guide: Skill or Code?
-
-When building GBrain features, use this decision guide:
-
-| Question | If YES | If NO |
-|----------|--------|-------|
-| Does the agent need to think, adapt, or ask questions? | **Skill** (recipe markdown) | Code |
-| Same input always produces same output? | **Code** (CLI command) | Skill |
-| Does it require judgment about the user's environment? | **Skill** | Code |
-| Is it a lookup, list, or status check? | **Code** | Probably skill |
-| Does it change behavior based on conversation context? | **Skill** | Code |
-
-**GBrain examples:**
- `gbrain integrations list` = **Code** (reads files, checks env vars, deterministic)
- `gbrain integrations status` = **Code** (checks env vars + heartbeat, deterministic)
- `gbrain integrations doctor` = **Code** (runs health checks, deterministic)
- `gbrain integrations stats` = **Code** (aggregates JSONL, deterministic)
- Recipe setup flow = **Skill** (asks for API keys, adapts to environment, validates)
- Recipe changelog surfacing = **Skill** (agent describes changes conversationally)
- Entity detection = **Skill** (reads message, decides what's important, creates pages)
- Meeting ingestion = **Skill** (reads transcript, extracts entities, updates pages)
-
-**The rule:** If it's a lookup table, it's code. If the agent needs to think, it's a skill.
@@ -1,330 +0,0 @@
-# Running real-world eval benchmarks against your gbrain changes
-
-Audience: gbrain maintainers and contributors. If you're touching retrieval
-(search, ranking, embeddings, intent classification, query expansion, source
-boost, hybrid fusion), this is the doc.
-
-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.
-
-## Prerequisite: turn on contributor mode
-
-Capture is **off by default** for production users (privacy-positive — no
-surprise data accumulation). Contributors flip it on with one line:
-
-```bash
-# In ~/.zshrc or ~/.bashrc:
-export GBRAIN_CONTRIBUTOR_MODE=1
-```
-
-Verify:
-
-```bash
-gbrain query "anything" >/dev/null
-psql $DATABASE_URL -c 'SELECT count(*) FROM eval_candidates'   # should be > 0
-```
-
-To override (force on/off regardless of env var), edit `~/.gbrain/config.json`:
-
-```json
-{"eval": {"capture": true}}    // force on
-{"eval": {"capture": false}}   // force off
-```
-
-Explicit config beats the env var both directions.
-
-## The 4-command loop
-
-```bash
-# ① Capture: writes to eval_candidates whenever CONTRIBUTOR_MODE is set.
-#   Inspect what's been collected:
-gbrain doctor                                     # surfaces capture failures
-psql $DATABASE_URL -c 'SELECT count(*) FROM eval_candidates'
-
-# ② Snapshot: freeze a baseline before your code change.
-gbrain eval export --since 7d > baseline.ndjson
-
-# ③ Code change: do whatever you want — tune RRF_K, swap embed model, edit
-#    hybrid.ts, add a new boost source, change the intent classifier.
-
-# ④ Replay: re-run every captured query against the current build.
-gbrain eval replay --against baseline.ndjson
-```
-
-Output:
-
-```
-Replaying 247 captured queries…
-  ...25/247
-  ...50/247
-  ...
-Replayed 247 of 247 captured queries (0 skipped, 0 errored)
-Mean Jaccard@k:    0.927
-Top-1 stability:   91.5%
-Mean latency Δ:    +14ms (current vs captured)
-
-Top 5 regression(s):
-  jaccard=0.20  captured=12  current=3   "find every reference to widget-co"
-  jaccard=0.43  captured=14  current=8   "show me everything tagged for review"
-  jaccard=0.50  captured=8   current=4   "what did alice say about the spec"
-  ...
-```
-
-Three numbers tell you whether the change is safe to land:
-
-| Metric | What it means | Healthy range |
-|---|---|---|
-| **Mean Jaccard@k** | Average overlap between captured retrieved slugs and current run's slugs. 1.0 = identical sets. | ≥0.85 for "neutral" changes. <0.7 means major retrieval shift. |
-| **Top-1 stability** | Fraction of queries whose #1 result didn't change. | ≥85% for tuning passes. <70% means top-of-funnel broke. |
-| **Mean latency Δ** | Current minus captured. Positive = slower now. | Within ±50ms of captured. >2× anywhere = regression alarm. |
-
-## What it actually does
-
-`gbrain eval replay` reads your NDJSON snapshot and, for each row:
-
-1. Re-executes the same op (`searchKeyword` for `tool_name='search'`,
-   `hybridSearch` for `tool_name='query'`) with the captured `detail` and
-   `expand_enabled` values threaded back in.
-2. Captures the current `retrieved_slugs` (deduped, in result order).
-3. Computes set-Jaccard between captured and current slug sets.
-4. Records top-1 match (was the #1 result the same slug?).
-5. Records latency delta vs captured `latency_ms`.
-
-It does NOT compute MRR or nDCG — those need ground-truth relevance labels,
-not a baseline comparison. For metric-against-truth eval, use
-`gbrain eval --qrels <path>` (the legacy IR-eval path, still supported). The
-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:
-
-1. **Brain state** — your brain probably has more pages now than when the
-   snapshot was taken. Unless you explicitly seed a fixed corpus, mean
-   Jaccard will drop simply because new pages are eligible.
-2. **Embedding source** — if you changed `OPENAI_API_KEY` between capture
-   and replay (or the embedding model rotated), vector-path results drift
-   even with identical code.
-3. **Capture cap** — captured `retrieved_slugs` is a deduped set; it doesn't
-   preserve internal ranking metadata. Two tools can return the same slug
-   set with different scores — Jaccard will say 1.0, but a downstream
-   consumer that orders by score may behave differently.
-
-The metrics are **regression alarms on real queries**, not a hash check.
-Pair them with manual inspection of the top regressions.
-
-## Cost
-
-Every `query` row in the snapshot embeds the query string via OpenAI to run
-the vector half of `hybridSearch`. Cost is identical to a normal `gbrain
-query` invocation — text-embedding-3-large at OpenAI list price, batched
-inside a single replay row.
-
-If you're iterating locally and don't want to pay per change, use
-`--limit 50` to cap rows replayed. The 50 most recent rows are usually
-enough to catch direction; expand for the final pre-merge run.
-
-```bash
-# Iteration mode — 50 most recent queries
-gbrain eval replay --against baseline.ndjson --limit 50
-
-# Pre-merge — full snapshot
-gbrain eval replay --against baseline.ndjson --top-regressions 20
-```
-
-## CI integration
-
-```bash
-gbrain eval replay --against baseline.ndjson --json > replay.json
-jq -e '.summary.mean_jaccard >= 0.85' replay.json || exit 1
-jq -e '.summary.top1_stability_rate >= 0.85' replay.json || exit 1
-```
-
-Stable JSON shape (schema_version: 1):
-
-```json
-{
-  "schema_version": 1,
-  "summary": {
-    "rows_total": 247,
-    "rows_replayed": 247,
-    "rows_skipped": 0,
-    "rows_errored": 0,
-    "mean_jaccard": 0.927,
-    "top1_stability_rate": 0.915,
-    "mean_latency_delta_ms": 14,
-    "rows_over_2x_latency": 0
-  }
-}
-```
-
-`--verbose` adds a `results: [...]` array with one entry per replayed row
-(useful for piping into jq or a notebook for deeper analysis).
-
-## When to run this
-
-Before merging anything that touches:
-
- `src/core/search/hybrid.ts` (RRF, fusion, dedup, two-pass retrieval)
- `src/core/search/source-boost.ts` / `sql-ranking.ts` (per-source ranking)
- `src/core/search/intent.ts` (auto-detail classification)
- `src/core/search/expansion.ts` (Haiku query expansion)
- `src/core/search/dedup.ts` (cross-page result collapse)
- `src/core/embedding.ts` or any embedding model swap
- `src/core/operations.ts` `query` or `search` op handlers (capture surface)
- `src/core/postgres-engine.ts` / `pglite-engine.ts` `searchKeyword` /
-  `searchVector` SQL
-
-Skip for: schema-only migrations, doc changes, tests-only PRs, CLI ergonomics
-that don't touch retrieval.
-
-## Building your own corpus
-
-If you don't have captured traffic yet (fresh install, can't dogfood for a
-week before merging), you can hand-author an NDJSON file:
-
-```jsonl
-{"schema_version":1,"id":1,"tool_name":"query","query":"who is alice","retrieved_slugs":["people/alice","people/alice-bio"],"expand_enabled":false,"detail":null,"latency_ms":0,"remote":false}
-{"schema_version":1,"id":2,"tool_name":"search","query":"acme deal","retrieved_slugs":["deals/acme-seed","companies/acme"],"latency_ms":0,"remote":false}
-```
-
-Then run `gbrain eval replay --against handcrafted.ndjson` to confirm the
-authoritative slugs come back. This is the seam between the BrainBench-Real
-pipeline (replay against live captures) and the BrainBench fixed-fixture
-pipeline (`gbrain eval --qrels` with the sibling
-[gbrain-evals](https://github.com/garrytan/gbrain-evals) corpus).
-
-## Off-switch
-
-Two ways to disable capture:
-
-```bash
-unset GBRAIN_CONTRIBUTOR_MODE             # easy: just unset the env var
-```
-
-Or force off regardless of the env var via `~/.gbrain/config.json`:
-
-```json
-{"eval": {"capture": false}}
-```
-
-Existing `eval_candidates` rows stay until you `gbrain eval prune
--older-than 0d` (or just drop the table).
-
-## Failure modes
-
-| What you see | What it means |
-|---|---|
-| `Mean Jaccard@k: 0.4`, top regressions all in one source dir | Source boost or hard-exclude regression on that prefix |
-| `Top-1 stability: 30%`, mean Jaccard still high | RRF tuning shifted the rank order without changing the set — re-tune `rrfK` |
-| `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.
@@ -1,160 +0,0 @@
-# Eval capture — NDJSON schema reference
-
-**Status:** stable from v0.21.0. Schema versioning via `schema_version`
-on every row; additive changes increment the minor version; removals
-are breaking-schema-v2.
-
-**Audience:** downstream consumers (primarily the sibling
-[gbrain-evals](https://github.com/garrytan/gbrain-evals) repo) that
-replay captured real-world queries as a BrainBench-Real fixture.
-
-## The pipeline
-
-```
-MCP / CLI / subagent tool-bridge caller
-     │
-     ▼
-src/core/operations.ts — query + search op handlers
-     │
-     │ (hybridSearch or searchKeyword)
-     │
-     ▼
-{results, meta: HybridSearchMeta}                 ┌── captureEvalCandidate
-     │                                             │    (fire-and-forget)
-     ▼                                             │
-return to caller                                   ▼
-                                            scrubPii(query) ←── src/core/eval-capture-scrub.ts
-                                                   │
-                                                   ▼
-                                           buildEvalCandidateInput
-                                                   │
-                                                   ▼
-                                           engine.logEvalCandidate
-                                                   │
-                                    ┌──────────────┴──────────────┐
-                                    │ success                     │ fail
-                                    ▼                             ▼
-                                INSERT into eval_candidates    engine.logEvalCaptureFailure
-                                                                 (reason: db_down | rls_reject |
-                                                                  check_violation |
-                                                                  scrubber_exception | other)
-```
-
-## `gbrain eval export` — the consumer contract
-
-```sh
-gbrain eval export [--since DUR] [--limit N] [--tool query|search]
-```
-
-Emits NDJSON to **stdout**. One JSON object per `\n`-terminated line.
-stderr receives progress heartbeats. Every line starts with
-`"schema_version": 1` so a forward-compat parser can fail loudly on
-schema v2 instead of silently misparsing.
-
-Typical usage from gbrain-evals:
-
-```sh
-# Snapshot the last week of real traffic for replay
-gbrain eval export --since 7d > brainbench-real.ndjson
-```
-
-```sh
-# Stream through jq for ad-hoc analysis
-gbrain eval export --tool query | jq -c 'select(.latency_ms > 500)'
-```
-
-## Row schema (v1)
-
-Every exported row has this shape. Field order in JSON output is not
-guaranteed; consumers MUST key by name, not position.
-
-| Field | Type | Notes |
-|---|---|---|
-| `schema_version` | number | Always `1` on v1 rows. Forward-compat gate. |
-| `id` | number | Autoincrement primary key. Stable across exports. |
-| `tool_name` | `"query"` \| `"search"` | Which MCP operation captured this row. |
-| `query` | string | **Already PII-scrubbed** by `scrubPii` unless `eval.scrub_pii: false`. Emails / phones / SSN / Luhn-verified credit cards / JWTs / bearer tokens replaced with `[REDACTED]`. Max length 50KB (CHECK-enforced). |
-| `retrieved_slugs` | string[] | Deduplicated slugs that came back in `SearchResult[]`. |
-| `retrieved_chunk_ids` | number[] | Every chunk id in result order (duplicates preserved — one per hit). |
-| `source_ids` | string[] | Distinct `sources.id` values across the result set (v0.18 multi-source). Empty for pre-v0.18 rows that lacked the column. |
-| `expand_enabled` | boolean \| null | Whether the caller **requested** Haiku expansion. `null` for `search` (no expansion concept). |
-| `detail` | `"low"` \| `"medium"` \| `"high"` \| null | Detail level the caller **requested**. `null` when omitted. |
-| `detail_resolved` | `"low"` \| `"medium"` \| `"high"` \| null | What `hybridSearch` **actually used** after auto-detect. `null` when neither caller nor heuristic classified. |
-| `vector_enabled` | boolean | True iff vector search actually ran. `false` when `OPENAI_API_KEY` was missing or the embed call failed. **Replay MUST respect this** — rows with `false` only exercised the keyword path. |
-| `expansion_applied` | boolean | True iff Haiku expansion actually produced variants (not just "was requested"). |
-| `latency_ms` | number | Wall-clock duration of the op handler (includes capture itself — negligible since it's fire-and-forget). |
-| `remote` | boolean | `true` for MCP callers (untrusted), `false` for local CLI. Partitions "real agent traffic" from "operator probing." |
-| `job_id` | number \| null | `OperationContext.jobId` when the caller was a subagent tool-bridge. Null for MCP + CLI. |
-| `subagent_id` | number \| null | `OperationContext.subagentId` for subagent-owned runs. |
-| `created_at` | string (ISO 8601) | UTC timestamp of insert. |
-
-## Ordering + determinism
-
-`listEvalCandidates` orders by `created_at DESC, id DESC`. Same-
-millisecond inserts tie on `created_at`; `id DESC` is the stable
-tiebreaker. Replay tools can consume rows in order and assume:
- no duplicate rows across calls with non-overlapping `--since` windows
- no missed rows across calls that chain `--since` windows (window end
-  of run 1 is the strict upper bound, not a soft cursor)
-
-## Schema versioning promise
-
- **v1 (shipped v0.21.0)** — this document. All fields listed above.
- **Additive changes** increment gbrain minor version (v0.25.0, v0.23.0
-  …) and ship with new optional fields. Consumers keyed on known fields
-  ignore unknown keys and keep working.
- **Breaking changes** (rename, type change, removal) increment
-  `schema_version` to 2. Consumers MUST branch on `schema_version` to
-  stay compatible.
-
-## `eval_capture_failures` — companion audit table
-
-Not exported by `gbrain eval export`. Surfaced via `gbrain doctor`:
-
-```sh
-gbrain doctor   # warns when failures in last 24h > 0
-```
-
-Reason enum (stable): `db_down` | `rls_reject` | `check_violation` |
-`scrubber_exception` | `other`. Cross-process visibility is the whole
-point — `gbrain doctor` runs in its own process and reads the table
-directly, so in-process counters wouldn't work.
-
-## Config + CONTRIBUTOR_MODE
-
-Capture is **off by default** as of v0.25.0 (was on for everyone in
-earlier drafts). Two paths to turn it on:
-
-**Path A — env var (contributor opt-in, the common case):**
-
-```bash
-export GBRAIN_CONTRIBUTOR_MODE=1     # in ~/.zshrc or ~/.bashrc
-```
-
-**Path B — explicit config (`~/.gbrain/config.json`, file-plane only):**
-
-```json
-{
-  "engine": "postgres",
-  "database_url": "...",
-  "eval": {
-    "capture": true,
-    "scrub_pii": true
-  }
-}
-```
-
-Resolution order (most explicit wins):
-
-1. `eval.capture: true` in config → on
-2. `eval.capture: false` in config → off (overrides CONTRIBUTOR_MODE=1)
-3. `GBRAIN_CONTRIBUTOR_MODE === '1'` → on
-4. otherwise → off
-
-`scrub_pii` defaults to `true` independent of capture. Set
-`eval.scrub_pii: false` to preserve raw query text (only if you control
-the brain's distribution).
-
-`gbrain config set eval.capture false` does **not** work — that
-command writes the DB-plane config, and the MCP server reads the
-file-plane. Edit the JSON directly or use the env var.
@@ -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.
@@ -1,124 +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.
-
-## 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.
-
---
-
-## 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.
-
@@ -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/anthropic-pricing.ts` 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.
@@ -1,129 +0,0 @@
-# The Brain-Agent Loop
-
-## Goal
-
-Every conversation makes the brain smarter. Every brain lookup makes responses
-better. The loop compounds daily.
-
-## What the User Gets
-
-Without this: the agent answers from stale context. You discuss a deal on Monday,
-and by Friday the agent has forgotten. Every conversation starts from zero.
-
-With this: six months in, the agent knows more about your world than you can hold
-in working memory. It never forgets. It never stops indexing.
-
-## The Loop
-
-```
-Signal arrives (message, meeting, email, tweet, link)
-  │
-  ▼
-DETECT entities (people, companies, concepts, original thinking)
-  │  → spawn sub-agent (see entity-detection.md)
-  │
-  ▼
-READ: check brain FIRST (before responding)
-  │  → gbrain search "{entity name}"
-  │  → gbrain get {slug} (if you know it)
-  │  → gbrain query "what do we know about {topic}"
-  │
-  ▼
-RESPOND with brain context (every answer is better with context)
-  │
-  ▼
-WRITE: update brain pages (new info → compiled truth + timeline)
-  │  → gbrain put {slug} (update page)
-  │  → add_timeline_entry (append to timeline)
-  │  → add_link (cross-reference to other entities)
-  │
-  ▼
-SYNC: gbrain indexes changes
-  │  → gbrain sync --no-pull --no-embed
-  │
-  ▼
-(next signal arrives — agent is now smarter)
-```
-
-## Implementation
-
-### On Every Inbound Message
-
-```
-on_message(text):
-  // 1. DETECT (async, don't block)
-  spawn_entity_detector(text)
-
-  // 2. READ (before composing response)
-  entities = extract_entity_names(text)  // quick regex/NER
-  context = []
-  for name in entities:
-    results = gbrain_search(name)
-    if results:
-      page = gbrain_get(results[0].slug)
-      context.append(page.compiled_truth)
-
-  // 3. RESPOND (with brain context injected)
-  response = compose_response(text, context)
-
-  // 4. WRITE (after responding, if new info emerged)
-  if response_contains_new_info(response):
-    for entity in mentioned_entities:
-      gbrain_add_timeline_entry(entity.slug, {
-        date: today,
-        summary: "Discussed {topic}",
-        source: "[Source: User, conversation, {date}]"
-      })
-
-  // 5. SYNC
-  gbrain_sync()
-```
-
-### The Two Invariants
-
-1. **Every READ improves the response.** If you answered a question about a
-   person without checking their brain page first, you gave a worse answer
-   than you could have. The brain almost always has something. External APIs
-   fill gaps, they don't start from scratch.
-
-2. **Every WRITE improves future reads.** If a meeting transcript mentioned
-   new information about a company and you didn't update the company page,
-   you created a gap that will bite you later.
-
-## Tricky Spots
-
-1. **Read BEFORE responding, not after.** The temptation is to respond first
-   and update the brain later. But the brain context makes the response better.
-   Read first.
-
-2. **Don't skip the write step.** "I'll update the brain later" means never.
-   Write immediately after the conversation, while the context is fresh.
-
-3. **Sync after every write batch.** Without sync, the brain search index is
-   stale. The next query won't find what you just wrote.
-
-4. **External APIs are fallback, not primary.** `gbrain search` before
-   Brave Search. `gbrain get` before Crustdata. The brain has relationship
-   history, your own assessments, meeting transcripts, cross-references.
-   No external API can provide that.
-
-## How to Verify It Works
-
-1. **Mention a person the brain knows.** Ask "what do we know about {name}?"
-   The agent should search the brain and return compiled truth, not hallucinate
-   or do a web search.
-
-2. **Discuss something new about a known entity.** Say "I heard Acme Corp
-   just raised Series B." After the conversation, check: does Acme Corp's
-   brain page have a new timeline entry?
-
-3. **Ask about the same person a day later.** The agent should immediately
-   pull brain context without you asking. If it doesn't reference the brain
-   page, the loop isn't running.
-
-4. **Check the sync.** After a conversation, run `gbrain search "{topic}"`
-   from the CLI. The new information should be searchable.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md). See also: [Entity Detection](entity-detection.md), [Brain-First Lookup](brain-first-lookup.md)*
@@ -1,85 +0,0 @@
-# Brain-First Lookup Protocol
-
-## Goal
-
-Check the brain before calling ANY external API. The brain almost always has
-something. External APIs fill gaps, they don't start from scratch.
-
-## What the User Gets
-
-Without this: the agent calls Brave Search for someone you've had 12 meetings with.
-You get a LinkedIn summary instead of your relationship history.
-
-With this: the agent pulls your compiled truth, recent timeline entries, and
-shared context before doing anything else. External APIs only fill gaps.
-
-## Implementation
-
-```
-lookup(name_or_topic):
-  // STEP 1: Keyword search (fast, works day one, no embeddings needed)
-  results = gbrain search "{name_or_topic}"
-  if results.length > 0:
-    page = gbrain get {results[0].slug}
-    return page  // done, brain had it
-
-  // STEP 2: Hybrid search (needs embeddings, finds semantic matches)
-  results = gbrain query "what do we know about {name_or_topic}"
-  if results.length > 0:
-    page = gbrain get {results[0].slug}
-    return page
-
-  // STEP 3: Direct slug (if you know or can guess the slug)
-  page = gbrain get "people/{slugify(name_or_topic)}"
-  if page: return page
-
-  // STEP 4: External API (FALLBACK ONLY)
-  // Only reach here if brain has nothing
-  return external_search(name_or_topic)
-```
-
-**This is mandatory.** An agent that calls Brave Search before checking the brain
-is wasting money and giving worse answers.
-
-## Why Brain First
-
-The brain has context no external API can provide:
- Relationship history (how you know them, what you discussed)
- Your own assessments (what you think of them, not their LinkedIn bio)
- Meeting transcripts (what was said, what was decided)
- Cross-references (who they know, what companies they're connected to)
- Timeline (what changed recently, what's trending)
-
-A LinkedIn scrape gives you their job title. The brain gives you: "co-founded
-Brex, you had coffee with him 3 times, last discussed the payments infrastructure
-thesis, he's interested in your take on AI agents."
-
-## Tricky Spots
-
-1. **Try keyword first, then hybrid.** Keyword search works without embeddings
-   (day one). Hybrid search needs embeddings but finds semantic matches. Try
-   both in sequence.
-
-2. **Fuzzy slug matching.** `gbrain get` supports fuzzy matching. If the exact
-   slug doesn't exist, it suggests alternatives. Use this for name variants
-   ("Pedro" → "pedro-franceschi").
-
-3. **Don't skip for "simple" questions.** Even "what's Acme Corp's address?"
-   should check the brain first. The brain might have it, and the lookup adds
-   no latency (< 100ms for keyword search).
-
-4. **Load compiled truth + recent timeline.** The compiled truth gives you the
-   state of play in 30 seconds. The timeline gives you what changed recently.
-   Both together = full context.
-
-## How to Verify
-
-1. Ask about someone in the brain. Verify the agent searched the brain FIRST
-   (check tool call order in the response).
-2. Ask about someone NOT in the brain. Verify the agent searched the brain,
-   found nothing, THEN fell back to external search.
-3. Ask the same question twice. Second time should be instant (brain has it).
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md). See also: [Brain-Agent Loop](brain-agent-loop.md), [Search Modes](search-modes.md)*
@@ -1,75 +0,0 @@
-# Brain vs Memory vs Session
-
-## Goal
-Know what goes in GBrain, what goes in agent memory, and what stays in session context -- so every piece of information lands in the right layer.
-
-## What the User Gets
-Without this: people dossiers get stored in agent memory (lost on agent reset), user preferences get stored in GBrain (cluttering knowledge pages), and the agent re-asks questions it already knows the answer to. With this: world knowledge persists in the brain, operational state persists in agent memory, and the agent never puts information in the wrong layer.
-
-## Implementation
-
-```
-on new_information(info):
-    # Three layers, three purposes -- route to the right one
-
-    if info.is_about_the_world:
-        # GBRAIN: people, companies, deals, meetings, concepts, ideas
-        # This is world knowledge -- facts about entities external to the agent
-        gbrain put <slug> --content "..."
-        # Examples:
-        #   "Pedro is CEO of Brex"           -> gbrain (person page)
-        #   "Brex raised Series D at $12B"   -> gbrain (company page)
-        #   "Tuesday's meeting covered Q2"   -> gbrain (meeting page)
-        #   "The meatsuit maintenance tax"   -> gbrain (originals page)
-
-    elif info.is_about_operations:
-        # AGENT MEMORY: preferences, decisions, tool config, session continuity
-        # This is how the agent operates -- not facts about the world
-        memory_write(info)
-        # Examples:
-        #   "User prefers concise formatting"      -> agent memory
-        #   "Deploy to staging before prod"        -> agent memory
-        #   "Use dark mode in code blocks"         -> agent memory
-        #   "API key for Crustdata goes in .env"   -> agent memory
-
-    elif info.is_current_conversation:
-        # SESSION CONTEXT: what was just said, current task, immediate state
-        # This is automatic -- already in the conversation window
-        # No storage action needed
-        # Examples:
-        #   "We were just discussing the board deck"  -> session
-        #   "You asked me to review this PR"          -> session
-        #   "The file I just shared"                  -> session
-
-# Lookup routing:
-on user_asks(question):
-    if question.about_person or question.about_company or question.about_meeting:
-        gbrain search "{entity}"    # -> world knowledge
-        gbrain get <slug>
-
-    elif question.about_preference or question.about_how_to_operate:
-        memory_search("{topic}")    # -> operational state
-
-    elif question.about_current_context:
-        # Already in session -- just reference conversation history
-        pass
-```
-
-## Tricky Spots
-
-1. **Don't store people in agent memory.** "Pedro prefers email over Slack" feels like a preference, but it's a fact about Pedro -- it goes in GBrain on Pedro's page. Agent memory is for the agent's own operational state, not facts about people in the world.
-2. **Don't store user preferences in GBrain.** "User likes bullet points over paragraphs" is about how the agent should behave, not about the world. It goes in agent memory. GBrain pages are for entities, not for agent configuration.
-3. **Synthesis of external ideas goes in GBrain.** "User's take on Peter Thiel's zero-to-one framework" is the user's original thinking -- it goes in GBrain under originals/, not in agent memory.
-4. **Agent memory doesn't survive agent resets on some platforms.** Critical world knowledge MUST be in GBrain, which is durable. If the agent loses memory, the brain still has everything.
-5. **When in doubt, ask: is this about the world or about how to operate?** World -> GBrain. Operations -> agent memory. Current conversation -> session.
-
-## How to Verify
-
-1. Ask the agent "Who is Pedro?" -- confirm it runs `gbrain search` or `gbrain get`, not `memory_search`. Person lookup should hit GBrain.
-2. Ask the agent "How should I format responses?" -- confirm it checks agent memory, not GBrain. Preferences are operational state.
-3. Check that no person or company pages exist in agent memory storage. Run `memory_search "person"` -- it should return preferences, not dossiers.
-4. Check that GBrain doesn't contain pages about agent behavior. Run `gbrain search "user prefers"` -- it should return nothing (preferences belong in agent memory).
-5. After an agent reset, confirm GBrain knowledge is still accessible. Run `gbrain get <any_slug>` -- world knowledge should survive the reset.
-
---
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,137 +0,0 @@
-# Compiled Truth + Timeline Pattern
-
-## Goal
-
-Every brain page has two zones: compiled truth (current synthesis, rewritten as
-evidence changes) and timeline (append-only evidence trail, never edited).
-
-## What the User Gets
-
-Without this: brain pages are append-only logs. To understand a person, you read
-200 timeline entries. The answer is buried in entry #147.
-
-With this: the compiled truth gives you the state of play in 30 seconds. The
-timeline is the proof. Six months of entries compress into a one-paragraph
-assessment that's always current.
-
-## Implementation
-
-### Page Structure
-
-```markdown
---
-type: person
-title: Sarah Chen
-tags: [engineering, acme-corp]
---
-
-## Executive Summary
-One paragraph. How you know them, why they matter.
-
-## State
-VP Engineering at Acme Corp. Managing 45-person team. Reports to CEO.
-
-## What They Believe
-Strong opinions on test coverage. "Ship it when the tests pass, not before."
-
-## What They're Building
-Leading the API migration from REST to GraphQL. Target: Q3 completion.
-
-## Assessment
-Sharp technical leader. Under-appreciated internally. Watch for signs of burnout.
-
-## Trajectory
-Ascending. Likely CTO track if the migration succeeds.
-
-## Relationship
-Met through Pedro. Had coffee 3x. Last: discussed API architecture thesis.
-
-## Contact
-sarah@acmecorp.com | @sarahchen | linkedin.com/in/sarahchen
-
---
-
-## Timeline
-
- **2026-04-07** | Met at team sync. Discussed API migration timeline.
-  Seemed energized about GraphQL pivot.
-  [Source: Meeting notes, 2026-04-07 2:00 PM PT]
- **2026-04-03** | Mentioned in email re Q2 planning. Taking lead on ops.
-  [Source: Gmail, sarah@acmecorp.com, 2026-04-03 10:30 AM PT]
- **2026-03-15** | First meeting. Intro from Pedro. Strong technical background.
-  [Source: User, direct conversation, 2026-03-15 3:00 PM PT]
-```
-
-### Updating a Page
-
-```
-update_brain_page(slug, new_info, source):
-  page = gbrain get {slug}
-
-  // TIMELINE: always APPEND (never edit existing entries)
-  gbrain add_timeline_entry {slug} {
-    date: today,
-    summary: new_info.summary,
-    detail: new_info.detail,
-    source: format_source(source)  // [Source: who, channel, date time tz]
-  }
-
-  // COMPILED TRUTH: REWRITE (not append)
-  // Read the existing compiled truth
-  // Integrate new information
-  // Write the updated synthesis
-  updated_truth = rewrite_compiled_truth(page.compiled_truth, new_info)
-  gbrain put {slug} {
-    compiled_truth: updated_truth,
-    // timeline is NOT passed — it's managed by add_timeline_entry
-  }
-```
-
-### The Rules
-
-| Zone | Action | Explanation |
-|------|--------|-------------|
-| Compiled truth | **REWRITE** | Current synthesis. Changes when evidence changes. |
-| Timeline | **APPEND** | Evidence trail. Never edited, only added to. |
-
-**Every compiled truth claim must trace to timeline entries.** If the Assessment
-says "under-appreciated internally," there should be timeline entries that
-support that claim.
-
-## Tricky Spots
-
-1. **REWRITE means rewrite, not append.** Don't add a new paragraph to compiled
-   truth. Rewrite the entire section with the new information integrated. Old
-   assessments that are no longer accurate should be updated, not kept alongside
-   contradictory new ones.
-
-2. **Timeline entries are immutable.** Never edit a timeline entry. If information
-   turns out to be wrong, add a NEW entry correcting it:
-   `- 2026-04-10 | Correction: Sarah is VP Eng, not CTO. Previous entry was wrong.`
-
-3. **GBrain search weights compiled truth higher.** `gbrain query` returns compiled
-   truth chunks with higher relevance than timeline chunks. This means the freshest
-   synthesis surfaces first in search results.
-
-4. **The --- separator matters.** GBrain uses the first standalone `---` after
-   frontmatter to split compiled_truth from timeline. Everything above is compiled
-   truth, everything below is timeline.
-
-5. **Don't skip the Assessment section.** The assessment is the value. "Strong
-   technical leader" is something no API can provide. It's YOUR read on this
-   person. That's what makes the brain page better than LinkedIn.
-
-## How to Verify
-
-1. **Update a person page.** Add new meeting info. Check: compiled truth was
-   REWRITTEN (not appended), timeline has new entry at the top.
-2. **Search for the person.** `gbrain query "Sarah Chen"`. The compiled truth
-   (current synthesis) should appear first, not a random timeline entry.
-3. **Check traceability.** Every claim in compiled truth should have a
-   corresponding timeline entry. Read both sections and verify.
-4. **Check immutability.** After update, old timeline entries should be unchanged.
-   Dates, sources, and content should match the originals exactly.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md). See also: [Source Attribution](source-attribution.md), [Entity Detection](entity-detection.md)*
@@ -1,136 +0,0 @@
-# Content and Media Ingestion
-
-## Goal
-YouTube videos, social media, PDFs, and documents become searchable brain pages with the agent's own analysis and full cross-references to every entity mentioned.
-
-## What the User Gets
-Without this: media links are bookmarks that decay -- you remember watching a video but can't find what was said, who said it, or why it mattered. With this: every piece of media is a permanent brain page with the agent's analysis layered on top, every mentioned entity gets a back-link, and the full content is searchable forever.
-
-## Implementation
-
-```
-on user_shares_media(url_or_file):
-
-    # PATTERN 1: YouTube Video Ingestion
-    if media.type == "youtube":
-        # Step 1: Get FULL transcript with speaker diarization
-        #   WHO said WHAT -- not just a wall of text
-        #   Use Diarize.io or equivalent service
-        transcript = diarize(video_url)  # speaker-attributed transcript
-        # NEVER use YouTube's auto-generated summary or AI summary
-
-        # Step 2: Agent writes OWN analysis (this is the value)
-        #   NOT a summary. NOT regurgitation. The agent's TAKE:
-        #   - What matters and why (given the user's worldview)
-        #   - Key quotes attributed to specific speakers
-        #   - Connections to existing brain pages
-        #   - Implications and follow-up angles
-        analysis = agent_analyze(transcript, user_context)
-
-        # Step 3: Create brain page
-        slug = f"media/youtube/{video_slug}"
-        gbrain put <slug> --content """
-            # {title}
-            **Channel:** {channel} | **Date:** {date} | **Link:** {url}
-
-            ## Analysis
-            {agent_analysis}
-
-            ## Key Quotes
-            - **{Speaker}** ({timestamp}): "{quote}" -- {why_it_matters}
-
-            ---
-            ## Full Transcript
-            {diarized_transcript}
-        """
-
-        # Step 4: Extract and cross-reference entities
-        for person in transcript.mentioned_people:
-            gbrain add_link <slug> <person_slug>
-            gbrain add_link <person_slug> <slug>
-            gbrain add_timeline_entry <person_slug> \
-                --entry "Discussed in {video_title}: {what_was_said}" \
-                --source "YouTube: {url}"
-
-    # PATTERN 2: Social Media Bundles
-    elif media.type == "tweet" or media.type == "social":
-        # Don't just save a tweet -- reconstruct FULL context
-        bundle = {
-            "original": fetch_tweet(url),
-            "thread": reconstruct_thread(url),        # quoted tweets, replies
-            "linked_articles": fetch_linked_urls(),    # fetch and summarize
-            "engagement": get_engagement_data(),       # what resonated
-        }
-
-        slug = f"media/social/{platform}-{author}-{date}"
-        gbrain put <slug> --content """
-            # {author}: {topic}
-            {agent_analysis_of_full_bundle}
-
-            ## Thread
-            {reconstructed_thread}
-
-            ## Linked Articles
-            {article_summaries}
-
-            ---
-            ## Raw
-            {original_tweet_text}
-        """
-
-        # Extract entities and cross-reference
-        for entity in bundle.mentioned_entities:
-            gbrain add_link <slug> <entity_slug>
-            gbrain add_link <entity_slug> <slug>
-
-    # PATTERN 3: PDFs and Documents
-    elif media.type == "pdf" or media.type == "document":
-        # OCR if needed (scanned PDFs)
-        content = ocr_if_needed(file) or extract_text(file)
-
-        # For books and long-form:
-        slug = f"sources/{document_slug}"
-        gbrain put <slug> --content """
-            # {title}
-            **Author:** {author} | **Date:** {date}
-
-            ## Chapter Summaries
-            {per_chapter_summary}
-
-            ## Key Quotes
-            - p.{page}: "{quote}" -- {why_it_matters}
-
-            ## Cross-References
-            {links_to_brain_pages_for_people_and_concepts}
-
-            ---
-            ## Source
-            {full_text_or_key_sections}
-        """
-
-        for entity in document.mentioned_entities:
-            gbrain add_link <slug> <entity_slug>
-            gbrain add_link <entity_slug> <slug>
-
-    # Always sync after ingestion
-    gbrain sync
-```
-
-## Tricky Spots
-
-1. **Always FULL transcript, never AI summary.** YouTube's auto-summary and AI-generated summaries lose the texture: who said what, exact phrasing, tone, what was left unsaid. The full diarized transcript is the evidence base. The agent's analysis goes above it.
-2. **The agent's OWN analysis is the value, not regurgitation.** "The video discussed AI safety" is worthless. "Dario made a specific claim about compute scaling that contradicts what Ilya said in the NeurIPS talk -- see media/youtube/ilya-neurips-2025" is useful. The analysis connects the new media to the existing brain.
-3. **Social media is a bundle, not a single tweet.** A tweet without its thread, quoted tweets, linked articles, and engagement context is a fragment. Reconstruct the full context before creating the brain page.
-4. **Cross-references make media pages alive.** A YouTube page without back-links to the people and companies mentioned is a dead archive. Every mentioned entity gets a link and a timeline entry.
-5. **Over time, `media/` becomes a searchable archive.** Every video, podcast, talk, interview, article, and tweet the user has consumed, with the agent's commentary layered on top. This is the memex at full power.
-
-## How to Verify
-
-1. Ingest a YouTube video. Run `gbrain get media/youtube/{slug}`. Confirm the page has: the agent's analysis (not just a summary), key quotes with speaker attribution, and the full diarized transcript.
-2. Run `gbrain get_links media/youtube/{slug}`. Confirm back-links exist to brain pages for every person and company mentioned in the video.
-3. Pick a person mentioned in the video. Run `gbrain get <person_slug>`. Confirm their timeline has a new entry referencing the video with specific context.
-4. Ingest a tweet. Confirm the brain page includes the thread context, linked article summaries, and entity cross-references -- not just the tweet text.
-5. Run `gbrain search "{topic_from_video}"`. Confirm the media page appears in search results (verifies the content is indexed and searchable).
-
---
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,193 +0,0 @@
-# Reference Cron Schedule
-
-## Goal
-
-A production brain runs 20+ recurring jobs that keep it alive, current, and
-compounding. This guide shows the schedule, the patterns, and how to set it up.
-
-## What the User Gets
-
-Without this: the brain only updates when you manually ingest data. Pages go
-stale, entities are thin, citations break, and the agent answers from old context.
-
-With this: the brain maintains itself. Email, social, calendar, and meetings
-flow in automatically. Thin pages get enriched overnight. Broken citations get
-fixed. You wake up and the brain is smarter than when you went to sleep.
-
-## The Schedule
-
-| Frequency | Job | Brain Interaction | Recipe |
-|-----------|-----|-------------------|--------|
-| Every 30 min | Email monitoring | Search sender, update people pages | [email-to-brain](../../recipes/email-to-brain.md) |
-| Every 30 min | X/Twitter collection | Create/update media pages, entity extraction | [x-to-brain](../../recipes/x-to-brain.md) |
-| 3x/day (weekdays) | Meeting sync | Full ingestion + attendee propagation | [meeting-sync](../../recipes/meeting-sync.md) |
-| Weekly | Calendar sync | Daily files + attendee enrichment | [calendar-to-brain](../../recipes/calendar-to-brain.md) |
-| Daily AM | Morning briefing | Search calendar attendees, deal status, active threads | [briefing skill](../../skills/briefing/SKILL.md) |
-| Weekly | Brain maintenance | `gbrain doctor`, embed stale, orphan detection | [maintain skill](../../skills/maintain/SKILL.md) |
-| Nightly | Dream cycle | Entity sweep, enrich thin spots, fix citations | See below |
-
-## Implementation: Setting Up Cron Jobs
-
-```bash
-# Email collector — every 30 minutes
-*/30 * * * * cd /path/to/email-collector && node email-collector.mjs collect && node email-collector.mjs digest
-
-# X/Twitter collector — every 30 minutes
-*/30 * * * * cd /path/to/x-collector && node x-collector.mjs collect >> /tmp/x-collector.log 2>&1
-
-# Meeting sync — 10 AM, 4 PM, 9 PM on weekdays
-0 10,16,21 * * 1-5 cd /path/to/meeting-sync && node meeting-sync.mjs >> /tmp/meeting-sync.log 2>&1
-
-# Calendar sync — Sundays at 10 AM
-0 10 * * 0 cd /path/to/calendar-sync && node calendar-sync.mjs --start $(date -v-7d +%Y-%m-%d) --end $(date +%Y-%m-%d)
-
-# Brain health — weekly Mondays at 6 AM
-0 6 * * 1 gbrain doctor --json >> /tmp/gbrain-health.log 2>&1 && gbrain embed --stale
-
-# Dream cycle — nightly at 2 AM
-0 2 * * * /path/to/dream-cycle.sh
-```
-
-### Quiet Hours Gate (MANDATORY)
-
-Every cron job that sends notifications MUST check quiet hours first.
-See [Quiet Hours](quiet-hours.md) for the full pattern.
-
-```bash
-# In every cron script:
-if ! bash scripts/quiet-hours-gate.sh; then
-  mkdir -p /tmp/cron-held
-  echo "$OUTPUT" > /tmp/cron-held/$(basename "$0" .sh).md
-  exit 0
-fi
-# Not quiet hours — send normally
-```
-
-### Travel-Aware Timezone Handling
-
-The agent reads your calendar for flights, hotels, and out-of-office blocks to
-infer your current location and timezone. All times shown in YOUR local timezone.
-
-```
-// Example: user flew to Tokyo
-// 2 PM Pacific = 3 AM Tokyo = quiet hours
-// Hold the notification, fold into morning briefing
-
-get_user_timezone():
-  calendar = gbrain search "flight" --type calendar --recent 7d
-  if recent_flight:
-    return infer_timezone(flight.destination)
-  return config.default_timezone  // fallback: US/Pacific
-```
-
-When you travel: cron jobs that would fire during your waking hours at home but
-hit your sleeping hours at the destination get held and folded into the next
-morning briefing. Zero config change needed.
-
-## The Dream Cycle
-
-The most important cron job. Runs while you sleep.
-
-### What It Does
-
-```
-dream_cycle():
-  // Phase 1: Entity Sweep
-  conversations = get_todays_conversations()
-  for message in conversations:
-    entities = detect_entities(message)
-    for entity in entities:
-      page = gbrain search "{entity.name}"
-      if not page:
-        create_page(entity)        // new entity, create + enrich
-      elif page.is_thin():
-        enrich_page(entity)        // thin page, fill it out
-      else:
-        update_timeline(entity)    // existing page, add today's mentions
-
-  // Phase 2: Fix Broken Citations
-  pages = gbrain list --type person --limit 100
-  for page in pages:
-    for entry in page.timeline:
-      if not entry.has_source_attribution():
-        fix_citation(entry)        // add [Source: ...] where missing
-      if entry.has_tweet_url() and not entry.url_is_valid():
-        fix_url(entry)             // broken tweet links
-
-  // Phase 3: Consolidate Memory
-  patterns = detect_patterns_across_conversations()
-  for pattern in patterns:
-    promote_to_memory(pattern)     // ephemeral → durable knowledge
-
-  // Phase 4: Sync
-  gbrain sync --no-pull --no-embed
-  gbrain embed --stale
-```
-
-### Setting Up the Dream Cycle
-
-**OpenClaw:** Ships with DREAMS.md as a default skill. Three phases (light,
-deep, REM) run automatically during quiet hours.
-
-**Hermes Agent:**
-```bash
-/cron add "0 2 * * *" "Dream cycle: search today's sessions for
-  entities I mentioned. For each person, company, or idea: check
-  if a brain page exists (gbrain search), create or update it if
-  thin. Fix any broken citations. Then consolidate: read MEMORY.md,
-  promote important signals, remove stale entries."
-  --name "nightly-dream-cycle"
-```
-
-**Claude Code / Custom agents:** Create a script:
-```bash
-#!/bin/bash
-# dream-cycle.sh
-
-# Check quiet hours (should be quiet — that's when we run)
-echo "Dream cycle starting at $(date)"
-
-# Phase 1: Entity sweep (spawn sub-agent)
-# Read today's conversation logs, extract entities, update brain
-
-# Phase 2: Citation hygiene
-gbrain doctor --json | jq '.checks[] | select(.status=="warn")'
-
-# Phase 3: Embed any stale content
-gbrain embed --stale
-
-echo "Dream cycle complete at $(date)"
-```
-
-## Tricky Spots
-
-1. **The dream cycle is NOT optional.** Without it, signal leaks out of every
-   conversation. With it, nothing is lost. This is the difference between an
-   agent that forgets and one that remembers.
-
-2. **Quiet hours gate on EVERY notification job.** If you skip it, the user
-   gets pinged at 3 AM. One 3 AM ping and they'll disable the whole system.
-
-3. **Don't over-cron.** 20+ jobs sounds like a lot. Start with: email (30 min),
-   dream cycle (nightly), brain health (weekly). Add more as you add
-   integration recipes.
-
-4. **Timezone changes are automatic.** Don't make the user reconfigure cron
-   when they travel. Read the calendar, infer the timezone, adjust delivery.
-
-5. **Held messages MUST be picked up.** If quiet hours hold a notification,
-   the morning briefing MUST include it. Otherwise information is lost.
-
-## How to Verify
-
-1. **Quiet hours:** Set quiet hours to current hour. Run a notification cron.
-   Verify output went to `/tmp/cron-held/`, not to messaging.
-2. **Dream cycle:** Run the dream cycle manually. Check that thin entity pages
-   got enriched and broken citations were fixed.
-3. **Email collector cron:** Wait 30 minutes. Check `data/digests/` for new digest.
-4. **Morning briefing:** Check that held messages appear in the briefing.
-5. **Health check:** Run `gbrain doctor --json`. All checks should pass.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md). See also: [Quiet Hours](quiet-hours.md), [Operational Disciplines](operational-disciplines.md)*
@@ -1,146 +0,0 @@
-# Deterministic Collectors: Code for Data, LLMs for Judgment
-
-## Goal
-
-Separate mechanical work (100% reliable code) from analytical work (LLM judgment) so that deterministic tasks never fail probabilistically.
-
-## What the User Gets
-
-Without this: the LLM generates Gmail links, formats tables, and tracks state.
-It follows the rule for the first 10 items, then drops a link on item 11. You
-write "NO EXCEPTIONS" in the prompt. It still fails. 90% reliability over 20
-items means visible failures twice per day. Trust is destroyed.
-
-With this: code handles URLs, formatting, and state (100% reliable). The LLM
-reads pre-formatted data and adds judgment, classification, and enrichment.
-Links are never wrong because the LLM never generates them.
-
-## Implementation
-
-```
-// The pattern: code collects, LLM analyzes
-
-// STEP 1: Deterministic collector (script, no LLM calls)
-collector_run():
-  messages = gmail_api.fetch_unread()
-  for msg in messages:
-    structured = {
-      id: msg.id,
-      from: msg.sender,
-      subject: msg.subject,
-      snippet: msg.snippet,
-      gmail_link: f"https://mail.google.com/mail/u/?authuser={account}#inbox/{msg.id}",
-      gmail_markdown: f"[Open in Gmail]({gmail_link})",
-      is_signature: regex_match(msg, DOCUSIGN_PATTERNS),
-      is_noise: regex_match(msg, NOISE_PATTERNS),
-      is_new: msg.id not in state.seen_ids
-    }
-    store(structured)
-    state.seen_ids.add(msg.id)
-  generate_markdown_digest(structured_messages)
-
-// STEP 2: LLM reads the pre-formatted digest
-llm_analyze():
-  digest = read("data/digests/today.md")  // links already baked in
-  classify_urgency(digest)                 // judgment call
-  add_commentary(digest)                   // contextual analysis
-  run_brain_enrichment(notable_entities)   // gbrain search + update
-  draft_replies(urgent_items)              // creative work
-  surface_to_user(final_output)            // delivery
-
-// STEP 3: Wire into cron
-cron_job():
-  collector_run()     // fast, cheap, deterministic
-  llm_analyze()       // slower, expensive, creative
-```
-
-### The Architecture
-
-```
-+-----------------------------+     +------------------------------+
-|  Deterministic Collector    |---->|       LLM Agent              |
-|  (Node.js / Python script)  |     |                              |
-|                             |     |  - Read the pre-formatted    |
-|  - Pull data from API       |     |    digest                    |
-|  - Store structured JSON    |     |  - Classify items            |
-|  - Generate links/URLs      |     |  - Add commentary            |
-|  - Detect patterns (regex)  |     |  - Run brain enrichment      |
-|  - Track state (seen/new)   |     |  - Draft replies             |
-|  - Output markdown digest   |     |  - Surface to user           |
-|                             |     |                              |
-|  CODE — deterministic,      |     |  AI — judgment, context,     |
-|  never forgets              |     |  creativity                  |
-+-----------------------------+     +------------------------------+
-```
-
-### File Structure
-
-```
-scripts/email-collector/
-├── email-collector.mjs     # No LLM calls, no external deps
-├── data/
-│   ├── state.json          # Last pull timestamp, known IDs, pending signatures
-│   ├── messages/           # Structured JSON per day
-│   │   └── 2026-04-09.json
-│   └── digests/            # Pre-formatted markdown
-│       └── 2026-04-09.md
-```
-
-### Where the Pattern Applies
-
-| Signal Source | Collector Generates | LLM Adds |
-|--------------|-------------------|----------|
-| **Email** | Gmail links, sender metadata, signature detection | Urgency classification, enrichment, reply drafts |
-| **X/Twitter** | Tweet links, engagement metrics, deletion detection | Sentiment analysis, narrative detection, content ideas |
-| **Calendar** | Event links, attendee lists, conflict detection | Prep briefings, meeting context from brain |
-| **Slack** | Channel links, thread links, mention detection | Priority classification, action item extraction |
-| **GitHub** | PR/issue links, diff stats, CI status | Code review context, priority assessment |
-
-### The Principle
-
-If a piece of output MUST be present and MUST be formatted correctly every
-time, generate it in code. If a piece of output requires judgment, context,
-or creativity, generate it with the LLM. Don't ask the LLM to do both in
-the same pass.
-
-## Tricky Spots
-
-1. **LLMs forget links -- bake them in code.** The LLM will follow the
-   "include a Gmail link" rule for the first 10 items, then silently drop
-   it on item 11. No amount of prompt engineering fixes probabilistic
-   formatting over long outputs. The fix: generate every link in the
-   collector script. The LLM reads pre-formatted markdown where links are
-   already embedded. It can't forget what it didn't generate.
-
-2. **Noise filtering must be deterministic.** Regex-based noise detection
-   (newsletters, automated receipts, marketing) belongs in the collector,
-   not the LLM. The LLM might classify a newsletter as "possibly important"
-   on one run and "noise" on the next. Code classifies the same input the
-   same way every time.
-
-3. **Atomic writes prevent corruption.** The collector writes to a state
-   file (`state.json`) that tracks which messages have been seen. If the
-   script crashes mid-write, the state file can be corrupted. Write to a
-   temp file first, then rename atomically. This also prevents the LLM
-   from reading a partial digest if the cron fires during a collection run.
-
-## How to Verify
-
-1. **Run the collector and check every link.** Execute the collector script
-   manually. Open the generated digest. Click every `[Open in Gmail]` link
-   (or equivalent). Every single link must resolve to the correct item. If
-   any link is broken or missing, the collector has a bug.
-
-2. **Verify noise filtering is consistent.** Run the collector twice on the
-   same input data. The noise classification (is_noise field) must be
-   identical both times. If it varies, a probabilistic element leaked into
-   the deterministic layer.
-
-3. **Verify the LLM reads structured output.** Run the full pipeline
-   (collector then LLM). Check that the LLM's analysis references data
-   from the structured digest, not from its own generation. The links in
-   the final output should be identical to the links in the digest file.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,151 +0,0 @@
-# Diligence Ingestion: Data Room to Brain Pages
-
-## Goal
-
-Turn pitch decks, financial models, and data room materials into searchable, cross-referenced brain pages with bull/bear analysis.
-
-## What the User Gets
-
-Without this: pitch decks sit in email attachments. Financial models in Google
-Drive. No cross-reference to the company brain page. You can't search "what
-were the key metrics from Acme Corp's Series A deck?"
-
-With this: every data room document is extracted, diarized, cross-referenced to
-the company page, and searchable. Index.md gives you the bull/bear case at a
-glance. `gbrain query "Acme Corp revenue growth"` finds the exact chart.
-
-## Implementation
-
-Recognize data room materials by PDF filenames containing "Data Deck", "Intro
-Deck", "Data Room", "Cap Table", "Financial Model", "Investor Memo", "Pitch
-Deck", or series round names. Spreadsheet tabs with Revenue, Retention, Cohorts,
-CAC, Gross Margin, Unit Economics, ARR. User language like "data room",
-"diligence", "deck", "pitch", "fundraise materials".
-
-### The 9-Step Pipeline
-
-**Step 1: Identify the Company.**
-From the document content or filename, identify the company name.
-Check if `brain/companies/{slug}.md` exists.
-
-**Step 2: Create Diligence Directory.**
-
-```bash
-mkdir -p brain/diligence/{company-slug}/.raw
-```
-
-**Step 3: Extract Content.**
-
- **PDFs:** Use PDF extraction tool. For scanned/image-heavy PDFs,
-  use OCR (e.g., Mistral OCR or similar).
- **Spreadsheets:** Export each sheet as CSV. For Google Sheets:
-  ```
-  https://docs.google.com/spreadsheets/d/{ID}/gviz/tq?tqx=out:csv&sheet={Sheet Name}
-  ```
-
-**Step 4: Diarize and Save.**
-Write extracted content to `brain/diligence/{company}/{doc-name}.md`:
- Document title and type
- Section-by-section breakdown with key metrics
- Notable footnotes or caveats
- Raw data tables where relevant
-
-**Step 5: Save Raw Files.**
-Copy original PDFs/files to `brain/diligence/{company}/.raw/`
-Preserve originals for reference. The diarized version is for search.
-
-**Step 6: Create or Update index.md.**
-Every diligence directory needs an `index.md`:
-
-```markdown
-# {Company Name} — Diligence
-
-## Round Details
- Stage: Series A
- Amount: $10M
- Date: 2026-04
-
-## Document Inventory
- [Pitch Deck](pitch-deck.md) — 25 slides, company overview + traction
- [Financial Model](financial-model.md) — 5 tabs, 3-year projections
- [Cap Table](cap-table.md) — current ownership + option pool
-
-## Key Findings
- Revenue growing 30% MoM for last 6 months
- CAC payback period: 4 months
- Net retention: 135%
-
-## Bull Case
- Strong product-market fit signal (NPS 72)
- Expanding into adjacent vertical
-
-## Bear Case
- Single customer represents 40% of revenue
- Burn rate increased 3x last quarter
-
-## Open Questions
- What's the path to profitability?
- How defensible is the moat?
-```
-
-**Step 7: Enrich Company Brain Page.**
-Update `brain/companies/{slug}.md`:
- Add document sources to frontmatter
- Update compiled truth with key findings
- Add "See Also" link to diligence directory
- If no company page exists, create one via the enrich skill
-
-**Step 8: Commit.**
-
-```bash
-cd brain/ && git add -A && git commit -m "diligence: {Company} — {doc type} ingestion" && git push
-```
-
-**Step 9: Publish (if asked).**
-When the user wants a shareable brief, create a password-protected
-published version. Strip internal notes and raw assessment language.
-
-### Quality Bar
-
-A good diligence page reads like an intelligence assessment:
- **What they say** vs **what the data shows** (the gap is the insight)
- Explicit bull/bear case (not just a summary)
- Key metrics highlighted, not buried
- Open questions that need answers before decision
-
-## Tricky Spots
-
-1. **PDF extraction is lossy.** Scanned decks and image-heavy PDFs lose
-   tables and charts during extraction. Always check the diarized output
-   against the original `.raw/` file. If key metrics are missing, re-extract
-   with OCR or transcribe manually.
-
-2. **Idempotency on re-ingestion.** If the user sends an updated deck for
-   the same company, don't create a duplicate directory. Check for an existing
-   `brain/diligence/{company-slug}/` and update in place. Append a version
-   suffix to the document file if the old version should be preserved.
-
-3. **index.md completeness.** The index.md is the entry point for the entire
-   diligence package. If it's missing the bull/bear case or open questions,
-   the diligence is incomplete. Always generate all sections even if some
-   require judgment calls -- flag uncertain assessments explicitly.
-
-## How to Verify
-
-1. **Search for key metrics.** After ingestion, run
-   `gbrain search "revenue growth"` or `gbrain search "{company name} CAC"`.
-   The diarized content should appear in results. If it doesn't, the sync
-   or embedding step was missed.
-
-2. **Check the company page cross-reference.** Open
-   `brain/companies/{slug}.md` and verify it links to the diligence directory.
-   The compiled truth section should include key findings from the deck.
-
-3. **Verify index.md has all sections.** Open
-   `brain/diligence/{company}/index.md` and confirm it has Round Details,
-   Document Inventory, Key Findings, Bull Case, Bear Case, and Open Questions.
-   Missing sections mean the pipeline stopped early.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,103 +0,0 @@
-# Enrichment Pipeline
-
-## Goal
-Enrich brain pages from external APIs with tiered spend -- full pipeline for key people, light touch for passing mentions, raw data preserved for auditability.
-
-## What the User Gets
-Without this: brain pages are thin shells with only what the user manually typed, API calls are wasted on nobodies, and enrichment data vanishes after the agent session ends. With this: key people have rich, multi-source portraits; spend scales to importance; raw API responses are preserved for re-processing; and cross-references connect the entire graph.
-
-## Implementation
-
-```
-on enrich(entity, trigger):
-    # trigger: meeting mention, email thread, social interaction, user request
-
-    # Step 1: Identify entities from the incoming signal
-    entities = extract_entities(signal)
-    #   people names, company names, associations
-
-    # Step 2: Check brain state -- UPDATE or CREATE path?
-    for entity in entities:
-        existing = gbrain search "{entity.name}"
-        if existing:
-            page = gbrain get <entity_slug>
-            path = "UPDATE"
-        else:
-            path = "CREATE"
-
-    # Step 3: Determine tier -- scale spend to importance
-    tier = classify_tier(entity):
-        # Tier 1 (10-15 API calls): key people, inner circle, business partners,
-        #         portfolio companies. Full pipeline, ALL data sources.
-        # Tier 2 (3-5 API calls): notable people, occasional interactions.
-        #         Web search + social + brain cross-reference.
-        # Tier 3 (1-2 API calls): minor mentions, everyone else worth tracking.
-        #         Brain cross-reference + social lookup if handle known.
-
-    # Step 4: Run external lookups (priority order, stop when enough signal)
-    data = {}
-    data["brain"] = gbrain search "{entity.name}"          # Always first (free)
-    if tier <= 2:
-        data["web"] = brave_search("{entity.name}")        # Background, press, talks
-    if tier <= 2:
-        data["twitter"] = twitter_lookup(entity.handle)    # Beliefs, building, network
-    if tier == 1:
-        data["linkedin"] = crustdata_enrich(entity.name)   # Career, connections
-        data["research"] = happenstance_research(entity)   # Career arcs, web presence
-        data["funding"] = captain_api(entity.company)      # Funding, valuation, team
-        data["meetings"] = circleback_search(entity.name)  # Transcript search
-        data["contacts"] = google_contacts(entity.email)   # Contact data
-
-    # Step 5: Store raw data (auditable, re-processable)
-    gbrain put_raw_data <entity_slug> \
-        --data '{"sources": {"crustdata": {"fetched_at": "...", "data": {...}}, ...}}'
-    # Overwrite on re-enrichment, don't append
-
-    # Step 6: Write to brain page
-    if path == "CREATE":
-        gbrain put <entity_slug> --content "<compiled_truth_from_all_sources>"
-        gbrain add_timeline_entry <entity_slug> --entry "Page created via enrichment"
-    elif path == "UPDATE":
-        # Append timeline, update compiled truth ONLY if materially new
-        gbrain add_timeline_entry <entity_slug> --entry "Enriched: {new_signal}"
-        # Flag contradictions -- don't silently resolve them
-
-    # Step 7: Cross-reference the graph
-    gbrain add_link <person_slug> <company_slug>       # person -> company
-    gbrain add_link <company_slug> <person_slug>       # company -> person
-    gbrain add_link <person_slug> <deal_slug>          # person -> deal
-    # Every entity page links to every other entity page that references it
-
-# People page sections (not a LinkedIn profile -- a living portrait):
-#   Executive Summary, State, What They Believe, What They're Building,
-#   What Motivates Them, Assessment, Trajectory, Relationship, Contact, Timeline
-# Facts are table stakes. TEXTURE is the value.
-
-# Extract texture, not just facts:
-#   Opinion expressed?        -> What They Believe
-#   Building or shipping?     -> What They're Building
-#   Emotion expressed?        -> What Makes Them Tick
-#   Who did they engage with? -> Network / Relationship
-#   Recurring topic?          -> Hobby Horses
-#   Committed to something?   -> Open Threads
-#   Energy level?             -> Trajectory
-```
-
-## Tricky Spots
-
-1. **Don't overwrite human-written assessments.** If the user wrote an Assessment section with their own read on someone, API enrichment NEVER overwrites it. API data goes into State, Contact, Timeline. The user's assessment is sacrosanct.
-2. **Don't re-enrich the same page more than once per week.** Check `put_raw_data` timestamps before running the pipeline again. Enrichment is expensive and data doesn't change that fast.
-3. **LinkedIn connection count < 20 means wrong person.** Crustdata sometimes returns a different person with the same name. If the LinkedIn profile has fewer than 20 connections, it's almost certainly a false match. Discard it.
-4. **X/Twitter is the most underrated data source.** When you have someone's handle, their tweets reveal beliefs, what they're building, hobby horses, network (reply patterns), and trajectory (posting frequency, tone shifts). This is richer than LinkedIn for "What They Believe" and "What Makes Them Tick."
-5. **Cross-references are not optional.** After enriching a person, update their company page. After enriching a company, update founder pages. An enriched page without cross-links is a dead end in the graph.
-
-## How to Verify
-
-1. Enrich a Tier 1 person. Run `gbrain get <slug>` and confirm the page has Executive Summary, State, What They Believe, Contact, and Timeline sections populated from multiple sources.
-2. Run `gbrain get_raw_data <slug>`. Confirm raw API responses are stored with `sources.{provider}.fetched_at` timestamps.
-3. Run `gbrain get_links <slug>`. Confirm cross-reference links exist to the person's company page, deal pages, and related entities.
-4. Check a page that was enriched AND has a user-written Assessment. Confirm the Assessment section was preserved, not overwritten by API data.
-5. Try to re-enrich the same person. Confirm the system checks the `fetched_at` timestamp and skips if less than a week old.
-
---
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,190 +0,0 @@
-# Entity Detection: Run It on Every Message
-
-## Goal
-
-Every inbound message gets scanned for original thinking AND entity mentions so the brain grows on every conversation, automatically.
-
-## What the User Gets
-
-Without this: the agent answers questions but forgets everything. You mention
-Pedro in a meeting, and next week the agent doesn't know who Pedro is.
-
-With this: every person, company, and idea mentioned in conversation gets a
-brain page. Next time Pedro comes up, the agent already has context. The
-brain compounds.
-
-## Implementation
-
-Spawn a lightweight sub-agent on EVERY inbound message. Do NOT wait for it
-to finish before responding. It runs in parallel.
-
-```
-on_every_message(message_text, source_context):
-
-  // 1. SPAWN ASYNC — don't block the response
-  spawn_subagent({
-    model: "sonnet-class",     // cheap + fast, not opus
-    timeout: 120,              // seconds
-    task: build_detection_prompt(message_text, source_context)
-  })
-
-  // 2. RESPOND TO USER NORMALLY
-  // The sub-agent runs in the background
-```
-
-### The Detection Prompt
-
-```
-build_detection_prompt(text, source):
-  return `
-SIGNAL DETECTION — scan this message for ideas AND entities:
-
-Message: "${text}"
-Source: [Source: User, ${source.topic}, ${source.platform}, ${source.timestamp}]
-
-STEP 1 — IDEAS FIRST (highest priority):
-Is the user expressing an original thought, observation, thesis, or framework?
-
-If yes:
-  - Create or update brain/originals/{slug}.md
-  - Use the user's EXACT phrasing (the language IS the insight)
-  - "The ambition-to-lifespan ratio has never been more broken" is better
-    than "tension between ambition and mortality"
-  - Include [Source: ...] citation with full context
-
-If the idea references a world concept: brain/concepts/{slug}.md
-If it's a product/business idea: brain/ideas/{slug}.md
-
-STEP 2 — ENTITIES:
-Extract all person names, company names, media titles.
-
-For each entity:
-  a. Run: gbrain search "{name}"
-  b. If page exists AND new info: append timeline entry
-     Format: - YYYY-MM-DD | {what happened} [Source: {who}, {context}, {date}]
-  c. If no page AND entity is notable: create page with web enrichment
-  d. If page is thin (< 5 lines compiled truth): spawn background enrichment
-
-STEP 3 — BACK-LINKING (mandatory):
-For every entity mentioned, add a back-link FROM their page TO this source.
-An unlinked mention is a broken brain.
-Format: - **YYYY-MM-DD** | Referenced in [{page title}]({path}) — {context}
-
-STEP 4 — SYNC:
-Run: gbrain sync --no-pull --no-embed
-
-If nothing to capture, reply "No signals detected" and exit.
-`
-```
-
-### Notability Filtering
-
-Before creating a new entity page, check notability:
-
-```
-is_notable(entity):
-  // CREATE a page for:
-  - People the user knows or discusses with specificity
-  - Companies the user is evaluating, working with, or investing in
-  - Media the user mentions with personal reaction
-  - Anyone the user has explicitly engaged with
-
-  // DON'T create a page for:
-  - Generic references or passing examples
-  - Low-engagement accounts who mentioned the user once
-  - Pure metaphors ("like the Roman Empire...")
-  - One-off encounters with no follow-up
-
-  // If notable AND no page: create FULL page (not a stub)
-  // If not notable: skip silently
-```
-
-### What Counts as Original Thinking
-
-| Capture | Don't Capture |
-|---------|---------------|
-| Original observations about how the world works | "ok", "do it", "sure" |
-| Novel connections between disparate things | Pure questions without observations |
-| Frameworks and mental models | Echoing back what the agent said |
-| Pattern recognition ("I keep seeing X in every Y") | Acknowledgments and reactions |
-| Hot takes with reasoning | Routine operational messages |
-| Metaphors that reveal new angles | Requests without embedded insight |
-
-### Filing Rules
-
-| Signal | Destination |
-|--------|-------------|
-| User generated the idea | `brain/originals/{slug}.md` |
-| User's synthesis of others' ideas | `brain/originals/` (the synthesis is original) |
-| World concept someone else coined | `brain/concepts/{slug}.md` |
-| Product or business idea | `brain/ideas/{slug}.md` |
-| Person mentioned | `brain/people/{slug}.md` |
-| Company mentioned | `brain/companies/{slug}.md` |
-| Media referenced | `brain/media/{type}/{slug}.md` |
-
-### The Iron Law of Back-Linking
-
-Every entity mention MUST create a back-link FROM the entity page TO the
-source. This is not optional.
-
-```
-// When message mentions "Pedro" and creates a meeting page:
-
-// 1. Update the meeting page (normal)
-brain/meetings/2026-04-10-board-sync.md:
-  - Pedro presented Q1 numbers
-
-// 2. ALSO update Pedro's page (back-link)
-brain/people/pedro-franceschi.md:
-  ## Timeline
-  - **2026-04-10** | Presented Q1 numbers at board sync
-    [Source: User, board meeting, 2026-04-10]
-```
-
-Without back-links, you can't traverse the graph. "Show me everything related
-to Pedro" only works if Pedro's page links back to every mention.
-
-## Tricky Spots
-
-1. **Don't block the conversation.** Entity detection runs async. The user
-   should see a response immediately, not wait 2 minutes while the sub-agent
-   enriches 5 entity pages.
-
-2. **Sonnet, not Opus.** Entity detection is pattern matching, not deep
-   reasoning. Sonnet is 5-10x cheaper and fast enough. Use Opus for the
-   main conversation.
-
-3. **Exact phrasing matters.** "Markdown is actually code" is an insight.
-   "Markdown can be used as code" is a summary. Capture the first version.
-
-4. **Don't create stubs.** If you create a page, make it good. Run a web
-   search, build out the compiled truth, add context. A stub page with just
-   a name is worse than no page (it gives false confidence).
-
-5. **Dedup before creating.** Always `gbrain search` before creating a page.
-   Variant spellings, nicknames, and company abbreviations cause duplicates.
-   "Pedro Franceschi" and "Pedro" might be the same person.
-
-## How to Verify
-
-1. **Send a message mentioning a person.** Say "I had coffee with Sarah Chen
-   from Acme Corp today." Verify: brain/people/sarah-chen.md was created or
-   updated, brain/companies/acme-corp.md was created or updated, both have
-   timeline entries with today's date.
-
-2. **Send a message with an original idea.** Say "What if we could distribute
-   software as markdown files that agents execute?" Verify:
-   brain/originals/{slug}.md was created with your exact phrasing.
-
-3. **Check back-links.** Open Sarah Chen's page. It should have a timeline
-   entry linking back to today's conversation. Open Acme Corp's page. Same.
-
-4. **Send a boring message.** Say "ok sounds good." Verify: nothing was
-   created. The detector should report "No signals detected."
-
-5. **Check for duplicates.** Mention "Pedro" then later "Pedro Franceschi."
-   Verify: one page, not two.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,109 +0,0 @@
-# Executive Assistant Pattern
-
-## Goal
-Email triage, meeting prep, and scheduling powered by brain context -- so every interaction is informed by the full history of the relationship.
-
-## What the User Gets
-Without this: the agent triages email mechanically ("you have 12 unread"), preps for meetings with generic LinkedIn bios, and schedules without relationship context. With this: the agent knows who every sender is before reading their email, surfaces shared history before every meeting, and nudges scheduling based on relationship temperature and open threads.
-
-## Implementation
-
-```
-# WORKFLOW 1: Email Triage
-on email_batch(emails):
-    for email in emails:
-        # Step 1: Search sender BEFORE reading the email body
-        #   Brain context makes triage 10x better
-        sender_page = gbrain search "{email.sender_name}"
-        if sender_page:
-            context = gbrain get <sender_slug>
-            #   Now you know: who they are, relationship history,
-            #   what they care about, open threads
-
-        # Step 2: Read the email WITH brain context loaded
-        #   Classification is now informed, not mechanical
-
-        # Step 3: Classify with context
-        if context.relationship == "inner_circle" or context.has_open_threads:
-            priority = "urgent"
-        elif context.is_known_entity:
-            priority = "normal"
-        else:
-            priority = "noise"  # unknown sender, no brain page
-
-        # Step 4: Draft reply with relationship context
-        if needs_reply(email):
-            draft = compose_reply(
-                email,
-                context=context,           # their brain page
-                open_threads=context.open_threads,  # what you're working on together
-                relationship=context.relationship   # tone calibration
-            )
-
-# WORKFLOW 2: Meeting Prep
-on upcoming_meeting(meeting):
-    briefing = {}
-    for attendee in meeting.attendees:
-        # Search brain for each attendee
-        results = gbrain search "{attendee.name}"
-        if results:
-            page = gbrain get <attendee_slug>
-            briefing[attendee] = {
-                "compiled_truth": page.compiled_truth,
-                "last_interaction": page.timeline[0],     # most recent
-                "open_threads": page.open_threads,
-                "relationship_temperature": page.relationship,
-                "relevant_deals": gbrain get_links <attendee_slug>,
-            }
-        else:
-            briefing[attendee] = "No brain page -- consider enriching"
-
-    # Surface: shared history, what to follow up on, what to watch for
-    # "Last time you discussed the Series B timeline. Pedro was concerned
-    #  about burn rate. Here's the latest from his company page."
-
-# WORKFLOW 3: Post-Inbox Brain Updates
-on inbox_cleared():
-    for email in processed_emails:
-        if email.contained_new_information:
-            # Update the sender's brain page with new signal
-            gbrain add_timeline_entry <sender_slug> \
-                --entry "Email re: {subject}. Key info: {extracted_signal}" \
-                --source "email from {sender} re {subject}, {date}"
-
-            # Update any mentioned entity pages too
-            for entity in email.mentioned_entities:
-                gbrain add_timeline_entry <entity_slug> \
-                    --entry "{what_was_said_about_them}" \
-                    --source "email from {sender}, {date}"
-
-# WORKFLOW 4: Scheduling Nudges
-on schedule_request(meeting):
-    for attendee in meeting.attendees:
-        page = gbrain get <attendee_slug>
-        if page.last_interaction > 6_weeks_ago:
-            nudge("You haven't met with {attendee} in {weeks} weeks")
-        if page.has_open_threads:
-            nudge("{attendee} has an open thread about {topic}")
-        if page.relationship_temperature == "cooling":
-            nudge("Relationship with {attendee} may need attention")
-```
-
-## Tricky Spots
-
-1. **Search sender BEFORE reading the email.** This is counterintuitive but critical. Loading brain context first means you know who they are, what you're working on together, and what they care about -- before you even see the subject line. The triage is informed, not mechanical.
-2. **Unknown senders with no brain page are almost always noise.** If `gbrain search` returns nothing for a sender, they're probably not important. Classify as low priority unless the email content signals otherwise.
-3. **Meeting prep is the highest-leverage EA workflow.** The user walks into every meeting already briefed on each attendee: last interaction, open threads, relationship history. This is the difference between "you have a meeting at 3" and "you have a meeting at 3 with Pedro -- last time you discussed the Series B, he was concerned about burn rate."
-4. **Post-inbox brain updates are where the brain compounds.** Every email is signal. If you clear the inbox without updating brain pages, the information is lost. This is the step most agents skip.
-5. **Scheduling nudges require timeline data.** "You haven't met with Diana in 6 weeks" only works if meeting pages have been ingested with proper entity propagation (see meeting-ingestion guide).
-
-## How to Verify
-
-1. Run meeting prep for tomorrow's calendar. For each attendee, confirm the agent ran `gbrain search` and loaded their brain page before generating the briefing.
-2. Triage 5 emails. Confirm the agent searched for each sender in the brain before classifying the email.
-3. After clearing an inbox, check 2 sender brain pages with `gbrain get <slug>`. Confirm new timeline entries were added with information from the emails.
-4. Check a scheduling suggestion. Confirm the agent referenced the attendee's brain page (last interaction date, open threads) in the nudge.
-5. Send a test email from someone with a brain page. Confirm the triage response references their relationship context, not just the email content.
-
---
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,190 +0,0 @@
-# Idea Capture: Originals, Depth, and Distribution
-
-## Goal
-
-Capture the user's original thinking with exact phrasing, deep context, and cross-links so the originals folder becomes the highest-value content in the brain.
-
-## What the User Gets
-
-Without this: brilliant ideas said in conversation disappear. The agent heard
-"the ambition-to-lifespan ratio has never been more broken" and forgot it.
-
-With this: every original observation is captured verbatim, cross-linked to
-the people and ideas that shaped it, and rated for publishing potential. Your
-intellectual archive grows with every conversation.
-
-## Implementation
-
-```
-capture_idea(message_text, source_context):
-
-  // 1. AUTHORSHIP TEST — where does this idea belong?
-  if user_generated_the_idea(message_text):
-    destination = "brain/originals/{slug}.md"
-  elif user_synthesis_of_others(message_text):
-    destination = "brain/originals/{slug}.md"  // synthesis IS original
-  elif world_concept(message_text):
-    destination = "brain/concepts/{slug}.md"
-  elif product_or_business_idea(message_text):
-    destination = "brain/ideas/{slug}.md"
-  elif ghostwritten_by_user(message_text):
-    destination = "brain/originals/{slug}.md"  // note ghostwriter in metadata
-  elif article_about_user(message_text):
-    destination = "brain/media/writings/{slug}.md"
-
-  // 2. CAPTURE WITH EXACT PHRASING — never paraphrase
-  page = create_or_update(destination, {
-    content: message_text,          // verbatim, not summarized
-    source: source_context,         // conversation, meeting, moment
-    reasoning_path: influences,     // what led to the insight
-    depth_context: emotional_nuance // the WHY behind the WHAT
-  })
-
-  // 3. ORIGINALITY RATING (for notable ideas)
-  if is_notable(message_text):
-    rate_originality(page, populations=[
-      "general_population", "tech_industry",
-      "intellectual_media", "political_establishment"
-    ])
-
-  // 4. CROSS-LINK (mandatory — an original without links is dead)
-  link_to_people(page, mentioned_people)
-  link_to_companies(page, mentioned_companies)
-  link_to_meetings(page, source_meeting)
-  link_to_media(page, influences)
-  link_to_other_originals(page, related_ideas)
-  link_to_concepts(page, referenced_concepts)
-
-  // 5. SYNC
-  gbrain sync --no-pull --no-embed
-```
-
-### The Authorship Test
-
-| Signal | Destination |
-|--------|-------------|
-| User generated the idea | `brain/originals/{slug}.md` |
-| User's unique synthesis of others' ideas | `brain/originals/` (the synthesis is original) |
-| World concept someone else coined | `brain/concepts/{slug}.md` |
-| Product or business idea | `brain/ideas/{slug}.md` |
-| User's ghostwritten book/essay | `brain/originals/` (note ghostwriter in metadata) |
-| Article ABOUT user | `brain/media/writings/` |
-
-### Capture Standards
-
-**Use the user's EXACT phrasing.** The language IS the insight.
-
-"The ambition-to-lifespan ratio has never been more broken" captures something that
-"tension between ambition and mortality" doesn't. Don't clean it up. Don't paraphrase.
-The vivid version is the real version.
-
-**What counts as worth capturing:**
- Original observations about how the world works
- Novel connections between disparate things
- Frameworks and mental models
- Pattern recognition moments ("I keep seeing X in every Y")
- Hot takes with reasoning behind them
- Metaphors that reveal new angles
- Emotional/psychological insights about self or others
-
-**What does NOT count:**
- Routine operational messages ("ok", "do it")
- Pure questions without embedded observations
- Echoing back something the agent said
- Acknowledgments and reactions
-
-### The Depth Test
-
-**Could someone unfamiliar with the user read this page and understand not
-just WHAT they think but WHY and HOW they got there?**
-
-If the answer is no, it needs more depth. Include:
- The reasoning path (what led to the insight)
- The influences (what they were reading/watching/experiencing)
- The context (conversation, meeting, moment)
- The emotional or psychological nuance
-
-### Originality Distribution Rating
-
-For notable ideas, rate originality 0-100 across different populations:
-
-```markdown
-## Originality Distribution
-
- **General population:** 72/100 — most people haven't encountered this framework
- **Tech industry:** 45/100 — common in startup circles but novel to most
- **Intellectual/media class:** 68/100 — would resonate, not yet articulated
- **Political establishment:** 82/100 — completely foreign to policy thinking
-
-**Publish signal:** Strong essay candidate. Best audience: founders, builders.
-```
-
-This tells the user which ideas are worth turning into essays, talks, or videos,
-and which audience would find them most novel.
-
-### Deep Cross-Linking Mandate
-
-**An original without cross-links is a dead original.** The connections ARE
-the intelligence.
-
-Every original MUST link to:
- **People** who shaped the thinking
- **Companies** where the idea played out
- **Meetings** where it was discussed
- **Books and media** that influenced it
- **Other originals** it connects to (ideas form clusters)
- **Concepts** it builds on or challenges
-
-### Notability Filtering
-
-Before creating any entity page, check notability:
-
-**Create a page for:**
- People you know or discuss with specificity
- Companies you're evaluating, working with, or investing in
- Media you mention with personal reaction
- Anyone you've explicitly engaged with
-
-**Don't create pages for:**
- Generic references or passing examples
- Low-engagement accounts who mentioned you once
- Pure metaphors ("like the Roman Empire...")
- One-off encounters with no follow-up
-
-**Decision:** If notable AND no page exists, create a full page with web
-search enrichment. No stubs. If you make a page, make it good.
-
-## Tricky Spots
-
-1. **Synthesis IS original.** When the user connects two existing ideas in a
-   new way, that synthesis belongs in `brain/originals/`, not `brain/concepts/`.
-   The novel combination is the insight, even if the component ideas aren't new.
-
-2. **Exact phrasing is non-negotiable.** Never paraphrase, summarize, or
-   "clean up" the user's language. "The ambition-to-lifespan ratio has never
-   been more broken" is the insight. "Tension between ambition and mortality"
-   is a corpse. Capture the first version.
-
-3. **Cross-links are mandatory, not optional.** An original without links to
-   the people, companies, meetings, and concepts that shaped it is a dead
-   original. The connections ARE the intelligence. Check every original for
-   at least 2 cross-links before considering it captured.
-
-## How to Verify
-
-1. **Generate an idea and check the page.** Say something original in
-   conversation (e.g., "What if markdown files are actually distributed
-   software?"). Verify that `brain/originals/{slug}.md` was created with
-   your exact phrasing, not a paraphrase.
-
-2. **Check cross-links exist.** Open the newly created original page. It
-   should link to at least the people or concepts mentioned. Open those
-   linked pages and verify they back-link to the original.
-
-3. **Verify the depth test passes.** Read the captured page as if you were
-   a stranger. Can you understand not just WHAT the user thinks but WHY?
-   If the reasoning path and context are missing, the capture is incomplete.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,138 +0,0 @@
-# Live Sync: Keep the Index Current
-
-## Goal
-
-Every markdown change in the brain repo is searchable within minutes, automatically, with no manual intervention.
-
-## What the User Gets
-
-Without this: you correct a hallucination in a brain page, but the vector DB
-keeps serving the old text because nobody ran `gbrain sync`. Stale search
-results erode trust. The brain becomes unreliable.
-
-With this: edits show up in search within minutes. The vector DB stays current
-with the brain repo automatically. You never have to remember to run sync.
-
-## Implementation
-
-### Prerequisite: Session Mode Pooler
-
-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: 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
-
-Always chain sync + embed:
-
-```bash
-gbrain sync --repo /path/to/brain && gbrain embed --stale
-```
-
- `gbrain sync --repo <path>` -- one-shot incremental sync. Detects changes via
-  `git diff`, imports only what changed. For small changesets (<= 100 files),
-  embeddings are generated inline during import.
- `gbrain embed --stale` -- backfill embeddings for any chunks that don't have
-  them. Safety net for large syncs (>100 files) or prior `--no-embed` runs.
- `gbrain sync --watch --repo <path>` -- foreground polling loop, every 60s
-  (configurable with `--interval N`). Embeds inline for small changesets. Exits
-  after 5 consecutive failures, so run under a process manager or pair with a
-  cron fallback.
-
-### Approach 1: Cron Job (recommended)
-
-Run every 5-30 minutes. Works with any cron scheduler.
-
-```bash
-gbrain sync --repo /data/brain && gbrain embed --stale
-```
-
-**OpenClaw:**
-```
-Name: gbrain-auto-sync
-Schedule: */15 * * * *
-Prompt: "Run: gbrain sync --repo /data/brain && gbrain embed --stale
-  Log the result. If sync fails with .begin() is not a function,
-  the DATABASE_URL is using Transaction mode pooler."
-```
-
-**Hermes:**
-```
-/cron add "*/15 * * * *" "Run gbrain sync --repo /data/brain &&
-  gbrain embed --stale. Log the result." --name "gbrain-auto-sync"
-```
-
-### Approach 2: Long-Lived Watcher
-
-For near-instant sync (60s polling). Run under a process manager that
-auto-restarts on exit. Pair with a cron fallback since `--watch` exits
-on repeated failures.
-
-```bash
-gbrain sync --watch --repo /data/brain
-```
-
-### Approach 3: Git Hook / Webhook
-
-Triggers sync on push events for instant sync (<5s).
-
- **GitHub webhook:** Set up the webhook to call
-  `gbrain sync --repo /data/brain && gbrain embed --stale`.
-  Verify `X-Hub-Signature-256` against a shared secret.
- **Git post-receive hook:** If the brain repo is on the same machine.
-
-### What Gets Synced
-
-Sync only indexes "syncable" markdown files. These are excluded by design:
- Hidden paths (`.git/`, `.raw/`, etc.)
- The `ops/` directory
- Meta files: `README.md`, `index.md`, `schema.md`, `log.md`
-
-### Sync is Idempotent
-
-Concurrent runs are safe. Two syncs on the same commit no-op because content
-hashes match. If both a cron and `--watch` fire simultaneously, no conflict.
-
-## Tricky Spots
-
-1. **Always chain sync + embed.** Running `gbrain sync` without
-   `gbrain embed --stale` leaves new chunks without embeddings. They exist
-   in the database but are invisible to vector search. Always run both
-   commands together. The `&&` ensures embed only runs if sync succeeds.
-
-2. **--watch polls, it doesn't stream.** The `--watch` flag polls every 60s
-   (configurable). It is not a filesystem watcher or git hook. It exits after
-   5 consecutive failures, so it needs a process manager (systemd, pm2) or a
-   cron fallback to stay alive. Don't assume it runs forever.
-
-3. **Webhook needs the server running.** If you use a GitHub webhook for
-   instant sync, the receiving server must be running and reachable. If the
-   server is down when a push happens, that sync is missed. Pair webhooks
-   with a cron fallback that catches anything the webhook missed.
-
-## How to Verify
-
-1. **Edit a file and search for the change.** Edit a brain markdown file,
-   commit, and push. Wait for the next sync cycle (cron interval or `--watch`
-   poll). Run `gbrain search "<text from the edit>"`. The updated content
-   should appear in results. If it returns old content, sync failed.
-
-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
-   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
-   `gbrain embed --stale` isn't running after sync, leaving chunks invisible
-   to vector search.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,80 +0,0 @@
-# Meeting Ingestion
-
-## Goal
-Meeting transcripts become brain pages that update every mentioned entity -- attendees, companies, deals, and action items all propagated in one pass.
-
-## What the User Gets
-Without this: meetings vanish into memory, action items are forgotten, and the agent has no idea what was discussed last time you met someone. With this: every meeting is a permanent record that enriches every person and company page it touches, and the user walks into every follow-up already briefed.
-
-## Implementation
-
-```
-on new_meeting_transcript(meeting):
-    # Step 1: Pull the COMPLETE transcript -- NOT the AI summary
-    #   AI summaries hallucinate framing ("it was agreed that...")
-    #   The transcript is ground truth
-    transcript = fetch_full_transcript(meeting.id)  # e.g., Circleback API
-    # Must have speaker diarization: WHO said WHAT
-
-    # Step 2: Create the meeting page
-    slug = f"meetings/{meeting.date}-{short_description}"
-    compiled_truth = agent_analysis(transcript):
-        # Above the bar: agent's OWN analysis, not a generic recap
-        #   - Reframe through the user's priorities
-        #   - Flag surprises, contradictions, implications
-        #   - Name real decisions (not performative ones)
-        #   - Call out what was left unsaid or unresolved
-    timeline = format_diarized_transcript(transcript)
-        # Below the bar: full transcript, append-only
-        #   Format: **Speaker** (HH:MM:SS): Words.
-
-    gbrain put <slug> --content "<compiled_truth>\n---\n<timeline>"
-
-    # Step 3: Propagate to ALL entity pages (MANDATORY -- most agents skip this)
-    for person in meeting.attendees + meeting.mentioned_people:
-        gbrain add_timeline_entry <person_slug> \
-            --entry "Met in '{meeting.title}' on {date}. Key points: ..." \
-            --source "Meeting notes '{meeting.title}', {date}"
-        # Update their State section if new information surfaced
-        # Update company pages for each person's company if relevant
-
-    for company in meeting.mentioned_companies:
-        gbrain add_timeline_entry <company_slug> \
-            --entry "Discussed in '{meeting.title}': {what_was_said}" \
-            --source "Meeting notes '{meeting.title}', {date}"
-
-    # Step 4: Extract action items
-    action_items = extract_action_items(transcript)
-    # Add to task list with owner attribution
-
-    # Step 5: Back-link everything (bidirectional graph)
-    for entity in all_entities_mentioned:
-        gbrain add_link <slug> <entity_slug>   # meeting -> entity
-        gbrain add_link <entity_slug> <slug>    # entity -> meeting
-
-    # Step 6: Sync so new pages are immediately searchable
-    gbrain sync
-
-# Schedule: cron 3x/day (10 AM, 4 PM, 9 PM) to catch new meetings
-# Source: Circleback (https://circleback.ai) or any service with
-#         speaker diarization + API/webhook access
-```
-
-## Tricky Spots
-
-1. **Always pull the COMPLETE transcript, never the AI summary.** AI summaries hallucinate framing -- they editorialize what was "agreed" or "decided" when no such agreement happened. The diarized transcript is ground truth.
-2. **Entity propagation is the step most agents skip.** A meeting is NOT fully ingested until every attendee's page, every mentioned person's page, and every company's page has a new timeline entry. The meeting page alone is useless without propagation.
-3. **Mentioned people are not just attendees.** If the meeting discussed "Sarah's team at Brex," then Sarah's page AND Brex's page need updates -- even though Sarah wasn't in the room.
-4. **The agent's analysis is the value, not a summary.** "They discussed Q2 targets" is worthless. "Pedro pushed back on the burn rate, Diana didn't commit to the timeline, and nobody addressed the pricing gap" is useful.
-5. **Back-links must be bidirectional.** The meeting page links to attendee pages AND attendee pages link back to the meeting. The graph is bidirectional. Always.
-
-## How to Verify
-
-1. After ingesting a meeting, run `gbrain get meetings/{date}-{slug}`. Confirm the page has the agent's analysis above the bar and the full diarized transcript below it.
-2. For each attendee, run `gbrain get <attendee_slug>`. Check that their timeline has a new entry referencing the meeting with specific insights (not just "attended meeting").
-3. Pick a company mentioned in the meeting. Run `gbrain get <company_slug>`. Confirm a timeline entry exists referencing what was discussed about the company.
-4. Run `gbrain get_links meetings/{date}-{slug}`. Verify back-links exist to all attendee and entity pages.
-5. Run `gbrain search "{meeting_topic}"`. Confirm the meeting page appears in search results (verifies sync ran).
-
---
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,13 +0,0 @@
-# Procfile — Render / Railway / Heroku.
-#
-# Fly.io users: see fly.toml.partial instead.
-#
-# Set secrets via the platform's env UI or CLI (e.g. `heroku config:set`,
-# `render env:set`, `railway variables set`). At minimum:
-#   DATABASE_URL=postgresql://...
-#   GBRAIN_ALLOW_SHELL_JOBS=1   # only if submitting shell jobs
-
-# Two-layer supervision: the platform restarts the container on host
-# events (OOM, deploy); `gbrain jobs supervisor` restarts the worker
-# on in-process crashes with exponential backoff.
-worker: gbrain jobs supervisor --concurrency 2
@@ -1,24 +0,0 @@
-# fly.toml — partial. Merge into your existing fly.toml.
-#
-# Set secrets once (never commit them):
-#   fly secrets set DATABASE_URL='postgresql://user:pass@host:6543/db?prepare=false'
-#   fly secrets set GBRAIN_ALLOW_SHELL_JOBS=1   # only if submitting shell jobs
-#   fly secrets set ANTHROPIC_API_KEY=...       # optional
-#
-# Two-layer supervision: Fly restarts the VM on host events; the
-# `gbrain jobs supervisor` process restarts the worker on in-process
-# crashes with exponential backoff and a structured audit trail.
-
-[processes]
-  worker = "gbrain jobs supervisor --concurrency 2"
-
-# Scale the worker process to 1 machine (job queue serializes work; more
-# machines means higher concurrency but also more Postgres connections).
-#   fly scale count worker=1
-
-# If you want the worker in its own VM size:
-# [[vm]]
-#   processes = ["worker"]
-#   memory = "512mb"
-#   cpu_kind = "shared"
-#   cpus = 1
@@ -1,35 +0,0 @@
-# /etc/gbrain.env — secrets + env for the gbrain worker.
-#
-# Install:
-#   sudo install -m 600 -o $GBRAIN_WORKER_USER -g $GBRAIN_WORKER_USER \
-#     gbrain.env.example /etc/gbrain.env
-#   sudoedit /etc/gbrain.env   # fill in real values
-#
-# Referenced from crontab via BASH_ENV=/etc/gbrain.env, or from systemd
-# via EnvironmentFile=/etc/gbrain.env. Never commit real secrets.
-
-# --- Required ---------------------------------------------------------------
-
-# Postgres connection string. For Supabase transaction pooler, include
-# prepare=false (see CLAUDE.md #284/#286).
-DATABASE_URL=postgresql://user:pass@host:6543/db?prepare=false
-
-# --- Required if you submit `shell` jobs ------------------------------------
-# Only the worker process needs this. Submitters do not.
-GBRAIN_ALLOW_SHELL_JOBS=1
-
-# --- Optional ---------------------------------------------------------------
-
-# LLM provider keys (needed for `subagent` handler, transcription, enrichment).
-# ANTHROPIC_API_KEY=
-# OPENAI_API_KEY=
-
-# Custom handler plugins (see docs/guides/plugin-handlers.md).
-# GBRAIN_PLUGIN_PATH=/etc/gbrain/plugins
-
-# Pool size tuning for Supabase transaction pooler (default 10; drop to 2
-# if you hit MaxClients during upgrade subprocess spawns).
-# GBRAIN_POOL_SIZE=2
-
-# Connection-level concurrency cap for Anthropic Messages API.
-# GBRAIN_ANTHROPIC_MAX_INFLIGHT=4
@@ -1,50 +0,0 @@
-[Unit]
-Description=gbrain minion worker
-Documentation=https://github.com/garrytan/gbrain/blob/master/docs/guides/minions-deployment.md
-After=network-online.target
-Wants=network-online.target
-
-[Service]
-Type=simple
-# Runs as an unprivileged user that owns the brain repo and any shell-job cwds.
-# Create with: sudo useradd --system --home /srv/gbrain --shell /usr/sbin/nologin gbrain
-User=gbrain
-Group=gbrain
-WorkingDirectory=/srv/gbrain
-
-# Env file is mode 600, owned by User=. Do not put secrets in this unit.
-EnvironmentFile=/etc/gbrain.env
-
-# Two-layer supervision: systemd restarts `gbrain jobs supervisor` on host
-# events (reboot, unit crash); the supervisor restarts `gbrain jobs work`
-# on in-process crashes with exponential backoff + structured audit.
-ExecStart=/usr/local/bin/gbrain jobs supervisor --concurrency 2
-
-# systemd restarts the supervisor on any non-zero exit. The supervisor
-# itself handles worker-level crash recovery.
-Restart=always
-RestartSec=10s
-
-# Graceful shutdown: SIGTERM → wait → SIGKILL. 30s matches worker grace
-# for in-flight jobs and the shell handler's 5s child SIGTERM window.
-KillSignal=SIGTERM
-TimeoutStopSec=30s
-
-StandardOutput=journal
-StandardError=journal
-SyslogIdentifier=gbrain-worker
-
-# Default 1024 is tight for Bun + Postgres pool + concurrent subagent LLM calls.
-LimitNOFILE=65535
-
-# Hardening (optional — remove if they break your deployment).
-NoNewPrivileges=true
-PrivateTmp=true
-ProtectSystem=strict
-ProtectHome=read-only
-# ReadWritePaths must include the brain workspace AND ~/.gbrain (PID file +
-# audit log written by the supervisor).
-ReadWritePaths=/srv/gbrain /home/gbrain/.gbrain
-
-[Install]
-WantedBy=multi-user.target
@@ -1,332 +0,0 @@
-# Minions Worker Deployment Guide
-
-Keep `gbrain jobs work` running across crashes, reboots, and Postgres
-connection blips. Written for agents to execute line-by-line.
-
-## The problem
-
-The persistent worker can die silently from:
-
- Database connection drops (Supabase/Postgres maintenance or network blips).
- Lock-renewal failures → the stall detector eventually dead-letters jobs.
- Bun process crashes with no automatic restart.
- Internal event-loop death (PID alive, worker loop stopped).
-
-When the worker dies, submitted jobs sit in `waiting` forever. The
-canonical answer is `gbrain jobs supervisor` — a first-class CLI that
-spawns `gbrain jobs work` as a child and auto-restarts it on crash.
-
-## Worker supervision
-
-### The canonical pattern
-
-`gbrain jobs supervisor` is an auto-restarting wrapper around
-`gbrain jobs work`. It writes a PID file, restarts the worker on crash
-with exponential backoff (1s → 60s cap), emits lifecycle events to an
-audit file, and drains gracefully on SIGTERM (35s worker-drain window
-before SIGKILL). Exit codes are documented so agents can branch on them.
-
-**Typical commands:**
-
-```bash
-# Start in the foreground (blocks; Ctrl-C to stop).
-gbrain jobs supervisor --concurrency 4
-
-# Start detached — returns {"event":"started","supervisor_pid":…} on stdout.
-gbrain jobs supervisor start --detach --json
-
-# Check liveness without reading log files.
-gbrain jobs supervisor status --json
-
-# Graceful stop (SIGTERM + drain wait + SIGKILL fallback).
-gbrain jobs supervisor stop
-```
-
-**Exit codes:**
-
-| Code | Meaning |
-|---|---|
-| 0 | Clean shutdown (SIGTERM/SIGINT received, worker drained) |
-| 1 | Max crashes exceeded (worker kept dying) |
-| 2 | Another supervisor holds the PID lock |
-| 3 | PID file unwritable (permission / path error) |
-
-An agent seeing exit=2 can safely treat it as "one is already running";
-exit=1 should page a human.
-
-### Which supervisor when?
-
-The supervisor solves in-process crash recovery. Platform-level
-supervision (systemd, Fly, Render) handles host-level failures. You
-usually want both.
-
-| Environment | Recommendation |
-|---|---|
-| **Container (Fly / Railway / Render / Heroku)** | `gbrain jobs supervisor` runs as PID 1. The platform restarts the container on OOM / host loss; supervisor restarts the worker on crash. See [Fly.io](#flyio) / [Render / Railway / Heroku](#render--railway--heroku). |
-| **Linux VM with systemd** | Two-layer recommended: systemd supervises `gbrain jobs supervisor`, which in turn supervises `gbrain jobs work`. Buys you automatic restart on reboot (systemd) plus fast crash recovery (supervisor). See [systemd](#systemd). |
-| **Dev laptop / macOS** | `gbrain jobs supervisor` in a terminal. Ctrl-C stops it. No system-level setup needed. |
-
-### Variables used in this guide
-
-Substitute these once before copy-pasting any snippet.
-
-| Variable | Meaning | Typical value |
-|---|---|---|
-| `$GBRAIN_BIN` | Absolute path to the `gbrain` binary | `$(command -v gbrain)` — often `/usr/local/bin/gbrain` or `~/.bun/bin/gbrain` |
-| `$GBRAIN_WORKER_USER` | OS user that owns the worker process | the same user that ran `gbrain init`; never `root` |
-| `$GBRAIN_WORKSPACE` | `cwd` for shell jobs submitted by this deployment | absolute path, e.g. `/srv/my-brain` |
-| `$GBRAIN_ENV_FILE` | Secrets file sourced by systemd / shell | `/etc/gbrain.env` (mode 600) |
-
-### Preconditions
-
-Run these before any deployment step.
-
-```bash
-# 1. gbrain is on PATH and resolves to an absolute location.
-command -v gbrain || { echo "gbrain not on PATH. Install, then retry."; exit 1; }
-
-# 2. DATABASE_URL points at reachable Postgres.
-#    (Supervisor is Postgres-only. PGLite's exclusive file lock blocks the
-#    separate worker process. If `config.engine === 'pglite'` the CLI rejects
-#    with a clear error.)
-gbrain doctor --fast --json | jq '.checks[] | select(.name=="db_connectivity")'
-
-# 3. Schema is up to date. If version=0 or status=="fail":
-#    gbrain apply-migrations --yes
-gbrain doctor --fast --json | jq '.checks[] | select(.name=="schema_version")'
-
-# 4. If you plan to submit `shell` jobs, pass --allow-shell-jobs to the
-#    supervisor (or export GBRAIN_ALLOW_SHELL_JOBS=1 before starting).
-#    Without the flag, the shell handler is disabled at worker startup.
-```
-
-## Agent usage (OpenClaw / Hermes / Cursor / Codex)
-
-Three-command pattern an agent can drive without shell archaeology:
-
-```bash
-# Start (returns PIDs + pid_file on stdout as JSON, then detaches)
-gbrain jobs supervisor start --detach --json
-# → {"event":"started","supervisor_pid":1234,"worker_pid":1235,"pid_file":"/Users/you/.gbrain/supervisor.pid"}
-
-# Check health (machine-parseable JSON, no log scraping)
-gbrain jobs supervisor status --json
-# → {"running":true,"supervisor_pid":1234,"last_start":"2026-04-23T15:30:22Z","crashes_24h":0, ...}
-
-# Stop cleanly (SIGTERM + 35s drain + SIGKILL fallback)
-gbrain jobs supervisor stop
-```
-
-Every lifecycle event (spawn, crash, backoff, health warning, max-crashes,
-shutdown) is also written to `${GBRAIN_AUDIT_DIR:-~/.gbrain/audit}/supervisor-YYYY-Www.jsonl`
-for historical inspection. `gbrain doctor` reads that file and surfaces
-a `supervisor` check in its health report.
-
-## Deployment: systemd
-
-For long-running Linux VMs with shell access.
-
-```bash
-# Create the worker user if it doesn't exist.
-sudo useradd --system --home "$GBRAIN_WORKSPACE" --shell /usr/sbin/nologin gbrain \
-  2>/dev/null || true
-sudo mkdir -p "$GBRAIN_WORKSPACE" && sudo chown gbrain:gbrain "$GBRAIN_WORKSPACE"
-
-# Install the env file (secrets stay out of the unit file).
-sudo install -m 600 -o gbrain -g gbrain \
-  docs/guides/minions-deployment-snippets/gbrain.env.example /etc/gbrain.env
-sudoedit /etc/gbrain.env
-# Fill in DATABASE_URL, optional GBRAIN_ALLOW_SHELL_JOBS=1.
-
-# Install the unit file, substituting /srv/gbrain → your workspace path.
-sudo install -m 644 docs/guides/minions-deployment-snippets/systemd.service \
-  /etc/systemd/system/gbrain-worker.service
-sudo sed -i "s|/srv/gbrain|$GBRAIN_WORKSPACE|g" \
-  /etc/systemd/system/gbrain-worker.service
-
-sudo systemctl daemon-reload
-sudo systemctl enable --now gbrain-worker
-sudo systemctl status gbrain-worker
-journalctl -u gbrain-worker -n 50
-```
-
-The shipped unit file invokes `gbrain jobs supervisor` (not `gbrain jobs work`
-directly) so you get two-layer supervision: systemd restarts the supervisor
-on host reboot, supervisor restarts the worker on in-process crash.
-
-`Restart=always` + `RestartSec=10s` handle the supervisor-level recovery.
-The unit runs as unprivileged `gbrain` with `PrivateTmp`, `ProtectSystem=strict`,
-and `ReadWritePaths=$GBRAIN_WORKSPACE,$HOME/.gbrain` (for the PID file and
-audit log). `LimitNOFILE=65535` covers Bun + Postgres pool + concurrent
-LLM subagent calls without hitting the default 1024 cap.
-
-## Deployment: Fly.io
-
-```bash
-# Merge the [processes] block from fly.toml.partial into your fly.toml.
-cat docs/guides/minions-deployment-snippets/fly.toml.partial >> fly.toml
-# Review + edit as needed.
-
-# Set secrets (Fly handles restart on crash).
-fly secrets set DATABASE_URL='postgres://…' GBRAIN_ALLOW_SHELL_JOBS=1
-```
-
-The `[processes]` block runs `gbrain jobs supervisor` as PID 1. Fly
-restarts the container on host failure; the supervisor restarts the
-worker on in-process crash.
-
-## Deployment: Render / Railway / Heroku
-
-Drop [`Procfile`](./minions-deployment-snippets/Procfile) at the repo
-root. The shipped Procfile calls `gbrain jobs supervisor`. Set
-`DATABASE_URL` + optional `GBRAIN_ALLOW_SHELL_JOBS=1` via the platform's
-env UI or CLI.
-
-## Deployment: inline `--follow` (no persistent worker)
-
-For short deterministic scripts on a fixed schedule where you don't need
-a persistent worker between runs. Each cron run brings its own temporary
-worker. `--follow` starts one on the queue and blocks until the
-just-submitted job reaches a terminal state (`completed` / `failed` /
-`dead` / `cancelled`). 2-3 s startup overhead per job; negligible vs job
-duration for scheduled work.
-
-```bash
-GBRAIN_ALLOW_SHELL_JOBS=1 gbrain jobs submit shell \
-  --queue nightly-enrich \
-  --params "{\"cmd\":\"$GBRAIN_BIN embed --stale\",\"cwd\":\"$GBRAIN_WORKSPACE\"}" \
-  --follow \
-  --timeout-ms 600000
-```
-
-Replace `gbrain embed --stale` with whichever gbrain subcommand you're
-scheduling (`sync`, `extract`, `orphans`, `doctor`, `check-backlinks`,
-`lint`, `autopilot`). For strict single-job semantics on shared queues,
-use a dedicated queue name like `nightly-enrich` above.
-
-## Upgrading from an older deployment
-
-### From `minion-watchdog.sh` (pre-v0.20)
-
-Earlier versions of this guide shipped a 68-line bash watchdog
-(`minion-watchdog.sh`). It's been replaced by `gbrain jobs supervisor`
-which handles everything the script did, plus atomic PID locking,
-structured audit events, queue-scoped health checks, and graceful
-drain on SIGTERM.
-
-**Migration:**
-
-```bash
-# 1. Stop and remove the old watchdog.
-sudo kill $(head -n1 /tmp/gbrain-worker.pid) 2>/dev/null
-sudo rm -f /usr/local/bin/minion-watchdog.sh /tmp/gbrain-worker.pid \
-           /tmp/gbrain-worker.log
-crontab -e   # delete the "*/5 * * * * /usr/local/bin/minion-watchdog.sh" line
-
-# 2. Start the supervisor (systemd users: reinstall the unit from
-#    docs/guides/minions-deployment-snippets/systemd.service, which
-#    now calls `gbrain jobs supervisor`).
-gbrain jobs supervisor start --detach --json
-# Or: sudo systemctl restart gbrain-worker
-
-# 3. Verify.
-gbrain jobs supervisor status --json
-gbrain doctor   # 'supervisor' check should report running=true
-```
-
-### Schema / migration hygiene
-
-Regardless of which deployment path you're upgrading from:
-
-1. **Stop the worker before upgrading.** `gbrain jobs supervisor stop`
-   (or `sudo systemctl stop gbrain-worker`). Skipping this risks an
-   in-flight job landing partial schema.
-2. **Run `gbrain upgrade`**. Then `gbrain apply-migrations --yes` if
-   `gbrain doctor` reports any migration as `partial` or `pending`.
-3. **If you run shell jobs:** from v0.14 onward, pass
-   `--allow-shell-jobs` to the supervisor (or keep
-   `GBRAIN_ALLOW_SHELL_JOBS=1` in `/etc/gbrain.env`). Submitters don't
-   need the flag; only the worker does.
-4. **Verify.** `gbrain doctor` should report zero `pending` or `partial`
-   migrations plus a healthy `supervisor` check. `gbrain jobs stats`
-   should show no unexplained growth in `dead` between pre- and
-   post-upgrade.
-
-## Known issues
-
-### Supabase connection drops
-
-The worker uses a single Postgres connection. If Supabase drops it
-(maintenance, connection limits, network blip), lock renewal fails
-silently. The stall detector then dead-letters the job after
-`max_stalled` misses.
-
-**Current defaults that make this worse:**
-
- `lockDuration: 30000` (30 s) — too short for long jobs during
-  connection blips.
- `max_stalled: 5` (schema column default — see `src/schema.sql` and
-  `src/core/pglite-schema.ts`). Five missed heartbeats before dead-letter.
- `stalledInterval: 30000` (30 s) — checks too aggressively.
-
-**Tune per-job today.** `gbrain jobs submit` accepts `--max-stalled N`,
-`--backoff-type fixed|exponential`, `--backoff-delay <ms>`,
-`--backoff-jitter 0..1`, and `--timeout-ms N` as first-class flags
-(since v0.13.1). These write onto the job row at submit time — which is
-what `handleStalled()` reads — so per-job tuning is the real knob today.
-
-### DO NOT pass `maxStalledCount` to `MinionWorker`
-
-It's a no-op. The stall detector reads the row's `max_stalled` column
-(set at submit time), not the worker opt in `src/core/minions/worker.ts:74`.
-Use `gbrain jobs submit --max-stalled N` per-job instead.
-
-### Zombie shell children
-
-When the Bun worker crashes hard, child processes from shell jobs can
-become zombies. The supervisor's SIGTERM → 35s drain → SIGKILL window
-covers the shell handler's 5 s child-kill grace (`KILL_GRACE_MS`). For
-long-running shell jobs, prefer timeouts via `--timeout-ms` on submit
-over relying on hard kills.
-
-## Smoke test
-
-```bash
-# Supervisor alive?
-gbrain jobs supervisor status --json | jq .running
-
-# Aggregate queue health.
-gbrain jobs stats
-
-# Jobs currently stalled (still `active` with expired lock_until, pre-requeue).
-gbrain jobs list --status active --limit 10
-
-# Dead-lettered jobs.
-gbrain jobs list --status dead --limit 10
-
-# Shell handler registered? (check supervisor audit log or worker stderr.)
-gbrain jobs supervisor status --json | jq '.worker_config.allow_shell_jobs'
-```
-
-## Uninstall
-
-**`gbrain jobs supervisor`** (foreground or `--detach`):
-
-```bash
-gbrain jobs supervisor stop
-```
-
-**systemd:**
-
-```bash
-sudo systemctl disable --now gbrain-worker
-sudo rm /etc/systemd/system/gbrain-worker.service /etc/gbrain.env
-sudo systemctl daemon-reload
-```
-
-**Fly / Render / Railway:** delete the `worker` process from `fly.toml`
-/ `Procfile` and redeploy. Secrets set via `fly secrets` persist until
-`fly secrets unset`.
-
-**Inline `--follow`:** remove the cron entry. Nothing else to clean up
-— temporary workers exit with their jobs.
@@ -1,159 +0,0 @@
-# Minions fix — repairing a half-migrated install
-
-**tl;dr:** on v0.11.1+ everything should self-heal. If Minions is partially
-set up (no `~/.gbrain/preferences.json`, autopilot still inline, cron jobs
-still on `agentTurn`), run:
-
-```bash
-gbrain apply-migrations --yes
-```
-
-It's idempotent. On v0.11.1 installs that already migrated it's a cheap
-no-op.
-
-## Context
-
-v0.11.0 shipped the Minions schema, queue, worker, and migration skill —
-but the migration skill itself never fired on upgrade. `runPostUpgrade`
-printed the feature pitch and stopped. v0.11.0 was never released
-publicly; v0.11.1 is the first public Minions ship and fixes the
-mega-bug (migration fires automatically on `gbrain upgrade` and via
-the `postinstall` hook).
-
-If you're on a pre-v0.11.1 branch build (e.g. running the
-`minions-jobs` branch before v0.11.1 tagged), Minions may be installed
-but not wired: schema is v7, but no `~/.gbrain/preferences.json`,
-autopilot still runs inline, cron jobs still call `agentTurn`.
-
-This guide covers both paths: the canonical v0.11.1+ fix, and the
-stopgap for pre-v0.11.1 binaries that don't have `apply-migrations`.
-
-## Detecting the half-migrated state
-
-```bash
-gbrain doctor
-```
-
-If the install is half-migrated, you'll see:
-
-```
-[FAIL] minions_migration: MINIONS HALF-INSTALLED (partial migration: 0.11.0). Run: gbrain apply-migrations --yes
-```
-
-or
-
-```
-[FAIL] minions_config: MINIONS HALF-INSTALLED (schema v7+ but no ~/.gbrain/preferences.json). Run: gbrain apply-migrations --yes
-```
-
-For a machine-readable report (cron-friendly):
-
-```bash
-gbrain skillpack-check --quiet && echo healthy || echo needs_action
-gbrain skillpack-check | jq -r '.actions[]'    # prints the exact commands to run
-```
-
-## The fix (v0.11.1 or later)
-
-```bash
-gbrain apply-migrations --yes
-```
-
-Reads `~/.gbrain/migrations/completed.jsonl`, diffs against the TS
-migration registry, runs whatever's pending. Seven phases:
-
-```
-A. Schema        gbrain init --migrate-only
-B. Smoke         gbrain jobs smoke
-C. Mode          prompt (or --yes default pain_triggered)
-D. Prefs         write ~/.gbrain/preferences.json
-E. Host          AGENTS.md marker injection + cron rewrites for gbrain
-                 builtins; JSONL TODOs for host-specific handlers
-F. Install       gbrain autopilot --install (env-aware)
-G. Record        append completed.jsonl status:"complete"
-```
-
-If Phase E emits TODOs for host-specific handlers (e.g. your OpenClaw's
-~29 non-gbrain crons), the migration finishes with `status: "partial"`.
-Your host agent walks the TODOs using `skills/migrations/v0.11.0.md` +
-`docs/guides/plugin-handlers.md`, ships handler registrations in the
-host repo, then re-runs `gbrain apply-migrations --yes`. Newly
-registerable cron entries get rewritten and the JSONL rows mark
-`status: "complete"`.
-
-## The stopgap (pre-v0.11.1 binary, no apply-migrations yet)
-
-If you're stuck on a branch build that doesn't have `apply-migrations`:
-
-```bash
-curl -fsSL https://raw.githubusercontent.com/garrytan/gbrain/v0.11.1/scripts/fix-v0.11.0.sh | bash
-```
-
-This bash script does what apply-migrations does from a shell environment:
-
-1. `gbrain init --migrate-only` — schema v7.
-2. `gbrain jobs smoke` — verify Minions health.
-3. Prompt for `minion_mode` (defaults `pain_triggered` on non-TTY).
-4. Write `~/.gbrain/preferences.json` atomically.
-5. Append `~/.gbrain/migrations/completed.jsonl` with `status: "partial"`
-   and `apply_migrations_pending: true`. That partial record is the
-   signal to v0.11.1's `apply-migrations` to pick up remaining phases
-   after the user upgrades.
-6. Detect host agent repos and PRINT rewrite instructions (never
-   auto-edits from a curl-piped script).
-7. Print the next step: `Run: gbrain autopilot --install`.
-
-Once v0.11.1 is installed, re-run `gbrain apply-migrations --yes` to
-finish the remaining phases (host rewrites + autopilot install). The
-stopgap's `status: "partial"` record is designed to resume cleanly
-(it doesn't poison the permanent migration path).
-
-## Verify the fix landed
-
-```bash
-# 1. Preferences exist and are readable
-cat ~/.gbrain/preferences.json
-
-# 2. Migration recorded
-cat ~/.gbrain/migrations/completed.jsonl
-
-# 3. Autopilot is supervising a Minions worker child
-gbrain autopilot --status
-ps aux | grep 'jobs work'
-
-# 4. Jobs show up in the queue
-gbrain jobs list
-
-# 5. Any host-specific TODOs still pending
-cat ~/.gbrain/migrations/pending-host-work.jsonl 2>/dev/null || echo "(none — all host work is done)"
-
-# 6. Doctor + skillpack-check should both be clean
-gbrain doctor
-gbrain skillpack-check --quiet && echo ok
-```
-
-## If the fix fails
-
-Each phase is idempotent. Re-running is safe. Common failure modes:
-
- **Phase B smoke fails:** the schema didn't apply. Check
-  `~/.gbrain/config.json` has a valid `database_url` (or `database_path`
-  for PGLite). Run `gbrain init --migrate-only` directly and look at
-  the error.
- **Phase F install fails:** your host environment doesn't match any
-  detected target. Pass `--target <macos|linux-systemd|ephemeral-container|linux-cron>`
-  explicitly.
- **Pending host work never clears:** your host agent hasn't shipped
-  handler registrations yet. Read
-  `~/.gbrain/migrations/pending-host-work.jsonl`, open
-  `skills/migrations/v0.11.0.md`, and follow the host-agent instruction
-  manual.
-
-## Related
-
- `skills/migrations/v0.11.0.md` — full migration skill for host agents.
- `skills/skillpack-check/SKILL.md` — when and how to run the health check.
- `docs/guides/plugin-handlers.md` — plugin contract for host-specific
-  handlers.
- `skills/conventions/cron-via-minions.md` — the canonical cron rewrite
-  pattern.
@@ -1,167 +0,0 @@
-# Minions shell jobs — move deterministic crons off the gateway
-
-## 30 seconds
-
-```bash
-# Run your first shell job:
-GBRAIN_ALLOW_SHELL_JOBS=1 gbrain jobs submit shell \
-  --params '{"cmd":"echo hello","cwd":"/tmp"}' --follow
-# → exit_code: 0, stdout_tail: "hello\n", duration_ms: 43
-```
-
-That's it. Your cron scripts now have a home with retry, backoff, DLQ, and
-`gbrain jobs list` visibility, without each one booting a full LLM session.
-
-**PGLite users:** `gbrain jobs work` does not run on PGLite (exclusive file
-lock). Every crontab invocation must use `--follow` for inline execution.
-Postgres users can run a persistent worker; see recipes below.
-
---
-
-## Why it exists
-
-If your agent runs deterministic scripts from cron (token refresh, API fetch,
-scrape + write), each one pays the cost of a full LLM session on the gateway.
-Fourteen simultaneous fires on a Series A deployment pin CPU at 100% and block
-live messages. None of those scripts need reasoning. They need a shell.
-
-Shell jobs move them to the Minions worker: one deterministic-script execution
-per cron, zero LLM tokens, unified visibility and retry.
-
---
-
-## Security model (read this)
-
-Shell exec is a large blast radius. We ship two independent gates, both must
-pass:
-
-1. **MCP boundary.** `submit_job` with `name: 'shell'` is rejected when
-   `ctx.remote === true` (MCP callers). Independent of the env flag. Remote
-   agents can never submit shell jobs. `MinionQueue.add('shell', ...)` has its
-   own guard too, so an in-process handler can't programmatically bypass this.
-2. **Env flag.** The worker only registers the shell handler when
-   `GBRAIN_ALLOW_SHELL_JOBS=1` is set on the worker process. Default: off. Your
-   agent opts in per-host.
-
-**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: { ... }`. 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
-with `GBRAIN_AUDIT_DIR`). Failures log to stderr and don't block submission, so
-a disk-full adversary could silently disable the trail. Good for "what did
-this cron submit last Tuesday", not for security-critical forensics.
-
-**The command text is logged as-is.** If you embed a secret in `cmd`
-(`curl -H 'Authorization: Bearer ...'`), it shows up in the audit file. Put
-secrets in `env:` instead.
-
---
-
-## Migrate a cron
-
-### Postgres worker (recommended)
-
-On one terminal, start a persistent worker:
-
-```bash
-GBRAIN_ALLOW_SHELL_JOBS=1 gbrain jobs work
-```
-
-Rewrite crontab to submit shell jobs (no `--follow`):
-
-```cron
-# Before (LLM gateway):
-#   OpenClaw cron: x-garrytan-unified
-# After (Minions worker):
-3 13,16,19,22,1,4,7,10 * * * \
-  gbrain jobs submit shell \
-    --params '{"cmd":"node scripts/x-garrytan-daily.mjs","cwd":"/data/.openclaw/workspace"}' \
-    --max-attempts 3 --timeout-ms 300000
-```
-
-Worker claims the job on next poll, runs it, records `exit_code` +
-`stdout_tail` + `stderr_tail` in the result. Failures retry per
-`--max-attempts` with exponential backoff.
-
-### PGLite (inline execution)
-
-PGLite doesn't support the persistent worker daemon. Every crontab invocation
-uses `--follow` to run inline:
-
-```cron
-# Each cron tick spawns a short-lived worker that runs the job inline.
-3 13,16,19,22,1,4,7,10 * * * \
-  GBRAIN_ALLOW_SHELL_JOBS=1 gbrain jobs submit shell \
-    --params '{"cmd":"node scripts/x-garrytan-daily.mjs","cwd":"/data/.openclaw/workspace"}' \
-    --follow --timeout-ms 300000
-```
-
-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.
-
-### Submitting with `argv` (no shell interpolation)
-
-For programmatic callers assembling commands from JSON, use `argv` instead of
-`cmd`. No shell, no injection surface:
-
-```bash
-gbrain jobs submit shell \
-  --params '{"argv":["node","scripts/fetch.mjs","--date","2026-04-19"],"cwd":"/data"}' \
-  --follow
-```
-
---
-
-## Debug a failed job
-
-```bash
-# List dead shell jobs
-gbrain jobs list --status dead
-
-# Inspect one
-gbrain jobs get 42
-# → error_text, stacktrace, result.stdout_tail, result.stderr_tail
-
-# Submission audit log (operator trail, not forensic)
-cat ~/.gbrain/audit/shell-jobs-*.jsonl | jq '.'
-
-# First-time failure mode: submitted without env flag on the worker
-gbrain jobs list --status waiting --name shell
-# If rows pile up here, no worker with GBRAIN_ALLOW_SHELL_JOBS=1 is running.
-```
-
---
-
-## Limitations
-
- **Filesystem reads are not sandboxed.** See "Security model" above. Don't
-  point `cwd` at a directory full of secrets.
- **Audit log is advisory.** Disk-full or EACCES silently disables it.
- **Cancel latency is lock-renewal-bounded** (~7-15 s by default). A cancelled
-  child keeps running until the next lock-renewal tick fails.
- **`--follow` claim order** is by priority/created_at. If another job is
-  waiting in the same queue at the time of `--follow`, that one runs first.
- **`cwd` symlink TOCTOU.** The absolute-path check doesn't guard against
-  symlinks pointing elsewhere at execution time. Operator-scope concern.
-
---
-
-## Errors {#errors}
-
-| Error | What it means | Fix |
-|---|---|---|
-| `shell: specify exactly one of cmd or argv` | `cmd` and `argv` are mutually exclusive. Both absent is also invalid. | Choose one. `cmd` for shell-interpolated strings; `argv` for structured args. |
-| `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}`. |
-| `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. |
-| `exit N: <stderr_tail_500>` | Script exited non-zero. | Read `stderr_tail` in `gbrain jobs get`. |
@@ -1,182 +0,0 @@
-# Multi-source brains
-
-**A single gbrain database can hold multiple knowledge repos.** Each one
-is a `source`: a logical brain-within-the-brain with its own slug
-namespace, its own sync state, and its own federation policy. The rest
-of this guide walks the three canonical scenarios.
-
-## The three scenarios
-
-### 1. Unified knowledge recall (wiki + gstack)
-
-You have a personal wiki and a `gstack` checkout. Both belong to you,
-both are knowledge you want your agent to recall across. When you ask
-"what did I learn about X?" you want the best hit whether it lives in
-the wiki or in a gstack plan.
-
-```bash
-# Register the gstack source, federate so it joins cross-source search
-gbrain sources add gstack --path ~/.gstack --federated
-
-# Pin the directory so `gbrain sync` knows which source it's walking
-cd ~/.gstack && gbrain sources attach gstack
-
-# Initial sync
-gbrain sync --source gstack
-
-# Now `gbrain search "retry budgets"` returns hits from BOTH wiki and
-# gstack. Each result includes source_id so the agent can cite properly.
-```
-
-Result: wiki pages and gstack plans are separate (different source_ids,
-different slug namespaces) but share the search surface.
-
-### 2. Purpose-separated brains (yc-media + garrys-list)
-
-You run two completely different content pipelines on the same backend.
-YC Media covers portfolio news and founder profiles. Garry's List is
-personal writing. You explicitly DON'T want them mixed in search — YC
-portfolio content leaking into essay searches is a bug, not a feature.
-
-```bash
-# Two sources, both isolated (federated=false)
-gbrain sources add yc-media --path ~/yc-media --no-federated
-gbrain sources add garrys-list --path ~/writing --no-federated
-
-# Pin each checkout directory
-(cd ~/yc-media && gbrain sources attach yc-media)
-(cd ~/writing && gbrain sources attach garrys-list)
-
-# Sync each independently
-gbrain sync --source yc-media
-gbrain sync --source garrys-list
-```
-
-Result: searching from neither directory returns the `default` source
-(your main brain). Searching from inside `~/yc-media` returns only yc-
-media hits. Searching from inside `~/writing` returns only garrys-list.
-Federation is opt-in, not leaked.
-
-To search across them explicitly on demand:
-
-```bash
-gbrain search "tech layoffs" --source yc-media,garrys-list
-```
-
-### 3. Mixed (wiki federated + sessions isolated)
-
-Your main wiki is federated with a few trusted sources. Your session
-transcripts (coming in v0.18) land in a separate isolated source so
-they don't dominate every search result.
-
-```bash
-# Federated sources
-gbrain sources add gstack --path ~/.gstack --federated
-
-# Isolated source (future v0.18 — sessions use this shape today for ingest)
-gbrain sources add sessions --path ~/.claude/sessions --no-federated
-```
-
-## Resolution priority
-
-When any command needs to pick a source, gbrain walks this list (highest
-first):
-
-1. Explicit `--source <id>` flag.
-2. `GBRAIN_SOURCE` environment variable.
-3. `.gbrain-source` dotfile in CWD or any ancestor directory.
-4. A registered source whose `local_path` contains the CWD (longest
-   prefix wins for nested checkouts).
-5. The brain-level default set via `gbrain sources default <id>`.
-6. The seeded `default` source.
-
-So inside `~/.gstack/plans/` on a brain that pinned `gstack` to
-`~/.gstack` via `.gbrain-source`, `gbrain put-page` implicitly writes to
-the `gstack` source. Outside any registered directory with no env/dotfile
-set, it writes to the default.
-
-## Federation flag
-
-Every source row stores `config.federated: boolean` in its JSONB config.
-
-| Value | Meaning |
-|-------|---------|
-| `true` | Source participates in unqualified `gbrain search "X"` results. |
-| `false` (default for new sources) | Source only searched when explicitly named via `--source <id>` or qualified citation. |
-
-The seeded `default` source is `federated=true` so pre-v0.17 brains
-behave exactly as before — every page appears in search.
-
-Flip later with `gbrain sources federate <id>` / `unfederate <id>`.
-
-## Commands
-
-Full subcommand reference:
-
-```
-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])?
-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).
-gbrain sources rename <id> <new-name>
-                               Change display name only; id is immutable.
-gbrain sources default <id>    Set the brain-level default.
-gbrain sources attach <id>     Write .gbrain-source in CWD (like kubectl context).
-gbrain sources detach          Remove .gbrain-source from CWD.
-gbrain sources federate <id>
-gbrain sources unfederate <id>
-```
-
-## Citation format for agents
-
-When agents receive multi-source results they MUST cite pages in
-`[source-id:slug]` form. Example:
-
-> You told me about the distillation protocol — see [wiki:topics/ai]
-> and [gstack:plans/multi-repo] for where this came from.
-
-The citation key is `sources.id` (immutable). Renaming a source via
-`gbrain sources rename` changes the display name only; existing
-citations keep working.
-
-## Writing to a specific source
-
-```bash
-# Pass --source explicitly
-gbrain put-page topics/ai ... --source wiki
-
-# Or rely on the dotfile / env / CWD match
-cd ~/.gstack && gbrain put-page plans/multi-repo ...
-# → source auto-resolves to gstack
-```
-
-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.
-
-## Upgrading an existing brain
-
-`gbrain upgrade` runs the v16 + v17 migrations automatically. Your
-existing pages all move under `source_id='default'`. Behavior is
-unchanged until you add a second source.
-
-To add one:
-
-```bash
-gbrain sources add gstack --path ~/.gstack --federated
-cd ~/.gstack && gbrain sources attach gstack && gbrain sync
-```
-
-Two commands. The existing default source is untouched.
-
-## Not in v0.18.0
-
- Session transcript ingest (`.jsonl`, raised size cap, session
-  PageType) — v0.18.
- Per-source retention/TTL (`gbrain sources prune`) — v0.18.
- ACL enforcement via caller-identity — v0.17.1.
- `gbrain sources import-from-github <url>` one-shot bootstrap — patch
-  release after the core plumbing stabilizes.
-
-All of these build on the `sources` primitive shipped here.
@@ -1,120 +0,0 @@
-# Operational Disciplines
-
-## Goal
-Five non-negotiable rules that separate a production brain from a demo -- signal detection, brain-first lookup, sync after every write, daily heartbeat, and nightly dream cycle.
-
-## What the User Gets
-Without this: the agent misses signals in conversation, wastes money on external APIs when the brain already has the answer, leaves search results stale after writes, and lets the brain rot quietly. With this: every message is scanned for entities, the brain is always consulted first, search is always current, health is monitored daily, and the brain compounds overnight.
-
-## Implementation
-
-```
-# DISCIPLINE 1: Signal Detection on Every Message (MANDATORY)
-on every_inbound_message(message):
-    # No exceptions. If the user thinks out loud and the brain doesn't
-    # capture it, the system is broken. This is the #1 discipline.
-
-    entities = detect_entities(message)
-    #   people, companies, deals, original ideas
-
-    for entity in entities:
-        existing = gbrain search "{entity.name}"
-        if existing:
-            gbrain add_timeline_entry <entity_slug> \
-                --entry "{what_was_said}" \
-                --source "User, direct message, {timestamp}"
-        # else: flag for enrichment if important enough
-
-    originals = detect_original_thinking(message)
-    for idea in originals:
-        gbrain put originals/{slug} --content "{user's exact phrasing}"
-
-# DISCIPLINE 2: Brain-First Lookup Before External APIs (MANDATORY)
-on information_needed(topic):
-    # ALWAYS check the brain before reaching for the web
-    brain_result = gbrain search "{topic}"
-    if brain_result:
-        page = gbrain get <slug>
-        # Use brain data first. External APIs FILL GAPS, not replace.
-    else:
-        # Brain has nothing -- now use external APIs
-        external_result = brave_search("{topic}")
-
-    # An agent that reaches for the web before checking its own brain
-    # is wasting money and giving worse answers.
-
-# DISCIPLINE 3: Sync After Every Write (MANDATORY)
-on brain_write_complete():
-    gbrain sync
-    # Without this, search results are stale.
-    # The page you just wrote won't appear in gbrain search or gbrain query
-    # until sync runs. Skipping this means the next lookup misses the
-    # most recent data.
-
-# DISCIPLINE 4: Daily Heartbeat Check
-on daily_schedule("09:00"):
-    gbrain doctor
-    # Checks: database connectivity, embedding health, sync status,
-    # page count, stale pages, broken links
-    # If doctor reports issues, fix them before doing anything else.
-
-# DISCIPLINE 5: Nightly Dream Cycle
-on nightly_schedule("02:00"):
-    # The dream cycle is the most important discipline.
-    # The brain COMPOUNDS overnight.
-
-    # 5a: Entity sweep -- find unlinked mentions
-    pages = gbrain list_pages
-    for page in pages:
-        mentions = extract_entity_mentions(page.content)
-        existing_links = gbrain get_links <page.slug>
-        for mention in mentions:
-            if mention not in existing_links:
-                gbrain add_link <page.slug> <mention_slug>  # fix broken graph
-
-    # 5b: Citation audit -- find facts without sources
-    for page in pages:
-        facts_without_sources = audit_citations(page.content)
-        if facts_without_sources:
-            flag_for_remediation(page, facts_without_sources)
-
-    # 5c: Memory consolidation -- update compiled truth from timeline
-    for page in stale_pages(older_than="7d"):
-        timeline = gbrain get_timeline <page.slug>
-        if timeline.has_new_entries_since_last_consolidation:
-            # Re-synthesize compiled truth from accumulated timeline
-            updated_truth = consolidate(page.compiled_truth, timeline.new_entries)
-            gbrain put <page.slug> --content updated_truth
-
-    # 5d: Sync everything
-    gbrain sync
-
-# BONUS: Durable Skills Over One-Off Work
-# If you do something twice, make it a skill + cron.
-#   1. Concept the process
-#   2. Run it manually for 3-10 items
-#   3. Revise -- iterate on quality
-#   4. Codify into a skill
-#   5. Add to cron -- automate it
-# Each entity type and signal source has exactly one owner skill.
-# Two skills creating the same page = coverage violation.
-```
-
-## Tricky Spots
-
-1. **The dream cycle is the most important discipline.** Brains compound overnight. Entity sweeps fix broken graphs, citation audits catch sourceless facts, and memory consolidation keeps compiled truth current. Skip the dream cycle and the brain slowly rots.
-2. **Skipping Discipline 3 (sync after write) means stale search results.** You write a page, then immediately search for it -- and get nothing back. The page exists but isn't indexed. Always sync after writes.
-3. **Signal detection must fire on EVERY message.** Not just messages that look important. The user says "I talked to Pedro yesterday about the board seat" in passing -- that's a timeline entry on Pedro's page, a potential update to his State section, and a signal about the board. If the agent doesn't catch it, the system is broken.
-4. **Brain-first saves money AND gives better answers.** The brain has context that external APIs don't: relationship history, meeting notes, the user's own assessment. An API lookup for "Pedro Franceschi" returns a LinkedIn profile. The brain returns the full picture including private context.
-5. **`gbrain doctor` catches silent failures.** Embedding pipelines can stall, sync can fail silently, database connections can drop. The daily heartbeat catches these before they compound into data loss.
-
-## How to Verify
-
-1. Send a message mentioning a person with a brain page. Confirm the agent detects the entity and adds a timeline entry to their page (`gbrain get_timeline <slug>`).
-2. Ask the agent about someone in the brain. Confirm it runs `gbrain search` or `gbrain get` BEFORE reaching for external APIs (check the tool call order).
-3. Write a new page with `gbrain put`, then immediately run `gbrain search` for it. Confirm it appears in results (verifies sync ran).
-4. Run `gbrain doctor`. Confirm it returns a health report with database status, page count, and any flagged issues.
-5. After a dream cycle runs, check a page that had unlinked entity mentions. Confirm new links were added (`gbrain get_links <slug>`).
-
---
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,87 +0,0 @@
-# The Originals Folder
-
-## Goal
-Capture the user's original thinking with their exact phrasing, deep cross-links, and full provenance -- so intellectual capital compounds instead of evaporating.
-
-## What the User Gets
-Without this: the user generates a brilliant framework in conversation and it vanishes when the session ends. Six months later, they vaguely remember the idea but can't find it, can't recall the exact phrasing, and can't trace what influenced it. With this: every original observation, thesis, framework, and hot take is captured verbatim in `brain/originals/`, cross-linked to the people, companies, and media that shaped it, and searchable forever.
-
-## Implementation
-
-```
-on user_message(message):
-    # Detect original thinking in every message
-    if contains_original_thinking(message):
-        # The authorship test:
-        #   User generated the idea?                   -> originals/{slug}.md
-        #   User's unique synthesis of someone else's?  -> originals/ (synthesis IS original)
-        #   World concept someone else coined?          -> concepts/{slug}.md
-        #   Product or business idea?                   -> ideas/{slug}.md
-
-        # Step 1: Use the user's EXACT phrasing for the slug
-        #   "meatsuit-maintenance-tax"
-        #   NOT "biological-needs-maintenance-overhead"
-        #   The vividness IS the concept.
-        slug = slugify(user_exact_phrase)
-
-        # Step 2: Create the originals page
-        gbrain put originals/{slug} --content """
-            # {User's Exact Phrase}
-
-            ## The Idea
-            {User's original thinking, captured in their own words.
-             Do NOT paraphrase. Do NOT clean up the language.
-             The raw phrasing is the intellectual artifact.}
-
-            ## Context
-            {What triggered this thinking. Meeting? Article? Conversation?
-             Include the source that sparked it.}
-            [Source: User, {context}, {date} {time} {tz}]
-
-            ## Connections
-            - Related to: [[{person_slug}]] -- {how they connect}
-            - Emerged from: [[{meeting_slug}]] -- {what was discussed}
-            - Influenced by: [[{book_or_media_slug}]] -- {what resonated}
-            - Builds on: [[{other_original_slug}]] -- {how ideas cluster}
-        """
-
-        # Step 3: Cross-link to everything that shaped the thinking
-        for entity in idea.influences:
-            gbrain add_link originals/{slug} <entity_slug>
-            gbrain add_link <entity_slug> originals/{slug}
-
-        # Step 4: Sync
-        gbrain sync
-
-# What counts as original thinking:
-#   - Novel frameworks ("the meatsuit maintenance tax")
-#   - Hot takes on someone else's work (synthesis IS original)
-#   - Pattern recognition across multiple entities
-#   - Predictions or bets about the future
-#   - Contrarian positions with reasoning
-
-# What does NOT go in originals/:
-#   - Facts about the world (-> entity pages)
-#   - Concepts someone else coined (-> concepts/)
-#   - Product ideas (-> ideas/)
-#   - Preferences (-> agent memory)
-```
-
-## Tricky Spots
-
-1. **Naming: the vividness IS the concept.** `meatsuit-maintenance-tax` not `biological-needs-maintenance-overhead`. `ambition-debt` not `deferred-career-risk-accumulation`. The user's colorful phrasing is the intellectual artifact. Never sanitize it into corporate-speak.
-2. **Synthesis IS original.** The user's take on Peter Thiel's zero-to-one framework goes in `originals/`, not `concepts/`. The original part is the user's synthesis, interpretation, or disagreement -- even though the underlying ideas came from someone else.
-3. **An original without cross-links is a dead original.** The connections ARE the intelligence. An idea about "ambition debt" that doesn't link to the people who exemplify it, the meeting where it was discussed, and the book that influenced it is just a note in a graveyard. Cross-link aggressively.
-4. **Originals form clusters.** Over time, the user's ideas connect to each other. "Meatsuit maintenance tax" connects to "ambition debt" connects to "founder energy budget." Link originals to other originals. The cluster IS the user's worldview.
-5. **Capture the trigger context.** What conversation, meeting, article, or moment sparked this idea? The context often matters as much as the idea itself for future retrieval. Include it in the page.
-
-## How to Verify
-
-1. Generate an original idea in conversation (e.g., "I call this the 'ambition debt' problem -- every year you delay going big, the compound interest works against you"). Confirm a new page appears at `brain/originals/ambition-debt` with `gbrain get originals/ambition-debt`.
-2. Check that the page uses the user's exact phrasing for the title and slug -- not a sanitized version.
-3. Run `gbrain get_links originals/ambition-debt`. Confirm cross-links exist to related people, meetings, or other originals.
-4. Express a take on someone else's idea (e.g., "I think Thiel's contrarian question is wrong because..."). Confirm it goes to `originals/` (synthesis is original), not `concepts/`.
-5. Run `gbrain search "ambition debt"`. Confirm the originals page appears in search results and is discoverable.
-
---
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
@@ -1,163 +0,0 @@
-# Plugin authors guide (v0.15)
-
-`gbrain` discovers subagent definitions from outside this repo via
-`GBRAIN_PLUGIN_PATH`. If you maintain a downstream agent (your OpenClaw
-deployment, a workflow host, a private tool) and want to ship custom
-subagents alongside it, drop a plugin directory on that env path.
-
-This guide is for plugin authors. The CLI user doesn't need to read it.
-
-## Minimum viable plugin
-
-```
-/path/to/my-plugin/
-├── gbrain.plugin.json
-└── subagents/
-    └── my-summarizer.md
-```
-
-`gbrain.plugin.json`:
-
-```json
-{
-  "name": "my-plugin",
-  "version": "1.0.0",
-  "plugin_version": "gbrain-plugin-v1"
-}
-```
-
-`subagents/my-summarizer.md`:
-
-```markdown
---
-name: my-summarizer
-model: claude-sonnet-4-6
-allowed_tools:
-  - brain_search
-  - brain_get_page
---
-
-You are a brain page summarizer. Given a slug, fetch the page and produce
-a 3-sentence summary.
-```
-
-## Turning it on
-
-```bash
-export GBRAIN_PLUGIN_PATH="/path/to/my-plugin"
-gbrain jobs work           # worker startup prints the plugin load line
-gbrain agent run "summarize meetings/2026-04-20" --subagent-def my-summarizer
-```
-
-Multiple plugins: colon-separated, just like `$PATH`.
-
-```bash
-export GBRAIN_PLUGIN_PATH="/path/to/plugin-a:/path/to/plugin-b"
-```
-
-## Rules (strict by design)
-
-**Path policy.** Absolute paths only. Relative paths, `~`-prefixed paths,
-and URL-style paths (`https://`, `file://`) are rejected with a warning.
-You control where your plugin lives on disk; `gbrain` doesn't guess.
-
-**Collision policy.** If two plugins ship a subagent with the same `name`,
-the one listed FIRST in `GBRAIN_PLUGIN_PATH` wins. The other is dropped
-with a warning naming both sources.
-
-**Trust policy.** Plugins ship subagent definitions ONLY in v0.15:
-
- You **cannot** declare new tools.
- You **cannot** extend the brain tool allow-list.
- You **cannot** override any `agentSafe` or similar flag.
- Your `allowed_tools:` frontmatter field MUST subset the derived brain
-  tool registry. Names not in the registry are rejected at plugin load
-  time (worker startup), NOT at subagent dispatch time — so a typo in
-  your plugin gives you a loud startup error, not a silent "tool never
-  fires" at 3am.
-
-v0.16+ may open up plugin-declared tools with a separate contract. Don't
-expect it.
-
-## `gbrain.plugin.json`
-
-| field            | type   | required | notes                                                              |
-|------------------|--------|----------|--------------------------------------------------------------------|
-| `name`           | string | yes      | Human-readable plugin id. Shows up in warnings and collision logs. |
-| `version`        | string | yes      | Your plugin's semver. Informational.                               |
-| `plugin_version` | string | yes      | Contract lock. Must equal `"gbrain-plugin-v1"` for v0.15.          |
-| `subagents`      | string | no       | Subdir name (default `subagents`). Escape-attempts are rejected.   |
-| `description`    | string | no       | Shown in future `gbrain plugin list`.                              |
-
-## Subagent definition files
-
-Plain markdown with YAML frontmatter. The body is the system prompt. The
-frontmatter controls runtime behavior.
-
-Recognized frontmatter fields:
-
-| field           | type     | required | notes                                                                                   |
-|-----------------|----------|----------|-----------------------------------------------------------------------------------------|
-| `name`          | string   | no       | Subagent identifier used as `--subagent-def`. Defaults to the file basename.            |
-| `model`         | string   | no       | Anthropic model id. Defaults to the handler default (sonnet).                           |
-| `max_turns`     | number   | no       | Cap on assistant turns. Defaults to 20.                                                 |
-| `allowed_tools` | string[] | no       | Whitelist of tool names. Must subset the derived brain registry. Rejected on mismatch.  |
-
-Unknown frontmatter fields are preserved but ignored by the handler. v0.16
-may consume more of them.
-
-## Caveats that will bite you
-
-1. **Plugin definitions can't change during a run.** The loader reads the
-   disk once at worker startup. Editing a subagent def doesn't re-take
-   effect until you restart the worker. This is deliberate — live
-   reloads would break crash-resumable replay.
-
-2. **`~/.gbrain/audit/subagent-jobs-*.jsonl` is local only.** If your
-   worker runs on a different host than the `gbrain agent logs` caller,
-   the CLI won't see heartbeats from that worker. v0.16 will unify this;
-   for now assume worker + CLI share a filesystem.
-
-3. **Tool calls always run with `ctx.remote = true`.** Even on local CLI
-   invocation. Tools that gate on `remote=true` (file_upload's strict
-   confinement, put_page's namespace check) will apply. Good default; a
-   subagent definition that wants local-filesystem reach beyond the brain
-   can't have it.
-
-4. **`put_page` writes are namespace-scoped.** A subagent with id 42 can
-   only write under `wiki/agents/42/...`. This is enforced both in the
-   tool schema (the slug pattern shown to the model) AND server-side in
-   the `put_page` operation (fail-closed if `viaSubagent=true`). Don't
-   try to route around it; you'll get `permission_denied`.
-
-## Example: a downstream-OpenClaw plugin
-
-```
-~/your-openclaw/
-└── gbrain-plugin/
-    ├── gbrain.plugin.json
-    └── subagents/
-        ├── meeting-ingestion.md
-        ├── signal-detector.md
-        └── daily-task-prep.md
-```
-
-`~/your-openclaw/gbrain-plugin/gbrain.plugin.json`:
-
-```json
-{
-  "name": "your-openclaw",
-  "version": "2026.4.20",
-  "plugin_version": "gbrain-plugin-v1",
-  "description": "Your OpenClaw's personal-brain subagents"
-}
-```
-
-Environment:
-
-```bash
-export GBRAIN_PLUGIN_PATH="$HOME/your-openclaw/gbrain-plugin"
-```
-
-Then your OpenClaw calls `gbrain agent run --subagent-def meeting-ingestion
--fanout-by transcript ...` and its definitions load automatically.
@@ -1,137 +0,0 @@
-# Plugin handlers — registering host-specific Minion handlers
-
-GBrain's Minion worker ships with seven built-in handlers: `sync`,
-`embed`, `lint`, `import`, `extract`, `backlinks`, `autopilot-cycle`.
-These cover every background operation the gbrain CLI itself performs.
-
-Host platforms (OpenClaw deployments, future hosts) register their own
-handlers via a plugin bootstrap that imports
-`gbrain/minions`. No `handlers.json`-style data file — handlers are
-code, loaded by the worker, with the same trust model as any other
-code in the host's repo.
-
-## Why code, not data
-
-An earlier design draft shipped `~/.claude/gbrain-handlers.json` where
-each entry was a shell command the worker would exec on job claim.
-Codex flagged this as a durable RCE surface: an agent-writable data
-file that spawns arbitrary shell. We dropped the data-file approach;
-handlers are code that the host imports explicitly and ships through
-code review.
-
-## The plugin contract
-
-A host worker bootstrap looks like this (TypeScript):
-
-```ts
-import { MinionQueue, MinionWorker } from 'gbrain/minions';
-import type { BrainEngine } from 'gbrain/engine';
-
-async function main() {
-  const engine: BrainEngine = /* your engine setup */;
-  await engine.connect({});
-
-  const worker = new MinionWorker(engine, { queue: 'default' });
-
-  // Register every host-specific handler the host's cron manifest references.
-  // Each handler returns a plain object (serialized as the job result).
-  // Throw on failure — the worker catches and retries per max_attempts.
-
-  worker.register('ea-inbox-sweep', async (ctx) => {
-    const slot = ctx.data.slot ?? new Date().toISOString();
-    // Host-specific agent turn: call your LLM, scan the inbox, write
-    // brain pages, return a summary. ctx.signal.aborted indicates the
-    // worker wants you to cooperate with shutdown — honor it.
-    return { swept: true, slot };
-  });
-
-  worker.register('morning-briefing', async (ctx) => {
-    /* host logic */
-    return { briefed: true };
-  });
-
-  // Call start() AFTER every handler is registered. The worker's
-  // stall-detector ignores jobs whose name is not in the registered set.
-  await worker.start();
-}
-
-main().catch(err => { console.error(err); process.exit(1); });
-```
-
-Ship this as a separate binary in the host repo (e.g. `your-openclaw-worker`)
-or as a side-effect module that the stock `gbrain jobs work` command
-auto-loads on startup (configurable via a host-provided entry point).
-
-## Handler contract
-
-Every handler receives a `MinionJobContext`:
-
-```ts
-interface MinionJobContext {
-  data: Record<string, unknown>;   // job params (whatever the cron submit passed)
-  job: MinionJob;                   // full job row (id, queue, attempts, etc.)
-  signal: AbortSignal;              // set to aborted when the worker is shutting down
-  inbox: MinionInbox;               // read messages sent to this job while it runs
-}
-```
-
-Return a serializable object on success. Throw on failure (the worker
-will log + retry per `max_attempts`).
-
-**Abort cooperation.** When `ctx.signal.aborted` becomes true, finish
-gracefully. The worker will wait 30s for you to return before SIGKILL.
-Long-running LLM calls should pass the signal through to whatever
-network library they use.
-
-**Idempotency.** The queue enforces unique `idempotency_key` at the DB
-layer, so you don't need to worry about double-submits from a cron that
-fires while the previous invocation is still running.
-
-## Gbrain's migration flow
-
-The v0.11.0 migration orchestrator (run by `gbrain apply-migrations`)
-detects cron entries whose handler name is NOT in GBrain's builtin set
-and emits a structured TODO to `~/.gbrain/migrations/pending-host-work.jsonl`.
-Each TODO has shape:
-
-```json
-{
-  "type": "cron-handler-needs-host-registration",
-  "handler": "ea-inbox-sweep",
-  "cron_schedule": "0 */30 * * *",
-  "manifest_path": "/path/to/cron/jobs.json",
-  "current_cmd": "agentTurn ea-inbox-sweep",
-  "recommendation": "Add a handler registration for `ea-inbox-sweep` in your host worker bootstrap per docs/guides/plugin-handlers.md. Once registered, re-run `gbrain apply-migrations` to auto-rewrite this entry.",
-  "status": "pending"
-}
-```
-
-The host agent walks these entries using `skills/migrations/v0.11.0.md`:
-
-1. Read `~/.gbrain/migrations/pending-host-work.jsonl`.
-2. For each `cron-handler-needs-host-registration` row, ship a handler
-   registration in the host's worker bootstrap following the pattern
-   above.
-3. Deploy the updated worker.
-4. Re-run `gbrain apply-migrations --yes`. The orchestrator now
-   recognizes the newly-registerable handler (worker writes the
-   registered names to a discovery file on startup) and rewrites the
-   cron entry to use `gbrain jobs submit`. The JSONL row is marked
-   `status: "complete"`.
-
-## Trust boundary
-
-Handler code runs inside the worker process with the same privileges
-as the rest of the host binary. There is no elevation. But there is
-also no runtime sandbox — handlers can read + write anywhere the
-worker user can. Review handler PRs the same way you review any other
-code that touches production data.
-
-## Related
-
- `skills/conventions/cron-via-minions.md` — the rewrite convention
-  for cron manifests.
- `skills/migrations/v0.11.0.md` — how the migration orchestrator
-  drives the host agent through this work.
- `skills/minion-orchestrator/SKILL.md` — patterns for submitting,
-  monitoring, steering, and replaying jobs once the handler is live.
@@ -1,76 +0,0 @@
-# Queue operations runbook
-
-"My queue looks wedged — what do I run?" The commands below are in the order
-you probably want them. Shipped with v0.19.1 after a production incident
-where the queue held for 90+ minutes before the operator noticed.
-
-## First signal: jobs aren't running
-
-```bash
-gbrain doctor --json | jq '.checks[] | select(.name == "queue_health")'
-```
-
-`queue_health` flags two patterns:
-
- **stalled-forever**: active job whose `started_at` is older than 1h.
- **waiting-depth**: any per-name queue deeper than 10 (override via
-  `GBRAIN_QUEUE_WAITING_THRESHOLD`). Signals a missing `maxWaiting`.
-
-## Triage commands
-
-```bash
-# Who's active right now?
-gbrain jobs list --status active
-
-# Who's waiting, biggest pile first?
-gbrain jobs list --status waiting --limit 50
-
-# What's wrong with a specific job?
-gbrain jobs get <id>
-```
-
-## Rescue actions (in order of escalation)
-
-```bash
-# Force-kill a single stuck job:
-gbrain jobs cancel <id>
-
-# Clear a specific job entirely (last resort):
-gbrain jobs delete <id>
-
-# Health smoke on the mechanism itself:
-gbrain jobs smoke --wedge-rescue
-```
-
-## What each subcheck means
-
- **stalled-forever** — A worker claimed a job, started executing, and has
-  held the row for over an hour. The wall-clock sweep evicts jobs past
-  2× `timeout_ms`; if one's still active, either no `timeout_ms` was set
-  or the sweep is newly deployed and this job predates it. Cancel it.
- **waiting-depth** — Submitters are piling up jobs faster than workers
-  drain them. Set `--max-waiting N` on the submission or on the programmatic
-  `queue.add()` call. If you want a taller pile, raise the threshold via
-  `GBRAIN_QUEUE_WAITING_THRESHOLD=50 gbrain doctor`.
-
-## Self-check: is a worker even running?
-
-```bash
-# If you're running autopilot with --no-worker, check that your external
-# worker (systemd / Docker / OpenClaw service-manager) is alive:
-gbrain jobs list --status active | head -5
-```
-
-If the list is empty AND your submissions keep piling up, no worker is
-claiming. Start one:
-
-```bash
-GBRAIN_ALLOW_SHELL_JOBS=1 gbrain jobs work --concurrency 4
-```
-
-## Follow-ups tracked for v0.20+
-
- B7 — `minion_workers` heartbeat table for ground-truth liveness (the
-  `--no-worker` probe and the dropped `queue_health` worker-heartbeat
-  subcheck both need this).
- B3 — `gbrain doctor --fix` learns to rescue queue wedges.
@@ -1,165 +0,0 @@
-# Quiet Hours and Timezone-Aware Delivery
-
-## Goal
-
-Hold all notifications during sleep hours, merge held messages into the morning briefing, and adjust automatically when the user travels.
-
-## What the User Gets
-
-Without this: 3 AM pings from cron jobs. One bad notification and the user
-disables the entire system.
-
-With this: the brain works overnight (dream cycle, collectors, enrichment)
-but notifications are held until morning. Travel to Tokyo? The system adjusts
-automatically from your calendar, no config change needed.
-
-## Implementation
-
-### Quiet Hours Gate
-
-Every cron job that sends notifications must check quiet hours FIRST.
-
-```
-QUIET_START = 23  // 11 PM local time
-QUIET_END = 8     // 8 AM local time
-
-is_quiet(local_hour):
-  return local_hour >= QUIET_START OR local_hour < QUIET_END
-```
-
-**Before sending any notification:**
-1. Determine user's current timezone (from config or heartbeat state)
-2. Convert current UTC time to local time
-3. If quiet hours: hold the message, don't send
-
-### Held Messages
-
-During quiet hours, output goes to a held directory instead of being sent:
-
-```
-if is_quiet():
-  mkdir -p /tmp/cron-held/
-  write("/tmp/cron-held/{job-name}.md", output)
-  exit  // don't send
-else:
-  send(output)
-```
-
-The morning briefing picks up held messages:
-
-```
-morning_briefing():
-  held_files = list("/tmp/cron-held/*.md")
-  if held_files:
-    briefing += "## Overnight Updates\n\n"
-    for file in held_files:
-      briefing += read(file)
-      delete(file)
-```
-
-This way nothing is lost. Overnight cron results get folded into the
-first thing the user sees in the morning.
-
-### Timezone Awareness
-
-The agent should know what timezone the user is in. Store it in
-the agent's operational state:
-
-```json
-{
-  "currentLocation": {
-    "timezone": "US/Pacific",
-    "city": "San Francisco"
-  }
-}
-```
-
-**Update the timezone when:**
- Calendar shows the user flying somewhere (check for airline/hotel events)
- User mentions being in a different city
- User's active hours shift (they're responding at 3 AM PT = they're probably traveling)
-
-**All times shown to the user should be in their LOCAL timezone.** Never
-show UTC or a timezone the user isn't in.
-
-### Shell Implementation
-
-```bash
-#!/bin/bash
-# quiet-hours-gate.sh — run before any notification
-
-TIMEZONE="${USER_TIMEZONE:-US/Pacific}"
-LOCAL_HOUR=$(TZ="$TIMEZONE" date +%H)
-
-if [ "$LOCAL_HOUR" -ge 23 ] || [ "$LOCAL_HOUR" -lt 8 ]; then
-  echo "QUIET_HOURS=true"
-  exit 1  # don't send
-fi
-
-echo "QUIET_HOURS=false"
-exit 0  # ok to send
-```
-
-**In cron job scripts:**
-```bash
-# Check quiet hours first
-if ! bash scripts/quiet-hours-gate.sh; then
-  mkdir -p /tmp/cron-held
-  echo "$OUTPUT" > /tmp/cron-held/$(basename "$0" .sh).md
-  exit 0
-fi
-
-# Not quiet hours — send normally
-send_notification "$OUTPUT"
-```
-
-### Configurable Hours
-
-Some users want different quiet hours. Store the config:
-
-```json
-{
-  "quiet_hours": {
-    "start": 23,
-    "end": 8,
-    "enabled": true
-  }
-}
-```
-
-Set `enabled: false` to disable quiet hours entirely (e.g., for 24/7 monitoring).
-
-## Tricky Spots
-
-1. **Gate on EVERY job.** The quiet hours check must run before every single
-   cron job that produces notifications. If even one job skips the gate, the
-   user gets a 3 AM ping and loses trust in the entire system. No exceptions.
-
-2. **Held messages MUST be picked up.** If the morning briefing doesn't read
-   `/tmp/cron-held/`, overnight results vanish silently. Verify the briefing
-   skill reads and clears the held directory. Orphaned held files mean the
-   pickup integration is broken.
-
-3. **Timezone auto-detection is fragile.** Calendar-based timezone detection
-   relies on the user having airline/hotel events with location data. If the
-   user books travel without calendar entries, the system won't detect the
-   move. Fall back to activity-hour analysis (responding at 3 AM PT = probably
-   not in PT anymore) and ask the user if uncertain.
-
-## How to Verify
-
-1. **Set quiet hours to the current hour.** Temporarily set `QUIET_START` to
-   one hour before now and `QUIET_END` to one hour after. Trigger a cron job.
-   Verify the output goes to `/tmp/cron-held/` instead of being sent.
-
-2. **Check held message pickup.** After step 1, run or simulate the morning
-   briefing. Verify the held message appears in the "Overnight Updates"
-   section and the file is deleted from `/tmp/cron-held/`.
-
-3. **Verify timezone adjustment.** Change the timezone config to a zone where
-   it's currently quiet hours. Trigger a notification. Verify it's held. Change
-   back to your real timezone during active hours. Trigger again. Verify it sends.
-
---
-
-*Part of the [GBrain Skillpack](../GBRAIN_SKILLPACK.md).*
--- a/Show More
+++ b/Show More