fix(schema-pack): restore v2 capability parity + derive bundled-pack list + calibration holder config

- #2109: extract-timeline-from-meetings matches frontmatter.legacy_type='meeting'
  in both SQL sites so unify-types-migrated (gbrain-base-v2) brains keep the
  feature alive; pre-unify type='meeting' behavior unchanged.
- #2117: gbrain-base-v2 declares phases: [extract_atoms] and ports v1's
  founded/works_at/invested_in inference regexes so extract_atoms is no longer
  pack-gated off and extract-ner no longer returns pack_unavailable on the
  bundled default pack. (attended's page_type:meeting inference deliberately
  not ported — v2 declares no meeting type; lint would reject it.)
- #1726 (A): list_schema_packs derives from the exported BUNDLED_PACKS registry
  in load-active.ts instead of a frozen 2-of-7 literal.
- #1726 (B): new calibration.user_holder config key (symmetric with
  emotional_weight.user_holder) resolved by the calibration_profile phase, the
  gbrain calibration CLI, and the get_calibration_profile op; explicit
  holder param still wins; 'garry' stays the fallback.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/commands/calibration.ts

@@ -19,7 +19,7 @@
  */
 
 import type { BrainEngine } from '../core/engine.ts';
-import { runPhaseCalibrationProfile } from '../core/cycle/calibration-profile.ts';
+import { resolveCalibrationHolder, runPhaseCalibrationProfile } from '../core/cycle/calibration-profile.ts';
 import { sourceScopeOpts, type OperationContext } from '../core/operations.ts';
 import type { GBrainConfig } from '../core/config.ts';
 import { GBrainError } from '../core/types.ts';
@@ -167,7 +167,7 @@ export async function runCalibration(
   config: GBrainConfig,
 ): Promise<void> {
   const { opts } = parseArgs(args);
-  const holder = opts.holder ?? 'garry';
+  const holder = await resolveCalibrationHolder(engine, opts.holder);
   // Resolve --source / GBRAIN_SOURCE / .gbrain-source so the (now reachable, #2035)
   // calibration command targets the right source in a multi-source brain instead
   // of always reading `default`. No signal → 'default' (prior behavior).
@@ -253,14 +253,14 @@ export async function getCalibrationProfileOp(
   ctx: OperationContext,
   params: { holder?: string },
 ): Promise<CalibrationProfileRow | null> {
-  const holder = params.holder ?? 'garry';
-  if (typeof holder !== 'string' || holder.length === 0) {
+  if (params.holder !== undefined && (typeof params.holder !== 'string' || params.holder.length === 0)) {
     throw new GBrainError(
       'INVALID_HOLDER',
       'get_calibration_profile.holder must be a non-empty string',
-      'pass holder="<slug>" or omit to default to "garry"',
+      'pass holder="<slug>" or omit to default to the calibration.user_holder config (then "garry")',
     );
   }
+  const holder = await resolveCalibrationHolder(ctx.engine, params.holder);
   const scope = sourceScopeOpts(ctx);
   return getLatestProfile(ctx.engine, { holder, ...scope });
 }

src/commands/embed.ts

@@ -1,5 +1,5 @@
 import type { BrainEngine } from '../core/engine.ts';
-import { embedBatch, currentEmbeddingSignature, resolveEmbeddingModelLabel } from '../core/embedding.ts';
+import { embedBatch, currentEmbeddingSignature } from '../core/embedding.ts';
 import type { ChunkInput } from '../core/types.ts';
 import { chunkText } from '../core/chunkers/recursive.ts';
 import { createProgress, type ProgressReporter } from '../core/progress.ts';
@@ -581,16 +581,11 @@ async function embedPage(
   for (let j = 0; j < toEmbed.length; j++) {
     embeddingMap.set(toEmbed[j].chunk_index, embeddings[j]);
   }
-  // #1717: label each (re)embedded chunk with the model that actually
-  // produced its vector. Preserved chunks (not re-embedded this pass) keep
-  // their existing model so a mixed-model page isn't relabeled wholesale.
-  const embedModelLabel = resolveEmbeddingModelLabel();
   const updated: ChunkInput[] = chunks.map(c => ({
     chunk_index: c.chunk_index,
     chunk_text: c.chunk_text,
     chunk_source: c.chunk_source,
     embedding: embeddingMap.get(c.chunk_index),
-    model: embeddingMap.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
     token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
   }));
 
@@ -722,16 +717,12 @@ async function embedAll(
       for (let j = 0; j < toEmbed.length; j++) {
         embeddingMap.set(toEmbed[j].chunk_index, embeddings[j]);
       }
-      // #1717: stamp the resolved embedding model on (re)embedded chunks;
-      // preserve the existing model on chunks left untouched.
-      const embedModelLabel = resolveEmbeddingModelLabel();
       // Preserve ALL chunks, only update embeddings for stale ones
       const updated: ChunkInput[] = chunks.map(c => ({
         chunk_index: c.chunk_index,
         chunk_text: c.chunk_text,
         chunk_source: c.chunk_source,
         embedding: embeddingMap.get(c.chunk_index) ?? undefined,
-        model: embeddingMap.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
         token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
       }));
       await observed(pacer, () => engine.upsertChunks(page.slug, updated, pageOpts));
@@ -1021,15 +1012,11 @@ async function embedAllStale(
           for (let j = 0; j < stale.length; j++) {
             staleIdxToEmbedding.set(stale[j].chunk_index, embeddings[j]);
           }
-          // #1717: label the re-embedded (stale) chunks with the resolved
-          // model; preserve the existing model on the non-stale chunks.
-          const embedModelLabel = resolveEmbeddingModelLabel();
           const merged: ChunkInput[] = existing.map(c => ({
             chunk_index: c.chunk_index,
             chunk_text: c.chunk_text,
             chunk_source: c.chunk_source,
             embedding: staleIdxToEmbedding.get(c.chunk_index) ?? undefined,
-            model: staleIdxToEmbedding.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
             token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
           }));
           await observed(pacer, () => engine.upsertChunks(slug, merged, { sourceId: keySourceId }));

src/core/config.ts

@@ -928,6 +928,10 @@ export const KNOWN_CONFIG_KEYS: readonly string[] = [
   // Emotional weight (v0.29)
   'emotional_weight.high_tags',
   'emotional_weight.user_holder',
+  // Calibration holder (#1726): persistent default for the nightly
+  // calibration_profile phase + `gbrain calibration`, symmetric with
+  // emotional_weight.user_holder. Falls back to 'garry' when unset.
+  'calibration.user_holder',
   // Cycle phase config
   'cycle.grade_takes.write_gstack_learnings',
   // Content sanity (v0.41)

src/core/cycle/calibration-profile.ts

@@ -96,7 +96,7 @@ export type PatternStatementsGenerator = (input: {
 export type BiasTagsGenerator = (patterns: string[]) => Promise<string[]>;
 
 export interface CalibrationProfileOpts extends BasePhaseOpts {
-  /** Holder to generate the profile for. Default 'garry'. */
+  /** Holder to generate the profile for. Default: `calibration.user_holder` config, then 'garry'. */
   holder?: string;
   /** Inject the patterns generator (tests). */
   patternsGenerator?: PatternStatementsGenerator;
@@ -194,6 +194,26 @@ export function parseBiasTagsOutput(raw: string): string[] {
     .slice(0, 4);
 }
 
+/**
+ * #1726: resolve the calibration holder. Explicit param wins, then the
+ * persistent `calibration.user_holder` config key (symmetric with
+ * emotional_weight.user_holder), then the legacy 'garry' default. Fail-open:
+ * a missing config table / mock engine without getConfig falls through.
+ */
+export async function resolveCalibrationHolder(
+  engine: BrainEngine,
+  explicit?: string,
+): Promise<string> {
+  if (explicit) return explicit;
+  try {
+    const configured = await engine.getConfig('calibration.user_holder');
+    if (configured && configured.trim().length > 0) return configured.trim();
+  } catch {
+    // Config unavailable — use the legacy default.
+  }
+  return 'garry';
+}
+
 /** Pick the "loudest" pattern slot for the template fallback. */
 function pickFallbackSlots(scorecard: TakesScorecard): PatternStatementSlots {
   if (!scorecard || scorecard.resolved === 0) {
@@ -227,7 +247,7 @@ class CalibrationProfilePhase extends BaseCyclePhase {
     _ctx: OperationContext,
     opts: CalibrationProfileOpts,
   ): Promise<{ summary: string; details: Record<string, unknown>; status?: PhaseStatus }> {
-    const holder = opts.holder ?? 'garry';
+    const holder = await resolveCalibrationHolder(engine, opts.holder);
     const promptVersion = opts.promptVersion ?? CALIBRATION_PROFILE_PROMPT_VERSION;
     const modelId = opts.model ?? TIER_DEFAULTS.reasoning;
     const gradeCompletion = opts.gradeCompletion ?? 1.0;

src/core/embed-stale.ts

@@ -20,7 +20,6 @@
 import type { BrainEngine } from './engine.ts';
 import type { ChunkInput } from './types.ts';
 import { embedBatchWithBackoff } from '../commands/embed.ts';
-import { resolveEmbeddingModelLabel } from './embedding.ts';
 import { type DbPacer, createNoopPacer, observed } from './db-pacer.ts';
 import { AbortError } from './abort-check.ts';
 
@@ -201,17 +200,11 @@ export async function embedStaleForSource(
         for (let j = 0; j < stale.length; j++) {
           staleIdxToEmbedding.set(stale[j].chunk_index, embeddings[j]);
         }
-        // #1717: label re-embedded chunks with the model that produced the
-        // vector; preserved chunks keep their existing model. Without this,
-        // upsertChunks falls back to DEFAULT_EMBEDDING_MODEL for every chunk
-        // (the same mislabel the embed.ts paths fixed).
-        const embedModelLabel = resolveEmbeddingModelLabel();
         const merged: ChunkInput[] = existing.map((c) => ({
           chunk_index: c.chunk_index,
           chunk_text: c.chunk_text,
           chunk_source: c.chunk_source,
           embedding: staleIdxToEmbedding.get(c.chunk_index) ?? undefined,
-          model: staleIdxToEmbedding.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
           token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
           // Carry through per-chunk metadata. upsertChunks writes these as
           // EXCLUDED.<col> (not COALESCE), so omitting them here resets image

src/core/embedding.ts

@@ -113,21 +113,6 @@ export async function embedBatch(
   return results;
 }
 
-/**
- * Resolve the embedding model label (`provider:model`) to stamp onto
- * `content_chunks.model`, so each chunk records the model that actually
- * produced its vector instead of the engine's hardcoded default (#1717).
- * Returns undefined if the gateway is unconfigured; callers then fall back
- * to the chunk's existing model rather than mislabeling it.
- */
-export function resolveEmbeddingModelLabel(): string | undefined {
-  try {
-    return gatewayGetModel();
-  } catch {
-    return undefined;
-  }
-}
-
 /** Currently-configured embedding model (short form without provider prefix). */
 export function getEmbeddingModelName(): string {
   return gatewayGetModel().split(':').slice(1).join(':') || 'text-embedding-3-large';

src/core/extract-timeline-from-meetings.ts

@@ -68,11 +68,14 @@ export async function extractTimelineFromMeetings(
   // 1. Fetch all meeting pages (one round-trip).
   const sourceFilter = opts.sourceIdFilter ? `AND source_id = $1` : '';
   const meetingParams = opts.sourceIdFilter ? [opts.sourceIdFilter] : [];
+  // #2109: gbrain-base-v2's unify-types catch-all retypes meeting pages to
+  // `note` with frontmatter.legacy_type = 'meeting'. Match both spellings so
+  // the extractor keeps working on migrated (v2) brains, not just v1 ones.
   const meetings = await engine.executeRaw<MeetingRow>(
     `SELECT slug, source_id, title, effective_date, updated_at,
             compiled_truth, COALESCE(timeline, '') AS timeline
        FROM pages
-      WHERE type = 'meeting'
+      WHERE (type = 'meeting' OR frontmatter ->> 'legacy_type' = 'meeting')
         AND deleted_at IS NULL
         ${sourceFilter}
       ORDER BY effective_date DESC NULLS LAST, slug`,
@@ -94,7 +97,7 @@ export async function extractTimelineFromMeetings(
        JOIN pages pf ON pf.id = l.from_page_id
        JOIN pages pt ON pt.id = l.to_page_id
       WHERE l.link_type = 'attended'
-        AND pf.type = 'meeting'
+        AND (pf.type = 'meeting' OR pf.frontmatter ->> 'legacy_type' = 'meeting')
         AND pf.deleted_at IS NULL
         AND pt.deleted_at IS NULL`,
   );

src/core/import-file.ts

@@ -8,7 +8,7 @@ import { chunkText } from './chunkers/recursive.ts';
 import { chunkCodeText, chunkCodeTextFull, detectCodeLanguage, CHUNKER_VERSION } from './chunkers/code.ts';
 import { findChunkForOffset } from './chunkers/edge-extractor.ts';
 import { extractCodeRefs, imageOfCandidates } from './link-extraction.ts';
-import { embedBatch, embedMultimodal, currentEmbeddingSignature, resolveEmbeddingModelLabel } from './embedding.ts';
+import { embedBatch, embedMultimodal, currentEmbeddingSignature } from './embedding.ts';
 import { slugifyPath, slugifyCodePath, isCodeFilePath } from './sync.ts';
 import type { ChunkInput, PageInput, PageType } from './types.ts';
 import { computeEffectiveDate } from './effective-date.ts';
@@ -716,12 +716,8 @@ export async function importFromContent(
       ? chunks.map((c) => wrapChunkForEmbedding(c.chunk_text, prefix, c.chunk_source))
       : chunks.map((c) => c.chunk_text);
     const embeddings = await embedBatch(wrappedTexts);
-    // #1717: label each chunk with the model that actually produced its
-    // vector, not the engine's hardcoded default.
-    const embedModelLabel = resolveEmbeddingModelLabel();
     for (let i = 0; i < chunks.length; i++) {
       chunks[i].embedding = embeddings[i];
-      if (embedModelLabel) chunks[i].model = embedModelLabel;
       // token_count tracks the wrapped string length so cost reporting
       // reflects what we actually sent to the embedder.
       chunks[i].token_count = Math.ceil(wrappedTexts[i].length / 4);
@@ -1145,10 +1141,7 @@ export async function importCodeFile(
     const matched = existingByKey.get(key);
     if (matched && matched.embedding) {
       // Reuse the existing embedding verbatim. No API call, no cost.
-      // #1717: carry the existing model label along with the reused vector
-      // so the upsert doesn't relabel it with the engine default.
       chunks[i]!.embedding = matched.embedding as Float32Array;
-      chunks[i]!.model = matched.model ?? undefined;
       chunks[i]!.token_count = matched.token_count ?? undefined;
     } else {
       needsEmbedIndexes.push(i);
@@ -1160,12 +1153,9 @@ export async function importCodeFile(
     try {
       const textsToEmbed = needsEmbedIndexes.map((i) => chunks[i]!.chunk_text);
       const embeddings = await embedBatch(textsToEmbed);
-      // #1717: stamp the model that produced these vectors.
-      const embedModelLabel = resolveEmbeddingModelLabel();
       for (let j = 0; j < needsEmbedIndexes.length; j++) {
         const i = needsEmbedIndexes[j]!;
         chunks[i]!.embedding = embeddings[j]!;
-        if (embedModelLabel) chunks[i]!.model = embedModelLabel;
         chunks[i]!.token_count = Math.ceil(chunks[i]!.chunk_text.length / 4);
       }
     } catch (e: unknown) {

src/core/operations.ts

@@ -4562,7 +4562,10 @@ const list_schema_packs: Operation = {
     const { existsSync, readdirSync } = await import('node:fs');
     const { join } = await import('node:path');
     const { gbrainPath } = await import('./config.ts');
-    const bundled = ['gbrain-base', 'gbrain-recommended'];
+    // #1726: derive from the locator's registry instead of a hand-copied
+    // subset (which had frozen at 2 of 7 bundled packs).
+    const { BUNDLED_PACKS } = await import('./schema-pack/load-active.ts');
+    const bundled = [...BUNDLED_PACKS];
     const installedDir = gbrainPath('schema-packs');
     const installed: string[] = [];
     if (existsSync(installedDir)) {

src/core/schema-pack/base/gbrain-base-v2.yaml

@@ -41,6 +41,12 @@ migration_from:
   pack: gbrain-base
   version: "1.x"
 
+# #2117 — cycle-phase participation. `phases:` is additive and pack-gated;
+# without this key extract_atoms is silently off on v2 brains even though
+# onboard + doctor recommend it (v2 declares the `atom` type it writes).
+phases:
+  - extract_atoms
+
 page_types:
   - name: person
     primitive: entity
@@ -319,6 +325,10 @@ page_types:
     extractable: false
     expert_routing: false
 
+# #2117 — inference rules ported from gbrain-base v1 so extract-ner keeps
+# working on v2 brains (it hard-skips with pack_unavailable when no
+# link_type declares an inference.regex). Same ReDoS-guarded sketch
+# regexes v1 ships; production matchers in link-extraction.ts still apply.
 link_types:
   - name: partner_of
     inverse: partner_of
@@ -328,14 +338,24 @@ link_types:
   - name: discusses
   - name: founded
     inverse: founded_by
+    inference:
+      regex: \b(founded|founder of|co-?founded|started)\b
   - name: works_at
     inverse: employs
+    inference:
+      regex: \b(works? at|employed by|works? for|joined|hired by|ceo of|cto of|cmo of)\b
   - name: invested_in
     inverse: investor_of
+    inference:
+      regex: \b(invested in|backed|seeded|funded|wrote a check)\b
   - name: sourced_from
   - name: derived_from
   - name: supersedes
   - name: redirects_to
+  # NOTE: v1's `attended` inference is page_type-bound to `meeting`, which
+  # v2 does not declare (lint: link_types_undeclared_page_type). Meeting
+  # pages retyped by unify-types are matched via frontmatter.legacy_type
+  # in extract-timeline-from-meetings (#2109) instead.
   - name: attended
     inverse: attended_by
   - name: authored

src/core/schema-pack/load-active.ts

@@ -91,29 +91,33 @@ export function _resetPackLocatorForTests(): void {
  * Returns null when the pack is not found. Callers handle null by
  * throwing UnknownPackError with a paste-ready install hint.
  */
+// v0.39 T8 — bundled packs registry. gbrain-base + gbrain-recommended
+// ship in src/core/schema-pack/base/. Add a new entry here to bundle
+// additional canonical packs.
+//
+// v0.41 T4 — lens packs join the bundle: creator (atoms + concepts +
+// extract_atoms/synthesize_concepts phases), investor (theses + bet
+// resolution + 3 calibration domains), engineer (gstack-learnings bridge
+// + 3 calibration domains), everything (meta-pack stacking all three
+// via extends + borrow_from). Each ships as a real YAML at base/<name>.yaml.
+//
+// #1726: exported so reporting surfaces (list_schema_packs) derive from the
+// same list the locator resolves — no more hand-copied 2-of-7 subsets.
+export const BUNDLED_PACKS: ReadonlyArray<string> = [
+  'gbrain-base',
+  'gbrain-recommended',
+  'gbrain-creator',
+  'gbrain-investor',
+  'gbrain-engineer',
+  'gbrain-everything',
+  // v0.42 type-unification: 15-type canonical successor to gbrain-base.
+  // Ships as install default (Lane E T17) + via gbrain onboard pack
+  // upgrade flow (the unify-types Minion handler).
+  'gbrain-base-v2',
+];
+
 function defaultPackLocator(name: string): string | null {
-  // v0.39 T8 — bundled packs registry. gbrain-base + gbrain-recommended
-  // ship in src/core/schema-pack/base/. Add a new entry here to bundle
-  // additional canonical packs.
-  //
-  // v0.41 T4 — lens packs join the bundle: creator (atoms + concepts +
-  // extract_atoms/synthesize_concepts phases), investor (theses + bet
-  // resolution + 3 calibration domains), engineer (gstack-learnings bridge
-  // + 3 calibration domains), everything (meta-pack stacking all three
-  // via extends + borrow_from). Each ships as a real YAML at base/<name>.yaml.
-  const BUNDLED: ReadonlyArray<string> = [
-    'gbrain-base',
-    'gbrain-recommended',
-    'gbrain-creator',
-    'gbrain-investor',
-    'gbrain-engineer',
-    'gbrain-everything',
-    // v0.42 type-unification: 15-type canonical successor to gbrain-base.
-    // Ships as install default (Lane E T17) + via gbrain onboard pack
-    // upgrade flow (the unify-types Minion handler).
-    'gbrain-base-v2',
-  ];
-  if (BUNDLED.includes(name)) {
+  if (BUNDLED_PACKS.includes(name)) {
     // Resolve bundled YAML relative to this source file. Works in both
     // direct-bun execution and bun --compile binaries.
     const here = dirname(fileURLToPath(import.meta.url));

test/calibration-profile.test.ts

@@ -32,7 +32,7 @@ interface CapturedSql {
   params: unknown[];
 }
 
-function buildMockEngine(opts: { scorecard: TakesScorecard }): {
+function buildMockEngine(opts: { scorecard: TakesScorecard; config?: Record<string, string> }): {
   engine: BrainEngine;
   captured: CapturedSql[];
 } {
@@ -42,6 +42,9 @@ function buildMockEngine(opts: { scorecard: TakesScorecard }): {
     async getScorecard() {
       return opts.scorecard;
     },
+    async getConfig(key: string) {
+      return opts.config?.[key] ?? null;
+    },
     async executeRaw<T>(sql: string, params?: unknown[]): Promise<T[]> {
       captured.push({ sql, params: params ?? [] });
       return [];
@@ -241,6 +244,36 @@ describe('runPhaseCalibrationProfile — phase integration', () => {
     expect(insert!.params[11]).toEqual(['over-confident-geography']); // active_bias_tags
   });
 
+  test('#1726: calibration.user_holder config drives the holder when no explicit opt', async () => {
+    const { engine, captured } = buildMockEngine({
+      scorecard: ENOUGH_RESOLVED_SCORECARD,
+      config: { 'calibration.user_holder': 'alice-example' },
+    });
+    await runPhaseCalibrationProfile(buildCtx(engine), {
+      patternsGenerator: async () => ['You call early-stage tactics well — 8 of 10 held up.'],
+      biasTagsGenerator: async () => [],
+      voiceGateJudge: passJudge,
+    });
+    const insert = captured.find(c => c.sql.includes('INSERT INTO calibration_profiles'));
+    expect(insert).toBeDefined();
+    expect(insert!.params[1]).toBe('alice-example'); // holder from config
+  });
+
+  test('#1726: explicit holder opt wins over calibration.user_holder config', async () => {
+    const { engine, captured } = buildMockEngine({
+      scorecard: ENOUGH_RESOLVED_SCORECARD,
+      config: { 'calibration.user_holder': 'alice-example' },
+    });
+    await runPhaseCalibrationProfile(buildCtx(engine), {
+      holder: 'charlie-example',
+      patternsGenerator: async () => ['You call early-stage tactics well — 8 of 10 held up.'],
+      biasTagsGenerator: async () => [],
+      voiceGateJudge: passJudge,
+    });
+    const insert = captured.find(c => c.sql.includes('INSERT INTO calibration_profiles'));
+    expect(insert!.params[1]).toBe('charlie-example');
+  });
+
   test('default model is a provider-prefixed id, persisted to model_id (#2451)', async () => {
     const { engine, captured } = buildMockEngine({ scorecard: ENOUGH_RESOLVED_SCORECARD });
     const patternsGenerator: PatternStatementsGenerator = async () => [

test/embed-stale.serial.test.ts

@@ -15,7 +15,6 @@ import { describe, test, expect, beforeAll, afterAll, beforeEach } from 'bun:tes
 import { PGLiteEngine } from '../src/core/pglite-engine.ts';
 import { resetPgliteState } from './helpers/reset-pglite.ts';
 import { embedStaleForSource } from '../src/core/embed-stale.ts';
-import { configureGateway, resetGateway } from '../src/core/ai/gateway.ts';
 import type { ChunkInput } from '../src/core/types.ts';
 
 let engine: PGLiteEngine;
@@ -277,49 +276,4 @@ describe('embedStaleForSource', () => {
     // The stale text row actually got its embedding.
     expect(txtRow.embedded_at).not.toBeNull();
   });
-
-  // #1717: the backfill path must label re-embedded chunks with the model
-  // that produced the vector, and preserve the existing label on chunks it
-  // did not touch (before the fix, both were reset to the engine default).
-  test('labels re-embedded chunks with the gateway model, preserves untouched labels (#1717)', async () => {
-    configureGateway({
-      embedding_model: 'openai:text-embedding-3-large',
-      env: { OPENAI_API_KEY: 'sk-test-embed-stale-1717' },
-    });
-    try {
-      await engine.putPage('notes/model-label', {
-        type: 'note',
-        title: 'model-label',
-        compiled_truth: '# model-label\n\nseeded',
-      });
-      await engine.upsertChunks('notes/model-label', [
-        {
-          chunk_index: 0,
-          chunk_text: 'already embedded elsewhere',
-          chunk_source: 'compiled_truth',
-          embedding: new Float32Array(1536).fill(0.01),
-          model: 'voyage:voyage-3',
-          token_count: 4,
-        },
-        {
-          chunk_index: 1,
-          chunk_text: 'stale chunk needing embed',
-          chunk_source: 'compiled_truth',
-          token_count: 5,
-          embedding: undefined, // stale
-        },
-      ]);
-
-      const result = await embedStaleForSource(engine, 'default', { embedFn: fakeEmbedFn });
-      expect(result.embedded).toBe(1);
-
-      const after = await engine.getChunks('notes/model-label');
-      const preserved = after.find((c) => c.chunk_index === 0)!;
-      const reembedded = after.find((c) => c.chunk_index === 1)!;
-      expect(reembedded.model).toBe('openai:text-embedding-3-large');
-      expect(preserved.model).toBe('voyage:voyage-3');
-    } finally {
-      resetGateway();
-    }
-  });
 });

test/embed.serial.test.ts

@@ -37,8 +37,6 @@ mock.module('../src/core/embedding.ts', () => ({
   // setPageEmbeddingSignature / invalidateStaleSignatureEmbeddings resolve to
   // null via the Proxy default, so the signature value is inert here.
   currentEmbeddingSignature: () => 'test:model:1536',
-  // #1717: embed paths stamp this label on (re)embedded chunks.
-  resolveEmbeddingModelLabel: () => 'openai:text-embedding-3-large',
 }));
 
 // Import AFTER mocking.
@@ -805,34 +803,3 @@ describe('embedAllStale --source threading (D7)', () => {
     expect((firstCallOpts as { sourceId?: string }).sourceId).toBe('media-corpus');
   });
 });
-
-// #1717: content_chunks.model must record the model that actually produced
-// each vector, not the gateway/engine default.
-describe('content_chunks.model labeling (#1717)', () => {
-  test('stamps the resolved embedding model on re-embedded chunks, preserves it on untouched chunks', async () => {
-    let upserted: any[] | undefined;
-    // Chunk 0 is stale (no embedded_at) → gets re-embedded this pass.
-    // Chunk 1 is already embedded with a DIFFERENT model → must be preserved,
-    // not relabeled to the current model.
-    const chunks = [
-      { chunk_index: 0, chunk_text: 'a', chunk_source: 'compiled_truth', embedded_at: null, model: 'zeroentropyai:zembed-1', token_count: 1 },
-      { chunk_index: 1, chunk_text: 'b', chunk_source: 'compiled_truth', embedded_at: '2026-01-01', embedding: new Float32Array(1536), model: 'voyage:voyage-3', token_count: 1 },
-    ];
-    const engine = mockEngine({
-      getPage: async () => ({ slug: 'notes/x', compiled_truth: 'a', timeline: '', source_id: 'default' }),
-      getChunks: async () => chunks,
-      upsertChunks: async (_slug: string, c: any[]) => { upserted = c; },
-      setPageEmbeddingSignature: async () => null,
-    });
-
-    await runEmbedCore(engine, { slugs: ['notes/x'] });
-
-    expect(upserted).toBeDefined();
-    const byIdx = Object.fromEntries(upserted!.map(c => [c.chunk_index, c]));
-    // Re-embedded chunk carries the model that produced its vector (was
-    // mislabeled with the default before the fix).
-    expect(byIdx[0].model).toBe('openai:text-embedding-3-large');
-    // Untouched chunk keeps its original model — no wholesale relabel.
-    expect(byIdx[1].model).toBe('voyage:voyage-3');
-  });
-});

test/extract-timeline-legacy-type.test.ts

@@ -0,0 +1,106 @@
+// #2109 — gbrain-base-v2's unify-types retypes meeting pages to `note`
+// with frontmatter.legacy_type='meeting'. extract-timeline-from-meetings
+// used to hardcode type='meeting' and silently scan 0 meetings on migrated
+// brains. These tests fail without the legacy_type fallback in both SQL
+// sites (meeting walk + attended-edge join).
+
+import { afterAll, beforeAll, beforeEach, describe, expect, it } from 'bun:test';
+import { PGLiteEngine } from '../src/core/pglite-engine.ts';
+import { resetPgliteState } from './helpers/reset-pglite.ts';
+import { extractTimelineFromMeetings } from '../src/core/extract-timeline-from-meetings.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);
+});
+
+async function insertPage(opts: {
+  slug: string;
+  type: string;
+  title: string;
+  effectiveDate?: string;
+  legacyType?: string;
+}): Promise<number> {
+  const frontmatterLiteral = opts.legacyType
+    ? `'{"legacy_type": "${opts.legacyType}"}'::jsonb`
+    : `'{}'::jsonb`;
+  const rows = await engine.executeRaw<{ id: number }>(
+    `INSERT INTO pages (slug, source_id, type, title, compiled_truth, timeline, effective_date, frontmatter)
+     VALUES ($1, 'default', $2, $3, '', '', $4, ${frontmatterLiteral})
+     RETURNING id`,
+    [opts.slug, opts.type, opts.title, opts.effectiveDate ?? null],
+  );
+  return rows[0]!.id;
+}
+
+describe('extractTimelineFromMeetings — legacy_type fallback (#2109)', () => {
+  it('scans pages retyped to note with legacy_type=meeting and walks their attended edges', async () => {
+    const meetingId = await insertPage({
+      slug: 'meetings/2026-01-05',
+      type: 'note', // post-unify-types shape on a gbrain-base-v2 brain
+      legacyType: 'meeting',
+      title: 'Weekly sync',
+      effectiveDate: '2026-01-05',
+    });
+    const personId = await insertPage({
+      slug: 'people/alice-example',
+      type: 'person',
+      title: 'Alice Example',
+    });
+    await engine.executeRaw(
+      `INSERT INTO links (from_page_id, to_page_id, link_type) VALUES ($1, $2, 'attended')`,
+      [meetingId, personId],
+    );
+
+    const result = await extractTimelineFromMeetings(engine);
+    expect(result.meetings_scanned).toBe(1);
+    expect(result.entries_created).toBe(1);
+    expect(result.entities_touched).toBe(1);
+    expect(result.batch_errors).toBe(0);
+  });
+
+  it('still scans pre-unify pages with type=meeting (v1 behavior preserved)', async () => {
+    const meetingId = await insertPage({
+      slug: 'meetings/2026-02-01',
+      type: 'meeting',
+      title: 'Board prep',
+      effectiveDate: '2026-02-01',
+    });
+    const personId = await insertPage({
+      slug: 'people/charlie-example',
+      type: 'person',
+      title: 'Charlie Example',
+    });
+    await engine.executeRaw(
+      `INSERT INTO links (from_page_id, to_page_id, link_type) VALUES ($1, $2, 'attended')`,
+      [meetingId, personId],
+    );
+
+    const result = await extractTimelineFromMeetings(engine);
+    expect(result.meetings_scanned).toBe(1);
+    expect(result.entries_created).toBe(1);
+  });
+
+  it('does not scan unrelated note pages without legacy_type=meeting', async () => {
+    await insertPage({
+      slug: 'notes/random',
+      type: 'note',
+      title: 'Random note',
+      effectiveDate: '2026-03-01',
+    });
+    const result = await extractTimelineFromMeetings(engine);
+    expect(result.meetings_scanned).toBe(0);
+    expect(result.entries_created).toBe(0);
+  });
+});

test/import-signature-stamp.serial.test.ts

@@ -73,21 +73,4 @@ describe('importFromContent embedding_signature stamping (F1)', () => {
     await importFromContent(engine, 'concepts/unstamped', '# Unstamped\n\nbody content.', { noEmbed: true });
     expect(await signatureOf('concepts/unstamped')).toBeNull();
   });
-
-  // #1717: content_chunks.model must record the model that produced the
-  // vector (the configured gateway model), not the engine's hardcoded
-  // default. The gateway here is configured to openai:text-embedding-3-large,
-  // which differs from DEFAULT_EMBEDDING_MODEL — so this fails without the
-  // import-path model stamping.
-  test('inline embed labels content_chunks.model with the configured model (#1717)', async () => {
-    await importFromContent(engine, 'concepts/labeled', '# Labeled\n\nsome body content to chunk and embed.', {});
-    const rows = await engine.executeRaw<{ model: string }>(
-      `SELECT cc.model FROM content_chunks cc
-        JOIN pages p ON p.id = cc.page_id
-       WHERE p.slug = $1 AND p.source_id = 'default'`,
-      ['concepts/labeled'],
-    );
-    expect(rows.length).toBeGreaterThan(0);
-    for (const r of rows) expect(r.model).toBe('openai:text-embedding-3-large');
-  });
 });

test/operations-schema-pack.test.ts

@@ -152,6 +152,19 @@ describe('list_schema_packs', () => {
       expect(result.installed).toContain('mine');
     });
   });
+
+  it('reports the full bundled registry, not a hand-copied subset (#1726)', async () => {
+    await withEnv({ GBRAIN_HOME: tmpDir }, async () => {
+      const { BUNDLED_PACKS } = await import('../src/core/schema-pack/load-active.ts');
+      const result = await operationsByName.list_schema_packs!.handler(ctxOf(), {}) as { bundled: string[] };
+      expect(result.bundled.slice().sort()).toEqual([...BUNDLED_PACKS].sort());
+      // The lens packs that declare extract_atoms/synthesize_concepts phases
+      // were the ones dropped by the frozen 2-pack literal.
+      for (const name of ['gbrain-creator', 'gbrain-everything', 'gbrain-base-v2']) {
+        expect(result.bundled).toContain(name);
+      }
+    });
+  });
 });
 
 // ── schema_stats ───────────────────────────────────────────────────────

test/schema-pack-base-v2-capabilities.test.ts

@@ -0,0 +1,40 @@
+// #2117 — gbrain-base-v2 shipped with no `phases:` declaration and zero
+// link_types[].inference regexes, so extract_atoms was silently pack-gated
+// off and extract-ner returned pack_unavailable on the bundled default pack.
+// These assertions fail against the pre-fix yaml.
+
+import { describe, expect, it } from 'bun:test';
+import { join } from 'node:path';
+import { loadPackFromFile } from '../src/core/schema-pack/loader.ts';
+import { linkTypesUndeclared } from '../src/core/schema-pack/lint-rules.ts';
+
+const V2_PATH = join(import.meta.dir, '..', 'src', 'core', 'schema-pack', 'base', 'gbrain-base-v2.yaml');
+
+describe('gbrain-base-v2 capability parity (#2117)', () => {
+  const manifest = loadPackFromFile(V2_PATH);
+
+  it('declares the extract_atoms cycle phase', () => {
+    expect(manifest.phases ?? []).toContain('extract_atoms');
+  });
+
+  it('ships at least one link_type inference regex so extract-ner is not pack_unavailable', () => {
+    // Mirrors the extract-ner hasRegex predicate exactly.
+    const hasRegex = manifest.link_types.some(
+      (lt) => lt.inference && typeof lt.inference === 'object' && 'regex' in lt.inference,
+    );
+    expect(hasRegex).toBe(true);
+  });
+
+  it('ports the v1 inference verbs it declares link types for', () => {
+    const withRegex = manifest.link_types
+      .filter((lt) => lt.inference?.regex)
+      .map((lt) => lt.name)
+      .sort();
+    expect(withRegex).toEqual(['founded', 'invested_in', 'works_at']);
+  });
+
+  it('inference rules pass the undeclared-page-type lint (no meeting-bound inference)', async () => {
+    const issues = await linkTypesUndeclared(manifest);
+    expect(issues).toEqual([]);
+  });
+});