fix(schema): block scalars keep '#' as literal content

Inside a YAML block scalar '#' is content, not a comment; parseBlockScalar
was routing lines through stripComment/isBlank, truncating descriptions
like 'see issue #2029' and blanking comment-looking lines. Use the raw
line inside the scalar. Adds a pinning test.

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

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/ai/anthropic-key.ts

@@ -18,12 +18,22 @@
 import { loadConfig } from '../config.ts';
 
 export function hasAnthropicKey(): boolean {
-  if (process.env.ANTHROPIC_API_KEY) return true;
+  return resolveAnthropicKey() !== undefined;
+}
+
+/**
+ * Resolve the actual key value: env first, then the gbrain config file.
+ * Callers constructing an Anthropic client directly (e.g. the legacy
+ * subagent path) must pass this as `apiKey` — a bare `new Anthropic()`
+ * only sees env, so launchd/MCP workers with config-stored keys fail.
+ */
+export function resolveAnthropicKey(): string | undefined {
+  if (process.env.ANTHROPIC_API_KEY) return process.env.ANTHROPIC_API_KEY;
   try {
     const cfg = loadConfig();
-    if (cfg?.anthropic_api_key) return true;
+    if (cfg?.anthropic_api_key) return cfg.anthropic_api_key;
   } catch {
     // loadConfig may throw on first-run installs; treat as no key available.
   }
-  return false;
+  return undefined;
 }

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/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/minions/handlers/subagent.ts

@@ -48,6 +48,7 @@ import {
   logSubagentHeartbeat,
 } from './subagent-audit.ts';
 import { resolveModel, isAnthropicProvider, TIER_DEFAULTS } from '../../model-config.ts';
+import { resolveAnthropicKey } from '../../ai/anthropic-key.ts';
 import { buildSystemPrompt, DEFAULT_SUBAGENT_SYSTEM } from '../system-prompt.ts';
 import { toolLoop as gatewayToolLoop } from '../../ai/gateway.ts';
 import type { ChatToolDef, ChatMessage, ChatBlock, ChatResult, ToolHandler } from '../../ai/gateway.ts';
@@ -186,7 +187,10 @@ export function makeSubagentHandler(deps: SubagentDeps) {
   // lives at sdk.messages.create. Assigning sdk.messages directly gets the
   // right object; JS method-call semantics preserve `this` at the call
   // site (subagent.ts invokes client.create(...) with client === sdk.messages).
-  const makeAnthropic = deps.makeAnthropic ?? (() => new Anthropic());
+  // Resolve the key env-first, then config (anthropic_api_key) — a bare
+  // new Anthropic() only reads env, so launchd/MCP workers whose key lives
+  // in the gbrain config file would fail auth (#2048).
+  const makeAnthropic = deps.makeAnthropic ?? (() => new Anthropic({ apiKey: resolveAnthropicKey() }));
   const client: MessagesClient = deps.client ?? makeAnthropic().messages;
   const config = deps.config ?? loadConfig() ?? ({ engine: 'postgres' } as GBrainConfig);
   const rateLeaseKey = deps.rateLeaseKey ?? DEFAULT_RATE_KEY;

src/core/operations.ts

@@ -4562,7 +4562,8 @@ 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'];
+    const { BUNDLED_PACK_NAMES } = await import('./schema-pack/bundled.ts');
+    const bundled = [...BUNDLED_PACK_NAMES];
     const installedDir = gbrainPath('schema-packs');
     const installed: string[] = [];
     if (existsSync(installedDir)) {

src/core/schema-pack/bundled.ts

@@ -0,0 +1,24 @@
+// Bundled schema-pack registry — single source of truth for the packs that
+// ship in src/core/schema-pack/base/. Keep every bundled-pack consumer
+// (CLI/MCP inspection, active-pack loading, mutation guards, upgrade
+// discovery) on this one list so they cannot drift.
+//
+// v0.39 T8 — gbrain-base + gbrain-recommended.
+// v0.41 T4 — lens packs: creator, investor, engineer, everything (meta-pack).
+// v0.42 type-unification — gbrain-base-v2, the 15-type canonical successor.
+
+export const BUNDLED_PACK_NAMES = [
+  'gbrain-base',
+  'gbrain-recommended',
+  'gbrain-creator',
+  'gbrain-investor',
+  'gbrain-engineer',
+  'gbrain-everything',
+  'gbrain-base-v2',
+] as const;
+
+export type BundledPackName = typeof BUNDLED_PACK_NAMES[number];
+
+export function isBundledPackName(name: string): name is BundledPackName {
+  return (BUNDLED_PACK_NAMES as readonly string[]).includes(name);
+}

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

@@ -37,6 +37,7 @@ import {
   type ResolutionInput,
   type ResolutionResult,
 } from './registry.ts';
+import { isBundledPackName } from './bundled.ts';
 
 /**
  * Inputs the caller (operations.ts handler / engine query path) provides.
@@ -92,28 +93,7 @@ export function _resetPackLocatorForTests(): void {
  * throwing UnknownPackError with a paste-ready install hint.
  */
 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 (isBundledPackName(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));

src/core/schema-pack/loader.ts

@@ -159,6 +159,29 @@ export function parseYamlMini(content: string): unknown {
     return parseMapping(baseIndent);
   }
 
+  function parseBlockScalar(parentIndent: number, folded: boolean): string {
+    const contentIndent = parentIndent + 2;
+    const out: string[] = [];
+    while (i < lines.length) {
+      const raw = lines[i];
+      // Inside a block scalar everything is literal content — '#' is NOT a
+      // comment here, so use the raw line (no stripComment / isBlank).
+      if (raw.trim() === '') {
+        out.push('');
+        i++;
+        continue;
+      }
+      const indent = indentOf(raw);
+      if (indent <= parentIndent) break;
+      out.push(raw.slice(Math.min(contentIndent, indent)));
+      i++;
+    }
+    if (folded) {
+      return out.join(' ').replace(/\s+$/u, '');
+    }
+    return out.join('\n').replace(/\n+$/u, '');
+  }
+
   function parseSequence(baseIndent: number): unknown[] {
     const result: unknown[] = [];
     while (i < lines.length) {
@@ -227,6 +250,10 @@ export function parseYamlMini(content: string): unknown {
           i++;
           if (rest2 === '') {
             map[key2] = parseBlock(nextIndent + 2);
+          } else if (rest2 === '|' || rest2 === '|-' || rest2 === '|+') {
+            map[key2] = parseBlockScalar(nextIndent, false);
+          } else if (rest2 === '>' || rest2 === '>-' || rest2 === '>+') {
+            map[key2] = parseBlockScalar(nextIndent, true);
           } else {
             map[key2] = parseScalar(rest2);
           }
@@ -257,6 +284,10 @@ export function parseYamlMini(content: string): unknown {
       i++;
       if (rest === '') {
         result[key] = parseBlock(indent + 2);
+      } else if (rest === '|' || rest === '|-' || rest === '|+') {
+        result[key] = parseBlockScalar(indent, false);
+      } else if (rest === '>' || rest === '>-' || rest === '>+') {
+        result[key] = parseBlockScalar(indent, true);
       } else {
         result[key] = parseScalar(rest);
       }

src/core/schema-pack/mutate.ts

@@ -65,6 +65,7 @@ import { invalidateQueryCache } from './query-cache-invalidator.ts';
 import { logMutationFailure, logMutationSuccess, type MutationActor, type MutationOp } from './mutate-audit.ts';
 import { runFilePlaneLintRules } from './lint-rules.ts';
 import { withPackLock, type PackLockOpts } from './pack-lock.ts';
+import { BUNDLED_PACK_NAMES as BUNDLED_PACK_NAME_LIST } from './bundled.ts';
 import type { BrainEngine } from '../engine.ts';
 
 export type PackFileFormat = 'json' | 'yaml';
@@ -93,7 +94,7 @@ export class SchemaPackMutationError extends Error {
   }
 }
 
-export const BUNDLED_PACK_NAMES = new Set(['gbrain-base', 'gbrain-recommended', 'gbrain-base-v2']);
+export const BUNDLED_PACK_NAMES = new Set<string>(BUNDLED_PACK_NAME_LIST);
 
 export interface MutateResult {
   /** Pack name that was mutated. */

test/ai/anthropic-key.test.ts

@@ -10,7 +10,7 @@ import { mkdtempSync, mkdirSync, writeFileSync, rmSync } from 'node:fs';
 import { tmpdir } from 'node:os';
 import { join } from 'node:path';
 import { withEnv } from '../helpers/with-env.ts';
-import { hasAnthropicKey } from '../../src/core/ai/anthropic-key.ts';
+import { hasAnthropicKey, resolveAnthropicKey } from '../../src/core/ai/anthropic-key.ts';
 
 const tmpDirs: string[] = [];
 function freshHome(withConfig?: Record<string, unknown>): string {
@@ -62,3 +62,35 @@ describe('hasAnthropicKey', () => {
     );
   });
 });
+
+describe('resolveAnthropicKey (#2048 — subagent config-key auth)', () => {
+  test('env wins over config', async () => {
+    const home = freshHome({ anthropic_api_key: 'sk-from-config' });
+    await withEnv(
+      { ANTHROPIC_API_KEY: 'sk-from-env', GBRAIN_HOME: home, DATABASE_URL: undefined, GBRAIN_DATABASE_URL: undefined },
+      async () => {
+        expect(resolveAnthropicKey()).toBe('sk-from-env');
+      },
+    );
+  });
+
+  test('config key returned when env unset', async () => {
+    const home = freshHome({ anthropic_api_key: 'sk-from-config' });
+    await withEnv(
+      { ANTHROPIC_API_KEY: undefined, GBRAIN_HOME: home, DATABASE_URL: undefined, GBRAIN_DATABASE_URL: undefined },
+      async () => {
+        expect(resolveAnthropicKey()).toBe('sk-from-config');
+      },
+    );
+  });
+
+  test('neither → undefined', async () => {
+    const home = freshHome();
+    await withEnv(
+      { ANTHROPIC_API_KEY: undefined, GBRAIN_HOME: home, DATABASE_URL: undefined, GBRAIN_DATABASE_URL: undefined },
+      async () => {
+        expect(resolveAnthropicKey()).toBeUndefined();
+      },
+    );
+  });
+});

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/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/lens-pack-manifests.test.ts

@@ -55,13 +55,13 @@ describe('v0.41 T4: all 4 bundled lens packs parse cleanly', () => {
 });
 
 describe('v0.41 T4: bundled registry includes lens packs', () => {
-  test('load-active.ts BUNDLED array source includes the 4 lens pack names', () => {
-    const loadActiveSrc = readFileSync(
-      join(here, '..', 'src', 'core', 'schema-pack', 'load-active.ts'),
-      'utf-8',
-    );
+  test('BUNDLED_PACK_NAMES includes the 4 lens pack names', async () => {
+    // The bundled list moved from load-active.ts to bundled.ts (the
+    // single source of truth); assert the array directly instead of
+    // grepping source text.
+    const { BUNDLED_PACK_NAMES } = await import('../src/core/schema-pack/bundled.ts');
     for (const name of PACK_NAMES) {
-      expect(loadActiveSrc).toContain(`'${name}'`);
+      expect(BUNDLED_PACK_NAMES).toContain(name);
     }
   });
 });

test/operations-schema-pack.test.ts

@@ -149,6 +149,9 @@ describe('list_schema_packs', () => {
       seedPack('mine');
       const result = await operationsByName.list_schema_packs!.handler(ctxOf(), {}) as { bundled: string[]; installed: string[] };
       expect(result.bundled).toContain('gbrain-base');
+      expect(result.bundled).toContain('gbrain-recommended');
+      expect(result.bundled).toContain('gbrain-base-v2');
+      expect(result.bundled).toContain('gbrain-investor');
       expect(result.installed).toContain('mine');
     });
   });

test/schema-cli.test.ts

@@ -64,11 +64,14 @@ describe('gbrain schema CLI (Phase C)', () => {
     expect(r.stdout + r.stderr).toMatch(/schema|active|list|show|validate|use/i);
   });
 
-  test('schema list shows gbrain-base bundled', () => {
+  test('schema list shows all bundled packs', () => {
     const r = gbrain(['schema', 'list']);
     expect(r.code).toBe(0);
     expect(r.stdout).toContain('Bundled packs:');
     expect(r.stdout).toContain('gbrain-base');
+    expect(r.stdout).toContain('gbrain-recommended');
+    expect(r.stdout).toContain('gbrain-base-v2');
+    expect(r.stdout).toContain('gbrain-investor');
   });
 
   test('schema show gbrain-base prints manifest details', () => {
@@ -97,6 +100,40 @@ describe('gbrain schema CLI (Phase C)', () => {
     expect(r.stdout).toContain('valid manifest');
   });
 
+  test('schema show/validate exposes bundled gbrain-recommended', () => {
+    const show = gbrain(['schema', 'show', 'gbrain-recommended']);
+    expect(show.code).toBe(0);
+    expect(show.stdout).toContain('gbrain-recommended v1.0.0');
+    expect(show.stdout).toContain('Page types (');
+    expect(show.stdout).toContain('meeting :: temporal');
+
+    const validate = gbrain(['schema', 'validate', 'gbrain-recommended']);
+    expect(validate.code).toBe(0);
+    expect(validate.stdout).toContain('valid manifest');
+  });
+
+  test('schema show exposes bundled gbrain-base-v2 successor pack', () => {
+    const r = gbrain(['schema', 'show', 'gbrain-base-v2']);
+    expect(r.code).toBe(0);
+    expect(r.stdout).toContain('gbrain-base-v2 v1.0.0');
+    expect(r.stdout).toContain('Page types (');
+    expect(r.stdout).toContain('Link verbs (14)');
+  });
+
+  test('schema active loads configured gbrain-recommended with real types', () => {
+    const home = mkdtempSync(join(tmpdir(), 'gbrain-schema-active-recommended-'));
+    try {
+      mkdirSync(join(home, '.gbrain'), { recursive: true });
+      writeFileSync(join(home, '.gbrain', 'config.json'), JSON.stringify({ schema_pack: 'gbrain-recommended' }), 'utf-8');
+      const r = gbrain(['schema', 'active'], { GBRAIN_HOME: home });
+      expect(r.code).toBe(0);
+      expect(r.stdout).toContain('Active pack: gbrain-recommended');
+      expect(r.stdout).not.toContain('Page types: 0');
+    } finally {
+      rmSync(home, { recursive: true, force: true });
+    }
+  });
+
   test('schema active reports default resolution', () => {
     const r = gbrain(['schema', 'active']);
     expect(r.code).toBe(0);

test/schema-pack-loader.test.ts

@@ -345,6 +345,34 @@ describe('YAML mini-parser', () => {
     expect(result.types[1].weight).toBe(2);
   });
 
+  test('parses block scalar without swallowing following keys', () => {
+    const yaml = `name: blocky
+description: |
+  First line.
+  Second line.
+page_types:
+  - name: meeting
+    primitive: temporal
+    path_prefixes:
+      - meetings/
+    aliases: []
+    extractable: true
+    expert_routing: false`;
+    const result = parseYamlMini(yaml) as { description: string; page_types: Array<Record<string, unknown>> };
+    expect(result.description).toBe('First line.\nSecond line.');
+    expect(result.page_types).toHaveLength(1);
+    expect(result.page_types[0].name).toBe('meeting');
+  });
+
+  test('block scalar keeps # as literal content, not a comment', () => {
+    const yaml = `description: |
+  See issue #2029 for context.
+name: hashy`;
+    const result = parseYamlMini(yaml) as Record<string, unknown>;
+    expect(result.description).toBe('See issue #2029 for context.');
+    expect(result.name).toBe('hashy');
+  });
+
   test('strips comments', () => {
     const result = parseYamlMini('# top comment\nname: value # inline comment') as Record<string, unknown>;
     expect(result.name).toBe('value');
@@ -374,6 +402,27 @@ extends: null`;
     const pack = loadPackFromString(json, 'fixture.json');
     expect(pack.name).toBe('json-pack');
   });
+
+  test('loads block-scalar pack descriptions without losing page types', () => {
+    const pack = loadPackFromString(`api_version: gbrain-schema-pack-v1
+name: recommended-fixture
+version: 1.0.0
+extends: gbrain-base
+description: |
+  Operational starter pack.
+page_types:
+  - name: meeting
+    primitive: temporal
+    path_prefixes:
+      - meetings/
+    aliases: []
+    extractable: true
+    expert_routing: false
+link_types: []`, 'fixture.yaml');
+    expect(pack.name).toBe('recommended-fixture');
+    expect(pack.extends).toBe('gbrain-base');
+    expect(pack.page_types.map((t) => t.name)).toContain('meeting');
+  });
 });
 
 describe('ReDoS guard', () => {

test/schema-pack-mutate.test.ts

@@ -103,7 +103,10 @@ describe('locateMutablePackFile — bundled guard', () => {
     expect(BUNDLED_PACK_NAMES.has('gbrain-recommended')).toBe(true);
     // v0.42 (T22): gbrain-base-v2 joins the bundled set.
     expect(BUNDLED_PACK_NAMES.has('gbrain-base-v2')).toBe(true);
-    expect(BUNDLED_PACK_NAMES.size).toBe(3);
+    // Derived from the single bundled registry — the lens packs (creator,
+    // investor, engineer, everything) are read-only too.
+    expect(BUNDLED_PACK_NAMES.has('gbrain-investor')).toBe(true);
+    expect(BUNDLED_PACK_NAMES.size).toBe(7);
   });
 
   it('rejects gbrain-base-v2 with PACK_READONLY (bundled guard)', () => {