fix(remediation): pass LINK_EXTRACTOR_VERSION_TS to the extraction-lag gate

countExtractionLag omitted versionTs, so its predicate diverged from the
counter it claims to share with doctor's links_extraction_lag check and
the extract --stale walk: pages stamped before an extractor version bump
(links_extracted_at < LINK_EXTRACTOR_VERSION_TS) lagged for doctor and
extract but never tripped the sync.repo/extract.all gate. Pass the stamp;
pin the version-bump arm with a backdated-page test (fails without it).

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

src/commands/autopilot.ts

@@ -686,6 +686,7 @@ export async function runAutopilot(engine: BrainEngine, args: string[]) {
       try {
         const { MinionQueue } = await import('../core/minions/queue.ts');
         const { computeRecommendations, embeddingProviderConfigured, HOSTED_EMBED_KEY_CONFIG } = await import('../core/brain-score-recommendations.ts');
+        const { countExtractionLag } = await import('../core/remediation/context.ts');
         const queue = new MinionQueue(engine);
         const slotMs = Math.floor(Date.now() / (baseInterval * 1000)) * baseInterval * 1000;
         const slot = new Date(slotMs).toISOString();
@@ -877,6 +878,9 @@ export async function runAutopilot(engine: BrainEngine, args: string[]) {
             return !!(process.env[envVar] || (cfgField ? embedKeyCfg[cfgField] : undefined));
           }),
           hasChatApiKey: !!(process.env.ANTHROPIC_API_KEY || await engine.getConfig('anthropic_api_key')),
+          // Real extraction-lag gate for sync.repo/extract.all — same counter
+          // loadRecommendationContext uses (replaces the health.stale_pages proxy).
+          extractionLagPages: await countExtractionLag(engine),
         };
         // v0.41.18.0 (A5 + A19 + A22, T15): consult onboard recommendations
         // ALONGSIDE doctor's brain-score recommendations. Onboard's 4 new

src/commands/import.ts

@@ -170,14 +170,10 @@ export async function runImport(
   // v0.22.13 (PR #490 Q2): shared parseWorkers helper rejects bad input
   // (--workers 0, -3, "foo") with a loud error instead of silently falling
   // through to 1. Mirrors sync.ts's flag handling.
-  const { parseWorkers, autoConcurrency } = await import('../core/sync-concurrency.ts');
-  // #1207: undefined (no --workers flag) defers to autoConcurrency below —
-  // the shared sync/import policy (PGLite → 1, >100 files → 4) — instead of
-  // hardcoding serial. Large Postgres imports stop paying one embedding
-  // round-trip per file in sequence.
-  let workerCount: number | undefined;
+  const { parseWorkers } = await import('../core/sync-concurrency.ts');
+  let workerCount: number;
   try {
-    workerCount = parseWorkers(workersArg ?? undefined);
+    workerCount = parseWorkers(workersArg ?? undefined) ?? 1;
   } catch (e) {
     console.error(e instanceof Error ? e.message : String(e));
     process.exit(1);
@@ -256,9 +252,8 @@ export async function runImport(
   }
   const files = resumeFilter(allFiles, dir, completed);
 
-  // Determine actual worker count. Explicit --workers wins; otherwise the
-  // shared autoConcurrency policy decides from engine kind + file count.
-  const actualWorkers = autoConcurrency(engine, files.length, workerCount);
+  // Determine actual worker count
+  const actualWorkers = workerCount > 1 ? workerCount : 1;
   if (actualWorkers > 1) {
     console.log(`Using ${actualWorkers} parallel workers`);
   }

src/core/ai/gateway.ts

@@ -1513,21 +1513,12 @@ export async function embed(texts: string[], opts?: EmbedOpts): Promise<Float32A
 
   const embedding = recipe.touchpoints?.embedding;
   const maxBatchTokens = embedding?.max_batch_tokens;
-  const maxBatchCount = embedding?.max_batch_count;
   const charsPerToken = embedding?.chars_per_token ?? DEFAULT_CHARS_PER_TOKEN;
 
-  // Pre-split is gated on max_batch_tokens / max_batch_count. Recipes with
-  // neither (e.g. OpenAI) ride the fast path: one embedMany call, no
-  // recursion safety net.
-  const batches = (maxBatchTokens || maxBatchCount)
-    ? splitByTokenBudget(
-        truncated,
-        maxBatchTokens
-          ? Math.floor(maxBatchTokens * effectiveSafetyFactor(recipe))
-          : Number.MAX_SAFE_INTEGER,
-        charsPerToken,
-        maxBatchCount,
-      )
+  // Pre-split is gated on max_batch_tokens. Recipes without it (e.g. OpenAI)
+  // ride the fast path: one embedMany call, no recursion safety net.
+  const batches = maxBatchTokens
+    ? splitByTokenBudget(truncated, Math.floor(maxBatchTokens * effectiveSafetyFactor(recipe)), charsPerToken)
     : [truncated];
 
   const allEmbeddings: Float32Array[] = [];
@@ -1577,9 +1568,6 @@ export async function embed(texts: string[], opts?: EmbedOpts): Promise<Float32A
  *   responsible for applying any safety-factor shrink before passing in.
  * @param charsPerToken - Provider-specific character density. Defaults to
  *   `DEFAULT_CHARS_PER_TOKEN` (4) when omitted, matching OpenAI tiktoken.
- * @param maxBatchCount - #1199: optional cap on INPUTS per sub-batch, for
- *   providers that reject batches by count (DashScope: 10). When omitted,
- *   only the token budget governs.
  *
  * @internal exported for tests; not part of the public gateway API.
  */
@@ -1587,17 +1575,15 @@ export function splitByTokenBudget(
   texts: string[],
   budgetTokens: number,
   charsPerToken: number = DEFAULT_CHARS_PER_TOKEN,
-  maxBatchCount?: number,
 ): string[][] {
   const ratio = charsPerToken > 0 ? charsPerToken : DEFAULT_CHARS_PER_TOKEN;
-  const maxCount = maxBatchCount !== undefined && maxBatchCount > 0 ? maxBatchCount : Infinity;
   const batches: string[][] = [];
   let current: string[] = [];
   let currentTokens = 0;
 
   for (const text of texts) {
     const estTokens = Math.ceil(text.length / ratio);
-    if (current.length > 0 && (currentTokens + estTokens > budgetTokens || current.length >= maxCount)) {
+    if (current.length > 0 && currentTokens + estTokens > budgetTokens) {
       batches.push(current);
       current = [];
       currentTokens = 0;
@@ -1623,11 +1609,7 @@ export function isTokenLimitError(err: unknown): boolean {
     /token.*limit.*exceeded/i.test(msg) ||
     // OpenAI embeddings: "Invalid 'input': maximum request size is 300000 tokens per request."
     /maximum request size.*tokens/i.test(msg) ||
-    /max.*tokens.*per.*request/i.test(msg) ||
-    // DashScope: "batch size is invalid, it should not be larger than 10." (#1199)
-    // Count-cap error, but recursive halving shrinks count too, so the same
-    // safety net converges.
-    /batch size is invalid/i.test(msg)
+    /max.*tokens.*per.*request/i.test(msg)
   );
 }
 

src/core/ai/recipes/dashscope.ts

@@ -31,10 +31,6 @@ export const dashscope: Recipe = {
       // path. Conservative declaration so the gateway pre-splits before
       // hitting whatever undocumented server-side limit exists.
       max_batch_tokens: 8192,
-      // #1199: DashScope hard-caps embeddings at 10 inputs per request
-      // ("batch size is invalid, it should not be larger than 10"). The
-      // token budget alone admits far more than 10 short chunks per batch.
-      max_batch_count: 10,
       // text-embedding-v3 mixes English + CJK heavily; the tokenizer is
       // closer to Voyage density than OpenAI tiktoken for CJK-dominant
       // content. Conservative chars_per_token=2 leaves headroom.

src/core/ai/recipes/google.ts

@@ -16,15 +16,6 @@ export const google: Recipe = {
       dims_options: [768, 1536, 3072],
       cost_per_1m_tokens_usd: 0.15,
       price_last_verified: '2026-04-20',
-      // #970: Gemini's documented limits are per-INPUT (2048 tokens,
-      // silently truncated beyond) and per-REQUEST count (batchEmbedContents
-      // caps at 100 inputs). There is no separate per-request token cap, so
-      // the token budget is derived: 100 inputs × 2048 tokens. The count cap
-      // binds first for typical chunk sizes. Do NOT copy the 2048 per-input
-      // limit into max_batch_tokens — that would over-split 50×.
-      max_batch_tokens: 204_800,
-      chars_per_token: 4,
-      max_batch_count: 100,
     },
     expansion: {
       models: ['gemini-2.0-flash', 'gemini-2.0-flash-lite'],

src/core/ai/recipes/index.ts

@@ -58,8 +58,5 @@ export function getRecipe(id: string): Recipe | undefined {
 }
 
 export function listRecipes(): Recipe[] {
-  // Read the map (not ALL) so there is one source of truth — getRecipe,
-  // model-resolver, and listRecipes all see the same registry, and tests
-  // can inject a synthetic recipe via RECIPES to exercise registry walks.
-  return [...RECIPES.values()];
+  return [...ALL];
 }

src/core/ai/types.ts

@@ -46,16 +46,6 @@ export interface EmbeddingTouchpoint {
    * Only consulted when `max_batch_tokens` is also set.
    */
   chars_per_token?: number;
-  /**
-   * #1199: maximum number of INPUTS per embedding request, for providers
-   * that hard-cap batch size by count rather than (or in addition to)
-   * tokens — DashScope text-embedding-v3 rejects batches > 10 with
-   * `InvalidParameter`, Gemini batchEmbedContents caps at 100 requests.
-   * When set, the gateway's pre-split flushes a sub-batch at this count
-   * even if the token budget still has room. Independent of
-   * `max_batch_tokens`; either alone triggers the pre-split.
-   */
-  max_batch_count?: number;
   /**
    * Budget-utilization ceiling in (0, 1]. The gateway pre-splits at
    * `safety_factor × max_batch_tokens` to leave headroom for tokenizer

src/core/brain-score-recommendations.ts

@@ -146,6 +146,16 @@ export interface RecommendationContext {
   chatModel?: string;
   /** Whether the chat provider has a usable API key. */
   hasChatApiKey?: boolean;
+  /**
+   * Count of pages needing link/timeline extraction — the SAME staleness the
+   * `gbrain extract --stale` walk and doctor's `links_extraction_lag` check use
+   * (`engine.countStalePagesForExtraction`). Gates the sync→extract pipeline
+   * (sync.repo / extract.all). Replaces the old `health.stale_pages` gate, which
+   * counted "pages whose updated_at predates their newest timeline entry" — a
+   * proxy that broke when the updated_at-on-timeline-insert trigger was dropped
+   * (migration v10) and never reflected real extraction work.
+   */
+  extractionLagPages?: number;
 }
 
 /** Triage result for one check. */
@@ -192,20 +202,28 @@ export function computeRecommendations(
   const source = ctx.sourceId ?? 'default';
 
   // ---------------------------------------------------------------------
-  // sync.repo — fires when sync hasn't run recently OR pages are stale
+  // sync.repo + extract.all — the materialization pipeline, gated on the REAL
+  // extraction lag (pages whose link/timeline edges are stale), NOT on the
+  // legacy `health.stale_pages` proxy. `extractionLagPages` comes from the same
+  // counter the `extract --stale` walk + doctor's `links_extraction_lag` use, so
+  // the recommendation can only fire when running extract will actually reduce
+  // it (and clear the rec). See RecommendationContext.extractionLagPages.
+  // sync.repo is the prerequisite: re-sync so pages are current before extract
+  // materializes their edges.
   // ---------------------------------------------------------------------
-  if (ctx.repoPath && health.stale_pages > 0) {
+  const extractionLag = ctx.extractionLagPages ?? 0;
+  if (ctx.repoPath && extractionLag > 0) {
     const params = { repoPath: ctx.repoPath, sourceId: ctx.sourceId, noEmbed: true };
     out.push({
       id: 'sync.repo',
       job: 'sync',
       params,
       idempotency_key: idemKey(source, 'sync', params),
-      severity: health.stale_pages > 50 ? 'high' : 'medium',
-      est_seconds: Math.min(600, 30 + health.stale_pages * 0.5),
+      severity: extractionLag > 50 ? 'high' : 'medium',
+      est_seconds: Math.min(600, 30 + extractionLag * 0.5),
       est_usd_cost: 0,  // sync is fs+DB only
       depends_on: [],
-      rationale: `${health.stale_pages} stale page${health.stale_pages === 1 ? '' : 's'} on disk`,
+      rationale: `Sync before extracting ${extractionLag} page${extractionLag === 1 ? '' : 's'} with stale link/timeline edges`,
       status: 'remediable',
     });
   }
@@ -237,7 +255,7 @@ export function computeRecommendations(
       est_seconds: Math.min(3600, 5 + health.missing_embeddings * 0.05),
       est_usd_cost,
       // sync should run first so embed sees fresh pages.
-      depends_on: ctx.repoPath && health.stale_pages > 0 ? ['sync.repo'] : [],
+      depends_on: ctx.repoPath && extractionLag > 0 ? ['sync.repo'] : [],
       rationale: `${health.missing_embeddings} chunk${health.missing_embeddings === 1 ? '' : 's'} invisible to vector search`,
       status: 'remediable',
     });
@@ -267,7 +285,7 @@ export function computeRecommendations(
   // Triggered when sync.repo fires (because sync was set to noEmbed:true,
   // and noExtract:true after T5 lands → extract job is the materializer).
   // ---------------------------------------------------------------------
-  if (ctx.repoPath && health.stale_pages > 0) {
+  if (ctx.repoPath && extractionLag > 0) {
     const params = { mode: 'all', dir: ctx.repoPath };
     out.push({
       id: 'extract.all',
@@ -278,7 +296,7 @@ export function computeRecommendations(
       est_seconds: Math.min(600, 30 + health.page_count * 0.01),
       est_usd_cost: 0,
       depends_on: ['sync.repo'],
-      rationale: 'Materialize link + timeline edges from fresh pages',
+      rationale: `Materialize link + timeline edges for ${extractionLag} page${extractionLag === 1 ? '' : 's'} with stale extraction`,
       status: 'remediable',
     });
   }

src/core/embedding.ts

@@ -79,34 +79,15 @@ export interface EmbedBatchOptions {
    * and amplify rate-limit pressure.
    */
   maxRetries?: number;
-  /**
-   * #1818: bounded parallelism across BATCH_SIZE sub-batches. Defaults to
-   * `GBRAIN_EMBED_BATCH_CONCURRENCY` env, else 4. Results are
-   * index-addressed so output order always matches input order. Set 1 to
-   * force the pre-v0.42 serial dispatch.
-   */
-  concurrency?: number;
 }
 
 /**
  * Embed a batch of texts via the gateway. Sub-batches of 100 so upstream
  * progress callbacks fire incrementally on large imports. The gateway owns
  * adaptive batch splitting and per-recipe token-budget logic; this paginator
- * owns progress-callback granularity and (#1818) bounded parallel dispatch
- * of the sub-batches — the embed-stale.ts worker-pool pattern, scoped down.
+ * is purely about progress-callback granularity.
  */
 const BATCH_SIZE = 100;
-const DEFAULT_EMBED_BATCH_CONCURRENCY = 4;
-
-function resolveEmbedBatchConcurrency(options: EmbedBatchOptions): number {
-  if (options.concurrency !== undefined) {
-    return Math.max(1, Math.floor(options.concurrency));
-  }
-  const env = Number(process.env.GBRAIN_EMBED_BATCH_CONCURRENCY);
-  if (Number.isFinite(env) && env >= 1) return Math.floor(env);
-  return DEFAULT_EMBED_BATCH_CONCURRENCY;
-}
-
 export async function embedBatch(
   texts: string[],
   options: EmbedBatchOptions = {},
@@ -122,44 +103,13 @@ export async function embedBatch(
   if (texts.length <= BATCH_SIZE && !options.onBatchComplete) {
     return gatewayEmbed(texts, gwOpts);
   }
-  // #1818: dispatch sub-batches through a bounded worker pool instead of a
-  // serial loop. Results are written into a preallocated index-addressed
-  // array so output order matches input order regardless of completion
-  // order; onBatchComplete reports a monotonic completed-embedding count.
-  const slices: Array<{ start: number; texts: string[] }> = [];
+  const results: Float32Array[] = [];
   for (let i = 0; i < texts.length; i += BATCH_SIZE) {
-    slices.push({ start: i, texts: texts.slice(i, i + BATCH_SIZE) });
+    const slice = texts.slice(i, i + BATCH_SIZE);
+    const out = await gatewayEmbed(slice, gwOpts);
+    results.push(...out);
+    options.onBatchComplete?.(results.length, texts.length);
   }
-  const results = new Array<Float32Array>(texts.length);
-  let next = 0;
-  let done = 0;
-  const numWorkers = Math.min(resolveEmbedBatchConcurrency(options), slices.length);
-  // Once any sub-batch fails, `failed` stops the surviving workers from
-  // dispatching FURTHER slices — the whole call is rejecting anyway, so
-  // continuing would burn real provider spend in the background and fire
-  // onBatchComplete after the caller already saw the failure (worst with
-  // embedBatchWithBackoff, whose 429 backoff assumes nothing is in flight).
-  // In-flight sibling calls still run to completion (bounded by numWorkers-1).
-  let failed = false;
-  const worker = async (): Promise<void> => {
-    while (!failed && next < slices.length) {
-      // NOTE: no local aborted-check here — an aborted signal makes the next
-      // gatewayEmbed call throw (SDK-side), which rejects the pool. Returning
-      // silently instead would resolve with holes in `results`.
-      const slice = slices[next++];
-      let out: Float32Array[];
-      try {
-        out = await gatewayEmbed(slice.texts, gwOpts);
-      } catch (err) {
-        failed = true;
-        throw err;
-      }
-      for (let j = 0; j < out.length; j++) results[slice.start + j] = out[j];
-      done += out.length;
-      if (!failed) options.onBatchComplete?.(done, texts.length);
-    }
-  };
-  await Promise.all(Array.from({ length: numWorkers }, () => worker()));
   return results;
 }
 

src/core/remediation/context.ts

@@ -8,6 +8,7 @@
 
 import type { BrainEngine } from '../engine.ts';
 import type { RecommendationContext } from '../brain-score-recommendations.ts';
+import { LINK_EXTRACTOR_VERSION_TS } from '../link-extraction.ts';
 
 // Re-export so consumers can `import { RecommendationContext } from '../remediation'`
 // — the canonical RecommendationContext type still lives in
@@ -68,5 +69,29 @@ export async function loadRecommendationContext(
     embeddingDimensions,
     embeddingProviderConfigured: embeddingConfigured,
     hasChatApiKey: !!(process.env.ANTHROPIC_API_KEY || fileCfg?.anthropic_api_key),
+    extractionLagPages: await countExtractionLag(engine),
   };
 }
+
+/**
+ * Real extraction-lag count — the SAME staleness `gbrain extract --stale`
+ * processes (engine.countStalePagesForExtraction with
+ * versionTs=LINK_EXTRACTOR_VERSION_TS, matching doctor's links_extraction_lag
+ * check — without versionTs, pages stamped before an extractor version bump
+ * would lag for doctor/extract but never trip this gate). Drives the
+ * sync→extract recommendation pipeline; replaces the legacy
+ * `health.stale_pages` proxy that no longer reflected real extraction work
+ * after the v10 trigger drop.
+ *
+ * Shared by loadRecommendationContext AND the D7 per-step recheck in
+ * runRemediation — the recheck MUST refresh this gate alongside getHealth,
+ * or a completed extract step keeps re-firing off the frozen initial count.
+ */
+export async function countExtractionLag(engine: BrainEngine): Promise<number> {
+  try {
+    return await engine.countStalePagesForExtraction({ versionTs: LINK_EXTRACTOR_VERSION_TS });
+  } catch {
+    /* counter unavailable (very old brain / mid-migration) — treat as 0 */
+    return 0;
+  }
+}

src/core/remediation/run.ts

@@ -16,7 +16,7 @@ import {
   computeRecommendations,
 } from '../brain-score-recommendations.ts';
 import type { RemediationStep } from '../remediation-step.ts';
-import { loadRecommendationContext } from './context.ts';
+import { countExtractionLag, loadRecommendationContext } from './context.ts';
 import { computeRemediationPlan } from './plan.ts';
 import type {
   RemediationHooks,
@@ -65,7 +65,7 @@ export async function runRemediation(
     clearRemediationCheckpoint,
   } = await import('../remediation-checkpoint.ts');
 
-  const ctx = await loadRecommendationContext(engine);
+  let ctx = await loadRecommendationContext(engine);
 
   // Pre-flight ceiling check via the shared plan computation.
   const initialPlan = await computeRemediationPlan(engine, { targetScore });
@@ -305,6 +305,11 @@ export async function runRemediation(
       // steps with bumped retry suffix (D1).
       if (recs.length === 0 || stepCount >= maxJobs) break;
       const freshHealth = await engine.getHealth();
+      // Refresh the extraction-lag gate alongside health: ctx was loaded once
+      // before the loop, and a completed sync/extract step is exactly what
+      // drives the count down. Reusing the frozen initial count would re-fire
+      // sync.repo/extract.all every recheck until maxJobs.
+      ctx = { ...ctx, extractionLagPages: await countExtractionLag(engine) };
       recs = computeRecommendations(freshHealth, ctx).filter((r) => r.status === 'remediable');
     }
   };

src/core/types.ts

@@ -1423,6 +1423,15 @@ export interface BrainStats {
 export interface BrainHealth {
   page_count: number;
   embed_coverage: number;
+  /**
+   * LEGACY proxy: count of pages whose `updated_at` predates their newest
+   * timeline entry. This bumped meaningfully only while a trigger updated
+   * `pages.updated_at` on timeline insert; that trigger was dropped in
+   * migration v10, so the metric no longer reflects real "needs work" state.
+   * NO LONGER gates remediations — the sync→extract pipeline now gates on
+   * `RecommendationContext.extractionLagPages` (the real extraction-lag from
+   * `countStalePagesForExtraction`). Retained for the CLI health line + back-compat.
+   */
   stale_pages: number;
   /**
    * Islanded pages — zero inbound AND zero outbound links. A hub page

test/ai/adaptive-embed-batch.test.ts

@@ -39,8 +39,6 @@ import {
   __getShrinkStateForTests,
 } from '../../src/core/ai/gateway.ts';
 import { AIConfigError, AITransientError } from '../../src/core/ai/errors.ts';
-import { RECIPES } from '../../src/core/ai/recipes/index.ts';
-import type { Recipe } from '../../src/core/ai/types.ts';
 
 // The last test in this file leaves the gateway configured with a remote
 // provider + fake key and a REAL embed transport. Without a final reset,
@@ -95,14 +93,6 @@ function configureGoogle(): void {
   });
 }
 
-function configureDashscope(): void {
-  configureGateway({
-    embedding_model: 'dashscope:text-embedding-v3',
-    embedding_dimensions: 1024,
-    env: { DASHSCOPE_API_KEY: 'sk-fake' },
-  });
-}
-
 // --------- 1. Pure helpers ---------
 
 describe('splitByTokenBudget (pure helper)', () => {
@@ -159,27 +149,6 @@ describe('splitByTokenBudget (pure helper)', () => {
     expect(splitByTokenBudget(texts, 96_000, 0)).toEqual(splitByTokenBudget(texts, 96_000, 4));
     expect(splitByTokenBudget(texts, 96_000, -1)).toEqual(splitByTokenBudget(texts, 96_000, 4));
   });
-
-  // #1199: count cap for providers that reject batches by input count.
-  test('max_batch_count flushes even when token budget has room', () => {
-    const texts = Array.from({ length: 25 }, (_, i) => `t${i}`);
-    const result = splitByTokenBudget(texts, 1_000_000, 4, 10);
-    expect(result.map(b => b.length)).toEqual([10, 10, 5]);
-    expect(result.flat()).toEqual(texts);
-  });
-
-  test('token budget still governs alongside max_batch_count', () => {
-    const texts = ['a'.repeat(50_000), 'b'.repeat(50_000), 'c'.repeat(50_000)];
-    const result = splitByTokenBudget(texts, 96_000, 1, 10);
-    expect(result).toHaveLength(3);
-  });
-
-  test('undefined / zero / negative max_batch_count is ignored', () => {
-    const texts = Array.from({ length: 25 }, () => 'x');
-    expect(splitByTokenBudget(texts, 1_000_000, 4, undefined)).toHaveLength(1);
-    expect(splitByTokenBudget(texts, 1_000_000, 4, 0)).toHaveLength(1);
-    expect(splitByTokenBudget(texts, 1_000_000, 4, -5)).toHaveLength(1);
-  });
 });
 
 describe('isTokenLimitError (pure helper)', () => {
@@ -210,12 +179,6 @@ describe('isTokenLimitError (pure helper)', () => {
     expect(isTokenLimitError(new Error('Exceeded 300000 max tokens per request'))).toBe(true);
   });
 
-  test('matches DashScope batch-count error (#1199)', () => {
-    expect(isTokenLimitError(new Error(
-      'InvalidParameter: batch size is invalid, it should not be larger than 10.',
-    ))).toBe(true);
-  });
-
   test('does not match unrelated errors', () => {
     expect(isTokenLimitError(new Error('Connection refused'))).toBe(false);
     expect(isTokenLimitError(new Error('Invalid API key'))).toBe(false);
@@ -424,92 +387,26 @@ describe('shrink-on-miss adaptive cache', () => {
   });
 });
 
-// --------- 8. Pre-split count cap through public embed() (#1199 / #970) ---------
-
-describe('embed() pre-split honors max_batch_count', () => {
-  beforeEach(() => resetGateway());
-  afterEach(() => __setEmbedTransportForTests(null));
-
-  test('dashscope never dispatches more than 10 inputs per call (#1199)', async () => {
-    configureDashscope();
-    const stub = mock(async ({ values }: { values: string[] }) => fakeEmbeddings(values, 1024));
-    __setEmbedTransportForTests(stub as any);
-
-    // 25 short texts fit trivially in the 8192-token budget; without the
-    // count cap they'd ship as ONE batch and DashScope would reject it.
-    const texts = Array.from({ length: 25 }, (_, i) => `short-${i}`);
-    const result = await embed(texts);
-
-    expect(result).toHaveLength(25);
-    const callLengths = stub.mock.calls.map(([arg]) => (arg as { values: string[] }).values.length);
-    expect(Math.max(...callLengths)).toBeLessThanOrEqual(10);
-    expect(callLengths.reduce((a, b) => a + b, 0)).toBe(25);
-    // Order preserved across sub-batches.
-    expect((stub.mock.calls[0][0] as { values: string[] }).values[0]).toBe('short-0');
-  });
-
-  test('google pre-splits at 100 inputs per batchEmbedContents call (#970)', async () => {
-    configureGoogle();
-    const stub = mock(async ({ values }: { values: string[] }) => fakeEmbeddings(values, 768));
-    __setEmbedTransportForTests(stub as any);
-
-    const texts = Array.from({ length: 250 }, (_, i) => `g${i}`);
-    const result = await embed(texts);
-
-    expect(result).toHaveLength(250);
-    const callLengths = stub.mock.calls.map(([arg]) => (arg as { values: string[] }).values.length);
-    expect(callLengths).toEqual([100, 100, 50]);
-  });
-});
-
 // --------- 7. Startup warning (D9-B) ---------
 
 describe('startup warning for recipes missing max_batch_tokens', () => {
   beforeEach(() => resetGateway());
 
-  // #970 closed google's missing cap, so no registered recipe is capless
-  // anymore. Inject a synthetic capless recipe to keep the warning path
-  // covered for the NEXT recipe that forgets the field.
-  const caplessRecipe: Recipe = {
-    id: 'capless-test',
-    name: 'Capless Test Provider',
-    tier: 'openai-compat',
-    implementation: 'openai-compatible',
-    base_url_default: 'https://example.invalid/v1',
-    auth_env: { required: [] },
-    touchpoints: {
-      embedding: { models: ['capless-embed-1'], default_dims: 768 },
-    },
-  };
-
-  function configureCapless(): void {
-    configureGateway({
-      embedding_model: 'capless-test:capless-embed-1',
-      embedding_dimensions: 768,
-      env: {},
-    });
-  }
-
   test('configured missing-cap recipe warns once; unrelated recipes stay quiet', () => {
     const warnings: string[] = [];
     const original = console.warn;
     console.warn = (msg: string) => warnings.push(String(msg));
-    RECIPES.set(caplessRecipe.id, caplessRecipe);
     try {
       configureOpenAI();
       expect(warnings.length).toBe(0);
-      // #970 regression: google now declares max_batch_tokens → quiet.
       configureGoogle();
-      expect(warnings.length).toBe(0);
-      configureCapless();
       const firstCallCount = warnings.length;
       // Reconfigure: the warning should NOT re-fire for the same recipes
       // within one process (we already told the operator).
-      configureCapless();
+      configureGoogle();
       expect(warnings.length).toBe(firstCallCount);
     } finally {
       console.warn = original;
-      RECIPES.delete(caplessRecipe.id);
     }
 
     // The warning text should match the documented contract.
@@ -518,12 +415,11 @@ describe('startup warning for recipes missing max_batch_tokens', () => {
     );
     expect(contractMatch.length).toBe(1);
 
-    // Voyage + google declare max_batch_tokens → suppressed. OpenAI is the
-    // canonical fast-path recipe → also suppressed by id. All must be
-    // absent from the warnings; only the synthetic capless recipe fires.
+    // Voyage declares max_batch_tokens → suppressed. OpenAI is the
+    // canonical fast-path recipe → also suppressed by id. Both must be
+    // absent from the warnings.
     expect(warnings.find(w => w.includes('"voyage"'))).toBeUndefined();
     expect(warnings.find(w => w.includes('"openai"'))).toBeUndefined();
-    expect(warnings.find(w => w.includes('"google"'))).toBeUndefined();
-    expect(warnings.find(w => w.includes('"capless-test"'))).toBeDefined();
+    expect(warnings.find(w => w.includes('"google"'))).toBeDefined();
   });
 });

test/ai/no-batch-cap-suppression.serial.test.ts

@@ -52,7 +52,16 @@ describe('v0.32 #779: no_batch_cap suppresses the missing-max_batch_tokens warni
     }
   });
 
-  test('configureGateway does NOT warn for google now that it declares batch caps (#970)', () => {
+  test('configureGateway warns for google only when google embedding is configured', () => {
+    warnSpy.mockClear();
+    resetGateway();
+    configureGateway({ env: {} });
+    let messages = warnSpy.mock.calls.map(c => String(c[0] ?? ''));
+    expect(
+      messages.some(m => m.includes('"google"') && m.includes('without max_batch_tokens')),
+      'google should not warn while OpenAI default is configured',
+    ).toBe(false);
+
     warnSpy.mockClear();
     resetGateway();
     configureGateway({
@@ -60,20 +69,11 @@ describe('v0.32 #779: no_batch_cap suppresses the missing-max_batch_tokens warni
       embedding_dimensions: 768,
       env: { GOOGLE_GENERATIVE_AI_API_KEY: 'fake' },
     });
-    const messages = warnSpy.mock.calls.map(c => String(c[0] ?? ''));
+    messages = warnSpy.mock.calls.map(c => String(c[0] ?? ''));
     expect(
       messages.some(m => m.includes('"google"') && m.includes('without max_batch_tokens')),
-      'google declares max_batch_tokens/max_batch_count since #970 — no warning',
-    ).toBe(false);
-  });
-
-  test('google recipe declares its derived batch caps (#970)', () => {
-    const e = getRecipe('google')!.touchpoints.embedding!;
-    // Count cap is the REAL Gemini limit (batchEmbedContents: 100 inputs);
-    // the token budget is derived (100 × 2048 per-input tokens), NOT the
-    // 2048 per-input limit — copying that verbatim would over-split 50×.
-    expect(e.max_batch_count).toBe(100);
-    expect(e.max_batch_tokens).toBe(204_800);
+      'google should warn when configured because it has fixed-cap models',
+    ).toBe(true);
   });
 
   test('every recipe with empty models[] declares user_provided_models OR has openai-fast-path', () => {

test/ai/recipe-dashscope.test.ts

@@ -55,11 +55,6 @@ describe('recipe: dashscope', () => {
     expect(r.touchpoints.embedding!.chars_per_token).toBeGreaterThan(0);
   });
 
-  test('declares max_batch_count: 10 — DashScope rejects larger batches (#1199)', () => {
-    const r = getRecipe('dashscope')!;
-    expect(r.touchpoints.embedding!.max_batch_count).toBe(10);
-  });
-
   test('dimsProviderOptions threads dimensions for text-embedding-v3 (Matryoshka)', async () => {
     // Codex finding #1: DashScope text-embedding-v3 is Matryoshka 64-1024.
     // Without `dimensions` on the wire, user-selected non-default dims are

test/brain-score-recommendations.test.ts

@@ -119,13 +119,12 @@ describe('computeRecommendations', () => {
     expect(recs.find((r) => r.id === 'embed.stale')).toBeUndefined();
   });
 
-  test('stale pages + dead links produce sync + backlinks + extract', () => {
+  test('extraction lag + dead links produce sync + backlinks + extract', () => {
     const health = makeHealth({
-      stale_pages: 25,
       dead_links: 8,
       brain_score: 70,
     });
-    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true });
+    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true, extractionLagPages: 25 });
     const ids = recs.map((r) => r.id);
     expect(ids).toContain('sync.repo');
     expect(ids).toContain('backlinks.fix');
@@ -133,18 +132,17 @@ describe('computeRecommendations', () => {
   });
 
   test('extract.all depends on sync.repo (D14: stable ids)', () => {
-    const health = makeHealth({ stale_pages: 10 });
-    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true });
+    const health = makeHealth();
+    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true, extractionLagPages: 10 });
     const extract = recs.find((r) => r.id === 'extract.all');
     expect(extract?.depends_on).toContain('sync.repo');
   });
 
-  test('embed.stale depends on sync.repo when sync also needed', () => {
+  test('embed.stale depends on sync.repo when extraction also needed', () => {
     const health = makeHealth({
-      stale_pages: 10,
       missing_embeddings: 100,
     });
-    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true });
+    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true, extractionLagPages: 10 });
     const embed = recs.find((r) => r.id === 'embed.stale');
     expect(embed?.depends_on).toContain('sync.repo');
   });
@@ -159,9 +157,9 @@ describe('computeRecommendations', () => {
   test('severity ordering: critical before high before medium', () => {
     const health = makeHealth({
       missing_embeddings: 100,  // critical
-      stale_pages: 80,          // high
     });
-    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true });
+    // extractionLagPages > 50 → sync.repo fires at 'high' severity.
+    const recs = computeRecommendations(health, { repoPath: '/brain', embeddingProviderConfigured: true, extractionLagPages: 80 });
     const critIdx = recs.findIndex((r) => r.severity === 'critical');
     const highIdx = recs.findIndex((r) => r.severity === 'high');
     expect(critIdx).toBeLessThan(highIdx);
@@ -170,11 +168,10 @@ describe('computeRecommendations', () => {
   // D6 #5 — THE critical regression test for the agent contract.
   test('D6 #5: determinism — same input twice produces identical output', () => {
     const health = makeHealth({
-      stale_pages: 10,
       missing_embeddings: 50,
       dead_links: 3,
     });
-    const ctx = { repoPath: '/brain', embeddingProviderConfigured: true, sourceId: 'default' };
+    const ctx = { repoPath: '/brain', embeddingProviderConfigured: true, sourceId: 'default', extractionLagPages: 10 };
     const run1 = computeRecommendations(health, ctx);
     const run2 = computeRecommendations(health, ctx);
     expect(JSON.stringify(run1)).toBe(JSON.stringify(run2));

test/embed-batch-concurrency.test.ts

@@ -1,161 +0,0 @@
-/**
- * #1818: embedBatch dispatches its 100-input sub-batches through a bounded
- * worker pool (the embed-stale.ts concurrency pattern) instead of a serial
- * `for` loop. This file pins:
- *
- *   - output order matches input order regardless of completion order
- *     (index-addressed results)
- *   - parallelism actually happens (max in-flight > 1) and stays bounded
- *     (max in-flight <= configured concurrency)
- *   - concurrency: 1 restores the serial pre-#1818 dispatch
- *   - GBRAIN_EMBED_BATCH_CONCURRENCY env is honored when the option is unset
- *   - onBatchComplete reports a monotonic completed count ending at total
- *
- * Transport is stubbed via the gateway's __setEmbedTransportForTests seam
- * (same pattern as test/ai/adaptive-embed-batch.test.ts). OpenAI recipe =
- * fast path (no pre-split), so each embedBatch sub-batch is exactly one
- * transport call.
- */
-
-import { afterAll, afterEach, beforeEach, describe, expect, test } from 'bun:test';
-import {
-  configureGateway,
-  resetGateway,
-  __setEmbedTransportForTests,
-} from '../src/core/ai/gateway.ts';
-import { embedBatch } from '../src/core/embedding.ts';
-import { withEnv } from './helpers/with-env.ts';
-
-const DIMS = 1536;
-
-function configureOpenAI(): void {
-  configureGateway({
-    embedding_model: 'openai:text-embedding-3-large',
-    embedding_dimensions: DIMS,
-    env: { OPENAI_API_KEY: 'sk-fake' },
-  });
-}
-
-/**
- * Install a transport whose returned embedding encodes the GLOBAL input
- * index in dim 0 (texts are `t<N>`), so order can be asserted end-to-end.
- * Tracks the max number of concurrently in-flight transport calls.
- */
-function installTrackingTransport(delayMs = 5): { maxInFlight: () => number } {
-  let inFlight = 0;
-  let maxInFlight = 0;
-  __setEmbedTransportForTests((async ({ values }: { values: string[] }) => {
-    inFlight++;
-    maxInFlight = Math.max(maxInFlight, inFlight);
-    await new Promise(r => setTimeout(r, delayMs));
-    inFlight--;
-    return {
-      embeddings: values.map(v => {
-        const idx = Number(v.slice(1));
-        return Array.from({ length: DIMS }, (_, j) => (j === 0 ? idx : 0.1));
-      }),
-    };
-  }) as any);
-  return { maxInFlight: () => maxInFlight };
-}
-
-const texts = Array.from({ length: 250 }, (_, i) => `t${i}`);
-
-afterAll(() => resetGateway());
-
-describe('embedBatch bounded parallelism (#1818)', () => {
-  beforeEach(() => {
-    resetGateway();
-    configureOpenAI();
-  });
-  afterEach(() => {
-    __setEmbedTransportForTests(null);
-  });
-
-  test('default pool dispatches sub-batches in parallel, order preserved', async () => {
-    const tracker = installTrackingTransport();
-    const result = await embedBatch(texts, { onBatchComplete: () => {} });
-    expect(result).toHaveLength(250);
-    for (let i = 0; i < 250; i++) {
-      expect(result[i][0]).toBe(i);
-    }
-    // 250 texts → 3 sub-batches; default concurrency 4 → all 3 in flight.
-    expect(tracker.maxInFlight()).toBeGreaterThan(1);
-    expect(tracker.maxInFlight()).toBeLessThanOrEqual(4);
-  });
-
-  test('concurrency: 1 keeps the serial dispatch', async () => {
-    const tracker = installTrackingTransport();
-    const result = await embedBatch(texts, { concurrency: 1, onBatchComplete: () => {} });
-    expect(result).toHaveLength(250);
-    expect(tracker.maxInFlight()).toBe(1);
-  });
-
-  test('GBRAIN_EMBED_BATCH_CONCURRENCY env bounds the pool when option unset', async () => {
-    const tracker = installTrackingTransport();
-    await withEnv({ GBRAIN_EMBED_BATCH_CONCURRENCY: '2' }, async () => {
-      await embedBatch(texts, { onBatchComplete: () => {} });
-    });
-    expect(tracker.maxInFlight()).toBeGreaterThan(1);
-    expect(tracker.maxInFlight()).toBeLessThanOrEqual(2);
-  });
-
-  test('onBatchComplete reports a monotonic count ending at total', async () => {
-    installTrackingTransport();
-    const seen: number[] = [];
-    await embedBatch(texts, {
-      onBatchComplete: (done, total) => {
-        expect(total).toBe(250);
-        seen.push(done);
-      },
-    });
-    expect(seen).toHaveLength(3); // 100 + 100 + 50 sub-batches
-    for (let i = 1; i < seen.length; i++) {
-      expect(seen[i]).toBeGreaterThan(seen[i - 1]);
-    }
-    expect(seen[seen.length - 1]).toBe(250);
-  });
-
-  test('a failing sub-batch rejects the whole call', async () => {
-    let call = 0;
-    __setEmbedTransportForTests((async ({ values }: { values: string[] }) => {
-      call++;
-      if (call === 2) throw new Error('boom');
-      await new Promise(r => setTimeout(r, 2));
-      return { embeddings: values.map(() => Array.from({ length: DIMS }, () => 0.1)) };
-    }) as any);
-    await expect(embedBatch(texts, { onBatchComplete: () => {} })).rejects.toThrow();
-  });
-
-  test('after a failure, surviving workers stop dispatching new slices', async () => {
-    // 1000 texts → 10 slices, concurrency 2. First call fails immediately;
-    // without the `failed` flag the second worker would keep draining all
-    // 10 slices in the background AFTER embedBatch already rejected —
-    // burning provider spend and firing onBatchComplete post-rejection.
-    let calls = 0;
-    const completions: number[] = [];
-    __setEmbedTransportForTests((async ({ values }: { values: string[] }) => {
-      calls++;
-      if (calls === 1) throw new Error('boom');
-      await new Promise(r => setTimeout(r, 5));
-      return { embeddings: values.map(() => Array.from({ length: DIMS }, () => 0.1)) };
-    }) as any);
-    const many = Array.from({ length: 1000 }, (_, i) => `t${i}`);
-    await expect(
-      embedBatch(many, { concurrency: 2, onBatchComplete: d => completions.push(d) }),
-    ).rejects.toThrow('boom');
-    const callsAtRejection = calls;
-    await new Promise(r => setTimeout(r, 50)); // would-be background drain window
-    expect(calls).toBe(callsAtRejection); // no new dispatch after rejection
-    expect(calls).toBeLessThanOrEqual(2); // only the in-flight sibling ran
-    expect(completions).toHaveLength(0); // no progress reported after failure
-  });
-
-  test('single small batch without callback stays on the one-call fast path', async () => {
-    const tracker = installTrackingTransport(1);
-    const result = await embedBatch(['t0', 't1', 't2']);
-    expect(result).toHaveLength(3);
-    expect(result[1][0]).toBe(1);
-    expect(tracker.maxInFlight()).toBe(1);
-  });
-});

test/helpers/legacy-embedding-preload.ts

@@ -19,7 +19,7 @@
  * overwrites this preload.
  */
 import { configureGateway, getEmbeddingDimensions } from '../../src/core/ai/gateway.ts';
-import { afterEach, beforeEach } from 'bun:test';
+import { beforeEach } from 'bun:test';
 
 const LEGACY_CONFIG = {
   embedding_model: 'openai:text-embedding-3-large',
@@ -52,7 +52,7 @@ applyLegacy();
 //   2. file-local beforeAll → may overwrite to ZE/1280
 // Since beforeAll runs once per file BEFORE the first beforeEach,
 // file-local beforeAll wins for that file's tests. ✓
-function applyLegacyIfEmpty() {
+beforeEach(() => {
   try {
     // Only re-apply if the gateway was reset (or never configured).
     // Tests that explicitly configured a different model in their
@@ -62,28 +62,4 @@ function applyLegacyIfEmpty() {
   } catch {
     applyLegacy();
   }
-}
-
-beforeEach(applyLegacyIfEmpty);
-
-// PR #3130 shard-order fix: beforeEach alone leaves ONE window open — a file
-// whose LAST afterEach calls resetGateway() poisons the NEXT file's
-// beforeAll, which runs BEFORE any beforeEach fires. A beforeAll there that
-// does engine.initSchema() then sizes the embedding column from the gateway
-// DEFAULTS (zembed-1/1280d) instead of the pinned legacy 1536, and every
-// 1536-d Float32Array fixture in that file dies with
-// "expected 1280 dimensions, not 1536". Which file pair collides is a
-// function of shard composition, so adding/removing ANY test file can
-// surface it (that is exactly how it bit shard 9).
-//
-// Preload hooks are registered before any file-local hooks, and bun runs
-// after-hooks inside-out (file-local afterEach first, then this one), so
-// this repairs the empty slot immediately after the poisoning reset —
-// before the next file's beforeAll can observe it.
-//
-// Known remaining window: a file whose afterAll() resets the gateway (no
-// hook runs between its afterAll and the next file's beforeAll). Files
-// that reset in afterAll and can precede a schema-creating file should
-// re-apply their own config, or the victim file should configureGateway()
-// explicitly in its beforeAll.
-afterEach(applyLegacyIfEmpty);
+});

test/import-default-workers.test.ts

@@ -1,69 +0,0 @@
-/**
- * #1207: `gbrain import` without `--workers` used to hardcode workerCount=1,
- * so a large Postgres import paid one serial embedding round-trip per file.
- * runImport now routes the default through the shared autoConcurrency policy
- * (PGLite → 1, >100 files on Postgres → DEFAULT_PARALLEL_WORKERS), while an
- * explicit `--workers N` still wins.
- *
- * The engine here is a minimal postgres-kind stub with no database_url in
- * config — runImport's parallel branch then falls back to serial processing
- * (its PR #490 guard) but the WORKER-COUNT DECISION (the thing #1207 fixes)
- * is still observable via the "Using N parallel workers" log line. Per-file
- * imports fail against the stub engine and are swallowed by runImport's
- * per-file catch; that's fine — this test pins the policy, not the import.
- */
-
-import { afterEach, beforeEach, describe, expect, test } from 'bun:test';
-import { mkdtempSync, writeFileSync, mkdirSync, rmSync, realpathSync } from 'fs';
-import { tmpdir } from 'os';
-import { join } from 'path';
-import { withEnv } from './helpers/with-env.ts';
-import { runImport } from '../src/commands/import.ts';
-
-const fakePostgresEngine = {
-  kind: 'postgres',
-  executeRaw: async () => [],
-  logIngest: async () => {},
-  setConfig: async () => {},
-  getConfig: async () => null,
-} as any;
-
-let workspace: string;
-let brainDir: string;
-let logs: string[];
-const realLog = console.log;
-
-beforeEach(() => {
-  workspace = mkdtempSync(join(tmpdir(), 'gbrain-import-workers-home-'));
-  mkdirSync(join(workspace, '.gbrain'), { recursive: true });
-  brainDir = realpathSync(mkdtempSync(join(tmpdir(), 'gbrain-import-workers-brain-')));
-  // 101 files: one past AUTO_CONCURRENCY_FILE_THRESHOLD (100).
-  for (let i = 0; i < 101; i++) {
-    writeFileSync(join(brainDir, `page-${i}.md`), `# Page ${i}\n\nbody ${i}\n`);
-  }
-  logs = [];
-  console.log = (msg?: unknown) => logs.push(String(msg));
-});
-
-afterEach(() => {
-  console.log = realLog;
-  rmSync(workspace, { recursive: true, force: true });
-  rmSync(brainDir, { recursive: true, force: true });
-});
-
-describe('import default worker count (#1207)', () => {
-  test('no --workers flag → autoConcurrency picks 4 for >100 files on Postgres', async () => {
-    await withEnv({ GBRAIN_HOME: join(workspace, '.gbrain'), GBRAIN_SOURCE: undefined }, async () => {
-      await runImport(fakePostgresEngine, [brainDir, '--no-embed'], { sourceId: 'default' });
-    });
-    expect(logs.some(l => l.includes('Using 4 parallel workers'))).toBe(true);
-  });
-
-  test('explicit --workers 2 still wins over the auto policy', async () => {
-    await withEnv({ GBRAIN_HOME: join(workspace, '.gbrain'), GBRAIN_SOURCE: undefined }, async () => {
-      await runImport(fakePostgresEngine, [brainDir, '--no-embed', '--workers', '2'], { sourceId: 'default' });
-    });
-    expect(logs.some(l => l.includes('Using 2 parallel workers'))).toBe(true);
-    expect(logs.some(l => l.includes('Using 4 parallel workers'))).toBe(false);
-  });
-});

test/remediation-context-extraction-lag.test.ts

@@ -0,0 +1,62 @@
+// test/remediation-context-extraction-lag.test.ts
+//
+// Pins the v-next fix: the sync→extract remediation pipeline gates on REAL
+// extraction lag, not the legacy `health.stale_pages` proxy (which counted
+// "updated_at predates newest timeline entry" — meaningless after the v10
+// trigger drop). loadRecommendationContext now populates `extractionLagPages`
+// from `engine.countStalePagesForExtraction` — the SAME counter the
+// `gbrain extract --stale` walk and doctor's `links_extraction_lag` use — so a
+// recommendation can only fire when running extract will actually reduce it.
+
+import { afterAll, beforeAll, describe, expect, it } from 'bun:test';
+import { PGLiteEngine } from '../src/core/pglite-engine.ts';
+import { loadRecommendationContext } from '../src/core/remediation/context.ts';
+
+let engine: PGLiteEngine;
+
+beforeAll(async () => {
+  engine = new PGLiteEngine();
+  await engine.connect({});
+  await engine.initSchema();
+});
+
+afterAll(async () => {
+  await engine.disconnect();
+});
+
+describe('loadRecommendationContext — extractionLagPages wiring', () => {
+  it('is 0 on an empty brain (nothing to extract)', async () => {
+    const ctx = await loadRecommendationContext(engine);
+    expect(ctx.extractionLagPages).toBe(0);
+  });
+
+  it('reflects the real extraction-lag count once a page needs extraction', async () => {
+    // A freshly-imported page has links_extracted_at = NULL, which the canonical
+    // countStalePagesForExtraction predicate counts as stale-for-extraction.
+    await engine.putPage('p0', {
+      title: 'p0',
+      type: 'note' as never,
+      compiled_truth: 'body that is long enough to pass any minimum-length guards in the codebase',
+      timeline: '',
+      frontmatter: {},
+      source_path: 'p0.md',
+    });
+    const ctx = await loadRecommendationContext(engine);
+    expect(ctx.extractionLagPages).toBeGreaterThan(0);
+  });
+
+  it('counts pages stamped before LINK_EXTRACTOR_VERSION_TS (version-bump arm)', async () => {
+    // Backdate p0 so BOTH the NULL arm and the updated_at arm are quiet:
+    // updated_at < links_extracted_at, but links_extracted_at predates the
+    // extractor version stamp. doctor's links_extraction_lag and
+    // `extract --stale` both count this page; the remediation gate must too.
+    await engine.executeRaw(
+      `UPDATE pages SET updated_at = '2020-01-01T00:00:00Z'::timestamptz,
+                        links_extracted_at = '2020-01-02T00:00:00Z'::timestamptz
+        WHERE slug = 'p0'`,
+      [],
+    );
+    const ctx = await loadRecommendationContext(engine);
+    expect(ctx.extractionLagPages).toBeGreaterThan(0);
+  });
+});

test/remediation-run-d7-refresh.serial.test.ts

@@ -0,0 +1,81 @@
+// test/remediation-run-d7-refresh.serial.test.ts
+//
+// Pins the D7-recheck half of the extraction-lag gate fix: runRemediation
+// loads RecommendationContext ONCE before the step loop, and the per-step
+// recheck (D7) must REFRESH ctx.extractionLagPages alongside getHealth.
+// Without the refresh, a completed sync/extract step keeps re-firing off
+// the frozen initial count — the plan never converges and the loop burns
+// steps until maxJobs.
+//
+// SERIAL (R2): uses top-level mock.module for the minion queue +
+// wait-for-completion so no real worker is needed — mocks leak across
+// files in a shard process, so this file must run in its own process.
+
+import { describe, expect, mock, test } from 'bun:test';
+
+// The fake brain: sync.repo clears the extraction lag when it "runs"
+// (today's sync materializes link/timeline edges; extract.all is the
+// explicit re-materializer). The frozen-ctx bug makes runRemediation
+// ignore that and resubmit sync.repo on every D7 recheck.
+let extractionLag = 25;
+const submittedJobs: string[] = [];
+
+mock.module('../src/core/minions/queue.ts', () => ({
+  MinionQueue: class {
+    constructor(_engine: unknown) {}
+    async add(job: string): Promise<{ id: number }> {
+      submittedJobs.push(job);
+      if (job === 'sync' || job === 'extract') extractionLag = 0;
+      return { id: submittedJobs.length };
+    }
+  },
+}));
+
+mock.module('../src/core/minions/wait-for-completion.ts', () => ({
+  waitForCompletion: async () => ({ status: 'completed' }),
+}));
+
+const health = () => ({
+  page_count: 100,
+  embed_coverage: 1.0,
+  stale_pages: 0, // legacy proxy stays 0 — the real counter drives the gate
+  orphan_pages: 0,
+  missing_embeddings: 0,
+  brain_score: 70,
+  dead_links: 0,
+  link_coverage: 1.0,
+  timeline_coverage: 1.0,
+  most_connected: [],
+  embed_coverage_score: 35,
+  link_density_score: 25,
+  timeline_coverage_score: 15,
+  no_orphans_score: 15,
+  no_dead_links_score: 10,
+});
+
+const fakeEngine = {
+  kind: 'pglite' as const,
+  getHealth: async () => health(),
+  getConfig: async (key: string) =>
+    key === 'sync.repo_path' ? '/tmp/brain-example' : null,
+  countStalePagesForExtraction: async () => extractionLag,
+};
+
+describe('runRemediation D7 recheck — extraction-lag gate refresh', () => {
+  test('a completed materializer step clears the gate; the pipeline is not resubmitted', async () => {
+    const { runRemediation } = await import('../src/core/remediation/run.ts');
+    const result = await runRemediation(
+      // Only the methods the orchestrator touches are needed.
+      fakeEngine as never,
+      { targetScore: 0, maxJobs: 6 },
+    );
+    // Frozen-ctx bug: extractionLagPages stays 25 forever, so every D7
+    // recheck re-introduces the sync/extract pipeline and the loop burns
+    // all 6 maxJobs. With the refresh, the plan converges after the first
+    // completed step: no step id is ever submitted twice.
+    const ids = result.submitted.map((s) => s.id);
+    expect(new Set(ids).size).toBe(ids.length);
+    expect(submittedJobs.length).toBeLessThan(3);
+    expect(extractionLag).toBe(0);
+  });
+});