fix(chunker): estimated-token hard cap — URL-dense/CJK-fallback chunks overflow strict embedding-server token limits

Takeover of #2847 (rebased onto current master). Fixes #2826.

- cjk.ts: estimateEmbeddingTokens() — conservative per-char-class token
  estimate (CJK 1.0, other 0.75, whitespace 0.1 per code unit).
- recursive.ts (MARKDOWN_CHUNKER_VERSION 3→4): countWords floored at
  ceil(nonWhitespaceChars/6); capByEstimatedTokens() final pass with
  ChunkOptions.maxTokens (default 1500).
- code.ts (CHUNKER_VERSION 4→5): capCodeChunks() applies the same cap to
  AST-path chunks that splitLargeNode can't subdivide.

Co-authored-by: paul-0320 <paul-0320@users.noreply.github.com>

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

src/commands/autopilot.ts

@@ -686,7 +686,6 @@ 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();
@@ -878,9 +877,6 @@ 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/core/brain-score-recommendations.ts

@@ -146,16 +146,6 @@ 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. */
@@ -202,28 +192,20 @@ export function computeRecommendations(
   const source = ctx.sourceId ?? 'default';
 
   // ---------------------------------------------------------------------
-  // 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.
+  // sync.repo — fires when sync hasn't run recently OR pages are stale
   // ---------------------------------------------------------------------
-  const extractionLag = ctx.extractionLagPages ?? 0;
-  if (ctx.repoPath && extractionLag > 0) {
+  if (ctx.repoPath && health.stale_pages > 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: extractionLag > 50 ? 'high' : 'medium',
-      est_seconds: Math.min(600, 30 + extractionLag * 0.5),
+      severity: health.stale_pages > 50 ? 'high' : 'medium',
+      est_seconds: Math.min(600, 30 + health.stale_pages * 0.5),
       est_usd_cost: 0,  // sync is fs+DB only
       depends_on: [],
-      rationale: `Sync before extracting ${extractionLag} page${extractionLag === 1 ? '' : 's'} with stale link/timeline edges`,
+      rationale: `${health.stale_pages} stale page${health.stale_pages === 1 ? '' : 's'} on disk`,
       status: 'remediable',
     });
   }
@@ -255,7 +237,7 @@ 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 && extractionLag > 0 ? ['sync.repo'] : [],
+      depends_on: ctx.repoPath && health.stale_pages > 0 ? ['sync.repo'] : [],
       rationale: `${health.missing_embeddings} chunk${health.missing_embeddings === 1 ? '' : 's'} invisible to vector search`,
       status: 'remediable',
     });
@@ -285,7 +267,7 @@ 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 && extractionLag > 0) {
+  if (ctx.repoPath && health.stale_pages > 0) {
     const params = { mode: 'all', dir: ctx.repoPath };
     out.push({
       id: 'extract.all',
@@ -296,7 +278,7 @@ 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 for ${extractionLag} page${extractionLag === 1 ? '' : 's'} with stale extraction`,
+      rationale: 'Materialize link + timeline edges from fresh pages',
       status: 'remediable',
     });
   }

src/core/chunkers/code.ts

@@ -18,8 +18,9 @@
  * at runtime.
  */
 
-import { chunkText as recursiveChunk } from './recursive.ts';
+import { chunkText as recursiveChunk, capByEstimatedTokens, DEFAULT_MAX_EST_TOKENS } from './recursive.ts';
 import { buildQualifiedName } from './qualified-names.ts';
+import { estimateEmbeddingTokens } from '../cjk.ts';
 
 // Embed the tree-sitter runtime + per-language grammars as files.
 // `with { type: 'file' }` returns a path (string) at runtime. Bun bundles
@@ -111,7 +112,15 @@ import G_ZIG from '../../assets/wasm/grammars/tree-sitter-zig.wasm' with { type:
 // chunks get the new columns populated. Without this, the v28 backfill
 // gives every existing chunk a search_vector but subsequent Layer 5 AST
 // work would silently no-op.
-export const CHUNKER_VERSION = 4;
+//
+// v5: estimated-token hard cap on AST-path chunks (capCodeChunks). A node
+// splitLargeNode can't subdivide (giant single-statement function, huge
+// literal) previously shipped WHOLE regardless of size and could overflow
+// strict per-request embedding-token limits (local llama-server crashes
+// past ~2,050 tokens, measured). Mirrors the markdown
+// chunker's v4 cap; fallback-path chunks are already capped inside
+// recursiveChunk.
+export const CHUNKER_VERSION = 5;
 
 // Lazy-loaded tree-sitter module (v0.22.x API: Parser is default export)
 let Parser: typeof import('web-tree-sitter') | null = null;
@@ -708,7 +717,7 @@ export async function chunkCodeTextFull(
     if (chunks.length === 0) {
       return { chunks: fallbackChunks(source, filePath, language, opts), edges: rawEdges };
     }
-    return { chunks: mergeSmallSiblings(chunks, chunkTarget), edges: rawEdges };
+    return { chunks: capCodeChunks(mergeSmallSiblings(chunks, chunkTarget)), edges: rawEdges };
   } catch {
     return { chunks: fallbackChunks(source, filePath, language, opts), edges: [] };
   } finally {
@@ -791,6 +800,33 @@ function mergeSmallSiblings(chunks: CodeChunk[], chunkTarget: number): CodeChunk
   return merged;
 }
 
+/**
+ * v5 final safety pass for AST-path chunks: split any chunk whose
+ * ESTIMATED embedding tokens (conservative per-char-class heuristic,
+ * cjk.ts) exceed DEFAULT_MAX_EST_TOKENS. Reaches chunks the AST logic
+ * can't subdivide — splitLargeNode returns [] for nodes with < 2 body
+ * children (giant single-statement functions, huge literals), which
+ * previously shipped whole at any size.
+ *
+ * Split pieces inherit the source chunk's metadata verbatim; start/end
+ * lines become approximate for pieces after the first. Acceptable —
+ * these chunks exist for embedding + retrieval, and the alternative was
+ * an embedding request the server rejects (or worse, crashes on).
+ */
+function capCodeChunks(chunks: CodeChunk[]): CodeChunk[] {
+  if (chunks.every((c) => estimateEmbeddingTokens(c.text) <= DEFAULT_MAX_EST_TOKENS)) {
+    return chunks;
+  }
+  const out: CodeChunk[] = [];
+  for (const c of chunks) {
+    const pieces = capByEstimatedTokens(c.text, DEFAULT_MAX_EST_TOKENS);
+    for (const piece of pieces) {
+      out.push({ ...c, text: piece, index: out.length, metadata: { ...c.metadata } });
+    }
+  }
+  return out;
+}
+
 function buildMergedChunk(group: CodeChunk[], index: number): CodeChunk {
   const first = group[0]!;
   const last = group[group.length - 1]!;

src/core/chunkers/recursive.ts

@@ -17,7 +17,13 @@
  * Lossless invariant: non-overlapping portions reassemble to original.
  */
 
-import { countCJKAwareWords, CJK_SENTENCE_DELIMITERS, CJK_CLAUSE_DELIMITERS } from '../cjk.ts';
+import {
+  countCJKAwareWords,
+  CJK_SENTENCE_DELIMITERS,
+  CJK_CLAUSE_DELIMITERS,
+  charEmbedTokenWeight,
+  estimateEmbeddingTokens,
+} from '../cjk.ts';
 
 /**
  * Markdown chunker version. Folded into the per-page chunker_version column
@@ -33,8 +39,20 @@ import { countCJKAwareWords, CJK_SENTENCE_DELIMITERS, CJK_CLAUSE_DELIMITERS } fr
  * re-embed (not re-chunk) so existing pages pick up the wrapper on the
  * post-upgrade reembed sweep. See
  * `src/core/contextual-retrieval-service.ts`.
+ *
+ * v4: estimated-token hard cap + whitespace-word undercount fix. The word
+ * pipeline counted a 150-char URL as ONE whitespace word, so URL/phone/
+ * email-dense docs (CJK density < 0.30 → whitespace fallback) produced
+ * 3-4K-char chunks that overflow strict per-request embedding-token
+ * limits (measured: local llama-server crashes past ~2,050 tokens; URL
+ * soup tokenizes at ~1.6 chars/token). Two changes:
+ *   1. countWords() floors the count at ceil(nonWhitespaceChars/6) so a
+ *      URL counts roughly per-character, not as one word.
+ *   2. capByEstimatedTokens() final pass guarantees every chunk fits
+ *      `maxTokens` (default 1500) under a conservative per-char-class
+ *      token estimate, regardless of how word counting misjudged it.
  */
-export const MARKDOWN_CHUNKER_VERSION = 3;
+export const MARKDOWN_CHUNKER_VERSION = 4;
 
 const DELIMITERS: string[][] = [
   ['\n\n'],                          // L0: paragraphs
@@ -48,8 +66,20 @@ export interface ChunkOptions {
   chunkSize?: number;    // target words per chunk (default 300)
   chunkOverlap?: number; // overlap words (default 50)
   maxChars?: number;     // hard cap on any chunk's char length (default 6000)
+  /**
+   * v4: hard cap on any chunk's ESTIMATED embedding tokens (default 1500).
+   * Estimate = conservative per-char-class weights (see cjk.ts
+   * estimateEmbeddingTokens) — deliberately high, so the real tokenizer
+   * count stays below this value. Default leaves headroom for the
+   * contextual-retrieval wrapper (≤ ~630 chars) under a ~2,050-token
+   * per-request embedding server limit.
+   */
+  maxTokens?: number;
 }
 
+/** v4 default for ChunkOptions.maxTokens — see the field doc above. */
+export const DEFAULT_MAX_EST_TOKENS = 1500;
+
 export interface TextChunk {
   text: string;
   index: number;
@@ -73,6 +103,7 @@ export function chunkText(text: string, opts?: ChunkOptions): TextChunk[] {
   const chunkSize = opts?.chunkSize || 300;
   const chunkOverlap = opts?.chunkOverlap || 50;
   const maxChars = opts?.maxChars || 6000;
+  const maxTokens = opts?.maxTokens || DEFAULT_MAX_EST_TOKENS;
 
   if (!text || text.trim().length === 0) return [];
 
@@ -89,8 +120,9 @@ export function chunkText(text: string, opts?: ChunkOptions): TextChunk[] {
 
   const wordCount = countWords(stripped);
   if (wordCount <= chunkSize) {
-    // Single-chunk path: still apply the maxChars cap.
-    const capped = capByChars(stripped.trim(), maxChars);
+    // Single-chunk path: still apply the maxChars + maxTokens caps.
+    const capped = capByChars(stripped.trim(), maxChars)
+      .flatMap((t) => capByEstimatedTokens(t, maxTokens));
     return capped.map((t, i) => ({ text: t, index: i }));
   }
 
@@ -101,9 +133,14 @@ export function chunkText(text: string, opts?: ChunkOptions): TextChunk[] {
   // v0.32.7: hard char cap. Catches pathological CJK + whitespace-less text
   // that the word-level pipeline can't bound (a single Chinese paragraph can
   // exceed 8192 OpenAI embedding tokens at any word count).
+  // v4: estimated-token cap on top — the char cap alone passes token-dense
+  // content (URL soup at ~1.6 chars/token) that overflows strict embedding
+  // server limits.
   const capped: string[] = [];
   for (const chunk of withOverlap) {
-    capped.push(...capByChars(chunk.trim(), maxChars));
+    for (const piece of capByChars(chunk.trim(), maxChars)) {
+      capped.push(...capByEstimatedTokens(piece, maxTokens));
+    }
   }
   return capped.map((t, i) => ({ text: t, index: i }));
 }
@@ -132,6 +169,68 @@ function capByChars(text: string, maxChars: number): string[] {
   return out;
 }
 
+/**
+ * How far back (in chars) the token cap looks for a friendly cut point
+ * before falling back to a hard cut. 300 covers typical rollup/list line
+ * lengths so forced splits land at line starts, not mid-URL.
+ */
+const TOKEN_CAP_CUT_LOOKBACK = 300;
+
+/**
+ * v4: hard-cap a chunk's ESTIMATED embedding tokens. Final safety pass —
+ * runs after capByChars on every chunk, so no upstream miscounting
+ * (whitespace-word fallback, overlap inflation, char-cap survivors) can
+ * emit a chunk past `maxTokens`.
+ *
+ * Cut placement prefers, within the last TOKEN_CAP_CUT_LOOKBACK chars of
+ * the window: a newline, then any whitespace, then a hard cut. This keeps
+ * forced splits off mid-line/mid-URL positions for list-shaped content
+ * and inside code fences. No overlap is added (pieces stay lossless
+ * modulo the trims the char cap already applies).
+ *
+ * @internal exported for the code chunker (code.ts) and tests.
+ */
+export function capByEstimatedTokens(text: string, maxTokens: number): string[] {
+  if (text.length === 0) return [];
+  if (estimateEmbeddingTokens(text) <= maxTokens) return [text];
+
+  const out: string[] = [];
+  let start = 0;
+  while (start < text.length) {
+    // Greedily extend the window until the next char would break the cap.
+    // Always take at least one char so the loop makes forward progress.
+    let est = 0;
+    let end = start;
+    while (end < text.length) {
+      const w = charEmbedTokenWeight(text.charCodeAt(end));
+      if (est + w > maxTokens && end > start) break;
+      est += w;
+      end++;
+    }
+
+    if (end < text.length) {
+      const windowStart = Math.max(start + 1, end - TOKEN_CAP_CUT_LOOKBACK);
+      let cut = text.lastIndexOf('\n', end - 1);
+      if (cut < windowStart) {
+        cut = -1;
+        for (let i = end - 1; i >= windowStart; i--) {
+          const code = text.charCodeAt(i);
+          if (code === 0x20 || (code >= 0x09 && code <= 0x0d)) {
+            cut = i;
+            break;
+          }
+        }
+      }
+      if (cut >= windowStart) end = cut + 1;
+    }
+
+    const slice = text.slice(start, end).trim();
+    if (slice.length > 0) out.push(slice);
+    start = end;
+  }
+  return out;
+}
+
 function recursiveSplit(text: string, level: number, target: number): string[] {
   if (level >= DELIMITERS.length) {
     // Level 4: split on whitespace
@@ -317,7 +416,19 @@ function extractTrailingContext(text: string, targetWords: number): string {
  * Delegated to src/core/cjk.ts so the slugify whitelist, expansion
  * detection, and PGLite keyword fallback all agree on what "CJK enough"
  * means.
+ *
+ * v4: floored at ceil(nonWhitespaceChars/6). The whitespace fallback
+ * counts a 150-char URL as ONE word, so URL/phone/email-dense docs
+ * (whose ASCII mass pushes CJK density below the 0.30 threshold) were
+ * sized at a fraction of their real bulk and merged into 3-4K-char
+ * chunks. The floor makes long whitespace-less runs count roughly
+ * per-character while leaving normal Latin prose untouched (average
+ * English word ≈ 5 chars < 6, so the whitespace count still wins).
+ * Kept local to the chunker — search/expansion.ts keeps the original
+ * countCJKAwareWords semantics for its query-length check.
  */
 function countWords(text: string): number {
-  return countCJKAwareWords(text);
+  const cjkAware = countCJKAwareWords(text);
+  const nonWhitespace = text.replace(/\s/g, '').length;
+  return Math.max(cjkAware, Math.ceil(nonWhitespace / 6));
 }

src/core/cjk.ts

@@ -65,3 +65,64 @@ export function countCJKAwareWords(s: string): number {
 export function escapeLikePattern(s: string): string {
   return s.replace(/\\/g, '\\\\').replace(/%/g, '\\%').replace(/_/g, '\\_');
 }
+
+/**
+ * Conservative per-char-class embedding-token weights (markdown chunker v4).
+ *
+ * Why this exists: the chunker's "word" counting drastically UNDER-counts
+ * whitespace-less ASCII runs (a 150-char URL = 1 whitespace word), so
+ * word-based size targets can emit chunks that overflow an embedding
+ * server's per-request token limit. Measured on a local Qwen3-embedding
+ * llama-server stack:
+ *   - URL/phone/email-dense text tokenizes at ~1.6 chars/token
+ *   - base64-ish / minified blobs approach ~1.3 chars/token (worst case)
+ *   - Korean prose tokenizes NO WORSE than 1 char/token in practice
+ *
+ * Weights are deliberately HIGH (tokens are overestimated) so any cap
+ * based on this estimate is safe against real tokenizers:
+ *   - CJK char        → 1.0 token  (real CJK prose is cheaper)
+ *   - other non-space → 0.75 token (≈1.33 chars/token, covers base64)
+ *   - whitespace      → 0.1 token  (mostly folds into neighbor tokens)
+ */
+export const EMBED_TOKEN_WEIGHT_CJK = 1.0;
+export const EMBED_TOKEN_WEIGHT_OTHER = 0.75;
+export const EMBED_TOKEN_WEIGHT_WS = 0.1;
+
+/** BMP CJK check by UTF-16 code unit — same ranges as CJK_SLUG_CHARS. */
+export function isCJKCodeUnit(code: number): boolean {
+  return (
+    (code >= 0x4e00 && code <= 0x9fff) || // Han
+    (code >= 0x3040 && code <= 0x309f) || // Hiragana
+    (code >= 0x30a0 && code <= 0x30ff) || // Katakana
+    (code >= 0xac00 && code <= 0xd7af)    // Hangul Syllables
+  );
+}
+
+/**
+ * Per-code-unit token weight. Unrecognized whitespace (exotic Unicode
+ * spaces) intentionally falls into OTHER — that only overestimates.
+ */
+export function charEmbedTokenWeight(code: number): number {
+  if (isCJKCodeUnit(code)) return EMBED_TOKEN_WEIGHT_CJK;
+  if (
+    code === 0x20 || (code >= 0x09 && code <= 0x0d) ||
+    code === 0xa0 || code === 0x3000
+  ) {
+    return EMBED_TOKEN_WEIGHT_WS;
+  }
+  return EMBED_TOKEN_WEIGHT_OTHER;
+}
+
+/**
+ * Tokenizer-free embedding-token estimate (conservative overestimate).
+ * See weight docs above. Astral chars count as 2 OTHER code units —
+ * another overestimate, which is the safe direction.
+ */
+export function estimateEmbeddingTokens(s: string): number {
+  if (s.length === 0) return 0;
+  let est = 0;
+  for (let i = 0; i < s.length; i++) {
+    est += charEmbedTokenWeight(s.charCodeAt(i));
+  }
+  return Math.ceil(est);
+}

src/core/remediation/context.ts

@@ -8,7 +8,6 @@
 
 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
@@ -69,29 +68,5 @@ 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 { countExtractionLag, loadRecommendationContext } from './context.ts';
+import { 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');
 
-  let ctx = await loadRecommendationContext(engine);
+  const ctx = await loadRecommendationContext(engine);
 
   // Pre-flight ceiling check via the shared plan computation.
   const initialPlan = await computeRemediationPlan(engine, { targetScore });
@@ -305,11 +305,6 @@ 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

@@ -1423,15 +1423,6 @@ 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/brain-score-recommendations.test.ts

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

test/chunker-version-gate.test.ts

@@ -15,19 +15,22 @@ import { describe, test, expect } from 'bun:test';
 import { CHUNKER_VERSION } from '../src/core/chunkers/code.ts';
 
 describe('Layer 12 — CHUNKER_VERSION constant', () => {
-  test('bumped to 4 for Cathedral II', () => {
+  test('bumped to 5 for the estimated-token hard cap', () => {
     // v3: v0.19.0 Chonkie parity (tokenizer + small-sibling merge).
     // v4: v0.20.0 Cathedral II (qualified names + parent scope + doc_comment
     //     + fence extraction + chunk-grain FTS). Folded into content_hash
     //     so any bump forces clean re-chunks on next sync.
-    expect(CHUNKER_VERSION).toBe(4);
+    // v5: estimated-token hard cap on AST-path chunks (capCodeChunks) so
+    //     un-subdividable giant nodes can't overflow strict embedding
+    //     server token limits.
+    expect(CHUNKER_VERSION).toBe(5);
   });
 
   test('is stable across imports (not recomputed at call time)', async () => {
     const a = (await import('../src/core/chunkers/code.ts')).CHUNKER_VERSION;
     const b = (await import('../src/core/chunkers/code.ts')).CHUNKER_VERSION;
     expect(a).toBe(b);
-    expect(a).toBe(4);
+    expect(a).toBe(5);
   });
 });
 

test/chunkers/code.test.ts

@@ -10,8 +10,8 @@ import { describe, test, expect } from 'bun:test';
 import { chunkCodeText, detectCodeLanguage, CHUNKER_VERSION } from '../../src/core/chunkers/code.ts';
 
 describe('CHUNKER_VERSION', () => {
-  test('v0.20.0 Cathedral II Layer 12 bumped to 4', () => {
-    expect(CHUNKER_VERSION).toBe(4);
+  test('v5: estimated-token hard cap on AST-path chunks', () => {
+    expect(CHUNKER_VERSION).toBe(5);
   });
 });
 

test/chunkers/recursive.test.ts

@@ -135,13 +135,14 @@ describe('Recursive Text Chunker', () => {
 });
 
 describe('CJK chunking (v0.32.7)', () => {
-  test('MARKDOWN_CHUNKER_VERSION is 3', async () => {
+  test('MARKDOWN_CHUNKER_VERSION is 4', async () => {
     // v0.40.3.0: bumped 2→3 to signal the post-upgrade reembed sweep that
-    // contextual retrieval wrapping is now applied at embed time. Chunk
-    // boundaries themselves are unchanged; the bump forces re-embed for
-    // pages where chunker_version < 3.
+    // contextual retrieval wrapping is now applied at embed time.
+    // v4: estimated-token hard cap + whitespace-word undercount floor
+    // (URL-dense docs produced chunks past strict embedding server token
+    // limits). Boundary change → forces re-chunk for chunker_version < 4.
     const mod = await import('../../src/core/chunkers/recursive.ts');
-    expect(mod.MARKDOWN_CHUNKER_VERSION).toBe(3);
+    expect(mod.MARKDOWN_CHUNKER_VERSION).toBe(4);
   });
 
   test('long pure-Chinese paragraph splits into multiple chunks', () => {

test/chunkers/token-cap.test.ts

@@ -0,0 +1,195 @@
+/**
+ * Markdown chunker v4 / code chunker v5 — estimated-token hard cap
+ * regression tests.
+ *
+ * Reproduces a field failure: a local llama-server embedding backend
+ * (`-ub 2048`) crashes deterministically (trace/BPT trap → EOF at the
+ * client) when a single chunk exceeds ~2,050 real tokens. Two content
+ * shapes triggered it:
+ *
+ *   1. Korean docs carrying one long source URL per line.
+ *      The URLs' ASCII mass pushes CJK density below 0.30, flipping
+ *      countCJKAwareWords to whitespace counting, where a 150-char URL
+ *      counts as ONE word → chunks ballooned to 3-4K chars ≈ 2,000+
+ *      real tokens (URL soup tokenizes at ~1.6 chars/token).
+ *
+ *   2. Large JSON code blocks (~7K chars) that the word pipeline
+ *      undercounts the same way (few whitespace tokens).
+ *
+ * The fix: every emitted chunk must satisfy
+ *   estimateEmbeddingTokens(chunk) <= maxTokens (default 1500)
+ * where the estimate deliberately OVERSTATES real tokenizer counts.
+ */
+
+import { describe, test, expect } from 'bun:test';
+import { chunkText, capByEstimatedTokens, DEFAULT_MAX_EST_TOKENS } from '../../src/core/chunkers/recursive.ts';
+import { chunkCodeText } from '../../src/core/chunkers/code.ts';
+import { estimateEmbeddingTokens } from '../../src/core/cjk.ts';
+
+/** Synthesize the failing shape: Korean rollup lines each ending in a long Notion URL. */
+function urlDenseKoreanRollup(lines: number): string {
+  const out: string[] = ['# 링크가 줄마다 붙는 한국어 예시 문서', ''];
+  for (let i = 0; i < lines; i++) {
+    const hex32 = (i * 2654435761 >>> 0).toString(16).padStart(8, '0').repeat(4);
+    out.push(
+      `- **항목 ${i}**: 이 줄은 청커 동작 검증을 위한 의미 없는 한국어 예시 문장입니다 · 전화 000-0000-${String(1000 + i)} · ` +
+      `이메일 user${i}@example.com · 링크: https://docs.example.com/pages/${hex32}?v=abcdef0123456789&ref=sample`,
+    );
+  }
+  return out.join('\n');
+}
+
+/** Synthesize a large pretty-printed JSON block with CJK values. */
+function bigJsonBlock(targetChars: number): string {
+  const entries: string[] = [];
+  let i = 0;
+  let len = 0;
+  while (len < targetChars) {
+    const row =
+      `  "item_${i}": { "name": "예시-${i}", "url": "https://example.com/api/v2/items/${i}?token=abc${i}def", "qty": ${i % 100}, "memo": "한국어 값이 섞인 예시 데이터" }`;
+    entries.push(row);
+    len += row.length;
+    i++;
+  }
+  return `{\n${entries.join(',\n')}\n}`;
+}
+
+describe('v4 estimated-token cap — URL-dense Korean doc (field-failure shape)', () => {
+  test('every chunk stays under the estimated-token cap', () => {
+    const md = urlDenseKoreanRollup(60);
+    const chunks = chunkText(md);
+    expect(chunks.length).toBeGreaterThan(0);
+    for (const c of chunks) {
+      expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS);
+    }
+  });
+
+  test('no chunk reaches the measured 3K-char danger zone for URL soup', () => {
+    const md = urlDenseKoreanRollup(60);
+    const chunks = chunkText(md);
+    // 1500 est tokens at the OTHER weight (0.75/char) bounds chunks to
+    // ~2,000 chars for pure ASCII — well under the ~3,300 chars where
+    // URL-dense content crosses ~2,050 real tokens (1.6 chars/token).
+    for (const c of chunks) {
+      expect(c.text.length).toBeLessThanOrEqual(2600);
+    }
+  });
+
+  test('content is preserved (no lines dropped by the cap)', () => {
+    const md = urlDenseKoreanRollup(60);
+    const chunks = chunkText(md);
+    const joined = chunks.map((c) => c.text).join('\n');
+    // Spot-check first / middle / last rollup lines survive chunking.
+    for (const marker of ['항목 0', '항목 30', '항목 59']) {
+      expect(joined).toContain(marker);
+    }
+  });
+});
+
+describe('v4 estimated-token cap — large JSON blocks', () => {
+  test('7K-char pretty JSON through the prose path stays under the cap', () => {
+    const md = `설정 파일 원문 보존:\n\n\`\`\`\n${bigJsonBlock(7000)}\n\`\`\`\n`;
+    const chunks = chunkText(md);
+    expect(chunks.length).toBeGreaterThan(1);
+    for (const c of chunks) {
+      expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS);
+    }
+  });
+
+  test('7K-char minified JSON (single whitespace-less token) stays under the cap', () => {
+    const minified = bigJsonBlock(7000).replace(/\n\s*/g, '');
+    const chunks = chunkText(minified);
+    expect(chunks.length).toBeGreaterThan(1);
+    for (const c of chunks) {
+      expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS);
+    }
+  });
+
+  test('json fence via the code chunker stays under the cap (+header slack)', async () => {
+    const chunks = await chunkCodeText(bigJsonBlock(7000), 'fence.json');
+    expect(chunks.length).toBeGreaterThan(0);
+    for (const c of chunks) {
+      // buildChunk prepends a short "[JSON] fence.json:…" header AFTER the
+      // body-level cap; allow ~60 est tokens of header slack. Real-token
+      // safety margin (2,050 − overestimated 1,500) absorbs this easily.
+      expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS + 60);
+    }
+  });
+});
+
+describe('v4 word-count floor — behavior preserved for normal content', () => {
+  test('Latin prose chunking is unchanged by the floor (avg word < 6 chars)', () => {
+    const prose = Array.from({ length: 120 }, (_, i) =>
+      `This is sentence number ${i} and it talks about ordinary things in plain words.`,
+    ).join(' ');
+    const chunks = chunkText(prose);
+    // Historical behavior: ~1,560 whitespace words → multiple ~300-word chunks.
+    expect(chunks.length).toBeGreaterThan(3);
+    for (const c of chunks) {
+      const words = c.text.split(/\s+/).length;
+      expect(words).toBeLessThanOrEqual(300 * 1.5 + 50); // merge cap + overlap
+    }
+  });
+
+  test('Korean prose (CJK-dense, no URLs) never triggers the token cap', () => {
+    const prose = Array.from({ length: 80 }, (_, i) =>
+      `이 문장은 순수 한국어 산문의 청킹 동작을 확인하기 위한 ${i}번째 예시 문장입니다.`,
+    ).join(' ');
+    const chunks = chunkText(prose);
+    expect(chunks.length).toBeGreaterThan(1);
+    for (const c of chunks) {
+      // CJK-dense chunks are char-counted (≈450 max) — nowhere near 1500.
+      expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(700);
+    }
+  });
+});
+
+describe('capByEstimatedTokens unit behavior', () => {
+  test('returns input unchanged when under the cap', () => {
+    expect(capByEstimatedTokens('short text', 1500)).toEqual(['short text']);
+    expect(capByEstimatedTokens('', 1500)).toEqual([]);
+  });
+
+  test('prefers newline cut points within the lookback window', () => {
+    const line = 'x'.repeat(100);
+    const text = Array.from({ length: 40 }, () => line).join('\n');
+    const pieces = capByEstimatedTokens(text, 1000);
+    expect(pieces.length).toBeGreaterThan(1);
+    for (const p of pieces) {
+      // Every piece should be whole lines (multiples of the 100-char line).
+      for (const l of p.split('\n')) {
+        expect(l).toBe(line);
+      }
+    }
+  });
+
+  test('makes forward progress on whitespace-less input (hard cut)', () => {
+    const blob = 'a'.repeat(10_000);
+    const pieces = capByEstimatedTokens(blob, 1000);
+    expect(pieces.length).toBeGreaterThan(1);
+    expect(pieces.join('')).toBe(blob);
+    for (const p of pieces) {
+      expect(estimateEmbeddingTokens(p)).toBeLessThanOrEqual(1000);
+    }
+  });
+});
+
+describe('estimateEmbeddingTokens — weight sanity', () => {
+  test('overestimates URL-dense ASCII (0.75/char ≥ measured ~0.63/char)', () => {
+    const url = 'https://docs.example.com/pages/a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4?v=abc&ref=sample';
+    const est = estimateEmbeddingTokens(url);
+    expect(est).toBeGreaterThanOrEqual(Math.floor(url.length * 0.7));
+  });
+
+  test('counts CJK at 1 token/char', () => {
+    expect(estimateEmbeddingTokens('가나다라마')).toBe(5);
+  });
+
+  test('whitespace is nearly free', () => {
+    expect(estimateEmbeddingTokens('   \n\t  ')).toBeLessThanOrEqual(1);
+  });
+
+  test('empty string is 0', () => {
+    expect(estimateEmbeddingTokens('')).toBe(0);
+  });
+});

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

@@ -1,62 +0,0 @@
-// 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

@@ -1,81 +0,0 @@
-// 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);
-  });
-});