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7be17261bc |
v0.32.3.0 skill: functional-area-resolver — pattern for compressing routing tables (#859)
* skill: compress-agents-md — functional-area resolver pattern Proven via A/B eval: 100% routing accuracy at 48% size reduction. Converts granular per-skill resolver rows into functional-area dispatchers with '(dispatcher for: ...)' sub-skill lists. Includes: - SKILL.md with full pattern docs, before/after examples, eval results - routing-eval.jsonl with 5 fixtures - Anti-patterns (resolver-of-resolvers pipe table = 15% accuracy) * skill: rename compress-agents-md → functional-area-resolver, cite prior art The contribution is a pattern (functional-area dispatcher with `(dispatcher for: ...)` clauses), not a file. Rename describes the contribution; triggers broaden to cover both AGENTS.md and RESOLVER.md phrasings. SKILL.md rewrite: - Three-model A/B table (Opus 4.7 / Sonnet 4.6 / Haiku 4.5) replaces the original Sonnet-only claim. Functional-areas beats baseline by +13 to +17pp training (lenient) across all three models at 48% the size. - Strict + lenient scoring documented side by side. Lenient (predicted shares dispatcher area with expected) matches production agent behavior. - Preconditions added: refuse to compress if file <12KB or working tree dirty. - Multi-file routing precedence section for the v0.31.7 RESOLVER.md/AGENTS.md merge case. - Mandatory verification step (≥95% via the harness). - Daily-doctor.mjs reference scrubbed (didn't exist in gbrain). - Three prior-art citations: AnyTool (arXiv:2402.04253), RAG-MCP (arXiv:2505.03275), Anthropic Agent Skills progressive disclosure. The pattern is the static-prompt analog of runtime hierarchical routing. routing-eval.jsonl: 8 positive (5 original + 3 broadened triggers) + 4 adversarial negatives targeting skillify, skill-creator, book-mirror, concept-synthesis to prove broadened triggers don't over-capture adjacent meta-skills. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * evals: A/B harness for functional-area-resolver (gateway-routed, strict + lenient scoring) evals/functional-area-resolver/ lives outside skills/ deliberately. The skillpack bundler walks skills/<skill>/ recursively, so an eval surface in there would copy harness + variants + fixtures + tests into every downstream install. The pattern (in SKILL.md) ships everywhere; the eval evidence stays in the gbrain repo. What ships: - Three variant resolvers in variants/ — baseline.md (verbose 25KB) and functional-areas.md (compressed 13KB) extracted from a real production AGENTS.md at git commits 93848ff3b^ and 93848ff3b (owner PII scrubbed). resolver-of-resolvers.md derived mechanically by stripping (dispatcher for: ...) clauses — the ablation case. - 20 hand-authored training fixtures + 5 held-out blind fixtures. - harness-runner.ts — TypeScript runner via gbrain gateway. Flags: --model {opus|sonnet|haiku|<full-id>}, --variants-dir, --variants for description-length sweeps, --parallel N (rate-lease bound), --limit N for smoke runs, --yes for non-TTY. - Every output row carries BOTH `correct` (strict) and `correct_lenient` (predicted shares dispatcher area with expected). Lenient matches production behavior. - Receipt header binds (model, prompt_template_hash, fixtures_hash, harness_sha, ts, cmd_args). Re-runs are auditable. - harness.mjs — thin Node shim that spawns the TS runner via bun. - rescore.mjs — zero-cost lenient re-score of an existing JSONL. - harness-runner.test.ts — 45 unit tests (no API key needed) covering every pure function plus the dispatcher-list parser. The prompt template is load-bearing: without the "drill into (dispatcher for: ...) list" instruction, every compression variant collapses to ~30-60%. Documented in SKILL.md and README.md. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * evals: baseline receipts (Opus 4.7 + Sonnet 4.6 + Haiku 4.5, 2026-05-11) Three canonical 225-row receipts (3 variants × 25 fixtures × 3 seeds per model). Each receipt header binds (model, prompt_template_hash, fixtures_hash, harness_sha, ts) so the published SKILL.md numbers are reproducible. Training corpus (n=20, lenient): baseline | Opus 81.7% | Sonnet 86.7% | Haiku 73.3% | 25KB functional-areas | Opus 98.3% | Sonnet 100% | Haiku 88.3% | 13KB resolver-of-resolvers | Opus 63.3% | Sonnet 41.7% | Haiku 65.0% | 10KB functional-areas beats baseline by +13 to +17pp across all three models at 48% the size. resolver-of-resolvers' Sonnet collapse (41.7%) is the SKILL.md "compression without dispatcher clause is broken" claim, observed. Held-out (n=5, lenient) saturates at 100% across most cells (Sonnet × resolver-of-resolvers is 73.3% — the same failure mode visible on a smaller sample). ~$3 API spend across all three runs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * skill: wire functional-area-resolver into RESOLVER.md + manifests skills/RESOLVER.md gets a new row in Operational, adjacent to skillify. Triggers: "Compress my resolver", "AGENTS.md too large", "RESOLVER.md too big", "functional area dispatcher", "shrink routing table". skills/manifest.json adds the new entry and bumps manifest version 0.25.1 → 0.32.3.0 (loadOrDeriveManifest reads this for sync-guard). openclaw.plugin.json adds functional-area-resolver to the skills array and bumps version 0.25.1 → 0.32.3.0 so install receipts stop being stale (src/core/skillpack/installer.ts:307-311 uses manifest version on every install). Verified: - gbrain check-resolvable --json: 42/42 reachable, 0 errors. - gbrain routing-eval: 70/70 pass (100% structural). - bun test test/skillpack-sync-guard.test.ts: passes (manifest in sync). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * v0.32.3.0 skill: functional-area-resolver — pattern for compressing routing tables Headline: compress a 25KB AGENTS.md down to 13KB without losing routing accuracy. Pattern proven across Opus 4.7, Sonnet 4.6, and Haiku 4.5 — beats the verbose baseline by +13 to +17pp at 48% the size. Empirical (training, n=20, 3 seeds, lenient): baseline 25KB: Opus 81.7% | Sonnet 86.7% | Haiku 73.3% functional-areas 13KB: Opus 98.3% | Sonnet 100% | Haiku 88.3% resolver-of-resolvers 10KB: Opus 63.3% | Sonnet 41.7% | Haiku 65.0% The (dispatcher for: ...) clause is the load-bearing signal. Strip it (the resolver-of-resolvers variant) and Sonnet collapses to 41.7% — the failure case the pattern's authors predicted, now observed. Files in this release: - VERSION + package.json bumped to 0.32.3.0 (4-segment per CLAUDE.md). - CHANGELOG.md: full empirical story, cross-model table, three prior-art citations (AnyTool, RAG-MCP, Anthropic Agent Skills progressive disclosure). - TODOS.md: nine v0.33.x follow-ups (dogfood on gbrain's own RESOLVER.md, CLI promotion to gbrain routing-eval --ab-compare, held-out corpus growth, cross-vendor Gemini+GPT verification, per-row description length sweep, structural compression to ~10KB, hierarchical area-of-areas, embedding pre-router, adversarial fixtures, prompt-design ablation doc). - llms-full.txt regenerated. Bisect-friendly history on this branch: |
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1d78013c07 |
v0.28.5 fix(wave): PGLite upgrade wedge + embedding dim corruption + bun-link foot-gun (#697)
* fix(engines): pre-add v0.20 + v0.26.3 forward-reference columns in bootstrap The forward-reference bootstrap (PostgresEngine + PGLiteEngine applyForwardReferenceBootstrap) covered v0.18 + v0.19 + v0.26.5 columns but missed two later groups. Brains upgrading from v0.14-era to current master crash before the migration ladder runs: 1. v0.20 Cathedral II — content_chunks.search_vector, parent_symbol_path, doc_comment, symbol_name_qualified. `CREATE INDEX idx_chunks_search_vector` and `CREATE INDEX idx_chunks_symbol_qualified` in schema.sql/PGLITE_SCHEMA_SQL crash with "column search_vector does not exist" / "column symbol_name_qualified does not exist". 2. v0.26.3 — mcp_request_log.agent_name, params, error_message. `CREATE INDEX idx_mcp_log_agent_time ON mcp_request_log(agent_name,...)` crashes with "column agent_name does not exist". Reproduces deterministically on a v0.13/v0.14 brain upgraded straight to current master. The user hits the wall before any of v15-v36 can run. Both engines now probe for these columns and pre-add them via `ALTER TABLE ADD COLUMN IF NOT EXISTS` before SCHEMA_SQL runs. Migrations v26, v27, v33 still run later via runMigrations and remain idempotent (they handle backfill on top of the bootstrap-added columns). Test coverage extended in test/schema-bootstrap-coverage.test.ts: REQUIRED_BOOTSTRAP_COVERAGE now lists 6 new forward references; the strip-and-rebuild block drops the corresponding indexes/triggers so the test exercises a brain that pre-dates v0.20 + v0.26.3 migrations. Repro: brain on schema v13/v14 + run `gbrain init --migrate-only` against current master → fails. With this patch → succeeds; ladder runs to v36. * fix(engines): pre-add v0.27 subagent_messages.provider_id in bootstrap PR #682 covered v0.20 (chunks) + v0.26.3 (mcp_request_log) but missed v0.27's subagent_messages.provider_id. The composite index `idx_subagent_messages_provider ON subagent_messages (job_id, provider_id)` in PGLITE_SCHEMA_SQL crashes on brains pinned at v0.18-v0.26 because provider_id is the SECOND column in the composite — array-extraction patterns that scan only first-column references miss it entirely. This is the wedge surfaced by issue #670 (v0.22.0 → v0.27.0 init --migrate-only crashes with "column 'provider_id' does not exist") and contributing to #661/#657. Both engines now probe for subagent_messages.provider_id and pre-add the column via ALTER TABLE ADD COLUMN IF NOT EXISTS before SCHEMA_SQL runs. Migration v36 (subagent_provider_neutral_persistence_v0_27) still runs later via runMigrations and remains idempotent. Note on the test side: REQUIRED_BOOTSTRAP_COVERAGE is hand-maintained and just gained a v0.27 entry. v0.28.5's Step 3 replaces this array with a SQL parser that auto-derives coverage from PGLITE_SCHEMA_SQL, including composite-index columns. This commit is the targeted follow-up to PR #682's cherry-pick; A2's parser closes the class permanently. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(cli): conditional schema-init on connect (closes #651) Adds `hasPendingMigrations(engine)` next to `runMigrations` in migrate.ts: single getConfig('version') probe, returns true when current < LATEST_VERSION, defensively returns true on getConfig failure (treats wedged-config as pending). `connectEngine` in cli.ts now wraps `engine.initSchema()` in a probe gate: short-lived CLI calls (gbrain stats, query, doctor, etc.) on already-migrated brains skip the bootstrap-probe + SCHEMA_SQL replay + ledger-check entirely. Wedged brains still auto-heal — the probe says "yes pending" and initSchema runs as before. Building on oyi77's investigation in PR #652. Same correctness as #652's unconditional initSchema-on-every-connect, but no perf regression on the hot path. Failure non-fatal: if probe or init throws, log a hint and let subsequent operations surface the real error in context. Test coverage in test/migrate.test.ts: 3 cases covering fully-migrated (false), version-rewound (true), and missing-version-config (defensive true). Pairs with v0.28.5's X1 (post-upgrade auto-apply) — the upgrade path runs initSchema explicitly while every other code path that goes through connectEngine gets the cheap probe. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(upgrade): post-upgrade auto-applies pending schema migrations (X1) Prior behavior: `gbrain upgrade` → `gbrain post-upgrade` → `apply-migrations` only WARNs at apply-migrations.ts:296-302 when schema version is behind LATEST_VERSION, telling the user to run `gbrain init --migrate-only`. 11 wedge incidents over 2 years have proven users don't read that WARN — they file an issue instead. This commit makes `runPostUpgrade` explicitly call `engine.initSchema()` after the orchestrator migration pass, mirroring `init --migrate-only`'s flow. Side-effect: `gbrain upgrade` now walks away with a healthy brain in the cluster A wedge case (#670, #661, #657, #651, #625, #615, #609). Defensive: wrapped in try/catch so a connection or DDL failure falls back to the existing user-facing WARN. The hint to run `gbrain init --migrate-only` is preserved as the manual escape hatch. Pairs with v0.28.5's A1 (hasPendingMigrations probe in connectEngine): the upgrade path runs initSchema explicitly here, while every other code path that goes through connectEngine gets the cheap probe. Codex outside-voice review caught this gap during plan review: "the plan still does not prove `upgrade` will actually run schema migrations." This is the load-bearing fix that makes v0.28.5's headline outcome ("run upgrade, brain works") literally true for cluster A. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test(bootstrap): auto-derive coverage from PGLITE_SCHEMA_SQL (A2) Replaces the hand-maintained REQUIRED_BOOTSTRAP_COVERAGE assertion with a SQL-parser-backed structural check. The new test: 1. parseIndexColumnReferences(PGLITE_SCHEMA_SQL) extracts every column referenced by every CREATE INDEX — including composite-index second and third columns. Codex outside-voice review caught that earlier first-col-only patterns missed v0.27's `idx_subagent_messages_provider ON subagent_messages (job_id, provider_id)`, which is exactly how the v0.28.5 wedge happened. 2. parseBaseTableColumns(PGLITE_SCHEMA_SQL) extracts every column declared in CREATE TABLE bodies (including via ALTER TABLE ADD COLUMN inside the schema blob). 3. parseAlterAddColumns(pglite-engine.ts source) extracts every column that applyForwardReferenceBootstrap adds. 4. Static contract: every (table, column) pair from step 1 must appear in either step 2 or step 3. Otherwise the test fails loud, names every uncovered pair, and points at the bootstrap function for the fix. Self-updating: any future CREATE INDEX added to PGLITE_SCHEMA_SQL on a column that bootstrap doesn't yet provide fails this test at PR time. No human required to remember to update an array. Closes the 11-incident wedge class identified in CLAUDE.md (#239, #243, #266, #357, #366, #374, #375, #378, #395, #396). Helper parsers also have their own unit tests covering composite-index second columns, function-wrapped columns (lower(col)), HNSW operator-class suffixes (vector_cosine_ops), and ALTER TABLE column extraction. Existing REQUIRED_BOOTSTRAP_COVERAGE-based tests preserved as a coarse-grained lower bound; the new parser-based test is the load-bearing structural gate going forward. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: support Voyage 2048d schema setup * fix: harden Voyage schema templating * feat: Voyage 4 embedding support + doctor eval - Add voyage-4-large/4/4-lite/4-nano + domain models to Voyage recipe - Fix AI SDK compatibility: strip encoding_format (Voyage rejects 'float'), patch response to add prompt_tokens from total_tokens - Add embedding_provider doctor check: live smoke test verifying model, API key, dimensions, and DB column alignment - Add embedding provider eval qrels for post-migration quality testing Closes: Voyage AI integration for gbrain embedding pipeline * fix: adaptive embed batch sizing for Voyage token limits Voyage's tokenizer is 3-4x denser than OpenAI tiktoken, causing batches of 50+ texts to exceed the 120K token-per-batch limit even when DB token counts (from tiktoken) suggest they'd fit. Changes: - Add max_batch_tokens to EmbeddingTouchpoint type (provider-declared limit) - Set Voyage recipe to 120K token limit - Gateway embed() now auto-splits batches using conservative char-to-token estimate (1:1 ratio, 80% budget utilization) - On token-limit errors, embedSubBatch recursively halves and retries (down to single-text batches before giving up) - Reduce embedding.ts BATCH_SIZE from 100 to 50 as a secondary guard - Add tests for batch splitting logic and error pattern matching Fixes infinite retry loops where the same oversized batch would fail repeatedly because WHERE embedding IS NULL re-fetches identical rows. * fix(init): error on existing-brain dim mismatch + embedding-migration recipe Adds A4 hard-error path: when `gbrain init --embedding-dimensions N` is run against an existing brain whose `content_chunks.embedding` column is a different `vector(M)`, init exits 1 with an inline four-step ALTER recipe and a pointer to docs/embedding-migrations.md. This kills the silent-corruption pattern surfaced by issue #673: the v0.27 schema seeded `('embedding_dimensions', '1536')` regardless of the flag, so users got a config saying 768 but a column at 1536 — first sync write blew up with "expected 1536, got 768." A4's contract: 1. Connect to engine BEFORE saveConfig so we can read the live column type 2. If column exists AND dim != requested, exit 1 (loud failure) 3. If column doesn't exist (fresh init) OR dim matches, proceed normally Recipe in docs/embedding-migrations.md (and inlined in init's error output) covers all four destructive steps codex's plan-review caught: 1. DROP INDEX IF EXISTS idx_chunks_embedding (HNSW won't survive ALTER) 2. ALTER TABLE content_chunks ALTER COLUMN embedding TYPE vector(N) 3. UPDATE content_chunks SET embedding = NULL, embedded_at = NULL 4. CREATE INDEX HNSW *only if N <= 2000* (pgvector cap) Step 4 is conditional: dims > 2000 (e.g. Voyage 4 Large 2048d) cannot be HNSW-indexed in pgvector; the recipe explicitly says "Skip reindex" in that case so the user doesn't paste a CREATE INDEX that crashes. Helper `readContentChunksEmbeddingDim` and message builder `embeddingMismatchMessage` live in src/core/embedding-dim-check.ts so doctor 8b (next commit) can reuse the same source of truth. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(gateway): correct dim-mismatch error to point at manual ALTER recipe (#672) Previous error message recommended running `gbrain migrate --embedding-model … --embedding-dimensions …`, but `gbrain migrate` only handles engine migration (postgres ↔ pglite), not embedding reconfiguration. Following that hint produced a different error and confused users further. New message: - Names the actual options: change models OR migrate the existing brain - Inlines a one-line quick recipe (DROP INDEX → ALTER → UPDATE NULL → config set → embed --stale) - Points at docs/embedding-migrations.md (added in commit |