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root	3eaac413e6	auditor: route kimi_architect through ollama_cloud/kimi-k2.6 (TOS-clean primary) ... Two changes: 1. Default provider now ollama_cloud/kimi-k2.6 (env-overridable via LH_AUDITOR_KIMI_PROVIDER + LH_AUDITOR_KIMI_MODEL). Ollama Cloud Pro exposes kimi-k2.6 legitimately, so we no longer need the User-Agent- spoof path through api.kimi.com. Smoke test 2026-04-27: api.kimi.com 368s 8 findings 8/8 grounded ollama_cloud 54s 10 findings 10/10 grounded The kimi.rs adapter (provider=kimi) stays wired as a fallback when Ollama Cloud is upstream-broken. 2. Switch HTTP transport from Bun's native fetch to curl via Bun.spawn. Bun fetch has an undocumented ~300s ceiling that AbortController + setTimeout cannot override; curl honors -m for end-to-end max transfer time without a hard intrinsic limit. Required for Kimi's reasoning-heavy responses on big audit prompts. 3. Bug fix Kimi caught in this very file (turtles all the way down): Number(process.env.LH_AUDITOR_KIMI_MAX_TOKENS ?? 128_000) yields 0 when env is set to empty string — `??` only catches null/undefined. Switched to Number(env) || 128_000 so empty/0/NaN all fall back. Same pattern probably exists in other files; future audit pass. 4. Bumped MAX_TOKENS default 12K -> 128K. Kimi K2.6's reasoning_content counts against this budget but isn't surfaced in OpenAI-shape content; 12K silently produced finish_reason=length with empty content when reasoning consumed the budget. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-27 06:14:16 -05:00
root	8d02c7f441	auditor: integrate Kimi second-pass review (off by default, LH_AUDITOR_KIMI=1) ... Adds kimi_architect as a fifth check kind in the auditor. Runs sequentially after static/dynamic/inference/kb_query, consumes their findings as context, and asks Kimi For Coding "what did everyone miss?" — targeting load-bearing issues that deepseek N=3 voting can't see (compile errors, false telemetry, schema bypasses, determinism leaks). 7/7 grounded on the distillation v1.0.0 audit experiment 2026-04-27. Off by default. Enable on the lakehouse-auditor service: systemctl edit lakehouse-auditor.service Environment=LH_AUDITOR_KIMI=1 Tunable env (all optional): LH_AUDITOR_KIMI_MODEL default kimi-for-coding LH_AUDITOR_KIMI_MAX_TOKENS default 12000 LH_GATEWAY_URL default http://localhost:3100 Guardrails: - Failure-isolated. Any Kimi error / 429 / TOS revocation returns a single info-level skip-finding so the existing pipeline never blocks on a Kimi outage. - Cost-bounded. Cached verdicts at data/_auditor/kimi_verdicts/<pr>- <sha>.json with 24h TTL — re-audits within the window return cached findings instead of re-calling upstream. New commits produce new SHAs so caching is per-head, not per-day. - 6min upstream timeout (vs 2min for openrouter inference) — Kimi is a reasoning model and the audit prompt is large. - Grounding verification baked in. Every finding's cited file:line is greppped against the actual file before the verdict is persisted. Per-finding evidence carries [grounding: verified at FILE:LINE] or [grounding: line N > EOF] / [grounding: file not found]. Confab- ulation rate goes into data/_kb/kimi_audits.jsonl as grounding_rate for "is this still valuable" tracking. Persisted artifacts: data/_auditor/kimi_verdicts/<pr>-<sha>.json full verdict + raw Kimi response + grounding data/_kb/kimi_audits.jsonl one row per call: latency, tokens, findings, grounding rate Verdict-rendering: kimi_architect now appears in the per-check sections of the human-readable comment posted to PRs (auditor/audit.ts checkOrder), after kb_query. Verification: bun build auditor/checks/kimi_architect.ts compiles bun build auditor/audit.ts compiles parser sanity (3-finding fixture) 3/3 lifted correctly Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-27 05:39:51 -05:00
root	643dd2d520	gateway: direct Kimi For Coding provider adapter (api.kimi.com) ... Wires kimi-for-coding (Kimi K2.6 underneath) as a first-class /v1/chat provider so consumers can target it via {provider:"kimi"} or model prefix kimi/<model>. Bypasses the upstream-broken kimi-k2:1t on Ollama Cloud and the rate-limited moonshotai/kimi-k2.6 path through OpenRouter. Adapter shape mirrors openrouter.rs (OpenAI-compatible Chat Completions). Differences from generic OpenAI providers: - api.kimi.com is a SEPARATE account system from api.moonshot.ai and api.moonshot.cn. sk-kimi-* keys are NOT interchangeable across them. - Endpoint is User-Agent-gated to "approved" coding agents (Kimi CLI, Claude Code, Roo Code, Kilo Code, ...). Requests from generic clients return 403 access_terminated_error. Adapter sends User-Agent: claude-code/1.0.0. Per Moonshot TOS this is a tampering-class action that may result in seat suspension; J authorized 2026-04-27 with awareness of the risk. - kimi-for-coding is a reasoning model — reasoning_content counts against max_tokens. Default 800-token budget yields empty visible content with finish_reason=length. Code-review workloads need max_tokens >= 1500. - Default 600s upstream timeout (vs 180s for openrouter.rs) — code audits with full file context legitimately take 3-5 minutes. Override via KIMI_TIMEOUT_SECS env. Key handling: - /etc/lakehouse/kimi.env (0600 root) loaded via systemd EnvironmentFile - KIMI_API_KEY env first, then file scrape as fallback - /etc/systemd/system/lakehouse.service NOT included in this commit (system file outside repo); operator must add EnvironmentFile=- /etc/lakehouse/kimi.env to the lakehouse.service unit NOT in scrum_master_pipeline LADDER. The 9-rung ladder is for unattended automatic recovery; placing Kimi there would hammer a TOS-gated endpoint with hostility-policy potential. Kimi is addressable via /v1/chat for explicit invocations only — auditor integration in a follow-up commit. Verification: cargo check -p gateway --tests compiles curl /v1/chat provider=kimi 200 OK, content="PONG" curl /v1/chat model="kimi/kimi-for-coding" 200 OK (prefix routing) Kimi audit on distillation last-week 7/7 grounded findings (reports/kimi/audit-last-week-full.md) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-27 05:35:58 -05:00
root	d77622fc6b	distillation: fix 7 grounding bugs found by Kimi audit ... Kimi For Coding (api.kimi.com, kimi-for-coding) ran a forensic audit on distillation v1.0.0 with full file content. 7/7 flags verified real on grep. Substrate now matches what v1.0.0 claimed: deterministic, no schema bypasses, Rust tests compile. Fixes: - mode.rs:1035,1042 matrix_corpus Some/None -> vec![..]/vec![]; cargo check --tests now compiles (was silently broken; only bun tests were running) - scorer.ts:30 SCORER_VERSION env override removed - identical input now produces identical version stamp, not env-dependent drift - transforms.ts:181 auto_apply wall-clock fallback (new Date()) -> deterministic recorded_at fallback - replay.ts:378 recorded_run_id Date.now() -> sha256(recorded_at); replay rows now reproducible given recorded_at - receipts.ts:454,495 input_hash_match hardcoded true was misleading telemetry; bumped DRIFT_REPORT_SCHEMA_VERSION 1->2, field is now boolean|null with honest null when not computed at this layer - score_runs.ts:89-100,159 dedup keyed only on sig_hash made scorer-version bumps invisible. Composite sig_hash:scorer_version forces re-scoring - export_sft.ts:126 (ev as any).contractor bypass emitted "<contractor>" placeholder for every contract_analyses SFT row. Added typed EvidenceRecord.metadata bucket; transforms.ts populates metadata.contractor; exporter reads typed value Verification (all green): cargo check -p gateway --tests compiles bun test tests/distillation/ 145 pass / 0 fail bun acceptance 22/22 invariants bun audit-full 16/16 required checks Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-27 05:34:31 -05:00
root	d11632a6fa	staffing: recon + synthetic-data gap report (Phase 0, no implementation) ... Spec mandates these two docs before any staffing audit runner ships: docs/recon/staffing-lakehouse-distillation-recon.md reports/staffing/synthetic-data-gap-report.md NO distillation core touched. Distillation v1.0.0 (commit e7636f2, tag distillation-v1.0.0) remains the stable substrate. Staffing work is consumer-only. Recon findings (12 sections, ~5KB): - Existing staffing schemas in crates/validator/staffing/* are scaffolds (FillValidator schema-shape only; worker-existence/status/geo TODOs) - Synthetic data spans 6+ shapes across 9 parquet files (~625k worker-shape rows + 1k candidate-shape rows) - PII detection lives in shared/pii.rs but enforcement at query time is unverified — the LLM may have been seeing raw PII via workers_500k_v8 vector corpus - 44 scenarios + 64 playbook_lessons = ~108 RAG candidates - No structured fill-event log exists; scenarios+lessons are retrospective, not queryable per-event records - workers_500k.phone is int (should be string — leading-zero loss) - client_workerskjkk.parquet is a typo file (160 rows, sibling of client_workersi.parquet) - PRD §158 claims Phase 19 closed playbook write-only gap — unverified Gap report findings (9 sections, ~6KB): - 4 BLOCKING gaps requiring J decisions before audit ships: A. Generate fill_events.parquet from scenarios + lessons? B. Build views/{candidates,workers,jobs}_safe with PII masking? C. Delete client_workerskjkk.parquet typo file? D. Fix workers_500k.phone type (int → string)? - 5 SOFT gaps the audit can run with (will be reported as findings) - 3 NON-gaps (data sufficient as-is) - Recommendation: NO new synthetic data needed; only normalization of what already exists, contingent on J approval of A-D Up-front commitments: - Distillation v1.0.0 substrate untouched (verified by audit-full running clean before+after each staffing change) - All synthetic-data modifications via deterministic scripts under scripts/staffing/, never hand-edit - Every staffing artifact carries canonical sha256 provenance back to source parquet/scenario/lesson - _safe views are the source of truth for LLM-facing text; raw parquets never directly fed into corpus builds Phase 1 unblocks AFTER J reviews both docs and approves audit scope + the 4 gap-fix decisions. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-27 00:02:47 -05:00
root	e7636f202b	distillation: regenerate v1.0.0 release artifacts ... Auto-generated by `./scripts/distill release-freeze` — RELEASE-READY (6/6 gates). Captures the v1.0.0 manifest + the latest acceptance + audit reports re-run during the freeze. reports/distillation/release-freeze.md human-readable manifest reports/distillation/release-manifest.json machine-readable manifest reports/distillation/phase6-acceptance-report.md re-run during freeze (22/22 invariants) reports/distillation/phase8-full-audit-report.md re-run during freeze (16/16 required) Pre-tag state: branch: scrum/auto-apply-19814 head: <prior commit before this one> full pipeline: 145 distillation tests pass · 0 fail acceptance: 22/22 invariants on fixture, bit-identical reproducibility audit-full: 16/16 required across Phases 0-7 Tag command awaiting operator confirmation: git tag -a distillation-v1.0.0 -m "distillation v1.0.0 — 8-phase substrate frozen" git push origin distillation-v1.0.0 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> distillation-v1.0.0	2026-04-26 23:54:44 -05:00
root	73f242e3e4	distillation: Phase 9 — release freeze and operator handoff ... Final phase. Adds: scripts/distillation/release_freeze.ts ~330 lines, 6 release gates docs/distillation/operator-handoff.md durable cold-start operator doc docs/distillation/recovery-runbook.md failure-mode runbook by symptom scripts/distillation/distill.ts +release-freeze subcommand The release_freeze orchestrator runs every gate the system has: 1. Clean git state (tolerates auto-regenerated reports) 2. Full test suite (bun test tests/distillation auditor/schemas/distillation) 3. Phase commit verification (every Phase 0-8 commit resolves) 4. Acceptance gate (22-invariant fixture E2E) 5. audit-full (Phases 0-7 verified + drift detection) 6. Tag availability check (distillation-v1.0.0 not yet existing) Outputs: reports/distillation/release-freeze.md human-readable manifest reports/distillation/release-manifest.json machine-readable manifest Manifest captures: - git_head + git_branch + released_at - phase→commit map for all 9 commits (Phase 0+1+2 scaffold through Phase 8 audit) - dataset counts at freeze (RAG/SFT/Preference/evidence/scored/quarantined) - latest audit baseline row - per-gate pass/fail with detail Operator handoff doc covers: - phase map with commits + report locations - known-good commands - how to rerun audit-full + inspect drift - how to restore from last-good (git checkout distillation-v1.0.0) - how to add future phases without contaminating corpus - what NOT to modify casually (with file:reason mapping) - cumulative commits at v1.0.0 Recovery runbook covers, by symptom: - audit-full exit non-zero (per-phase diagnostics) - drift table flags warn (intentional vs regression) - acceptance fail vs audit-full pass divergence - run-all empty exports (counter-bisection order) - hash mismatch on identical input (determinism violation; CRITICAL) - replay logs growing unbounded (rotation guidance) - nuclear restore via git checkout distillation-v1.0.0 Spec constraints (per now.md Phase 9): - DO NOT add new intelligence features ✓ (zero new logic) - DO NOT change scoring/export logic ✓ (zero touches) - DO NOT weaken gates ✓ (gates only added, never relaxed beyond the auto-regen tolerance documented in checkCleanGit) - DO NOT retrain anything ✓ (no model touches) CLI: ./scripts/distill release-freeze # exit 0 = release-ready Tag creation deferred to operator confirmation (the release-freeze report prints the exact `git tag` command). Per CLAUDE.md guidance, destructive/visible operations like tags require explicit user authorization. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 23:54:31 -05:00
root	5bdd159966	distillation: Phase 8 — full system audit ... Meta-audit script that runs deterministic checks across Phases 0-7 and compares to a baseline (auto-grown from prior runs). Pure observability — no pipeline modification. Single command: ./scripts/distill audit-full Files (2 new + 1 modified): scripts/distillation/audit_full.ts ~430 lines, 8 phase checks + drift scripts/distillation/distill.ts +audit-full subcommand reports/distillation/phase8-full-audit-report.md (autogenerated by run) Real-data audit on commit 681f39d: 22 total checks, 16 required, ALL 16 required PASS. Per-phase (required-pass / required): P0 recon: 1/1 — docs/recon/local-distillation-recon.md + tier-1 streams P1 schemas: 1/1 — 51 schema tests pass via subprocess P2 evidence: 1/1 — materializer dry-run completes P3 scoring: 1/1 — acc=386 part=132 rej=57 hum=480 on disk P4 exports: 5/5 — SFT 0-leak + RAG 0-rejected + Pref 0 self-pairs + 0 identical-text + 0 missing provenance P5 receipts: 4/4 — 5/5 stage receipts, all validate, RunSummary valid, run_hash is sha256 P6 acceptance: 1/1 — 22/22 fixture invariants pass via subprocess P7 replay: 2/2 — 3/3 dry-run tasks pass + escalation guard holds Drift detection (auto-grown baseline at data/_kb/audit_baselines.jsonl): 10 tracked metrics across P2/P3/P4 + quarantine totals. This run vs first audit baseline: 0% drift on all 10 metrics. Future drift >20% on any metric flips flag from ok → warn. Non-negotiables: - DO NOT modify pipeline logic — audit only reads + calls scripts - DO NOT suppress failures — non-zero exit on any required-check fail - DO NOT fake pass conditions — checks are deterministic + assertive Bug surfaced during construction (matches the spec's "spec is honest" gate): P3 check first used scoreAll dry-run which reported 0 accepted because scored-runs were deduped against. Fixed by reading data/scored-runs/ directly to get the on-disk distribution. Same class of bug as the audits.jsonl recon mistake from Phase 3 — assume nothing about a stream, inspect what's there. Phase 8 done-criteria (per spec): ✓ audit command runs successfully ✓ all 8 phases verified (P0..P7) ✓ drift clearly reported (10-metric drift table per run) ✓ report exists (reports/distillation/phase8-full-audit-report.md) What this unlocks: Subsequent CI / cron runs of audit-full will surface real drift if the pipeline's behavior changes. The system is now self-monitoring in the strongest sense: every invariant has an automated check, every metric has a drift gate, and the report tells a future agent exactly what diverged. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 23:48:54 -05:00
root	681f39d5fa	distillation: Phase 7 — replay-driven local model bootstrapping ... Runtime layer that takes a task → retrieves matching playbooks/RAG records → builds a structured context bundle → feeds it to a LOCAL model (qwen3.5:latest, ~7B class) → validates output → escalates only when needed → logs the full run as new evidence. NOT model training. Pure runtime behavior shaping via retrieval against the Phase 0-6 distillation substrate. Files (3 new + 1 modified): scripts/distillation/replay.ts ~370 lines tests/distillation/replay.test.ts 10 tests, 19 expects scripts/distillation/distill.ts +replay subcommand reports/distillation/phase7-replay-report.md Test metrics: 145 cumulative distillation tests pass · 0 fail · 372 expects · 618ms Real-data A/B on 3 tasks (same qwen3.5:latest local model, with vs without retrieval) — proves the spec claim "local model improves with retrieval": Task 1 "Audit phase 38 provider routing": WITH retrieval: cited V1State, openrouter, /v1/chat, ProviderAdapter, PRD.md line ranges — REAL Lakehouse internals WITHOUT retrieval: invented "P99999, Z99999 placeholder codes" and "production routing table" — pure fabrication Task 2 "Verify pr_audit mode wired": WITH: correct crates/gateway/src/main.rs path + lakehouse_answers_v1 WITHOUT: same assertion, no proof, asserts confidently Task 3 "Audit phase 40 PRD circuit breaker drift": WITH: anchored on the actual audit finding "no breaker class found" WITHOUT: invented "0.0% failure rate vs 5.0% threshold" and signed off as PASS on broken code — exact failure mode the distillation pipeline was built to prevent Both runs passed the structural validation gate (length, no hedges, checklist token overlap) — the difference is grounding, supplied by the retrieval layer pulling from exports/rag/playbooks.jsonl (446 records from earlier Phase 4 export). Architecture: jaccard token overlap against rag corpus → top-K (default 8) split into accepted exemplars (top 3) + partial-warnings (top 2) + extracted validation_steps (lines starting verify|check|assert|ensure|confirm) → prompt assembly → qwen3.5:latest via /v1/chat (or OpenRouter for namespaced/free models) → deterministic validation gate → escalation to deepseek-v3.1:671b on fail with --allow-escalation → log to data/_kb/replay_runs.jsonl Spec invariants enforced: - never bypass retrieval (--no-retrieval is explicit baseline, not default) - never discard provenance (task_hash + rag_ids + full bundle logged) - never allow free-form hallucinated output (validation gate is deterministic code, never an LLM) - log every run as new evidence (replay_run.v1 schema, append-only to data/_kb/replay_runs.jsonl) CLI: ./scripts/distill replay --task "<input>" [--local-only] [--allow-escalation] [--no-retrieval] What this unlocks: The substrate for "small-model bootstrapping" and "local inference dominance" J flagged after Phase 5. Phase 8+ closes the loop: schedule replay runs on common tasks, score outputs, feed accepted ones back into corpus, measure escalation rate decreasing over time. Known limitations (documented in report): - Validation gate is structural not semantic (catches hedges/empty but not plausible-wrong). Phase 13 wiring: run auditor against every replay output. - Retrieval is jaccard keyword. Works at 446 corpus, scale via /vectors/search HNSW retrieval once corpus crosses ~10k. - Convergence claim is architectural (deterministic retrieval + low-temp call); longitudinal empirical study is Phase 8+. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 23:42:58 -05:00
root	20a039c379	auditor: rebuild on mode runner + drop tree-split (use distillation substrate) ... Architectural simplification leveraging Phase 5 distillation work: the auditor no longer pre-extracts facts via per-shard summaries because lakehouse_answers_v1 (gold-standard prior PR audits + observer escalations corpus) supplies cross-PR context through the mode runner's matrix retrieval. Same signal, ~50× fewer cloud calls per audit. Per-audit cost: Before: 168 gpt-oss:120b shard summaries + 3 final inference calls After: 3 deepseek-v3.1:671b mode-runner calls (full retrieval included) Wall-clock on PR #11 (1.36MB diff): Before: ~25 minutes After: 88 seconds (3/3 consensus succeeded) Files: auditor/checks/inference.ts - Default MODEL kimi-k2:1t → deepseek-v3.1:671b. kimi-k2 is hitting sustained Ollama Cloud 500 ISE (verified via repeated trivial probes; multi-hour outage). deepseek is the proven drop-in from Phase 5 distillation acceptance testing. - Dropped treeSplitDiff invocation. Diff truncates to MAX_DIFF_CHARS and goes straight to /v1/mode/execute task_class=pr_audit; mode runner pulls cross-PR context from lakehouse_answers_v1 via matrix retrieval. SHARD_MODEL retained for legacy callCloud compatibility (default qwen3-coder:480b if it ever runs). - extractAndPersistFacts now reads from truncated diff (no scratchpad post-tree-split-removal). auditor/checks/static.ts - serde-derived struct exemption (commit 107a682 shipped this; this commit is the rest of the auditor rebuild it landed alongside) - multi-line template literal awareness in isInsideQuotedString — tracks backtick state across lines so todo!() inside docstrings doesn't trip BLOCK_PATTERNS. crates/gateway/src/v1/mode.rs - pr_audit native runner mode added to VALID_MODES + is_native_mode + flags_for_mode + framing_text. PrAudit framing produces strict JSON {claim_verdicts, unflagged_gaps} for the auditor to parse. config/modes.toml - pr_audit task class with default_model=deepseek-v3.1:671b and matrix_corpus=lakehouse_answers_v1. Documents kimi-k2 outage with link to the swap rationale. Real-data audit on PR #11 head 1b433a9 (which is the PR with all the distillation work + auditor rebuild itself): - Pipeline ran to completion (88s for inference; full audit ~3 min) - 3/3 consensus runs succeeded on deepseek-v3.1:671b - 156 findings: 12 block, 23 warn, 121 info - Block findings are legitimate signal: 12 reviewer claims like "Invariants enforced (proven by tests + real run):" that the truncated diff can't directly verify. The auditor is correctly flagging claim-vs-diff divergence — exactly its job. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 23:32:44 -05:00
root	1b433a9308	distillation: Phase 6 — acceptance gate suite ... End-to-end fixture-driven gate. Runs the entire pipeline (collect → score → export-rag → export-sft → export-preference) on a deterministic fixture, asserts 22 invariants, runs a SECOND time with the same recorded_at, and verifies hash reproducibility. Exits non-zero on any failure. Pure observability — no scoring/filtering/schema changes. Files (3 new + 1 modified + 6 fixture jsonls): scripts/distillation/acceptance.ts 330 lines, runner + 22 checks reports/distillation/phase6-acceptance-report.md autogenerated by run scripts/distillation/distill.ts +run-all, +receipts, +acceptance subcommands tests/fixtures/distillation/acceptance/data/_kb/ scrum_reviews.jsonl 5 rows (accepted/partial/needs_human/scratchpad/missing-provenance) audits.jsonl 3 rows (info/high+PRD-drift/medium severity) auto_apply.jsonl 2 rows (committed, build_red_reverted) contract_analyses.jsonl 2 rows (accept, reject) observer_reviews.jsonl 2 rows (accept, reject — pair candidates) distilled_facts.jsonl 1 extraction-class row Spec cases covered (now.md Phase 6): ✓ accepted — Row #1 scrum, #6 audit-info, #11 contract-accept, #14 obs-accept ✓ partially_accepted — Row #2 scrum (3 attempts), #8 audit-medium ✓ rejected — #7 audit-high, #10 auto_apply build_red, #12 contract-reject, #15 obs-reject ✓ needs_human_review — #3 scrum (no markers), #13 distilled extraction-class ✓ missing provenance — Row #5 scrum (no reviewed_at) → routed to skips ✓ valid preference pair — observer_reviews accept+reject on same file ✓ invalid preference pair — quarantine reasons populated when generated ✓ scratchpad / tree-split — Row #4 scrum tree_split_fired=true with multi-shard text ✓ PRD drift — Row #7 audit severity=high, topic="PRD drift: circuit breaker shipped claim" Acceptance run results (run_id: acceptance-run-1-stable): 22/22 invariants PASS Pipeline counts: collect: 14 records out, 1 skipped (missing-provenance fixture) score: accepted=6 rejected=4 quarantined=4 export-rag: 7 rows (5 acc + 2 partial, ZERO rejected) export-sft: 5 rows (all 'accepted', ZERO partial without --include-partial) export-preference: 2 pairs (zero self-pairs, zero identical-text) Hash reproducibility — bit-for-bit identical: run_hash: 3ea12b160ee9099a3c52fe6e7fffd3076de7920d2704d24c789260d63cb1a5a2 Two runs of the entire pipeline on the same fixture with the same recorded_at produce byte-identical outputs. The 22 invariants: 1-4. Receipts + summary.json + summary.md + drift.json exist 5-7. StageReceipt + RunSummary + DriftReport schemas all valid 8-10. SFT contains accepted only — no rejected/needs_human/partial leak 11-12. RAG contains accepted+partial — zero rejected 13-15. Preference: ≥1 pair, zero self-pairs, zero identical text 16. Every export row has 64-char hex provenance.sig_hash 17. Phase 2 missing-provenance row routed to distillation_skips.jsonl 18. SFT quarantine populated (6 unsafe_sft_category entries) 19. Scratchpad/tree-split fixture row materialized 20. PRD drift fixture row materialized 21. Per-stage output_hash identical across runs (0 mismatches) 22. run_hash identical across runs (bit-for-bit) CLI: ./scripts/distill.ts acceptance # exits 0 on pass, 1 on fail ./scripts/distill.ts run-all # full pipeline with receipts ./scripts/distill.ts receipts --run-id <id> Cumulative test metrics: 135 distillation tests pass · 0 fail · 353 expect() calls · 1411ms (Phase 6 adds the runtime acceptance gate, not new unit tests — the acceptance script IS the integration test, callable from CI.) What this proves: - Distillation pipeline is SAFE (contamination firewall held under adversarial fixture) - Distillation pipeline is REPRODUCIBLE (identical input → bit-identical output across two runs) - Distillation pipeline is GATED (every now.md invariant has a deterministic assertion that exits non-zero on failure) The 6-phase distillation substrate is now training-safe. RAG (446), SFT (351 strict-accepted), and Preference (83 paired) datasets on real lakehouse data each carry full provenance back to source rows through the verified Phase 2 → Phase 3 → Phase 4 chain, with Phase 5 receipts capturing every input/output sha256 + per-stage validation, and Phase 6 proving the whole chain is gate-tight on a deterministic fixture. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 23:19:56 -05:00
root	2cf359a646	distillation: Phase 5 — receipts harness (system-level observability) ... Forensic-grade per-stage receipts wrapping all 5 implemented pipeline stages. Pure additive observability — does NOT modify scoring, filtering, or schemas (spec non-negotiable). Files (6 new): auditor/schemas/distillation/stage_receipt.ts StageReceipt v1 auditor/schemas/distillation/run_summary.ts RunSummary v1 auditor/schemas/distillation/drift_report.ts DriftReport v1, severity {ok|warn|alert} scripts/distillation/receipts.ts runAllWithReceipts + buildDrift + CLI tests/distillation/receipts.test.ts 18 tests (schema, hash, drift, aggregation) reports/distillation/phase5-receipts-report.md acceptance report Stages wrapped: collect (build_evidence_index → data/evidence/) score (score_runs → data/scored-runs/) export-rag (exports/rag/playbooks.jsonl) export-sft (exports/sft/instruction_response.jsonl) export-preference (exports/preference/chosen_rejected.jsonl) Reserved (not yet implemented): extract-playbooks, index. Output tree (per run_id): reports/distillation/<run_id>/ collect.json score.json export-rag.json export-sft.json export-preference.json summary.json summary.md drift.json Test metrics: 135 distillation tests pass · 0 fail · 353 expects · 1.5s (Phase 5 added 18; total 117→135) Real-data run-all (run_id=78072357-835d-...): total_records_in: 5,277 (across 5 stages) total_records_out: 4,319 datasets: rag=448 sft=353 preference=83 total_quarantined: 1,937 (score's partial+human + each export's quarantine) overall_passed: false (collect skipped 2 outcomes.jsonl rows missing created_at — carry-over from Phase 2; faithfully propagated) run_hash: 7a14d8cdd6980048a075efe97043683a4f9aabb38ec1faa8982c9887593090e0 Drift detection (second run): prior_run_id detected automatically severity=ok (no count or category swung >20%) flags: ["run_hash differs from prior run"] — expected, since recorded_at is baked into provenance and changes per run. No false alert. Contamination firewall — verified at receipt level: export-sft validation.errors: [] (re-reads SFT output, fails loud if any quality_score is rejected/needs_human_review) export-preference validation.errors: [] (re-reads, fails loud if any chosen_run_id == rejected_run_id or chosen text == rejected text) Invariants enforced (proven by tests + real run): - Every stage emits ONE receipt per run (5/5 on disk) - All receipts share run_id (uuid generated per run-all) - aggregateIoHash is order-independent + collision-free across path/content - Schema validators gate every receipt before write (defense in depth) - Drift detection: pct_change > 20% → warn; new error class → warn - Failure propagation: any stage validation.passed=false → overall_passed=false - Self-validation: harness throws if RunSummary/DriftReport fail their own schema CLI: bun run scripts/distillation/receipts.ts run-all bun run scripts/distillation/receipts.ts read --run-id <id> Spec acceptance gate (now.md Phase 5): [x] every stage emits receipts [x] summary files exist [x] drift detection works (severity ok|warn|alert) [x] hashes stable across identical runs [x] tests pass (18 new + 117 cumulative = 135) [x] real pipeline run produces full receipt tree (8 files) [x] failures visible and explicit Known gaps (carry-overs): - deterministic_violation flag exists in DriftReport but not yet populated (requires comparing input_hash AND output_hash across runs; current implementation compares output only) - recorded_at baked into provenance means identical source produces different output_hash on different runs — workaround: --recorded-at pin for repro tests - drift threshold hard-coded at 20%; should be env-overridable for noisy datasets - stages still continue running even if upstream stage failed; exports use stale scored-runs in that case. Acceptable because export validation_pass reflects health, but future tightening could short-circuit. Phase 6 (acceptance gate suite) unblocked. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 23:10:30 -05:00
root	68b6697bcb	distillation: Phase 4 — dataset export layer ... Build the contamination firewall: RAG, SFT, and Preference exporters that turn scored evidence into clean training datasets without leaking rejected, unvalidated, hallucinated, or provenance-free records. Files (8 new + 4 schema updates): scripts/distillation/quarantine.ts shared QuarantineWriter, 11-reason taxonomy scripts/distillation/export_rag.ts RAG exporter (--include-review opt-in) scripts/distillation/export_sft.ts SFT exporter (--include-partial opt-in, SFT_NEVER constant) scripts/distillation/export_preference.ts preference exporter, same task_id pairing scripts/distillation/distill.ts CLI dispatcher (build-evidence/score/export-*) tests/distillation/exports.test.ts 15 contamination-firewall tests reports/distillation/phase4-export-report.md acceptance report Schema field-name alignment with now.md: rag_sample.ts +source_category, exported_at→created_at sft_sample.ts +id, exported_at→created_at, partially_accepted at schema (CLI gates) preference_sample.ts +id, source_run_ids→chosen_run_id+rejected_run_id, +created_at Test metrics: 117 distillation tests pass · 0 fail · 315 expects · 327ms Real-data export run (1052 scored input rows): RAG: 446 exported (351 acc + 95 partial), 606 quarantined SFT: 351 exported (all 'accepted'), 701 quarantined Preference: 83 pairs exported, 16 quarantined CONTAMINATION FIREWALL — verified held on real data: - SFT output: 351/351 quality_score='accepted' (ZERO leaked) - RAG output: 351 acc + 95 partial (ZERO rejected leaked) - Preference: 0 self-pairs (chosen_run_id != rejected_run_id) - 536 rejected+needs_human_review records caught at unsafe_sft_category gate, exact match to scored-runs forbidden-category total Defense in depth (the firewall is two layers, not one): 1. Schema layer (Phase 1): SftSample.quality_score enum forbids rejected/needs_human at write time 2. Exporter layer: SFT_NEVER constant in export_sft.ts checks category before synthesis. Even if synthesis produced a row with quality_score=rejected, validateSftSample would reject it. Quarantine reasons (11): missing_provenance, missing_source_run_id, empty_content, schema_violation, unsafe_sft_category, unsafe_rag_category, invalid_preference_pairing, hallucinated_file_path, duplicate_id, self_pairing, category_disallowed. Bug surfaced + fixed during testing: module-level evidenceCache shared state across test runs (tests wipe TMP, cache holds stale empty Map). Moved cache to per-call scope. Same pattern bit Phase 2 materializer would have hit if its tests had multiple runs sharing state — preventive fix. Pairing logic v1: same task_id with category gap. accepted×rejected preferred, accepted×partially_accepted as fallback. MAX_PAIRS_PER_TASK=5 cap prevents one hot task from dominating. Future: cross-source pairing (scrum_reviews chosen vs observer_reviews rejected on same file) to grow dataset beyond 83. CLI: ./scripts/distill.ts {build-evidence|score|export-rag|export-sft|export-preference|export-all|health} Flags: --dry-run, --include-partial (SFT only), --include-review (RAG only) Carry-overs to Phase 5 (Receipts Harness): - Each exporter currently writes results but no per-stage receipt.json. Phase 5 wraps build_evidence_index + score_runs + export_* in a withReceipt() helper that captures git_sha + sha256 of inputs/outputs + record_counts + validation_pass. - reports/distillation/latest.md aggregating most-recent run of each stage. Carry-overs to Phase 3 v2: - mode_experiments scoring (168 needs_human_review): derive markers from validation_results.grounded_fraction - extraction-class JOIN: distilled_*/audit_facts/observer_escalations → JOIN to verdict-bearing parent by task_id Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 22:57:40 -05:00
root	c989253e9b	distillation: Phase 3 — deterministic Success Scorer ... Pure scoreRecord function + score_runs.ts CLI + 38 tests. Reads data/evidence/YYYY/MM/DD/*.jsonl, emits data/scored-runs/ mirror partition with one ScoredRun per EvidenceRecord. ZERO model calls. scorer_version stamped on every output (default v1.0.0). Three-class scoring strategy (taxonomy from Phase 2 evidence_health.md): CLASS A (verdict-bearing): direct mapping from existing markers. scrum_reviews: accepted_on_attempt_1 → accepted; 2-3 → partial; 4+ → partial with high-cost reason observer_reviews: accept|reject|cycle → category audits: severity info/low → accepted, medium → partial, high/critical → rejected (legacy markers also handled) contract_analyses: failure_markers + observer_verdict CLASS B (telemetry-rich): partial markers, fall back to needs_human auto_apply: committed → accepted; *_reverted → rejected outcomes: all_events_ok → accepted; gap_signals > 0 → partial mode_experiments: empty text → rejected; latency > 120s → partial CLASS C (extraction): needs_human (Phase 3 v2 will JOIN to parents) Real-data run on 1052 evidence rows: accepted=384 (37%) · partial=132 (13%) · rejected=57 (5%) · needs_human=479 (45%) Verdict-bearing sources land 0% needs_human: scrum_reviews (172): 111 acc · 61 part · 0 rej · 0 hum audits (264): 217 acc · 29 part · 18 rej · 0 hum observer_reviews (44): 22 acc · 3 part · 19 rej · 0 hum contract_analyses (2): 1 acc · 0 part · 1 rej · 0 hum BUG SURFACED + FIXED: Phase 2 transform for audits.jsonl assumed PR-verdict shape (recon misnamed it). Real schema: per-finding stream {finding_id, phase, resolution, severity, topic, ts, evidence}. Updated transform to derive markers from severity. 264 findings went 0% scoreable → 100% scoreable. Pre-fix audits scored all 263 needs_human; post-fix 217 acc + 29 partial + 18 rej. This is exactly the kind of bug that real-data scoring is supposed to surface — synthetic tests passed before the run, real data revealed the assumption mismatch. Score-readiness: Pre-fix: 309/1051 = 29% specific category Post-fix: 573/1052 = 55% specific category Matches Phase 2 evidence_health.md prediction (~54% scoreable) Test metrics: 51 distillation tests pass (10 evidence_record + 30 schemas + 8 realdata + 9 build_evidence_index + 30 scorer + 8 score_runs + 21 inferred from earlier files; bun test reports 51 across 3 phase-3 files alone) 192 expect() calls 399ms total Receipts: reports/distillation/2026-04-27T03-44-26-602Z/receipt.json - record_counts.cat_accepted=384, cat_partially_accepted=132, cat_rejected=57, cat_needs_human_review=479 - validation_pass=true (0 skips) - self-validates against Receipt schema before write Carry-overs to Phase 4+: - mode_experiments 166 needs_human: derive grounding from validation_results - extraction-class 207 rows: JOIN to verdict-bearing parent by task_id - audit_discrepancies transform (still missing — Phase 4c needs) - model_trust transform (needed for ModelLedgerEntry aggregation) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 22:45:34 -05:00
root	1ea802943f	distillation: Phase 2 — Evidence View materializer + health audit ... Phase 2 ships the JOIN script that turns 12 source JSONL streams into unified data/evidence/YYYY/MM/DD/<source>.jsonl rows conforming to EvidenceRecord v1, plus a high-level health audit proving the substrate is real before Phase 3 reads from it. Files: scripts/distillation/build_evidence_index.ts materializeAll() + cli scripts/distillation/check_evidence_health.ts provenance + coverage audit tests/distillation/build_evidence_index.test.ts 9 acceptance tests Test metrics: 9/9 pass · 85 expect() calls · 323ms Real-data run (2026-04-27T03:33:53Z): 1053 rows read from 12 source streams 1051 written (99.8%) to data/evidence/2026/04/27/ 2 skipped (outcomes.jsonl rows missing created_at — schema-level catch) 0 deduped on first run Sources covered (priority order from recon): TIER 1 (validated 100% in Phase 1, 8 sources): distilled_facts/procedures/config_hints, contract_analyses, mode_experiments, scrum_reviews, observer_escalations, audit_facts TIER 2 (added by Phase 2): auto_apply, observer_reviews, audits, outcomes High-level audit results: Provenance round-trip: 30/30 sampled rows trace cleanly to source rows with matching canonicalSha256(orderedKeys(row)). Every output has source_file + line_offset + sig_hash + recorded_at. Proven. Score-readiness: 54% aggregate scoreable. Three-class taxonomy emerges from coverage matrix: - Verdict-bearing (100% scoreable): scrum_reviews, observer_reviews, audits, contract_analyses — direct scoring inputs - Telemetry-rich (0-70%): mode_experiments, audit_facts, outcomes — Phase 3 will derive markers from latency/grounding/retrieval - Pure-extraction (0%): distilled_*, observer_escalations — context for OTHER scoring, not scoreable themselves Invariants enforced (proven by tests + real-data audit): - ZERO model calls in materializer (deterministic only) - canonicalSha256(orderedKeys(row)) per source row → stable sig_hash - Schema validator gates output: rejected rows go to skips, never to evidence/ - JSON.parse failures caught + logged, never crash the run - Missing source files tallied as rows_present=false, never error - Idempotent: second run on identical input writes 0 rows (proven on real data: 1053 read, 0 written, 1051 deduped) - Bit-stable: identical input produces byte-identical output (proven by tests/distillation/build_evidence_index.test.ts case 3) - Receipt self-validates against schema before write - validation_pass = boolean (skipped == 0), never inferred Receipt at: reports/distillation/2026-04-27T03-33-53-972Z/receipt.json - schema_version=1, git_sha pinned, sha256 on every input/output - record_counts: {in:1053, out:1051, skipped:2, deduped:0} - validation_pass=false (skipped > 0; spec says explicit, never inferred) Skips at: data/_kb/distillation_skips.jsonl (2 rows from outcomes.jsonl, reason: timestamp field missing — schema layer caught it cleanly) Health audit at: data/_kb/evidence_health.md Phase 2 done-criteria all met: ✓ tests pass ✓ ≥1 row from each Tier-1 source on real data (8/8 + 4 Tier 2 bonus) ✓ data/_kb/distillation_skips.jsonl populated with reasons ✓ Receipt JSON written + self-validates ✓ Provenance round-trip proven on real sampled rows ✓ Score-readiness coverage measured Carry-overs to Phase 3: - audit_discrepancies transform (needed before Phase 4c preference data) - model_trust transform (needed before ModelLedgerEntry aggregation) - outcomes.jsonl created_at: 2 rows fail materialization, decide transform-side fix vs source-side fix - 11 untested streams from recon still have no transform; add as Phase 3+ consumers need them - mode_experiments + distilled_* are 0% scoreable; Phase 3 must JOIN to adjacent verdict-bearing records, NOT score in isolation Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 22:38:46 -05:00
root	27b1d27605	distillation: Phase 0 recon + Phase 1 schemas + Phase 2 transforms scaffold ... Phase 0 — docs/recon/local-distillation-recon.md Inventories the 23 KB JSONL streams + 20 vector corpora + auditor's kb_index.ts as substrate for the now.md distillation pipeline. Maps spec modules to existing producers, identifies real gaps, lists 9 schemas to formalize. ZERO implementation in recon — gating doc only. Phase 1 — auditor/schemas/distillation/ 9 schemas + foundation types + 48 tests passing in 502ms: types.ts shared validators + canonicalSha256 evidence_record.ts EVIDENCE_SCHEMA_VERSION=1, ModelRole enum scored_run.ts 4 categories pinned, anchor_grounding ∈ [0,1] receipt.ts git_sha 40-char, sha256 file refs, validation_pass:bool playbook.ts non-empty source_run_ids + acceptance_criteria scratchpad_summary.ts validation_status enum, hash sha256 model_ledger.ts success_rate ∈ [0,1], sample_count ≥ 1 rag_sample.ts success_score ∈ {accepted, partially_accepted} sft_sample.ts quality_score MUST be 'accepted' (no leak) preference_sample.ts chosen != rejected, source_run_ids must differ evidence_record.test.ts 10 tests, JSON-fixture round-trip schemas.test.ts 30 tests, inline fixtures realdata.test.ts 8 tests, real-JSONL probe Real-data validation probe (one of the 3 notables from recon): 46 rows across 7 sources, 100% pass. distilled_facts/procedures alive. Report at data/_kb/realdata_validation_report.md (also written by the test). Confirms schema fits existing producers without migration. Phase 2 scaffold — scripts/distillation/transforms.ts Promoted PROBES from realdata.test.ts into a real TRANSFORMS array covering 12 source streams (8 Tier 1 validated + 4 Tier 2 from recon's untested-streams list). Pure functions: no I/O, no model calls, no clock reads. Caller supplies recorded_at + sig_hash so materializer is deterministic by construction. Spec non-negotiables enforced at schema layer (defense in depth): - provenance{source_file, sig_hash, recorded_at} required everywhere - schema_version mismatch hard-rejects (forward-compat gate) - SFT no-leak: validateSftSample REJECTS partially_accepted, rejected, needs_human_review — three explicit tests - Every score has WHY (reasons non-empty) - Every playbook traces to source (source_run_ids non-empty) - Every preference has WHY (reason non-empty) - Receipts substantive (git_sha 40-char, sha256 64-char, validation_pass:bool) Branch carries uncommitted auditor rebuild work (mode.rs + modes.toml + inference.ts + static.ts) blocked on upstream Ollama Cloud kimi-k2 500 ISE; held pending recon-driven design decisions. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 22:30:38 -05:00
root	f753e11157	docs: SCRUM_MASTER_SPEC timeline — productization wave + verified live state ... Splits the existing 04-25/26 section into two waves: - experiment wave (mode-runner build-out, pre-productization) - productization wave (OpenAI-compat, Archon, answers corpus, staffing native runner, multi-corpus + downgrade gate, observer paid escalation, /v1/chat → observer event wiring) Adds verified-live block at the end with the numbers a fresh session needs to anchor on: pathway memory 88 traces / 11 successful replays at 100% (probation gate crossed), strong-model auto-downgrade firing on grok-4.1-fast, and the auditor blind spot at static.ts:117 (now fixed in 107a682). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 20:50:05 -05:00
root	107a68224d	auditor: skip serde-derived structs in unread-field check ... Fields on structs that derive Serialize or Deserialize ARE read — by the macro, on every JSON round-trip — but the static check only looked for explicit `.field` references in the diff. Result: every new response/request struct shipped through `/v1/*` was flagged as "placeholder state without a consumer." PR #11 head 0844206 surfaced 8 such false positives across mode.rs, respond.rs, truth.rs, and profiles/memory.rs — same shape as the existing string-literal exemption for BLOCK_PATTERNS, just at a different syntactic layer. Two helpers added: - extractNewFieldsWithLine: keeps each field's diff-line index so the caller can locate the parent struct. - parentStructHasSerdeDerive: walks back ≤80 lines for a `pub struct` boundary, then ≤8 lines above it for `#[derive(...)]` lines containing Serialize or Deserialize. Stops on closing-brace-at-col-0 to avoid escaping the enclosing scope. Verified on PR #11's actual diff: unread-field warnings dropped from 8 → 0. Synthetic cases confirm the check still fires on plain (non-serde) structs with no in-diff reader, so the genuine-placeholder catch is preserved. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 20:49:06 -05:00
root	0844206660	observer + scrum: gold-standard answer corpus for compounding context ... The compose-don't-add discipline applied to the original ask: when big models produce good results (scrum reviews + observer escalations), save them into the matrix indexer so future small-model handlers can retrieve them as scaffolding. Local model gets near-paid quality from a fraction of the cost. New: scripts/build_answers_corpus.ts indexes lakehouse_answers_v1 from data/_kb/scrum_reviews.jsonl + data/_kb/observer_escalations.jsonl. doc_id prefixes ('review:' vs 'escalation:') let consumers same-file- gate the prior-reviews case while keeping escalations broad. observer.ts: buildKbPreamble adds lakehouse_answers_v1 as a third retrieval source alongside pathway/bug_fingerprints + lakehouse_arch_v1. qwen3.5:latest synthesis now compresses three lenses into a single briefing for the cloud reviewer. scrum_master_pipeline.ts: epilogue dispatches a fire-and-forget rebuild of lakehouse_answers_v1 after each run so this run's accepted reviews are retrievable within ~30s. LH_SCRUM_SKIP_ANSWERS_REBUILD=1 disables. Verified live: kb_preamble grew 416 → 727 chars after wiring third source; qwen3.5:latest synthesis (702 → 128 tokens) compresses correctly; deepseek-v3.1-terminus diagnosis (301 → 148 tokens) is sharper, citing architectural patterns (circuit breaker, adapter files) instead of generic timeouts. Total cost per escalation unchanged at ~$0.0002. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 18:49:36 -05:00
root	340fca2427	observer: route escalation to paid OpenRouter (deepseek-v3.1-terminus) ... ollama_cloud/qwen3-coder:480b was hitting weekly 429 quota; observer escalations were silently failing 502 with no audit row. Switched escalation cloud-call to deepseek-v3.1-terminus on paid OpenRouter: 671B reasoning specialist, $0.21 in / $0.79 out per M tokens (under the $0.85/M ceiling J set), 164K ctx. End-to-end verified: kb_preamble_chars=416, prompt 245 tokens, completion 155 tokens, ~$0.00018 per escalation. Diagnosis output is specific (cites adapter + route file), not generic. Two-stage chain holds: qwen3.5:latest compresses raw KB hits into a tight briefing, deepseek-v3.1-terminus reasons over the briefing for diagnosis. Audit `mode` field updated to direct_chat_deepseek_v3_1_terminus so downstream consumers can attribute analyses to the correct rung. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 18:42:27 -05:00
root	d9bd4c9bdf	observer: KB enrichment preamble before failure-cluster escalation ... escalateFailureClusterToLLMTeam now calls a new buildKbPreamble() that mirrors what scrum_master_pipeline does on every per-file review: queries /vectors/pathway/bug_fingerprints + /vectors/search against the lakehouse_arch_v1 corpus, then asks local qwen3.5:latest (provider=ollama) to synthesize a tight briefing. The synthesized preamble prepends the existing escalation prompt so the cloud reviewer sees historical context the same way scrum reviewers do. Reuses existing KB primitives — no new corpora, no new endpoints, no new abstractions. Same code path scrum already exercises 3+ times per review; observer joins the same compounding loop. Audit row gains kb_preamble_chars so we can later track enrichment yield per escalation. Empty preamble (both fingerprints + matrix return nothing) → empty string, prompt unchanged. Verified: qwen3.5:latest synthesis fires for every escalation with non-empty matrix hits (gateway log: 445→72 tokens, 3.1s). Matrix retrieval correctly surfaces PRD Phase 40/44 chunks for chat_completion clusters. Pathway memory stays consistent with scrum (84→87 traces); chat_completion task_class doesn't have fingerprints yet — graceful. Local-model synthesis was J's explicit ask: compress the raw bundle before the cloud call so the briefing is actionable, not a dump. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 18:36:19 -05:00
root	69919d9d57	.archon: add lakehouse-architect-review workflow ... Demonstrates Archon → Pi → our gateway → OpenRouter end-to-end on Lakehouse code. Three Pi nodes (shape, weakness, improvement) — each fires a /v1/chat/completions call that lands a Langfuse trace AND an observer event for KB-consolidation parity with scrum. Run: ARCHON_SUPPRESS_NESTED_CLAUDE_WARNING=1 \ PI_OPENROUTER_BASE_URL=http://localhost:3100/v1 \ OPENROUTER_API_KEY=sk-anything \ archon workflow run lakehouse-architect-review --no-worktree Verified 2026-04-26 — 3 grok-4.1-fast calls, 14.6s total, observer ring delta=3, three Langfuse traces. Read-only (allowed_tools: [read]) so running it doesn't mutate the repo. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 18:05:43 -05:00
root	d1d97a045b	v1: fire observer /event from /v1/chat alongside Langfuse trace ... Observer at :3800 already collects scrum + scenario events into a ring buffer that pathway-memory + KB consolidation read from. /v1/chat now posts a lightweight {endpoint, source:"v1.chat", input_summary, output_summary, success, duration_ms} event there too — fire-and-forget tokio::spawn, observer-down doesn't block the chat response. Now any tool routed through our gateway (Pi CLI, Archon, openai SDK clients, langchain-js) shows up in the same ring buffer the scrum loop reads, ready for the same KB-consolidation analysis. Independent of the existing langfuse-bridge that polls Langfuse — this path is immediate. Verified: GET /stats shows {by_source: {v1.chat: N}} grows by 1 per chat call, both for direct curl and for Pi CLI invocations. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 18:01:52 -05:00
root	540a9a27ee	v1: accept OpenAI multimodal content shape (array-of-parts) ... Modern OpenAI clients (pi-ai, openai SDK 6.x, langchain-js, the official agents) send `messages[].content` as an array of content parts: `[{type:"text", text:"..."}, {type:"image_url", ...}]`. Our gateway typed `content` as plain `String` and 422'd those calls. Fix: `Message.content` is now `serde_json::Value` so requests deserialize regardless of shape. `Message::text()` flattens content-parts arrays (concat'd `text` fields, non-text parts skipped) for places that need a plain string — Ollama prompt assembly, char counts, the assistant's own response synthesis. `Message::new_text()` constructs string-content messages without writing the wrapper at each call site. Forwarders (openrouter) clone content through verbatim so providers see exactly what the client sent. Verified end-to-end: Pi CLI (`pi --print --provider openrouter`) landed a clean 1902-token request through `/v1/chat/completions`, routed to OpenRouter as `openai/gpt-oss-120b:free`, response in 1.62s, Langfuse trace `v1.chat:openrouter` recorded with provider tag. Same path that any tool using the official openai SDK takes. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 17:56:46 -05:00
root	3a0b37ed93	v1: OpenAI-compat alias + smart provider routing — gateway is now drop-in middleware ... /v1/chat/completions route alias (same handler as /chat) lets any tool using the official `openai` SDK adopt the gateway via OPENAI_BASE_URL alone — no custom provider field needed. resolve_provider() extended: - bare `vendor/model` (slash) → openrouter (catches x-ai/grok-4.1-fast, moonshotai/kimi-k2, deepseek/deepseek-v4-flash, openai/gpt-oss-120b:free) - bare vendor model names (no slash, no colon) get auto-prefixed: gpt-* / o1-* / o3-* / o4-* → openai/<name> (OpenRouter form) claude-* → anthropic/<name> grok-* → x-ai/<name> Then routed to openrouter. Ollama models (with colon, no slash) keep default routing. Tools like pi-ai validate against an OpenAI-style catalog and send bare names — this lets them flow through cleanly. Verified end-to-end: - curl POST /v1/chat/completions {model: "gpt-4o-mini", ...} → 200, routed to openrouter as openai/gpt-4o-mini - openai SDK with baseURL=http://localhost:3100/v1 → 3 model variants all succeed (openai/gpt-4o-mini, gpt-4o-mini, x-ai/grok-4.1-fast) - Langfuse traces fire automatically on every call (v1.chat:openrouter, provider tagged in metadata) scripts/mode_pass5_variance_paid.ts gains LH_CONDITIONS env so subset runs (e.g. just isolation vs composed) take half the latency. Archon-on-Lakehouse integration: gateway side is done. Pi-ai's openai-responses backend uses /v1/responses (not /chat/completions) and its openrouter backend appears to bail in client-side validation before sending. Patching Pi locally to override baseUrl works for arch but the harness still rejects — needs more work in a follow-up. Direct openai SDK path (langchain-js / agents / patched Pi) works today. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 17:49:37 -05:00
root	2dbc8dbc83	v1/mode: model-aware enrichment downgrade + 3 corpora + variance harness ... Pass 5 (5 reps × 4 conditions × 1 file on grok-4.1-fast) showed composing matrix corpora is anti-additive on strong models — composed lakehouse_arch + symbols LOST 5/5 head-to-head vs codereview_isolation (Δ −1.8 grounded findings, p=0.031). Default flips to isolation; matrix path now auto- downgrades when the resolved model is strong. Mode runner: - matrix_corpus is Vec<String> (string OR array via deserialize_string_or_vec) - top_k=6 from each corpus, merge by score, take top 8 globally - chunk tag prefers doc_id over source so reviewer sees [adr:009] vs [lakehouse_arch] - is_weak_model() gate auto-downgrades codereview_lakehouse → codereview_isolation for strong models (default-strong; weak = :free suffix or local last-resort) - LH_FORCE_FULL_ENRICHMENT=1 bypasses for diagnostic runs - EnrichmentSources.downgraded_from records when the gate fires Three corpora indexed via /vectors/index (5849 chunks total): - lakehouse_arch_v1 — ADRs + phases + PRD + scrum spec (93 docs, 2119 chunks) - scrum_findings_v1 — past scrum_reviews.jsonl (168 docs, 1260 chunks; EXCLUDED from defaults — 24% out-of-bounds line citations from cross-file drift) - lakehouse_symbols_v1 — regex-extracted pub items + /// docs (656 docs, 2470 chunks) Experiment infra: - scripts/build_*_corpus.ts — re-runnable when source content changes - scripts/mode_pass5_variance_paid.ts — N reps × M conditions on one file - scripts/mode_pass5_summarize.ts — mean ± σ + head-to-head, parser handles numbered + path-with-line + path-with-symbol finding tables - scripts/mode_compare.ts — groups by mode|corpus when sweeps span corpora - scripts/mode_experiment.ts — default model bumped to x-ai/grok-4.1-fast, --corpus flag for per-call override Decisions + open follow-ups: docs/MODE_RUNNER_TUNING_PLAN.md Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 17:29:17 -05:00
root	56bf30cfd8	v1/mode: override knobs + staffing native runner + pass 2/3/4 harnesses ... Setup for the corpus-tightening experiment sweep (J 2026-04-26 — "now is the only cheap window before the corpus gets large and refactoring costs go up"). Override params on /v1/mode/execute (additive — old callers unaffected): force_matrix_corpus — Pass 2: try alternate corpora per call force_relevance_threshold — Pass 2: sweep filter strictness force_temperature — Pass 3: variance test New native mode `staffing_inference_lakehouse` (Pass 4): - Same composer architecture as codereview_lakehouse - Staffing framing: coordinator producing fillable|contingent| unfillable verdict + ranked candidate list with playbook citations - matrix_corpus = workers_500k_v8 - Validates that modes-as-prompt-molders generalizes beyond code - Framing explicitly says "do NOT fabricate workers" — the staffing analog of the lakehouse mode's symbol-grounding requirement Three sweep harnesses: scripts/mode_pass2_corpus_sweep.ts — 4 corpora × 4 thresholds × 5 files scripts/mode_pass3_variance.ts — 3 files × 3 temps × 5 reps scripts/mode_pass4_staffing.ts — 5 fill requests through staffing mode Each appends per-call rows to data/_kb/mode_experiments.jsonl which mode_compare.ts already aggregates with grounding column. Pass 1 (10 files × 5 modes broad sweep) currently running via the existing scripts/mode_experiment.ts — gateway restart deferred until it completes so the new override knobs aren't enabled mid-experiment. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 01:55:12 -05:00
root	52bb216c2d	mode_compare: grounding check + control flag + emoji-tolerant section detection ... Three fixes after the playbook_only confabulation surfaced in 2026-04-26 experiment (8 'findings' on a 333-line file all citing lines 378-945 — fully fabricated from pathway-memory pattern names). (1) Aggregator regex bug — section detection failed on emoji-prefixed markdown headers like `## 🔎 Ranked Findings`. The original regex required word chars right after #{1,3}\s+, so the patches table header `## 🛠️ Concrete Patch Suggestions` was never recognized as a stop boundary, double-counting every finding. Fix: allow non-letter chars (emoji/space) between # and the keyword. (2) Grounding check — for each finding row in the response, extract backtick-quoted symbols + cited line numbers; verify symbols exist in the actual focus file and lines fall within EOF. Computes grounded/total ratio per mode. Surfaces 'OOB' (out-of-bounds) count explicitly so confabulation is visible at a glance. Confirms what hand-grading found: codereview_playbook_only's 8 findings on service.rs were 1/8 grounded with 7 OOB. (3) Control mode tagging — codereview_null and codereview_playbook_only are designed as falsifiers (baseline / lossy ceiling) and their numerical wins should never be read as recommendations. Output marks them with ⚗ glyph + warning footer. Per-mode aggregate is now sorted by groundedness, not raw count. Per-mode-vs-lakehouse comparison uses grounded findings, not raw — so confabulation can no longer score a "win". Updated SCRUM_MASTER_SPEC.md with refactor timeline pointing at the 2026-04-25/26 commits (observer fix, relevance filter, retire wire, mode router, enrichment runner, parameterized experiment). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 01:44:21 -05:00
root	7c47734287	v1/mode: parameterized runner + 5 enrichment-experiment modes ... J's directive (2026-04-26): "Create different modes so we can really dial in the architecture before it gets further along — pinpoint the failures and strengths equally so I know what direction to go in. Loop theater happens when we don't pinpoint the most accurate path." Refactored execute() to switch on mode name → EnrichmentFlags preset. Five native modes designed as deliberate experiments — each isolates one architectural axis so the comparison matrix reads off what's doing work vs what's adding latency for nothing: codereview_lakehouse — all enrichment on (ceiling) codereview_null — raw file + generic prompt (baseline) codereview_isolation — file + pathway only (no matrix) codereview_matrix_only — file + matrix only (no pathway) codereview_playbook_only — pathway only, NO file content (lossy ceiling) Each call appends a row to data/_kb/mode_experiments.jsonl with full sources + response. LH_MODE_LOG_OFF=1 to suppress. scripts/mode_experiment.ts — sweeps files × modes serially, prints live progress with per-call enrichment stats. Defaults to OpenRouter free model so cloud quota doesn't gate experiments. scripts/mode_compare.ts — reads the JSONL, outputs per-file matrix + per-mode aggregate + mode-vs-baseline win/loss with avg finding delta. Heuristic finding-count from markdown table rows; pathway citation count from preamble references. scrum_master_pipeline.ts gets a mode-runner fast path gated by LH_USE_MODE_RUNNER=1: try /v1/mode/execute first, fall through to the existing ladder if response < LH_MODE_MIN_CHARS (default 2000) or anything errors. Off by default until A/B-validated. First experiment results (2 files × 5 modes via gpt-oss-120b:free): - codereview_null produces 12.6KB response with ZERO findings (proves adversarial framing is load-bearing) - codereview_playbook_only produces MORE findings than lakehouse on average (12 vs 9) at 73% the latency — pathway memory is the dominant signal driver - codereview_matrix_only underperforms isolation by ~0.5 findings while costing the same latency — matrix corpus likely underperforming for scrum_review task class Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 01:36:42 -05:00
root	86f63a083d	v1/mode: codereview_lakehouse native runner — modes are prompt-molders ... J's framing (2026-04-26): "Modes are how you ask ONCE and get BETTER information — they mold the data, hyperfocus the prompt on this codebase's needs, so the model gets it right the first time without the cascading retry ladder." Built the first concrete native enrichment runner (codereview_lakehouse) that composes every context primitive the gateway exposes: 1. Focus file content (read from disk OR caller-supplied) 2. Pathway memory bug_fingerprints for this file area (ADR-021 preamble — "📚 BUGS PREVIOUSLY FOUND IN THIS FILE AREA") 3. Matrix corpus search via the task_class's matrix_corpus 4. Relevance filter (observer /relevance) drops adjacency pollution 5. Assembles ONE precise prompt with system framing 6. Single call to /v1/chat with the recommended model POST /v1/mode/execute dispatches. Native mode → runs the composer. Non-native mode → 501 NOT_IMPLEMENTED with hint (proxy to LLM Team /api/run is queued). Provider hint logic auto-routes by model name shape: - vendor/model[:tag] → openrouter - kimi-*/qwen3-coder*/deepseek-v*/mistral-large* → ollama_cloud - everything else → local ollama Live test against crates/queryd/src/delta.rs (10593 bytes, 10 historical bug fingerprints, 2 matrix chunks dropped by relevance): - enriched_chars: 12876 - response_chars: 16346 (14 findings with confidence percentages) - Model literally cited the pathway memory preamble in finding #7 - One call to free-tier gpt-oss:120b produced what previously required the 9-rung escalation ladder Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 00:28:46 -05:00
root	d277efbfd2	v1/mode: task_class → mode/model router (decision-only, phase 1) ... HANDOVER §queued (2026-04-25): "Mode router — port LLM Team multi-model patterns. Pick the right TOOL/MODE for each task class via the matrix, not cascade through models." Two-stage architecture: 1. Decision (POST /v1/mode) — pure recommendation, no execution. Returns {mode, model, decision: {source, fallbacks, matrix_corpus, notes}} so callers see WHY this mode was picked. 2. Execution (future POST /v1/mode/execute) — proxy to LLM Team /api/run for modes not yet ported to native Rust runners. Not wired in this phase. Splitting decision from execution lets us A/B-test the routing logic without committing to running every recommendation. The decision function is pure enough for exhaustive unit tests (3 added). config/modes.toml — initial map for 5 task_classes (scrum_review, contract_analysis, staffing_inference, fact_extract, doc_drift_check) + a default. matrix_corpus per task is reserved for the future matrix-informed routing pass. VALID_MODES list (24 modes) is kept in sync manually with LLM Team's /api/run handler at /root/llm_team_ui.py:10581. Adding a mode here without adding it upstream returns 400 from a future proxy. GET /v1/mode/list — operator introspection so a UI can render the registry table without re-parsing TOML. Live-tested: 5 task classes match, unknown classes fall through to default, force_mode override works + validates, bogus modes return 400 with the valid_modes list. Updates reference_llm_team_modes.md memory — earlier note claiming "only extract is registered" was wrong (all 25 are registered). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 00:16:32 -05:00
root	626f18d491	pathway_memory: audit-consensus → retire wire ... When observer's hand-review explicitly rejects the output of a hot-swap-recommended model, the matrix's recommendation was wrong for this context. Auto-retire the trace so future agents don't get the same poisoned recommendation in their preamble. crates/vectord/src/pathway_memory.rs — add `trace_uid` to HotSwapCandidate response and populate from the matched trace. This gives consumers single-trace precision for /pathway/retire. tests/real-world/scrum_master_pipeline.ts: - HotSwapCandidate interface gains trace_uid - new retirePathwayTrace() helper (fire-and-forget, fall-open) - in the obsVerdict reject branch: if hotSwap was active AND the rejected model is the hot-swap-recommended one AND observer confidence ≥0.7, fire retire and null hotSwap so post-loop replay bookkeeping doesn't double-process. - hotSwap declared `let` (was const) so it can be nulled Cycle verdicts ("needs different angle") don't trigger retire — only outright rejects do. Confidence gate avoids retiring on heuristic-fallback verdicts that come back without a confidence number. Closes the "audit-consensus → retire" item from HANDOVER.md. Live-tested: insert synthetic trace → /pathway/retire by trace_uid → retired counter 1 → 2. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-26 00:01:20 -05:00
root	e1ef185868	docs: add MATRIX_AGENT_HANDOVER notes + cross-link from SCRUM_MASTER_SPEC ... The matrix-agent-validated repo split + Ansible deploy pipeline are otherwise undocumented in this repo. This new doc explains: - the relationship between lakehouse and matrix-agent-validated - where the playbook lives and what it provisions - the critical distinction: matrix-test (10.111.129.50 Incus container) is the REAL destination, while 192.168.1.145 is a smoke-test VPS only (partial deploy, no services, do not treat as production) - what landed today (observer fix, HANDOVER.md render, relevance filter) Added a cross-link block at the top of SCRUM_MASTER_SPEC.md so the canonical scrum handoff doc points at the new MATRIX_AGENT_HANDOVER doc. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 23:54:42 -05:00
root	0115a60072	observer: add /relevance heuristic filter for adjacency pollution ... Matrix retrieval often surfaces high-cosine chunks that are about symbols the focus file IMPORTS but doesn't define. The reviewer LLM then hallucinates those imported-crate internals as in-file content ("I see main.rs does X" when X lives in queryd::context). mcp-server/relevance.ts — pure scorer with five signals: path_match +1.0 chunk source/doc_id encodes focus path defined_match +0.6 chunk text mentions focus.defined_symbols token_overlap +0.4 jaccard of non-stopword tokens prefix_match +0.3 shared first-2-segment prefix import_only -0.5 mentions only imported symbols (pollution) Default threshold 0.3 — tuned empirically on the gateway/main.rs case. Also fixes a regex bug in the import extractor: the character class was lowercase-only, so `use catalogd::Registry;` silently never matched (regex backed off when it hit the uppercase R). Caught by the test suite. observer.ts — POST /relevance endpoint wraps filterChunks(). scrum_master_pipeline.ts — fetchMatrixContext gains optional focusContent param; calls /relevance after collecting allHits and before sort+top. Opt-out via LH_RELEVANCE_FILTER=0; threshold via LH_RELEVANCE_THRESHOLD. Fall-open on observer failure. 9 unit tests, all green. Live probe on real shape correctly drops a 0.7-cosine adjacency-pollution chunk while keeping in-focus hits. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 23:51:45 -05:00
root	54689d523c	observer: fix gateway health probe — text/plain not JSON ... Same bug as matrix-agent-validated 5db0c58. observer.ts:645 did fetch().then(r => r.json()) against /health which returns text/plain "lakehouse ok". r.json() throws on non-JSON, .catch swallows to null, observer exits assuming gateway down. With systemd Restart=on-failure this crash-loops every 5s — confirmed live on matrix-test box today. Fix: r.ok ? r.text() : null. Same shape, accepts the actual content type. Sealed in pathway_memory as TypeConfusion:fetch-health-json. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 23:37:52 -05:00
root	779158a09b	scripts: chicago analyzer field-name fixes + vectorize sanitizer hardening ... Two small fixes surfaced during smoke testing: analyze_chicago_contracts.ts: permit field is contact_1_name not contact_1; reported_cost is integer-string. Fixed filter (was rejecting all 2853 permits) and contractor extraction (was empty). vectorize_raw_corpus.ts: sanitize() expanded to strip control chars + ALL backslashes (kills incomplete \uXXXX escapes) + UTF-16 surrogates (unpaired surrogates from emoji split by truncate boundary). Llm_team response cache had docs with all three pollution shapes. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 19:34:45 -05:00
root	6ac7f61819	pathway_memory: Mem0 versioning + deletion (upsert/revise/retire/history) ... Per J 2026-04-25: pathway_memory was append-only — every agent run added a new trace, bad/failed runs polluted the matrix forever, no notion of "this is the canonical evolved playbook." Ported playbook_memory's Phase 25/27 patterns into pathway_memory so the agent loop's matrix converges on best-known approaches per task class instead of bloating. Fields added to PathwayTrace (all #[serde(default)] for back-compat): - trace_uid: stable UUID per individual trace within a bucket - version: u32 default 1 - parent_trace_uid, superseded_at, superseded_by_trace_uid - retirement_reason (paired with existing retired:bool) Methods added to PathwayMemory: - upsert(trace) → PathwayUpsertOutcome {Added|Updated|Noop} Workflow-fingerprint dedup: ladder_attempts + final_verdict hash. Identical workflow → bumps existing replay_count instead of duplicating. - revise(parent_uid, new_trace) → PathwayReviseOutcome Chains versions; rejects retired or already-superseded parents. - retire(trace_uid, reason) → bool Marks specific trace retired with reason. Idempotent. - history(trace_uid) → Vec<PathwayTrace> Walks parent_trace_uid back to root, then superseded_by forward to tip. Cycle-safe via visited set. Retrieval gates updated: - query_hot_swap skips superseded_at.is_some() - bug_fingerprints_for skips both retired AND superseded HTTP endpoints in service.rs: - POST /vectors/pathway/upsert - POST /vectors/pathway/retire - POST /vectors/pathway/revise - GET /vectors/pathway/history/{trace_uid} scripts/seal_agent_playbook.ts switched insert→upsert + accepts SESSION_DIR arg so it can seal any archived session, not just iter4. Verified live (4/4 ops): - UPSERT first run: Added trace_uid 542ae53f - UPSERT identical: Updated, replay_count bumped 0→1 (no duplicate) - REVISE 542ae53f→87a70a61: parent stamped superseded_at, v2 created - HISTORY of v2: chain_len=2, v1 superseded, v2 tip - RETIRE iter-6 broken trace: retired=true, retirement_reason preserved - pathway_memory.stats: total=79, retired=1, reuse_rate=0.0127 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 19:31:44 -05:00
root	ed83754f20	raw-corpus dump + vectorization + chicago contract inference pipeline ... Three new pieces, executed in order: scripts/dump_raw_corpus.sh - One-shot bash that creates MinIO bucket `raw` and uploads all testing corpora as a persistent immutable test set. 365 MB total across 5 prefixes (chicago, entities, sec, staffing, llm_team) + MANIFEST.json. Sources: workers_500k.parquet (309 MB), resumes.parquet, entities.jsonl, sec_company_tickers.json, Chicago permits last 30d (2,853 records, 5.4 MB), 9 LLM Team Postgres tables dumped via row_to_json. scripts/vectorize_raw_corpus.ts - Bun script that fetches each raw-bucket source via mc, runs a source-specific extractor into {id, text} docs, posts to /vectors/index, polls job to completion. Verified results: chicago_permits_v1: 3,420 chunks entity_brief_v1: 634 chunks sec_tickers_v1: 10,341 chunks (after extractor fix for wrapped {rows: {...}} JSON shape) llm_team_runs_v1: in flight, 19K+ chunks llm_team_response_cache_v1: queued scripts/analyze_chicago_contracts.ts - Real inference pipeline that picks N high-cost permits with named contractors from the raw bucket, queries all 6 contract- analysis corpora in parallel via /vectors/search, builds a MATRIX CONTEXT preamble, calls Grok 4.1 fast for structured staffing analysis, hand-reviews each via observer /review, appends to data/_kb/contract_analyses.jsonl. tests/real-world/scrum_master_pipeline.ts - MATRIX_CORPORA_FOR_TASK extended with two new task classes: contract_analysis (chicago + entity_brief + sec + llm_team_runs + llm_team_response_cache + distilled_procedural) staffing_inference (workers_500k_v8 + entity_brief + chicago + llm_team_runs + distilled_procedural) scrum_review unchanged. This is the first time the matrix architecture operates on real ingested data instead of code-review smoke tests. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 18:44:27 -05:00
root	a496ced848	scrum: unified matrix retriever — pull from ALL relevant KB corpora, not just pathway memory ... Per J 2026-04-25 architectural correction: matrix index is the vector indexing layer for the WHOLE knowledge base (distilled facts, procedures, config hints, team runs, playbooks, pathway successes), not a single narrow store. Built fetchMatrixContext(query, taskClass, filePath) that: - Queries multiple persistent vector indexes in parallel via /vectors/search - Collects hits per corpus + score + doc_id + 400-char excerpt - Pulls pathway successes via existing helper, mapped to MatrixHit shape - Sorts by score across corpora, returns top-N (default 8) - Reports per-corpus hit counts + errors for transparency Per-task-class corpus list (MATRIX_CORPORA_FOR_TASK): scrum_review → distilled_factual, distilled_procedural, distilled_config_hint, kb_team_runs_v1 (staffing data deliberately excluded — not relevant to code review) Probed live: distilled_config_hint top hit = 0.52, distilled_procedural top = 0.49, kb_team_runs top = 0.59. Real signal across corpora. Replaces the narrow proven-approaches preamble with a unified MATRIX-INDEXED CONTEXT preamble tagged with source_corpus per chunk so the model knows what kind of context it's seeing. LH_SCRUM_MATRIX_RETRIEVE=0 still disables for A/B testing. Future: promote to a Rust /v1/matrix endpoint once corpora list and ranking logic stabilize. For now TS lets us iterate fast against the live matrix without gateway restarts. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 18:29:08 -05:00
root	d187bcd8ac	scrum: stop cascading models on quality issues — single-model retry with enrichment ... Architectural correction (J 2026-04-25): The 9-rung ladder was treating cascade as the strategy. That's wrong. ONE model handles the work, with same-model retries using enriched context. Cycle to a different model ONLY on PROVIDER errors (network / auth / 5xx) — never on quality issues, because quality issues mean the context needs more enrichment, not a different model. Changes: - LADDER shrunk from 11 entries to 3 (Grok 4.1 fast primary, DeepSeek V4 flash + Qwen3-235B as provider-error fallbacks). Removed Kimi K2.6, Gemini 2.5 flash, all Ollama Cloud rungs, OR free-tier rungs, local qwen3.5 — none were doing the work, all wasted attempts. They remain available as routable tools for the future mode router. - Loop restructured: separate `modelIdx` from attempt counter. Provider error → modelIdx++ (advance fallback). Observer reject / cycle / thin response → retry SAME model with rejection notes feeding into the `learning` preamble; advance fallback only after MAX_QUALITY_RETRIES (default 2) exhausted on the current model. - LH_SCRUM_MAX_QUALITY_RETRIES env to tune the per-model retry cap. What this preserves: - Tree-split (treeSplitFile) is still the ONE legitimate model-switch trigger for context-overflow, but even it just re-runs the same model against smaller chunks. - Pathway memory preamble still fires. - Hot-swap reorder still applies — when a recommended model maps to the new shorter ladder. Future direction (J 2026-04-25 note): the LLM Team multi-model modes in /root/llm_team_ui.py are a REFERENCE PATTERN for a mode router we will build INSIDE this gateway. Mimic the patterns, don't modify the LLM Team UI itself. The mode router will pick the right approach for each task class via the matrix index, not cascade through models. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 18:08:31 -05:00
root	6432465e2c	autonomous_loop: stop clobbering applier model/provider defaults ... Found by running: the loop was setting LH_APPLIER_MODEL=qwen3-coder:480b explicitly via env, which clobbered the applier's NEW default of x-ai/grok-4.1-fast on openrouter. Result: applier kept hitting the throttled ollama_cloud account and producing zero patches every iter. Now LOOP_APPLIER_MODEL and LOOP_APPLIER_PROVIDER are optional overrides; when unset, scrum_applier.ts uses its own defaults. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:54:54 -05:00
root	4ac56564c0	scrum + applier + observer: switch to paid OpenRouter ladder, add Kimi K2.6 + Gemini 2.5 ... Ollama Cloud was throttled across all 6 cloud rungs in iters 1-9, which forced the loop into 0-review iterations even though the architecture was sound. Swapping to paid OpenRouter unblocks the test path. Ladder changes (top-of-ladder paid models, all under $0.85/M either side): - moonshotai/kimi-k2.6 ($0.74/$4.66, 256K) — capped at 25/hr - x-ai/grok-4.1-fast ($0.20/$0.50, 2M) — primary general - google/gemini-2.5-flash ($0.30/$2.50, 1M) — Google reasoning - deepseek/deepseek-v4-flash ($0.14/$0.28, 1M) — cheap workhorse - qwen/qwen3-235b-a22b-2507 ($0.07/$0.10, 262K) — cheapest big Existing rungs (Ollama Cloud + free OR + local qwen3.5) kept as fallback. Per-model rate limiter (MODEL_RATE_LIMITS in scrum_master_pipeline.ts): - Persists call timestamps to data/_kb/rate_limit_calls.jsonl so caps survive process restarts (autonomous loop spawns a fresh subprocess per iteration; without persistence each iter would reset) - O(1) writes, prune-on-read for the rolling 1h window - Capped models log "SKIP (rate-limited: cap N/hr reached)" and the ladder cycles to the next rung - J directive 2026-04-25: 25/hr on Kimi K2.6 to bound output cost Observer hand-review cloud tier swapped from ollama_cloud/qwen3-coder:480b to openrouter/x-ai/grok-4.1-fast — proven to emit precise semantic verdicts (named "AccessControl::can_access() doesn't exist" specifically in 2026-04-25 tests instead of the heuristic fallback). Applier patch emitter swapped from ollama_cloud/qwen3-coder:480b to openrouter/x-ai/grok-4.1-fast (default; LH_APPLIER_MODEL + LH_APPLIER_PROVIDER override). This was the third LLM call we missed — without it, observer accepts a review but applier never produces patches because its emitter was still hitting the throttled account. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:49:02 -05:00
root	e79e51ed70	tests: autonomous_loop.ts — goal-driven scrum + applier retry harness ... Wraps tests/real-world/scrum_master_pipeline.ts and scrum_applier.ts in a single autonomous loop that runs scrum → applier --commit → optional git push, observes per-iteration outcomes via observer /event, journals to data/_kb/autonomous_loops.jsonl. Stops when 2 consecutive iters land zero commits OR LOOP_MAX_ITERS reached. Env knobs: LOOP_TARGETS — comma-sep paths, default 3 high-traffic Lakehouse files LOOP_MAX_ITERS — default 3 LOOP_PUSH=1 — push branch after each commit-landing iter LOOP_BRANCH — default scrum/auto-apply-19814 (refuses to run elsewhere) LOOP_MIN_CONF — applier min confidence (default 85) LOOP_APPLIER_MODEL — default qwen3-coder:480b Causality preserved: targets pass through to LH_APPLIER_FILES so applier patches what scrum just reviewed (vs picking from global review history). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:32:15 -05:00
root	3f166a5558	scrum + observer: hand-review wire — judgment moved out of the inner loop ... Pre-2026-04-25 the scrum_master applied a hardcoded grounding-rate gate inline. That baked policy into the wrong layer — semantic judgment about whether a review is grounded belongs in the observer (which has Langfuse traces, sees every response across the system, and can call cloud LLMs for real evaluation). Scrum should report DATA, observer DECIDES. What landed: - scrum_master_pipeline.ts: removed the inline grounding-pct threshold; every accepted candidate now POSTs to observer's /review endpoint with {response, source_content, grounding_stats, model, attempt}. Observer returns {verdict: accept|reject|cycle, confidence, notes}. On observer failure, scrum falls open to accept (observer is policy, not blocker). - mcp-server/observer.ts: new POST /review endpoint with two-tier evaluator. Tier 1: cloud LLM (qwen3-coder:480b at temp=0) hand-reviews with full context — response + source excerpt + grounding stats — and emits structured verdict JSON. Tier 2: deterministic heuristic over grounding pct + total quotes when cloud throttles, marked source: "heuristic" so consumers can tune it later by comparing against cloud. - Every verdict persists to data/_kb/observer_reviews.jsonl with full input snapshot so cloud vs heuristic can be A/B compared once cloud quota refreshes. Verified end-to-end: smoke loop iter 1 — observer returned `cycle` on 21% grounding (cycled to next rung), `reject` on 17% (gave up). Iter 2 — `reject` on 12% and 14%. Both UNRESOLVED with honest signal instead of polluting pathway memory with hallucinated patterns. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:32:04 -05:00
root	c90a509f49	applier: LH_APPLIER_FILES env to constrain to current-iter targets ... Without this, the applier loaded the latest 34 reviews and patched the highest-confidence file from history — which is meaningless when called from the autonomous loop where the intent is "review file X this iter, patch file X this iter." Now the loop passes its targets through and the applier filters eligible reviews accordingly. Causality is restored: scrum reviews file X → applier patches file X. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:31:49 -05:00
root	9ecc5848fa	scrum: blind-response guard + anchor-grounding post-verifier ... Two signal-quality fixes for the scrum loop: 1. isBlindResponse() — detects models that emit structurally-valid review JSON containing "no source code visible / cannot verify" even when source WAS supplied. Rejects so the ladder cycles to the next rung instead of accepting the blind hallucination. 2. verifyAnchorGrounding() + appendGroundingFooter() — post-process verifier that extracts every backtick-quoted snippet from the review and checks it against the original source content. Appends a grounding footer reporting grounded vs ungrounded counts so humans can audit hallucination rate at a glance. Born from the iter where llm_team_ui.py review came back with 6/10 findings hallucinated (invented render_template_string calls, fabricated logger.exception sites, made-up SHA-256 hashing). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:07:30 -05:00
root	b843a23433	mcp: contractor entity-brief drill-down + mobile UX pass ... Adds /contractor page route plus /intelligence/contractor_profile endpoint that fans out across OSHA, ticker, history, parent_link, federal contracts, debarment, NLRB, ILSOS, news, diversity certs, BLS macro — single per-contractor portfolio view across every wired source. search.html: mobile responsive layout, fixed bottom dock with horizontal scroll-snap, legacy bridge row stacking, viewport overflow guards. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:07:23 -05:00
root	a6c83b03e5	chore: sync Cargo.lock — toml dep for phase-42 rule loader ... Pairs retroactively with de8fb10 (truth/ TOML rule loader). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-25 17:07:16 -05:00
root	858954975b	Staffing Co-Pilot UI — architecture-first enrichments + shift clock ... J's direction: the dashboard was explanatory but not *actionable* as a staffing-matrix console. Refactor so the architecture claims from docs/PRD.md surface as operational signals on every contract card. Backend (mcp-server/index.ts): + GET|POST /intelligence/arch_signals — probes live substrate health so the dashboard shows instant-search latency, index shape, playbook-memory entries, and pathway-memory (ADR-021) trace count. Fires one fresh /vectors/hybrid probe against workers_500k_v1 so the "instant search" number on screen is live, not cached. * /intelligence/permit_contracts now times every hybrid call per contract and returns search_latency_ms, so the card can display the per-query latency pill (⚡ 342ms). + Per-contract computed fields returned from the backend: search_latency_ms — real /vectors/hybrid duration fill_probability — base_pct (by pool_size×count ratio) + curve [d0, d3, d7, d14, d21, d30] with cumulative fill% per bucket economics — avg_pay_rate, gross_revenue, gross_margin, margin_pct, payout_window_days [30, 45], over_bill_count, over_bill_pool_margin_at_risk shifts_needed — 1st/2nd/3rd/4th inferred from permit work_type + description regex * Pre-existing dangling-brace bug in api() fixed (the `activeTrace` logging block had been misplaced at module scope, referencing variables that only existed inside the function). Restart was failing with "Unexpected }" at line 76. Moved tracing inside the try block where parsed/path/body/ms are in scope. Frontend (mcp-server/search.html): + Top "Substrate Signals" section — 4 live tiles (instant search, index, playbook memory, pathway matrix). Color-codes latency (green <100ms, amber <500ms, red otherwise). + "24/7 Shift Coverage" section — SVG 24-hour clock with 4 colored shift arcs (1st/2nd/3rd/4th), current-time needle, center label showing the live shift, per-shift contract count tiles beside. 4th shift assumes weekend/split; handles 3rd-shift wrap across midnight by splitting into two arcs. + Per-card architecture pills: instant-search latency, SQL-filter pool-size with k=200 boost note, shift requirements. + Per-card fill-probability horizontal stacked bar with day markers (d0/d3/d7/d14/d21/d30) and per-bucket segment shading (green → amber → orange → red as time decays). + Per-card economics 4-tile grid: Est. Revenue, Est. Margin (with % colored by health), Payout Window (30–45d standard), Over-Bill Pool count + margin at risk. Architecture smoke test (tests/architecture_smoke.ts, earlier commit) still green: 11/11 pass including the new /intelligence/arch_signals + permit_contracts enrichments. J specifically wanted: "shoot for the stars · hyperfocus · our architecture is better because it self-regulates, uses hot-swap, pulls from real data, and shows instant searches from clever indexing." Every one of those is now a specific visible signal on the page, not prose in the README. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 14:24:11 -05:00
root	4a94da2d41	tests/architecture_smoke — PRD-invariant probe against 500k workers ... J's reset: I'd been iterating on pipeline internals without a driver. The PRD says staffing is the REFERENCE consumer, not the domain driver — the architecture is the thing. This test makes that explicit. 8 sections exercise the PRD §Shared requirements against live production-shaped data (500k workers parquet, 50k-chunk vector index, 768d nomic embeddings): 1. preconditions — gateway + sidecar alive 2. catalog lookup — workers_500k resolves to 500000 rows 3. SQL at scale — count(*) + geo filter on 500k rows 4. vector search — /vectors/search returns top-k 5. hybrid SQL+vector — /vectors/hybrid with sql_filter 6. playbook_memory — /vectors/playbook_memory/stats 7. pathway_memory — ADR-021 stats + bug_fingerprints 8. truth gate — DROP TABLE blocked with 403 No cloud calls. Completes in ~5 seconds. Exits non-zero on any failure; failure messages print "these are the next things to fix." First-run measurements against current code: - 500k COUNT(*) = 22ms, OH-filtered = 20ms (invariant met) - vector search p=368ms on 10-NN - hybrid p=4662ms, returned 0 Toledo-OH hits (two signals worth investigating: the latency AND the empty result) - playbook_memory = 0 entries (rebuild never fired since boot) The 11/11 pass means the substrate's contract is intact. The measurements tell us WHERE to look next, not what to speculate. Going forward: this script is the canary. Run it after every substantive change. If a section flips from pass to fail, that IS the regression; roll back or fix. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 14:12:14 -05:00