Coherence pass — PRD/PHASES updates, config snapshot wired, unit tests

J flagged the audit: "make sure everything flows coherently, no
pseudocode or unnecessary patches or ignoring any particular part of
what we built." This is that pass.

PRD.md updates:
- Phase 19 refinement block — geo-filter + role-prefilter WIRED with
  citation density numbers (0.32 → 1.38, and 2 → 28 on same scenario).
- Phase 20 rewrite — mistral dropped, qwen3.5 + qwen3 local hot path,
  think:false as the key mechanical finding, kimi-k2.6 upgrade path.
- Phase 21 status block — think plumbing + cloud executor routing
  added after original commit.
- Phase 22 item B (cloud rescue) — pivot sanitizer, rescue verified
  1/3 on stress_01.
- Phase 23 NEW — staffer identity + tool_level + competence-weighted
  retrieval + kb_staffer_report. Auto-discovered worker labels called
  out with real numbers (Rachel Lewis 12× across 4 staffers).
- Phase 24 NEW — Observer/Autotune integration gap DOCUMENTED, not
  fixed. Observer has been idle at 0 ops for 3600+ cycles because
  scenarios hit gateway:3100 directly, bypassing MCP:3700 which the
  observer wraps. This is the honest "we're not using it in these
  tests" signal J surfaced. Fix deferred; gap visible now.

PHASES.md:
- Appended Phases 20-23 as checked, Phase 24 as unchecked gap.
- Updated footer count: 102 unit tests across all layers.
- Latest line updated with 14× citation lift + 46.4pt tool-asymmetry
  finding.

scenario.ts:
- snapshotConfig() was defined but never called. Now fires at every
  scenario start with a stable sha256 hash over the active model set +
  tool_level + cloud flags. config_snapshots.jsonl finally populates,
  which the error_corrections diff path needs to work correctly.

kb.test.ts (new): 4 signature invariant tests — stability across
unrelated fields (date, contract, staffer), sensitivity to role/city/
count changes, digest shape. All pass under `bun test`.

service.rs: 6 Rust extractor tests for extract_target_geo +
extract_target_role — basic, missing-state-returns-none, word
boundary (civilian != city), multi-word role, absent role, quoted
value parse. All pass under `cargo test -p vectord --lib extractor_tests`.

Dangling items now honestly documented rather than silently pending:
- Chunking cache (config/models.json SPEC, not wired) — flagged
- Playbook versioning (SPEC, not wired) — flagged
- Observer integration (WIRED but disconnected) — new Phase 24

docs/PRD.md

@@ -380,17 +380,37 @@ Make successful playbooks actually improve future searches. Today `successful_pl
 - Hard guarantees about recall lift magnitude. "Measurably better on the demo query" is the gate, not a universal quality claim.
 - Real-time recomputation on every playbook. Batched refresh via the existing stale-marking path is sufficient.
 
+### Phase 19 refinement (WIRED 2026-04-21): geo-filter + role prefilter on boost
+
+Item-3 diagnostic pass surfaced that `compute_boost_for` was ranking playbooks globally by cosine similarity, while candidates came from an SQL-filtered city. Result: boost map had 170 endorsed workers, 0 intersected the 50 Nashville-filtered candidates. Zero citations where there should have been dozens.
+
+Fix — in `crates/vectord/src/playbook_memory.rs`:
+- `compute_boost_for_filtered(target_geo)` — skip playbooks from other cities before cosine sort.
+- `compute_boost_for_filtered_with_role(target_geo, target_role)` — multi-strategy: exact (role, city, state) match earns similarity=1.0 and fills up to half the top_k; cosine fallback fills the rest. Mirrors Mem0/Zep 2026 guidance on parallel-strategy rerank.
+
+In `crates/vectord/src/service.rs`:
+- `extract_target_geo` and `extract_target_role` pull both from the executor's SQL filter.
+- `tracing::info!` emits `playbook_boost: boosts=N sources=N parsed=N matched=N target_geo=? target_role=?` on every hybrid_search. Silent-truncation class of bug now visible.
+
+Citation lift measured: avg citations per run 0.32 → 1.38 after geo filter; then 2 → 28 in the single-scenario Riverfront Steel re-run after role prefilter landed. 14× delta on same scenario.
+
+Unit tests: `extract_target_geo_basic`, `_missing_state_returns_none`, `_word_boundary` (rejects "civilian" substring), `extract_target_role_basic`, `_none_when_absent`, `_multi_word` — all pass (`cargo test -p vectord --lib extractor_tests`).
+
 ### Phase 20: Model Matrix + Overseer Tiers (WIRED 2026-04-21)
 
-Five-tier routing declared in `config/models.json`. Hot path (T1/T2) stays on local mistral + qwen2.5. Cloud is consulted sparingly for overview (T3 gpt-oss:120b), strategic (T4 qwen3.5:397b), and gatekeeper decisions (T5 kimi-k2-thinking). Every tier declares `context_window` + `context_budget` + `overflow_policy`. See ADR-021 (to add).
+Five-tier routing declared in `config/models.json`. Hot path (T1/T2) stays local (qwen3.5 + qwen3 after mistral was dropped for 0/14 fill rate on complex scenarios). Cloud for overview (T3 gpt-oss:120b), strategic (T4 qwen3.5:397b), and gatekeeper (T5 kimi-k2-thinking). Every tier declares `context_window` + `context_budget` + `overflow_policy`.
 
-- T1 hot: 50-200 calls/scenario, local only
-- T2 review: 5-14 calls/event, local only
-- T3 overview: 1-3 calls/scenario, cloud primary
+- T1 hot: 50-200 calls/scenario, local only — `qwen3.5:latest` executor, `think:false`
+- T2 review: 5-14 calls/event, local only — `qwen3:latest` reviewer, `think:false`
+- T3 overview: 1-3 calls/scenario, cloud primary — `gpt-oss:120b` on Ollama Cloud, thinking on
 - T4 strategic: 1-10 calls/day, cloud primary
 - T5 gatekeeper: 1-5 calls/day, audit-logged
 
-T3 checkpoints + cross-day lessons are wired. Lessons archive to `data/_playbook_lessons/` and load back at next scenario start as `prior_lessons` in executor context.
+T3 checkpoints + cross-day lessons wired. Lessons archive to `data/_playbook_lessons/` and load back at next scenario start as `prior_lessons` in executor context. Cloud passthrough verified on stress_01 scenario with `LH_OVERVIEW_CLOUD=1` — `gpt-oss:120b` response latency consistently 4-8s, diagnosing city-pivot ("Gary IN → Chicago IL, 40mi") when target city has zero supply.
+
+`think:false` is the key mechanical finding — qwen3.5 burns ~650 tokens of hidden reasoning before emitting response; hot-path JSON emitters MUST disable thinking or continuation has to paper over empty returns. T3/T4 overseers KEEP thinking (that's the point).
+
+**Kimi-k2.6 upgrade path:** Current Ollama Cloud key returns 403 on kimi-k2.6 (`ollama run kimi-k2.6:cloud` requires `ollama signin` with pro-tier account). kimi-k2.5 substitutes on the current tier — same family, strong at tool calling. Swap to k2.6 is a one-line change in `applyToolLevel` once the subscription lands.
 
 ### Phase 21: Scratchpad + Tree-Split Continuation
 
@@ -421,6 +441,16 @@ T3 checkpoints + cross-day lessons are wired. Lessons archive to `data/_playbook
 
 **Status:** TS primitives WIRED. Rust port pending. The escalation path (tree split → bigger-context cloud model → kimi-k2:1t's 1M window → split decision into sub-decisions) is declared in `config/models.json` under `context_management.overflow_policies`.
 
+### Phase 21 status update (WIRED 2026-04-21 evening)
+
+Additional primitives landed after the initial commit:
+
+- **`think: boolean`** flag plumbed through `generate()`, `generateCloud()`, `generateContinuable()`, and into sidecar's `/generate` endpoint. Enables per-call opt-out of hidden reasoning for hot-path JSON emitters. Verified: qwen3.5 with `think:false` + `num_predict:400` returns clean `{"worker_id":...}` on first call; without `think:false`, 650 tokens eaten by reasoning, response empty.
+
+- **Cloud executor routing** — `ACTIVE_EXECUTOR_CLOUD` / `ACTIVE_REVIEWER_CLOUD` flags let per-staffer tool_level route executor to Ollama Cloud when weak local model (qwen2.5) would collapse. Verified on kimi-k2.5 via Ollama Cloud: clean JSON emission, think:false honored.
+
+Rust port of continuation + tree-split primitives remains queued for next sprint (`crates/aibridge/src/continuation.rs`, `tree_split.rs`).
+
 ### Phase 22: Internal Knowledge Library (KB)
 
 Meta-layer over Phase 19 playbook_memory. Playbook memory answers "which WORKERS worked for this event." The KB answers "which CONFIG worked for this playbook signature." Subject changes from workers to the system itself — model choice, budget hints, overflow policies, pathway notes.
@@ -451,9 +481,55 @@ Meta-layer over Phase 19 playbook_memory. Playbook memory answers "which WORKERS
 - budget_hints {executor_max_tokens, reviewer_max_tokens, executor_think}
 - pathway_notes (concrete pre-run advice)
 
-**Status (WIRED 2026-04-21):** `tests/multi-agent/kb.ts` holds all primitives. scenario.ts reads rec at start, indexes + recommends at end. Cold start gracefully writes a "low confidence, no history" rec so the second run has a floor to build on.
+**Status (WIRED 2026-04-21):** `tests/multi-agent/kb.ts` holds all primitives. scenario.ts reads rec at start, indexes + recommends at end. Cold start gracefully writes a "low confidence, no history" rec so the second run has a floor to build on. `snapshotConfig()` wired to fire at every scenario start — active model set + tool_level + cloud flags hashed and appended to `config_snapshots.jsonl`.
 
-### Phase 23+: Further horizon
+**Phase 22 item B — cloud rescue (WIRED):** When an event fails and cloud T3 is enabled, `requestCloudRemediation()` feeds the failure trace (SQL filters attempted, row counts, reviewer drift reasons, gap signals, contract terms) to cloud and parses a JSON remediation with new_city / new_state / new_role / new_count / rationale. Event retries once with the pivot. Verified 1/3 rescues succeeded on stress_01 (Gary IN → South Bend IN pivot filled a Welder that local drift-aborted). Sanitizer splits "City, ST" comma-packed outputs so downstream SQL doesn't get `Hammond, IN, IN`.
+
+### Phase 23: Staffer identity + competence-weighted retrieval (WIRED 2026-04-21)
+
+Answers "who handled this" as a first-class dimension of the matrix index. Senior staffers' playbooks rank higher than juniors' on similar scenarios via competence × similarity score. Auto-discovers "reliable performer" worker labels via cross-staffer endorsement overlap.
+
+**Schema (`scenario.ts` ScenarioSpec):**
+- `contract?: ContractTerms` — deadline, budget_per_hour_max, local_bonus_per_hour, local_bonus_radius_mi, fill_requirement. Propagates into T3 checkpoint + cloud rescue prompts so cloud reasons about trade-offs (pivot-within-radius before budget-pivot-further).
+- `staffer?: Staffer` — {id, name, tenure_months, role, tool_level}. tool_level controls subsystems available to this run:
+  - `full` — qwen3.5 + qwen3 local + cloud T3 + cloud rescue
+  - `local` — qwen3.5 + qwen3 local + local gpt-oss:20b T3 + rescue
+  - `basic` — **kimi-k2.5 cloud** exec + qwen3 local reviewer + local T3, no rescue
+  - `minimal` — kimi-k2.5 cloud exec + qwen3 local reviewer, NO T3, NO rescue — tests whether playbook inheritance carries knowledge alone
+
+**KB staffer indexing (`data/_kb/staffers.jsonl`):**
+- Recomputed per-staffer on every run: total_runs, fill_rate, avg_turns_per_event, avg_citations_per_run, rescue_rate, competence_score.
+- `competence_score = 0.45·fill_rate + 0.20·turn_efficiency + 0.20·citation_density + 0.15·rescue_rate`. Bounded 0..1.
+
+**Weighted neighbor retrieval:**
+- `findNeighbors` in `kb.ts` returns `weighted_score = cosine × max_staffer_competence` (floor 0.3). Senior playbooks rank above junior playbooks on similar scenarios.
+- `pathway_recommendations` include `best_staffer_id` / `best_staffer_competence` so cloud knows WHOSE playbook it's synthesizing from.
+
+**Cross-staffer auto-discovery:**
+- `scripts/kb_staffer_report.py` emits leaderboard + workers endorsed across ≥2 staffers on same signature.
+- Validated output: Rachel D. Lewis (Welder Nashville) endorsed 12× across 4 staffers; Christina Watson (Machine Op Indianapolis) 11×. These are the highest-confidence "reliable performer" labels the system produced without human tagging.
+
+**Demo infrastructure:**
+- `tests/multi-agent/gen_staffer_demo.ts` — 4 personas × 3 contracts = 12 scenario specs.
+- `scripts/run_staffer_demo.sh` — sequential batch with cloud T3.
+- `scripts/kb_staffer_report.py` — leaderboard + top/bottom differential + cross-staffer overlap.
+
+### Phase 24: Observer / Autotune integration (NOT YET WIRED — honest gap)
+
+J flagged this 2026-04-21 evening: the `lakehouse-observer.service` systemd unit has been running for 3600+ cycles but shows `total_ops=0 successes=0 failures=0` because `tests/multi-agent/scenario.ts` hits the Rust gateway directly on port 3100, bypassing the Bun MCP layer on 3700 that observer wraps.
+
+Result: our test scenarios are INVISIBLE to the observer and the autotune pipeline. Autotune's HNSW parameter learning runs on its own schedule, but no signal from scenario outcomes flows into it.
+
+**Target architecture:**
+- Scenarios emit per-event outcome summaries to a path the observer polls (or POST to observer's ingest endpoint directly).
+- Observer's ERROR ANALYZER + PLAYBOOK BUILDER loops consume those summaries alongside the MCP-layer ops.
+- Autotune agent subscribes to a metric stream the observer writes.
+
+**Why deferred:** this is a real architecture change (coherent data path from scenario → observer → autotune → vectord index) and needs care. The observer's current `observed_operations` ingest uses REPLACE semantics (flagged in `feedback_ingest_replace_semantics.md`) — naive appending will wipe prior ops.
+
+**Status:** GAP DOCUMENTED, not fixed. Scenarios continue to populate KB directly. The parallel pipelines are coherent but separate; Phase 24 connects them.
+
+### Phase 25+: Further horizon
 
 - Specialized fine-tuned models per domain (staffing matcher, resume parser)
 - Video/audio transcript ingest + multimodal embeddings