v0.3.0-rc.1: live skill registry + fast advisor scaffold

Roots out the v0.2.x failure mode: Pi-extracted lessons routinely named
hallucinated skill ids (relocate.surface, choose.safe.surface,
survive.shelter, gather.visible_log, …). All 47 Pi-lessons in the live DB
had applied_count=0 because normalisePreferSkill couldn't find them.

Fix:
1. runtime/skill-registry.js — single source of truth derived from
   skills/index.js. Exports listSkillIds, isRegistered, and a
   prompt-ready block (skillRegistryPrompt) grouped by namespace.
2. Pi prompts (coach/postmortem, coach/reflect) embed the live registry
   with a "USE ONLY THESE, never invent" instruction. Lessons are
   filtered at write-time too — anything not in the registry and not a
   known mode name gets dropped.
3. coach/advice.js — normalisePreferSkill now returns null for unknown
   ids, hardening consult() against any hallucinations that slip
   through. Warn-logged for visibility.

Also lays the LLM substrate for the rest of v0.3.0:

- runtime/llm/provider.js — OpenAI-compatible chat client. Configured
  via PEPA_FAST_LLM_{BASE_URL,API_KEY,MODEL,TIMEOUT_MS}. Safe no-op
  unless API_KEY is set. Supports JSON-mode.
- runtime/coach/fast-advisor.js — tactical advisor tier (scaffold).
  Exposes advise() that asks the fast LLM what to do RIGHT NOW when
  the reflex is wedged/stuck. Rejects hallucinated skill ids using the
  registry. Rate-limited 6/h, 30s cooldown. Not auto-triggered yet —
  wired into reflex in rc.3 (awareness layer).

Tests: 279 green (+24 vs rc.3): 5 registry, 9 provider, 10 advisor.

See dev/v0.3.0/PLAN.md for the full iteration design (manifesto needs
ladder, event-driven awareness, skill pre-emption) and STATUS.md for
shipped/pending tracking.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

dev/v0.3.0/STATUS.md

@@ -0,0 +1,86 @@
+# pepa v0.3.0 — status
+
+Live tracking document for the v0.3.0 iteration ("Maslow + Awareness").
+See [`PLAN.md`](./PLAN.md) for the full design.
+
+## Shipped
+
+### rc.1 — Live skill registry + Fast advisor scaffold
+**Root problem solved**: 47/47 Pi-extracted lessons in v0.2.x had
+`applied_count = 0` because Pi was hallucinating skill ids
+(`relocate.surface`, `choose.safe.surface`, `survive.shelter`,
+`gather.visible_log`, …) that don't exist in the registry. Both halves
+fixed: (a) Pi now sees the real registry in its system prompt,
+(b) anything that still slips through gets rejected at consult time.
+
+- [`runtime/skill-registry.js`](../../runtime/skill-registry.js) —
+  single source of truth wrapping `skills/index.js`. Exports:
+  - `listSkillIds()` — live id list
+  - `isRegistered(id)` — bool check
+  - `describeSkill(id)` — id/title/timeoutMs
+  - `skillRegistryPrompt({ limit })` — prompt-ready block grouped by
+    namespace, with "USE ONLY THESE, never invent" instruction
+- [`runtime/llm/provider.js`](../../runtime/llm/provider.js) —
+  OpenAI-compatible chat client, env-driven:
+  - `PEPA_FAST_LLM_BASE_URL` (default `https://api.openai.com/v1`)
+  - `PEPA_FAST_LLM_API_KEY` (required to enable; safe no-op otherwise)
+  - `PEPA_FAST_LLM_MODEL` (required)
+  - `PEPA_FAST_LLM_TIMEOUT_MS` (default 8000)
+  - Supports JSON-mode via `response_format: { type: "json_object" }`
+  - Surfaces `not_configured`, `no_model`, `http_<status>`,
+    `network_error`, `timeout`, `bad_json` codes
+- [`runtime/coach/fast-advisor.js`](../../runtime/coach/fast-advisor.js)
+  — tactical "what now?" tier. Scaffold only in rc.1; auto-trigger
+  comes in rc.3.
+  - `advise({snapshot, reason, recentSkillIds, lessonsTail})` →
+    `{action: 'switch_skill'|'continue'|'wait', skillId?, rationale}`
+  - Rejects any returned `skill_id` not in the live registry
+  - Rate-limit: 6 calls/hour, 30s cooldown between calls
+  - System prompt embeds registry; user prompt carries snapshot + trigger
+- [`runtime/coach/advice.js`](../../runtime/coach/advice.js):
+  - `normalisePreferSkill()` now returns `null` for anything not in
+    registry/mode-map (was: passed through unchanged → dispatcher
+    crashed at `runSkill()`)
+  - Logs `warn` line when a hallucinated prefer_skill is dropped
+- [`runtime/coach/postmortem.js`](../../runtime/coach/postmortem.js):
+  - Pi prompt includes the live registry block (`skillRegistryPrompt`)
+    with a "CRITICAL: USE ONLY THESE" instruction
+  - On insert, drops `prefer_skill`/`avoid_skill` that's neither a
+    registered id nor a known mode name; warn-logs the count
+- [`runtime/coach/reflect.js`](../../runtime/coach/reflect.js) — same
+  treatment as postmortem (registry in prompt + write-time filter)
+
+Tests: 279 green (was 257 on rc.3). Added:
+- `runtime/skill-registry.test.js` — 5 tests
+- `runtime/llm/provider.test.js` — 9 tests
+- `runtime/coach/fast-advisor.test.js` — 10 tests
+
+### rc.2 — (pending) Manifesto / Needs ladder
+### rc.3 — (pending) Event-driven awareness + skill pre-emption
+
+## Next session quick start
+
+1. **Read PLAN.md** for the full design and per-rc breakdown.
+2. **Check live DB** to see if Pi-lesson application is improving:
+   ```bash
+   sqlite3 state/play.xmatic.team_25565/knowledge.db \
+     "SELECT source, COUNT(*) AS n, SUM(applied_count > 0) AS applied
+      FROM lessons GROUP BY source ORDER BY n DESC;"
+   ```
+   After rc.1 deploys, expect Pi-coach/Pi-reflect `applied` count to
+   start growing as the registry feedback closes the loop.
+3. **Set fast-advisor env when ready to test**:
+   ```bash
+   export PEPA_FAST_LLM_BASE_URL="https://<timeweb-endpoint>/v1"
+   export PEPA_FAST_LLM_API_KEY="<key>"
+   export PEPA_FAST_LLM_MODEL="gpt-5-mini"
+   ```
+   The advisor still isn't auto-triggered in rc.1 — it's wired in rc.3.
+4. **Pick the next rc** from PLAN.md.
+
+## Workflow notes
+
+- main is protected — only operator merges PRs
+- Tests: `npm test` (279 green at last check), isolated under `/tmp/`
+- The bot supervisor hot-restarts on file changes in `runtime/**/*.js`
+- If something regresses badly, revert to v0.2.0-rc.3 commit `865aae1`