pepa-pi-bot/dev/v0.3.0/STATUS.md

pepa v0.3.0 — status

Live tracking document for the v0.3.0 iteration ("Maslow + Awareness"). See 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 — 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 — 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 — 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:
  • 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:
  • 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 — 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:

   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:

   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