feat(v0.3.0): paradigm shift — TimeWeb-only LLM + persistent advisor trail + improvement queue

This is the rc.4 batch the user requested:

  1. Emergency triggers (low HP + close hostile, lava-under-foot)
     bypass the long cooldown so the LLM is consulted BEFORE the bot
     dies, not after.
  2. Active manifesto need is now included in the advisor user prompt
     — the LLM picks suggestions that satisfy the bot's current
     concrete need (L2 tools_wood → "gather logs nearby" not
     "explore further").
  3. Every advisor recommendation is persisted to SQLite
     (advisor_recommendations table) with full token usage. The
     reflex marks 'applied=1' when it dispatches and updates
     outcome_ok/code when the dispatch completes. Ground truth for
     "is the LLM actually helping" lives in the DB, not in logs.
  4. Pi CLI is OUT of every background loop. coach/postmortem and
     coach/reflect now go through the same TimeWeb endpoint
     fast-advisor uses, via the shared coach/llm-call.js helper.
     Pi is reserved for manual operator commands.
  5. The LLM (postmortem, reflect, advisor) can flag "structural
     gaps" — missing skills/features the operator should implement.
     These land in the new improvement_requests table. Dedup by
     title bumps `votes` instead of inserting duplicates so the
     queue doesn't bloat. Operator views via
     `node scripts/list-improvements.js`.
  6. A deterministic trigger-tuner runs hourly: reads 24h of
     recommendation stats, flags triggers whose success rate is
     below 25% (sample ≥ 5) or whose prompts are expensive (>1000
     input tokens) with mediocre payoff. Improvements get
     source="tuner", category="tuning". No LLM call.

New files:
  runtime/coach/llm-call.js        — askAnalytical() helper
  runtime/coach/trigger-tuner.js   — stats → improvements
  runtime/coach/trigger-tuner.test.js
  scripts/list-improvements.js     — operator CLI

Schema additions:
  advisor_recommendations: id, ts, trigger_reason, planned_skill,
    recommended_skill, action, rationale, active_need, tokens_in,
    tokens_out, latency_ms, applied, outcome_ok, outcome_code, outcome_at
  improvement_requests: id, ts, source, category, title, description,
    context, priority, status, duplicate_of, votes, implemented_at, notes

Renamed env-var consumers:
  Pi-coach drainOnce({ askPi })   → drainOnce({ askAnalyticalFn? })
  Pi-reflect runOnce({ askPi })   → runOnce({ askAnalyticalFn? })
  bot.js attachCoach/attachReflect no longer pass askPi
  attachTuner() added to bot.js spawn handler
  lessons.source 'pi-coach'   → 'timeweb-coach'
  lessons.source 'pi-reflect' → 'timeweb-reflect'

Token cost measured live:
  ~705 input + 45 output = ~750 total per advisor call
  worst case @ 6 calls/hour rate cap = ~108K tokens/day
  OpenAI gpt-5-mini reference price: ~$0.60/month

Operator usage:
  node scripts/list-improvements.js                # open queue
  node scripts/list-improvements.js --stats        # advisor performance
  node scripts/list-improvements.js --done 17 "shipped in 0.3.1"
  node scripts/list-improvements.js --reject 18 "duplicate"

Tests: 360 green (was 332, +28 new).

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

runtime/bot.js

@@ -60,6 +60,7 @@ import { createOwnedBlocksLedger } from "./owned-blocks.js";
 import { initKnowledge } from "./knowledge/index.js";
 import { attach as attachCoach } from "./coach/postmortem.js";
 import { attach as attachReflect } from "./coach/reflect.js";
+import { attach as attachTuner } from "./coach/trigger-tuner.js";
 import { attach as attachChatter } from "./persona/chatter.js";
 import { attachAwareness } from "./awareness/events.js";
 
@@ -689,8 +690,12 @@ function connect() {
 		// v0.2.0 — self-learning coach + persona narration. Both are
 		// import-safe; they just attach listeners and (for coach) a periodic
 		// Pi-drain timer. See docs/v0.2.0-self-learning.md.
-		try { attachCoach(bot, { stateDir, askPi }); } catch (e) { warn("coach", `attach: ${e?.message ?? e}`); }
-		try { attachReflect({ bot, stateDir, askPi, getSnapshot: () => lastSnapshot }); } catch (e) { warn("reflect", `attach: ${e?.message ?? e}`); }
+		// v0.3.0 — coach/reflect run on TimeWeb (fast LLM). Pi CLI is no
+		// longer wired into background loops; it remains available for
+		// manual operator commands only.
+		try { attachCoach(bot, { stateDir }); } catch (e) { warn("coach", `attach: ${e?.message ?? e}`); }
+		try { attachReflect({ bot, stateDir, getSnapshot: () => lastSnapshot }); } catch (e) { warn("reflect", `attach: ${e?.message ?? e}`); }
+		try { attachTuner(); } catch (e) { warn("tuner", `attach: ${e?.message ?? e}`); }
 		try { attachChatter(bot, { getSnapshot: () => lastSnapshot }); } catch (e) { warn("persona", `attach: ${e?.message ?? e}`); }
 		// v0.3.0-rc.3 — awareness layer: listens to bot.on('move'/'health'/
 		// 'entitySpawn'/'blockUpdate') and aborts the current dispatch via