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/coach/fast-advisor.js

@@ -66,6 +66,7 @@ export async function advise({
 	reason = "unknown",
 	recentSkillIds = [],
 	lessonsTail = [],
+	activeNeed = null,
 	force = false,
 } = {}) {
 	if (!isAvailable()) {
@@ -82,7 +83,7 @@ export async function advise({
 	}
 
 	const system = buildSystemPrompt();
-	const user = buildUserPrompt({ snapshot, reason, recentSkillIds, lessonsTail });
+	const user = buildUserPrompt({ snapshot, reason, recentSkillIds, lessonsTail, activeNeed });
 
 	_callTimes.push(now);
 	_lastCallAt = now;
@@ -163,16 +164,24 @@ function buildSystemPrompt() {
 	].join("\n");
 }
 
-function buildUserPrompt({ snapshot, reason, recentSkillIds, lessonsTail }) {
+function buildUserPrompt({ snapshot, reason, recentSkillIds, lessonsTail, activeNeed }) {
 	const pos = snapshot?.position;
 	const inv = snapshot?.inventory ? Object.keys(snapshot.inventory).slice(0, 10).join(", ") : "(empty)";
 	const recent = (recentSkillIds ?? []).slice(-8).join(" → ") || "(none)";
 	const lessons = (lessonsTail ?? []).slice(0, 4).map((l) => `  - ${l.text ?? l}`).join("\n");
+	const needLine = activeNeed
+		? `L${activeNeed.need.level} ${activeNeed.need.id} (${activeNeed.need.title}) — manifesto wants ${activeNeed.skillId}`
+		: "(no active need)";
+	const hostile = snapshot?.closestHostile
+		? `${snapshot.closestHostile.name}@${snapshot.closestHostile.distance}b`
+		: "(none)";
 
 	return [
 		`Trigger: ${reason}`,
 		`Position: ${pos ? `(${Math.round(pos.x)}, ${Math.round(pos.y)}, ${Math.round(pos.z)})` : "?"}`,
 		`HP: ${snapshot?.health ?? "?"} food: ${snapshot?.food ?? "?"} day: ${snapshot?.isDay ? "yes" : "no"}`,
+		`Active need (Maslow ladder): ${needLine}`,
+		`Closest hostile: ${hostile}`,
 		`Active skill: ${snapshot?.activeSkill ?? "(idle)"}`,
 		`Recent dispatches: ${recent}`,
 		`Inventory keys: ${inv}`,
@@ -181,6 +190,7 @@ function buildUserPrompt({ snapshot, reason, recentSkillIds, lessonsTail }) {
 		"",
 		lessons ? `Relevant lessons:\n${lessons}\n` : "",
 		"What should the bot do RIGHT NOW? Return the JSON decision.",
+		"Prefer a skill that helps satisfy the active need unless an emergency forces another action.",
 	].filter(Boolean).join("\n");
 }