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/trigger-tuner.test.js

@@ -0,0 +1,98 @@
+import { test } from "node:test";
+import assert from "node:assert/strict";
+import { mkdtempSync, rmSync } from "node:fs";
+import { tmpdir } from "node:os";
+import { join } from "node:path";
+
+import { initKnowledge, isAvailable, listImprovements } from "../knowledge/index.js";
+import { closeStore, __resetForTests } from "../knowledge/store.js";
+import { runOnce, __testing } from "./trigger-tuner.js";
+
+const { MIN_SAMPLE } = __testing;
+
+async function bootstrap() {
+	const tmp = mkdtempSync(join(tmpdir(), "pepa-tuner-test-"));
+	__resetForTests();
+	await initKnowledge({ stateDir: tmp });
+	return tmp;
+}
+
+function cleanup(tmp) {
+	closeStore();
+	try { rmSync(tmp, { recursive: true, force: true }); } catch {}
+}
+
+test("runOnce: empty stats → ok with 0 flagged", async () => {
+	const tmp = await bootstrap();
+	if (!isAvailable()) { cleanup(tmp); return; }
+	const r = runOnce({ stats: [] });
+	assert.equal(r.ok, true);
+	assert.equal(r.flagged, 0);
+	cleanup(tmp);
+});
+
+test("runOnce: ignores small samples (below MIN_SAMPLE)", async () => {
+	const tmp = await bootstrap();
+	if (!isAvailable()) { cleanup(tmp); return; }
+	const stats = [
+		{ trigger_reason: "wedged_60s", total: 2, applied: 2, succeeded: 0, failed: 2, avg_in: 700, avg_out: 40, avg_latency_ms: 5000 },
+	];
+	const r = runOnce({ stats });
+	assert.equal(r.flagged, 0, "applied=2 is below MIN_SAMPLE; skipped");
+	cleanup(tmp);
+});
+
+test("runOnce: flags low success-rate trigger as improvement", async () => {
+	const tmp = await bootstrap();
+	if (!isAvailable()) { cleanup(tmp); return; }
+	const stats = [
+		{ trigger_reason: "wedged_60s", total: 10, applied: 10, succeeded: 1, failed: 9, avg_in: 700, avg_out: 40, avg_latency_ms: 5000 },
+	];
+	const r = runOnce({ stats });
+	assert.equal(r.flagged, 1);
+	const requests = listImprovements({ source: "tuner" });
+	assert.ok(requests.some((req) => req.title.includes("wedged_60s") && req.title.includes("low success")));
+	cleanup(tmp);
+});
+
+test("runOnce: flags expensive prompt with mediocre payoff", async () => {
+	const tmp = await bootstrap();
+	if (!isAvailable()) { cleanup(tmp); return; }
+	const stats = [
+		{ trigger_reason: "repeat_4_explore.far", total: 10, applied: 10, succeeded: 4, failed: 6, avg_in: 1500, avg_out: 50, avg_latency_ms: 7000 },
+	];
+	const r = runOnce({ stats });
+	assert.equal(r.flagged, 1);
+	const requests = listImprovements({ source: "tuner", category: "tuning" });
+	assert.ok(requests.some((req) => req.title.includes("expensive")));
+	cleanup(tmp);
+});
+
+test("runOnce: healthy trigger does NOT get flagged", async () => {
+	const tmp = await bootstrap();
+	if (!isAvailable()) { cleanup(tmp); return; }
+	const stats = [
+		{ trigger_reason: "emergency_hp4_creeper@3", total: 8, applied: 8, succeeded: 7, failed: 1, avg_in: 700, avg_out: 40, avg_latency_ms: 5000 },
+	];
+	const r = runOnce({ stats });
+	assert.equal(r.flagged, 0);
+	cleanup(tmp);
+});
+
+test("runOnce: re-running with same low-success stats bumps votes, not row count", async () => {
+	const tmp = await bootstrap();
+	if (!isAvailable()) { cleanup(tmp); return; }
+	const stats = [
+		{ trigger_reason: "wedged_unique_label", total: 10, applied: 10, succeeded: 1, failed: 9, avg_in: 700, avg_out: 40, avg_latency_ms: 5000 },
+	];
+	runOnce({ stats });
+	runOnce({ stats });
+	const requests = listImprovements({ source: "tuner" }).filter((r) => r.title.includes("wedged_unique_label"));
+	assert.equal(requests.length, 1, "single row for the same title");
+	assert.ok(requests[0].votes >= 2, "votes bumped on re-flagging");
+	cleanup(tmp);
+});
+
+test("MIN_SAMPLE constant is reasonable", () => {
+	assert.ok(MIN_SAMPLE >= 3 && MIN_SAMPLE <= 10);
+});