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>
This commit is contained in:
2026-05-27 19:12:48 +03:00
co-authored by Claude Opus 4.7
parent bc381b2a4b
commit 0d97ccccfa
18 changed files with 1188 additions and 197 deletions
+60 -6
View File
@@ -21,6 +21,7 @@
import { advise, isAvailable as advisorAvailable } from "./fast-advisor.js";
import { isRegistered } from "../skill-registry.js";
import { insertRecommendation } from "../knowledge/index.js";
import { info, warn } from "../log.js";
const TRIGGER_COOLDOWN_MS = 90_000;
@@ -28,6 +29,11 @@ const RECOMMENDATION_TTL_MS = 60_000;
const WEDGED_THRESHOLD_MS = 60_000;
const REPEAT_THRESHOLD = 4;
const PREEMPT_WINDOW_MS = 30_000;
// Emergency triggers — bypass cooldown because waiting another 90s
// when the bot is about to die is not useful.
const EMERGENCY_HP = 6;
const EMERGENCY_HOSTILE_DIST = 8;
const EMERGENCY_COOLDOWN_MS = 20_000;
let _lastTriggerAt = 0;
let _inFlight = false;
@@ -57,9 +63,6 @@ export function tickAdvisor(ctx, { plannedSkillId } = {}) {
if (_inFlight) return { fired: false, reason: "in_flight" };
const now = Date.now();
if (now - _lastTriggerAt < TRIGGER_COOLDOWN_MS) {
return { fired: false, reason: "cooldown" };
}
// Drop a recommendation that's already aged out.
if (ctx.advisorRecommendation && now - ctx.advisorRecommendation.at > RECOMMENDATION_TTL_MS) {
@@ -69,18 +72,42 @@ export function tickAdvisor(ctx, { plannedSkillId } = {}) {
const reason = detectTrigger(ctx, now, plannedSkillId);
if (!reason) return { fired: false, reason: "no_trigger" };
// Emergency triggers use a much shorter cooldown — waiting 90s with
// HP=4 and a creeper at 3 blocks is exactly when we MUST hit the LLM.
const isEmergency = reason.startsWith("emergency_");
const cooldownMs = isEmergency ? EMERGENCY_COOLDOWN_MS : TRIGGER_COOLDOWN_MS;
if (now - _lastTriggerAt < cooldownMs) {
return { fired: false, reason: "cooldown" };
}
_lastTriggerAt = now;
_inFlight = true;
const snapshot = ctx.snapshot ?? null;
const recentSkillIds = (ctx.recentSkillIds ?? []).slice(-8);
const activeNeed = ctx.activeNeed ?? null;
info("advisor-trigger", `firing because ${reason} (planned=${plannedSkillId ?? "?"})`);
info("advisor-trigger", `firing because ${reason} (planned=${plannedSkillId ?? "?"}, need=${activeNeed?.need?.id ?? "?"})`);
// Fire-and-forget. The promise's resolution writes ctx.advisorRecommendation.
advise({ snapshot, reason, recentSkillIds, lessonsTail: ctx.recentLessons ?? [], force: true })
advise({ snapshot, reason, recentSkillIds, lessonsTail: ctx.recentLessons ?? [], activeNeed, force: true })
.then((result) => {
_inFlight = false;
const needLabel = activeNeed
? `L${activeNeed.need.level} ${activeNeed.need.id}`
: null;
if (result.ok && result.action === "switch_skill" && isRegistered(result.skillId)) {
const recId = insertRecommendation({
triggerReason: reason,
plannedSkill: plannedSkillId ?? null,
recommendedSkill: result.skillId,
action: "switch_skill",
rationale: result.rationale,
activeNeed: needLabel,
tokensIn: result.usage?.in,
tokensOut: result.usage?.out,
latencyMs: result.latencyMs,
});
ctx.advisorRecommendation = {
id: recId,
at: Date.now(),
skillId: result.skillId,
action: "switch_skill",
@@ -89,9 +116,21 @@ export function tickAdvisor(ctx, { plannedSkillId } = {}) {
latencyMs: result.latencyMs,
usage: result.usage ?? null,
};
info("advisor-trigger", `recommendation cached: ${result.skillId} (${result.latencyMs}ms, in=${result.usage?.in ?? "?"}t/out=${result.usage?.out ?? "?"}t)`);
info("advisor-trigger", `recommendation cached: ${result.skillId} (${result.latencyMs}ms, in=${result.usage?.in ?? "?"}t/out=${result.usage?.out ?? "?"}t, db=${recId ?? "-"})`);
} else if (result.ok && (result.action === "wait" || result.action === "continue")) {
const recId = insertRecommendation({
triggerReason: reason,
plannedSkill: plannedSkillId ?? null,
recommendedSkill: null,
action: result.action,
rationale: result.rationale,
activeNeed: needLabel,
tokensIn: result.usage?.in,
tokensOut: result.usage?.out,
latencyMs: result.latencyMs,
});
ctx.advisorRecommendation = {
id: recId,
at: Date.now(),
action: result.action,
rationale: result.rationale,
@@ -113,6 +152,21 @@ export function tickAdvisor(ctx, { plannedSkillId } = {}) {
}
function detectTrigger(ctx, now, plannedSkillId) {
const snap = ctx.snapshot ?? {};
// 0. EMERGENCY: low HP + hostile near — call BEFORE the bot dies.
// Checked first so reason string starts with "emergency_" → bypasses
// the long trigger cooldown via the caller's isEmergency check.
const hp = snap.health ?? 20;
const hostile = snap.closestHostile;
if (hp <= EMERGENCY_HP && hostile && (hostile.distance ?? Infinity) <= EMERGENCY_HOSTILE_DIST) {
return `emergency_hp${Math.round(hp)}_${hostile.name ?? "hostile"}@${Math.round(hostile.distance)}`;
}
// 0b. EMERGENCY: drowning / lava / lethal fluid
if (snap.hazards?.footBlock === "lava") {
return "emergency_lava";
}
// 1. Wedged > threshold
if (ctx.lastSignificantMoveAt && (now - ctx.lastSignificantMoveAt) > WEDGED_THRESHOLD_MS) {
return `wedged_${Math.round((now - ctx.lastSignificantMoveAt) / 1000)}s`;