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>
172 lines
6.6 KiB
JavaScript
172 lines
6.6 KiB
JavaScript
import { test } from "node:test";
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import assert from "node:assert/strict";
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import { mkdtempSync, rmSync, writeFileSync } from "node:fs";
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import { tmpdir } from "node:os";
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import { join } from "node:path";
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import { initKnowledge, unanalysedDeaths, recall } from "../knowledge/index.js";
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import { closeStore, __resetForTests, isAvailable } from "../knowledge/store.js";
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import { attach, detach, drainOnce, __testing } from "./postmortem.js";
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const { captureDeath, extractJson, inferCause, buildPrompt } = __testing;
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function mockBot({ pos = { x: 100, y: 64, z: 200 }, hp = 0, food = 14, entities = {} } = {}) {
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const handlers = {};
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return {
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entity: { position: { ...pos, distanceTo(o) { return Math.hypot(pos.x - o.x, pos.z - o.z); } } },
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health: hp,
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food,
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time: { timeOfDay: 18000 },
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entities,
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isRaining: false,
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inventory: { items: () => [] },
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on(ev, fn) { handlers[ev] = fn; },
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emit(ev, payload) { handlers[ev]?.(payload); },
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};
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}
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test("inferCause: hostile present → 'hostile'", () => {
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assert.equal(inferCause({ bot: { food: 20, entity: { position: { y: 64 } } }, hostile: "creeper" }), "hostile");
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assert.equal(inferCause({ bot: { food: 0, entity: { position: { y: 64 } } }, hostile: null }), "starvation");
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assert.equal(inferCause({ bot: { food: 20, entity: { position: { y: 20 } } }, hostile: null }), "fall");
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assert.equal(inferCause({ bot: { food: 20, entity: { position: { y: 64 } } }, hostile: null }), "unknown");
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});
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test("extractJson: tolerates fences and surrounding text", () => {
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assert.deepEqual(extractJson('```json\n{"a": 1}\n```'), { a: 1 });
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assert.deepEqual(extractJson('Reply: {"a": 2} done'), { a: 2 });
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assert.equal(extractJson("not json"), null);
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assert.equal(extractJson(""), null);
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});
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test("buildPrompt: returns {system, user}, includes all death rows + improvements schema", () => {
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const rows = [
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{ id: 1, ts: Date.now(), x: 100, y: 64, z: 200, cause: "hostile", hostile: "creeper", last_skill: "gather.logs", last_skill_code: "timeout", food_at_death: 14, context_blob: JSON.stringify({ recentScenarios: [{ skillId: "gather.logs", code: "timeout" }] }) },
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{ id: 2, ts: Date.now(), x: 102, y: 64, z: 201, cause: "hostile", hostile: "creeper", last_skill: "explore.far", last_skill_code: "done", food_at_death: 12, context_blob: null },
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];
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const { system, user } = buildPrompt(rows);
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assert.match(user, /death id=1/);
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assert.match(user, /death id=2/);
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assert.match(user, /creeper/);
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assert.match(system, /Reply with ONE JSON object/);
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assert.match(system, /improvements/);
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assert.match(system, /Valid skill ids/);
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});
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test("captureDeath: builds a row with context blob and inferred cause", () => {
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const bot = mockBot({ entities: {
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1: { type: "hostile", name: "zombie", position: { x: 101, y: 64, z: 200, distanceTo: (p) => Math.hypot(101 - p.x, 200 - p.z) } },
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}});
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const stateDir = mkdtempSync(join(tmpdir(), "pepa-coach-test-"));
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writeFileSync(join(stateDir, "current-task.json"), JSON.stringify({ label: "gather.logs", lastCode: "timeout" }));
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const death = captureDeath(bot, { stateDir });
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assert.equal(death.cause, "hostile");
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assert.equal(death.hostile, "zombie");
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assert.equal(death.lastSkill, "gather.logs");
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assert.equal(death.lastSkillCode, "timeout");
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assert.equal(death.x, 100);
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assert.ok(death.contextBlob.snapshot);
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rmSync(stateDir, { recursive: true, force: true });
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});
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test("attach + emit('death'): inserts row in knowledge DB", async () => {
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const stateDir = mkdtempSync(join(tmpdir(), "pepa-coach-test-"));
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__resetForTests();
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await initKnowledge({ stateDir });
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if (!isAvailable()) {
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// Without sqlite the no-op contract is enough.
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assert.ok(true);
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rmSync(stateDir, { recursive: true, force: true });
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return;
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}
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const bot = mockBot();
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attach(bot, { stateDir });
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bot.emit("death");
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// Insert is sync.
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const pending = unanalysedDeaths({ limit: 10 });
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assert.ok(pending.length >= 1, "death row inserted");
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detach();
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closeStore();
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rmSync(stateDir, { recursive: true, force: true });
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});
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test("drainOnce: respects budget and parses analytical LLM reply (incl. improvements)", async () => {
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const stateDir = mkdtempSync(join(tmpdir(), "pepa-coach-test-"));
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__resetForTests();
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await initKnowledge({ stateDir });
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if (!isAvailable()) {
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assert.ok(true);
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rmSync(stateDir, { recursive: true, force: true });
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return;
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}
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// Seed one unanalysed death.
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const bot = mockBot();
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attach(bot, { stateDir });
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bot.emit("death");
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detach();
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const lessonsBefore = recall({ category: "combat" }).length;
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// TimeWeb path needs env vars to satisfy the llmAvailable check.
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const prevKey = process.env.TIMEWEB_API_KEY;
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const prevModel = process.env.TIMEWEB_MODEL;
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process.env.TIMEWEB_API_KEY = "test-key";
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process.env.TIMEWEB_MODEL = "test-model";
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const fakeReply = {
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cause: "creeper_explosion_unarmed",
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next_action: "shelter at dusk",
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lessons: [{
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lesson: "Stop attacking creepers without armour; dig down 2 instead.",
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category: "combat",
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trigger_hostile: "creeper",
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avoid_skill: "attack creeper",
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prefer_skill: "survive.flee",
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confidence: 0.85,
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}],
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improvements: [
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{ title: "Add craft.shield skill", description: "No skill to craft a shield when creepers are around.", category: "skill", priority: 2 },
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],
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};
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const askAnalyticalFn = async () => fakeReply;
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const result = await drainOnce({ stateDir, force: true, askAnalyticalFn });
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if (prevKey === undefined) delete process.env.TIMEWEB_API_KEY;
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else process.env.TIMEWEB_API_KEY = prevKey;
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if (prevModel === undefined) delete process.env.TIMEWEB_MODEL;
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else process.env.TIMEWEB_MODEL = prevModel;
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assert.equal(result.ok, true);
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assert.equal(result.analysed, 1);
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assert.equal(result.lessons, 1);
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assert.equal(result.improvements, 1);
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const after = recall({ hostile: "creeper", category: "combat" });
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assert.ok(after.length > lessonsBefore, "new lesson recorded");
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const stillPending = unanalysedDeaths({ limit: 10 });
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assert.equal(stillPending.length, 0, "death marked analysed");
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closeStore();
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rmSync(stateDir, { recursive: true, force: true });
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});
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test("drainOnce: skipped when LLM not configured", async () => {
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const stateDir = mkdtempSync(join(tmpdir(), "pepa-coach-test-"));
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__resetForTests();
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await initKnowledge({ stateDir });
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if (!isAvailable()) {
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rmSync(stateDir, { recursive: true, force: true });
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return;
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}
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const prevKey = process.env.TIMEWEB_API_KEY;
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delete process.env.TIMEWEB_API_KEY;
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const result = await drainOnce({ stateDir, force: true });
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if (prevKey !== undefined) process.env.TIMEWEB_API_KEY = prevKey;
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assert.equal(result.ok, false);
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assert.equal(result.reason, "llm not configured");
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closeStore();
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rmSync(stateDir, { recursive: true, force: true });
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});
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