Files
pepa-pi-bot/runtime/coach/advisor-trigger.test.js
T
mayatnikovandClaude Opus 4.7 0d97ccccfa 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>
2026-05-27 19:12:48 +03:00

245 lines
7.1 KiB
JavaScript

import { test } from "node:test";
import assert from "node:assert/strict";
import {
tickAdvisor,
consumeFreshRecommendation,
getTriggerState,
_resetForTest,
__testing,
} from "./advisor-trigger.js";
import { _resetForTest as resetAdvisor } from "./fast-advisor.js";
const { detectTrigger, WEDGED_THRESHOLD_MS, REPEAT_THRESHOLD, PREEMPT_WINDOW_MS, RECOMMENDATION_TTL_MS } = __testing;
const API_KEY = "TIMEWEB_API_KEY";
const MODEL = "TIMEWEB_MODEL";
function withEnv(env, fn) {
const prev = {};
for (const k of Object.keys(env)) {
prev[k] = process.env[k];
if (env[k] === undefined) delete process.env[k];
else process.env[k] = env[k];
}
return Promise.resolve(fn()).finally(() => {
for (const [k, v] of Object.entries(prev)) {
if (v === undefined) delete process.env[k];
else process.env[k] = v;
}
});
}
function stubFetch(reply, latency = 0) {
const orig = globalThis.fetch;
globalThis.fetch = async () => {
if (latency) await new Promise((r) => setTimeout(r, latency));
return {
ok: true,
json: async () => ({
choices: [{ message: { content: typeof reply === "string" ? reply : JSON.stringify(reply) } }],
usage: { prompt_tokens: 1500, completion_tokens: 40, total_tokens: 1540 },
}),
};
};
return () => { globalThis.fetch = orig; };
}
test("detectTrigger: returns null when nothing matches", () => {
const r = detectTrigger({ recentSkillIds: [] }, Date.now(), "gather.logs");
assert.equal(r, null);
});
test("detectTrigger: low HP + hostile near → emergency_hp", () => {
const now = Date.now();
const r = detectTrigger({
recentSkillIds: [],
snapshot: { health: 4, closestHostile: { name: "creeper", distance: 3 } },
}, now, "gather.logs");
assert.match(r, /^emergency_hp4_creeper@3/);
});
test("detectTrigger: foot in lava → emergency_lava", () => {
const now = Date.now();
const r = detectTrigger({
recentSkillIds: [],
snapshot: { health: 18, hazards: { footBlock: "lava" } },
}, now, "explore.far");
assert.equal(r, "emergency_lava");
});
test("detectTrigger: emergency wins over wedged when both present", () => {
const now = Date.now();
const r = detectTrigger({
recentSkillIds: [],
snapshot: { health: 4, closestHostile: { name: "skeleton", distance: 5 } },
lastSignificantMoveAt: now - 120_000,
}, now, "x");
assert.match(r, /^emergency_/);
});
test("detectTrigger: wedged > 60s fires", () => {
const now = Date.now();
const r = detectTrigger(
{ recentSkillIds: ["x"], lastSignificantMoveAt: now - WEDGED_THRESHOLD_MS - 5000 },
now,
"explore.far",
);
assert.match(r, /^wedged_\d+s/);
});
test("detectTrigger: 4 same dispatches in row + same planned → repeat", () => {
const now = Date.now();
const r = detectTrigger(
{ recentSkillIds: ["explore.far", "explore.far", "explore.far", "explore.far"] },
now,
"explore.far",
);
assert.match(r, /^repeat_4_explore\.far/);
});
test("detectTrigger: same skill repeated but planned is different → no repeat trigger", () => {
const now = Date.now();
const r = detectTrigger(
{ recentSkillIds: ["explore.far", "explore.far", "explore.far", "explore.far"] },
now,
"gather.logs",
);
assert.equal(r, null);
});
test("detectTrigger: recent preempt + same skill re-planned → preempt_retry", () => {
const now = Date.now();
const r = detectTrigger(
{
recentSkillIds: ["gather.logs"],
lastPreempt: { at: now - 5000, reason: "forced_move" },
},
now,
"gather.logs",
);
assert.equal(r, "preempt_retry_forced_move");
});
test("detectTrigger: old preempt (> window) does not trigger", () => {
const now = Date.now();
const r = detectTrigger(
{
recentSkillIds: ["gather.logs"],
lastPreempt: { at: now - PREEMPT_WINDOW_MS - 5000, reason: "forced_move" },
},
now,
"gather.logs",
);
assert.equal(r, null);
});
test("tickAdvisor: disabled when TIMEWEB_API_KEY missing", async () => {
await withEnv({ [API_KEY]: undefined }, () => {
_resetForTest();
const r = tickAdvisor({ recentSkillIds: [] }, { plannedSkillId: "x" });
assert.equal(r.fired, false);
assert.equal(r.reason, "disabled");
});
});
test("tickAdvisor: no_trigger when ctx has nothing interesting", async () => {
await withEnv({ [API_KEY]: "k", [MODEL]: "m" }, () => {
_resetForTest();
resetAdvisor();
const r = tickAdvisor({ recentSkillIds: [] }, { plannedSkillId: "gather.logs" });
assert.equal(r.fired, false);
assert.equal(r.reason, "no_trigger");
});
});
test("tickAdvisor: fires on wedged trigger and caches recommendation", async () => {
const restore = stubFetch({
action: "switch_skill",
skill_id: "survive.flee",
rationale: "Wedged here, retreat instead.",
});
try {
await withEnv({ [API_KEY]: "k", [MODEL]: "m" }, async () => {
_resetForTest();
resetAdvisor();
const ctx = {
recentSkillIds: ["explore.far"],
lastSignificantMoveAt: Date.now() - 120_000,
};
const r = tickAdvisor(ctx, { plannedSkillId: "explore.far" });
assert.equal(r.fired, true);
assert.match(r.reason, /^wedged_/);
assert.equal(getTriggerState().inFlight, true);
// Wait for the in-flight promise to settle.
await new Promise((res) => setTimeout(res, 20));
assert.equal(getTriggerState().inFlight, false);
assert.ok(ctx.advisorRecommendation, "recommendation cached");
assert.equal(ctx.advisorRecommendation.skillId, "survive.flee");
assert.equal(ctx.advisorRecommendation.usage.total, 1540);
});
} finally { restore(); }
});
test("tickAdvisor: cooldown blocks second trigger right after", async () => {
const restore = stubFetch({ action: "continue", rationale: "ok" });
try {
await withEnv({ [API_KEY]: "k", [MODEL]: "m" }, async () => {
_resetForTest();
resetAdvisor();
const ctx = {
recentSkillIds: ["explore.far"],
lastSignificantMoveAt: Date.now() - 120_000,
};
const r1 = tickAdvisor(ctx, { plannedSkillId: "explore.far" });
assert.equal(r1.fired, true);
await new Promise((res) => setTimeout(res, 20));
const r2 = tickAdvisor(ctx, { plannedSkillId: "explore.far" });
assert.equal(r2.fired, false);
assert.equal(r2.reason, "cooldown");
});
} finally { restore(); }
});
test("consumeFreshRecommendation: returns + clears switch_skill recommendation", () => {
_resetForTest();
const ctx = {
advisorRecommendation: {
at: Date.now(),
action: "switch_skill",
skillId: "survive.flee",
rationale: "x",
},
};
const r = consumeFreshRecommendation(ctx);
assert.ok(r);
assert.equal(r.skillId, "survive.flee");
assert.equal(ctx.advisorRecommendation, null);
});
test("consumeFreshRecommendation: stale (> TTL) recommendation dropped", () => {
_resetForTest();
const ctx = {
advisorRecommendation: {
at: Date.now() - RECOMMENDATION_TTL_MS - 1000,
action: "switch_skill",
skillId: "survive.flee",
},
};
const r = consumeFreshRecommendation(ctx);
assert.equal(r, null);
assert.equal(ctx.advisorRecommendation, null);
});
test("consumeFreshRecommendation: continue/wait recommendations are not consumed for skill swap", () => {
_resetForTest();
const ctx = {
advisorRecommendation: { at: Date.now(), action: "continue", rationale: "ok" },
};
const r = consumeFreshRecommendation(ctx);
assert.equal(r, null);
// stays cached for telemetry
assert.ok(ctx.advisorRecommendation);
});