mayatnikovandClaude Opus 4.7
0d97ccccfa
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>
45 lines
1.5 KiB
JavaScript
45 lines
1.5 KiB
JavaScript
// Shared helper for the slow-analytical coach loops (postmortem.js,
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// reflect.js). Replaces the old askPi-based subprocess path with a
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// direct TimeWeb / OpenAI-compatible HTTP call.
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//
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// Why this split: postmortem and reflect each took 5-15s via Pi CLI
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// (with its own subprocess + auth + sometimes a fresh MC connection)
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// and were rate-limited by the subscription. Now they take 5-15s via
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// the same TimeWeb endpoint the fast-advisor uses — but they're
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// analytical, NOT tactical, so they ask for a different prompt shape
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// and a longer reply.
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//
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// The Pi CLI is no longer driven from background timers. It remains
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// available for manual operator commands.
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import { complete } from "../llm/provider.js";
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import { warn } from "../log.js";
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const ANALYTICAL_TIMEOUT_MS = 30_000;
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/**
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* askAnalytical({ system, user, json }) → text|object|null
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*
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* Higher-timeout, lower-temperature companion to fast-advisor's
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* complete(). Returns just the parsed text/object on success or null
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* on failure (so callers can keep their old "no reply" branch).
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*
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* Token usage is logged via the underlying provider — no extra
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* accounting here.
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*/
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export async function askAnalytical({ system, user, json = true, timeoutMs } = {}) {
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const res = await complete({
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system,
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user,
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json,
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timeoutMs: timeoutMs ?? ANALYTICAL_TIMEOUT_MS,
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});
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if (!res.ok) {
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warn("coach-llm", `analytical call failed: ${res.code} (${res.detail})`);
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return null;
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}
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return res.text;
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}
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export { ANALYTICAL_TIMEOUT_MS };
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