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>
204 lines
7.1 KiB
JavaScript
204 lines
7.1 KiB
JavaScript
// Fast tactical advisor — second LLM tier, parallel to Pi.
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//
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// Pi (the CLI coach) is great for deep post-mortems and 30-min reflection,
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// but it's slow (5-15s) and rate-limited. When the reflex detects the bot
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// is wedged, stuck, or just took an environment shock (forced teleport,
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// HP plunge, hostile spawn), we want a sub-2-second "what do I do?"
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// answer from a cheap, hosted model. That's this module.
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//
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// In rc.1 this is a scaffold: complete() + advise() + rate-limiting +
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// integration tests, but no auto-trigger from the reflex yet. rc.3 wires
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// the trigger paths (awareness layer) into here.
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//
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// The advisor MUST return a JSON shape whose `prefer_skill` field is a
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// real, registered skill id — anything else is rejected. The system
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// prompt embeds the live registry so the model has the source of truth.
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import { complete, isAvailable as llmAvailable } from "../llm/provider.js";
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import { isRegistered, skillRegistryPrompt } from "../skill-registry.js";
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import { info, warn } from "../log.js";
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const HOURLY_BUDGET = 6;
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const COOLDOWN_MS = 30_000;
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let _callTimes = [];
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let _lastCallAt = 0;
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let _tokensIn = 0;
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let _tokensOut = 0;
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let _calls = 0;
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export function isAvailable() {
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return llmAvailable();
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}
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export function getUsageSnapshot() {
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const now = Date.now();
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const hourAgo = now - 3600_000;
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const recentCalls = _callTimes.filter((t) => t > hourAgo).length;
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return {
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callsLastHour: recentCalls,
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callsTotal: _calls,
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tokensInTotal: _tokensIn,
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tokensOutTotal: _tokensOut,
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hourlyBudget: HOURLY_BUDGET,
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};
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}
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export function _resetForTest() {
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_callTimes = [];
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_lastCallAt = 0;
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_tokensIn = 0;
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_tokensOut = 0;
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_calls = 0;
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}
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/**
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* advise({ snapshot, reason, recentSkillIds, lessonsTail }) →
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* { ok: true, action: 'switch_skill'|'continue'|'wait', skillId?, rationale, raw, latencyMs }
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* | { ok: false, code, detail, latencyMs }
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*
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* `reason` is a free-text trigger ("wedged_60s", "forced_move",
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* "hp_plunge", "stuck_3_dispatches"). It goes verbatim into the prompt
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* so the model can tailor its advice.
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*/
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export async function advise({
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snapshot,
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reason = "unknown",
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recentSkillIds = [],
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lessonsTail = [],
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activeNeed = null,
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force = false,
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} = {}) {
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if (!isAvailable()) {
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return { ok: false, code: "not_configured", detail: "set TIMEWEB_API_KEY", latencyMs: 0 };
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}
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const now = Date.now();
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_callTimes = _callTimes.filter((t) => t > now - 3600_000);
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if (!force && _callTimes.length >= HOURLY_BUDGET) {
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return { ok: false, code: "budget_exhausted", detail: `${_callTimes.length}/${HOURLY_BUDGET} per hour`, latencyMs: 0 };
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}
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if (!force && now - _lastCallAt < COOLDOWN_MS) {
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return { ok: false, code: "cooldown", detail: `${Math.round((COOLDOWN_MS - (now - _lastCallAt)) / 1000)}s`, latencyMs: 0 };
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}
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const system = buildSystemPrompt();
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const user = buildUserPrompt({ snapshot, reason, recentSkillIds, lessonsTail, activeNeed });
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_callTimes.push(now);
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_lastCallAt = now;
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const res = await complete({ system, user, json: true });
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_calls += 1;
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if (res.usage) {
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_tokensIn += res.usage.in;
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_tokensOut += res.usage.out;
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}
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if (!res.ok) {
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warn("advisor", `complete failed: ${res.code} (${res.detail})`);
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return { ok: false, code: res.code, detail: res.detail, latencyMs: res.latencyMs };
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}
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const parsed = res.text;
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if (!parsed || typeof parsed !== "object") {
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return { ok: false, code: "bad_shape", detail: "no object in reply", latencyMs: res.latencyMs };
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}
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const action = String(parsed.action ?? "").toLowerCase();
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const skillId = parsed.skill_id ?? parsed.prefer_skill ?? null;
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const rationale = parsed.rationale ?? parsed.reason ?? "";
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if (action === "switch_skill") {
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if (!skillId || !isRegistered(skillId)) {
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warn("advisor", `rejected hallucinated skill "${skillId}"`);
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return {
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ok: false,
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code: "hallucinated_skill",
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detail: skillId ?? "(null)",
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rationale,
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raw: parsed,
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latencyMs: res.latencyMs,
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usage: res.usage,
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};
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}
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info("advisor", `switch_skill → ${skillId} (${rationale.slice(0, 80)})`);
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return {
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ok: true,
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action: "switch_skill",
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skillId,
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rationale,
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raw: parsed,
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latencyMs: res.latencyMs,
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usage: res.usage,
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};
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}
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if (action === "continue" || action === "wait") {
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info("advisor", `${action} (${rationale.slice(0, 80)})`);
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return { ok: true, action, rationale, raw: parsed, latencyMs: res.latencyMs, usage: res.usage };
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}
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return { ok: false, code: "bad_action", detail: action || "missing", raw: parsed, latencyMs: res.latencyMs, usage: res.usage };
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}
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function buildSystemPrompt() {
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return [
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"You are the tactical advisor for pepa, an autonomous Minecraft survival bot.",
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"You are called when the bot's reflex layer detects something wrong (wedged, stuck,",
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"forced move, HP plunge). Your job: produce a single fast decision.",
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"",
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"Reply STRICTLY with a JSON object:",
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'{',
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' "action": "switch_skill" | "continue" | "wait",',
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' "skill_id": "<registered skill id or null>",',
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' "rationale": "<≤25 words explaining why>"',
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'}',
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"",
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"Rules:",
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'- "switch_skill" REQUIRES skill_id to be one of the registered ids below.',
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'- "continue" means current skill is fine, just give it more time.',
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'- "wait" means stop dispatching for ~10s (e.g. waiting for night to pass).',
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'- If unsure, return "continue".',
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"",
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skillRegistryPrompt({ limit: 1800 }),
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].join("\n");
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}
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function buildUserPrompt({ snapshot, reason, recentSkillIds, lessonsTail, activeNeed }) {
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const pos = snapshot?.position;
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const inv = snapshot?.inventory ? Object.keys(snapshot.inventory).slice(0, 10).join(", ") : "(empty)";
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const recent = (recentSkillIds ?? []).slice(-8).join(" → ") || "(none)";
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const lessons = (lessonsTail ?? []).slice(0, 4).map((l) => ` - ${l.text ?? l}`).join("\n");
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const needLine = activeNeed
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? `L${activeNeed.need.level} ${activeNeed.need.id} (${activeNeed.need.title}) — manifesto wants ${activeNeed.skillId}`
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: "(no active need)";
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const hostile = snapshot?.closestHostile
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? `${snapshot.closestHostile.name}@${snapshot.closestHostile.distance}b`
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: "(none)";
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return [
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`Trigger: ${reason}`,
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`Position: ${pos ? `(${Math.round(pos.x)}, ${Math.round(pos.y)}, ${Math.round(pos.z)})` : "?"}`,
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`HP: ${snapshot?.health ?? "?"} food: ${snapshot?.food ?? "?"} day: ${snapshot?.isDay ? "yes" : "no"}`,
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`Active need (Maslow ladder): ${needLine}`,
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`Closest hostile: ${hostile}`,
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`Active skill: ${snapshot?.activeSkill ?? "(idle)"}`,
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`Recent dispatches: ${recent}`,
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`Inventory keys: ${inv}`,
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`Nearby threats: ${formatThreats(snapshot?.threats)}`,
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`No-progress reason: ${snapshot?.noProgressReason ?? "(none)"}`,
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"",
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lessons ? `Relevant lessons:\n${lessons}\n` : "",
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"What should the bot do RIGHT NOW? Return the JSON decision.",
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"Prefer a skill that helps satisfy the active need unless an emergency forces another action.",
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].filter(Boolean).join("\n");
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}
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function formatThreats(threats) {
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if (!Array.isArray(threats) || threats.length === 0) return "(none)";
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return threats.slice(0, 3).map((t) => `${t.name ?? "?"}@${Math.round(t.distance ?? 0)}m`).join(", ");
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}
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// Test exports
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export const __testing = { buildSystemPrompt, buildUserPrompt, formatThreats, HOURLY_BUDGET, COOLDOWN_MS };
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