0d97ccccfa65764150e7976ee697bf47a9bd578e
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Commits
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  mayatnikovandClaude Opus 4.7
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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>
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mayatnikovandClaude Opus 4.7
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fcfa2277ba |
v0.3.0-rc.1: live skill registry + fast advisor scaffold
Roots out the v0.2.x failure mode: Pi-extracted lessons routinely named
hallucinated skill ids (relocate.surface, choose.safe.surface,
survive.shelter, gather.visible_log, …). All 47 Pi-lessons in the live DB
had applied_count=0 because normalisePreferSkill couldn't find them.
Fix:
1. runtime/skill-registry.js — single source of truth derived from
skills/index.js. Exports listSkillIds, isRegistered, and a
prompt-ready block (skillRegistryPrompt) grouped by namespace.
2. Pi prompts (coach/postmortem, coach/reflect) embed the live registry
with a "USE ONLY THESE, never invent" instruction. Lessons are
filtered at write-time too — anything not in the registry and not a
known mode name gets dropped.
3. coach/advice.js — normalisePreferSkill now returns null for unknown
ids, hardening consult() against any hallucinations that slip
through. Warn-logged for visibility.
Also lays the LLM substrate for the rest of v0.3.0:
- runtime/llm/provider.js — OpenAI-compatible chat client. Configured
via PEPA_FAST_LLM_{BASE_URL,API_KEY,MODEL,TIMEOUT_MS}. Safe no-op
unless API_KEY is set. Supports JSON-mode.
- runtime/coach/fast-advisor.js — tactical advisor tier (scaffold).
Exposes advise() that asks the fast LLM what to do RIGHT NOW when
the reflex is wedged/stuck. Rejects hallucinated skill ids using the
registry. Rate-limited 6/h, 30s cooldown. Not auto-triggered yet —
wired into reflex in rc.3 (awareness layer).
Tests: 279 green (+24 vs rc.3): 5 registry, 9 provider, 10 advisor.
See dev/v0.3.0/PLAN.md for the full iteration design (manifesto needs
ladder, event-driven awareness, skill pre-emption) and STATUS.md for
shipped/pending tracking.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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865aae1213 |
v0.2.0-rc.3: pillar-up escape + advice in fallback + danger POI (#22)
* v0.2.0-rc.3: pillar-up escape + advice everywhere + danger POI
Closes the gap rc.2 left open. Live observation showed:
- Pi-coach extracted 5 high-quality lessons (do not explore.far at
night near zombies, etc.) but none of them fired (applied_count=0
across the board). Root cause: dispatcher consulted advice only on
the main curriculum path; the bot was falling into the wander/
explore.far FALLBACK after each gather attempt bailed, which
bypassed consult().
- Bot was wedged in a pit on (608, 90) with stone walls. recovery.
tunnel-out kept failing ("Digging aborted") because mining stone
with fists takes ~10s/block; pathfinder watchdog kills it.
This patch:
1. survive.pillar-up (runtime/skills/pillar-up.js) — new escape skill.
Places a placeable block under the bot and jumps onto it; repeats
up to 8 steps. No pickaxe required. Works in dirt/cobble/planks/
sand/gravel/wool/etc. The bot's vertical exit from any pit it can
stand in.
2. Wedged-emergency reflex (runtime/reflex.js). At the top of
curriculumReflex, if noProgressReason is wedged-like AND position
hasn't shifted ≥16 blocks in 60s AND no hostile in 6m AND pillar
block in inventory → dispatch survive.pillar-up. 2-min cooldown
between attempts.
3. consult() now also runs on the WANDER/explore.far fallback path
(runtime/reflex.js curriculumReflex). Pi-coach lessons can finally
take effect. If the fallback skill is overridden to a non-eligible
skill but the bot has a placeable block, falls back to pillar-up.
Outcomes feed reportAdviceOutcome so confidence stays grounded.
4. recordPOI("danger") on death (runtime/coach/postmortem.js). Spatial
memory now flags where the bot died, expires after 6h. POI table
was empty in rc.2.
5. SAFE_OVERRIDES extended (runtime/coach/advice.js): adds
survive.pillar-up and village.choose-base so coach lessons can
route there.
Tests: 255/255 green (+9 pillar-up).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* v0.2.0-rc.3 fixup: relax wedged-escape trigger
Drop the WEDGED_REASONS check — noProgressReason is a string that
may or may not be set when the bot is stuck. Fire pillar-up purely on
"no horizontal progress ≥ 60s, no hostile in 6m, placeable block in
inv". Pillar-up is a constructive no-op when it's not needed (places
one dirt under self) so the false-positive cost is small.
Live observation: rc.3 was deployed and bot was wedged with tunnel-out
repeatedly aborted on stone, but wedged-escape never fired because
the runtime's noProgressReason wasn't in my whitelist. Removing the
gate lets the trigger actually engage.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
---------
Co-authored-by: Yuriy Mayatnikov <mayatnikov@me.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
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mayatnikovandClaude Opus 4.7
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5449ad2e0d |
feat(runtime/coach,persona): death post-mortem coach + Russian chat narration
Coach (runtime/coach/postmortem.js):
- bot.on('death') → captures context (last skill, hostile, recent
scenarios, journal nearby, snapshot) → inserts row into knowledge.deaths
- Periodic drain (every 5min, ≤3 Pi calls/hour, 12min cooldown):
batches up to 8 unanalysed deaths, asks Pi to extract 1-3 generalised
lessons in JSON, persists to knowledge.lessons + knowledge.postmortems
- Pi prompt asks for structured advice (trigger_skill, trigger_hostile,
avoid_skill, prefer_skill, confidence) so future dispatch can act on it
Persona (runtime/persona/chatter.js):
- Polls snapshot every 5s, narrates Russian lines on transitions:
skill start, threat spotted, dusk/dawn, respawn, stuck, milestone done
- Rate-limited: min 75s gap, max 8/hour, duplicate suppression
- ~14 template buckets covering gather/craft/build/travel/combat/weather
Wire-up in bot.js (5 lines):
- initKnowledge({stateDir}) fire-and-forget at module load
- attachCoach + attachChatter inside bot.once("spawn")
Tests: 14 new (7 coach, 7 persona). Total suite 230 green.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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