7d96e448049a0c4bdb5dca0a3c6614c3d41f58b2
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7d96e44804 |
v0.3.0: Maslow + Awareness — self-learning bot with needs ladder, event-driven reflex, and TimeWeb fast advisor (#27)
* 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>
* v0.3.0-rc.2: manifesto / needs ladder L0-L10
Adds an explicit hierarchical needs catalogue that the reflex consults
on every tick. The bot now pursues tangible intermediate goals (food,
wood tools, shelter, stone tools, ...) instead of inheriting whatever
the curriculum thought was "next".
Ladder:
L0 alive HP>5, food>0, not in lava, not panic-near hostile
L1 food ≥6 food items in inventory (or sated + any food)
L2 tools_wood wooden_pickaxe + wooden_axe + wooden_sword
L3 shelter_basic bed placed nearby or in inventory
L4 tools_stone stone-tier triplet
L5 armor_basic any chestplate (pursue=null until craft.leather-*
lands; ladder gracefully skips)
L6 food_security ≥16 food items
L7 tools_iron iron-tier triplet (pursue=gather.stone for now)
L8 armor_iron iron chestplate (pursue=null for now)
L9 village_seed bed + chest in nearby blocks
L10 village_full never detected, falls through to curriculum
Each need has detect(snapshot) → bool and pursue(snapshot) →
{skillId, args} | null. The ladder picks the LOWEST unsatisfied
pursuable need. Needs whose pursue is null get recorded as
blockedNeeds and the walk continues — no stalling on missing skills.
Wired into curriculumReflex: manifesto takes precedence over
curriculum.plan when it has a concrete suggestion. Tests can pass
ctx.disableManifesto=true to exercise the curriculum branch
in isolation (existing reflex tests keep passing this way).
Pi self-reflection prompt now includes
"activeNeed (Maslow ladder L0-L10): L2 tools_wood → gather.logs"
so Pi advises at the right level instead of giving generic guidance.
skillId returned by pursue() is validated against the live registry
(rc.1 plumbing) — manifesto cannot accidentally dispatch a
hallucinated skill name.
Tests: 315 green (was 279 on rc.1, +36 new):
- runtime/manifesto/needs.test.js — 24 tests (per-need detect/pursue,
helper sums)
- runtime/manifesto/state.test.js — 10 tests (ladder walk, hostile
takeover at L0, armor skipping, caching)
- runtime/reflex.test.js — 2 integration tests (manifesto overrides
curriculum plan; well-fed bot pursues tools_stone)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* v0.3.0-rc.3: event-driven awareness + skill pre-emption
Adds a reactive layer on top of the polling reflex. The bot now
notices environmental shocks (forced moves, HP plunges, hostile
spawns) within ~100ms instead of waiting for the next DISPATCH tick,
and the in-flight skill is preempted so the next reflex cycle can
re-plan against the current world state.
This is the rc that wires the "rc.1 plumbing + rc.2 manifesto" into
a feedback loop:
- awareness fires preempt → dispatch aborts
- reflex tick re-evaluates → manifesto walks the ladder
- new dispatch picks the right skill for the new world state
Pieces:
- runtime/awareness/events.js (new) — bot.on listeners:
- move: single-tick Δposition ≥ 5 blocks → forced_move flag + preempt
- health: HP drop ≥ 2 → health_plunge flag + preempt
- entitySpawn: hostile mob within 12 blocks → hostile_added + preempt
- blockUpdate: nearby block change → env_changed flag (no preempt,
throttled 800ms; otherwise gather skills would self-preempt
every dig)
- runtime/skills/index.js — RUNNER_CODES.PREEMPTED + raceWithAbort()
wraps every execute() against ctx.abortSignal. Existing skills get
preemption for free; they don't have to check the signal manually.
- runtime/bot.js:
- dispatchAction creates a fresh AbortController per dispatch and
stores it on reflexCtx.currentAbort
- attachAwareness fires controller.abort() when something disrupts
the active skill; runSkill returns code: "preempted" and the
reflex moves on
- reflexCtx.lastPreempt records the most recent shock
Tests: 332 green (was 315 on rc.2, +17 new):
- runtime/awareness/events.test.js — 12 tests (each event type +
thresholds + throttling + passive-mob filter)
- runtime/skills/contract.test.js — 3 abortSignal tests
(mid-flight, pre-armed, clean signal)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(.env): add PEPA_FAST_LLM_* placeholders for v0.3.0 fast advisor
Empty values keep the fast-advisor tier disabled (safe no-op). Fill
in BASE_URL + API_KEY + MODEL to enable. TimeWeb-style endpoint
example included.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(v0.3.0): rename fast-LLM env vars to TIMEWEB_* (match other projects)
Aligns with the user's other repos (proso) which use TIMEWEB_API_GROK /
TIMEWEB_URL_GROK. Single naming convention across projects avoids the
'which env var was it for this repo' mental tax.
PEPA_FAST_LLM_BASE_URL → TIMEWEB_BASE_URL
PEPA_FAST_LLM_API_KEY → TIMEWEB_API_KEY
PEPA_FAST_LLM_MODEL → TIMEWEB_MODEL
PEPA_FAST_LLM_TIMEOUT_MS → TIMEWEB_TIMEOUT_MS
Provider still works with any OpenAI-compatible endpoint — TimeWeb is
the default but the variable name doesn't lock us in. Tests + docs +
.env / .env.example updated.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* feat(scripts): TimeWeb smoke test + bump default LLM timeout to 20s
scripts/check-timeweb.js — three probes: plain text, JSON mode, full
fast-advisor stack (registry injection + skill validation). Loads .env,
prints {ok, latency, reply preview} for each. Doesn't touch bot state.
Bumped DEFAULT_TIMEOUT_MS 8s → 20s in runtime/llm/provider.js. TimeWeb's
hosted agent endpoint takes 5-15s for the fast-advisor prompt
(registry block + snapshot context), so 8s was producing spurious
timeouts. OpenAI direct returns much faster; env var TIMEWEB_TIMEOUT_MS
overrides if needed.
Smoke verified live (PR #27 branch):
probe 1: 6.3s, plain prompt → "pepa hears you"
probe 2: 5.4s, JSON mode → {"alive":true,"name":"pepa"}
probe 3: 14.9s, advise() → action=switch_skill, skill=recovery.tunnel-out
(correct registered skill, sensible rationale — registry
injection successfully prevents hallucination)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* feat(v0.3.0): auto-trigger fast-advisor + token usage tracking
Closes the awareness → LLM → action loop that the rc.1/2/3 sequence
left as a followup. When the bot is wedged, looping, or just suffered
a preempt-then-retry, the reflex fires advise() in the background;
when the recommendation lands it overrides the next dispatch.
Async by design: advise() takes 5-15s on TimeWeb's hosted endpoint —
too slow for a synchronous reflex tick. tickAdvisor() is fire-and-
forget, the result lands on ctx.advisorRecommendation, and the *next*
tick reads and consumes it. Recommendations age out after 60s.
Components:
- runtime/coach/advisor-trigger.js — policy + async fire path
- tickAdvisor(ctx, {plannedSkillId}) checks three triggers:
1. wedged > 60s (no significant move)
2. last 4+ dispatches are the same skill AND it's planned again
3. preempt within last 30s + same skill being retried
- 90s trigger cooldown, single-in-flight guard
- consumeFreshRecommendation(ctx) reads/clears the cache
- runtime/reflex.js — curriculumReflex calls tickAdvisor() every tick
and consumes a fresh recommendation BEFORE dispatching. ctx flag
disableAdvisor=true for tests.
- runtime/bot.js — dispatchAction maintains a rolling 8-slot
reflexCtx.recentSkillIds for the loop-detection trigger.
Token usage:
- runtime/llm/provider.js — normaliseUsage() reads OpenAI/TimeWeb-
style {prompt_tokens, completion_tokens, total_tokens} from the
response. Returned on every complete() result and logged at info
level as "in=Nt/out=Mt".
- runtime/coach/fast-advisor.js — getUsageSnapshot() aggregates
total tokens across all calls in the session.
Measured on live TimeWeb endpoint (gpt-5.4-mini agent):
per call: ~705 input + 45 output = ~750 tokens
rate limit: 6 calls/hour
worst case at full budget: ~108K tokens/day
estimated cost (OpenAI gpt-5-mini reference price): ~$0.60/month
Well within any reasonable budget — model can run hot 24/7.
Smoke verified: scripts/check-timeweb.js probe 4 produces
trigger fired: true (wedged_90s)
recommendation: recovery.tunnel-out
rationale: "Stuck wedged for 90s; exploration is failing."
latency: 5302ms
Tests: 345 green (was 332, +13 advisor-trigger).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* 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>
---------
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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042793a53f |
docs+chore: v0.2.0 README refresh + auto-patch opens PR (not direct merge)
- README: update lede, architecture block, self-improvement section, status. Mentions knowledge.db, coach/advice loop, persona narration, and the new PR-based auto-patch flow. - scripts/auto-patch.js: replace cherry-pick-to-main with `git push` + `gh pr create`. The operator is now the only one who can merge into main (enforced by branch protection rules on the remote). Legacy direct-merge path remains behind PEPA_AUTO_PATCH_MERGE=cherry-pick for emergencies. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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mayatnikovandClaude Opus 4.7
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4ae63dabe1 |
feat(runtime): v0.1.0 — adopt Voyager critic + Mindcraft modes/library/lint
Five concrete patterns from Voyager and Mindcraft, applied in our shape
without abandoning the git-as-evolution-substrate that makes pepa
distinct. Plus a first multi-agent surface so two bots from the same
repo can share intent.
1. runtime/critic.js (Voyager critic.txt)
- Spawns `pi -p` with a JSON-only critic prompt before a proposal is
written. {reasoning, success, critique}.
- success=true short-circuits the proposal (bot recovered between
detector tripping and now), saving Pi tokens on false positives.
- critique is spliced into the proposal body via attachCritique() so
the downstream auto-patcher has a sharp spec.
- Graceful: pi missing / timeout / unparseable JSON → proposal still
filed without the critic block.
2. scripts/lint-patch.js (Mindcraft coder._lintCode)
- Pre-flight gate between Pi commit and npm test: node --check, dynamic
import (catches missing named exports), regex extraction of
runSkill("id") calls cross-checked against the live registry.
- Cheaper than npm test, fails fast with a clear reason.
3. runtime/stuck-incident.renderActionTemplate (Voyager action_template.txt)
- All proposal bodies now follow the same fixed-section layout: Task /
Last result / Execution error / State / Metrics / Journal /
Scenarios / Critique / Fix / Edit scope / Forbidden.
4. runtime/skill-library.js (Mindcraft skill_library.getRelevantSkillDocs)
- Word-overlap ranking (Mindcraft's offline fallback) — zero deps,
deterministic. auto-patch.js injects top-3 similar skills into the
Pi prompt as "look at these patterns".
5. runtime/modes.js (Mindcraft modes.js)
- Declarative {name, interrupts, on, active, update(ctx)} chain that
runs BEFORE the curriculum each tick.
- Ships self_preservation (low HP → eat/flee), hunger (food<14 → eat),
night_shelter (night + bed in hand → sleep). Cleaner than ad-hoc
lastFleeAttempt cooldowns in reflex.js.
6. runtime/social/conversation.js + cmd:conv-say/conv-recent/conv-list
- File-JSONL topic channel so two bots from the same repo (different
usernames, different host dirs under state/) can append turns and
read peers. Skeleton — multi-agent collaboration on top later.
Differentiator preserved: every Pi-written skill still lands on main via
auto-patch.js (real git branch + smoke gate + cherry-pick). Voyager
keeps skills in a Chroma JSON, Mindcraft keeps them in RAM — pepa keeps
them as versioned source code reviewable in `git log`.
package.json: 0.0.1 → 0.1.0. 174/174 tests pass. README + AGENTS updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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mayatnikovandClaude Opus 4.7
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69d1298fbd |
fix(auto-patch): lockfile-coordinated supervisor restarts
When Pi writes a multi-file runtime patch, the supervisor's file watcher can fire between two consecutive writes, kill the bot mid-edit, and load a half-saved file with a SyntaxError. Loop until the operator stops it. scripts/auto-patch.js now creates state/auto-patch.lock with its PID right after the branch checkout (before spawning pi -p), and removes it on every exit path. runtime/supervisor.js defers any watch-triggered restart while the lock holder is alive, polling every 2 s; once the lock drops it waits 1.5 s for the final write to settle, then runs \`node --check\` on the changed file and only restarts if it parses. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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82a8250a12 |
feat(auto-patch): enforce proposal editScope + run npm test before cherry-pick (#19)
Two safety rails on the unattended self-improvement loop:
1. scripts/edit-scope.js + .test.js: pure helpers that parse
`editScope: [...]` out of a proposal frontmatter (the field Phase 6
started writing) and validate a list of changed files against it.
13 tests covering null/missing/malformed frontmatter, directory
prefix matching, exact-file matching, default-scope fallback.
2. scripts/auto-patch.js:
- reads editScope from the proposal (falls back to ["runtime/"])
- injects the allowed paths into the Pi prompt so Pi knows the
boundaries up front
- validates the diff against scope + auto-allows any
runtime/**/*.test.js files Pi added
- runs `npm test` on the patched branch BEFORE cherry-picking;
refuses to land a patch that breaks the suite
Closes the "Smoke checks run before applying patch" item from
plans/autonomous-survival-bot-prd.md §7 Phase 6. npm test now 92/92.
Co-authored-by: Yuriy Mayatnikov <mayatnikov@me.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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7797dd3d5a |
feat(runtime): fully autonomous self-healing — no operator approval (#10)
Operator feedback: "бот должен быть полностью автономным — сам себя улучшать и чинить, в этом и есть смысл; все что я вижу пока что он стоит на месте и кидает proposals на каждый чих — это кардинально не то что я хочу". Acted on: 1. Trigger filter — proposals only on real bugs. runtime/bot.js classifies failure detail into bug / timeout / feature-gap / other. The 5-in-a-row trigger fires only when the run contains a bug (TypeError / Cannot read / is not defined …) OR is entirely timeouts on the same operation. Feature gaps like "no reachable log within 32 blocks", "no food in inventory", "no bed in range", "no target in reach" are SKIPPED — the reflex layer routes around them (noTreesUntil → wander, etc). The LLM has no business patching code for missing inventory. Threshold raised 3 → 5 in a row. Cooldown unchanged (30 min). 2. Auto-apply, no operator-in-the-loop. New runtime/auto-improve.js polls proposals/ every 2s. When it sees a new .md and 10s have passed since first sighting (debounce), spawns scripts/auto-patch.js detached. New scripts/auto-patch.js: refuses on dirty tree, moves proposal pending → approved/, branches `auto/<slug>` off main, runs `pi -p` with 10-min timeout. If Pi committed AND every changed file is under runtime/ → cherry-picks onto main. Otherwise discards the branch. No push, no PR. Audit trail in state/<host>/proposals/approved/. Rate limit: 15-min cooldown between finished runs + 4/hour hard cap. 3. Auto-rollback on bad patches. runtime/supervisor.js: when MAX_RESTARTS_PER_MINUTE is exceeded AND `git log -1 HEAD` is younger than 15 min AND HEAD touched runtime/, runs `git reset --hard HEAD~1`. Up to MAX_ROLLBACKS=3 lifetime, then exits 1 for manual investigation. Restart counters are reset after a successful rollback so the next attempt isn't immediately killed. 4. current-task.json slim. No longer stores the full perception snapshot (was ~3 KB per write × every action). Position only — sufficient as a resume anchor. Slim snapshot still goes into the proposal markdown for context. docs/runtime.md — rewrote the self-improvement section: full flow diagram, classification rules, all rate-limit knobs, manual escape hatches kept but documented as rarely-needed. Also cleared 5 stale proposals from previous smoke tests so the first production run isn't burning Pi tokens on stale bugs that have since been fixed. Co-authored-by: Yuriy Mayatnikov <mayatnikov@me.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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2ecadd3bb2 |
fix(supervisor): pidfile lock prevents two supervisors racing on the nickname (#9)
User report 2026-05-25: launched 'npm run bot' fresh, MC server kicked
every login with "Игрок с данным никнеймом уже играет на сервере" and
the bot fell into a perpetual reconnect-then-kicked loop. Root cause:
a smoke-test supervisor from an earlier shell was still running in the
background, holding the pepa_bot session open. Two supervisors racing
on the same nickname is undefined behaviour from the server's side and
results in this exact failure mode.
Changes:
runtime/supervisor.js — acquires state/<host>/supervisor.pid before
spawning the child. If another supervisor is alive (kill -0 check), the
new one exits with a clear message telling the operator how to recover.
On SIGINT/SIGTERM/exit the lock is released; stale pidfiles are detected
when the recorded PID is no longer alive.
scripts/stop.sh — emergency cleanup helper:
- kills any supervisor or bot.js processes matching this repo
- removes pidfile + bot.sock
- reminds the operator to wait ~30s for the MC server to drop the old
session before re-launching
package.json — new `npm run stop` script.
Smoke-tested:
- first 'node runtime/supervisor.js' acquires lock, writes pid
- second call refuses with diagnostic message
- first SIGTERM ⇒ pidfile removed automatically
Co-authored-by: Yuriy Mayatnikov <mayatnikov@me.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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cd14bbf89a |
feat(runtime): state persistence + proposals + supervisor hot-restart (#6)
Closes the self-improvement loop end-to-end:
reflex fails 3× → proposal file → operator approves in TUI →
`npm run propose:apply <file>` spawns Pi on a feature branch →
Pi commits the patch → supervisor watches runtime/*.js and
restarts the child on change.
runtime/state-store.js — atomic current-task.json writes, daily diary
append, proposals/ + proposals/approved/ helpers.
runtime/bot.js:
- ctx.dispatch writes current-task.json on start and updates it on
completion / failure / throw.
- failure tracker: 3 consecutive same-label failures → writeProposal()
with the snapshot, labels, and a suggested-next-step section.
30-min cooldown prevents proposal spam.
- on startup, surfaces resume info (previous task + pending proposal
count); on death, clears current-task.json + writes diary line.
- new IPC commands: PROPOSAL_LATEST returns the newest pending
proposal body; PROPOSAL_APPROVE moves it to proposals/approved/.
tui/tui.tsx — status bar shows `[proposals N, press y]` badge when
bot.pendingProposals > 0. Hotkey 'y' opens the proposal panel; 'y'
approves, 'n'/Esc closes.
scripts/propose-apply.js — given an approved proposal filename, creates
a `feat/proposal-<slug>` branch and spawns `pi -p` with the proposal
+ repo-conventions prompt. Refuses on dirty tree. No auto-push, no
auto-merge — operator reviews the diff and decides.
runtime/supervisor.js — forks bot.js as a child, watches runtime/*.js,
restarts on file change or on child exit code 42. Rate-limited at 5
restarts/minute. SIGINT/SIGTERM forward cleanly. `npm run bot` now
goes through the supervisor; `npm run bot:bare` skips it.
Smoke-tested: supervisor spawned, bot connected to MC, spawned at
expected coords, diary line written, state cleanup on SIGTERM correct.
Co-authored-by: Yuriy Mayatnikov <mayatnikov@me.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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