Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/megamen32/lasthumancommit/worker-bugfixnpx skills add megamen32/LastHumanCommit --skill worker-bugfixgit clone --depth 1 https://github.com/megamen32/LastHumanCommitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00087 | $0.00442 |
| Opus 5 | $0.00044 | $0.00221 |
| Sonnet 5 | $0.00017 | $0.00088 |
| Haiku 4.5 | $0.00009 | $0.00044 |
Grade A, and why
worker-bugfix scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Worker Bugfix
Fix one proven failure without turning it into a hardening project.
Procedure
- Search the reusable code map for the symptom, consumer, and prior
failure-shieldentries. Check freshness and verify decisive locations with targetedrg. - Reproduce the real failure with the cheapest discriminating proof: an existing check, focused regression, protocol probe, or black-box canary. Write a new unit test only when it is the cheapest strong red proof.
- Trace the failing consumer path and reject nearby but unused adapters or fixtures.
- Change the smallest coherent source slice that makes the same proof green.
- Re-run that proof and only proportional direct-regression checks.
- If the root cause or false route is likely to recur, resolve
../worker-research/scripts/code_map.pyfrom this skill and upsert a compactfailure-shieldentry with verified locations and the discriminating probe. Replace or remove knowledge invalidated by the fix. - Stop when the accepted behavior claim is proven.
Ask Lead non-blockingly instead of guessing when product intent, accepted fallback, data migration, blast radius, or proof strength changes the fix. Continue only answer-independent work while waiting.
Do not
- Do not widen the fix into cleanup, abstraction, security hardening, logging, compatibility, or exhaustive edge cases.
- Do not create a fake-contract test when a real consumer probe is cheaper and stronger.
- Do not claim production or UI recovery from source tests alone.
- Do not keep retrying after two failed hypotheses; report evidence and the shortest changed route to Lead.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 41 lines · 87 tokens per session scan A bcf05b179877
worker-bugfix is a skill published in the GitHub repository megamen32/LastHumanCommit (2 stars, last pushed 2d ago), licensed MIT. It adds 87 tokens to every session and 442 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
planning-with-files-ar
تخطيط مستمر قائم على الملفات لعمل وكلاء الذكاء الاصطناعي متعدد الخطوات. يحتفظ بملفات taskplan.md و findings.md و progress.md على القرص، وتحقن خطافات دورة الحياة سياق التخطيط المحدد للمشروع. تقرأ الاستعادة التلقائية ملفات تخطيط المشروع فقط. يمكن للأمر الصريح session-catchup.py --metadata فحص بيانات وصفية لجلسات الوكيل…
kl-consistency-test
Write, calibrate, and debug the prefill-vs-decode logprob (KL) consistency tests in sglang -- the two independent conditions a zero requires (every operator batch-invariant, and the two paths computing the same function), which helper separates them, how to pick a threshold once they hold, and how to localize a…
i18n-localization
Internationalization and localization patterns. Detecting hardcoded strings, managing translations, locale files, RTL support.
dsh-web-documentation
Use when adding or editing dsh-web README files, docs, AGENTS.md instructions, user-facing configuration text, or bilingual documentation pairs.
baoyu-youtube-transcript
Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面"…
indication-dossier
Build a source-backed biomedical indication dossier. Use when a research task asks for disease biology, target rationale, patient segmentation, biomarkers, trials, drugs, competitive landscape, or translational evidence.