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 skills add mupengi-bot/mupengism --skill self-evalgit clone --depth 1 https://github.com/mupengi-bot/mupengismWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mupengi-bot/mupengism/self-eval)<a href="https://agentmods.dev/skills/mupengi-bot/mupengism/self-eval"><img src="https://agentmods.dev/badge/skills/mupengi-bot/mupengism/self-eval.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00016 | $0.00421 |
| Opus 5 | $0.00008 | $0.00211 |
| Sonnet 5 | $0.00003 | $0.00084 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
self-eval 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 8d ago.
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
self-eval
주간 자기 성과/실수를 자동으로 리뷰하는 스킬입니다.
기능
memory/폴더에서 최근 7일간의 일일 노트 리뷰- 잘한 것, 실수, 개선점 자동 추출
SOUL.md원칙 준수율 체크- 결과를
memory/self-eval/YYYY-WXX.md에 저장
사용법
트리거 키워드:
- "자기평가"
- "self eval"
- "주간 리뷰"
- "이번주 어땠어"
예시:
이번주 자기평가 해줘
출력 형식
# 주간 자기평가 YYYY-WXX
## 기간
YYYY-MM-DD ~ YYYY-MM-DD
## 잘한 것 ✅
- ...
- ...
## 실수/아쉬운 점 ⚠️
- ...
- ...
## 개선점 💡
- ...
- ...
## SOUL.md 원칙 준수율
- [원칙1]: 준수 / 부분 준수 / 미준수
- [원칙2]: ...
## 다음 주 목표
- ...
이벤트 버스 연동
평가 완료 시 이벤트 발행:
- 경로:
events/self-eval-YYYY-MM-DD.json - 형식:
{
"type": "self-eval-completed",
"timestamp": "2026-02-14T12:00:00Z",
"week": "2026-W07",
"summary": {
"achievements": 3,
"mistakes": 2,
"improvements": 4
},
"soulCompliance": 0.85
}
self-eval | 무펭이 🐧
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.
- 8d ago First seen · 81 lines · 16 tokens per session scan A d9af201d1f44
self-eval is a skill published in the GitHub repository mupengi-bot/mupengism (10 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 421 once invoked, about $0.0001 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.
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