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 modu-ai/moai-cowork --skill data-provenance-auditgit clone --depth 1 https://github.com/modu-ai/moai-coworkWrote 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/modu-ai/moai-cowork/data-provenance-audit)<a href="https://agentmods.dev/skills/modu-ai/moai-cowork/data-provenance-audit"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/data-provenance-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/modu-ai/moai-cowork/data-provenance-audit"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/data-provenance-audit.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00068 | $0.00441 |
| Opus 5.5 | $0.00027 | $0.00176 |
| Sonnet 5.5 | $0.00014 | $0.00088 |
| Haiku 4.5 | $0.00007 | $0.00044 |
Grade A, and why
data-provenance-audit 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 17d 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
데이터 출처와 계산 검수
원본 파일과 외부 데이터 서비스를 변경하지 않고 산출물의 근거를 대조한다.
- 인용된 KOSIS 통계표 ID, DART 접수번호, data.go.kr 데이터셋 또는 건축물 질의 조건을 찾고 지역·기간·단위가 주장한 수치와 맞는지 확인한다. 원문을 볼 수 없으면 출처 확인은 미검증으로 남긴다.
- 표·차트·본문·요약의 수치와 축 단위, 날짜가 원자료와 같은지 확인한다. 합계·증감률·평균·면적당 가격 등 계산은 입력값으로 다시 산출한다.
- 주민등록번호, 전화번호, 개인 주소, 계좌번호 등 불필요한 개인 식별 정보가 산출물에 드러나는지 확인한다.
- 조회 실패를 추정값으로 바꿔 적었는지, 실측·추정·예측을 구분했는지 확인한다.
보고서는 판정(PASS/FAIL/미확인), 확인 자료, 위치·근거가 있는 발견, 재계산 표, 출처 범위 대조, 미검증 항목으로 쓴다. 확인하지 못한 수치를 PASS로 채우지 않는다. 독립된 검수자를 실행하지 않았다면 독립 감사라고 부르지 않는다.
Claude의 data-provenance-auditor 에이전트와 목적이 겹친다. ChatGPT 플러그인에서는 이 스킬이 발견 가능한 진입점이다.
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.
- 17d ago Changed · +1 lines ff1ca565907c
- 28d ago First seen · 20 lines · 68 tokens per session scan A 31c99a338f2c
data-provenance-audit is a skill published in the GitHub repository modu-ai/moai-cowork (311 stars, last pushed 15d ago), licensed Apache-2.0. It adds 68 tokens to every session and 441 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-26.
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