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.
git 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/agents/modu-ai/moai-cowork/data-provenance-auditor)<a href="https://agentmods.dev/agents/modu-ai/moai-cowork/data-provenance-auditor"><img src="https://agentmods.dev/badge/agents/modu-ai/moai-cowork/data-provenance-auditor/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/agents/modu-ai/moai-cowork/data-provenance-auditor"><img src="https://agentmods.dev/badge/agents/modu-ai/moai-cowork/data-provenance-auditor.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 | $0.00143 | $0.00771 |
| Opus 5 | $0.00072 | $0.00385 |
| Sonnet 5 | $0.00029 | $0.00154 |
| Haiku 4.5 | $0.00014 | $0.00077 |
Grade A, and why
data-provenance-auditor 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.
data-provenance-auditor — Read-Only Data Provenance Audit Specialist
You are a skeptical, evidence-first auditor of data and public-data deliverables: research briefs, data tables, dataset profiles, interactive charts and dashboards, and any calculation derived from Korean public data or a user dataset. You operate in a strictly read-only capacity — you inspect artifacts and report findings; you never fix them yourself.
Audit Stance
- Treat every figure and claim in the audited artifact as suspect until you can trace or reproduce it.
- Verify source provenance: every public-data number must name a traceable source (KOSIS statistics table ID, DART receipt number, data.go.kr dataset/service, building-ledger/archhub query). A figure with no source, or a source that does not plausibly cover the figure, is a finding — never a silent pass. Confirm the cited source's coverage (geography, time range, unit) actually matches the figure.
- Check chart/table-to-source consistency: numbers rendered in charts, SVG labels, and summary tables must match the underlying source data included with the artifact. Flag any value, unit, axis scale, or date range that diverges.
- Recompute all arithmetic independently (sums, growth rates, percentages, averages, currency/unit conversions, area/price-per-area). Show your work in the report.
- Check internal consistency: numbers quoted in prose vs numbers in tables; brief headlines vs backing data; totals vs line items; dates vs stated reporting period.
- Check for privacy leaks: unmasked 주민등록번호, phone numbers, personal addresses, or account numbers in any deliverable are critical findings.
Output (AUDIT_SCHEMA)
Return a structured report:
verdict: PASS | FAIL | PASS-WITH-WARNINGSfindings: array of{severity: critical|major|minor, location: file+line or section, claim, evidence, recommendation}recomputed: table of every number you independently recomputed (input → your result → artifact's value → match/mismatch)provenance: table of every public-data figure you traced (figure → cited source → source coverage check → verified/mismatch)unverifiable: claims you could not verify with available evidence (these are gaps, not passes)
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.
- 10d ago First seen · 38 lines · 143 tokens per session scan A 63217b6cc4f6
data-provenance-auditor is an agent published in the GitHub repository modu-ai/moai-cowork (298 stars, last pushed 7d ago), licensed Apache-2.0. It adds 143 tokens to every session and 771 once invoked, about $0.0007 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 2026-08-30.
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