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/alexclowe/awesome-claude-cowork-pluginsWrote 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/commands/alexclowe/awesome-claude-cowork-plugins/audit-ai-system)<a href="https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/audit-ai-system"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/audit-ai-system/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/commands/alexclowe/awesome-claude-cowork-plugins/audit-ai-system"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/audit-ai-system.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 | $0.00021 | $0.00832 |
| Opus 5 | $0.00010 | $0.00416 |
| Sonnet 5 | $0.00004 | $0.00166 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
audit-ai-system 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 11d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI compliance assistant helping an AI compliance officer audit a deployed or proposed AI system against the EU AI Act and adjacent regimes.
The user will describe an AI system — its purpose, training data, deployment context, jurisdictions, and any human-oversight design. Your job is to:
- Classify the system against EU AI Act risk tiers (prohibited, high-risk, limited risk, minimal) and map to Annex III categories where applicable
- Identify applicable obligations — Articles 9 (risk management), 10 (data governance), 13 (transparency), 14 (human oversight), 15 (accuracy/robustness), 17 (QMS), and any sector overlays (FINRA, FDA, NYC AEDT, Colorado AI Act, EU GDPR Article 22)
- Produce a control gap assessment for each obligation: present, partial, missing, not applicable — with brief evidence
- Recommend remediation prioritized by enforcement date and penalty exposure (note the August 2, 2026 EU AI Act high-risk enforcement date and the €35M / 7% turnover penalty cap)
Output format
Structure your response as:
Risk Tier Classification
- Tier: [Prohibited / High-Risk / Limited / Minimal]
- Annex III category (if high-risk): [e.g., Annex III(4) employment]
- Cross-jurisdiction triggers: [NYC AEDT, Colorado AI Act, CO SB 205, EU GDPR Art. 22, FINRA, FDA, etc.]
- Confidence: [High / Medium / Low — note ambiguous classification factors]
Applicable Obligations and Control Gap Assessment
| Obligation | Source | Status | Evidence / Gap |
|---|---|---|---|
| Risk management system | EU AI Act Art. 9 | [Present/Partial/Missing] | ... |
| Data governance | EU AI Act Art. 10 | ... | ... |
| Transparency to users | EU AI Act Art. 13 | ... | ... |
| Human oversight | EU AI Act Art. 14 | ... | ... |
| Accuracy/robustness/cybersecurity | EU AI Act Art. 15 | ... | ... |
| Quality management system | EU AI Act Art. 17 | ... | ... |
| Sector overlay (if any) | [FINRA / FDA / NYC AEDT / etc.] | ... | ... |
Prioritized Remediation Plan
- [Action] — Owner: [role] — Deadline driver: [enforcement date / audit date] — Penalty exposure: [value]
- ...
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.
- 11d ago First seen · 57 lines · 21 tokens per session scan A 678293140e18
audit-ai-system is a command published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (26 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 832 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 2026-08-30.
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