Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/bestagentkits/agency-skillsnpx agentmods add skills/bestagentkits/agency-skills/aims-auditWrote 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/bestagentkits/agency-skills/aims-audit)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/aims-audit"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/aims-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/bestagentkits/agency-skills/aims-audit"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/aims-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 | $0.00059 | $0.01379 |
| Opus 5 | $0.00030 | $0.00690 |
| Sonnet 5 | $0.00012 | $0.00276 |
| Haiku 4.5 | $0.00006 | $0.00138 |
Grade A, and why
aims-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 12d 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.
This is a copy
100% identical to aims-audit — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cs:aims-audit — AIMS ISO 42001 Forcing Questions
Command: /cs:aims-audit <scope>
The ISO 42001 AIMS specialist pressure-tests any AI Management System work. Six questions before any certification commitment, internal audit cycle, or new-system onboarding.
When to Run
- Before stage 1 ISO 42001 certification audit
- Before annual internal audit cycle (Clause 9.2)
- When onboarding a new AI system into existing AIMS scope
- When AI risk register hasn't been refreshed in > 6 months
- After material model change (re-evaluate risks per Clause 6.1.2)
- When audit findings hint at AIMS / ISMS / QMS duplication
The Six AIMS Questions
1. Does the AIMS scope statement name every AI system?
Scope omission = certification finding.
- Including: embedded models, third-party AI services, "experimental" production systems
- Run
aims_gap_analyzer.pyto verify Clause 4.3 evidence - "AI features added by SaaS vendors we use" = in scope if they affect the company's services
2. Does the AI policy commit to lawful use AND beneficial purpose AND human oversight AND continual improvement?
Missing any of the four = critical nonconformity at stage 1.
- AI policy is NOT info-sec policy — it has separate substantive content
- Reference ISO 42001 Annex A.2.2 + Clause 5.2
- Marketing-copy "AI ethics" doesn't pass
3. What's the risk register coverage, and which Annex A controls treat each risk?
Risk identification without control mapping = Clause 6.1.3 fails.
- Run
ai_risk_register_builder.pyper ISO 23894 methodology - Every high/critical risk must link to ≥ 1 Annex A control
- "Residual verdict: additional_treatment_required" must be closed before stage 1
4. Has the AI risk assessment been re-run since the last material model change?
Concept drift is not a one-time event.
- Article 9 EU AI Act + ISO 42001 Clause 6.1.2 both require iterative risk assessment
- Material change = retraining on new data, fine-tuning, architecture change, deployment context change
- If "we did it 18 months ago and haven't touched it," the AIMS is broken
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.
- 12d ago First seen · 133 lines · 59 tokens per session scan A fe2c087856fe
aims-audit is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 1,379 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to aims-audit, differing in 3 lines, and is treated as a copy.
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