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 leodisa/compliance-review-skill --skill compliance-reviewgit clone --depth 1 https://github.com/leodisa/compliance-review-skillWrote 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/leodisa/compliance-review-skill/compliance-review)<a href="https://agentmods.dev/skills/leodisa/compliance-review-skill/compliance-review"><img src="https://agentmods.dev/badge/skills/leodisa/compliance-review-skill/compliance-review/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/leodisa/compliance-review-skill/compliance-review"><img src="https://agentmods.dev/badge/skills/leodisa/compliance-review-skill/compliance-review.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 | $0.00238 | $0.02871 |
| Opus 5 | $0.00119 | $0.01435 |
| Sonnet 5 | $0.00048 | $0.00574 |
| Haiku 4.5 | $0.00024 | $0.00287 |
Grade A, and why
compliance-review 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.
How it starts
The opening of the file, as written. The whole thing — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compliance Review & Design
Help code meet the EU AI Act, the GDPR, the NIS2 Directive (EU 2022/2555), and secure-development (SSDLC) best practice — either by reviewing an existing repository or by building it in from the start. The EU frameworks are public legal texts (articles cited directly); the SSDLC pillar aligns with OWASP SAMM / ASVS and NIST SSDF. The reference files are the shared source of truth in all modes:
- EU AI Act →
references/eu-ai-act.md - GDPR →
references/gdpr-quickcheck.md - NIS2 →
references/nis2.md - Secure development (SSDLC) →
references/secure-development.md - Data masking (safe dataset handling) →
references/data-masking.md
Read this first: scope and honesty (applies to all modes)
A codebase is only part of an organisation's compliance posture. NIS2 and GDPR are largely governance regimes — policies, risk treatment, training, and reporting to authorities live outside any code. Be honest about that boundary: address only what code can actually carry, and flag the rest as organisational.
- This is an advisory aid, not a certification or legal opinion, and not legal advice. It helps prioritise and build correctly; it does not prove conformity.
- Never read, print, or hardcode secret values. Secrets come from env vars or a secret store — never committed or embedded. If you find a committed credential, report its location and that it must be rotated; do not print it.
Avoid double-counting across frameworks
The four references overlap on purpose — NIS2 Art. 21(2)(e), the SSDLC practices, and GDPR Art. 32 all touch SAST, dependency scanning, encryption, access control and vulnerability handling. Assess each underlying fact once: record the full finding (evidence, severity, remediation) in the most specific section — usually SSDLC for engineering practice, NIS2 for the legal measure, GDPR when personal data is the point — and in the other tables add a one-line cross-reference (e.g. "see SSDLC §2.2") instead of repeating it. Count each underlying gap once in the severity totals.
What ships with it
9 files 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 · 240 lines · 238 tokens per session scan A bb28263f3358
compliance-review is a skill published in the GitHub repository leodisa/compliance-review-skill (3 stars, last pushed 1mo ago), licensed MIT. It adds 238 tokens to every session and 2,871 once invoked, about $0.0012 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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