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 mastepanoski/claude-skills --skill iso-27090-ai-securitygit clone --depth 1 https://github.com/mastepanoski/claude-skillsWrote 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/mastepanoski/claude-skills/iso-27090-ai-security)<a href="https://agentmods.dev/skills/mastepanoski/claude-skills/iso-27090-ai-security"><img src="https://agentmods.dev/badge/skills/mastepanoski/claude-skills/iso-27090-ai-security/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/mastepanoski/claude-skills/iso-27090-ai-security"><img src="https://agentmods.dev/badge/skills/mastepanoski/claude-skills/iso-27090-ai-security.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 | $0.00041 | $0.02520 |
| Opus 5.5 | $0.00016 | $0.01008 |
| Sonnet 5.5 | $0.00008 | $0.00504 |
| Haiku 4.5 | $0.00004 | $0.00252 |
Grade A, and why
iso-27090-ai-security 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 today.
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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ISO/IEC 27090 AI Security Assessment
This skill enables AI agents to conduct a structured gap assessment of an organization's AI systems against ISO/IEC 27090:2026 (Information security, cybersecurity and privacy protection — Artificial intelligence — Guidance for AI security), published by ISO/IEC JTC 1/SC 27.
It assesses how an organization (from small-and-medium enterprises to large institutions) protects its AI systems against AI-specific threats across seven security domains: data poisoning, model theft, prompt injection, evasion, output manipulation, supply chain dependencies, and operational drift.
Before Starting: Scope & Certification Boundaries
- Guidance only: ISO/IEC 27090:2026 is guidance, not a certifiable management-system standard. There is no "ISO 27090 certification".
- Deliverable type: The output is strictly a gap assessment, never a "compliant / certified" verdict.
- Certifiable counterparts: When the organization holds or pursues formal certification, map findings to ISO/IEC 27001:2022 (ISMS / Annex A controls) and ISO/IEC 42001:2023 (Artificial Intelligence Management System).
- Do not invent clause numbers: ISO/IEC 27090 clause numbering is copyright-protected. If the client or auditor provides a licensed copy or table of contents, map to their verified clauses. Otherwise, reference findings by topic domain and threat category (e.g.,
Threat: Data Poisoning,Domain: Model Protection). - Language: Deliver the assessment, interview notes, and final report in the user's requested language.
When to Use This Skill
Invoke this skill when:
- Assessing AI cybersecurity posture for custom models, fine-tuned models, or third-party AI services.
- Evaluating threats specific to AI (prompt injection, model theft, training data poisoning, inference attacks).
- Reviewing AI supply chains, vendor contracts, and AI Bill of Materials (AIBOM).
- Preparing an SME or enterprise for AI security audits before product launch or procurement.
- Mapping technical AI security controls into ISO/IEC 27001 or ISO/IEC 42001 risk registers.
- Responding to AI security incidents or evaluating adversarial robustness.
What ships with it
2 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.
- today First seen · 165 lines · 41 tokens per session scan A caca8d503c18
iso-27090-ai-security is a skill published in the GitHub repository mastepanoski/claude-skills (59 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 2,520 once invoked, about $0.0002 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 2026-10-30.
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