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 nist-ai-rmfgit 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/nist-ai-rmf)<a href="https://agentmods.dev/skills/mastepanoski/claude-skills/nist-ai-rmf"><img src="https://agentmods.dev/badge/skills/mastepanoski/claude-skills/nist-ai-rmf/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/nist-ai-rmf"><img src="https://agentmods.dev/badge/skills/mastepanoski/claude-skills/nist-ai-rmf.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 | $0.00044 | $0.05416 |
| Opus 5 | $0.00022 | $0.02708 |
| Sonnet 5 | $0.00009 | $0.01083 |
| Haiku 4.5 | $0.00004 | $0.00542 |
Grade A, and why
nist-ai-rmf 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 — 575 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NIST AI Risk Management Framework (AI RMF 1.0)
This skill enables AI agents to perform a comprehensive AI risk assessment using the NIST AI Risk Management Framework (AI RMF 1.0), published January 2023 by the National Institute of Standards and Technology.
The AI RMF is a voluntary, technology- and sector-agnostic framework designed to help organizations manage risks associated with AI systems throughout their lifecycle. It promotes trustworthy AI development by addressing risks that affect individuals, organizations, and society.
Use this skill to identify, assess, and manage AI risks; establish governance structures; ensure trustworthy AI characteristics; and align with international AI risk management best practices.
Combine with "ISO 42001 AI Governance" for comprehensive compliance coverage or "OWASP LLM Top 10" for security-focused assessment.
When to Use This Skill
Invoke this skill when:
- Assessing risks of AI systems before deployment
- Establishing AI governance and accountability structures
- Evaluating trustworthiness of AI products and services
- Preparing for regulatory compliance (EU AI Act, state AI laws)
- Conducting periodic AI risk reviews
- Evaluating third-party AI tools and vendors
- Building organizational AI risk management programs
- Documenting AI system risks for stakeholders
Inputs Required
When executing this assessment, gather:
- ai_system_description: Description of the AI system (purpose, capabilities, deployment context, users, data sources) [REQUIRED]
- system_lifecycle_stage: Current stage (design, development, deployment, monitoring, decommissioning) [OPTIONAL, defaults to deployment]
- organization_context: Organization size, industry, risk tolerance, regulatory environment [OPTIONAL]
- deployment_domain: Sector and setting, including whether the system supports critical infrastructure [OPTIONAL]
- existing_controls: Current risk management processes or controls in place [OPTIONAL]
- specific_concerns: Known risks, incidents, or areas of focus [OPTIONAL]
- stakeholders: Key stakeholders and affected communities [OPTIONAL]
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 · 575 lines · 44 tokens per session scan A 9696a60f866a
nist-ai-rmf is a skill published in the GitHub repository mastepanoski/claude-skills (53 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 5,416 once invoked, about $0.0002 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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