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 vaquarkhan/compliance-agent-skills --skill nist-ai-rmf-governancegit clone --depth 1 https://github.com/vaquarkhan/compliance-agent-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/vaquarkhan/compliance-agent-skills/nist-ai-rmf-governance)<a href="https://agentmods.dev/skills/vaquarkhan/compliance-agent-skills/nist-ai-rmf-governance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/compliance-agent-skills/nist-ai-rmf-governance/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/vaquarkhan/compliance-agent-skills/nist-ai-rmf-governance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/compliance-agent-skills/nist-ai-rmf-governance.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 | $0.00163 | $0.01972 |
| Opus 5 | $0.00081 | $0.00986 |
| Sonnet 5 | $0.00033 | $0.00394 |
| Haiku 4.5 | $0.00016 | $0.00197 |
Grade A, and why
nist-ai-rmf-governance 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NIST AI RMF Governance
Overview
The NIST AI Risk Management Framework (AI RMF 1.0) (NIST AI 100-1, January 2023) provides voluntary guidance for trustworthy AI across four core functions:
| Function | Purpose | Agent/LLM relevance |
|---|---|---|
| GOVERN | Culture, policies, accountability, workforce | AI governance board, agent approval process |
| MAP | Context, categorization, impacts, benefits/risks | Agent use-case inventory, stakeholder harm analysis |
| MEASURE | Metrics, evaluation, TEVV, documentation | Redaction FN/FP rates, prompt injection tests, bias evals |
| MANAGE | Prioritize, respond, recover, communicate | Kill switches, MCP allowlists, incident playbooks |
Trustworthy AI characteristics (NIST): valid & reliable, safe, secure & resilient, accountable & transparent, explainable & interpretable, privacy-enhanced, fair with harmful bias managed.
Companion resources: NIST AI RMF Playbook, Generative AI profile (NIST AI 600-1), cross-walk to NIST CSF 2.0 (nist-csf-2-assessment).
This skill is the primary AI-specific governance path for compliance-agent-skills deployments.
When to Use
Use this skill when:
- Deploying or auditing LLM agents, MCP servers, or GenAI in regulated workflows
- Building AI governance policies (acceptable use, human oversight, model selection)
- Mapping AI risks for compliance agents processing PHI, PCI, or PII
- TEVV (test, evaluation, verification, validation) for agent outputs and tool calls
- Executive/board AI risk reporting using NIST taxonomy
- Harmonizing with SOC 2 (CC8 change, CC7 monitoring) and ISO 27001 for AI systems
- Customer AI due diligence questionnaires (NIST AI RMF, SOC 2 + AI addendum)
Do not use this skill when:
- Pure infrastructure security with no AI/ML (use
nist-csf-2-assessment) - PHI redaction tuning only (use
hipaa-phi-redaction-pipeline) - Children's data FTC rules (use
coppa-children-privacy) - Student education records (use
ferpa-education-records)
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 · 150 lines · 163 tokens per session scan A 611193b7940a
nist-ai-rmf-governance is a skill published in the GitHub repository vaquarkhan/compliance-agent-skills (2 stars, last pushed 18d ago), licensed MIT. It adds 163 tokens to every session and 1,972 once invoked, about $0.0008 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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