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 LegalQuants/lq-skills --skill nist-ai-rmfgit clone --depth 1 https://github.com/LegalQuants/lq-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/legalquants/lq-skills/nist-ai-rmf)<a href="https://agentmods.dev/skills/legalquants/lq-skills/nist-ai-rmf"><img src="https://agentmods.dev/badge/skills/legalquants/lq-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/legalquants/lq-skills/nist-ai-rmf"><img src="https://agentmods.dev/badge/skills/legalquants/lq-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 | $0.00182 | $0.02830 |
| Opus 5.5 | $0.00073 | $0.01132 |
| Sonnet 5.5 | $0.00036 | $0.00566 |
| Haiku 4.5 | $0.00018 | $0.00283 |
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 1mo 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NIST AI Risk Management Framework
What this skill does
Applies the NIST AI RMF — by name, by Subcategory, by Action ID — to whatever AI use case, governance question, or assessment the user brings. Three modes; pick one based on the user's question, default to consult if unsure.
- Consult — fast lookup. "What should I do per the AI RMF for X?" Returns applicable risks (for GenAI) and the relevant Suggested Actions / Subcategories, quoted verbatim. Best for quick gut-check questions.
- Governance plan — structured plan. "What should our governance plan include per the AI RMF?" Organized around the GOVERN function's Subcategories, with GenAI-specific actions layered in where applicable. Best for standing up or auditing an AI governance program.
- Assessment — full impact assessment. "Run a NIST AI RMF impact assessment for X." Walks all four functions for one specific system. Best when the user wants a documented artifact.
All three modes share the same source-of-truth: verbatim NIST text in references/. Quote the files; don't invent or paraphrase.
Source and scope
Two NIST publications underlie the skill. The verbatim extracted markdown ships in references/; the raw source HTMLs and maintainer-only re-extraction tooling live outside this distribution.
- NIST AI 100-1 (AI RMF 1.0, January 2023) — the Core framework. Applies to any AI system. Defines Govern, Map, Measure, Manage; their Categories and Subcategories; and seven Trustworthy AI characteristics. Extracted into
references/core/. - NIST AI 600-1 (Generative AI Profile, July 2024) — the GenAI-specific overlay. 12 enumerated GAI risks and 211 Suggested Actions coded
GV-X.Y-NNNetc., each mapped to a Core Subcategory. Extracted intoreferences/gai-profile/.
The Core applies to any AI system. The Profile is an overlay on top of the Core for generative systems. So:
- Non-GenAI system → Core only. Don't pull GAI Profile actions; many won't apply.
- GenAI system → Core for the framework + Profile for GenAI-specific risks and actions.
- Mixed pipeline → split per component.
What ships with it
20 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.
- LICENSE 1.5 KB
- README.md 3.2 KB
- references/core/functions.md 957 B
- references/core/glossary.md 8.0 KB
- references/core/govern.md 5.0 KB
- references/core/manage.md 3.3 KB
- references/core/map.md 4.8 KB
- references/core/measure.md 5.2 KB
- references/core/trustworthy-characteristics.md 14 KB
- references/crosswalk.md 4.8 KB
- references/gai-profile/actions-govern.md 22 KB
- references/gai-profile/actions-manage.md 16 KB
- references/gai-profile/actions-map.md 14 KB
- references/gai-profile/actions-measure.md 27 KB
- references/gai-profile/glossary.md 38 KB
- references/gai-profile/risks.md 24 KB
- references/README.md 4.6 KB
- references/templates/assessment.md 4.2 KB
- references/templates/consult.md 3.2 KB
- references/templates/governance-plan.md 3.3 KB
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.
- 1mo ago First seen · 159 lines · 182 tokens per session scan A 6f81b593337f
nist-ai-rmf is a skill published in the GitHub repository LegalQuants/lq-skills (64 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 182 tokens to every session and 2,830 once invoked, about $0.0007 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 2026-08-30.
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