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Nist Ai Rmf

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mastepanoski
nist-ai-rmf

AI risk assessment using NIST AI RMF 1.0 framework. Evaluate AI systems across 4 core functions (Govern, Map, Measure, Manage) for trustworthy and responsible AI deployment.

Overview

Publishermastepanoski
Repositoryclaude-skills
Skill namenist-ai-rmf
Stars
63
Forks
4
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mastepanoski on GitHub. Read the source before you install it.

Installation

Install the Nist Ai Rmf AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mastepanoski/claude-skills/tree/main/skills/nist-ai-rmf
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mastepanoski/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/nist-ai-rmf .claude/skills/nist-ai-rmf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nist Ai Rmf in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nist Ai Rmf on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nist Ai Rmf is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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]

Trustworthy AI Characteristics

The AI RMF identifies seven characteristics of trustworthy AI that serve as evaluation criteria across all functions:

  1. Valid and Reliable: System performs as intended with consistent results
  2. Safe: System does not endanger human life, health, property, or the environment
  3. Secure and Resilient: System withstands adverse events and recovers gracefully
  4. Accountable and Transparent: Information about the system is available to stakeholders
  5. Explainable and Interpretable: Mechanisms and outputs can be understood
  6. Privacy-Enhanced: Human autonomy and data rights are protected
  7. Fair with Harmful Bias Managed: System does not produce discriminatory outcomes

The 4 Core Functions

The AI RMF Core has four functions, each broken into categories and subcategories. Category-level guidance and evaluation questions are in references/core-functions.md.

FunctionPurpose
GOVERNPolicies, processes, and accountability for AI risk management (cross-cutting)
MAPEstablish context and identify AI risks
MEASUREAnalyze, assess, and track identified risks
MANAGEPrioritize and act on risks; respond and recover

Audit Procedure

Follow these steps systematically:

Step 1: System Understanding (15 minutes)

  1. Review AI system:

    • Analyze ai_system_description and system_lifecycle_stage
    • Identify system type (classifier, generative, recommender, autonomous, etc.)
    • Document data sources, models, and deployment environment
    • Note stakeholders and affected communities

  2. Understand context:

    • Review organization_context and regulatory environment
    • Identify applicable laws and standards
    • Note risk tolerance and existing controls

  3. Define scope:

    • Determine which functions and categories to assess
    • Prioritize based on lifecycle stage and concerns

Step 2: GOVERN Assessment (20 minutes)

Evaluate organizational governance:

  • G1: Are AI risk policies in place and transparent?
  • G1.1: Legal/regulatory requirements understood and documented?
  • G1.2: Trustworthy AI characteristics in organizational policies?
  • G1.5: Monitoring and review processes planned?
  • G1.6: AI system inventory maintained?
  • G2: Accountability structures defined?
  • G2.1: Roles and responsibilities clear?
  • G2.3: Executive leadership accountable?
  • G3.1: Diverse team informing decisions?
  • G4: Risk culture fostered?
  • G5: Stakeholder engagement processes in place?
  • G6: Third-party risks addressed?

Step 3: MAP Assessment (20 minutes)

Evaluate risk identification and context:

  • M1.1: Intended purposes and deployment context documented?
  • M1.5: Risk tolerances determined?
  • M2.1: AI tasks and methods defined?
  • M2.2: Knowledge limits and human oversight documented?
  • M3.1: Benefits examined and documented?
  • M3.2: Costs from AI errors documented?
  • M3.5: Human oversight processes defined?
  • M4.1: Component risks mapped?
  • M5.1: Impact likelihood and magnitude documented?

Step 4: MEASURE Assessment (25 minutes)

Evaluate risk measurement and monitoring:

  • ME1.1: Risk metrics selected and applied?
  • ME2.3: Performance criteria measured?
  • ME2.4: Production behavior monitored?
  • ME2.5: Validity and reliability demonstrated?
  • ME2.6: Safety risks evaluated?
  • ME2.7: Security and resilience evaluated?
  • ME2.9: Model explainability documented?
  • ME2.10: Privacy risk examined?
  • ME2.11: Fairness and bias evaluated?
  • ME3.1: Risk tracking in place?
  • ME3.3: User feedback mechanisms established?

Step 5: MANAGE Assessment (20 minutes)

Evaluate risk response and treatment:

  • MA1.1: System achieves intended purposes?
  • MA1.2: Risk treatment prioritized?
  • MA1.3: Response plans for high-priority risks?
  • MA2.1: Non-AI alternatives considered?
  • MA2.3: Unknown risk response procedures?
  • MA2.4: Deactivation mechanisms in place?
  • MA3.1: Third-party risks monitored?
  • MA4.1: Post-deployment monitoring implemented?
  • MA4.3: Incident communication and recovery documented?

Step 6: Report Generation (20 minutes)

Compile assessment findings with ratings and recommendations.

Output Format

The copy-ready assessment report template (executive summary, per-function assessment, trustworthiness characteristics, action plan) is in references/report-template.md.

Scoring Guide

Use this scale for subcategory ratings:

RatingDescription
Not ImplementedNo evidence of activity or documentation
PartialSome activity but inconsistent or incomplete
SubstantialMostly implemented with minor gaps
FullFully implemented and regularly maintained

Use this scale for trustworthiness characteristics:

ScoreDescription
1Not addressed
2Minimally addressed
3Partially addressed
4Substantially addressed
5Fully addressed and monitored

Generative AI Considerations

For generative AI systems, additionally evaluate (per NIST AI 600-1 GenAI Profile, July 2024):

  • Content provenance: Mechanisms to track AI-generated content origin
  • Confabulation risk: Controls for hallucinated or fabricated outputs
  • Data privacy: Training data protections and consent
  • Environmental impact: Computational resource consumption
  • Information security: Prompt injection and adversarial robustness
  • Harmful content: Filters and safeguards for toxic or dangerous outputs
  • Third-party risks: Foundation model and API dependencies
  • Human-AI interaction: User awareness that they are interacting with AI

Critical Infrastructure Note

If the AI system supports critical infrastructure operations, also check current NIST AI RMF profile material for trustworthy AI in critical infrastructure. NIST released a concept note for this profile on April 7, 2026; treat it as additional context rather than a replacement for AI RMF 1.0.

Best Practices

  1. Start with GOVERN: Establish governance before mapping risks
  2. Iterate continuously: Risk management is ongoing, not one-time
  3. Engage stakeholders: Include diverse perspectives in assessments
  4. Document everything: Maintain evidence for accountability
  5. Align with existing frameworks: Integrate with NIST CSF, ISO 42001, SOC 2
  6. Tailor to context: Adapt depth of assessment to system risk level
  7. Test in production: Monitor deployed systems, not just pre-deployment
  8. Plan for failure: Have incident response and decommissioning procedures
  9. Consider societal impact: Look beyond organizational risks
  10. Stay current: Monitor evolving AI regulations and standards

Version

1.0 - Initial release (NIST AI RMF 1.0 compliant)

Remember: The NIST AI RMF is voluntary and risk-based. Not all subcategories apply to every system. Tailor the assessment depth to the system's risk profile and organizational context.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nist Ai Rmf AI skill do?

AI risk assessment using NIST AI RMF 1.0 framework. Evaluate AI systems across 4 core functions (Govern, Map, Measure, Manage) for trustworthy and responsible AI deployment.

Why use Nist Ai Rmf on TypingMind?

Because you install it once and use it with any model. Nist Ai Rmf is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nist Ai Rmf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mastepanoski/claude-skills/tree/main/skills/nist-ai-rmf. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nist Ai Rmf?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nist Ai Rmf?

As many as you like. As long as a model supports skills, you can use Nist Ai Rmf with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nist Ai Rmf AI skill free?

Yes. It is published on GitHub by mastepanoski under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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