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Rai Standards

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From the author

Consolidated Responsible AI standards reference: NIST AI RMF 1.0, AI STRIDE threat-modeling overlay, EU AI Act risk tiers, and an open-standards catalog with phase mapping
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The rai-standards skill provides a structured repository of reference material for Responsible AI (RAI) planning. It consolidates key frameworks such as the NIST AI Risk Management Framework (RMF) 1.0, an AI-specific adaptation of the STRIDE threat-modeling methodology, and a paraphrased overview of EU AI Act risk tiers. Designed to support AI planners, this skill maps specific RAI phases to relevant standards, ensuring that governance, risk management, and security analysis are grounded in established industry and regulatory references.

What it does

  • Provides a centralized index of NIST AI RMF trustworthiness characteristics.
  • Maps RAI project phases to corresponding standards and governance frameworks.
  • Offers an AI-specific extension of the STRIDE threat-modeling methodology.
  • Summarizes EU AI Act risk tiers and high-risk obligation themes.
  • Maintains a catalog of open-standard resources for AI management and risk assessment.
  • Defines a protocol for layering custom frameworks onto the default NIST baseline.

When to use it

  • When scoping an AI project and identifying necessary governance frameworks.
  • During the risk classification phase to align with regulatory tiers.
  • When performing security model analysis using AI-adapted threat modeling.
  • When documenting risk management, data governance, and human oversight requirements.
  • When integrating sector-specific or regional AI standards into an existing RAI workflow.

Advantages

  • Centralizes disparate RAI standards into a single, reusable package.
  • Facilitates consistent mapping of project phases to regulatory and industry standards.
  • Extends traditional security threat modeling to address AI-specific risks like data poisoning and model tampering.
  • Supports interoperability by allowing custom frameworks to be layered over a NIST baseline.
  • Provides clear attribution and licensing guidance for all included reference materials.

Usage examples

“What are the NIST AI RMF trustworthiness characteristics?”
The agent lists the seven characteristics including Valid and Reliable, Safe, Secure and Resilient, Accountable and Transparent, Explainable and Interpretable, Privacy-Enhanced, and Fair with Harmful Bias Managed.
“How should I approach threat modeling for an AI inference endpoint?”
The agent provides the AI STRIDE matrix, highlighting concerns like inference exfiltration, data poisoning, and identity confusion, and suggests treating the endpoint as a trust boundary.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/rai-standards/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip rai-standards.zip -d .agents/skills

Documentation by Microsoft

RAI Standards Skill

This skill is the reusable standards package for the RAI Planner. It consolidates the embedded NIST AI RMF content, the AI STRIDE threat-modeling overlay, and a paraphrased EU AI Act reference so the phase playbook can stay focused on workflow and orchestration.

Attribution and licensing posture

  • NIST AI RMF 1.0 is a U.S. Government document and is reproduced here as public-domain reference material with attribution. Public-domain material carries no license obligation, so it adds no term to the package license expression.

  • EU AI Act content in this skill is paraphrased and attributed rather than quoted verbatim, consistent with the open legal-text posture used in the repository. The paraphrase is this repository's own expression.

  • The AI STRIDE overlay is Microsoft-authored reference material for threat-modeling reuse in the RAI workflow.

Every file in this package is therefore governed by the repository-original license. The table records the source each file draws on.

PathLicenseOrigin
references/nist-ai-rmf-govern.mdCC-BY-4.0Adapted from NIST AI RMF 1.0 (public domain)
references/nist-ai-rmf-map.mdCC-BY-4.0Adapted from NIST AI RMF 1.0 (public domain)
references/nist-ai-rmf-measure.mdCC-BY-4.0Adapted from NIST AI RMF 1.0 (public domain)
references/nist-ai-rmf-manage.mdCC-BY-4.0Adapted from NIST AI RMF 1.0 (public domain)
references/eu-ai-act.mdCC-BY-4.0Paraphrase of EU AI Act 2024/1689, not reproduced
references/ai-stride-overlay.mdCC-BY-4.0Repository-original
SKILL.md and remaining package contentCC-BY-4.0Repository-original

Framework index

NIST AI RMF trustworthiness characteristics

KeyCharacteristicDescription
validReliableValid and ReliableBase characteristic for correctness, robustness, and stability
safeSafeSafety and harm prevention under normal and adversarial conditions
secureResilientSecure and ResilientResistance to attack, misuse, and failure
accountableTransparentAccountable and TransparentGovernance, auditability, and decision provenance
explainableInterpretableExplainable and InterpretableUnderstandability of model behavior and outputs
privacyEnhancedPrivacy-EnhancedProtection of personal data and confidentiality
fairBiasManagedFair with Harmful Bias ManagedBias detection, mitigation, and equitable outcomes

Phase-to-framework mapping

RAI phasePrimary standards packageNotes
Phase 1 ScopingNIST AI RMF Govern + MapContext, purpose, stakeholders, and policy framing
Phase 2 Risk ClassificationNIST AI RMF GovernGovernance culture, DEI&A, and stakeholder engagement
Phase 3 Standards MappingNIST AI RMF Govern + MeasureCore standards mapping and TEVV alignment
Phase 4 Security Model AnalysisAI STRIDE overlay + MeasureThreat modeling and overlap with security analysis
Phase 5 Impact AssessmentNIST AI RMF ManageRisk prioritization, mitigation, and monitoring
Phase 6 Review and HandoffNIST AI RMF Manage + EU AI ActRegulatory review, incident response, and evidence handoff

Customer extension pattern

The default framework remains NIST AI RMF 1.0. When a customer supplies an additional framework, preserve the default NIST mapping as a baseline and layer the custom framework on top with explicit attribution. This keeps the planner interoperable while allowing organizations to add ISO, sector, or regional references without rewriting the core playbook.

Open-standards catalog

Use the links below as the reference catalog for open standards and governance resources. Do not reproduce normative text verbatim when the license posture does not allow it.

Files

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Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

Security

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Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/hve-core · .github/skills/rai/rai-standards · commit f425e94
Licence
Creative Commons Attribution 4.0 International (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download rai-standards.zip