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/ai-use-case-intake

Intake a proposed AI use case and risk-classify it (EU AI Act tier / NIST AI RMF context), then gate it with the controls and documentation required before it can proceed. Use for AI governance intake, an AI use-case review board, or deciding what rigor a new AI project needs.

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awesome-claude-security
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$ npx -y skills add jassics/awesome-claude-security --skill ai-use-case-intake --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/ai-use-case-intake

Context preview

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Intake a proposed AI use case and risk-classify it (EU AI Act tier / NIST AI RMF context), then gate it with the controls and documentation required before it can proceed. Use for AI governance intake, an AI use-case review board, or deciding what rigor a new AI project needs.

SKILL.md

ai-use-case-intake.SKILL.md
name: ai-use-case-intake
description: >-
  Intake a proposed AI use case and risk-classify it (EU AI Act tier / NIST AI RMF
  context), then gate it with the controls and documentation required before it can
  proceed. Use for AI governance intake, an AI use-case review board, or deciding
  what rigor a new AI project needs.

Goal

A governance decision record for a proposed AI use case: its risk classification, the obligations that follow, the required controls/evidence, and a clear proceed / proceed-with-conditions / do-not-proceed outcome.

Steps

1. **Capture the use case** — purpose, users and affected people, data used, autonomy/impact on decisions, deployment context, and vendor/build status. 2. **Classify risk:**

  • **EU AI Act tier** — unacceptable (prohibited) / high-risk / limited

(transparency) / minimal. High-risk and prohibited categories drive most obligations; check whether the use case falls in a listed high-risk area.

  • **NIST AI RMF context** — characterize impact, stakeholders, and harm

potential (reuse `ai-safety:harm-modeling`). 3. **Derive obligations** from the tier: required documentation (model/data cards, intended-use + limitations), human oversight, transparency/disclosure, data governance, testing/eval evidence, logging, and post-market monitoring. 4. **Map required controls to evidence** — which already exist vs. gaps. Pull from `ai-safety:safety-evaluation`, `bias-fairness-assessment`, `guardrail-review`, `responsible-ai-assessment`. 5. **Decide & record** — proceed / conditions / stop, with owner, conditions, and review date. Add to the AI inventory/register.

Output

An intake decision record: use case · risk tier · obligations · required controls · evidence status · decision · conditions · owner · review date. Use `security-reporting` for the record and an executive summary; this also feeds the org's AI inventory.

Notes

Classify the risk tier first — it sets how much rigor everything else needs, and it catches prohibited uses before effort is spent. Keep the register current; intake is the front door of the governance program, not a one-time form. Verify current EU AI Act / NIST AI RMF text and any sector rules.

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