a2a-security-review
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity, capability-negotiation trust, and delegation-chain…
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.
$ npx -y skills add jassics/awesome-claude-security --skill ai-use-case-intake --agent claude-codeHow it fires
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/ai-use-case-intakeContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
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.
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.
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:**
(transparency) / minimal. High-risk and prohibited categories drive most obligations; check whether the use case falls in a listed high-risk area.
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.
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.
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.
A Claude Code plugin marketplace for the full cybersecurity & GenAI-security lifecycle — from recon and threat modeling to detection engineering, GRC, and CISO-level strategy. A pentester knows which OWASP test bends a broken-access-control endpoint.
Repo: jassics/awesome-claude-security
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