a2a-security-review
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity,…
Assemble a structured assurance / safety case for deploying an AI system — an explicit argument that it is acceptably safe for its context, backed by evidence (harm model, evals, guardrails, fairness, governance). Use to support a go/no-go deployment decision or an
Claude Code
$ npx -y skills add jassics/awesome-claude-security --skill safety-case --agent claude-code
Codex
$ npx skills add jassics/awesome-claude-security --skill safety-case -a codex -y
Cursor
$ npx skills add jassics/awesome-claude-security --skill safety-case -a cursor -y
Gemini CLI
$ npx skills add jassics/awesome-claude-security --skill safety-case -a gemini-cli -y
GitHub Copilot
$ npx skills add jassics/awesome-claude-security --skill safety-case -a github-copilot -y
OpenCode
$ npx skills add jassics/awesome-claude-security --skill safety-case -a opencode -y
Windsurf
$ npx skills add jassics/awesome-claude-security --skill safety-case -a windsurf -y
name: safety-case description: >- Assemble a structured assurance / safety case for deploying an AI system — an explicit argument that it is acceptably safe for its context, backed by evidence (harm model, evals, guardrails, fairness, governance). Use to support a go/no-go deployment decision or an audit/sign-off.
A defensible safety case: a top-level claim that the system is acceptably safe for its intended use and context, decomposed into arguments, each supported by concrete evidence and with residual risks stated honestly.
1. **Top claim** — "System X is acceptably safe to deploy for use case Y in context Z," with the acceptance criteria and who owns the decision. 2. **Sub-claims / arguments** — typically:
`ai-safety:safety-evaluation`, `ai-safety:safety-red-team`).
`ai-safety:guardrail-review`).
`ai-safety:responsible-ai-assessment`). 3. **Evidence** — link each argument to the actual artifacts/results; note their date, scope, and limitations. 4. **Residual risk & conditions** — what remains, why it's acceptable, and the conditions/monitoring/kill-switches that keep it acceptable post-deployment.
1. State the deployment context and acceptance criteria first (they set the bar). 2. Build the argument tree; for each leaf, attach evidence or mark it as a gap. 3. Make gaps explicit — a safety case with honest gaps beats one that hides them. 4. Conclude with a clear go / no-go / go-with-conditions recommendation.
A safety-case document (claims → arguments → evidence → residual risk → recommendation). Render the argument tree with `security-diagramming` and produce the document + executive summary with `security-reporting`.
A safety case is an honest argument, not a checklist or a rubber stamp. Unsupported arguments and unaddressed residual risks are the point — surface them so the decision-maker owns them explicitly.
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.
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity,…
Test the agent execution harness/runtime itself — LangChain/LangGraph, AutoGen, CrewAI, custom ReAct-style loops, or…
Assess an autonomous / tool-using AI agent for security end-to-end: tool privileges, autonomy and approval boundaries, excessive…
Test what an AI agent will actually do without human confirmation, including under injected-goal / prompt-injection scenarios, to…
Review the security of MCP (Model Context Protocol) servers/clients an agent uses: server trust tier, tool/resource description…
Inventory the tools/functions an AI agent can call and audit their privileges, side effects, and approval requirements to find…
Use for authorized security assessment of REST, GraphQL, WebSocket, or SOAP APIs, including discovery, authentication,…
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys)…
Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting…