secure-ai-agent-coding

Installation
SKILL.md

secure-ai-agent-coding

Build or review AI agents and LLM applications so their blast radius stays small, their behavior is auditable, and high-impact actions require explicit control.

Decision Tree

What is the user asking for?

  • Build a new AI agent or LLM feature: Read references/implementation-patterns.md, then apply references/controls.md before writing integration code.
  • Review an existing codebase or design: Run scripts/scan_patterns.py /path/to/project --json if source code is available, then read references/review-workflow.md.
  • Add tools, system calls, code execution, database writes, API calls, or email/message sending: Treat it as a high-risk action surface. Read references/implementation-patterns.md and require allowlisted tools, per-action authorization, rate limits, rollback, and approval gates.
  • Handle user data, production data, documents, web pages, email, vector stores, embeddings, or fine-tuning data: Read references/governance.md and references/threat-model.md before designing prompts or retrieval flows.
  • Debug a safety incident, unexpected model behavior, prompt injection, data leak, or harmful automation: Read references/review-workflow.md and references/gotchas.md, preserve logs, stop autonomous actions, and recover from a known safe state.
  • The request is only generic web app security with no AI, model, RAG, tool-call, or agentic workflow:
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