ai-mcp-audit

# MCP Audit — On-Demand Skill & MCP Server Security Audit ## Quick start ``` /ai-mcp-audit scan # coherence analysis (all surfaces) /ai-mcp-audit scan --target <skill-name> # scoped scan (cost-saving) /ai-mcp-audit audit-update <skill-name> # rug-pull detection vs baseline /ai-mcp-audit baseline set --target all # anchor known-good snapshot ``` ## Workflow Cold-path LLM-driven security audit (spec-107 D-107-08). Three modes: 1. **Coherence analysis** — declared `description` vs observed code behavior. 2. **Rug-pull detection** — diff post-update files against trusted baseline. 3. **Baseline anchoring** — tamper-evident reference for future audits. Counterpart to **hot-path** runtime control: - **Hot path (Capa 1)** — `prompt-injection-guard.py` PreToolUse hook, $0 cost, deterministic IOC matching, immune to prompt injection of payload (D-107-06). - **Cold path (Capa 2, this skill)** — on-demand LLM analysis. Apt for post-install review, pre-merge audit. Does NOT replace `/ai-security` (CVE/SBOM), `/ai-governance` (compliance), `/ai-verify` (quality). ## When to Use - After installing a new skill or MCP server (`scan`). - After updating an existing skill, especially auto-update (`audit-update <skill>`). - After fresh-cloning or anchoring known-good state (`baseline set`). - Before merging PRs touching `.agents/skills/`, `.codex/skills/`, `.agents/skills/`, `.github/skills/`. - NOT for runtime payload inspection (use prompt-injection...