agent-architecture-audit

npx skills add mturac/everything-openai-codex --skill agent-architecture-audit --agent codex

Same command for any agent — swap --agent for claude-code, cursor, copilot.

# Agent Architecture Audit A diagnostic workflow for agent systems that hide failures behind wrapper layers, stale memory, retry loops, or transport/rendering mutations. ## When to Activate **MANDATORY for:** - Releasing any agent or LLM-powered application to production - Shipping features with tool calling, memory, or multi-step workflows - Agent behavior degrades after adding wrapper layers - User reports "the agent is getting worse" or "tools are flaky" - Same model works in playground but breaks inside your wrapper - Debugging agent behavior for more than 15 minutes without finding root cause **Especially critical when:** - You've added new prompt layers, tool definitions, or memory systems - Different agents in your system behave inconsistently - The model was fine yesterday but is hallucinating today - You suspect hidden repair/retry loops silently mutating responses **Do not use for:** - General code debugging — use `agent-introspection-debugging` - Code review — use language-specific reviewer agents - Security scanning — use `security-review` or `security-review/scan` - Agent performance benchmarking — use `agent-eval` - Writing new features — use the appropriate workflow

Tool requirements expressed only in prompt text (not code)
rg "must.*tool|必须.*工具|required.*call" --type md
Tool execution without validation
rg "tool_call|toolCall|tool_use" --type py --type ts
Hidden LLM calls outside main agent loop
rg "completion|chat\.create|messages\.create|llm\.invoke"
Memory admission without user-correction priority
rg "memory.*admit|long.*term.*update|persist.*memory" --type py --type ts
Fallback loops that run additional LLM calls
rg "fallback|retry.*llm|repair.*prompt|re-?prompt" --type py --type ts