Agent Architecture Audit
This is a diagnostic workflow that systematically checks the 12-layer stack of agent applications for common failure patterns like wrapper regression, memory pollution, and hidden repair loops. You run it when your agent worked fine in the playground but breaks in production, or when users report degrading behavior after you've added prompt layers or memory systems. It searches your codebase for anti-patterns (tool requirements only in prompts, not code), maps failures to specific layers, and gives you severity-ranked findings with code-first fixes. The core insight is that most agent failures happen in the wrapper layers between the model and the user, not in the model itself. Worth running before any production release with tool calling or multi-step workflows.
- skills.sh installs
- 5.8K
- GitHub stars
- 300.3K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add affaan-m/ecc --skill agent-architecture-audit --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
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- Repository
- github.com/affaan-m/ecc
- Categories
- Code Review & Quality, AI & Agent Building
- First seen
- Jul 14, 2026
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