testing-skills-with-subagents

You test AI agent skills for compliance by simulating realistic pressure scenarios involving time constraints, sunk costs, and authority. This methodology identifies and mitigates common agent rationalizations and loopholes before deployment.

The RED-GREEN-REFACTOR cycle for skill testing is a TDD methodology adapted for AI development. It uses pressure scenarios to expose behavioral flaws, then guides you to refine skill documentation until the agent passes compliance tests under duress.

You identify and close documentation loopholes by using rationalization tables during the TDD process. This exposes common agent excuses and allows you to refine the skill's instructions, ensuring it resists manipulation and adheres strictly to standards.

Yes, understanding TDD principles is required for effective application in skill development and validation. This approach adapts traditional test-driven development concepts to rigorously refine AI agent behavior and ensure documentation compliance.

You test AI agent compliance by developing realistic pressure scenarios that simulate high-stress production environments. This verifies whether the agent will adhere to coding standards and constraints even when facing pressures like sunk costs or authority.