writing-skills

TDD for AI skills involves writing test scenarios to observe agent failures without the skill (RED), authoring the skill to address those failures (GREEN), and refactoring to ensure compliance under pressure. This methodology ensures skills are discoverable and robust against rationalization.

AI agent rationalization occurs when agents bypass or ignore skill rules under pressure, such as tight deadlines or complex inputs. TDD for skills addresses this by testing against these specific pressures during the refactor phase to close loopholes and enforce compliance.

You test AI agent behavior under pressure by creating scenarios where the agent must perform a task without the skill, simulating constraints like tight deadlines or complex code. Observing these failures establishes the RED phase for test-driven skill creation.

The best way to prevent AI agents from bypassing skill rules is to implement rationalization countermeasures through a TDD cycle. You test the agent against edge cases and pressures, then refactor the skill document to close loopholes and ensure strict compliance.

Yes, you can apply the RED-GREEN-REFACTOR cycle to AI documentation and skill authoring. The cycle is adapted to first test unwritten agent behavior, then write the skill to fix failures, and finally refactor to ensure the agent follows the skill under pressure.

You need a test-driven approach for refining AI skills when you must ensure they remain effective and compliant under complex conditions. It is essential for closing loopholes that allow agents to rationalize bypassing rules when facing tight deadlines or difficult tasks.