writing-skills

Test-driven development for AI skill creation enforces a RED-GREEN-REFACTOR cycle on skill documentation, starting with writing failing pressure scenarios before building the minimal skill. This ensures robust agent behavior and effective documentation.

Pressure testing in AI agent behavior involves developing scenarios to test skills under realistic constraints like time, sunk cost, and authority. It identifies loopholes and ensures the agent adheres to strict testing protocols before deployment.

To write AI skills that resist manipulation, you must explicitly identify and address common rationalizations agents use to bypass rules. Iterative refactoring closes loopholes by enforcing strict testing protocols against baseline failure analysis.

Start the TDD cycle for skill documentation by writing failing test scenarios first, then writing the minimal skill to pass them, and finally refactoring to close loopholes. This baseline failure analysis guides the entire creation process.

Use TDD methodology for refining AI skills when you need to ensure documentation is robust and effective against manipulation. It is necessary when developing skills for enforcing coding standards where agents might be tempted to cut corners.

A limitation of using TDD for skill development is that it requires strict adherence to testing protocols before deployment. You must accurately anticipate pressure scenarios and rationalization countermeasures, as missing edge cases leaves loopholes open.