Testing Skills With Subagents

Testing skill documentation with TDD involves running a RED-GREEN-REFACTOR cycle adapted for AI rules. You write pressure scenarios that cause agent failures, create minimal skill code to fix those failures, and iteratively refactor the documentation to eliminate any compliance loopholes.

Yes, TDD methodology can be applied to AI skill documentation by treating agent compliance failures as failing tests. You baseline test the agent without the skill, write minimal documentation to address the failures, and refactor the skill to ensure robust rule enforcement under pressure.

To prevent AI agents from cutting corners, you simulate realistic scenarios with tight deadlines and sunk costs to expose weaknesses in your skill. The testing process reveals exactly how the skill documentation must be rephrased or augmented to stop agents from bypassing coding standards.

The limitation of pressure scenario testing is that it requires iteratively refining documentation to close every loophole. If the pressure scenarios do not accurately reflect real-world authority or time constraints, the skill may still fail to enforce compliance when deployed to actual production environments.