An Eval Harness for Agent Skills: How We Change Behavior on Purpose

The text discusses a methodology for evaluating changes to AI agent skills used in security tools, emphasizing the importance of evidence-based improvements rather than relying on subjective feelings of enhancement. The process involves formulating a hypothesis about the expected behavior change, then running controlled experiments across multiple real-world code repositories to isolate the impact of the skill change. By using an unbiased grading system that includes a skill-blind judge and deterministic process-checks, the methodology ensures that any observed improvements are genuine and not influenced by subjective biases. This rigorous approach helps maintain trust in the security tool by ensuring that skill enhancements lead to measurable and reliable outcomes, as demonstrated with a recent skill rewrite that improved the AI's ability to use documentation for application discovery without sacrificing correctness.

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