skill-creator

To benchmark AI agent skills, you spawn parallel test runs with and without the skill, grade assertions, and aggregate quantitative benchmarks to compare outputs across iterations. An HTML review viewer visualizes the performance differences.

Eval-driven iterative improvement for agent development is a structured workflow that generates test cases, runs parallel baseline comparisons, and validates skill performance quantitatively across multiple refinement cycles.

You optimize skill descriptions for triggering accuracy by applying a machine learning loop that tests realistic user queries, measuring activation rates, and refining the description to maximize correct skill selection.

Yes, you can compare baseline performance against a modified AI skill by spawning parallel test runs, grading assertion outputs, and aggregating the benchmarks into a visual HTML reviewer for direct comparison.

The best way to create and refine AI skills end-to-end is using a structured eval-driven workflow that guides you from initial intent capture and drafting to test case generation, benchmarking, and description optimization.