skill-evaluator

Benchmarking AI agent skills involves comparing outputs against a baseline to quantify improvements in accuracy, time, and token usage. The evaluation loop runs test cases with and without the skill to measure performance differences.

Evaluating agent capabilities requires designing diverse prompts and edge cases as test scenarios. You execute these test cases, write assertions to grade the outputs, and analyze patterns to progressively refine the agent's instructions.

Structured testing effectively supports regression testing of agent capabilities. By applying consistent test cases and assertions across iterations, you identify performance degradation and verify that new changes do not break existing functionality.

Writing custom assertions is required for output grading during skill evaluation. Assertions define the specific correctness criteria for your test cases, allowing the evaluation framework to automatically grade outputs and identify areas for improvement.

Iterative refinement improves agent skill quality by creating a feedback loop based on evaluation data and human review. Developers use graded test results to identify weaknesses and enhance the skill's instructions for better performance.