Testing Agent Skills Systematically with Evals

Iterating on skills for agents like Codex poses challenges in determining whether changes improve performance or merely alter behavior, with regressions such as skipped steps or leftover files complicating the process. A skill, comprising prompts and instructions for a large language model (LLM), is best improved by evaluating it like any LLM prompt through "evals," which assess if the model's outputs and process align with intended goals. This involves defining measurable success criteria, such as outcome, process, style, and efficiency goals, and using a pattern akin to lightweight end-to-end tests to validate performance. The post illustrates this with a skill for setting up a React demo app, emphasizing the importance of clear naming and descriptions in the SKILL.md file for reliable skill invocation. Manual testing reveals hidden assumptions, and a small, targeted prompt set helps catch regressions early. Evals leverage both deterministic checks and qualitative, rubric-based grading to ensure skills meet both functional and stylistic expectations. As skills mature, evals can be expanded with additional checks to maintain and improve performance consistency, shifting from intuitive improvements to evidence-based validations.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.