skill-creator

To create and test Claude skills, use an iterative eval loop to draft prompts, run controlled evaluations, analyze benchmark results, and rewrite based on feedback. This structured workflow progressively improves skill quality and triggering accuracy.

Automating skill evaluation involves running eval prompts through a configurable testing framework to collect results and benchmark performance. Basic analytics help analyze outputs, ensuring robust behavior and accurate triggering for AI agents.

Frontmatter-driven metadata improves triggering accuracy by enforcing a structured metadata model that defines skill descriptions. Generating and refining this frontmatter ensures the correct skill activates for the appropriate user intent.

Yes, you can co-maintain AI agent skills with human users. The workflow supports guided collaboration, allowing teams to design, test, and iteratively improve skills together for scalable development.

Yes, pyyaml is required as a dependency. It supports the configurable testing framework used to run eval prompts, collect benchmark results, and manage the frontmatter-driven metadata model.

The iterative workflow for skill optimization requires structured frontmatter and controlled evals to function correctly. Without running benchmark analytics, the rewriting phase lacks the feedback needed to improve triggering accuracy and behavior.