skill-creator

To validate AI agent skills, you can run automated frontmatter and SKILL.md body structure checks. This process ensures your skill directory meets formatting standards before packaging into a distributable archive.

The best way to package AI skills is to bundle the skill directory with optional resources like scripts and assets into a distributable .skill archive. This ensures repeatable, scalable deployment across hosts like Claude Code and Codex.

You can benchmark AI skills by running trigger-eval experiments across various agent hosts and aggregating the results. This allows you to measure performance and reduce regression risk during updates.

An evaluation-and-improvement lifecycle works by iteratively refining the skill description based on regression suite outcomes. You draft a skill, run evaluations, review benchmark results, and iterate until stable.

No specific external dependencies are required to automate skill authoring and packaging. The workflow handles frontmatter validation, structured body generation, and resource bundling natively.

A skill description regression suite fails after updates when changes introduce structural misalignments or trigger mismatches. Running iterative evaluation loops helps identify and correct these regressions before distribution.