skill-creator

You can automate Claude Code skill creation by running an evaluation loop that drafts SKILL.md content, tests triggers across train and test sets, and iteratively refines descriptions based on quantitative feedback. This handles the end-to-end workflow from drafting to deployment packaging.

To start building Claude skills, you need a repository containing a SKILL.md file with frontmatter (name and description) and a configured model. The automation uses these inputs to generate updated SKILL.md files and optional history or benchmark data.

The iterative improvement process works by running trigger and evaluation loops across train and test sets, capturing quantitative metrics, and generating benchmarks. It then uses this feedback to refine skill descriptions across multiple iterations and holdouts.

Yes, the skill creation workflow includes frontmatter validation to ensure your SKILL.md name and description fields are correctly formatted before proceeding to multi-stage content drafting and the evaluation loop.

The best way to generate benchmarks for Claude Code skills is to run trigger and evaluation loops across train and test datasets, capturing metrics throughout the iterative refinement process to produce quantitative performance data.

A limitation of automating skill creation is that it requires a pre-existing repository with properly structured SKILL.md frontmatter and a configured model, meaning it cannot generate skills from scratch without these initial inputs in place.