skill-creator

To create a Claude skill, draft a structured SKILL.md with trigger guidance, test it against realistic prompts, review benchmark results, and package the validated instructions into a distributable .skill file. This process ensures reliable triggering and consistent outcomes for repeated workflows.

Skill benchmarking evaluates Claude trigger accuracy by running comparison tests with and without the skill using realistic prompts. Analyzing pass rates, timing, and token usage from these evaluation outputs reveals trigger failures so you can iteratively refine and tighten the trigger descriptions.

Yes, pyyaml is a required dependency for the skill-creator. You need this Python package installed in your environment to properly parse SKILL.md frontmatter and execute the bundled scripts used during skill drafting, evaluation, and packaging.

Yes, you can revise existing Claude skills by using bundled scripts, references, and assets alongside evaluation outputs. This allows you to inspect failure transcripts, tighten instructions, add supporting references, and refine trigger optimization for improved behavior.

The best way to validate Claude skill behavior is to generate realistic test prompts, run comparative benchmarking with and without the skill, and inspect pass rates alongside token usage. Iterating on instructions based on these evaluation outputs ensures the skill triggers reliably before packaging.

Claude skills fail to trigger reliably when instructions and trigger descriptions are too broad or misaligned with actual user prompts. Benchmarking test runs, reviewing failure transcripts, and iteratively tightening the SKILL.md frontmatter and operating instructions resolves these triggering inaccuracies.