skill-creator

To create and test Claude skills, draft a SKILL.md file with a clear name and description, bundle reusable scripts and references, then run structured evaluations to measure trigger accuracy and iterate based on the results.

Evaluating skill trigger accuracy involves running structured evals against diverse prompts to quantify performance, then iterating on the SKILL.md description and bundled assets to improve triggering reliability across different use cases.

Yes, automating the skill lifecycle requires Python along with the anthropic and pyyaml libraries to execute evaluation scripts, parse configuration files, and benchmark triggering performance.

Bundling assets and references accelerates skill development by packaging reusable scripts and contextual documentation directly with the SKILL.md, enriching the skill and streamlining the iteration workflow.

Yes, you can automate the entire skill lifecycle from idea to deployment by drafting the initial SKILL.md, running evaluation sets to obtain trigger scores, and iterating based on benchmarking results.

You should run evals immediately after drafting a new SKILL.md to obtain an initial trigger score, then continue running structured evaluations after each iteration to guide improvements and quantify triggering performance.