skill-creator

You can automate Claude skill development by drafting a SKILL.md, running initial evals, reviewing benchmark results, and using description optimization to iterate until triggering is reliable.

The best way to measure triggering accuracy is by running benchmarks and evals across train/test splits to evaluate how reliably the skill activates in the correct context.

Yes, you can optimize skill descriptions by running an end-to-end improvement loop that collects eval feedback, adjusts the description, and re-runs tests to maximize triggering accuracy.

Yes, the skill benchmarking and evaluation workflow requires a YAML dependency to process the SKILL.md configuration and drive the iterative improvement loop.

Your skill triggers incorrectly when the description lacks optimization, which you can fix by running iterative evals, analyzing benchmark results, and refining the description across train/test splits.

Yes, you can adapt existing skills by reading the current SKILL.md, running fresh evals to benchmark performance, and using the automated feedback loop to refine descriptions and triggering logic.