skill-creator

To create Claude skills, you capture the design intent, draft the SKILL.md file with frontmatter and instructions, then run evaluation cycles to test trigger accuracy and iterate on improvements. This structured workflow reduces trial-and-error during skill design.

Evaluating Claude skill performance involves running trigger evaluations, collecting metrics, and comparing results against baselines. You can use benchmarking scripts to produce human- and machine-readable reports that quantify progress and guide iterative improvements across testing cycles.

Iterating on a SKILL.md file involves harnessing evaluation results to refine descriptions, scripts, and test prompts, then re-running evaluation cycles. This loop analyzes prompts and triggers to coordinate optional benchmarking scripts that measure progress across iterations.

Yes, PyYAML is used to manage the skill creation workflow. It supports the structured SKILL.md creation process by handling the YAML frontmatter required to formalize instructions, references, and evaluation triggers within Claude skills.

Benchmarking scripts in Claude skill evaluation provide quantitative progress reports but require coordinating optional cycles. They measure trigger accuracy and iteration progress but do not replace manual review of intent capture, prompt analysis, or description optimization.