skill-creator

To create and test AI skills, you draft a SKILL.md file, generate realistic test prompts, and run paired with-skill versus baseline evaluations. This workflow ensures reproducible skill behavior and objective expectations through quantitative benchmarking across iterations.

The best way to benchmark prompt engineering changes is to run parallel with-skill and baseline evaluations, collect timing and token metrics, grade expectations, and aggregate benchmark statistics. This provides quantitative measurement to validate skill improvements.

Skill description optimization works by running an improvement loop that proposes description revisions and re-evaluates them on train and holdout splits. It optionally calls the Claude CLI to refine descriptions until trigger reliability reaches an acceptable threshold.

Yes, you need Python scripts to run evaluations, aggregate benchmark statistics, and view results. The provided scripts integrate with the workflow to spawn parallel runs, collect timing and token data, and generate viewer reports for human feedback.

Yes, you can use YAML to define metadata and operational instructions when drafting a SKILL.md file. Capturing user intent and structuring the skill definition in YAML ensures clear operational instructions and reproducible behavior.

A limitation of automating skill development is that quantitative benchmarks cannot fully replace human judgment. While the improve loop proposes description revisions and generates viewer reports, developers must still inspect results and iterate based on qualitative feedback.