skill-creator

You can automate AI skill creation by running an evaluation loop that drafts skills, tests prompts, benchmarks iterations, and auto-improves descriptions using history-aware optimization until achieving stable performance.

Trigger-evaluation loop iteration is a testing mechanism that repeatedly runs prompts against holdout test sets, benchmarks the results, and refines the skill description to produce consistent, traceable upgrades with measurable improvements.

Yes, you can benchmark AI skill performance across multiple prompts and holdout test sets by using built-in evaluation scripts that compare iterations and visualize results to deliver measurable, traceable skill improvements.

Yes, AI skill development requires integration with evaluation scripts like run_eval.py and run_loop.py to perform frontmatter validation, script packaging, and optional live reporting for consistent, traceable skill upgrades.

The best way to package a distributable AI skill artifact is to use the built-in packaging and distribution support, which compiles the validated scripts and assets into a reusable .skill artifact for deployment across multiple projects.

Your AI skill description may not converge during iteration testing if the history-aware optimization lacks sufficient holdout test sets or if the trigger-evaluation loop fails to apply consistent benchmarking across multiple prompts.