skill-creator

Automating Claude skill creation involves using an eval-driven workflow that guides you from drafting SKILL.md to executing evals, benchmarking, and iteratively refining descriptions for better triggering accuracy.

Eval-driven iteration is a process of generating prompts, running triggers, comparing results with baselines, and capturing outcomes to propose better skill descriptions and tooling for continuous performance improvement.

Refining SKILL.md descriptions uses stored evaluation results to drive automated improvements, proposing better skill descriptions and tooling configurations before re-running eval cycles to validate triggering accuracy.

Yes, the skill uses PyYAML scripts to store benchmark-ready outputs, enabling streaming analysis and comparison against baselines during the iterative evaluation and improvement cycles.

Structuring a minimal eval set requires providing an initial prompt, creating test triggers, and starting iterative cycles to compare results against baselines and refine skill performance.

Incorrect triggering occurs when skill descriptions lack precision; leveraging captured eval results to propose refined descriptions and tooling, then re-running cycles, corrects the triggering behavior.