skill-creator

To create and test AI skills, you define the skill intent, write the `SKILL.md` file, set up test cases, and run evaluations to measure performance against a baseline. This iterative development loop refines skill descriptions for optimal triggering accuracy.

Measuring AI agent performance involves running benchmarks with and without the skill, analyzing the evaluation results, and optimizing the prompt engineering in the skill description to improve triggering accuracy and overall task execution.

Yes, iterative workflow automation improvement is supported through testing loops that evaluate performance metrics, gather feedback, and refine the skill logic until the AI agent performs optimally for your specific use case.

Skill development handles edge cases by interviewing users during the creation phase to identify potential scenarios, which are then translated into test cases for evaluation, ensuring the prompt engineering is robust against unexpected inputs.

No prior coding experience is strictly required to write and evaluate `SKILL.md` files, as the process guides users of all technical backgrounds through defining intent, setting up test cases, and analyzing benchmark results.

An AI skill might not trigger correctly if the skill description lacks optimization, requiring you to run performance tuning evaluations, analyze benchmark results, and iteratively refine the description for improved accuracy.