skill-creator

You can optimize Claude skill triggering accuracy by using an automated trigger-eval loop that tests description variations against realistic prompts, analyzes variance-aware benchmark results, and iterates until the skill meets your success criteria.

Benchmarking Claude skills involves running trigger-detection evaluations across multiple configurations, generating test sets, grading results with variance-aware comparison, and iterating through the test-and-benchmark loop until performance meets your goals.

Creating a new Claude skill requires defining your goal, specifying desired trigger contexts, drafting a SKILL.md file, generating aligned test prompts, and iterating through eval and benchmark loops to converge on reliable behavior.

Yes, you need Python and pyyaml installed to run the eval and benchmark scripts, generate test sets, and execute the automated trigger-eval loop for skill optimization.

Claude skills fail to trigger correctly when the SKILL.md description lacks coverage for realistic input variations; you can fix this by generating targeted test sets, running trigger-detection evaluations, and optimizing the description through iterative benchmarking.

Yes, you can improve an existing Claude skill by providing your current draft SKILL.md, defining expected outcomes for new test prompts, and using the benchmark loop to grade and analyze results for targeted workflow improvements.