A skill is just an agent. So measure your changes.
Blog post from Arize
Skills for coding agents can be evaluated like other agents because they combine reusable prompt instructions with an agent harness, making their behavior traceable and measurable despite LLM variability. Using the arize-instrumentation skill as an example, the approach relies on a golden dataset of real applications, isolated container sandboxes that prevent agents from finding existing solutions, experiment runs that capture agent traces, token use, and latency, and evaluators that assess both generated code and the resulting telemetry. Teams establish a baseline, modify the skill, rerun the same experiment, and inspect failing traces to identify instructions that caused poor decisions. In one tested pull request, revisions that reduced duplicated guidance and improved manual and session tracing instructions increased trace correctness from 74% to 83%, raised an overall tracing grade from 13% to 50%, improved correct session use from 83% to 100%, and reduced token consumption by 9% and latency by 22%.
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