skill-testing-output

Benchmark skill effectiveness by running two parallel generations—one with the skill and one without—then using a grader to compare output quality. This structured A/B testing approach requires an evals.json file with prompts to produce per-eval results and an overall benchmark.

A/B output comparison is a method to quantify quality differences by evaluating outputs generated with a skill against a baseline without it. It provides objective evidence of improvement for code, docs, data transformations, or content creation tasks.

Yes, skill evaluation applies to any generation task including code, documentation, data transformations, and content creation. It measures output quality wherever measurable improvement matters and evidence-based validation is required.

Yes, an evals.json file containing prompts is required to run automated grading benchmarks. The evaluation process collects timing and output data from two parallel runs, then applies a grader to produce per-eval results and an overall benchmark.

The best way to measure output quality improvements is through structured A/B testing that compares baseline outputs against skill-enhanced outputs. This method captures timing data and quality metrics to deliver objective benchmark evidence of value.

Set up controlled A/B tests by defining prompts in an evals.json file, executing two parallel runs with and without the skill, collecting timing and output data from both, and applying a grader to generate per-eval results and an overall benchmark.