What problem does it solve? Proving that a code change actually improves performance requires more than a single timing run. This Skill runs controlled A/B benchmarks of an original and an optimized Node.js function, then applies statistical tests (p-value, significance, improvement percentage) to confirm whether the optimization is genuinely effective. ## Core Features & Use Cases - Controlled A/B Benchmarking: Runs the original and optimized implementations with identical arguments, mock setup, and benchmark configuration so the comparison is statistically valid. - Statistical Verdicts: Compares results using p-value (< 0.05) and improvement percentage (> 25%) to produce an optimization_effective verdict, with up to 3 optimized retry attempts. - Structured Reporting: Generates a markdown report with ops/sec, histograms, variance assessment, and a side-by-side comparison table, optionally saved to .nsolid/assets/. - Use Case: After a CPU spike analysis identifies a hot function and proposes a fix, use this Skill to benchmark both versions and obtain statistical proof that the fix improves throughput before applying it. ## Quick Start Ask the assistant to validate the proposed optimization of the hot function by running an A/B benchmark against the original implementation.
