What problem does it solve? Performance claims are often based on vibes, debug builds, or single lucky runs. This Skill turns benchmarking into a controlled experiment: it defines the workload, stabilizes the environment, collects enough independent samples, and reports results with uncertainty and caveats so performance decisions are trustworthy. ## Core Features & Use Cases - Rigorous Measurement Discipline: Enforces production-equivalent builds, warm-up, independent runs, and harness-native statistics instead of averages or single samples. - Full Metric Coverage: Guides capture of latency p50/p95/p99, throughput, error rate, CPU, memory, allocations, binary/bundle/image size, and cold vs warm startup. - Regression Comparison: Compares baseline and candidate under identical conditions with predeclared thresholds, randomized run order, and a structured report template. - Use Case: Before merging a pull request that rewrites a hot parsing function, use this Skill to run a Criterion microbenchmark plus a hyperfine CLI comparison, then report whether the change beats the noise floor with p99 and binary size included. ## Quick Start Ask the agent to benchmark the current change against the main branch and report p50, p95, p99 latency, throughput, and binary size with exact commands and environment details.