What problem does it solve? Go performance work fails when developers draw conclusions from single benchmark runs, misread pprof profiles, or cannot tell a real regression from CI noise. This Skill provides the full measurement methodology — writing correct benchmarks, profiling hot paths, and proving improvements with statistical rigor. ## Core Features & Use Cases - Benchmark Authoring: Write benchmarks with b.Loop() (Go 1.24+), memory tracking via b.ReportAllocs(), custom metrics via b.ReportMetric(), and table-driven sub-benchmarks. - Profiling & Analysis: Capture CPU, memory, and execution trace profiles from benchmarks, then interpret them with pprof commands, escape analysis, inlining diagnostics, and SSA/assembly inspection. - Statistical Comparison & CI Gating: Compare runs with benchstat (p-values, confidence intervals, filters), and set up CI regression detection with benchdiff, cob, or gobenchdata including noisy-runner mitigation. - Use Case: After optimizing a JSON parser, run both versions with -count=10, compare with benchstat, and paste the statistically significant result into the commit body to document the improvement. ## Quick Start Ask the agent to write a benchmark for your function, run it with -count=10 and -benchmem, then compare the before and after results with benchstat.
