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: Generate CPU, memory, and execution trace profiles directly 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 go test -bench=. -count=10 before and after, compare with benchstat, and paste the statistically significant result into the commit message to document the improvement. ## Quick Start Ask the agent to write a Go 1.24 benchmark for your function, run it ten times with memory stats, and compare the results against your baseline using benchstat.
