SkillsAdd

lvtd-llc/skills

go-performance-testing

Design, run, compare, and interpret Go performance evidence using benchmarks, B.Loop, benchstat, pprof, traces, escape analysis, cache locality, false sharing, sync.Pool, GC limits, container CPU behavior, and PGO.

Installation

$ npx skills add lvtd-llc/skills --skill go-performance-testing
View on skills.sh ↗ GitHub repository ↗

Summary

  • Design, run, compare, and interpret Go performance evidence using benchmarks, B.Loop, benchstat, pprof, traces, escape analysis, cache locality, false sharing, sync.Pool, GC limits, container CPU behavior, and PGO.
  • Use when investigating Go latency, throughput, CPU, memory, allocations, contention, runtime behavior, performance regressions, or optimization claims.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version0.2.0
LicenseMIT
CompatibilityCodex, Claude Code, and other Agent Skills-compatible clients.
Declared agents claude-code codex
More metadata
version
0.2.0
displayName
Go Performance Testing
category
Go
tags
go,golang,benchmarks,profiling,pprof,performance

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,925 B
  • docs SUMMARY.md 396 B

History

  1. First recorded snapshot · 1 installs

SKILL.md

Go Performance Testing

Optimize only after establishing a representative, repeatable measurement and a specific hypothesis.

Core Workflow

  1. Define the user-visible metric and representative workload.
  2. Stabilize correctness before measuring performance.
  3. Write focused benchmarks with realistic inputs and controlled setup.
  4. Collect repeated baseline samples in comparable conditions.
  5. Use allocations, profiles, and traces to form a causal hypothesis.
  6. Make one bounded change.
  7. Compare repeated before/after samples statistically.
  8. Recheck correctness and the end-to-end workload.

Read Next

Task Load
Benchmark a function or package guidelines.md, workflows/benchmark-code.md
Investigate a regression workflows/investigate-performance.md
Evaluate profile-guided optimization workflows/evaluate-pgo.md
Choose benchmark and profile controls references/performance-testing/rules.md
Interpret metrics and profiles references/performance-testing/knowledge.md
Review benchmark patterns references/performance-testing/examples.md

Guardrails

  • Do not optimize from one benchmark line or an unrepresentative microbenchmark.
  • Do not compare runs made under materially different environments.
  • Do not treat coverage or race detection as performance evidence.
  • Name the profile sample type and distinguish flat from cumulative cost.
  • Prefer B.Loop only when the pinned Go version supports it.
  • Keep production profile endpoints protected and operationally controlled.
  • Do not encode cache-line size, escape output, or inliner budgets as portable facts.
  • Do not use sync.Pool as a cache or resource owner.

Source Notes

Guidance is transformed and paraphrased from Inanc Gumus, Go by Example: Programmer's Guide to Idiomatic and Testable Programs (Manning, 2025), especially Chapter 3. Examples are original.

Diagnostics, locality, allocation, GC, and container guidance also incorporates transformed material from Teiva Harsanyi, 100 Go Mistakes and How to Avoid Them (Manning, 2022), Chapter 12.

Book: https://www.manning.com/books/go-by-example

Verify current behavior against https://pkg.go.dev/testing, https://go.dev/doc/diagnostics, and https://go.dev/doc/pgo.