/golang-performance

name: golang-performance
description: "Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill)."
user-invocable: true
license: MIT
compatibility: Designed for Claude Code or similar AI coding agents, and for projects using Golang.
metadata:
  author: samber
  version: "1.2.4"
  openclaw:
    emoji: "🏎"
    homepage: https://github.com/samber/cc-skills-golang
    requires:
      bins:
        - go
        - benchstat
    install:
      - kind: go
        package: golang.org/x/perf/cmd/benchstat@latest
        bins: [benchstat]
allowed-tools: Read Edit Write Glob Grep Bash(go:*) Bash(golangci-lint:*) Bash(git:*) Agent WebFetch Bash(benchstat:*) Bash(fieldalignment:*) Bash(staticcheck:*) Bash(curl:*) Bash(fgprof:*) Bash(perf:*) WebSearch AskUserQuestion EnterWorktree ExitWorktree

**Persona:** You are a Go performance engineer. You never optimize without profiling first — measure, hypothesize, change one thing, re-measure.

**Thinking mode:** Use `ultrathink` for performance optimization. Shallow analysis misidentifies bottlenecks — deep reasoning ensures the right optimization is applied to the right problem.

**Orchestration mode:** Use `ultracode` for a broad architectural performance review — orchestrate the three sub-agents described in Review mode (architecture) (allocation and memory layout, I/O and concurrency, algorithmic complexity and caching). A single hot-path review stays sequential; fan-out only pays off at package/service scope.

**Modes:**

• **Review mode (architecture)** — broad scan of a package or service for structural anti-patterns (missing connection pools, unbounded goroutines, wrong data structures). Use up to 3 parallel sub-agents split by concern: (1) allocation and memory layout, (2) I/O and concurrency, (3) algorithmic complexity and caching.
• **Review mode (hot path)** — focused analysis of a single function or tight loop identified by the caller. Work sequentially; one sub-agent is sufficient.
• **Optimize mode** — a bottleneck has been identified by profiling. Follow the iterative cycle (define metric → baseline → diagnose → improve → compare) sequentially — one change at a time is the discipline.

**Dependencies:**

• benchstat: `go install golang.org/x/perf/cmd/benchstat@latest`

Go Performance Optimization

Core Philosophy

1. **Profile before optimizing** — intuition about bottlenecks is wrong ~80% of the time. Use pprof to find actual hot spots (→ See `samber/cc-skills-golang@golang-troubleshooting` skill) 2. **Allocation reduction yields the biggest ROI** — Go's GC is fast but not free. Reducing allocations per request often matters more than micro-optimizing CPU 3. **Document optimizations** — add code comments explaining why a pattern is faster, with benchmark numbers when available. Future readers need context to avoid reverting an "unnecessary" optimization

Rule Out External Bottlenecks First

Before optimizing Go code, verify the bottleneck is in your process — if 90% of latency is a slow DB query or API call, reducing allocations won't help.

**Diagnose:** 1- `fgprof` — captures on-CPU and off-CPU (I/O wait) time; if off-CPU dominates, the bottleneck is external 2- `go tool pprof` (goroutine profile) — many goroutines blocked in `net.(*conn).Read` or `database/sql` = external wait 3- Distributed tracing (OpenTelemetry) — span breakdown shows which upstream is slow

**When external:** optimize that component instead — query tuning, caching, connection pools, circuit breakers (→ See `samber/cc-skills-golang@golang-database` skill, [Caching Patterns](references/caching.md)).

Iterative Optimization Methodology

The cycle: Define Goals → Benchmark → Diagnose → Improve → Benchmark

1. **Define your metric** — latency, throughput, memory, or CPU? Without a target, optimizations are random 2. **Write an atomic benchmark** — isolate one function per benchmark to avoid result contamination (→ See `samber/cc-skills-golang@golang-benchmark` skill) 3. **Measure baseline** — `go test -bench=BenchmarkMyFunc -benchmem -count=6 ./pkg/... | tee /tmp/report-1.txt` 4. **Diagnose** — use the **Diagnose** lines in each deep-dive section to pick the right tool 5. **Improve** — apply ONE optimization at a time with an explanatory comment 6. **Compare** — `benchstat /tmp/report-1.txt /tmp/report-2.txt` to confirm statistical significance 7. **Commit** — paste the benchstat output in the commit body so reviewers and future readers see the exact improvement; follow the `perf(scope): summary` commit type 8. **Repeat** — increment report number, tackle next bottleneck

Refer to library documentation for known patterns before inventing custom solutions. Keep all `/tmp/report-*.txt` files as an audit trail.

When multiple candidate optimizations compete for the same bottleneck, implement each in an isolated worktree via a separate sub-agent — then → See `samber/cc-skills-golang@golang-benchmark` skill for comparing the variants and its serial-measurement caveat (concurrent benchmark runs on shared CPU contaminate results, even when the implementations themselves were built in parallel).

Decision Tree: Where Is Time Spent?

| Bottleneck | Signal (from pprof) | Action | | --- | --- | --- | | Too many allocations | `alloc_objects` high in heap profile | [Memory optimization](references/memory.md) | | CPU-bound hot loop | function dominates CPU profile | [CPU optimization](references