| 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 golang-benchmark skill) or debugging workflow (see 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":"1.1.2"} |
| 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 |
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.
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.
Go Performance Optimization
Core Philosophy
- 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)
- 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
- 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).
