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

Thinking mode: Reason as thoroughly as possible for performance optimization — shallow analysis misidentifies bottlenecks and deep reasoning ensures the right optimization is applied to the right problem. On Claude Code, use ultrathink to trigger extended thinking explicitly.

Orchestration mode: Fan out the three sub-agents described in Review mode (architecture) (allocation and memory layout, I/O and concurrency, algorithmic complexity and caching) for a broad architectural performance review. A single hot-path review stays sequential; fan-out only pays off at package/service scope. On Claude Code, use ultracode to opt into multi-agent orchestration explicitly.

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).

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
CPU-bound hot loop	function dominates CPU profile	CPU optimization
GC pauses / OOM	high GC%, container limits	Runtime tuning
Network / I/O latency	goroutines blocked on I/O	I/O & networking
Repeated expensive work	same computation/fetch multiple times	Caching patterns
Wrong algorithm	O(n²) where O(n) exists	Algorithmic complexity
Lock contention	mutex/block profile hot	→ See samber/cc-skills-golang@golang-concurrency skill
Slow queries	DB time dominates traces	→ See samber/cc-skills-golang@golang-database skill

Common Mistakes

Mistake	Fix
Optimizing without profiling	Profile with pprof first — intuition is wrong ~80% of the time
Default http.Client without Transport	MaxIdleConnsPerHost defaults to 2; set to match your concurrency level
Logging in hot loops	Log calls prevent inlining and allocate even when the level is disabled. Use slog.LogAttrs
panic/recover as control flow	panic allocates a stack trace and unwinds the stack; use error returns
unsafe without benchmark proof	Only justified when profiling shows >10% improvement in a verified hot path
No GC tuning in containers	Set GOMEMLIMIT to 80-90% of container memory to prevent OOM kills
reflect.DeepEqual in production	50-200x slower than typed comparison; use slices.Equal, maps.Equal, bytes.Equal

Deep Dives

• Memory Optimization — allocation patterns, backing array leaks, sync.Pool, struct alignment
• CPU Optimization — inlining, cache locality, false sharing, ILP, reflection avoidance
• I/O & Networking — HTTP transport config, streaming, JSON performance, cgo, batch operations
• Runtime Tuning — GOGC, GOMEMLIMIT, GC diagnostics, GOMAXPROCS, PGO
• Caching Patterns — algorithmic complexity, compiled patterns, singleflight, work avoidance
• Production Observability — Prometheus metrics, PromQL queries, continuous profiling, alerting rules

CI Regression Detection

Automate benchmark comparison in CI to catch regressions before they reach production. → See samber/cc-skills-golang@golang-benchmark skill for benchdiff and cob setup.

Cross-References

• → See samber/cc-skills-golang@golang-benchmark skill for benchmarking methodology, benchstat, and b.Loop() (Go 1.24+)
• → See samber/cc-skills-golang@golang-troubleshooting skill for pprof workflow, escape analysis diagnostics, and performance debugging
• → See samber/cc-skills-golang@golang-data-structures skill for slice/map preallocation and strings.Builder
• → See samber/cc-skills-golang@golang-concurrency skill for worker pools, sync.Pool API, goroutine lifecycle, and lock contention
• → See samber/cc-skills-golang@golang-safety skill for defer in loops, slice backing array aliasing
• → See samber/cc-skills-golang@golang-database skill for connection pool tuning and batch processing
• → See samber/cc-skills-golang@golang-observability skill for continuous profiling in production