Golang Performance Agent Skill

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

• 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).

Iterative Optimization Methodology

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

• Define your metric — latency, throughput, memory, or CPU? Without a target, optimizations are random
• Write an atomic benchmark — isolate one function per benchmark to avoid result contamination (→ See samber/cc-skills-golang@golang-benchmark skill)
• Measure baseline — go test -bench=BenchmarkMyFunc -benchmem -count=6 ./pkg/... | tee /tmp/report-1.txt
• Diagnose — use the Diagnose lines in each deep-dive section to pick the right tool
• Improve — apply ONE optimization at a time with an explanatory comment
• Compare — benchstat /tmp/report-1.txt /tmp/report-2.txt to confirm statistical significance
• 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
• 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