golang-performance

Profile Go CPU and memory usage with pprof and optimize allocations.

4|Updated Dec 23, 2025
One-click install
npx skills add https://github.com/89jobrien/steve --skill golang-performance
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: golang-performance
Source: https://github.com/89jobrien/steve/tree/main/steve/skills/golang-performance
Command: npx skills add https://github.com/89jobrien/steve --skill golang-performance

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Go applications can suffer from CPU hotspots, memory bloat, and concurrency pitfalls. This Skill provides profiling, memory optimization, concurrency patterns, and escape analysis guidance to improve Go performance.

Core Features & Use Cases

  • Profiling & Diagnosis: pprof workflows for CPU and memory
  • Memory Optimization: reduce allocations and GC pressure
  • Concurrency Patterns: efficient worker pools and synchronization
  • Escape Analysis: understand heap vs stack allocations
  • Production Considerations: latency, throughput, and observable metrics

Quick Start

Profile a Go HTTP server with pprof and identify the top hotspot functions.

Frequently Asked Questions about golang-performance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile CPU and memory usage in a Go application?

Use pprof to profile Go applications by importing the net/pprof package and accessing profiling endpoints. pprof captures CPU hotspots, memory allocations, and goroutine data, which you analyze with go tool pprof to identify bottlenecks in your services or command-line tools.

What's the best way to reduce memory allocations in Go?

Reduce allocations by preallocating slices with known capacity, reusing objects via sync.Pool, and avoiding unnecessary heap escapes. These techniques lower garbage collection pressure and improve throughput in production applications handling high request volumes.

How do I implement efficient worker pools and concurrency patterns in Go?

Use buffered channels and goroutine pools to process tasks concurrently without spawning unbounded goroutines. Worker pool patterns limit resource consumption, improve latency predictability, and scale throughput in server applications and long-running services.

What is escape analysis and why does it matter for Go performance?

Escape analysis determines whether variables are allocated on the stack or heap. Stack allocations are faster and don't trigger garbage collection; understanding escape analysis helps you redesign code to keep allocations on the stack and reduce GC overhead.

Can I use pprof to measure latency and throughput in production Go services?

Yes, pprof provides CPU, memory, and goroutine profiling suited for production environments. Combined with observable metrics collection, pprof data reveals latency hotspots and throughput constraints in HTTP servers and background workers.

When should I optimize Go performance versus accepting current resource usage?

Optimize when profiling reveals CPU or memory hotspots impacting latency or causing excessive garbage collection. Start with pprof diagnosis to confirm the bottleneck exists before investing in concurrency tuning, allocation reduction, or escape analysis fixes.