# Performance Profiling

> pprof usage and performance analysis. Use when profiling applications. Use when this capability is needed.

- Skill: `tomevault-io/performance-profiling` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/performance-profiling`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/performance-profiling/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/performance-profiling

---


# Performance Profiling Skill

Using pprof for performance analysis.

## When to Use

Use when identifying performance bottlenecks.

## CPU Profiling

```bash
# During tests
go test -cpuprofile=cpu.out -bench=.

# Analyze
go tool pprof cpu.out

# Web interface
go tool pprof -http=:8080 cpu.out
```

## Memory Profiling

```bash
# During tests
go test -memprofile=mem.out -bench=.

# Analyze
go tool pprof mem.out
```

## HTTP Endpoint

```go
import _ "net/http/pprof"

func main() {
    go func() {
        log.Println(http.ListenAndServe("localhost:6060", nil))
    }()
    // your code
}
```

Access profiles at:
- `http://localhost:6060/debug/pprof/`
- `http://localhost:6060/debug/pprof/heap`
- `http://localhost:6060/debug/pprof/goroutine`

## pprof Commands

```
(pprof) top      # Top functions
(pprof) list     # Source code view
(pprof) web      # Graph view
(pprof) pdf      # PDF output
```

## Best Practices

- Profile in production-like conditions
- Profile before optimizing
- Focus on hot paths
- Check both CPU and memory
- Use flamegraphs for visualization

---
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<!-- tomevault:4.0:skill_md:2026-04-13 -->