go-profiling-optimization

A guide for measuring and improving the performance of Go programs. It covers CPU and memory profiling, allocation checks, benchmarks, tracing, and runtime tuning.

In plain words
What is it for?
Use it to investigate slow code, unexpected memory allocations, concurrency bottlenecks, or performance changes in benchmarks and code reviews.
Why use it?
It replaces guesswork with measurements that show where a Go program spends time, uses memory, or gets blocked.

Skill for Claude CodeCodex

Part of the go-dev plugin — 3 skills, 10 commands shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/gopherguides/gopher-ai/go-profiling-optimization
Any agent
npx skills add gopherguides/gopher-ai --skill go-profiling-optimization
Clone the repo
git clone --depth 1 https://github.com/gopherguides/gopher-ai

Made for: Claude Code, Codex.

Or install go-dev, the plugin that ships this one along with the rest of its 3 skills, 10 commands.

Per session 101 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,177 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00101 $0.01177
Opus 5 $0.00051 $0.00589
Sonnet 5 $0.00020 $0.00235
Haiku 4.5 $0.00010 $0.00118

Measured 3d ago against content hash 5d5928c40b20, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

go-profiling-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/go-dev/skills/go-profiling-optimization/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Persona: You are a Go performance engineer. You measure before you optimize, you optimize one thing at a time, and you verify with statistical rigor.

Modes:

  • Coding mode — optimizing code. Follow the profiling workflow: baseline, profile, isolate, optimize, verify.
  • Review mode — reviewing a PR for performance. Check for unnecessary allocations, missing benchmarks, premature optimization.
  • Audit mode — auditing performance across a codebase. Use up to 4 parallel sub-agents targeting: CPU hotspots, memory allocations, concurrency bottlenecks, and I/O patterns.

Principle: "Never optimize without profiling data. You will optimize the wrong thing."

Go Profiling & Optimization

For hands-on profiling with automatic bottleneck detection and optimization, use /profile <target>.

The Profiling Workflow

NEVER optimize without profiling data. Every optimization must be driven by evidence.

Baseline → Profile → Identify Bottleneck → Isolate → Optimize → Verify → Repeat
  1. Establish a benchmark baseline with go test -bench=. -benchmem -count=6
  2. Profile (CPU, then memory, then trace if concurrent)
  3. Read pprof output — find the top 3 hotspots by cumulative time/allocations
  4. Create isolation benchmarks for each hotspot
  5. Apply ONE optimization at a time
  6. Re-benchmark and compare with benchstat old.bench new.bench
  7. Verify p-value < 0.05 (statistically significant improvement)
  8. Repeat until diminishing returns

Profile Types — When to Use Each

Profile Flag Use When
CPU -cpuprofile=cpu.pprof Function is slow, high CPU usage
Memory (heap) -memprofile=mem.pprof High memory usage, GC pressure, many allocations
Block -blockprofile=block.pprof Goroutines blocked on channels or mutexes
Mutex -mutexprofile=mutex.pprof Lock contention suspected
Goroutine runtime/pprof.Lookup("goroutine") Goroutine leaks, too many goroutines
Trace -trace=trace.out Scheduling latency, GC pauses, concurrency issues

Read the full file on GitHub · 93 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 93 lines · 101 tokens per session scan A 5d5928c40b20

Subscribe to this mod's changes

go-profiling-optimization is a skill published in the GitHub repository gopherguides/gopher-ai (21 stars, last pushed 3d ago), licensed MIT. It adds 101 tokens to every session and 1,177 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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