performance-profiling

A workflow for finding and fixing performance problems in Go services. It uses pprof, a Go profiling tool, to inspect CPU, memory, goroutines, blocking, and lock contention.

In plain words
What is it for?
Use it to investigate latency, high CPU, memory problems, bottlenecks, or suspected leaks, then compare before-and-after benchmark results.
Why use it?
It helps identify the code causing slowness or high resource use and checks that an optimization preserves existing behaviour.

Skill for Claude CodeCodex

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/luisfelipemoro/harness-devkit/performance-profiling
Any agent
npx skills add LuisFelipeMoro/Harness-devkit --skill performance-profiling
Clone the repo
git clone --depth 1 https://github.com/LuisFelipeMoro/Harness-devkit

Made for: Claude Code, Codex.

Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 459 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 $0.00070 $0.00459
Opus 5 $0.00035 $0.00230
Sonnet 5 $0.00014 $0.00092
Haiku 4.5 $0.00007 $0.00046

Measured 2d ago against content hash 154acd7db282, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

performance-profiling 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 2d 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/engineering/skills/performance-profiling/SKILL.md · 26 lines

What it actually says

Optimization must never change behaviour: a characterization test pins the current output before any change and must stay GREEN through every optimization. The pprof endpoint must stay on an internal port, never a public one.

Contract

  • Input: a Go service with a suspected performance problem.
  • Output: a profiled diagnosis plus an optimization guarded by a characterization test and a before/after benchmark.
  • Tool boundary: profiling is read-only; optimizations land only behind a GREEN characterization test and a measured benchmark.
  • Done when: the benchmark shows before/after numbers and the characterization test stays GREEN.

Steps

  1. Enable the pprof endpoint on an internal port per references/pprof-setup-and-capture.md.
  2. Capture CPU, heap, goroutine, block, and mutex profiles with the commands in references/pprof-setup-and-capture.md.
  3. Analyse the profiles at the pprof prompt (top10, list, flame graph) per references/pprof-setup-and-capture.md; the widest bars at the top of the flame mark the hot path.
  4. Lock behaviour first: a characterization test pins the hot path's current output, per references/optimize-and-benchmark.md.
  5. Apply the matching entry from the common-fixes table in references/optimize-and-benchmark.md.
  6. Benchmark before and after per references/optimize-and-benchmark.md, then report the numbers in the PR description.
  7. For production visibility, the continuous-profiling options in references/pprof-setup-and-capture.md apply.

References

  • references/pprof-setup-and-capture.md — endpoint setup, capture commands, pprof-shell analysis, continuous profiling.
  • references/optimize-and-benchmark.md — characterization test, common-fixes table, before/after benchmark.
Files

What ships with it

2 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. 2d ago First seen · 26 lines · 70 tokens per session scan A 154acd7db282

Subscribe to this mod's changes

performance-profiling is a skill published in the GitHub repository LuisFelipeMoro/Harness-devkit (10 stars, last pushed 2d ago), licensed MIT. It adds 70 tokens to every session and 459 once invoked, about $0.0003 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 2026-08-31.

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