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/workers-profiling

Profile or debug CPU usage and memory allocation in deployed Cloudflare Workers and Durable Objects. Then use this information to optimize your code.

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claude-codex-settings
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Install
$ npx -y skills add fcakyon/claude-codex-settings --skill workers-profiling --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/workers-profiling

Context preview

The summary Claude sees to decide when to auto-load this skill.

Profile or debug CPU usage and memory allocation in deployed Cloudflare Workers and Durable Objects. Then use this information to optimize your code.

SKILL.md

workers-profiling.SKILL.md
name: workers-profiling
description: Profile or debug CPU usage and memory allocation in deployed Cloudflare Workers and Durable Objects. Then use this information to optimize your code.
license: Apache-2.0

Workers profiling

Find expensive functions or memory allocations in deployed code and come up with ways to fix them.

Retrieve the docs

Start with the [production profiling guide](https://developers.cloudflare.com/workers/observability/profiling-in-production/index.md) to understand how Workers and Durable Objects profiling works.

Profiling steps

1. Identify the symptom and affected workload: CPU usage, latency, allocation pressure, or memory growth. 2. Confirm the account, Worker, environment, and deployed version with the user. 3. For a Durable Object, also confirm its owning Worker, namespace, and instance. 4. Choose CPU or heap profiling from the current guide's supported types and meanings.

Where possible, use the [`cf` CLI](https://developers.cloudflare.com/cf/index.md) to capture profiles. If the user requests `cf`, inspect the installed version's command help and schema where available.

If `cf` isn't available, consider prompting the user whether they would like to install it.

The profiling API will return a pprof file which you can analyze directly, or using appropriate tools like Go's `pprof` utility.

Inspect the evidence

  • Read the pprof file and analyse it for hotspots
  • Map hotspots to functions and source locations when evidence supports it; report missing symbols or source mappings.
  • If filenames and function names are pointing at the generated JavaScript, consider suggesting to the user to enable source maps and re-do the profiling.

Optimise

Propose the smallest code change supported by the capture which resolves the issues identified.

Report findings

Return a concise report with:

  • Evidence for each finding: file, function, source location, and metric with units, where available.
  • The smallest actionable change and how to check its effect.

Skill feedback

If the user reports that this skill gave incorrect or outdated guidance, or is missing information, read [references/skill-feedback.md](references/skill-feedback.md).

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