workers-profiling

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 ## Skill feedback If the user reports that this skill gave incorrect or outdated guidance, or is missing information, read [references/skill-feedback.md](references/skill-feedback.md).