spencerpauly/awesome-cursor-skills

profiling-performance

Profile a running web application's CPU performance using Cursor's built-in browser profiler.

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill profiling-performance

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

Performance Profile

Use this skill when a web application feels slow, janky, or unresponsive. Cursor's built-in browser has CPU profiling tools that capture real call stacks and timing data.

How It Works

The cursor-ide-browser MCP provides browser_profile_start and browser_profile_stop tools that capture Chrome DevTools-format CPU profiles. Profile data is written to ~/.cursor/browser-logs/ as both raw JSON and a human-readable summary.

Steps

  1. Ensure the app is running — start the dev server if it isn't already running.
  1. Navigate to the slow page:
   Tool: browser_navigate
   Arguments: { "url": "http://localhost:3000/slow-page" }
  1. Start profiling:
   Tool: browser_profile_start
  1. Reproduce the slow interaction — use browser tools to trigger the slow behavior:
  • Click buttons, scroll, type in inputs, navigate between pages
  • Use browser_click, browser_scroll, browser_fill to interact
  • Wait a few seconds for the interaction to complete
  1. Stop profiling:
   Tool: browser_profile_stop

This writes two files to ~/.cursor/browser-logs/:

  • cpu-profile-{timestamp}.json — raw Chrome DevTools profile
  • cpu-profile-{timestamp}-summary.md — human-readable summary
  1. Analyze the results — read both files. Key things to look for in the raw JSON:
  • profile.nodes[].hitCount — how many samples hit each function
  • profile.nodes[].callFrame.functionName — the function names
  • profile.samples.length — total number of samples collected

Cross-reference with the summary to identify:

  • Functions consuming the most CPU time
  • Unexpected re-renders or layout thrashing
  • Expensive third-party library calls
  • Synchronous operations blocking the main thread
  1. Suggest fixes — based on the profile data, recommend specific optimizations:
  • Memoize expensive computations
  • Debounce rapid event handlers
  • Move heavy work to a Web Worker
  • Lazy-load components or routes
  • Virtualize long lists

Notes

  • Always read the raw .json profile to verify the summary — the summary can miss nuances.
  • Profile in development mode first, but be aware that React dev mode adds overhead. For accurate measurements, profile a production build.
  • Short profiles (2-5 seconds of interaction) are usually more useful than long ones.
  • Compare before/after profiles to verify your optimization actually helped.

Verwandte Aufgaben

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