spencerpauly/awesome-cursor-skills

profiling-performance

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

Ver código fuente

Cómo usar

  1. Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
  2. Consulta las herramientas, credenciales y entornos necesarios en el documento original.
  3. Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill profiling-performance

Antes de empezar

  • La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
  • Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill

Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.

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.

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