profiling-performance

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.

2. Navigate to the slow page:

   Tool: browser_navigate
   Arguments: { "url": "http://localhost:3000/slow-page" }
   

3. Start profiling:

   Tool: browser_profile_start
   

4. 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

5. 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

6. 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

7. 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