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profiling-performance

profiling-performance

github.com/spencerpaulyDevelopment868416 installsUpdated 13w ago
Repository

About profiling-performance

A skill for profiling CPU activity in a web application through Cursor's browser tools. It guides developers from reproducing a slow interaction to reviewing Chrome DevTools profiles and suggesting optimizations.

Key features

  1. 01Capture CPU profiles with browser tools
  2. 02Save raw JSON and readable summaries
  3. 03Inspect call stacks and sample counts
  4. 04Compare profiles before and after changes

Use cases

  1. 01Investigate slow page interactions
  2. 02Find CPU-intensive functions
  3. 03Check for re-renders or blocking work
  4. 04Assess optimization results

MCP support

Uses the cursor-ide-browser MCP tools browser_profile_start and browser_profile_stop to capture CPU profiles, alongside browser navigation and interaction tools.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill profiling-performance

After installing, type/profiling-performancein Claude Code to confirm it loaded.

Details

From the project README

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

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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