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Profiling Performance

  • 230 installs
  • 655 repo stars
  • Updated August 2, 2026
  • spencerpauly/awesome-cursor-skills

Helps with ai & agent building tasks.

About

profiling-performance is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • profiling-performance
  • AI & Agent Building
  • AI-coding skill

Profiling Performance by the numbers

  • 230 all-time installs (skills.sh)
  • +27 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #2,697 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill profiling-performance

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Listed on Skillselion
Installs230
repo stars655
Last updatedAugust 2, 2026
Repositoryspencerpauly/awesome-cursor-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

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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