
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)
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| Installs | 230 |
|---|---|
| repo stars | ★ 655 |
| Last updated | August 2, 2026 |
| Repository | spencerpauly/awesome-cursor-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
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_start4. 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_fillto interact - Wait a few seconds for the interaction to complete
5. Stop profiling:
Tool: browser_profile_stopThis writes two files to ~/.cursor/browser-logs/:
cpu-profile-{timestamp}.json— raw Chrome DevTools profilecpu-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 functionprofile.nodes[].callFrame.functionName— the function namesprofile.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
.jsonprofile 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.