Profile a running web application's CPU performance using Cursor's built-in browser profiler. Captures call stacks, identifies slow functions, and suggests optimizations. Use when a page feels slow or janky.
Scanned 9/10/2026
Install to Claude Code
npx -y skills add 0xAidan/polymarket-bot-test --skill profiling-performance --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: profiling-performance
description: Profile a running web application's CPU performance using Cursor's built-in browser profiler. Captures call stacks, identifies slow functions, and suggests optimizations. Use when a page feels slow or janky.
---
# 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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