spencerpauly/awesome-cursor-skills

profiling-performance

Profile a running web application's CPU performance using Cursor's built-in browser profiler.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill profiling-performance

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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.
  1. Navigate to the slow page:
   Tool: browser_navigate
   Arguments: { "url": "http://localhost:3000/slow-page" }
  1. Start profiling:
   Tool: browser_profile_start
  1. 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
  1. 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
  1. 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
  1. 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.

관련 작업

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