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Agent skill

Profiling Performance

by spencerpauly in spencerpauly/awesome-cursor-skills

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

CC0-1.0Auto-check passedDevelopment

Install Profiling Performance

skills CLI
$ npx skills add spencerpauly/awesome-cursor-skills --skill profiling-performance -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install spencerpauly/awesome-cursor-skills profiling-performance --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/spencerpauly/awesome-cursor-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/resources/profiling-performance .claude/skills/profiling-performance && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
profiling-performance
GitHub stars
842
Token cost
~686 tokens
SKILL.md length
290 words
Files
1
Skills in repo
57
Repo updated
First seen
Licence
CC0-1.0

At a glance

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

  • Works in 7 steps: Ensure the app is running — start the… → Navigate to the slow page → Start profiling → …
  • A page feels slow
  • SKILL.md covers How It Works, Steps and Notes
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Profiling Performance is an agent skill from spencerpauly/awesome-cursor-skills. 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.

Its SKILL.md is about 690 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Performance optimization. The repository describes itself as: A curated list of awesome skills for Cursor. The licence is CC0-1.0.

When your agent uses it

  • A page feels slow
  • Tasks that involve Performance optimization

Example prompts

  • “s CPU performance using Cursor”
  • “/profiling-performance”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Ensure the app is running — start the dev server if it isn't already running.
  2. Navigate to the slow page
  3. Start profiling
  4. Reproduce the slow interaction — use browser tools to trigger the slow behavior
  5. Stop profiling
  6. Analyze the results — read both files. Key things to look for in the raw JSON
  7. Suggest fixes — based on the profile data, recommend specific optimizations

What it can do on your machine

Read from SKILL.md and the folder at commit 99cd265. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Profiling Performance loads about 686 tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 290 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~686

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from spencerpauly/awesome-cursor-skills at commit 99cd265, republished under its CC0-1.0 licence (© spencerpauly). 290 words, ~686 tokens.

Download SKILL.mdSave it as .claude/skills/profiling-performance/SKILL.md (or your agent's skills folder).
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.

© spencerpauly, CC0-1.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in resources/profiling-performance of spencerpauly/awesome-cursor-skills.

Open the folder on GitHubat commit 99cd265

Compare with similar skills

Profiling Performance next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Profiling Performance compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Profiling Performance this skillspencerpauly/awesome-cursor-skills842—~686Automated safety check: PassCC0-1.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
LLM Torch Profiler Analysissgl-project/sglang37k2 repos~6.4kAutomated safety check: PassApache-2.0
Pycrazyguitar/pysheeet8.2k—~886Automated safety check: PassMIT
Cmux Debugging Guidemanaflow-ai/cmux28k1 repos~1.1kAutomated safety check: PassCustom licence
Electron Heap Snapshot Analysiskeybase/client9.3k—~875Automated safety check: PassBSD-3-Clause

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Categories

Questions about Profiling Performance

What does Profiling Performance do?

Profile a running web application's CPU performance using Cursor's built-in browser profiler. Profiling Performance is an agent skill from spencerpauly/awesome-cursor-skills. Profile a running web application's CPU performance using Cursor's built-in browser profiler.

When should I use Profiling Performance?

Profiling Performance fits situations like: A page feels slow; tasks that involve Performance optimization.

How do I install Profiling Performance in Claude Code?

Run `npx skills add spencerpauly/awesome-cursor-skills --skill profiling-performance -a claude-code`. Or copy the skill folder (resources/profiling-performance in spencerpauly/awesome-cursor-skills) into .claude/skills/profiling-performance in your project. Claude Code loads it when a task matches its description.

How do I install Profiling Performance in Codex?

Run `npx skills add spencerpauly/awesome-cursor-skills --skill profiling-performance -a codex`. Or copy the skill folder (resources/profiling-performance in spencerpauly/awesome-cursor-skills) into .agents/skills/profiling-performance in your project. Codex loads it when a task matches its description.

Can I use Profiling Performance in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add spencerpauly/awesome-cursor-skills --skill profiling-performance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/profiling-performance, .gemini/skills/profiling-performance, .github/skills/profiling-performance and .opencode/skills/profiling-performance in your project.

What does Profiling Performance need to run?

SKILL.md names no scripts, command-line tools or credentials: Profiling Performance is instructions for the agent only.

Does Profiling Performance access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Profiling Performance safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Profiling Performance use?

Profiling Performance is published under the CC0-1.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Profiling Performance use?

About 686 tokens (SKILL.md is roughly 2.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Profiling Performance?

Skills that share tags, products or a category with Profiling Performance: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Py (crazyguitar/pysheeet, 8.2k stars) and Cmux Debugging Guide (manaflow-ai/cmux, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Profiling Performance?

spencerpauly (a GitHub user) maintains it in spencerpauly/awesome-cursor-skills, which has 842 GitHub stars. The repository holds 57 skills in this directory. The repository was last updated on August 2, 2026.

Source: spencerpauly/awesome-cursor-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.