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React Component Performance

CommunityPopular
Dimillian
react-component-performance

Analyze and optimize React component performance issues (slow renders, re-render thrash, laggy lists, expensive computations). Use when asked to profile or improve a React component, reduce re-renders, or speed up UI updates in React apps.

Overview

PublisherDimillian
RepositorySkills
Skill namereact-component-performance
Stars
4K
Forks
206
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Dimillian on GitHub. Read the source before you install it.

Installation

Install the React Component Performance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dimillian/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/react-component-performance .claude/skills/react-component-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable React Component Performance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads React Component Performance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so React Component Performance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

React Component Performance

Overview

Identify render hotspots, isolate expensive updates, and apply targeted optimizations without changing UI behavior.

Workflow

  1. Reproduce or describe the slowdown.
  2. Identify what triggers re-renders (state updates, props churn, effects).
  3. Isolate fast-changing state from heavy subtrees.
  4. Stabilize props and handlers; memoize where it pays off.
  5. Reduce expensive work (computation, DOM size, list length).
  6. Validate: open React DevTools Profiler → record the interaction → inspect the Flamegraph for components rendering longer than ~16 ms → compare against a pre-optimization baseline recording.

Checklist

  • Measure: use React DevTools Profiler or log renders; capture baseline.
  • Find churn: identify state updated on a timer, scroll, input, or animation.
  • Split: move ticking state into a child; keep heavy lists static.
  • Memoize: wrap leaf rows with memo only when props are stable.
  • Stabilize props: use useCallback/useMemo for handlers and derived values.
  • Avoid derived work in render: precompute, or compute inside memoized helpers.
  • Control list size: window/virtualize long lists; avoid rendering hidden items.
  • Keys: ensure stable keys; avoid index when order can change.
  • Effects: verify dependency arrays; avoid effects that re-run on every render.
  • Style/layout: watch for expensive layout thrash or large Markdown/diff renders.

Optimization Patterns

Isolate ticking state

Move a timer or animation counter into a child so the parent list never re-renders on each tick.

tsx
// ❌ Before – entire parent (and list) re-renders every second
function Dashboard({ items }: { items: Item[] }) {
  const [tick, setTick] = useState(0);
  useEffect(() => {
    const id = setInterval(() => setTick(t => t + 1), 1000);
    return () => clearInterval(id);
  }, []);
  return (
    <>
      <Clock tick={tick} />
      <ExpensiveList items={items} /> {/* re-renders every second */}
    </>
  );
}

// ✅ After – only <Clock> re-renders; list is untouched
function Clock() {
  const [tick, setTick] = useState(0);
  useEffect(() => {
    const id = setInterval(() => setTick(t => t + 1), 1000);
    return () => clearInterval(id);
  }, []);
  return <span>{tick}s</span>;
}

function Dashboard({ items }: { items: Item[] }) {
  return (
    <>
      <Clock />
      <ExpensiveList items={items} />
    </>
  );
}

Stabilize callbacks with useCallback + memo

tsx
// ❌ Before – new handler reference on every render busts Row memo
function List({ items }: { items: Item[] }) {
  const handleClick = (id: string) => console.log(id); // new ref each render
  return items.map(item => <Row key={item.id} item={item} onClick={handleClick} />);
}

// ✅ After – stable handler; Row only re-renders when its own item changes
const Row = memo(({ item, onClick }: RowProps) => (
  <li onClick={() => onClick(item.id)}>{item.name}</li>
));

function List({ items }: { items: Item[] }) {
  const handleClick = useCallback((id: string) => console.log(id), []);
  return items.map(item => <Row key={item.id} item={item} onClick={handleClick} />);
}

Prefer derived data outside render

tsx
// ❌ Before – recomputes on every render
function Summary({ orders }: { orders: Order[] }) {
  const total = orders.reduce((sum, o) => sum + o.amount, 0); // runs every render
  return <p>Total: {total}</p>;
}

// ✅ After – recomputes only when orders changes
function Summary({ orders }: { orders: Order[] }) {
  const total = useMemo(() => orders.reduce((sum, o) => sum + o.amount, 0), [orders]);
  return <p>Total: {total}</p>;
}

Additional patterns

  • Split rows: extract list rows into memoized components with narrow props.
  • Defer heavy rendering: lazy-render or collapse expensive content until expanded.

Profiling Validation Steps

  1. Open React DevTools → Profiler tab.
  2. Click Record, perform the slow interaction, then Stop.
  3. Switch to Flamegraph view; any bar labeled with a component and time > ~16 ms is a candidate.
  4. Use Ranked chart to sort by self render time and target the top offenders.
  5. Apply one optimization at a time, re-record, and compare render counts and durations against the baseline.

Example Reference

Load references/examples.md when the user wants a concrete refactor example.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the React Component Performance AI skill do?

Analyze and optimize React component performance issues (slow renders, re-render thrash, laggy lists, expensive computations). Use when asked to profile or improve a React component, reduce re-renders, or speed up UI updates in React apps.

Why use React Component Performance on TypingMind?

Because you install it once and use it with any model. React Component Performance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install React Component Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dimillian/Skills/tree/main/react-component-performance. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use React Component Performance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with React Component Performance?

As many as you like. As long as a model supports skills, you can use React Component Performance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the React Component Performance AI skill free?

Yes. It is published on GitHub by Dimillian under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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