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Frontend Review Performance

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mizchi
frontend-review-performance

Use when reviewing React rendering performance — profiler-first diagnosis, memo/useCallback/useMemo correctness, virtual scroll, useTransition/useDeferredValue, and canvas/WebGL separation for data-heavy UIs. Covers checklist 24-rendering-performance.md.

Overview

Publishermizchi
Repositoryskills
Skill namefrontend-review-performance
Stars
333
Forks
4
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Frontend Review 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/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/frontend-review-performance .claude/skills/frontend-review-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend Review 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 Frontend Review 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 Frontend Review 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.

Frontend Review — Rendering Performance

You are reviewing the rendering performance of a React frontend. The most common AI-generated problems are: applying memo / useCallback / useMemo everywhere without measuring (or never at all), missing virtual scroll on large lists, and Context changes re-rendering unrelated components.

Procedure

  1. Check package.json for performance-related packages (@tanstack/react-virtual, react-window, @welldone-software/why-did-you-render, etc.).
  2. Grep for existing memo usage:
    bash
    grep -rn "React\.memo\|useMemo\|useCallback" src/ --include='*.tsx' --include='*.ts' | wc -l
    grep -rn "useVirtualizer\|FixedSizeList\|VariableSizeList" src/ --include='*.tsx' | wc -l
    grep -rn "useTransition\|useDeferredValue\|startTransition" src/ --include='*.tsx' | wc -l
  3. Find the largest list-rendering components (look for .map( on arrays with no size guard).
  4. Look for Context providers that change frequently and might cause wide re-renders.
  5. For iot-ops / map / chart apps: check whether heavy rendering is in React state or in a canvas/WebGL ref.

Profiler-First Principle

Do not recommend memo / useCallback / useMemo without first profiling. Premature memoization adds cognitive overhead and can slow things down (each hook has a cost).

When writing the report, prefix every optimization recommendation with: "After profiling confirms X re-renders per interaction, consider Y."

Memoization Correctness

When memoization IS present (or being recommended), check for these common mistakes:

React.memo

  • Is React.memo applied to components that receive stable props from their parents?
  • Is the parent passing new object/array/function references on every render (negating memo)?
tsx
// Bad: new array on every render — memo is useless
<List items={data.filter(x => x.active)} />

// Good: stable reference with useMemo
const activeItems = useMemo(() => data.filter(x => x.active), [data]);
<List items={activeItems} />

useCallback

  • Is useCallback used when passing callbacks to memo-wrapped children?
  • Are dependency arrays accurate (no missing or unnecessary deps)?
tsx
// Bad: new function reference every render
<Button onClick={() => handleDelete(id)} />

// Good: stable reference
const handleDeleteClick = useCallback(() => handleDelete(id), [id, handleDelete]);
<Button onClick={handleDeleteClick} />

useMemo

  • Is useMemo applied to expensive computations (filter/sort/aggregate on large arrays), not trivial ones (string concat, boolean check)?
  • Are dependency arrays correct?

Virtual Scroll

For lists with 100+ items, virtual scroll is almost always necessary for acceptable performance.

Recommended: @tanstack/react-virtual (works with any layout, no CSS constraints).

tsx
const rowVirtualizer = useVirtualizer({
  count: items.length,
  getScrollElement: () => parentRef.current,
  estimateSize: () => 48,
});

return (
  <div ref={parentRef} style={{ height: '400px', overflow: 'auto' }}>
    <div style={{ height: `${rowVirtualizer.getTotalSize()}px`, position: 'relative' }}>
      {rowVirtualizer.getVirtualItems().map(vItem => (
        <div key={vItem.key} style={{ position: 'absolute', top: vItem.start, height: vItem.size }}>
          <ListItem item={items[vItem.index]} />
        </div>
      ))}
    </div>
  </div>
);

Flag any list that maps over an array > 100 items without virtual scroll.

Concurrent Features (React 18+)

  • useTransition — wrap heavy non-urgent state updates so the UI stays responsive:
tsx
const [isPending, startTransition] = useTransition();
const handleFilterChange = (q: string) => {
  startTransition(() => setFilterQuery(q));
};
  • useDeferredValue — defer a value that drives expensive rendering:
tsx
const deferredQuery = useDeferredValue(filterQuery);
const filtered = useMemo(() => items.filter(i => i.name.includes(deferredQuery)), [deferredQuery, items]);

Flag heavy filter/sort operations that block the main thread on every keystroke — these are candidates for useTransition.

Canvas / WebGL Separation (iot-ops / map / chart apps)

For data-dense UIs (real-time dashboards, map overlays, charting), React state is the wrong tool for per-frame updates.

Check whether:

  • High-frequency data (sensor readings, map tile updates, chart data) bypasses React state and goes directly to canvas/WebGL via useRef.
  • React only controls the layout shell and control panel; the canvas/WebGL layer handles rendering independently.
ts
// Pattern: React controls mount; canvas reads data via ref
const canvasRef = useRef<HTMLCanvasElement>(null);
useEffect(() => {
  const renderer = new WebGLRenderer(canvasRef.current!);
  const unsub = sensorStream.subscribe(data => renderer.update(data)); // no setState
  return unsub;
}, []);

Output

Write <client-repo>/.frontend-review/report/latest/md/performance-review.md with:

  • Profiling recommendation: what to measure first and how (React DevTools Profiler, why-did-you-render)
  • Memoization gaps / misuse: file:line references for each finding
  • Virtual scroll candidates: component name, estimated list size
  • Concurrent feature opportunities: interactions that block the thread
  • Canvas/WebGL assessment (if applicable): is high-frequency data bypassing React state?
  • Recommended PRs: one optimization per PR, profiling benchmark in PR description

Keep under 200 lines. Recommendations without profiling evidence must be explicitly flagged as "unconfirmed — profile first."

Boundaries

  • Do NOT run profiling sessions — describe what to measure and how.
  • Do NOT propose optimization without a measurement plan.
  • Do NOT touch source files in the client repo.
  • State management architecture (store design, selector granularity) is covered by frontend-review-state.

Reference

  • Checklist: 24-rendering-performance.md, 23-state-management.md, C2-lighthouse.md
  • Tools: React DevTools Profiler, @welldone-software/why-did-you-render, @tanstack/react-virtual

Frequently asked questions

What does the Frontend Review Performance AI skill do?

Use when reviewing React rendering performance — profiler-first diagnosis, memo/useCallback/useMemo correctness, virtual scroll, useTransition/useDeferredValue, and canvas/WebGL separation for data-heavy UIs. Covers checklist 24-rendering-performance.md.

Why use Frontend Review Performance on TypingMind?

Because you install it once and use it with any model. Frontend Review 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 Frontend Review Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mizchi/skills/tree/main/frontend-review-performance. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Frontend Review 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 Frontend Review Performance?

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

Is the Frontend Review Performance AI skill free?

It is published on GitHub by mizchi. Check the repository for licensing terms. 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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