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Performance Optimization

CommunityPopular
rohitg00
performance-optimization

Web performance optimization including bundle analysis, lazy loading, caching strategies, and Core Web Vitals

Overview

Publisherrohitg00
Repositoryawesome-claude-code-toolkit
Skill nameperformance-optimization
Stars
2.6K
Forks
963
Bundled files
Instructions only
LicenseApache-2.0
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Performance Optimization 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/rohitg00/awesome-claude-code-toolkit.git /tmp/awesome-claude-code-toolkit
mkdir -p .claude/skills
cp -r /tmp/awesome-claude-code-toolkit/skills/performance-optimization .claude/skills/performance-optimization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Performance Optimization 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 Performance Optimization 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 Performance Optimization 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.

Performance Optimization

Bundle Analysis and Code Splitting

typescript
// Dynamic import for route-level code splitting
const Dashboard = lazy(() => import("./pages/Dashboard"));
const Settings = lazy(() => import("./pages/Settings"));

function App() {
  return (
    <Suspense fallback={<PageSkeleton />}>
      <Routes>
        <Route path="/dashboard" element={<Dashboard />} />
        <Route path="/settings" element={<Settings />} />
      </Routes>
    </Suspense>
  );
}
javascript
// vite.config.ts - manual chunk splitting
export default defineConfig({
  build: {
    rollupOptions: {
      output: {
        manualChunks: {
          vendor: ["react", "react-dom"],
          charts: ["recharts", "d3"],
          editor: ["@monaco-editor/react"],
        },
      },
    },
  },
});
bash
# Analyze bundle composition
npx vite-bundle-visualizer
npx source-map-explorer dist/assets/*.js

Image Optimization

tsx
import Image from "next/image";

function ProductImage({ src, alt }: { src: string; alt: string }) {
  return (
    <Image
      src={src}
      alt={alt}
      width={800}
      height={600}
      sizes="(max-width: 768px) 100vw, (max-width: 1200px) 50vw, 33vw"
      placeholder="blur"
      blurDataURL={generateBlurHash(src)}
      loading="lazy"
    />
  );
}
html
<!-- Native lazy loading with aspect ratio -->
<img
  src="product.webp"
  alt="Product photo"
  width="800"
  height="600"
  loading="lazy"
  decoding="async"
  fetchpriority="low"
/>

<!-- Preload LCP image -->
<link rel="preload" as="image" href="/hero.webp" fetchpriority="high" />

Caching Headers

typescript
function setCacheHeaders(res: Response, options: CacheOptions) {
  if (options.immutable) {
    res.setHeader("Cache-Control", "public, max-age=31536000, immutable");
    return;
  }

  if (options.revalidate) {
    res.setHeader("Cache-Control", `public, max-age=0, s-maxage=${options.revalidate}, stale-while-revalidate=${options.staleWhileRevalidate ?? 86400}`);
    return;
  }

  res.setHeader("Cache-Control", "no-cache, no-store, must-revalidate");
}

app.use("/assets", (req, res, next) => {
  setCacheHeaders(res, { immutable: true });
  next();
});

app.use("/api", (req, res, next) => {
  setCacheHeaders(res, { revalidate: 60, staleWhileRevalidate: 3600 });
  next();
});

Virtual Lists for Large Data

tsx
import { useVirtualizer } from "@tanstack/react-virtual";

function VirtualList({ items }: { items: Item[] }) {
  const parentRef = useRef<HTMLDivElement>(null);

  const virtualizer = useVirtualizer({
    count: items.length,
    getScrollElement: () => parentRef.current,
    estimateSize: () => 50,
    overscan: 5,
  });

  return (
    <div ref={parentRef} style={{ height: "600px", overflow: "auto" }}>
      <div style={{ height: `${virtualizer.getTotalSize()}px`, position: "relative" }}>
        {virtualizer.getVirtualItems().map((virtualRow) => (
          <div
            key={virtualRow.key}
            style={{
              position: "absolute",
              top: 0,
              transform: `translateY(${virtualRow.start}px)`,
              height: `${virtualRow.size}px`,
              width: "100%",
            }}
          >
            <ItemRow item={items[virtualRow.index]} />
          </div>
        ))}
      </div>
    </div>
  );
}

Core Web Vitals Monitoring

typescript
import { onCLS, onINP, onLCP } from "web-vitals";

function sendMetric(metric: { name: string; value: number; id: string }) {
  navigator.sendBeacon("/api/vitals", JSON.stringify(metric));
}

onCLS(sendMetric);
onINP(sendMetric);
onLCP(sendMetric);
  • LCP (Largest Contentful Paint): < 2.5s. Preload hero images, optimize server response time.
  • INP (Interaction to Next Paint): < 200ms. Avoid long tasks, use requestIdleCallback.
  • CLS (Cumulative Layout Shift): < 0.1. Set explicit dimensions on images and embeds.

Anti-Patterns

  • Loading all JavaScript upfront instead of code-splitting by route
  • Serving unoptimized images (no WebP/AVIF, no responsive sizes)
  • Missing width and height on images (causes layout shift)
  • Using Cache-Control: no-cache on static assets with content hashes
  • Rendering thousands of DOM nodes instead of virtualizing lists
  • Blocking the main thread with synchronous computation

Checklist

  • Routes lazy-loaded with dynamic import() and Suspense
  • Bundle analyzed and vendor chunks separated
  • Images served in WebP/AVIF with responsive sizes attribute
  • LCP image preloaded with fetchpriority="high"
  • Static assets cached with immutable headers and content hashes
  • Lists with 100+ items use virtualization
  • Core Web Vitals monitored in production (LCP, INP, CLS)
  • No render-blocking resources in the critical path

Frequently asked questions

What does the Performance Optimization AI skill do?

Web performance optimization including bundle analysis, lazy loading, caching strategies, and Core Web Vitals

Why use Performance Optimization on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/awesome-claude-code-toolkit/tree/main/skills/performance-optimization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Performance Optimization?

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 Performance Optimization?

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

Is the Performance Optimization AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 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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