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Frontend Excellence

CommunityPopular
rohitg00
frontend-excellence

Modern frontend patterns for React Server Components, performance optimization, and Core Web Vitals

Overview

Publisherrohitg00
Repositoryawesome-claude-code-toolkit
Skill namefrontend-excellence
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 Frontend Excellence 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/frontend-excellence .claude/skills/frontend-excellence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend Excellence 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 Excellence 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 Excellence 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 Excellence

React Server Components

Server Components run on the server and send rendered HTML to the client. They can directly access databases, filesystems, and internal APIs without exposing them to the browser.

tsx
// app/products/page.tsx (Server Component by default)
async function ProductsPage() {
  const products = await db.query("SELECT * FROM products WHERE active = true");
  return (
    <main>
      <h1>Products</h1>
      <ProductList products={products} />
      <AddToCartButton />  {/* Client Component */}
    </main>
  );
}

Rules:

  • Server Components cannot use useState, useEffect, or browser APIs
  • Mark interactive components with 'use client' at the top of the file
  • Pass serializable props from Server to Client Components (no functions, no classes)
  • Keep 'use client' boundary as deep in the tree as possible

Streaming SSR

tsx
import { Suspense } from 'react';

export default function Dashboard() {
  return (
    <div>
      <Header />  {/* renders immediately */}
      <Suspense fallback={<ChartSkeleton />}>
        <AnalyticsChart />  {/* streams when ready */}
      </Suspense>
      <Suspense fallback={<TableSkeleton />}>
        <RecentOrders />  {/* streams independently */}
      </Suspense>
    </div>
  );
}

Each Suspense boundary streams independently. Place boundaries around data-fetching components to avoid blocking the entire page.

Code Splitting

tsx
import dynamic from 'next/dynamic';

const HeavyEditor = dynamic(() => import('@/components/Editor'), {
  loading: () => <EditorSkeleton />,
  ssr: false,
});

const AdminPanel = dynamic(() => import('@/components/AdminPanel'));

Split on:

  • Route boundaries (automatic in Next.js App Router)
  • Conditionally rendered components (modals, drawers, admin panels)
  • Heavy libraries (chart libraries, rich text editors, maps)
  • Below-the-fold content

Bundle Optimization

javascript
// next.config.js
module.exports = {
  experimental: {
    optimizePackageImports: ['lucide-react', '@heroicons/react', 'lodash-es'],
  },
};

Checklist:

  • Run npx next build and review the output size per route
  • Use @next/bundle-analyzer to identify large dependencies
  • Replace moment with date-fns or dayjs (save ~200KB)
  • Import specific functions: import { debounce } from 'lodash-es/debounce'
  • Prefer CSS over JS for animations (no runtime cost)
  • Tree-shake icon libraries: import { Search } from 'lucide-react'

Core Web Vitals Targets

MetricGoodNeeds WorkPoor
LCP (Largest Contentful Paint)<2.5s2.5-4.0s>4.0s
INP (Interaction to Next Paint)<200ms200-500ms>500ms
CLS (Cumulative Layout Shift)<0.10.1-0.25>0.25

LCP Optimization

  • Preload hero images: <link rel="preload" as="image" href="..." />
  • Use priority prop on above-the-fold <Image> components
  • Inline critical CSS, defer non-critical stylesheets
  • Avoid client-side rendering for above-the-fold content
  • Set explicit width/height on images to prevent layout shifts

Image Optimization

tsx
import Image from 'next/image';

<Image
  src="/hero.jpg"
  alt="Descriptive alt text"
  width={1200}
  height={630}
  priority              // preload for LCP images
  sizes="(max-width: 768px) 100vw, 50vw"
  placeholder="blur"
  blurDataURL={base64}  // inline tiny placeholder
/>
  • Use next/image or equivalent (automatic WebP/AVIF, responsive srcset)
  • Set sizes attribute to avoid downloading oversized images
  • Use placeholder="blur" with a base64 data URL for perceived performance
  • Lazy load below-the-fold images (default behavior)

Font Loading Strategy

tsx
// app/layout.tsx
import { Inter } from 'next/font/google';

const inter = Inter({
  subsets: ['latin'],
  display: 'swap',       // show fallback font immediately
  preload: true,
  variable: '--font-inter',
});

export default function RootLayout({ children }) {
  return (
    <html className={inter.variable}>
      <body>{children}</body>
    </html>
  );
}
  • Use next/font for zero-CLS font loading with automatic subsetting
  • Set display: 'swap' to avoid invisible text during load
  • Self-host fonts instead of loading from Google CDN (saves DNS lookup)
  • Limit to 2 font families maximum

CLS Prevention

  • Always set width and height on images and videos
  • Use aspect-ratio CSS for responsive media containers
  • Reserve space for dynamic content (ads, embeds) with min-height
  • Avoid inserting content above existing content after load
  • Use CSS contain: layout for components that change size

Performance Monitoring

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

onCLS(console.log);
onINP(console.log);
onLCP(console.log);

Measure real user metrics (RUM), not just lab scores. Vercel Analytics and Google Search Console provide field data.

Frequently asked questions

What does the Frontend Excellence AI skill do?

Modern frontend patterns for React Server Components, performance optimization, and Core Web Vitals

Why use Frontend Excellence on TypingMind?

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

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

Which AI models can use Frontend Excellence?

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 Excellence?

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

Is the Frontend Excellence 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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