Performance logo

Performance

CommunityPopular
addyosmani
performance

Optimize web performance for faster loading and better user experience. Use when asked to "speed up my site", "optimize performance", "reduce load time", "fix slow loading", "improve page speed", or "performance audit".

Overview

Publisheraddyosmani
Repositoryweb-quality-skills
Skill nameperformance
Stars
2.8K
Forks
246
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 addyosmani on GitHub. Read the source before you install it.

Installation

Install the 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/addyosmani/web-quality-skills.git /tmp/web-quality-skills
mkdir -p .claude/skills
cp -r /tmp/web-quality-skills/skills/performance .claude/skills/performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Performance optimization

Evidence-led performance optimization using real-user signals for prioritization and browser traces for diagnosis. Focuses on loading speed, runtime responsiveness, and resource delivery.

How it works

  1. If a page can run, read the measurement workflow and establish a field-plus-lab baseline before editing.
  2. Prioritize poor real-user Core Web Vitals. Use a DevTools performance trace and its focused insights to find the cause.
  3. Inspect and change only the code or assets connected to measured bottlenecks.
  4. Re-run equivalent lab measurements and report before/after values, conditions, and uncertainty. Field verification remains pending until enough new user data arrives.

When no runnable page exists, perform static inspection but call findings hypotheses, not measured regressions. Include the command or browser workflow that can verify each high-impact hypothesis.

Prefer a browser tool that records a performance trace and exposes focused insights. With Chrome DevTools MCP, use performance_start_trace and performance_analyze_insight; do not route performance through lighthouse_audit, which covers non-performance Lighthouse categories.

Starting performance budget

Budgets must reflect the product's target devices, networks, page types, and user journeys. The values below are initial guardrails for a typical content or commerce page, not universal pass/fail criteria. Preserve an existing project budget when one is already defined.

ResourceBudgetRationale
Total page weight< 1.5 MBBounds transfer time and data cost on constrained target networks; calibrate with representative pages
JavaScript (compressed)< 300 KBProtect parse and execution cost
CSS (compressed)< 100 KBLimit render-blocking work
Images (above-fold)< 500 KBProtect likely LCP resources
Fonts< 100 KBLimit critical font transfer
Third-party< 200 KBBound code outside product control

Critical rendering path

Server response

  • TTFB < 800ms. Time to First Byte should be fast. Use CDN, caching, and efficient backends.
  • Enable compression. Gzip or Brotli for text assets. Brotli preferred (15-20% smaller).
  • HTTP/2 or HTTP/3. Multiplexing reduces connection overhead.
  • Edge caching. Cache HTML at CDN edge when possible.
  • Consider Early Hints (HTTP 103) for measured document latency. If a trace shows slow HTML generation and stable critical subresources, send an interim 103 with Link headers before the normal final response from the same request. Use HTTP/2 or later. A CDN may synthesize the 103 from Link headers on an earlier 200, or the origin/edge handler can emit it directly. Unsupported clients continue to the final response, but confirm current browser and infrastructure support. Limit hints to proven critical preloads or preconnects: inaccurate hints waste bandwidth. Cloudflare reported a 20–30% LCP improvement in an artificial, image-heavy test; treat that as a vendor case study, not an expected saving, and measure your result. See MDN's 103 implementation example and the Cloudflare study.

Resource loading

Preconnect to required origins:

html
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://cdn.example.com" crossorigin>

Preload critical resources:

Preload only resources whose late discovery is visible in the trace. Each preload competes for bandwidth and an unnecessary high-priority request can delay LCP.

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

<!-- Critical font -->
<link rel="preload" href="/font.woff2" as="font" type="font/woff2" crossorigin>

Prerender likely-next navigations with the Speculation Rules API:

html
<script type="speculationrules">
{
  "prerender": [{
    "where": { "href_matches": "/*" },
    "eagerness": "moderate"
  }]
}
</script>

moderate waits for a stronger intent signal than eager modes. Measure prediction hit rate, transferred bytes, and server cost; a wrong prerender is roughly an unused navigation. See core-web-vitals → LCP for the tradeoffs and the prerenderingchange gating needed for analytics.

Defer non-critical CSS:

html
<!-- Critical CSS inlined -->
<style>/* Above-fold styles */</style>

<!-- Non-critical CSS -->
<link rel="preload" href="/styles.css" as="style" onload="this.onload=null;this.rel='stylesheet'">
<noscript><link rel="stylesheet" href="/styles.css"></noscript>

JavaScript optimization

Defer non-essential scripts:

html
<!-- Parser-blocking (avoid) -->
<script src="/critical.js"></script>

<!-- Deferred (preferred) -->
<script defer src="/app.js"></script>

<!-- Async (for independent scripts) -->
<script async src="/analytics.js"></script>

<!-- Module (deferred by default) -->
<script type="module" src="/app.mjs"></script>

Code splitting patterns:

javascript
// Route-based splitting
const Dashboard = lazy(() => import('./Dashboard'));

// Component-based splitting
const HeavyChart = lazy(() => import('./HeavyChart'));

// Feature-based splitting
if (user.isPremium) {
  const PremiumFeatures = await import('./PremiumFeatures');
}

Tree shaking best practices:

javascript
// ❌ Imports entire library
import _ from 'lodash';
_.debounce(fn, 300);

// ✅ Imports only what's needed
import debounce from 'lodash/debounce';
debounce(fn, 300);

Image optimization

Format selection

FormatUse caseBrowser support
AVIFPhotos, best compression92%+
WebPPhotos, good fallback97%+
PNGGraphics with transparencyUniversal
SVGIcons, logos, illustrationsUniversal

Responsive images

html
<picture>
  <!-- AVIF for modern browsers -->
  <source 
    type="image/avif"
    srcset="hero-400.avif 400w,
            hero-800.avif 800w,
            hero-1200.avif 1200w"
    sizes="(max-width: 600px) 100vw, 50vw">
  
  <!-- WebP fallback -->
  <source 
    type="image/webp"
    srcset="hero-400.webp 400w,
            hero-800.webp 800w,
            hero-1200.webp 1200w"
    sizes="(max-width: 600px) 100vw, 50vw">
  
  <!-- JPEG fallback -->
  <img 
    src="hero-800.jpg"
    srcset="hero-400.jpg 400w,
            hero-800.jpg 800w,
            hero-1200.jpg 1200w"
    sizes="(max-width: 600px) 100vw, 50vw"
    width="1200" 
    height="600"
    alt="Hero image"
    loading="lazy"
    decoding="async">
</picture>

LCP image priority

html
<!-- Above-fold LCP image: eager loading, high priority -->
<img 
  src="hero.webp" 
  fetchpriority="high"
  loading="eager"
  decoding="sync"
  alt="Hero">

<!-- Below-fold images: lazy loading -->
<img 
  src="product.webp" 
  loading="lazy"
  decoding="async"
  alt="Product">

Font optimization

Loading strategy

css
/* System font stack as fallback */
body {
  font-family: 'Custom Font', -apple-system, BlinkMacSystemFont, 
               'Segoe UI', Roboto, sans-serif;
}

/* Prevent invisible text */
@font-face {
  font-family: 'Custom Font';
  src: url('/fonts/custom.woff2') format('woff2');
  font-display: swap; /* or optional for non-critical */
  font-weight: 400;
  font-style: normal;
  unicode-range: U+0000-00FF; /* Subset to Latin */
}

Preloading critical fonts

html
<link rel="preload" href="/fonts/heading.woff2" as="font" type="font/woff2" crossorigin>

Variable fonts

css
/* One file instead of multiple weights */
@font-face {
  font-family: 'Inter';
  src: url('/fonts/Inter-Variable.woff2') format('woff2-variations');
  font-weight: 100 900;
  font-display: swap;
}

Caching strategy

Cache-Control headers

# HTML (short or no cache)
Cache-Control: no-cache, must-revalidate

# Static assets with hash (immutable)
Cache-Control: public, max-age=31536000, immutable

# Static assets without hash
Cache-Control: public, max-age=86400, stale-while-revalidate=604800

# API responses
Cache-Control: private, max-age=0, must-revalidate

Service worker caching

javascript
// Cache-first for static assets
self.addEventListener('fetch', (event) => {
  if (event.request.destination === 'image' ||
      event.request.destination === 'style' ||
      event.request.destination === 'script') {
    event.respondWith(
      caches.match(event.request).then((cached) => {
        return cached || fetch(event.request).then((response) => {
          const clone = response.clone();
          caches.open('static-v1').then((cache) => cache.put(event.request, clone));
          return response;
        });
      })
    );
  }
});

Runtime performance

Avoid layout thrashing

javascript
// ❌ Forces multiple reflows
elements.forEach(el => {
  const height = el.offsetHeight; // Read
  el.style.height = height + 10 + 'px'; // Write
});

// ✅ Batch reads, then batch writes
const heights = elements.map(el => el.offsetHeight); // All reads
elements.forEach((el, i) => {
  el.style.height = heights[i] + 10 + 'px'; // All writes
});

Debounce expensive operations

javascript
function debounce(fn, delay) {
  let timeout;
  return (...args) => {
    clearTimeout(timeout);
    timeout = setTimeout(() => fn(...args), delay);
  };
}

// Debounce scroll/resize handlers
window.addEventListener('scroll', debounce(handleScroll, 100));

Use requestAnimationFrame

javascript
// ❌ May cause jank
setInterval(animate, 16);

// ✅ Synced with display refresh
function animate() {
  // Animation logic
  requestAnimationFrame(animate);
}
requestAnimationFrame(animate);

Virtualize long lists

javascript
// For lists > 100 items, render only visible items
// Use libraries like react-window, vue-virtual-scroller, or native CSS:
.virtual-list {
  content-visibility: auto;
  contain-intrinsic-size: 0 50px; /* Estimated item height */
}

Smooth navigations with View Transitions

The View Transitions API lets the browser cross-fade (or custom-animate) between two DOM states using a single GPU-composited snapshot — no double-render, no layout thrash, and the snapshot doesn't count toward CLS.

Same-document (SPA-style) — Baseline 2026:

javascript
// Wrap the DOM mutation that swaps the view
function navigate(newView) {
  if (!document.startViewTransition) return swapDOM(newView);
  document.startViewTransition(() => swapDOM(newView));
}

Cross-document (MPA-style) — Chromium-stable, progressive enhancement elsewhere:

css
/* On both source and destination pages */
@view-transition { navigation: auto; }

That's the entire integration — same-origin navigations now fade automatically. To opt specific elements into shared-element transitions (e.g. a thumbnail expanding into a hero), give them a matching view-transition-name:

css
.product-thumb[data-id="42"], .product-hero { view-transition-name: product-42; }

Pair this with Speculation Rules (above) for instant + animated navigations.

Third-party scripts

Load strategies

javascript
// ❌ Blocks main thread
<script src="https://analytics.example.com/script.js"></script>

// ✅ Async loading
<script async src="https://analytics.example.com/script.js"></script>

// ✅ Delay until interaction
<script>
document.addEventListener('DOMContentLoaded', () => {
  const observer = new IntersectionObserver((entries) => {
    if (entries[0].isIntersecting) {
      const script = document.createElement('script');
      script.src = 'https://widget.example.com/embed.js';
      document.body.appendChild(script);
      observer.disconnect();
    }
  });
  observer.observe(document.querySelector('#widget-container'));
});
</script>

Facade pattern

html
<!-- Show static placeholder until interaction -->
<div class="youtube-facade" 
     data-video-id="abc123" 
     onclick="loadYouTube(this)">
  <img src="/thumbnails/abc123.jpg" alt="Video title">
  <button aria-label="Play video"></button>
</div>

Measurement

Use the measurement workflow whenever a URL is runnable. It defines Chrome DevTools MCP routing, CrUX and fallback sources, repeatable lab conditions, and a compact evidence format.

MetricKindInterpretation
LCP, INP, CLS at p75FieldUser-outcome Core Web Vitals; use for pass/fail prioritization
LCP, CLS in a traceLabReproducible diagnostic values for one navigation
TBTLabMain-thread blocking diagnostic and a rough INP proxy, not field INP
FCP, Speed IndexLabLoading diagnostics, not Core Web Vitals

Raw PerformanceObserver snippets are useful for the current browser session but are not real-user data by themselves. When the user wants production telemetry, read the first-party RUM reference and prefer web-vitals over a hand-rolled metric implementation.

References

For Core Web Vitals specific optimizations, see Core Web Vitals.

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 Performance AI skill do?

Optimize web performance for faster loading and better user experience. Use when asked to "speed up my site", "optimize performance", "reduce load time", "fix slow loading", "improve page speed", or "performance audit".

Why use Performance on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/addyosmani/web-quality-skills/tree/main/skills/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 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 Performance?

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

Is the Performance AI skill free?

Yes. It is published on GitHub by addyosmani 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇