Core Web Vitals logo

Core Web Vitals

CommunityPopular
addyosmani
core-web-vitals

Optimize Core Web Vitals (LCP, INP, CLS) for better page experience using field and lab evidence. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "optimize INP", "page experience optimization", or "fix layout shifts".

Overview

Publisheraddyosmani
Repositoryweb-quality-skills
Skill namecore-web-vitals
Stars
2.8K
Forks
246
Bundled files
3
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.

  • 3 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 Core Web Vitals 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/core-web-vitals .claude/skills/core-web-vitals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Core Web Vitals 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 Core Web Vitals 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 Core Web Vitals 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.

Core Web Vitals optimization

Targeted optimization for the three Core Web Vitals using field data to identify user impact and browser traces to diagnose causes.

Measure before optimizing

When a runnable URL is available, read the performance measurement workflow. Prefer this sequence:

  1. Check page-level CrUX p75 data, with a clearly labeled origin fallback when page data is unavailable.
  2. Record a browser performance trace under stated conditions. With Chrome DevTools MCP, trace summaries can include CrUX alongside the observed lab metrics.
  3. Analyze only the insights associated with the failing metric, then inspect the implicated code and resources.
  4. Re-run equivalent lab measurements after the fix. Do not claim an immediate field improvement; CrUX and first-party RUM need new user visits.

If only source code is available, identify likely causes but do not claim that LCP, INP, or CLS is failing without runtime evidence.

The three metrics

MetricMeasuresGoodNeeds workPoor
LCPLoading≤ 2.5s2.5s – 4s> 4s
INPInteractivity≤ 200ms200ms – 500ms> 500ms
CLSVisual Stability≤ 0.10.1 – 0.25> 0.25

Google measures at the 75th percentile — 75% of page visits must meet "Good" thresholds.


LCP: Largest Contentful Paint

LCP measures when the largest visible content element renders. Usually this is:

  • Hero image or video
  • Large text block
  • Background image
  • <svg> element

Common LCP issues

1. Slow server response (TTFB > 800ms)

Fix: CDN, caching, optimized backend, edge rendering

2. Render-blocking resources

html
<!-- ❌ Blocks rendering -->
<link rel="stylesheet" href="/all-styles.css">

<!-- ✅ Critical CSS inlined, rest deferred -->
<style>/* Critical above-fold CSS */</style>
<link rel="preload" href="/styles.css" as="style" 
      onload="this.onload=null;this.rel='stylesheet'">

3. Slow resource load times

html
<!-- ❌ LCP image is discovered only after a stylesheet loads -->
<div class="hero"></div>

<!-- ✅ Discoverable in initial HTML and prioritized -->
<link rel="preload" href="/hero.webp" as="image" fetchpriority="high">
<img src="/hero.webp" alt="Hero" fetchpriority="high">

Prefer a discoverable <img> with fetchpriority="high". Add the preload only when the trace shows that the resource would otherwise be discovered late; duplicate or speculative preloads can compete for bandwidth.

4. Client-side rendering delays

javascript
// ❌ Content loads after JavaScript
useEffect(() => {
  fetch('/api/hero-text').then(r => r.json()).then(setHeroText);
}, []);

// ✅ Server-side or static rendering
// Use SSR, SSG, or streaming to send HTML with content
export async function getServerSideProps() {
  const heroText = await fetchHeroText();
  return { props: { heroText } };
}

5. Make navigations instant with the Speculation Rules API

For sites with predictable same-origin journeys, prerendering a likely next page can make a successful subsequent navigation much faster. Treat this as a measured navigation optimization, not a substitute for fixing the current page's LCP.

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

Current Chrome behavior is specific enough to guide the choice:

eagernessTrigger
conservativePointer or touch down
moderateDesktop: 200ms hover, or earlier pointer down; mobile: viewport heuristics
eagerChrome 143+: desktop 10ms hover; mobile 50ms after the anchor enters the viewport
immediateAs soon as the rules are observed

Start conservatively and measure prediction hit rate, transferred bytes, server load, and navigation improvement before expanding the rules. Recheck Chrome's maintained eagerness documentation before hardcoding timing-sensitive behavior.

Caveats:

  • Bandwidth/CPU cost. Each prerender is roughly a full page load. Scope where carefully (href_matches patterns, exclude logout/checkout) and avoid immediate outside small sites.
  • Side effects fire early. Analytics, ads, and any code that runs on load will fire when the prerender starts, not when the user navigates. Gate side effects on the prerenderingchange event or document.prerendering.
  • Chromium-only. Safari and Firefox ignore the script — it's a progressive enhancement, never a regression.

LCP optimization checklist

markdown
- [ ] TTFB < 800ms (use CDN, edge caching)
- [ ] LCP resource is discoverable in initial HTML and prioritized; preload only if the trace shows late discovery
- [ ] LCP image optimized (WebP/AVIF, correct size)
- [ ] Critical CSS inlined (< 14KB)
- [ ] No render-blocking JavaScript in <head>
- [ ] Fonts don't block text rendering (font-display: swap)
- [ ] LCP element in initial HTML (not JS-rendered)
- [ ] Speculation Rules added for likely-next navigations (moderate eagerness)

LCP element identification

This snippet diagnoses the current page session. It is not field data.

javascript
// Find your LCP element
new PerformanceObserver((list) => {
  const entries = list.getEntries();
  const lastEntry = entries[entries.length - 1];
  console.log('LCP element:', lastEntry.element);
  console.log('LCP time:', lastEntry.startTime);
}).observe({ type: 'largest-contentful-paint', buffered: true });

INP: Interaction to Next Paint

INP measures responsiveness across clicks, taps, and key presses during a visit. Diagnose its input delay, processing time, and presentation delay separately; a slow interaction may involve main-thread contention before the handler, expensive application work, or delayed rendering after it.

When field INP is poor or a trace identifies a slow interaction, read the INP reference for trace interpretation, yielding patterns, third-party and rendering causes, a single-session observer, and first-party attribution.


CLS: Cumulative Layout Shift

CLS measures unexpected layout shifts across a page visit. Use field attribution or a trace to identify the shifted node and the trigger; do not assume the visible victim caused the shift.

When field CLS is poor or a trace reports shifts, read the CLS reference for reserved-space patterns, dynamic content, font and animation fixes, a debugging observer, and a verification checklist.


Measurement sources

SourceUse
Browser performance trace (Chrome DevTools MCP: performance_start_trace)Observe one load or interaction and diagnose focused insights; use included CrUX context when available
CrUX or Search ConsolePrioritize aggregated real-user outcomes at p75
Lighthouse CLI or PageSpeed InsightsControlled lab fallback when DevTools tools are unavailable
First-party RUMSegment current production experience by route, device, release, and attribution
Raw PerformanceObserverInspect one page session during debugging

Do not route performance through Chrome DevTools MCP's lighthouse_audit; that capability intentionally covers non-performance Lighthouse categories. Do not compare a single lab value directly with a field p75 as if they were equivalent samples.

When adding or reviewing production collection, read the first-party RUM reference. Prefer the web-vitals library because raw browser APIs do not by themselves implement every Core Web Vital's lifecycle and reporting rules.


Framework quick fixes

Next.js

jsx
// LCP: Use next/image with priority
import Image from 'next/image';
<Image src="/hero.jpg" priority fill alt="Hero" />

// INP: Use dynamic imports
const HeavyComponent = dynamic(() => import('./Heavy'), { ssr: false });

// CLS: Image component handles dimensions automatically

React

jsx
// LCP: Preload in head
<link rel="preload" href="/hero.jpg" as="image" fetchpriority="high" />

// INP: Memoize and useTransition
const [isPending, startTransition] = useTransition();
startTransition(() => setExpensiveState(newValue));

// CLS: Always specify dimensions in img tags

Vue/Nuxt

vue
<!-- LCP: Use nuxt/image with preload -->
<NuxtImg src="/hero.jpg" preload loading="eager" />

<!-- INP: Use async components -->
<component :is="() => import('./Heavy.vue')" />

<!-- CLS: Use aspect-ratio CSS -->
<img :style="{ aspectRatio: '16/9' }" />

References

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 Core Web Vitals AI skill do?

Optimize Core Web Vitals (LCP, INP, CLS) for better page experience using field and lab evidence. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "optimize INP", "page experience optimization", or "fix layout shifts".

Why use Core Web Vitals on TypingMind?

Because you install it once and use it with any model. Core Web Vitals 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 Core Web Vitals in TypingMind?

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

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 Core Web Vitals?

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

Is the Core Web Vitals 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 👇