performance

addyosmani/web-quality-skills · updated Apr 8, 2026

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$npx skills add https://github.com/addyosmani/web-quality-skills --skill performance
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summary

Lighthouse-based performance optimization with budgets, critical rendering path guidance, and Core Web Vitals alignment.

  • Defines resource budgets (1.5 MB total, <300 KB JS, <100 KB CSS) and provides server response optimization (TTFB <800ms, HTTP/2, edge caching)
  • Covers resource loading strategies: preconnect/preload directives, deferred CSS, script deferral patterns, and code splitting techniques
  • Includes image optimization (AVIF/WebP selection, responsive markup, LCP
skill.md

Performance optimization

Deep performance optimization based on Lighthouse performance audits. Focuses on loading speed, runtime efficiency, and resource optimization.

How it works

  1. Identify performance bottlenecks in code and assets
  2. Prioritize by impact on Core Web Vitals
  3. Provide specific optimizations with code examples
  4. Measure improvement with before/after metrics

Performance budget

Resource Budget Rationale
Total page weight < 1.5 MB 3G loads in ~4s
JavaScript (compressed) < 300 KB Parsing + execution time
CSS (compressed) < 100 KB Render blocking
Images (above-fold) < 500 KB LCP impact
Fonts < 100 KB FOIT/FOUT prevention
Third-party < 200 KB Uncontrolled latency

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.

Resource loading

Preconnect to required origins:

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

Preload critical resources:

<!-- 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>

Defer non-critical CSS:

<!-- 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:

<!-- 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:

// 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:

// ❌ 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

Format Use case Browser support
AVIF Photos, best compression 92%+
WebP Photos, good fallback 97%+
PNG Graphics with transparency Universal
SVG Icons, logos, illustrations Universal

Responsive images

<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

<!-- Above-fold LCP image: eager loading, high priority -->
<img 
  src="hero.webp" 
  fetchpriority
how to use performance

How to use performance on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add performance
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/addyosmani/web-quality-skills --skill performance

The skills CLI fetches performance from GitHub repository addyosmani/web-quality-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/performance

Reload or restart Cursor to activate performance. Access the skill through slash commands (e.g., /performance) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.534 reviews
  • Hiroshi Gupta· Dec 16, 2024

    We added performance from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Tariq Taylor· Dec 4, 2024

    performance has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Sakura Ramirez· Nov 23, 2024

    performance fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yuki Park· Nov 7, 2024

    Solid pick for teams standardizing on skills: performance is focused, and the summary matches what you get after install.

  • Yuki Agarwal· Oct 26, 2024

    performance has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kaira Ndlovu· Oct 14, 2024

    We added performance from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Maya Robinson· Sep 9, 2024

    performance has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Sep 5, 2024

    performance has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Layla Thomas· Aug 28, 2024

    Solid pick for teams standardizing on skills: performance is focused, and the summary matches what you get after install.

  • Dhruvi Jain· Aug 24, 2024

    Solid pick for teams standardizing on skills: performance is focused, and the summary matches what you get after install.

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