performance-optimization▌
OWNER/REPO · updated May 7, 2026
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Optimizes application performance by identifying and fixing bottlenecks based on profiling data.
| name | performance-optimization |
| description | Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing. |
Performance Optimization
Overview
Measure before optimizing. Performance work without measurement is guessing — and guessing leads to premature optimization that adds complexity without improving what matters. Profile first, identify the actual bottleneck, fix it, measure again. Optimize only what measurements prove matters.
When to Use
- Performance requirements exist in the spec (load time budgets, response time SLAs)
- Users or monitoring report slow behavior
- Core Web Vitals scores are below thresholds
- You suspect a change introduced a regression
- Building features that handle large datasets or high traffic
When NOT to use: Don't optimize before you have evidence of a problem. Premature optimization adds complexity that costs more than the performance it gains.
Core Web Vitals Targets
| Metric | Good | Needs Improvement | Poor |
|---|---|---|---|
| LCP (Largest Contentful Paint) | ≤ 2.5s | ≤ 4.0s | > 4.0s |
| INP (Interaction to Next Paint) | ≤ 200ms | ≤ 500ms | > 500ms |
| CLS (Cumulative Layout Shift) | ≤ 0.1 | ≤ 0.25 | > 0.25 |
The Optimization Workflow
1. MEASURE → Establish baseline with real data
2. IDENTIFY → Find the actual bottleneck (not assumed)
3. FIX → Address the specific bottleneck
4. VERIFY → Measure again, confirm improvement
5. GUARD → Add monitoring or tests to prevent regression
Step 1: Measure
Two complementary approaches — use both:
- Synthetic (Lighthouse, DevTools Performance tab): Controlled conditions, reproducible. Best for CI regression detection and isolating specific issues.
- RUM (web-vitals library, CrUX): Real user data in real conditions. Required to validate that a fix actually improved user experience.
Frontend:
# Synthetic: Lighthouse in Chrome DevTools (or CI)
# Chrome DevTools → Performance tab → Record
# Chrome DevTools MCP → Performance trace
# RUM: Web Vitals library in code
import { onLCP, onINP, onCLS } from 'web-vitals';
onLCP(console.log);
onINP(console.log);
onCLS(console.log);
Backend:
# Response time logging
# Application Performance Monitoring (APM)
# Database query logging with timing
# Simple timing
console.time('db-query');
const result = await db.query(...);
console.timeEnd('db-query');
Where to Start Measuring
Use the symptom to decide what to measure first:
What is slow?
├── First page load
│ ├── Large bundle? --> Measure bundle size, check code splitting
│ ├── Slow server response? --> Measure TTFB in DevTools Network waterfall
│ │ ├── DNS long? --> Add dns-prefetch / preconnect for known origins
│ │ ├── TCP/TLS long? --> Enable HTTP/2, check edge deployment, keep-alive
│ │ └── Waiting (server) long? --> Profile backend, check queries and caching
│ └── Render-blocking resources? --> Check network waterfall for CSS/JS blocking
├── Interaction feels sluggish
│ ├── UI freezes on click? --> Profile main thread, look for long tasks (>50ms)
│ ├── Form input lag? --> Check re-renders, controlled component overhead
│ └── Animation jank? --> Check layout thrashing, forced reflows
├── Page after navigation
│ ├── Data loading? --> Measure API response times, check for waterfalls
│ └── Client rendering? --> Profile component render time, check for N+1 fetches
└── Backend / API
├── Single endpoint slow? --> Profile database queries, check indexes
├── All endpoints slow? --> Check connection pool, memory, CPU
└── Intermittent slowness? --> Check for lock contention, GC pauses, external deps
Step 2: Identify the Bottleneck
Common bottlenecks by category:
Frontend:
| Symptom | Likely Cause | Investigation |
|---|---|---|
| Slow LCP | Large images, render-blocking resources, slow server | Check network waterfall, image sizes |
| High CLS | Images without dimensions, late-loading content, font shifts | Check layout shift attribution |
| Poor INP | Heavy JavaScript on main thread, large DOM updates | Check long tasks in Performance trace |
| Slow initial load | Large bundle, many network requests | Check bundle size, code splitting |
Backend:
| Symptom | Likely Cause | Investigation |
|---|---|---|
| Slow API responses | N+1 queries, missing indexes, unoptimized queries | Check database query log |
| Memory growth | Leaked references, unbounded caches, large payloads | Heap snapshot analysis |
| CPU spikes | Synchronous heavy computation, regex backtracking | CPU profiling |
| High latency | Missing caching, redundant computation, network hops | Trace requests through the stack |
Step 3: Fix Common Anti-Patterns
N+1 Queries (Backend)
// BAD: N+1 — one query per task for the owner
const tasks = await db.tasks.findMany();
for (const task of tasks) {
task.owner = await db.users.findUnique({ where: { id: task.ownerId } });
}
// GOOD: Single query with join/include
const tasks = await db.tasks.findMany({
include: { owner: true },
});
Unbounded Data Fetching
// BAD: Fetching all records
const allTasks = await db.tasks.findMany();
// GOOD: Paginated with limits
const tasks = await db.tasks.findMany({
take: 20,
skip: (page - 1) * 20,
orderBy: { createdAt: 'desc' },
});
Missing Image Optimization (Frontend)
<!-- BAD: No dimensions, no format optimization -->
<img src="/hero.jpg" />
<!-- GOOD: Hero / LCP image — art direction + resolution switching, high priority -->
<!--
Two techniques combined:
- Art direction (media): different crop/composition per breakpoint
- Resolution switching (srcset + sizes): right file size per screen density
-->
<picture>
<!-- Mobile: portrait crop (8:10) -->
<source
media="(max-width: 767px)"
srcset="/hero-mobile-400.avif 400w, /hero-mobile-800.avif 800w"
sizes="100vw"
width="800"
height="1000"
type="image/avif"
/>
<source
media="(max-width: 767px)"
srcset="/hero-mobile-400.webp 400w, /hero-mobile-800.webp 800w"
sizes="100vw"
width="800"
height="1000"
type="image/webp"
/>
<!-- Desktop: landscape crop (2:1) -->
<source
srcset="/hero-800.avif 800w, /hero-1200.avif 1200w, /hero-1600.avif 1600w"
sizes="(max-width: 1200px) 100vw, 1200px"
width="1200"
height="600"
type="image/avif"
/>
<source
srcset="/hero-800.webp 800w, /hero-1200.webp 1200w, /hero-1600.webp 1600w"
sizes="(max-width: 1200px) 100vw, 1200px"
width="1200"
height="600"
type="image/webp"
/>
<img
src="/hero-desktop.jpg"
width="1200"
height="600"
fetchpriority="high"
alt="Hero image description"
/>
</picture>
<!-- GOOD: Below-the-fold image — lazy loaded + async decoding -->
<img
src="/content.webp"
width="800"
height="400"
loading="lazy"
decoding="async"
alt="Content image description"
/>
Unnecessary Re-renders (React)
// BAD: Creates new object on every render, causing children to re-render
function TaskList() {
return <TaskFilters options={{ sortBy: 'date', order: 'desc' }} />;
}
// GOOD: Stable reference
const DEFAULT_OPTIONS = { sortBy: 'date', order: 'desc' } as const;
function TaskList() {
return <TaskFilters options={DEFAULT_OPTIONS} />;
}
// Use React.memo for expensive components
const TaskItem = React.memo(function TaskItem({ task }: Props) {
return <div>{/* expensive render */}</div>;
});
// Use useMemo for expensive computations
function TaskStats({ tasks }: Props) {
const stats = useMemo(() => calculateStats(tasks), [tasks]);
return <div>{stats.completed} / {stats.total}</div>;
}
Large Bundle Size
// Modern bundlers (Vite, webpack 5+) handle named imports with tree-shaking automatically,
// provided the dependency ships ESM and is marked `sideEffects: false` in package.json.
// Profile before changing import styles — the real gains come from splitting and lazy loading.
// GOOD: Dynamic import for heavy, rarely-used features
const ChartLibrary = lazy(() => import('./ChartLibrary'));
// GOOD: Route-level code splitting wrapped in Suspense
const SettingsPage = lazy(() => import('./pages/Settings'));
function App() {
return (
<Suspense fallback={<Spinner />}>
<SettingsPage />
</Suspense>
);
}
Missing Caching (Backend)
// Cache frequently-read, rarely-changed data
const CACHE_TTL = 5 * 60 * 1000; // 5 minutes
let cachedConfig: AppConfig | null = null;
let cacheExpiry = 0;
async function getAppConfig(): Promise<AppConfig> {
if (cachedConfig && Date.now() < cacheExpiry) {
return cachedConfig;
}
cachedConfig = await db.config.findFirst();
cacheExpiry = Date.now() + CACHE_TTL;
return cachedConfig;
}
// HTTP caching headers for static assets
app.use('/static', express.static('public', {
maxAge: '1y', // Cache for 1 year
immutable: true, // Never revalidate (use content hashing in filenames)
}));
// Cache-Control for API responses
res.set('Cache-Control', 'public, max-age=300'); // 5 minutes
Performance Budget
Set budgets and enforce them:
JavaScript bundle: < 200KB gzipped (initial load)
CSS: < 50KB gzipped
Images: < 200KB per image (above the fold)
Fonts: < 100KB total
API response time: < 200ms (p95)
Time to Interactive: < 3.5s on 4G
Lighthouse Performance score: ≥ 90
Enforce in CI:
# Bundle size check
npx bundlesize --config bundlesize.config.json
# Lighthouse CI
npx lhci autorun
See Also
For detailed performance checklists, optimization commands, and anti-pattern reference, see references/performance-checklist.md.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We'll optimize later" | Performance debt compounds. Fix obvious anti-patterns now, defer micro-optimizations. |
| "It's fast on my machine" | Your machine isn't the user's. Profile on representative hardware and networks. |
| "This optimization is obvious" | If you didn't measure, you don't know. Profile first. |
| "Users won't notice 100ms" | Research shows 100ms delays impact conversion rates. Users notice more than you think. |
| "The framework handles performance" | Frameworks prevent some issues but can't fix N+1 queries or oversized bundles. |
Red Flags
- Optimization without profiling data to justify it
- N+1 query patterns in data fetching
- List endpoints without pagination
- Images without dimensions, lazy loading, or responsive sizes
- Bundle size growing without review
- No performance monitoring in production
React.memoanduseMemoeverywhere (overusing is as bad as underusing)
Verification
After any performance-related change:
- Before and after measurements exist (specific numbers)
- The specific bottleneck is identified and addressed
- Core Web Vitals are within "Good" thresholds
- Bundle size hasn't increased significantly
- No N+1 queries in new data fetching code
- Performance budget passes in CI (if configured)
- Existing tests still pass (optimization didn't break behavior)
How to use performance-optimization on Cursor
AI-first code editor with Composer
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-optimization
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performance-optimization from GitHub repository OWNER/REPO and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performance-optimization. Access the skill through slash commands (e.g., /performance-optimization) 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
Use Cases▌
Accelerate Code Development
Use skill to generate boilerplate code, refactor legacy code, and write tests faster
Example
Generate React component with TypeScript types, styled-components, and comprehensive test suite in minutes
Reduce development time by 40-60% for repetitive coding tasks
Code Review Automation
Systematically review code for bugs, security issues, and style violations
Example
Analyze pull requests for common anti-patterns, suggest performance improvements, flag security vulnerabilities
Catch 70%+ of code issues before human review, improve code quality
Debug Complex Issues
Trace errors through stack traces and identify root causes faster
Example
Analyze error logs, suggest probable causes, recommend fixes with code examples
Cut debugging time by 30-50%, especially for unfamiliar codebases
Learn New Technologies
Get explanations, examples, and best practices for unfamiliar frameworks
Example
Understand Next.js app router, learn Rust ownership, grasp Kubernetes concepts with practical examples
Accelerate learning curve by 2-3x, reduce onboarding time for new tech stacks
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill installation support
- ›Basic understanding of programming concepts and version control (Git)
- ›Code editor or IDE for testing generated code (VS Code, JetBrains, etc.)
- ›Test environment separate from production for validating skill outputs
Time Estimate
15-30 minutes to install and see first useful output
Installation Steps
- 1.Install the skill using provided installation command
- 2.Verify skill is loaded in Claude Desktop (check ~/.claude/skills directory)
- 3.Test skill with simple prompt: 'Help me review this code snippet'
- 4.Gradually increase complexity: code generation → refactoring → architecture advice
- 5.Review all generated code before committing to repository
- 6.Iterate on prompts to improve output quality and relevance
- 7.Share effective prompts with team for consistency
Common Pitfalls
- ⚠Blindly trusting generated code without testing—always run tests and manual review
- ⚠Not providing enough context about your project structure and coding standards
- ⚠Expecting perfection on first generation—iteration and refinement are normal
- ⚠Sharing proprietary code or API keys in prompts—maintain confidentiality
- ⚠Over-relying on skill for critical security or business logic code
- ⚠Skipping documentation of why AI-generated code was chosen over alternatives
Best Practices▌
✓ Do
- +Always review and test AI-generated code before merging
- +Provide clear context: language, framework, coding standards, constraints
- +Use for boilerplate, tests, docs—areas where mistakes are easily caught
- +Iterate on prompts: start broad, refine with specific requirements
- +Combine AI suggestions with human judgment and domain expertise
- +Document successful prompt patterns for team reuse
- +Keep version control so you can rollback if needed
- +Use skill for learning and exploration, not production-critical features initially
✗ Don't
- −Don't commit AI code without thorough testing and review
- −Don't expose sensitive code, credentials, or proprietary algorithms
- −Don't use for security-critical code (auth, crypto, payments) without expert review
- −Don't skip peer review process just because AI generated it
- −Don't assume code follows your team's conventions—verify
- −Don't let junior developers skip learning fundamentals by relying solely on AI
- −Don't ignore compiler warnings or test failures in generated code
💡 Pro Tips
- ★Describe desired patterns explicitly: 'Use async/await, avoid callbacks'
- ★Ask for alternatives: 'Show 3 approaches to solve this, with tradeoffs'
- ★Request explanations: 'Explain why this approach is better than X'
- ★Use skill for 70% generation + 30% manual refinement for best results
- ★Build a prompt library for common patterns (API endpoints, components, tests)
- ★Pair program with AI: describe problem → review solution → iterate → refine
When to Use This▌
✓ Use When
Use coding skills for boilerplate generation, code reviews, refactoring legacy code, writing tests, learning new frameworks, and debugging non-critical issues. Best for repetitive tasks where errors are easy to catch.
✗ Avoid When
Avoid for production security features (auth, encryption, payment processing), complex business logic requiring deep domain knowledge, performance-critical algorithms, or when learning fundamentals is more valuable than speed.
Learning Path▌
- 1Start with simple tasks: generate functions, write tests, explain code
- 2Progress to code review: analyze PRs, suggest improvements
- 3Advanced: architectural decisions, refactoring strategies, performance optimization
- 4Expert: use for exploring new paradigms, researching best practices, mentoring juniors
Integration▌
- →VS Code
- →JetBrains IDEs
- →Cursor
- →GitHub Copilot
- →Git workflows
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★36 reviews- ★★★★★Ama Martinez· Dec 24, 2024
Registry listing for performance-optimization matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chaitanya Patil· Dec 16, 2024
Keeps context tight: performance-optimization is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Henry White· Dec 12, 2024
performance-optimization fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Henry Rao· Dec 4, 2024
performance-optimization is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ama Thompson· Nov 23, 2024
performance-optimization reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Aditi Zhang· Nov 15, 2024
Keeps context tight: performance-optimization is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Piyush G· Nov 7, 2024
Registry listing for performance-optimization matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Shikha Mishra· Oct 26, 2024
performance-optimization reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Henry Khan· Oct 14, 2024
Registry listing for performance-optimization matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Nia Flores· Oct 6, 2024
performance-optimization is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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