Performance Optimizer

Performance Optimizer

Find and fix performance bottlenecks. Measure, optimize, verify. Make it fast.

When to Use This Skill

• App is slow or laggy
• User complains about performance
• Page load times are high
• API responses are slow
• Database queries take too long
• User mentions "slow", "lag", "performance", or "optimize"

The Optimization Process

1. Measure First

Never optimize without measuring:

// Measure execution time
console.time('operation');
await slowOperation();
console.timeEnd('operation'); // operation: 2341ms

What to measure:

• Page load time
• API response time
• Database query time
• Function execution time
• Memory usage
• Network requests

2. Find the Bottleneck

Use profiling tools to find the slow parts:

Browser:

DevTools → Performance tab → Record → Stop
Look for long tasks (red bars)

Node.js:

node --prof app.js
node --prof-process isolate-*.log > profile.txt

Database:

EXPLAIN ANALYZE SELECT * FROM users WHERE email = 'test@example.com';

3. Optimize

Fix the slowest thing first (biggest impact).

Common Optimizations

Database Queries

Problem: N+1 Queries

// Bad: N+1 queries
const users = await db.users.find();
for (const user of users) {
  user.posts = await db.posts.find({ userId: user.id }); // N queries
}

// Good: Single query with JOIN
const users = await db.users.find()
  .populate('posts'); // 1 query

Problem: Missing Index

-- Check slow query
EXPLAIN SELECT * FROM users WHERE email = 'test@example.com';
-- Shows: Seq Scan (bad)

-- Add index
CREATE INDEX idx_users_email ON users(email);

-- Check again
EXPLAIN SELECT * FROM users WHERE email = 'test@example.com';
-- Shows: Index Scan (good)

**Problem: SELECT ***

// Bad: Fetches all columns
const users = await db.query('SELECT * FROM users');

// Good: Only needed columns
const users = await db.query('SELECT id, name, email FROM users');

Problem: No Pagination

// Bad: Returns all records
const users = await db.users.find();

// Good: Paginated
const users = await db.users.find()
  .limit(20)
  .skip((page - 1) * 20);

API Performance

Problem: No Caching

// Bad: Hits database every time
app.get('/api/stats', async (req, res) => {
  const stats = await db.stats.calculate(); // Slow
  res.json(stats);
});

// Good: Cache for 5 minutes
const cache = new Map();
app.get('/api/stats', async (req, res) => {
  const cached = cache.get('stats');
  if (cached && Date.now() - cached.time < 300000) {
    return res.json(cached.data);
  }
  
  const stats = await db.stats.calculate();
  cache.set('stats', { data: stats, time: Date.now() });
  res.json(stats);
});

Problem: Sequential Operations

// Bad: Sequential (slow)
const user = await getUser(id);
const posts = await getPosts(id);
const comments = await getComments(id);
// Total: 300ms + 200ms + 150ms = 650ms

// Good: Parallel (fast)
const [user, posts, comments] = await Promise.all([
  getUser(id),
  getPosts(id),
  getComments(id)
]);
// Total: max(300ms, 200ms, 150ms) = 300ms

Problem: Large Payloads

// Bad: Returns everything
res.json(users); // 5MB response

// Good: Only needed fields
res.json(users.map(u => ({
  id: u.id,
  name: u.name,
  email: u.email
}))); // 500KB response

Frontend Performance

Problem: Unnecessary Re-renders

// Bad: Re-renders on every parent update
function UserList({ users }) {
  return users.map(user => <UserCard user={user} />);
}

// Good: Memoized
const UserCard = React.memo(({ user }) => {
  return <div>{user.name}</div>;
});

Problem: Large Bundle

// Bad: Imports entire library
import _ from 'lodash'; // 70KB

// Good: Import only what you need
import debounce from 'lodash/debounce'; // 2KB

Problem: No Code Splitting

// Bad: Everything in one bundle
import HeavyComponent from './HeavyComponent';

// Good: Lazy load
const HeavyComponent = React.lazy(() => import('./HeavyComponent'));

Problem: Unoptimized Images

<!-- Bad: Large image -->
<img src="photo.jpg" /> <!-- 5MB -->

<!-- Good: Optimized and responsive -->
<img 
  src="photo-small.webp" 
  srcset="photo-small.webp 400w, photo-large.webp 800w"
  loading="lazy"
  width="400"
  height="300"
/> <!-- 50KB -->

Algorithm Optimization

Problem: Inefficient Algorithm

// Bad: O(n²) - nested loops
function findDuplicates(arr) {
  const duplicates = [];
  for (let i = 0; i < arr.length; i++) {
    for (let j = i + 1; j < arr.length; j++) {
      if (arr[i] === arr[j]) duplicates.push(arr[i]);
    }
  }
  return duplicates;
}

// Good: O(n) - single pass with Set
function findDuplicates(arr) {
  const seen = new Set();
  const duplicates = new Set();
  for (const item of arr) {
    if (seen.has(item)) duplicates.add(item);
    seen.add(item);
  }
  return Array.from(duplicates);
}

Problem: Repeated Calculations

// Bad: Calculates every time
function getTotal(items) {
  return items.reduce((sum, item) => sum + item.price * item.quantity, 0);
}
// Called 100 times in render

// Good: Memoized
const getTotal = useMemo(() => {
  return items.reduce((sum, item) => sum + item.price * item.quantity, 0);
}, [items]);

Memory Optimization

Problem: Memory Leak

// Bad: Event listener not cleaned up
useEffect(() => {
  window.addEventListener('scroll', handleScroll);
  // Memory leak!
}, []);

// Good: Cleanup
useEffect(() => {
  window.addEventListener('scroll', handleScroll);
  return () => window.removeEventListener('scroll', handleScroll);
}, []);

Problem: Large Data in Memory

// Bad: Loads entire file into memory
const data = fs.readFileSync('huge-file.txt'); // 1GB

// Good: Stream it
const stream = fs.createReadStream('huge-file.txt');
stream.on('data', chunk => process(chunk));

Measuring Impact

Always measure before and after:

// Before optimization
console.time('query');
const users = await db.users.find();
console.timeEnd('query');
// query: 2341ms

// After optimization (added index)
console.time('query');
const users = await db.users.find();
console.timeEnd('query');
// query: 23ms

// Improvement: 100x faster!

Performance Budgets

Set targets:

Page Load: < 2 seconds
API Response: < 200ms
Database Query: < 50ms
Bundle Size: < 200KB
Time to Interactive: < 3 seconds

Tools

Browser:

• Chrome DevTools Performance tab
• Lighthouse (audit)
• Network tab (waterfall)

Node.js:

• node --prof (profiling)
• clinic (diagnostics)
• autocannon (load testing)

Database:

• EXPLAIN ANALYZE (query plans)
• Slow query log
• Database profiler

Monitoring:

• New Relic
• Datadog
• Sentry Performance

Quick Wins

Easy optimizations with big impact:

1. Add database indexes on frequently queried columns
2. Enable gzip compression on server
3. Add caching for expensive operations
4. Lazy load images and heavy components
5. Use CDN for static assets
6. Minify and compress JavaScript/CSS
7. Remove unused dependencies
8. Use pagination instead of loading all data
9. Optimize images (WebP, proper sizing)
10. Enable HTTP/2 on server

Optimization Checklist

• Measured current performance
• Identified bottleneck
• Applied optimization
• Measured improvement
• Verified functionality still works
• No new bugs introduced
• Documented the change

When NOT to Optimize

• Premature optimization (optimize when it's actually slow)
• Micro-optimizations (save 1ms when page takes 5 seconds)
• Readable code is more important than tiny speed gains
• If it's already fast enough

Key Principles

• Measure before optimizing
• Fix the biggest bottleneck first
• Measure after to prove improvement
• Don't sacrifice readability for tiny gains
• Profile in production-like environment
• Consider the 80/20 rule (20% of code causes 80% of slowness)

Related Skills

• @database-design - Query optimization
• @codebase-audit-pre-push - Code review
• @bug-hunter - Debugging

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

# Check for prior backend/API context before starting
python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Performance Optimizer"

Storing Results

After completing work, store backend/API decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \
  --type decision --project <project> \
  --tags performance-optimizer backend

Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>

Agent Team: Code Review

After implementation, dispatch code_review_team for two-stage review (spec compliance + code quality) before merging.