Agent Skills: Performance Engineer

Performance Engineer

Specialist in analyzing and optimizing application performance, identifying bottlenecks, and implementing efficiency improvements.

When This Skill Activates

Activates when you:

• Report performance issues
• Need performance optimization
• Mention "slow" or "latency"
• Want to improve efficiency

Performance Analysis Process

Phase 1: Identify the Problem

1. Define metrics

   • What's the baseline?
   • What's the target?
   • What's acceptable?

2. Measure current performance

   # Response time
   curl -w "@curl-format.txt" -o /dev/null -s https://example.com/users
   
   # Database query time
   # Add timing logs to queries
   
   # Memory usage
   # Use profiler
   

3. Profile the application

   # Node.js
   node --prof app.js
   
   # Python
   python -m cProfile app.py
   
   # Go
   go test -cpuprofile=cpu.prof
   

Phase 2: Find the Bottleneck

Common bottleneck locations:

| Layer | Common Issues | |-------|---------------| | Database | N+1 queries, missing indexes, large result sets | | API | Over-fetching, no caching, serial requests | | Application | Inefficient algorithms, excessive logging | | Frontend | Large bundles, re-renders, no lazy loading | | Network | Too many requests, large payloads, no compression |

Phase 3: Optimize

Database Optimization

N+1 Queries:

// Bad: N+1 queries
const users = await User.findAll();
for (const user of users) {
  user.posts = await Post.findAll({ where: { userId: user.id } });
}

// Good: Eager loading
const users = await User.findAll({
  include: [{ model: Post, as: 'posts' }]
});

Missing Indexes:

-- Add index on frequently queried columns
CREATE INDEX idx_user_email ON users(email);
CREATE INDEX idx_post_user_id ON posts(user_id);

API Optimization

Pagination:

// Always paginate large result sets
const users = await User.findAll({
  limit: 100,
  offset: page * 100
});

Field Selection:

// Select only needed fields
const users = await User.findAll({
  attributes: ['id', 'name', 'email']
});

Compression:

// Enable gzip compression
app.use(compression());

Frontend Optimization

Code Splitting:

// Lazy load routes
const Dashboard = lazy(() => import('./Dashboard'));

Memoization:

// Use useMemo for expensive calculations
const filtered = useMemo(() =>
  items.filter(item => item.active),
  [items]
);

Image Optimization:

• Use WebP format
• Lazy load images
• Use responsive images
• Compress images

Phase 4: Verify

1. Measure again
2. Compare to baseline
3. Ensure no regressions
4. Document the improvement

Performance Targets

| Metric | Target | Critical Threshold | |--------|--------|-------------------| | API Response (p50) | < 100ms | < 500ms | | API Response (p95) | < 500ms | < 1s | | API Response (p99) | < 1s | < 2s | | Database Query | < 50ms | < 200ms | | Page Load (FMP) | < 2s | < 3s | | Time to Interactive | < 3s | < 5s | | Memory Usage | < 512MB | < 1GB |

Common Optimizations

Caching Strategy

// Cache expensive computations
const cache = new Map();

async function getUserStats(userId: string) {
  if (cache.has(userId)) {
    return cache.get(userId);
  }

  const stats = await calculateUserStats(userId);
  cache.set(userId, stats);

  // Invalidate after 5 minutes
  setTimeout(() => cache.delete(userId), 5 * 60 * 1000);

  return stats;
}

Batch Processing

// Bad: Individual requests
for (const id of userIds) {
  await fetchUser(id);
}

// Good: Batch request
await fetchUsers(userIds);

Debouncing/Throttling

// Debounce search input
const debouncedSearch = debounce(search, 300);

// Throttle scroll events
const throttledScroll = throttle(handleScroll, 100);

Performance Monitoring

Key Metrics

• Response Time: Time to process request
• Throughput: Requests per second
• Error Rate: Failed requests percentage
• Memory Usage: Heap/RAM used
• CPU Usage: Processor utilization

Monitoring Tools

| Tool | Purpose | |------|---------| | Lighthouse | Frontend performance | | New Relic | APM monitoring | | Datadog | Infrastructure monitoring | | Prometheus | Metrics collection |

Scripts

Profile application:

python scripts/profile.py

Generate performance report:

python scripts/perf_report.py

References

• references/optimization.md - Optimization techniques
• references/monitoring.md - Monitoring setup
• references/checklist.md - Performance checklist