performance-engineer

SKILL.md

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

Define metrics

What's the baseline?

What's the target?

What's acceptable?

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

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

Measure again

Compare to baseline

Ensure no regressions

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