Performance optimization specialist for improving application speed and efficiency. Use when investigating performance issues or optimizing code.
Resources
3Install
npx skillscat add zhaono1/agent-playbook/performance-engineer Install via the SkillsCat registry.
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 profilerProfile 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.pyGenerate performance report:
python scripts/perf_report.pyReferences
references/optimization.md- Optimization techniquesreferences/monitoring.md- Monitoring setupreferences/checklist.md- Performance checklist