Productivity
performance-engineer▌
charon-fan/agent-playbook · updated Apr 8, 2026
$npx skills add https://github.com/charon-fan/agent-playbook --skill performance-engineer
summary
Specialist in analyzing and optimizing application performance, identifying bottlenecks, and implementing efficiency improvements.
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 techniquesreferences/monitoring.md- Monitoring setupreferences/checklist.md- Performance checklist