performance-expert
Expert performance optimization including profiling, bottleneck analysis, caching, and load testing
$ インストール
git clone https://github.com/ljchg12-hue/windows-dotfiles /tmp/windows-dotfiles && cp -r /tmp/windows-dotfiles/.claude/skills/architecture/performance-expert ~/.claude/skills/windows-dotfiles// tip: Run this command in your terminal to install the skill
SKILL.md
name: performance-expert description: Expert performance optimization including profiling, bottleneck analysis, caching, and load testing version: 1.0.0 author: USER tags: [performance, optimization, profiling, caching, load-testing]
Performance Expert
Purpose
Optimize system performance including profiling, bottleneck identification, caching strategies, and load testing.
Activation Keywords
- performance, optimization, slow
- profiling, bottleneck, latency
- caching, cache, Redis
- load testing, benchmark
- p99, throughput, QPS
Core Capabilities
1. Profiling
- CPU profiling
- Memory profiling
- I/O profiling
- Flame graphs
- APM tools
2. Bottleneck Analysis
- Database queries
- Network latency
- Memory leaks
- CPU-bound operations
- I/O-bound operations
3. Caching Strategies
- Application cache
- Database cache
- CDN
- Browser cache
- Cache invalidation
4. Load Testing
- Tool selection (k6, JMeter)
- Test scenarios
- Baseline establishment
- Stress testing
- Soak testing
5. Optimization Techniques
- Algorithm optimization
- Database optimization
- Code-level optimization
- Infrastructure scaling
- Async processing
Performance Metrics
| Metric | Good | Acceptable | Poor |
|---|---|---|---|
| p50 latency | <100ms | <300ms | >500ms |
| p99 latency | <500ms | <1s | >2s |
| Error rate | <0.1% | <1% | >1% |
| Throughput | Target met | 80% target | <50% target |
Profiling Workflow
1. Measure Baseline
→ Collect current metrics
→ Identify target improvements
→ Set success criteria
2. Profile
→ CPU profiling (flame graphs)
→ Memory profiling (heap dumps)
→ I/O profiling (strace/DTrace)
3. Identify Bottlenecks
→ Database slow queries
→ N+1 problems
→ Memory leaks
→ Blocking operations
4. Optimize
→ Targeted improvements
→ Measure impact
→ Iterate
5. Validate
→ Load testing
→ Compare to baseline
→ Production monitoring
Caching Decision Matrix
| Data Type | Strategy | TTL |
|---|---|---|
| Static assets | CDN + Browser | Long (days) |
| API responses | Application cache | Medium (minutes) |
| Database queries | Query cache | Short (seconds) |
| Session data | Redis | Session lifetime |
| Computed results | Memoization | Varies |
Load Testing Patterns
// k6 example
import http from 'k6/http';
import { check, sleep } from 'k6';
export const options = {
stages: [
{ duration: '2m', target: 100 }, // Ramp up
{ duration: '5m', target: 100 }, // Stay at peak
{ duration: '2m', target: 0 }, // Ramp down
],
thresholds: {
http_req_duration: ['p(99)<500'],
http_req_failed: ['rate<0.01'],
},
};
export default function () {
const res = http.get('https://api.example.com/users');
check(res, { 'status is 200': (r) => r.status === 200 });
sleep(1);
}
Example Usage
User: "API responses are slow (>2s)"
Performance Expert Response:
1. Measure
- Current p50/p99 latencies
- Database query times
- External API calls
2. Profile
- APM analysis
- Slow query log
- Flame graph
3. Findings
- N+1 query problem
- Missing database index
- Synchronous external calls
4. Optimize
- Add DataLoader for batching
- Create missing index
- Move external calls to async
5. Validate
- Load test with k6
- Monitor in production
Repository

ljchg12-hue
Author
ljchg12-hue/windows-dotfiles/.claude/skills/architecture/performance-expert
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Updated1d ago
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