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Test application performance, scalability, and resilience. Use when planning load testing, stress testing, or optimizing system performance.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/proffesor-for-testing/sentinel-api-testing --skill performance-testing명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SKILL.md 표시 중
SOC 직업 분류 기준
| name | performance-testing |
| description | Test application performance, scalability, and resilience. Use when planning load testing, stress testing, or optimizing system performance. |
| category | specialized-testing |
| priority | high |
| tokenEstimate | 1100 |
| agents | ["qe-performance-tester","qe-quality-analyzer","qe-production-intelligence"] |
| implementation_status | optimized |
| optimization_version | 1 |
| last_optimized | 2025-12-02T00:00:00.000Z |
| dependencies | [] |
| quick_reference_card | true |
| tags | ["performance","load-testing","stress-testing","scalability","k6","bottlenecks"] |
<default_to_action> When testing performance or planning load tests:
Quick Test Type Selection:
Critical Success Factors:
| Type | Purpose | When |
|---|---|---|
| Load | Expected traffic | Every release |
| Stress | Beyond capacity | Quarterly |
| Spike | Sudden surge | Before events |
| Endurance | Memory leaks | After code changes |
| Scalability | Scaling validation | Infrastructure changes |
| Metric | Target | Why |
|---|---|---|
| p95 response | < 200ms | User experience |
| Throughput | 10k req/min | Capacity |
| Error rate | < 0.1% | Reliability |
| CPU | < 70% | Headroom |
| Memory | < 80% |
| Stability |
qe-performance-tester: Load test orchestrationqe-quality-analyzer: Results analysisqe-production-intelligence: Production comparisonBad: "The system should be fast" Good: "p95 response time < 200ms under 1,000 concurrent users"
export const options = {
thresholds: {
http_req_duration: ['p(95)<200'], // 95% < 200ms
http_req_failed: ['rate<0.01'], // < 1% failures
},
};
Bad: Every user hits homepage repeatedly Good: Model actual user behavior
// Realistic distribution
// 40% browse, 30% search, 20% details, 10% checkout
export default function () {
const action = Math.random();
if (action < 0.4) browse();
else if (action < 0.7) search();
else if (action < 0.9) viewProduct();
else checkout();
sleep(randomInt(1, 5)); // Think time
}
Symptoms: Slow queries under load, connection pool exhaustion Fixes: Add indexes, optimize N+1 queries, increase pool size, read replicas
// BAD: 100 orders = 101 queries
const orders = await Order.findAll();
for (const order of orders) {
const customer = await Customer.findById(order.customerId);
}
// GOOD: 1 query
const orders = await Order.findAll({ include: [Customer] });
Problem: Blocking operations in request path (sending email during checkout) Fix: Use message queues, process async, return immediately
Detection: Endurance testing, memory profiling Common causes: Event listeners not cleaned, caches without eviction
Solutions: Aggressive timeouts, circuit breakers, caching, graceful degradation
// performance-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';
export const options = {
stages: [
{ duration: '1m', target: 50 }, // Ramp up
{ duration: '3m', target: 50 }, // Steady
{ duration: '1m', target: 0 }, // Ramp down
],
thresholds: {
http_req_duration: ['p(95)<200'],
http_req_failed: ['rate<0.01'],
},
};
export default function () {
const res = http.get('https://api.example.com/products');
check(res, {
'status is 200': (r) => r.status === 200,
'response time < 200ms': (r) => r.timings.duration < 200,
});
sleep(1);
}
# GitHub Actions
- name: Run k6 test
uses: grafana/k6-action@v0.3.0
with:
filename: performance-test.js
Load: 1,000 users | p95: 180ms | Throughput: 5,000 req/s
Error rate: 0.05% | CPU: 65% | Memory: 70%
Load: 1,000 users | p95: 3,500ms ❌ | Throughput: 500 req/s ❌
Error rate: 5% ❌ | CPU: 95% ❌ | Memory: 90% ❌
| ❌ Anti-Pattern | ✅ Better |
|---|---|
| Testing too late | Test early and often |
| Unrealistic scenarios | Model real user behavior |
| 0 to 1000 users instantly | Ramp up gradually |
| No monitoring during tests | Monitor everything |
| No baseline | Establish and track trends |
| One-time testing | Continuous performance testing |
// Comprehensive load test
await Task("Load Test", {
target: 'https://api.example.com',
scenarios: {
checkout: { vus: 100, duration: '5m' },
search: { vus: 200, duration: '5m' },
browse: { vus: 500, duration: '5m' }
},
thresholds: {
'http_req_duration': ['p(95)<200'],
'http_req_failed': ['rate<0.01']
}
}, "qe-performance-tester");
// Bottleneck analysis
await Task("Analyze Bottlenecks", {
testResults: perfTest,
metrics: ['cpu', 'memory', 'db_queries', 'network']
}, "qe-performance-tester");
// CI integration
await Task("CI Performance Gate", {
mode: 'smoke',
duration: '1m',
vus: 10,
failOn: { 'p95_response_time': 300, 'error_rate': 0.01 }
}, "qe-performance-tester");
aqe/performance/
├── results/* - Test execution results
├── baselines/* - Performance baselines
├── bottlenecks/* - Identified bottlenecks
└── trends/* - Historical trends
const perfFleet = await FleetManager.coordinate({
strategy: 'performance-testing',
agents: [
'qe-performance-tester',
'qe-quality-analyzer',
'qe-production-intelligence',
'qe-deployment-readiness'
],
topology: 'sequential'
});
Performance is a feature: Test it like functionality Test continuously: Not just before launch Monitor production: Synthetic + real user monitoring Fix what matters: Focus on user-impacting bottlenecks Trend over time: Catch degradation early
With Agents: Agents automate load testing, analyze bottlenecks, and compare with production. Use agents to maintain performance at scale.