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Profiles application performance under load using k6, Artillery, or JMeter to measure latency, throughput, and error rates. Use when planning load tests, stress tests, soak tests, benchmarking APIs, or identifying performance bottlenecks.
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Ruflo is a multi-agent orchestration platform for AI coding agents (Claude Code, Cursor, Codex, Copilot, Gemini, Amp, +12 more). Use this skill when the user wants to (1) install/init ruflo in a project, (2) run multi-agent swarms with hierarchical coordination, (3) use ruflo's 314+ MCP tools for memory, routing, hooks, sub-agents, or workflows, (4) check ruflo status/version/doctor health, or (5) discover which of ruflo's 30+ plugins fits their task.
Build dependency-aware execution plans for complex Agentic QE, Ruflo, integration, migration, or multi-stream engineering programs. Use when Codex must turn research or requirements into phased work, select a small AQE fleet, map critical paths and parallel streams, define acceptance gates, or sequence risky changes. Use aqe-plan-quality instead when the primary output is only a test or quality plan.
Conduct evidence-first technical research for Agentic QE, Ruflo, related ruvnet projects, or external tools. Use when Codex must investigate a repository, compare current upstream changes, trace dependencies and history, distinguish verified facts from inference, or synthesize findings into actionable engineering recommendations. Do not use for a simple known-answer lookup or an implementation-only task.
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正在显示 SKILL.md
| name | performance-testing |
| description | Profiles application performance under load using k6, Artillery, or JMeter to measure latency, throughput, and error rates. Use when planning load tests, stress tests, soak tests, benchmarking APIs, or identifying performance bottlenecks. |
| 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"] |
| trust_tier | 3 |
| validation | {"schema_path":"schemas/output.json","validator_path":"scripts/validate-config.json","eval_path":"evals/performance-testing.yaml"} |
<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.
After each performance test run, append results to run-history.json in this skill directory:
node -e "
const fs = require('fs');
const h = JSON.parse(fs.readFileSync('.claude/skills/performance-testing/run-history.json'));
h.runs.push({date: new Date().toISOString().split('T')[0], scenario: 'load', p95_ms: P95, throughput_rps: RPS, error_rate_pct: ERR});
fs.writeFileSync('.claude/skills/performance-testing/run-history.json', JSON.stringify(h, null, 2));
"
Read run-history.json before each run — compare with baselines. Alert if p95 increases >20% from baseline.