QA Engineer
Load Test Before You Launch β Your Agent Simulates 10,000 Users
Key Takeaway
The Load Testing skill designs and generates complete performance test suites β k6, JMeter, or Locust scripts with realistic user scenarios, ramp-up patterns, and SLA validation. Find your breaking point before your users do.
The Problem
Launch day. Marketing pushes the campaign. Traffic spikes 10x. Your app falls over. The database connection pool maxes out at 20 connections. The API response time goes from 200ms to 8 seconds. Redis fills up. The load balancer returns 502s.
You could have found all of this with a load test. But load testing is "hard" so it gets skipped:
- Writing realistic test scripts is time-consuming
- Setting up the load generation infrastructure is a project itself
- Analyzing results requires expertise
- Nobody knows what the SLAs should be
So you launch and pray. Sometimes prayer works. Usually it doesn't.
The Solution
The Load Testing skill generates complete test suites with realistic user scenarios, proper ramp-up patterns, and SLA validation.
The Process
View details
You: Create a load test for our e-commerce API:
Endpoints: product listing, search, add to cart, checkout
Expected peak: 5,000 concurrent users
SLAs: p95 < 500ms, p99 < 1s, error rate < 0.1%
Tool preference: k6
The agent generates a complete k6 test suite:
javascriptShow code
// === load-test.js ===
import http from 'k6/http';
import { check, group, sleep } from 'k6';
import { Rate, Trend } from 'k6/metrics';
// Custom metrics
const errorRate = new Rate('errors');
const checkoutDuration = new Trend('checkout_duration');
export const options = {
stages: [
{ duration: '2m', target: 500 }, // Warm up
{ duration: '5m', target: 2000 }, // Ramp to moderate
{ duration: '5m', target: 5000 }, // Ramp to peak
{ duration: '10m', target: 5000 }, // Sustain peak
{ duration: '3m', target: 0 }, // Ramp down
],
thresholds: {
http_req_duration: ['p(95)<500', 'p(99)<1000'],
errors: ['rate<0.001'],
checkout_duration: ['p(95)<2000'],
},
};
const BASE_URL = __ENV.BASE_URL || 'https://api.myshop.com';
// Realistic user journey weights:
// 60% browse, 25% search, 10% add to cart, 5% checkout
export default function () {
const rand = Math.random();
if (rand < 0.60) {
browseProducts();
} else if (rand < 0.85) {
searchProducts();
} else if (rand < 0.95) {
addToCart();
} else {
fullCheckout();
}
sleep(Math.random() * 3 + 1); // Think time: 1-4 seconds
}
function browseProducts() {
group('Browse Products', () => {
const res = http.get(`${BASE_URL}/v1/products?page=1&limit=20`);
check(res, {
'products 200': (r) => r.status === 200,
'has results': (r) => JSON.parse(r.body).data.length > 0,
}) || errorRate.add(1);
// Click into a product
sleep(0.5);
const productRes = http.get(`${BASE_URL}/v1/products/1`);
check(productRes, { 'product detail 200': (r) => r.status === 200 })
|| errorRate.add(1);
});
}
// ... search, addToCart, fullCheckout functions with
// realistic data, auth headers, and validation
The agent also includes:
- Smoke test (1 user, verify everything works)
- Stress test (ramp beyond expected peak to find breaking point)
- Soak test (moderate load for 2 hours β find memory leaks)
- Spike test (sudden 10x surge β simulates viral moment)
- Analysis guidance for interpreting results
The Results
| Metric | No Load Testing | AI-Generated Load Tests |
|---|---|---|
| Launch confidence | Pray | Data-driven |
| Breaking point known | After crash | Before launch |
| Test creation time | 1-2 days | 15 minutes |
| Scenario realism | Basic | Weighted user journeys |
| SLA validation | None | Automated thresholds |
| Regression detection | None | Run in CI/CD |
Setup on MrChief
yamlShow code
skills:
- load-testing
- monitoring # For correlating metrics during tests
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