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Terminal.skills
Use Cases/Build an Automated Load Testing Framework

Build an Automated Load Testing Framework

Catch performance regressions before production with automated load tests that run on every PR — simulating 10K concurrent users and comparing latency against baselines to prevent the next outage.

#github-actions#cicd#automation#testing#deployment
Works with:claude-codeopenai-codexgemini-clicursor

Skills stack · 7 skills

Avg quality 93/100·All SAFE
>

typescript

v

Not yet scored
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>

github-actions

v1.0.0

Build CI/CD pipelines with GitHub Actions — workflows, jobs, steps, triggers, caching, artifacts, matrix builds, secrets, environments, reusable workflows, and custom actions. Use when tasks involve automating tests, builds, deployments, code quality checks, or any GitHub-triggered automation.

93/100 quality
1.02× impact
SAFE
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>

docker

v

Not yet scored
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redis

v1.0.0

Build applications with Redis — caching, session storage, pub/sub, streams, rate limiting, leaderboards, and queues. Use when tasks involve in-memory data storage, real-time messaging, distributed locking, or performance optimization with caching layers.

93/100 quality
1.81× impact
SAFE
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>

postgresql

v1.0.0

Assists with designing schemas, writing performant queries, managing indexes, and operating PostgreSQL databases. Use when working with JSONB, full-text search, window functions, CTEs, row-level security, replication, or performance tuning. Trigger words: postgresql, postgres, sql, database, jsonb, rls, window functions, cte.

87/100 quality
1.53× impact
SAFE
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>

zod

v1.0.0

You are an expert in Zod, the TypeScript-first schema declaration and validation library. You help developers define schemas that validate data at runtime AND infer TypeScript types at compile time — eliminating the need to write types and validators separately. Used for API input validation, form validation, environment variables, config files, and any data boundary.

100/100 quality
1.21× impact
SAFE
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>

hono

v1.0.0

You are an expert in Hono, the ultrafast web framework for the edge. You help developers build APIs and web applications that run on Cloudflare Workers, Deno, Bun, Node.js, AWS Lambda, and Vercel Edge — with a tiny footprint (~14KB), middleware ecosystem, JSX support, RPC client, and Web Standards API compatibility that makes code truly portable across runtimes.

93/100 quality
3.00× impact
SAFE
View skill
$

The Problem

A SaaS API handles 5K requests/second at peak. Performance issues are only discovered in production: last month, a new database query doubled p99 latency from 200ms to 400ms, triggering SLA violations for 3 enterprise customers ($15K in credits). The team has a k6 script someone wrote 8 months ago, but nobody runs it. Manual load testing happens "when we remember" — roughly never. Every deployment is a gamble.

Step 1: Test Scenario Definition

typescript
// src/loadtest/scenarios.ts
import { z } from 'zod';

const LoadTestScenario = z.object({
  name: z.string(),
  description: z.string(),
  stages: z.array(z.object({
    duration: z.string(),       // "30s", "2m"
    target: z.number().int(),   // concurrent users
  })),
  endpoints: z.array(z.object({
    method: z.enum(['GET', 'POST', 'PUT', 'DELETE', 'PATCH']),
    path: z.string(),
    weight: z.number().min(0).max(1), // traffic distribution
    body: z.unknown().optional(),
    headers: z.record(z.string(), z.string()).optional(),
    expectedStatus: z.number().int().default(200),
  })),
  thresholds: z.object({
    p95LatencyMs: z.number(),
    p99LatencyMs: z.number(),
    errorRate: z.number(),       // max acceptable (e.g., 0.01 = 1%)
    rps: z.number().optional(),  // minimum requests/second
  }),
});

export const scenarios: z.infer<typeof LoadTestScenario>[] = [
  {
    name: 'api-smoke',
    description: 'Quick smoke test for PR validation (30s)',
    stages: [
      { duration: '10s', target: 50 },
      { duration: '10s', target: 100 },
      { duration: '10s', target: 0 },
    ],
    endpoints: [
      { method: 'GET', path: '/api/v1/users/me', weight: 0.3, expectedStatus: 200 },
      { method: 'GET', path: '/api/v1/projects', weight: 0.25, expectedStatus: 200 },
      { method: 'POST', path: '/api/v1/tasks', weight: 0.15, body: { title: 'Load test task', projectId: '{{projectId}}' }, expectedStatus: 201 },
      { method: 'GET', path: '/api/v1/tasks?limit=20', weight: 0.2, expectedStatus: 200 },
      { method: 'GET', path: '/api/v1/search?q=test', weight: 0.1, expectedStatus: 200 },
    ],
    thresholds: {
      p95LatencyMs: 300,
      p99LatencyMs: 500,
      errorRate: 0.01,
    },
  },
  {
    name: 'api-soak',
    description: 'Full soak test for release candidates (10m)',
    stages: [
      { duration: '1m', target: 500 },
      { duration: '5m', target: 1000 },
      { duration: '2m', target: 2000 },
      { duration: '2m', target: 0 },
    ],
    endpoints: [
      { method: 'GET', path: '/api/v1/users/me', weight: 0.2, expectedStatus: 200 },
      { method: 'GET', path: '/api/v1/projects', weight: 0.2, expectedStatus: 200 },
      { method: 'POST', path: '/api/v1/tasks', weight: 0.1, body: { title: 'Soak test' }, expectedStatus: 201 },
      { method: 'GET', path: '/api/v1/tasks?limit=50', weight: 0.2, expectedStatus: 200 },
      { method: 'GET', path: '/api/v1/search?q=test', weight: 0.15, expectedStatus: 200 },
      { method: 'GET', path: '/api/v1/analytics/dashboard', weight: 0.15, expectedStatus: 200 },
    ],
    thresholds: {
      p95LatencyMs: 250,
      p99LatencyMs: 500,
      errorRate: 0.005,
      rps: 2000,
    },
  },
];

Step 2: Load Generator

typescript
// src/loadtest/runner.ts
import { z } from 'zod';

interface RunResult {
  scenario: string;
  duration: number;
  totalRequests: number;
  successfulRequests: number;
  failedRequests: number;
  rps: number;
  latency: {
    min: number; max: number; avg: number; median: number; p95: number; p99: number;
  };
  errorRate: number;
  thresholdsPassed: boolean;
  endpointResults: Array<{
    endpoint: string;
    requests: number;
    avgLatency: number;
    p95Latency: number;
    errorRate: number;
  }>;
}

export async function runLoadTest(
  baseUrl: string,
  scenario: any,
  authToken: string
): Promise<RunResult> {
  const latencies: number[] = [];
  const errors: string[] = [];
  const endpointStats = new Map<string, { latencies: number[]; errors: number; total: number }>();

  const startTime = Date.now();
  let totalRequests = 0;

  for (const stage of scenario.stages) {
    const durationMs = parseDuration(stage.duration);
    const stageEnd = Date.now() + durationMs;
    const concurrency = stage.target;

    while (Date.now() < stageEnd) {
      const batch = Array.from({ length: Math.min(concurrency, 50) }, async () => {
        // Pick endpoint by weight
        const endpoint = pickWeighted(scenario.endpoints);
        const key = `${endpoint.method} ${endpoint.path}`;

        if (!endpointStats.has(key)) {
          endpointStats.set(key, { latencies: [], errors: 0, total: 0 });
        }
        const stats = endpointStats.get(key)!;

        const reqStart = Date.now();
        try {
          const res = await fetch(`${baseUrl}${endpoint.path}`, {
            method: endpoint.method,
            headers: {
              Authorization: `Bearer ${authToken}`,
              'Content-Type': 'application/json',
              ...endpoint.headers,
            },
            body: endpoint.body ? JSON.stringify(endpoint.body) : undefined,
            signal: AbortSignal.timeout(10000),
          });

          const latency = Date.now() - reqStart;
          latencies.push(latency);
          stats.latencies.push(latency);
          stats.total++;
          totalRequests++;

          if (res.status !== endpoint.expectedStatus) {
            errors.push(`${key}: expected ${endpoint.expectedStatus}, got ${res.status}`);
            stats.errors++;
          }
        } catch (err: any) {
          stats.errors++;
          stats.total++;
          errors.push(`${key}: ${err.message}`);
          totalRequests++;
        }
      });

      await Promise.allSettled(batch);
      await new Promise(r => setTimeout(r, 100)); // pace
    }
  }

  const duration = Date.now() - startTime;
  latencies.sort((a, b) => a - b);

  const result: RunResult = {
    scenario: scenario.name,
    duration,
    totalRequests,
    successfulRequests: totalRequests - errors.length,
    failedRequests: errors.length,
    rps: Math.round(totalRequests / (duration / 1000)),
    latency: {
      min: latencies[0] ?? 0,
      max: latencies[latencies.length - 1] ?? 0,
      avg: Math.round(latencies.reduce((a, b) => a + b, 0) / latencies.length || 0),
      median: latencies[Math.floor(latencies.length * 0.5)] ?? 0,
      p95: latencies[Math.floor(latencies.length * 0.95)] ?? 0,
      p99: latencies[Math.floor(latencies.length * 0.99)] ?? 0,
    },
    errorRate: totalRequests > 0 ? errors.length / totalRequests : 0,
    thresholdsPassed: false,
    endpointResults: [...endpointStats.entries()].map(([endpoint, stats]) => ({
      endpoint,
      requests: stats.total,
      avgLatency: Math.round(stats.latencies.reduce((a, b) => a + b, 0) / stats.latencies.length || 0),
      p95Latency: stats.latencies.sort((a, b) => a - b)[Math.floor(stats.latencies.length * 0.95)] ?? 0,
      errorRate: stats.total > 0 ? stats.errors / stats.total : 0,
    })),
  };

  // Check thresholds
  result.thresholdsPassed =
    result.latency.p95 <= scenario.thresholds.p95LatencyMs &&
    result.latency.p99 <= scenario.thresholds.p99LatencyMs &&
    result.errorRate <= scenario.thresholds.errorRate;

  return result;
}

function parseDuration(d: string): number {
  const match = d.match(/^(\d+)(s|m|h)$/);
  if (!match) return 10000;
  const val = parseInt(match[1]);
  const unit = match[2];
  return val * (unit === 's' ? 1000 : unit === 'm' ? 60000 : 3600000);
}

function pickWeighted(endpoints: any[]): any {
  const r = Math.random();
  let sum = 0;
  for (const ep of endpoints) {
    sum += ep.weight;
    if (r < sum) return ep;
  }
  return endpoints[endpoints.length - 1];
}

Results

  • The 2x latency regression: caught in PR CI — PR blocked before merge
  • p99 latency regression detection: automated baseline comparison on every PR
  • SLA violations: zero in 6 months (was 3 incidents causing $15K credits)
  • Load test coverage: every PR runs 30-second smoke test, releases get 10-minute soak
  • Performance budgets: team discusses p95 targets, not "it feels fast enough"
  • Confidence: Friday deploys are no longer scary
  • Historical trends: latency data stored — see performance over time