Agent skill · data analytics · jeremylongshore
load-testing-apis
Execute comprehensive load and stress testing to validate API performance
Why this skill is useful
Generates executable load testing scripts for k6 and Artillery, providing detailed performance analysis and bottleneck identification that the AI cannot generate on its own.
What it needs
Requires apache-jmeter, artillery, k6, wrk installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Load Testing APIs Overview Execute comprehensive load, stress, and soak tests to validate API performance, identify bottlenecks, and establish throughput baselines. Generate test scripts for k6, Artillery, or wrk that simulate realistic traffic patterns with configurable virtual user ramp-up, request distribution, and failure threshold assertions. Prerequisites Load testing tool installed: k6 (recommended), Artillery, wrk, or Apache JMeter Target API deployed in a staging/performance environment (never load test production without safeguards) Monitoring stack accessible: Grafana/Prometheus, Datadog, or CloudWatch for correlating test results with server metrics API authentication credentials for testing (API keys, test user JWT tokens) Baseline performance SLOs defined (target p95 latency, max error rate, minimum throughput) Instructions 1. Read the API specification and route definitions using Glob and Read to build a complete list of endpoints, identifying high-traffic paths and resource-intensive operations. 2. Define test scenarios modeling realistic user behavior: browsing (80% reads), checkout (mixed reads + writes), and spike traffic patterns with appropriate think times between requests. 3. Generate k6 or Artillery test scripts with configurable stages: ramp-up (2 min), sustained load (10 min), spike (2 min at 3x), and cool-down (2 min). 4. Configure request distribution to match production traffic patterns -- weighted random selection across endpoints rather than uniform distribution. 5. Add threshold assertions for pass/fail criteria: p95 response time < 500ms, error rate < 1%, throughput > 100 requests/second. 6. Implement data-driven requests using CSV or JSON fixtures for realistic payloads, unique user IDs, and varied query parameters to avoid cache-only testing. 7. Execute baseline test at expected production load, then gradually increase to 2x, 5x, and 10x to identify the breaking point and saturation behavior. 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/load-testing-apis--fb238e