What problem does it solve? Teams often ship APIs without knowing how they behave under real traffic, discovering bottlenecks only after production incidents. This Skill provides structured k6-based performance testing guidance so you can establish baselines, enforce SLA/SLO thresholds, and catch capacity limits before deployment. ## Core Features & Use Cases - Scenario Design: Pre-built patterns for smoke, load, stress, spike, and soak tests with appropriate VU counts and durations. - Threshold Quality Gates: Define p95/p99 latency and error-rate thresholds with abortOnFail so CI pipelines fail automatically on regressions. - CI/CD Integration: Ready-to-adapt GitHub Actions workflow that installs k6, runs tests, and archives JSON results. - Use Case: Before launching a new API endpoint, generate a load test script with p95 < 500ms and error rate < 1% thresholds, wire it into your pull request pipeline, and record the performance baseline for future comparison. ## Quick Start Ask the AI to create a k6 load test script for your API endpoint with p95 latency and error-rate thresholds, then integrate it into your GitHub Actions workflow.