worker-benchmarks

You can benchmark worker system performance by running a suite that measures latency, throughput, and memory usage across detection, registry, selection, caching, and concurrency workloads. It outputs structured results to guide optimization decisions.

Measuring worker concurrency and throughput in production requires running defined benchmark types with specific targets and iterations. This process compares resource usage and structured metrics to evaluate reliable behavior under real workloads.

Yes, you can compare before and after performance metrics by running benchmarks after a workflow change. The Skill provides comparable structured results that quantify improvements in latency, throughput, and memory usage.

Yes, the benchmark suite works without external dependencies because it has none. You can run the full benchmark suite with a single command to generate a complete performance report in both development and production scenarios.

Profiling agent selection and cache workloads measures latency, throughput, and memory usage. The benchmark uses defined targets and iterations to output structured metrics for comparing these specific worker operations.

Dedicated benchmark types evaluate specific worker operations like trigger-detection, registry, and memory-keys, whereas generic profiling lacks targeted targets and iterations. This structured approach yields comparable results that directly guide optimization decisions.