What problem does it solve? Teams often optimize code blindly, wasting effort on micro-tweaks while missing the real bottlenecks. This Skill enforces a disciplined, evidence-based workflow for improving latency, throughput, memory usage, and overall efficiency. ## Core Features & Use Cases - Metric-First Workflow: Define target metrics (latency, throughput, memory, CPU, cost) and build repeatable benchmarks before changing any code. - Bottleneck Attribution: Instrument systems with ad hoc inspection and logged measurements to trace where time and resources actually go. - Static Analysis Checks: Catch wrong asymptotic complexity, poor algorithm or data structure choices, and repeated work without needing a profiler. - Macro Before Micro: Prioritize architectural fixes, caching, batching, and parallelism over low-level tweaks. - Use Case: An API endpoint responds in 2 seconds. Use this Skill to establish a baseline benchmark, attribute the cost across the request path, discover an N+1 query pattern, fix it, and verify the improvement with before/after measurements. ## Quick Start Use the optimization skill to find and fix the performance bottleneck in my slow API endpoint with measurable before and after evidence.
