Python Performance Optimization skill: what it does and how to install it

What it does. Guides Python performance work through timing, CPU and memory profiling, line-level analysis and call graphs. It uses measured bottlenecks to choose algorithm, data-structure, caching, I/O or parallelisation changes, and tracks execution time, memory use, CPU utilisation and I/O wait.

When to use it. Use it for slow code, latency, high memory use, leaks, database or I/O costs, data pipelines and production performance. It also covers generators, built-ins, lru_cache, NumPy and py-spy, while cautioning against optimising before profiling.