Identifying performance bottlenecks
Reducing application latency
Optimizing CPU-intensive operations
Reducing memory consumption
Profiling production applications
Improving database query performance

When NOT To Use

Async concurrency - use python-async instead
CPU/GPU system monitoring - use conservation:cpu-gpu-performance
Async concurrency - use python-async instead
CPU/GPU system monitoring - use conservation:cpu-gpu-performance

Modules

This skill is organized into focused modules for progressive loading:

profiling-tools

CPU profiling with cProfile, line profiling, memory profiling, and production profiling with py-spy. Essential for identifying where your code spends time and memory.

Eleven proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, database operations, and loop transformations (what works in Python vs the compiler).

memory-management

Memory optimization techniques including leak tracking with tracemalloc and weak references for caches. Depends on profiling-tools.

benchmarking-tools

Benchmarking tools including custom decorators and pytest-benchmark for verifying performance improvements.

best-practices

Best practices, common pitfalls, and exit criteria for performance optimization work. Synthesizes guidance from profiling-tools and optimization-patterns.

Exit Criteria

Profiled code to identify bottlenecks
Applied appropriate optimization patterns
Verified improvements with benchmarks
Memory usage acceptable
No performance regressions

python-performance

Python Performance Optimization

Quick Start

When To Use