Which profiling tools does this Claude Code skill support?

The skill provides guidance and implementation patterns for PyInstrument (statistical profiling), cProfile (deterministic profiling), and memray (memory profiling).

How does it help with Python memory management?

It offers strategies for using generators to handle large datasets, deque for fast queue operations, and tools to generate flamegraphs for visualizing memory consumption.

Can this skill help with automated performance testing?

Yes, it includes specific patterns for using pytest-benchmark to establish performance baselines and detect speed regressions during CI/CD.

Does it provide specific code-level optimization tips?

Absolutely; it includes best practices for O(1) lookups using sets/dicts, efficient string building techniques, and using lru_cache for expensive computations.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: wdm0006

bywdm0006

Analytics & Monitoring

Optimizes Python application speed and memory efficiency through advanced profiling, benchmarking, and implementation strategies.

About

The Python Performance skill equips Claude with specialized knowledge to identify execution bottlenecks, resolve memory leaks, and implement highly efficient data structures. It integrates industry-standard tools like cProfile, PyInstrument, and memray while providing actionable guidance on algorithmic complexity and caching strategies. This skill is ideal for developers looking to scale Python applications, reduce infrastructure costs, and establish automated performance regression testing within their CI/CD pipelines by leveraging proven optimization patterns.

Key Features

Automated benchmarking and regression testing via pytest-benchmark
Statistical and deterministic profiling using PyInstrument and cProfile
Algorithmic optimization recommendations for core Python data structures
0 GitHub stars
Implementation of performance patterns like generators, deques, and lru_cache
Deep memory analysis and flamegraph generation with memray and tracemalloc

Use Cases

Reducing the memory footprint of large-scale data processing scripts
Setting up performance baselines to prevent speed regressions in pull requests
Diagnosing and fixing slow API response times in Python backend services

About

Key Features

Automated benchmarking and regression testing via pytest-benchmark
Statistical and deterministic profiling using PyInstrument and cProfile
Algorithmic optimization recommendations for core Python data structures
0 GitHub stars
Implementation of performance patterns like generators, deques, and lru_cache
Deep memory analysis and flamegraph generation with memray and tracemalloc

Use Cases

Reducing the memory footprint of large-scale data processing scripts
Setting up performance baselines to prevent speed regressions in pull requests
Diagnosing and fixing slow API response times in Python backend services