Does it offer benchmarking for code comparisons?

Yes, it includes a dedicated Benchmark class that calculates mean, standard deviation, and percentage improvements between different code implementations.

Which profiling tools does this skill support?

It provides patterns for industry-standard tools including cProfile, pstats, line_profiler, memory_profiler, tracemalloc, and the timeit module.

Can this skill profile asynchronous Python code?

Yes, it includes specialized patterns for profiling async coroutines, tracking execution duration across tasks, and printing async-specific stats.

How does it help with memory leak detection?

The skill utilizes tracemalloc snapshots and the memory-profiler package to track allocations and peak memory usage, helping you pinpoint where memory is being held.

Can I integrate these profiling patterns into my tests?

Absolutely. It includes patterns for using pytest to enforce performance budgets and detect regressions during your continuous integration process.

Performance Profiling & Optimization

Name: Performance Profiling & Optimization
Author: ricardoroche

byricardoroche

Analytics & Monitoring

Automates Python performance analysis using advanced profiling tools, memory tracking, and benchmarking strategies.

About

This skill empowers Claude to identify and resolve performance bottlenecks in Python applications through a comprehensive suite of profiling patterns. It provides standardized implementations for CPU and line-by-line analysis, detailed memory tracking with tracemalloc, asynchronous operation profiling, and rigorous benchmarking using timeit. Whether you are dealing with memory leaks, slow API responses, or inefficient algorithms, this skill ensures data-driven optimization by applying industry-standard tools and metrics to your development workflow.

Key Features

Granular line-by-line execution analysis for precise bottleneck detection
0 GitHub stars
Comprehensive memory tracking using memory_profiler and tracemalloc
Automated CPU profiling with cProfile decorators and stats analysis
Asynchronous-aware profiling for modern Python coroutines and tasks
Comparative benchmarking tools to validate performance improvements

Use Cases

Detecting and fixing memory leaks or high peak memory usage in data-heavy tasks
Identifying specific functions or lines of code causing slow execution times
Comparing multiple algorithm implementations to find the most efficient solution

About

Key Features

Granular line-by-line execution analysis for precise bottleneck detection
0 GitHub stars
Comprehensive memory tracking using memory_profiler and tracemalloc
Automated CPU profiling with cProfile decorators and stats analysis
Asynchronous-aware profiling for modern Python coroutines and tasks
Comparative benchmarking tools to validate performance improvements

Use Cases

Detecting and fixing memory leaks or high peak memory usage in data-heavy tasks
Identifying specific functions or lines of code causing slow execution times
Comparing multiple algorithm implementations to find the most efficient solution