Can I profile a running production application?

Yes, it includes patterns for using py-spy, which allows you to attach to and profile running Python processes without needing to restart them.

Does it provide advice on Python-specific coding patterns?

Absolutely. It covers best practices like using list comprehensions, efficient string concatenation, and local variable caching for micro-optimizations.

How does this help with memory management?

It teaches you how to use memory_profiler to find leaks and implement memory-efficient structures like generators and __slots__ to reduce your application's footprint.

What profiling tools does this skill support?

The skill provides implementation patterns for cProfile, line_profiler, memory_profiler, and py-spy to cover CPU, memory, and production-level profiling.

Will this help me speed up mathematical calculations?

Yes, the skill includes specific guides on using NumPy for vectorized operations, which can be significantly faster than standard Python loops.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: kivilaid

bykivilaid

•

Analytics & Monitoring

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory consumption, and improve execution speed.

About

This skill provides comprehensive guidance for identifying and resolving performance issues in Python applications. It equips developers with patterns for CPU and memory profiling using industry-standard tools like cProfile and line_profiler, alongside advanced optimization strategies such as caching, vectorized operations with NumPy, and memory-efficient data structures. It is particularly useful when debugging slow production code, optimizing data processing pipelines, or scaling resource-intensive services to improve overall application responsiveness.

Key Features

6 GitHub stars
Advanced optimization patterns for caching and local variable access
Production-ready monitoring and flamegraph generation with py-spy
Multi-level profiling including CPU, memory, and line-by-line analysis
High-performance numerical operations and vectorization with NumPy
Memory management techniques using generators and __slots__

Use Cases

Identifying and fixing memory leaks in long-running background processes
Optimizing large-scale data processing pipelines and ETL jobs
Reducing latency and response times in high-traffic web applications

About

Key Features

6 GitHub stars
Advanced optimization patterns for caching and local variable access
Production-ready monitoring and flamegraph generation with py-spy
Multi-level profiling including CPU, memory, and line-by-line analysis
High-performance numerical operations and vectorization with NumPy
Memory management techniques using generators and __slots__

Use Cases

Identifying and fixing memory leaks in long-running background processes
Optimizing large-scale data processing pipelines and ETL jobs
Reducing latency and response times in high-traffic web applications