Agent skill · data analytics · aiskillstore
python-performance-optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
Why this skill is useful
Provides specific profiling techniques and optimization strategies for Python that the AI wouldn't reliably generate on its own.
What it needs
Requires line-profiler installed locally. About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Python Performance Optimization Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices. When to Use This Skill Identifying performance bottlenecks in Python applications Reducing application latency and response times Optimizing CPU-intensive operations Reducing memory consumption and memory leaks Improving database query performance Optimizing I/O operations Speeding up data processing pipelines Implementing high-performance algorithms Profiling production applications Core Concepts 1. Profiling Types CPU Profiling: Identify time-consuming functions Memory Profiling: Track memory allocation and leaks Line Profiling: Profile at line-by-line granularity Call Graph: Visualize function call relationships 2. Performance Metrics Execution Time: How long operations take Memory Usage: Peak and average memory consumption CPU Utilization: Processor usage patterns I/O Wait: Time spent on I/O operations 3. Optimization Strategies Algorithmic: Better algorithms and data structures Implementation: More efficient code patterns Parallelization: Multi-threading/processing Caching: Avoid redundant computation Native Extensions: C/Rust for critical paths Quick Start Basic Timing Profiling Tools Pattern 1: cProfile - CPU Profiling Command-line profiling: Pattern 2: lineprofiler - Line-by-Line Profiling Manual line profiling: Pattern 3: memoryprofiler - Memory Usage Pattern 4: py-spy - Production Profiling Optimization Patterns Pattern 5: List Comprehensions vs Loops Pattern 6: Generator Expressions for Memory Pattern 7: String Concatenation Pattern 8: Dictionary Lookups vs List Searches Pattern 9: Local Variable Access Pattern 10: Function Call Overhead Advanced Optimization Pattern 11: NumPy for Numerical Operations Pattern 12: Caching with functools.lrucache Pattern 13: Using slots for Memory Pattern 14: Multiprocessing for CPU-Bound Tasks Pattern 15: Async I/O fo …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/python-performance-optimization--236e6f