name: python-performance description: 'Python performance profiling and optimization: bottleneck detection, memory tuning, benchmarking' version: 1.7.1 globs: "**/*.py" alwaysApply: false category: performance tags:
- python
- performance
- profiling
- optimization
- cProfile
- memory tools:
- profiler-runner
- memory-analyzer
- benchmark-suite usage_patterns:
- performance-analysis
- bottleneck-identification
- memory-optimization
- algorithm-optimization complexity: intermediate model_hint: standard estimated_tokens: 1200 progressive_loading: true modules:
- profiling-tools
- optimization-patterns
- memory-management
- benchmarking-tools
- best-practices
Python Performance Optimization
Profiling and optimization patterns for Python code.
Table of Contents
Quick Start
# Basic timing
import timeit
time = timeit.timeit("sum(range(1000000))", number=100)
print(f"Average: {time/100:.6f}s")Verification: Run the command with --help flag to verify availability.
When To Use
- 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.
optimization-patterns
Ten proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, and database operations.
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
Troubleshooting
Common Issues
Command not found Ensure all dependencies are installed and in PATH
Permission errors Check file permissions and run with appropriate privileges
Unexpected behavior
Enable verbose logging with --verbose flag