name: python-performance description: |

Triggers: memory, profiling, optimization, performance, python Profile and optimize Python code using cProfile, memory profilers, and performance best practices.

Triggers: profiling, optimization, cProfile, memory profiler, bottleneck, slow code, performance, benchmarking, py-spy, tracemalloc

Use when: debugging slow code, identifying bottlenecks, optimizing memory, benchmarking performance, production profiling

DO NOT use when: async concurrency - use python-async instead. DO NOT use when: CPU/GPU system monitoring - use conservation:cpu-gpu-performance.

Consult this skill for Python performance profiling and optimization. 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 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.

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

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