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name: python-performance description: | 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

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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")

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