version: "1.0.2" name: python-performance description: Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release. globs: "**/*.py" alwaysApply: false category: performance tags:

• python
• performance
• profiling
• optimization
• cProfile
• memory

tools: [] usage_patterns:

• performance-analysis
• bottleneck-identification
• memory-optimization
• algorithm-optimization

complexity: intermediate model_hint: standard estimated_tokens: 1200 progressive_loading: true modules:

• modules/profiling-tools.md
• modules/optimization-patterns.md
• modules/memory-management.md
• modules/benchmarking-tools.md
• modules/best-practices.md

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

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

Eleven proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, database operations, and loop transformations (what works in Python vs the compiler).

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