Python Performance Optimization
Unverified●30/40Claude Code◐PartialHas SKILL.md but declares no allowed-tools — Claude Code will ask for permission each time
Cursor◐PartialPlain prose you can paste in — but no Cursor rules file
Codex◐PartialPlain prose you can paste in — but no AGENTS.md
Gemini CLI◐PartialPlain prose you can paste in
Copilot◐PartialPlain prose you can paste in — but no Copilot instructions file
npx agentalley add python-performance-optimizationWho is stuck, and on what
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.
The whole source
Frontmatter — 2 properties
| name | python-performance-optimization |
|---|---|
| description | 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. |
| 1 | --- |
| 2 | name: python-performance-optimization |
| 3 | description: 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. |
| 4 | ---A5 — No allowed-tools declared — no way to tell what this skill may touch |
| 5 | |
| 6 | # Python Performance Optimization |
| 7 | |
| 8 | Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices. |
| 9 | |
| 10 | ## When to Use This Skill |
| 11 | |
| 12 | - Identifying performance bottlenecks in Python applications |
| 13 | - Reducing application latency and response times |
| 14 | - Optimizing CPU-intensive operations |
| 15 | - Reducing memory consumption and memory leaks |
| 16 | - Improving database query performance |
| 17 | - Optimizing I/O operations |
| 18 | - Speeding up data processing pipelines |
| 19 | - Implementing high-performance algorithms |
| 20 | - Profiling production applications |
| 21 | |
| 22 | ## Core Concepts |
| 23 | |
| 24 | ### 1. Profiling Types |
| 25 | |
| 26 | - **CPU Profiling**: Identify time-consuming functions |
| 27 | - **Memory Profiling**: Track memory allocation and leaks |
| 28 | - **Line Profiling**: Profile at line-by-line granularity |
| 29 | - **Call Graph**: Visualize function call relationships |
| 30 | |
| 31 | ### 2. Performance Metrics |
| 32 | |
| 33 | - **Execution Time**: How long operations take |
| 34 | - **Memory Usage**: Peak and average memory consumption |
| 35 | - **CPU Utilization**: Processor usage patterns |
| 36 | - **I/O Wait**: Time spent on I/O operations |
| 37 | |
| 38 | ### 3. Optimization Strategies |
| 39 | |
| 40 | - **Algorithmic**: Better algorithms and data structures |
| 41 | - **Implementation**: More efficient code patterns |
| 42 | - **Parallelization**: Multi-threading/processing |
| 43 | - **Caching**: Avoid redundant computation |
| 44 | - **Native Extensions**: C/Rust for critical paths |
| 45 | |
| 46 | ## Quick Start |
| 47 | |
| 48 | ### Basic Timing |
| 49 | |
| 50 | ```python |
| 51 | import time |
| 52 | |
| 53 | def measure_time(): |
| 54 | """Simple timing measurement.""" |
| 55 | start = time.time() |
| 56 | |
| 57 | # Your code here |
| 58 | result = sum(range(1000000)) |
| 59 | |
| 60 | elapsed = time.time() - start |
| 61 | print(f"Execution time: {elapsed:.4f} seconds") |
| 62 | return result |
| 63 | |
| 64 | # Better: use timeit for accurate measurements |
| 65 | import timeit |
| 66 | |
| 67 | execution_time = timeit.timeit( |
| 68 | "sum(range(1000000))", |
| 69 | number=100 |
| 70 | ) |
| 71 | print(f"Average time: {execution_time/100:.6f} seconds") |
| 72 | ``` |
| 73 | |
| 74 | ## Detailed patterns and worked examples |
| 75 | |
| 76 | Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient. |
| 77 | |
| 78 | ## Best Practices |
| 79 | |
| 80 | 1. **Profile before optimizing** - Measure to find real bottlenecks |
| 81 | 2. **Focus on hot paths** - Optimize code that runs most frequently |
| 82 | 3. **Use appropriate data structures** - Dict for lookups, set for membership |
| 83 | 4. **Avoid premature optimization** - Clarity first, then optimize |
| 84 | 5. **Use built-in functions** - They're implemented in C |
| 85 | 6. **Cache expensive computations** - Use lru_cache |
| 86 | 7. **Batch I/O operations** - Reduce system calls |
| 87 | 8. **Use generators** for large datasets |
| 88 | 9. **Consider NumPy** for numerical operations |
| 89 | 10. **Profile production code** - Use py-spy for live systems |
| 90 | |
| 91 | ## Common Pitfalls |
| 92 | |
| 93 | - Optimizing without profiling |
| 94 | - Using global variables unnecessarily |
| 95 | - Not using appropriate data structures |
| 96 | - Creating unnecessary copies of data |
| 97 | - Not using connection pooling for databases |
| 98 | - Ignoring algorithmic complexity |
| 99 | - Over-optimizing rare code paths |
| 100 | - Not considering memory usage |
| 101 |
Reviews
Installed this one?Write the first review and take the Trailblazer badge.
Alternatives
Subagent Driven DevelopmentUse when executing implementation plans with independent tasks in the current session◐◐◐◐◐●36/40Python Code Style & DocumentationPython code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards.◐····●35/40Competitor Price Analysis 💲Competitor pricing strategy analysis and market positioning. Price mapping, pricing gaps identification, elasticity signals evaluation, and strategic pricing optimization. Use when the user asks about competitor pricing, price analysis, pricing strategy, or co◐····●34/40Competitor Price Tracker 📊Set up competitor price tracking and monitoring workflows. Track price changes, detect promotions, analyze pricing patterns, and get alerts for competitive price movements.◐····●34/40