python-performance-optimization
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.
Profile →npx skills add wshobson/agents --skill python-performance-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Python Performance Optimization Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices. ## When to Use This Skill - Identifying performance bottlenecks in Python applications - Reducing application latency and response times - Optimizing CPU-intensive operations - Reducing memory consumption and memory leaks - Improving database query performance - Optimizing I/O operations - Speeding up data processing pipelines - Implementing high-performance algorithms - Profiling production applications ## Core Concepts ### 1. Profiling Types - **CPU Profiling**: Identify time-consuming functions - **Memory Profiling**: Track memory allocation and leaks - **Line Profiling**: Profile at line-by-line granularity - **Call Graph**: Visualize function call relationships ### 2. Performance Metrics - **Execution Time**: How long operations take - **Memory Usage**: Peak and average memory consumption - **CPU Utilization**: Processor usage patterns - **I/O Wait**: Time spent on I/O operations ### 3. Optimization Strategies - **Algorithmic**: Better algorithms and data structures - *
- When to Use This Skill
- Core Concepts
- 1. Profiling Types
- 2. Performance Metrics
- 3. Optimization Strategies
- Quick Start
- Basic Timing
- Detailed patterns and worked examples
- Best Practices
- Common Pitfalls
What does the python-performance-optimization skill do?
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.
How do I install it?
Run `npx skills add wshobson/agents --skill python-performance-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.