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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill python-performance-optimization- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads python-performance-optimization automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- Implementation: More efficient code patterns
- Parallelization: Multi-threading/processing
- Caching: Avoid redundant computation
- Native Extensions: C/Rust for critical paths
Quick Start
Basic Timing
import time
def measure_time():
"""Simple timing measurement."""
start = time.time()
# Your code here
result = sum(range(1000000))
elapsed = time.time() - start
print(f"Execution time: {elapsed:.4f} seconds")
return result
# Better: use timeit for accurate measurements
import timeit
execution_time = timeit.timeit(
"sum(range(1000000))",
number=100
)
print(f"Average time: {execution_time/100:.6f} seconds")
Detailed patterns and worked examples
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
Best Practices
- Profile before optimizing - Measure to find real bottlenecks
- Focus on hot paths - Optimize code that runs most frequently
- Use appropriate data structures - Dict for lookups, set for membership
- Avoid premature optimization - Clarity first, then optimize
- Use built-in functions - They're implemented in C
- Cache expensive computations - Use lru_cache
- Batch I/O operations - Reduce system calls
- Use generators for large datasets
- Consider NumPy for numerical operations
- Profile production code - Use py-spy for live systems
Common Pitfalls
- Optimizing without profiling
- Using global variables unnecessarily
- Not using appropriate data structures
- Creating unnecessary copies of data
- Not using connection pooling for databases
- Ignoring algorithmic complexity
- Over-optimizing rare code paths
- Not considering memory usage
FAQ
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 python-performance-optimization?
Run npx skills add https://github.com/wshobson/agents --skill python-performance-optimization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-performance-optimization/ (personal) or .claude/skills/python-performance-optimization/ (project).
Which AI agents can use python-performance-optimization?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is python-performance-optimization safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains python-performance-optimization?
It is published by wshobson in wshobson/agents. It has 35,886 recorded installs.
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