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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Python Performance Optimization
Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.
Use this skill when
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
Do not use this skill when
The task is unrelated to python performance optimization
You need a different domain or tool outside this scope
Instructions
Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
If detailed examples are required, open resources/implementation-playbook.md.
Resources
resources/implementation-playbook.md for detailed patterns and examples.
Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
