Profile CPU, memory, and I/O bottlenecks in Python applications.
npx skills add https://github.com/caraya/agent-skills --skill python-performance-caraya
Please help me install this Agent Skill. Skill: python-performance Source: https://github.com/caraya/agent-skills/tree/main/skills/python-performance Command: npx skills add https://github.com/caraya/agent-skills --skill python-performance-caraya
Profiling and optimizing Python applications to reduce CPU time, memory usage, and I/O bottlenecks, including handling GIL contention and async patterns.
Profile your Python project to locate the top CPU and memory hotspots and produce a baseline report.
High-intent search queries and answers about installing and using this skill.
Profile Python performance using cProfile, py-spy, tracemalloc, and memory_profiler to identify CPU, memory, and I/O bottlenecks, producing measurable baselines for actionable optimizations across web services and data pipelines.
GIL contention restricts multi-threaded Python performance, but profiling identifies where the Global Interpreter Lock impacts execution, suggesting alternatives like multiprocessing, Cython, or async patterns to bypass GIL limitations.
Async Python optimization is supported through structured analysis using profiling tools that measure baselines and identify I/O bottlenecks, providing guidance for async patterns and GIL-related contention issues.
Reduce memory usage in Python data pipelines by using tracemalloc and memory_profiler to locate hotspots, establish measurable baselines, and apply a guided optimization plan with actionable steps for code paths and dependencies.
Python profiling libraries such as cProfile, py-spy, tracemalloc, and memory_profiler are required to analyze startup overhead, produce measurable baselines, and generate actionable optimizations across typical Python workloads.
Use multiprocessing instead of async patterns when GIL-aware profiling identifies GIL contention as the primary bottleneck, as multiprocessing bypasses the Global Interpreter Lock while async patterns optimize I/O-bound workloads.
coolguy1771Profile and optimize Python code using cProfile, memory_profiler, line_profiler, and py-spy.
Pupfish-LLCAssess Qlik Sense app memory, reload time, and expression efficiency.
randalmurphalProfile Python CPU and memory bottlenecks using cProfile, line_profiler, memory_profiler, and py-spy.
kreuzberg-devOptimize PyO3 async Rust functions by managing the GIL and selecting spawn_blocking or block_in_place.
