performance-profiling
Automatically applies when profiling Python performance. Ensures proper use of profiling tools, async profiling, benchmarking, memory analysis, and optimization strategies.
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Automatically applies when profiling Python performance. Ensures proper use of profiling tools, async profiling, benchmarking, memory analysis, and optimization strategies.
Python code security analysis, performance optimization, and maintainability assessment
Profile and optimize Python backend code using cProfile, memory profilers, and performance best practices. Use when debugging slow FastAPI code, optimizing bottlenecks, or improving application performance.
Use when checking for data leaks, PII handling, and license risks in Python research code.
WHEN: Pandas/NumPy code review, data processing, vectorization, memory optimization WHAT: Vectorization patterns + Memory efficiency + Data validation + Performance optimization + Best practices WHEN NOT: Web framework → fastapi/django/flask-reviewer, General Python → python-reviewer
Use when you need to inspect detailed execution flow of a Python program, stepping through functions and inspecting variable contents with pdb. Triggers include requests to debug Python scripts, trace execution, or inspect runtime state using the pdb debugger.
Use when Python code runs slowly, needs profiling, requires async/await patterns, or needs concurrent execution - covers profiling tools, optimization patterns, and asyncio; measure before optimizing (plugin:python@dot-claude)
Use when you need a detailed execution trace of a Python script using sys.monitoring, including logging every operation with include/exclude event controls.
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. WHEN: Debugging slow Python code, profiling with cProfile/line_profiler, memory optimization, identifying bottlenecks, optimizing loops and data structures. WHEN NOT: Async optimization (use python-async-patterns), database query optimization (use sql-optimization-patterns), general debugging.
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.
Editing CCXT TypeScript exchange sources with transpilation-safe patterns for Python/Go/PHP/C#
Python type hints, mypy, Protocol, and static typing best practices
Use when Python's type system including type hints, mypy, Protocol, TypedDict, and Generics. Use when working with Python type annotations.
Use when writing Python code with the Affinity SDK, or when user asks about "affinity-sdk", "affinity package", typed IDs, async Affinity client, pagination, or Python scripts for Affinity CRM.