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Common Python anti-patterns to catch during code review and debugging. \n \n Covers 14+ anti-patterns across infrastructure, architecture, error handling, resources, type safety, and testing with before/after code examples \n Includes a quick review checklist and summary table for fast reference during code reviews \n Focuses on practical fixes: centralized retry logic, DTOs, repository pattern, specific exception handling, and async-native libraries \n Emphasizes validation at API boundaries, c

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust Python applications. \n \n Covers core principles including readability, explicit code, EAFP exception handling, and modern type hints with generics and protocols \n Includes practical patterns for error handling, context managers, comprehensions, generators, dataclasses, and decorators with runnable examples \n Addresses concurrency patterns for I/O-bound (threading, async/await) and CPU-bound (multiprocessing)

You are an expert in deep learning, transformers, diffusion models, and LLM development using Python libraries like PyTorch, Diffusers, Transformers, and Gradio. Follow these guidelines when writing deep learning code.

Generate complete, production-ready solutions for Dataverse SDK use cases with architecture guidance. \n \n Analyzes requirements across data volume, frequency, performance, and error tolerance to recommend appropriate patterns (transactional, batch, query, file management, scheduled, or real-time) \n Provides full Python implementation including authentication, singleton service classes, CRUD operations, bulk processing, and comprehensive error handling \n Includes data model design with table

Use the Kernel Python SDK when you need to:

Zarr is a Python library for storing large N-dimensional arrays with chunking and compression. Apply this skill for efficient parallel I/O, cloud-native workflows, and seamless integration with NumPy, Dask, and Xarray.

Comprehensive pytest testing strategies with TDD, fixtures, mocking, and coverage best practices. \n \n Covers TDD methodology (red-green-refactor cycle), parametrization, fixtures with multiple scopes, and mocking patterns for unit and integration testing \n Includes pytest fundamentals: assertions, markers for test selection, exception testing, and async test support with pytest-asyncio \n Provides practical patterns for testing APIs, databases, file operations, and class methods with real cod

Standards and best practices for Python development. Follow these guidelines when writing or modifying Python code.

Generate production-ready Python code for Dataverse SDK with error handling and best practices. \n \n Implements comprehensive error handling using DataverseError hierarchy with retry logic and exponential backoff for transient failures \n Enforces singleton client pattern for connection management and includes structured logging for audit trails and debugging \n Applies OData optimization techniques: server-side filtering, column selection, and pagination to reduce data transfer \n Provides typ

Pytest-based testing strategies for Temporal workflows with time-skipping, mocking, and replay validation. \n \n Covers unit testing (WorkflowEnvironment with time-skipping), integration testing (mocked activities), and replay testing for determinism validation \n Time-skipping enables month-long workflows to execute in seconds; ActivityEnvironment isolates activity logic for fast feedback \n Includes progressive disclosure resources for unit testing, integration testing, replay testing, and loc