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Modern Python tooling, naming conventions, type checking, and documentation standards for maintainable codebases. \n \n Configure ruff for unified linting and formatting, replacing flake8, isort, and black with a single fast tool \n Set up strict type checking with mypy or pyright to catch errors before runtime \n Follow PEP 8 naming conventions: snake_case for functions/variables, PascalCase for classes, SCREAMING_SNAKE_CASE for constants \n Write Google-style docstrings for all public APIs wit

Fundamental design principles for writing maintainable, testable Python code. \n \n Covers five core patterns: KISS (Keep It Simple), Single Responsibility Principle, Separation of Concerns, Composition Over Inheritance, and the Rule of Three \n Includes practical code examples contrasting anti-patterns with recommended approaches for each principle \n Provides layered architecture guidance (API, Service, Repository layers) with dependency injection patterns for testability \n Emphasizes explici

Modern Python project setup with uv, ruff, and ty for Python 3.11+. \n \n Replaces pip, Poetry, black, flake8, mypy, and pre-commit with faster, simpler alternatives from the Astral team \n Covers new project creation, dependency management via uv add / uv remove , and linting/formatting/type-checking workflows \n Includes migration paths from legacy tooling (requirements.txt, setup.py, flake8+black+isort, mypy/pyright) \n Provides decision tree for single-file scripts (PEP 723), simple projects

Structured input validation, exception design, and graceful failure handling for Python applications. \n \n Covers fail-fast validation patterns, meaningful exception hierarchies, and partial failure handling for batch operations \n Includes Pydantic integration for complex input validation with automatic error messages and custom exception types with context \n Demonstrates exception chaining to preserve debug trails, batch processing with per-item error tracking, and progress reporting for lon

Production-quality Python coding standards for writing clean, maintainable, modern Python code (versions 3.10-3.13).

You are an expert in Python development with uv package management.

A comprehensive guide to using CCXT in Python projects for cryptocurrency exchange integration.

Comprehensive testing strategies for Python using pytest, fixtures, mocking, and test-driven development. \n \n Covers unit, integration, functional, and performance testing with the AAA pattern (Arrange, Act, Assert) for test structure \n Includes 10 fundamental and advanced patterns: basic tests, fixtures with setup/teardown, parameterization, mocking, exception handling, async testing, monkeypatching, temporary files, custom fixtures, and property-based testing \n Provides test design princip

mojo-python-interop

Async task processing patterns for decoupling long-running work from request/response cycles. \n \n Covers core patterns including immediate job ID returns, task queue configuration with Celery, idempotency strategies, and job state management for visibility \n Includes advanced workflows: dead letter queues for failed tasks, status polling endpoints, task chaining, and parallel execution \n Provides examples for Celery, RQ, and Dramatiq, plus guidance on cloud-native alternatives like AWS SQS a