python▌

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 package creation with pyproject.toml, setuptools, and PyPI publishing. \n \n Covers source layout (recommended), flat layout, and multi-package project structures with complete pyproject.toml examples \n Supports CLI tools via Click or argparse with entry point configuration, dynamic versioning, and namespace packages \n Includes build, distribution, and automated publishing workflows for PyPI with GitHub Actions integration \n Provides patterns for data files, C extensions, editab

Execute arbitrary Python code in a sandboxed environment with 100+ pre-installed libraries. \n \n Supports data processing (NumPy, Pandas, SciPy), web scraping (requests, BeautifulSoup, Selenium, Playwright), and image/video manipulation (Pillow, OpenCV, MoviePy) \n Includes 3D model processing (trimesh, open3d), PDF generation (reportlab, pypdf2), and SVG creation (svgwrite, cairosvg) \n Configurable timeout (1–300 seconds), output capture, and working directory; optional high-memory variant (1

Production-ready Dataverse SDK patterns with error handling, batch operations, and optimization techniques. \n \n Demonstrates exponential backoff retry logic for transient errors, batch CRUD operations with error recovery, and OData query optimization using filters, selects, expands, and paging with correct logical names \n Covers table metadata creation and inspection, custom column definitions with IntEnum option sets, and cache flushing strategies when schema changes \n Includes configuratio

Guide for building reliable, fault-tolerant Python applications with DBOS durable workflows.

This skill provides comprehensive architecture review capabilities for Python backend applications, covering all aspects of system design from infrastructure to code organization.

mojo-python-interop

Comprehensive testing approaches for Temporal workflows using pytest, progressive disclosure resources for specific testing scenarios.

Expert guidance for writing Python code in n8n Code nodes.

Note: Use TablesDB (not the deprecated Databases class) for all new code. Only use Databases if the existing codebase already relies on it or the user explicitly requests it.