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Build Python serverless APIs on Cloudflare Workers with async-only execution, external package support, and multi-step workflow automation. \n \n Deploy Python APIs using the WorkerEntrypoint class pattern with pywrangler CLI; supports all Cloudflare bindings (D1, KV, R2, Workers AI, Durable Objects, Queues) \n Requires async-only code: use httpx or aiohttp for HTTP calls, avoid sync libraries like requests and native C extensions \n Python Workflows enable durable multi-step DAG automation with

Type-first Python development using dataclasses, discriminated unions, NewType, and Protocol to make illegal states unrepresentable. \n \n Define data models and function signatures before implementation; use frozen dataclasses, Literal-based discriminated unions, and NewType for domain primitives to prevent invalid states at type-check time \n Leverage Protocol for structural typing, TypedDict for external data shapes, and exhaustive pattern matching with match statements to catch incomplete lo

Structured logging, metrics, and distributed tracing patterns for Python production systems. \n \n Covers four core observability areas: structured JSON logging with structlog, Prometheus metrics for the four golden signals (latency, traffic, errors, saturation), correlation ID propagation across service boundaries, and OpenTelemetry distributed tracing \n Includes semantic log level guidance, bounded cardinality rules for metrics to prevent storage explosion, and context manager patterns for co

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

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

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

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

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.