Skill UI

Showcases modern Python practices, covering strict 3.12 type hints, dataclasses vs Pydantic decisions, asyncio/httpx patterns, src-layout projects, pyproject tool configs, virtual env workflows, and pytest guidance for reliable codebases.

This skill acts as an expert architect to generate complete, production-ready Python project structures. It supports various modern architectures, including FastAPI, Django (full-stack), standalone libraries, and CLI tools. It ensures adherence to best practices, includes dependency management (uv, pip), type hinting, and comprehensive testing skeletons.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.

A comprehensive technical guide covering the entire lifecycle of Python packaging. It teaches developers how to structure, build, test, and distribute robust Python libraries using modern standards like pyproject.toml, generating wheels, and publishing artifacts reliably to PyPI or private package indexes. Ideal for creating reusable, professional-grade Python code.

A concise collection of idiomatic Python best practices—from readability and explicit configuration to type hints, error handling, and context managers—aimed at building robust, maintainable applications.

A comprehensive guide for profiling, analyzing, and optimizing Python code. It addresses identifying performance bottlenecks related to CPU usage, excessive memory consumption, I/O operations, and slow data processing. Use this skill to improve application speed and efficiency in production environments.

A comprehensive skill set for building robust, type-safe, and highly performant Python 3.11+ applications. This specialist enforces best practices by generating type-annotated code, utilizing async/await patterns, structured dataclasses, and comprehensive error handling. The workflow mandates rigorous validation using tools like mypy (strict mode), black, ruff, and extensive pytest test suites to ensure code quality and reliability.

Provides a comprehensive pytest-focused testing strategy for Python applications, covering TDD cycles, fixtures, parametrization, coverage tools, and test infrastructure setup for reliable suites.

This comprehensive guide provides robust strategies for testing Python code using industry best practices. It covers essential techniques like pytest, fixtures, mocking, parameterization, and implementing Test-Driven Development (TDD). Use this skill when writing unit tests, setting up complex test suites, performing integration testing, or establishing continuous testing infrastructure in CI/CD pipelines.

Automated guidance for setting up RabbitMQ queues in backend services, covering patterns, code snippets, and validation for Node.js, Python, Go, and microservices messaging scenarios.

Guides backend engineers through planning, implementing, and validating rate limit middleware across Node.js, Python, Go, and microservice architectures.

Automates redis cache manager workflows by recommending backend patterns, generating production-ready code, and validating configurations for node.js, python, go, and microservice architectures.