Best Best AI Agent Skills for Python Development

This skill provides comprehensive guidance on PDF processing operations using Python libraries and command-line tools, including merging, splitting, extracting text and tables, creating PDFs, and performing general manipulations.

Better Auth is a TypeScript-first, framework-agnostic auth framework supporting email/password, OAuth, magic links, passkeys, and more via plugins.

Test-Driven Development (TDD) is a software development approach where tests are written before writing the code that needs to be tested. The process follows a cycle of writing a failing test (RED), writing minimal code to pass the test (GREEN), and then refactoring the code while keeping it working (REFACTOR).

Codex is a skill designed to leverage the GPT-5.2 model for various coding and reasoning workflows. It allows users to specify the reasoning effort required for tasks, configure command options, and execute commands within a flexible environment.

Smart dependency management for any language with automatic detection and safe updates.

The AI SDK provides tools and resources for developers to integrate artificial intelligence capabilities into their applications using the Vercel platform.

This skill biases toward the smallest possible codebase, aiming to reduce entropy by focusing on the measure of code after changes rather than the effort put into writing it.

Converts PRDs to prd.json format for ralph-tui autonomous execution.

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

Production-ready FastAPI project structures with async patterns, dependency injection, middleware, and best practices for building high-performance APIs.

Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and test-driven development practices.

This skill enables retrieval of current documentation for software libraries and components by querying the Context7 API via curl. Use it instead of relying on potentially outdated training data.

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

Converts OpenAPI 3.0 specifications to TypeScript interfaces and type guards.

Build resilient applications with robust error handling strategies that gracefully handle failures and provide excellent debugging experiences.

Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.

The Code Review skill provides a structured approach to reviewing code for security vulnerabilities, performance issues, code quality, and testing completeness. It helps developers identify areas that need fixing, considerations for improvement, and recognizes good coding practices.

Comprehensive guide to using uv, an extremely fast Python package installer and resolver written in Rust, for modern Python project management and dependency workflows.

Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration.

Comprehensive guide to creating, structuring, and distributing Python packages using modern packaging tools, pyproject.toml, and publishing to PyPI.

Clean Code - Pragmatic AI Coding Standards focuses on writing clear, concise, and maintainable code by following best practices such as Single Responsibility Principle, Don't Repeat Yourself, and proper function naming.

Master major dependency version upgrades, compatibility analysis, staged upgrade strategies, and comprehensive testing approaches.

Comprehensive guide to implementing CQRS (Command Query Responsibility Segregation) patterns.

Builds and validates trading strategy backtests using robust methodologies to avoid biases and errors. Developers gain increased confidence in their strategy's reliability through structured tests and comprehensive analysis techniques.

Guide to selecting and optimizing embedding models for vector search applications.

Automates the setup and analysis of fullstack applications by providing tools for scaffolding and quality checks. Developers benefit from increased efficiency and reduced errors, making it easier to adhere to best practices in their projects.

Implements best practices for structuring and animating scenes in ManimCE. This results in cleaner code, easier debugging, and faster animation workflows, enhancing productivity for developers.

Establishes decision-making processes for selecting frameworks and patterns in Python projects. By implementing these guidelines, developers can enhance their coding practices and streamline project setups to avoid common pitfalls.

Enables the deployment and execution of Python functions in a serverless cloud environment. By using Modal, developers can scale applications efficiently and only pay for the compute resources they utilize, significantly reducing operational costs.

MarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown, particularly useful for converting documents into LLM-friendly text format.

Facilitates the design and implementation of advanced AI and ML systems with a focus on prompt engineering and system optimization. By installing this skill, developers improve their capability to build scalable, production-grade architectures that enhance performance and reliability.

Handles the creation and management of AI function examples across various use cases. By installing this skill, developers can streamline their workflow with pre-built scripts, significantly reducing development time and minimizing the potential for errors in AI function implementations.

Facilitates the design and orchestration of AI agent teams with defined roles and tasks. Streamlines project management by enabling clear task delegation and improving collaboration among agents.

Leverage Python's type system to catch errors at static analysis time. Type annotations serve as enforced documentation that tooling validates automatically.