Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and test-driven development practices. Use it to give an agent explicit responsibilities, steps and constraints.

Design well-organized Python projects with clear module boundaries, explicit public interfaces, and maintainable directory structures. Good organization makes code discoverable and changes predictable. Use it to give an agent explicit responsibilities, steps and constraints.

Consistent code style and clear documentation make codebases maintainable and collaborative. This skill covers modern Python tooling, naming conventions, and documentation standards. Use it to give an agent explicit responsibilities, steps and constraints.

Decouple long-running or unreliable work from request/response cycles. Return immediately to the user while background workers handle the heavy lifting asynchronously. Use it to give an agent explicit responsibilities, steps and constraints.

A reference checklist of common mistakes and anti-patterns in Python code. Review this before finalizing implementations to catch issues early. Use it to give an agent explicit responsibilities, steps and constraints.

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems. Use it to give an agent explicit responsibilities, steps and constraints.

Python conventions following PEP 8 and modern best practices. Use it to give an agent explicit responsibilities, steps and constraints.

This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup, tool registration patterns, input validation with Pydantic, error handling, and complete working examples. Use it to ground design choices in named patterns, trade-offs and examples.

List files the agent wrote to /mnt/session/outputs/ during a session, then. Use it to ground design choices in named patterns, trade-offs and examples.

For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md. Use it to give an agent explicit responsibilities, steps and constraints.

Use with options() to override client settings for a single call without mutating the client:. Use it as a repeatable review, validation or hardening pass.

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to textract, but with a focus on preserving important document structure and content as Markdown (includin. Use it to build a structured path from fundamentals to hands-on practice.

Simple Python bindings for @ggerganov's llama.cpp library. This package provides:. Use it to ground design choices in named patterns, trade-offs and examples.

The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and. Use it to navigate the topic and choose relevant methods, papers or tools.

MedAlpaca expands upon both Stanford Alpaca and AlpacaLoRA to offer an advanced suite of large language models specifically fine-tuned for medical question-answering and dialogue applications. Our primary objective is to deliver an array of open-source language models, paving the way for seamless development of medical chatbot solutions.

That being said, some enhancements added compared to the original repo: :heavycheckmark: Possible to train in other languages :heavycheckmark: Hifigan added, allowing for faster inference at the cost of quality. :heavycheckmark: whisper-v3 added as a chooseable option for whisperx :heavycheckmark: Output conversion using RVC This is a fork of the repo originally located here: https://git.ecker.tech/mrq/ai-voice-cloning.

Implementation of Band Split Roformer , SOTA Attention network for music source separation out of ByteDance AI Labs. They beat the previous first place by a large margin.

Implementation of SoundStorm , Efficient Parallel Audio Generation from Google Deepmind, in Pytorch. They basically applied MaskGiT to the residual vector quantized codes from Soundstream .

🐸TTS is a library for advanced Text-to-Speech generation. 🛠️ Tools for training new models and fine-tuning existing models in any language.

SimpleTuner is geared towards simplicity, with a focus on making the code easily understood. This codebase serves as a shared academic exercise, and contributions are welcome.