This open-source library provides a comprehensive suite of AI research engineering skills, empowering AI agents to autonomously conduct research from hypothesis to experimental verification. It tackles the complexity of modern AI research infrastructure, enabling agents to manage tasks from data preparation to validation, thereby accelerating scientific discovery.

How It Works

The project curates 70 distinct "skills," each offering deep, production-ready expertise in specific AI frameworks and techniques. These skills cover the entire AI research lifecycle, from model architecture and fine-tuning to MLOps and multimodal applications. By packaging these specialized engineering abilities, the library allows AI agents to autonomously execute complex research experiments, abstracting away infrastructure challenges.

Quick Start & Requirements

Clone the repository for learning: git clone https://github.com/zechenzhangAGI/claude-ai-research-skills.git. Skills can be integrated with AI coding assistants (Claude, Gemini, etc.) by pointing them to skill folders or ingesting SKILL.md and reference files into RAG/Agent systems. No specific non-default prerequisites are listed for usage.

Highlighted Details

• 70 Skills, Full Lifecycle Coverage: Encompasses model architecture, tokenization, fine-tuning, distributed training, optimization, inference, serving, agents, RAG, multimodal, prompt engineering, MLOps, observability, and emerging techniques.
• Research-Grade Quality: Each skill offers expert guidance, real code examples, troubleshooting, and production-ready workflows, sourced from official documentation and battle-tested environments.
• Key Frameworks: Features specialized expertise in Mamba (5x faster), Megatron-Core (H100 MFU), vLLM (high-throughput serving), Unsloth (2x faster QLoRA), and DSPy (declarative prompting).
• Autonomous Research: Designed for AI agent integration to automate complex research tasks, abstracting infrastructure challenges.

Maintenance & Community

Maintained by Orchestra Research, the project encourages community contributions via PRs (48-hour review target). Contributions sync to the Orchestra marketplace. A detailed roadmap is available, promoting collaborative development.

Licensing & Compatibility

Licensed under MIT. Users must independently verify licenses of individual referenced libraries, as they may have different terms affecting commercial use or closed-source linking.

Limitations & Caveats

While 70 skills are claimed complete, users must independently assess the licensing and compatibility of each skill's underlying libraries for their specific use cases. Autonomous research effectiveness depends on the AI agent's ability to interpret and utilize the provided skill structures.