ai-research-skills is a skills library providing 98 skills across 23 categories of the AI research lifecycle, including fine-tuning, inference, and ML paper writing. It enables AI agents to autonomously handle tasks from literature survey through paper writing via a two-loop orchestration architecture.
Project overview
The project targets a concrete productivity bottleneck: AI researchers losing time to specialized ML infrastructure debugging. Its skill library covers the full research lifecycle and includes an autonomous orchestration layer plus an agent-native research artifact suite, offering a structured approach to reducing that overhead.
Project type
AI Coding · Model Runtime
Deployment
Refer to project documentation
License
MIT
Best for
AI researchers who want to reduce time spent on ML infrastructure debugging and instead focus on hypothesis testing
AI engineers and developers seeking reusable prompt-based skills for tasks such as fine-tuning, inference, and ML paper writing
Key capabilities
A collection of 98 skills spanning 23 categories across the AI research lifecycle, including fine-tuning, inference, and ML paper writing.
An autonomous research orchestration skill using a two-loop architecture that manages the full lifecycle from literature survey to paper writing.
Includes a compiler for research inputs, a research manager to extract decisions, and a rigor reviewer for epistemic review scoring.
Limitations and risks
Individual skills may reference libraries with different licenses. Users must check each project's license before adoption.
Getting started
Installation is rated easy and uses npx for both humans and AI agents. The first success path is to run npx @orchestra-research/ai-research-skills.