AI-Research-SKILLs
Orchestra-Research · Orchestra-Research/AI-Research-SKILLs
A comprehensive open-source library of skills that empowers AI agents to autonomously conduct AI research, from ideation and experimentation to paper writing.
Comprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent with full horsepower. Maintained by Orchestra Research.
Preview
What you can do with it
- Enable an AI agent to survey scientific literature and generate new AI research ideas.
- Use an AI agent to propose and fine-tune machine learning model architectures.
- Automate the process of evaluating model performance and drafting research papers.
Safety check
No risky patterns found in the files we scanned.
Automated scan of the repo files. Always review code you run.
From the README
AI Research Skills Library
The most comprehensive open-source skills library enabling AI agents to autonomously conduct AI research — from idea to paper
98 Skills Powering AI Research in 2026
View All 23 Categories
| Autoresearch (1) | Ideation (2) | ML Paper Writing (2) |
| Model Architecture (5) | Fine-Tuning (4) | Post-Training (8) |
| Distributed Training (6) | Optimization (6) | Inference (4) |
| Tokenization (2) | Data Processing (2) | Evaluation (3) |
| Safety & Alignment (4) | Agents (4) | RAG (5) |
| Multimodal (7) | Prompt Engineering (4) | MLOps (3) |
| Observability (2) | Infrastructure (3) | Mech Interp (4) |
| Emerging Techniques (6) | Agent-Native Research Artifact (3) |
Table of Contents
- Our Mission
- Path Towards AI Research Agent
- Available AI Research Engineering Skills
- Demos
- Skill Structure
- Roadmap
- Repository Structure
- Use Cases
- Contributors
- Citation
- Community
Our Mission
We enable AI agents to autonomously conduct AI research — from literature survey and idea generation through experiment execution to paper writing. The library provides both the research orchestration layer (autoresearch, ideation, paper writing) and the engineering skills (training, evaluation, deployment) needed at each stage.
System diagram of an AI research agent
Path Towards AI Research Agent
Modern AI research requires mastering dozens of specialized tools and frameworks. AI Researchers spend more time debugging infrastructure than testing hypotheses — slowing the pace of scientific discovery. We provide a comprehensive skills library that enables AI agents to autonomously conduct the full research lifecycle — from brainstorming ideas to writing the paper.
- Autonomous Research - The autoresearch skill orchestrates the entire research workflow using a two-loop architecture, routing to domain skills as needed
- Specialized Expertise - Each domain skill provides deep, production-ready knowledge of a specific framework (Megatron-LM, vLLM, TRL, etc.)
- End-to-End Coverage - 98 skills spanning the full AI research lifecycle, from ideation and literature survey to experiments and paper writing
- Research-Grade Quality - Documentation sourced from official repos, real GitHub issues, and battle-tested production workflows
Available AI Research Engineering Skills
Quality over quantity: Each skill provides comprehensive, expert-level guidance with real code examples, troubleshooting guides, and production-ready workflows.
📦 Quick Install (Recommended)
For humans — interactive installer with one command:
npx @orchestra-research/ai-research-skills
For AI agents — point your agent to the welcome doc and it handles the rest:
Read https://www.orchestra-research.com/ai-research-skills/welcome.md and follow the instructions to install and use AI Research Skills.
This installs all 98 skills, loads the autoresearch orchestration layer, and starts autonomous research.
What the installer does
- Auto-detects your installed coding agents (Claude Code, Hermes Agent, OpenCode, Qoder, Cursor, Gemini CLI, etc.)
- Installs skills to
~/.orchestra/skills/with symlinks to each agent (falls back to copy on Windows) - Offers everything, quickstart bundle, by category, or individual skills
- Updates installed skills with latest versions
- Uninstalls all or selected skills
CLI Commands
# Interactive installer (recommended)
npx @orchestra-research/ai-research-skills
# Direct commands
npx @orchestra-research/ai-research-skills list # View installed skills
npx @orchestra-research/ai-research-skills update # Update installed skills
Claude Code Marketplace (Alternative)
Install skill categories directly using the Claude Code CLI:
# Add the marketplace
/plugin marketplace add orchestra-research/AI-research-SKILLs
# Install by category (23 categories available)
/plugin install fine-tuning@ai-research-skills # Axolotl, LLaMA-Factory, PEFT, Unsloth
/plugin install post-training@ai-research-skills # TRL, GRPO, OpenRLHF, SimPO, verl, slime, miles, torchforge
/plugin install inference-serving@ai-research-skills # vLLM, TensorRT-LLM, llama.cpp, SGLang
/plugin install distributed-training@ai-research-skills
/plugin install optimization@ai-research-skills
All 23 Categories (98 Skills)
| Category | Skills | Included |
|---|---|---|
| Autoresearch | 1 | Autonomous research orchestration — central layer that manages the full lifecycle and routes to all other skills |
| Ideation | 2 | Research Brainstorming, Creative Thinking |
| ML Paper Writing | 2 | ML Paper Writing (LaTeX templates, citation verification), Academic Plotting |
| Model Architecture | 5 | LitGPT, Mamba, NanoGPT, RWKV, TorchTitan |
| Tokenization | 2 | HuggingFace Tokenizers, SentencePiece |
| Fine-Tuning | 4 | Axolotl, LLaMA-Factory, PEFT, Unsloth |
| Mech Interp | 4 | TransformerLens, SAELens, pyvene, nnsight |
| Data Processing | 2 | NeMo Curator, Ray Data |
| Post-Training | 8 | TRL, GRPO, OpenRLHF, SimPO, verl, slime, miles, torchforge |
| Safety | 4 | Constitutional AI, LlamaGuard, NeMo Guardrails, Prompt Guard |
| Distributed | 6 | DeepSpeed, FSDP, Accelerate, Megatron-Core, Lightning, Ray Train |
| Infrastructure | 3 | Modal, Lambda Labs, SkyPilot |
| Optimization | 6 | Flash Attention, bitsandbytes, GPTQ, AWQ, HQQ, GGUF |
| Evaluation | 3 | lm-eval-harness, BigCode, NeMo Evaluator |
| Inference | 4 | vLLM, TensorRT-LL |