AI-Research-SKILLs

# AI Research `Skills` Library

> **The most comprehensive open-source skills library enabling AI agents to autonomously conduct AI research — from idea to paper**

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<div align="center">

### **87 Skills Powering AI Research in 2026**

</div>

<details>
<summary><b>View All 22 Categories</b></summary>

<div align="center">

| | | |
|:---:|:---:|:---:|
| **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) | | |

</div>

</details>

---

## Table of Contents

- [Our Mission](#our-mission)
- [Path Towards AI Research Agent](#path-towards-ai-research-agent)
- [Available AI Research Engineering Skills](#available-ai-research-engineering-skills)
- [Demos](#demos)
- [Skill Structure](#skill-structure)
- [Roadmap](#roadmap)
- [Repository Structure](#repository-structure)
- [Use Cases](#use-cases)
- [Contributors](#contributors)
- [Citation](#citation)
- [Community](#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.
<p align="center">
  <img src="docs/skills.png" alt="AI Research Agent System" width="50%">
  <br>
  <em>System diagram of an AI research agent</em>
</p>

## 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 - 87 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:

```bash
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 87 skills, loads the **autoresearch** orchestration layer, and starts autonomous research.

<details>
<summary><b>What the installer does</b></summary>

- **Auto-detects** your installed coding agents (Claude Code, Hermes Agent, OpenCode, 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

</details>

<details>
<summary><b>CLI Commands</b></summary>

```bash
# 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
```

</details>

<details>
<summary><b>Claude Code Marketplace (Alternative)</b></summary>

Install skill categories directly using the **Claude Code CLI**:

```bash
# Add the marketplace
/plugin marketplace add orchestra-research/AI-research-SKILLs

# Install by category (22 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
```

</details>

### All 22 Categories (87 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-LLM, llama.cpp, SGLang |
| MLOps | 3 | W&B, MLflow, TensorBoard |
| Agents | 4 | LangChain, LlamaIndex, CrewAI, AutoGPT |
| RAG | 5 | Chroma, FAISS, Pinecone, Qdrant, Sentence Transformers |
| Prompt Eng | 4 | DSPy, Instructor, Guidance, Outlines |
| Observability | 2 | LangSmith, Phoenix |
| Multimodal | 7 | CLIP, Whisper, LLaVA, BLIP-2, SAM, Stable Diffusion, AudioCraft |
| Emerging | 6 | MoE, Model Merging, Long Context, Speculative Decoding, Distillation, Pruning |

<details>
<summary><b>View All 87 Skills in Details</b></summary>

### 🔬 Autoresearch (1 skill) — Central Orchestration Layer
- **[Autoresearch](0-autoresearch-skill/)** - Autonomous research orchestration using a two-loop architecture (inner optimization + outer synthesis). Manages the full lifecycle from literature survey to paper writing, routing to all domain-specific skills. Supports Claude Code /loop and OpenClaw heartbeat for continuous operation (390 lines + 3 refs)

### 🏗️ Model Architecture (5 skills)
- **[LitGPT](01-model-architecture/litgpt/)** - Lightning AI's 20+ clean LLM implementations with production training recipes (462 lines + 4 refs)
- **[Mamba](01-model-architecture/mamba/)** - State-space models with O(n) complexity, 5× faster than Transformers (253 lines + 3 refs)
- **[RWKV](01-model-architecture/rwkv/)** - RNN+Transformer hybrid, infinite context, Linux Foundation project (253 lines + 3 refs)
- **[NanoGPT](01-model-architecture/nanogpt/)** - Educational GPT in ~300 lines by Karpathy (283 lines + 3 refs)
- **[TorchTitan](01-model-architecture/torchtitan/)** - PyTorch-native distributed training for Llama 3.1 with 4D parallelism

### 🔤 Tokenization (2 skills)
- **[HuggingFace Tokenizers](02-tokenization/huggingface-tokenizers/)** - Rust-based, <20s/GB, BPE/WordPiece/Unigram algorithms (486 lines + 4 refs)
- **[SentencePiece](02-tokenization/sentencepiece/)** - Language-independent, 50k sentences/sec, used by T5/ALBERT (228 lines + 2 refs)

### 🎯 Fine-Tuning (4 skills)
- **[Axolotl](03-fine-tuning/axolotl/)** - YAML-based fine-tuning with 100+ models (156 lines + 4 refs)
- **[LLaMA-Factory](03-fine-tuning/llama-factory/)** - WebUI no-code fine-tuning (78 lines + 5 refs)
- **[Unsloth](03-fine-tuning/unsloth/)** - 2x faster QLoRA fine-tuning (75 lines + 4 refs)
- **[PEFT](03-fine-tuning/peft/)** - Parameter-efficient fine-tuning with LoRA, QLoRA, DoRA, 25+ methods (431 lines + 2 refs)

### 🔬 Mechanistic Interpretability (4 skills)
- **[TransformerLens](04-mechanistic-interpretability/transformer-lens/)** - Neel Nanda's library for mech interp with HookPoints, activation caching (346 l