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

| | | | |:---:|:---:|:---:| | 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.

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.

• 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

# 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

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-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 | | Agent-Native Research Artifact | 3 | ARA Compiler, Research Manager, Rigor Reviewer |

🔬 Autoresearch (1 skill) — Central Orchestration Layer

• Autoresearch - 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 - Lightning AI's 20+ clean LLM implementations with production training recipes (462 lines + 4 refs)
• Mamba - State-space models with O(n) complexity, 5× faster than Transformers (253 lines + 3 refs)
• RWKV - RNN+Transformer hybrid, infinite context, Linux Foundation project (253 lines + 3 refs)
• NanoGPT - Educational GPT in ~300 lines by Karpathy (283 lines + 3 refs)
• TorchTitan - PyTorch-native distributed training for Llama 3.1 with 4D parallelism

🔤 Tokenization (2 skills)

• HuggingFace Tokenizers - Rust-based, <20s/GB, BPE/WordPiece/Unigram algorithms (486 lines + 4 refs)
• SentencePiece - Language-independent, 50k sentences/sec, used by T5/ALBERT (228 lines + 2 refs)

🎯 Fine-Tuning (4 skills)

• Axolotl - YAML-based fine-tuning with 100+ models (156 lines + 4 refs)
• LLaMA-Factory - WebUI no-code fine-tuning (78 lines + 5 refs)
• Unsloth - 2x faster QLoRA fine-tuning (75 lines + 4 refs)
• PEFT - Parameter-efficient fine-tuning with LoRA, QLoRA, DoRA, 25+ methods (431 lines + 2 refs)

🔬 Mechanistic Interpretability (4 skills)

• **[TransformerLens](04-mech