AI-Research-SKILLs: Instructions file for Claude Code

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

AI Research Skills Library - A comprehensive open-source library of 98 AI research skills enabling AI agents to autonomously conduct AI research — from idea to paper. Each skill provides expert-level guidance (200-500 lines) with real code examples, troubleshooting guides, and production-ready workflows.

Mission: Enable AI agents to autonomously conduct AI research from hypothesis to experimental verification, covering the full lifecycle: literature survey, ideation, dataset preparation, training pipelines, model deployment, evaluation, and paper writing.

Repository Architecture

Directory Structure (98 Skills Across 23 Categories)

Skills are organized into numbered categories representing the AI research lifecycle:

• 0-autoresearch-skill/ - Autonomous research orchestration (1 skill: Autoresearch — central layer that manages the full lifecycle and routes to all other skills)
• 01-model-architecture/ - Model architectures (5 skills: TorchTitan, LitGPT, Mamba, RWKV, NanoGPT)
• 02-tokenization/ - Tokenizers (2 skills: HuggingFace Tokenizers, SentencePiece)
• 03-fine-tuning/ - Fine-tuning frameworks (4 skills: Axolotl, LLaMA-Factory, Unsloth, PEFT)
• 04-mechanistic-interpretability/ - Interpretability tools (4 skills: TransformerLens, SAELens, NNsight, Pyvene)
• 05-data-processing/ - Data curation (2 skills: Ray Data, NeMo Curator)
• 06-post-training/ - RLHF/DPO/GRPO (8 skills: TRL, GRPO, OpenRLHF, SimPO, verl, slime, miles, torchforge)
• 07-safety-alignment/ - Safety and guardrails (4 skills: Constitutional AI, LlamaGuard, NeMo Guardrails, Prompt Guard)
• 08-distributed-training/ - Distributed systems (6 skills: Megatron-Core, DeepSpeed, FSDP, Accelerate, PyTorch Lightning, Ray Train)
• 09-infrastructure/ - Cloud compute (3 skills: Modal, SkyPilot, Lambda Labs)
• 10-optimization/ - Optimization techniques (7 skills: Flash Attention, bitsandbytes, GPTQ, AWQ, HQQ, GGUF, ML Training Recipes)
• 11-evaluation/ - Benchmarking (3 skills: lm-evaluation-harness, BigCode, NeMo Evaluator)
• 12-inference-serving/ - Inference engines (4 skills: vLLM, TensorRT-LLM, llama.cpp, SGLang)
• 13-mlops/ - Experiment tracking (4 skills: Weights & Biases, MLflow, TensorBoard, SwanLab)
• 14-agents/ - Agent frameworks (5 skills: LangChain, LlamaIndex, CrewAI, AutoGPT, A-Evolve)
• 15-rag/ - Retrieval-augmented generation (5 skills: Chroma, FAISS, Sentence Transformers, Pinecone, Qdrant)
• 16-prompt-engineering/ - Structured output (4 skills: DSPy, Instructor, Guidance, Outlines)
• 17-observability/ - LLM observability (2 skills: LangSmith, Phoenix)
• 18-multimodal/ - Vision and speech (10 skills: CLIP, Whisper, LLaVA, Stable Diffusion, SAM, BLIP-2, AudioCraft, Cosmos Policy, OpenPI, OpenVLA-OFT)
• 19-emerging-techniques/ - Advanced methods (6 skills: MoE Training, Model Merging, Long Context, Speculative Decoding, Knowledge Distillation, Model Pruning)
• 20-ml-paper-writing/ - Paper writing (4 skills: ML Paper Writing with LaTeX templates for NeurIPS, ICML, ICLR, ACL, AAAI, COLM; Systems Paper Writing for OSDI, NSDI, ASPLOS, SOSP; Academic Plotting; Presenting Conference Talks)
• 21-research-ideation/ - Ideation (2 skills: Research Brainstorming, Creative Thinking)
• 22-agent-native-research-artifact/ - Agent-Native Research Artifact tooling (3 skills: ARA Compiler, ARA Research Manager, ARA Rigor Reviewer — ingestion, post-task provenance recording, and Seal Level 2 epistemic review)