AI-Research-SKILLs

Carries strong trust indicators from repository metadata

• Our Mission
• Path Towards AI Research Agent
• Available AI Research Engineering Skills
• Demos
• Skill Structure
• Roadmap
• Repository Structure
• Use Cases
• Contributors
• Citation
• Community

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, 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

# 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)

🔬 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 - Neel Nanda's library for mech interp with HookPoints, activation caching (346 lines + 3 refs)
• SAELens - Sparse Autoencoder training and analysis for feature discovery (386 lines + 3 refs)
• pyvene - Stanford's causal intervention library with declarative configs (473 lines + 3 refs)
• nnsight - Remote interpretability via NDIF, run experiments on 70B+ models (436 lines + 3 refs)

📊 Data Processing (2 skills)

• Ray Data - Distributed ML data processing, streaming execution, GPU support (318 lines + 2 refs)
• NeMo Curator - GPU-accelerated data curation, 16× faster deduplication (375 lines + 2 refs)

🎓 Post-Training (8 skills)

• TRL Fine-Tuning - Transformer Reinforcement Learning (447 lines + 4 refs)
• GRPO-RL-Training (TRL) - Group Relative Policy Optimization with TRL (569 lines, gold standard)
• OpenRLHF - Full RLHF pipeline with Ray + vLLM (241 lines + 4 refs)
• SimPO - Simple Preference Optimization, no reference model needed (211 lines + 3 refs)
• verl - ByteDance's HybridFlow RL framework, FSDP/Megatron + vLLM/SGLang backends (389 lines + 2 refs)
• slime - THUDM's Megatron+SGLang framework powering GLM-4.x models (464 lines + 2 refs)
• miles - Enterprise fork of slime with FP8, INT4, speculative RL for MoE training (315 lines + 2 refs)
• torchforge - Meta's PyTorch-native RL with Monarch+TorchTitan+vLLM (380 lines + 2 refs)

🛡️ Safety & Alignment (4 skills)

• Constitutional AI - AI-driven self-improvement via principles (282 lines)
• LlamaGuard - Safety classifier for LLM inputs/outputs (329 lines)
• NeMo Guardrails - Programmable guardrails with Colang (289 lines)
• Prompt Guard - Meta's 86M prompt injection & jailbreak detector, 99%+ TPR, <2ms GPU (313 lines)

⚡ Distributed Training (6 skills)

• Megatron-Core - NVIDIA's framework for training 2B-462B param models with 47% MFU on H100 (359 lines + 4 refs)
• DeepSpeed - Microsoft's ZeRO optimization (137 lines + 9 refs)
• PyTorch FSDP2 - Fully Sharded Data Parallel v2 with fully_shard and DTensor (231 lines + 12 refs)
• Accelerate - HuggingFace's 4-line distributed training API (324 lines + 3 refs)
• PyTorch Lightning - High-level training framework with Trainer class (339 lines + 3 refs)
• Ray Train - Multi-node orchestration and hyperparameter tuning (399 lines + 1 ref)

🚀 Optimization (6 skills)

• Flash Attention - 2-4x faster attention with memory efficiency (359 lines + 2 refs)
• bitsandbytes - 8-bit/4-bit quantization for 50-75% memory reduction (403 lines + 3 refs)
• GPTQ - 4-bit post-training quantization, 4× memory reduction, <2% accuracy loss (443 lines + 3 refs)
• AWQ - Activation-aware weight quantization, 4-bit with minimal accuracy loss (310 lines + 2 refs)
• HQQ - Half-Quadratic Quantization, no calibration data needed, multi-backend (370 lines + 2 refs)
• GGUF - llama.cpp quantization format, K-quant methods, CPU/Metal inference (380 lines + 2 refs)

📊 Evaluation (3 skills)

• lm-evaluation-harness - EleutherAI's standard for benchmarking LLMs across 60+ tasks (482 lines + 4 refs)
• BigCode Evaluation Harness - Code model benchmarking with HumanEval, MBPP, MultiPL-E, pass@k metrics (406 lines + 3 refs)
• NeMo Evaluator - NVIDIA's enterprise platform for 100+ benchmarks across 18+ harnesses with multi-backend execution (454 lines + 4 refs)

☁️ Infrastructure (3 skills)

• Modal - Serverless GPU cloud with Python-native API, T4-H200 on-demand (342 lines + 2 refs)
• SkyPilot - Multi-cloud orchestration across 20+ providers with spot recovery (390 lines + 2 refs)
• Lambda Labs - Reserved/on-demand GPU cloud with H100/A100, persistent filesystems (390 lines + 2 refs)

🔥 Inference & Serving (4 skills)

• vLLM - High-throughput LLM serving with PagedAttention (356 lines + 4 refs, production-ready)
• TensorRT-LLM - NVIDIA's fastest inference, 24k tok/s, FP8/INT4 quantization (180 lines + 3 refs)
• llama.cpp - CPU/Apple Silicon inference, GGUF quantization (251 lines + 3 refs)
• SGLang - Structured generation with RadixAttention, 5-10× faster for agents (435 lines + 3 refs)

🤖 Agents (4 skills)

• LangChain - Most popular agent framework, 500+ integrations, ReAct pattern (658 lines + 3 refs, production-ready)
• LlamaIndex - Data framework for LLM apps, 300+ connectors, RAG-focused (535 lines + 3 refs)
• CrewAI - Multi-agent orchestration, role-based collaboration, autonomous workflows (498 lines + 3 refs)
• AutoGPT - Autonomous AI agent platform, visual workflow builder, continuous execution (400 lines + 2 refs)

🔍 RAG (5 skills)

• Chroma - Open-source embedding database, local/cloud, 24k stars (385 lines + 1 ref)
• FAISS - Facebook's similarity search, billion-scale, GPU acceleration (295 lines)
• Sentence Transformers - 5000+ embedding models, multilingual, 15k stars (370 lines)
• Pinecone - Managed vector database, auto-scaling, <100ms latency (410 lines)
• Qdrant - High-performance vector search, Rust-powered, hybrid search with filtering (493 lines + 2 refs)

🎨 Multimodal (7 skills)

• CLIP - OpenAI's vision-language model, zero-shot classification, 25k stars (320 lines)
• Whisper - Robust speech recognition, 99 languages, 73k stars (395 lines)
• LLaVA - Vision-language assistant, image chat, GPT-4V level (360 lines)
• Stable Diffusion - Text-to-image generation via HuggingFace Diffusers, SDXL, ControlNet (380 lines + 2 refs)
• Segment Anything - Meta's SAM for zero-shot image segmentation with points/boxes (500 lines + 2 refs)
• BLIP-2 - Vision-language pretraining with Q-Former, image captioning, VQA (500 lines + 2 refs)
• AudioCraft - Meta's MusicGen/AudioGen for text-to-music and text-to-sound (470 lines + 2 refs)

🎯 Prompt Engineering (4 skills)

• DSPy - Declarative prompt programming with optimizers, Stanford NLP, 22k stars (438 lines + 3 refs)
• Instructor - Structured LLM outputs with Pydantic validation, 15k stars (726 lines + 3 refs)
• Guidance - Constrained generation with regex/grammars, Microsoft Research, 18k stars (485 lines + 3 refs)
• Outlines - Structured text with FSM, zero-overhead, 8k stars (601 lines + 3 refs)

📊 MLOps (3 skills)

• Weights & Biases - Experiment tracking, sweeps, artifacts, model registry (427 lines + 3 refs)
• MLflow - Model registry, tracking, deployment, autologging (514 lines + 3 refs)
• TensorBoard - Visualization, profiling, embeddings, scalars/images (538 lines + 3 refs)

👁️ Observability (2 skills)

• LangSmith - LLM observability, tracing, evaluation, monitoring for AI apps (422 lines + 2 refs)
• Phoenix - Open-source AI observability with OpenTelemetry tracing and LLM evaluation (380 lines + 2 refs)

🔬 Emerging Techniques (6 skills)

• MoE Training - Mixture of Experts training with DeepSpeed, Mixtral 8x7B, 5× cost reduction (515 lines + 3 refs)
• Model Merging - Combine models with TIES, DARE, SLERP using mergekit (528 lines + 3 refs)
• Long Context - Extend context windows with RoPE, YaRN, ALiBi, 32k-128k tokens (624 lines + 3 refs)
• Speculative Decoding - 1.5-3.6× faster inference with Medusa, Lookahead (379 lines)
• Knowledge Distillation - Compress models 70B→7B with MiniLLM, temperature scaling (424 lines)
• Model Pruning - 50% sparsity with Wanda, SparseGPT, <1% accuracy loss (417 lines)

📝 ML Paper Writing (2 skills)

• ML Paper Writing - Write publication-ready papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM with LaTeX templates, citation verification, and writing best practices (532 lines + 5 refs)
• Academic Plotting - Generate publication-quality figures for ML papers: architecture diagrams via Gemini AI and data-driven charts via matplotlib/seaborn with venue-specific styling (479 lines + 3 refs)

💡 Ideation (2 skills)

• Research Brainstorming - Structured ideation frameworks for discovering high-impact research directions with 10 complementary lenses (384 lines)
• Creative Thinking - Cognitive science frameworks (bisociation, structure-mapping, constraint manipulation) for genuinely novel research ideas (366 lines)

🧬 Agent-Native Research Artifact (3 skills)

• ARA Compiler - Compiles any research input (PDF papers, repos, experiment logs, raw notes) into a complete Agent-Native Research Artifact with claims, exploration graph, evidence, and code stubs (245 lines + 3 refs)
• ARA Research Manager - Post-task research recorder that runs at session end to extract decisions, experiments, dead ends, and pivots from conversation history into the ara/ directory with user-vs-AI provenance tags (324 lines + 3 refs)
• ARA Rigor Reviewer - ARA Seal Level 2 semantic epistemic review scoring six dimensions of research rigor (evidence relevance, falsifiability, scope, coherence, exploration integrity, methodology) with severity-ranked findings (322 lines + 1 ref)

Featured Demos: Two papers produced entirely by AI agents using the autoresearch skill. The Norm Heterogeneity paper demonstrates autonomous research pivoting — the agent refuted its own hypothesis and discovered a stronger finding. The RL Brain Scan paper demonstrates multi-skill orchestration — the agent trained RL models, analyzed internals with interpretability tools, and synthesized the insight that "DPO is rank-1 alignment." Both papers written end-to-end by the agent.