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

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, 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)

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 - 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)

Demos

All 98 skills in this repo are automatically synced to Orchestra Research, where you can add them to your projects with one click and use them with AI research agents.

See skills in action → demos/

We maintain a curated collection of demo repositories showing how to use skills for real AI research tasks:

Demo Skills Used What It Does
Norm Heterogeneity → LoRA Brittleness Autoresearch, ML Paper Writing, Ideation Agent autonomously discovered norm heterogeneity predicts fine-tuning difficulty (r=-0.99), pivoting from a null result on ETF overlaps
RL Algorithm Brain Scan Autoresearch, GRPO, TRL, SAELens, TransformerLens, ML Paper Writing Agent found DPO is a rank-1 perturbation (95.6% recovery from one SVD direction) while online RL is distributed and structure-preserving
NeMo Eval: GPQA Benchmark NeMo Evaluator Compare Llama 8B/70B/405B on graduate-level science questions
LoRA Without Regret Reproduction GRPO, TRL Reproduce SFT + GRPO RL experiments via prompting
Layer-Wise Quantization Experiment llama.cpp, GGUF Investigate optimal layer precision allocation—early layers at Q8 achieve 1.9× compression with 1.3% perplexity loss
Cross-Lingual Alignment Analysis FAISS Quantify how well multilingual embeddings align semantic concepts across 8 languages using FAISS similarity search
Scientific Plotting Demo Academic Plotting Generate publication-quality figures for the Andes QoE-aware LLM serving paper — Gemini AI architecture diagrams + matplotlib data charts (CDF, multi-panel grids, bar charts)

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.

Skill Structure

Each skill follows a battle-tested format for maximum usefulness:

skill-name/
├── SKILL.md                    # Quick reference (50-150 lines)
│   ├── Metadata (name, description, version)
│   ├── When to use this skill
│   ├── Quick patterns & examples
│   └── Links to references
│
├── references/                 # Deep documentation (300KB+)
│   ├── README.md              # From GitHub/official docs
│   ├── api.md                 # API reference
│   ├── tutorials.md           # Step-by-step guides
│   ├── issues.md              # Real GitHub issues & solutions
│   ├── releases.md            # Version history & breaking changes
│   └── file_structure.md      # Codebase navigation
│
├── scripts/                    # Helper scripts (optional)
└── assets/                     # Templates & examples (optional)
  • 300KB+ documentation from official sources
  • Real GitHub issues & solutions (when available)
  • Code examples with language detection
  • Version history & breaking changes
  • Links to official docs

Roadmap

The library spans 98 comprehensive skills across the full AI research lifecycle. See our detailed roadmap for the complete development plan.

View Full Roadmap →

Metric Current Target
Skills 87 (high-quality, standardized YAML) 80 ✅
Avg Lines/Skill 420 lines (focused + progressive disclosure) 200-600 lines
Documentation ~130,000 lines total (SKILL.md + references) 100,000+ lines
Gold Standard Skills 65 with comprehensive references 50+
Contributors 1 100+
Coverage Architecture, Tokenization, Fine-Tuning, Mechanistic Interpretability, Data Processing, Post-Training, Safety, Distributed, Optimization, Evaluation, Infrastructure, Inference, Agents, RAG, Multimodal, Prompt Engineering, MLOps, Observability, ML Paper Writing, Ideation, Autoresearch Full Lifecycle ✅

Recent Progress: npm package @orchestra-research/ai-research-skills for one-command installation across all coding agents

Philosophy: Quality > Quantity. Following Anthropic official best practices - each skill provides 200-500 lines of focused, actionable guidance with progressive disclosure.

Repository Structure

claude-ai-research-skills/
├── README.md                    ← You are here
├── CONTRIBUTING.md              ← Contribution guide
├── demos/                       ← Curated demo gallery (links to demo repos)
├── docs/
├── 0-autoresearch-skill/        (1 skill ✓ - Autonomous research orchestration)
├── 01-model-architecture/       (5 skills ✓ - LitGPT, Mamba, RWKV, NanoGPT, TorchTitan)
├── 02-tokenization/             (2 skills ✓ - HuggingFace Tokenizers, SentencePiece)
├── 03-fine-tuning/              (4 skills ✓ - Axolotl, LLaMA-Factory, Unsloth, PEFT)
├── 04-mechanistic-interpretability/ (4 skills ✓ - TransformerLens, SAELens, pyvene, nnsight)
├── 05-data-processing/          (2 skills ✓ - Ray Data, NeMo Curator)
├── 06-post-training/            (8 skills ✓ - TRL, GRPO, OpenRLHF, SimPO, verl, slime, miles, torchforge)
├── 07-safety-alignment/         (4 skills ✓ - Constitutional AI, LlamaGuard, NeMo Guardrails, Prompt Guard)
├── 08-distributed-training/     (6 skills ✓ - Megatron-Core, DeepSpeed, FSDP, Accelerate, Lightning, Ray Train)
├── 09-infrastructure/           (3 skills ✓ - Modal, SkyPilot, Lambda Labs)
├── 10-optimization/             (6 skills ✓ - Flash Attention, bitsandbytes, GPTQ, AWQ, HQQ, GGUF)
├── 11-evaluation/               (3 skills ✓ - lm-evaluation-harness, BigCode, NeMo Evaluator)
├── 12-inference-serving/        (4 skills ✓ - vLLM, TensorRT-LLM, llama.cpp, SGLang)
├── 13-mlops/                    (3 skills ✓ - Weights & Biases, MLflow, TensorBoard)
├── 14-agents/                   (4 skills ✓ - LangChain, LlamaIndex, CrewAI, AutoGPT)
├── 15-rag/                      (5 skills ✓ - Chroma, FAISS, Sentence Transformers, Pinecone, Qdrant)
├── 16-prompt-engineering/       (4 skills ✓ - DSPy, Instructor, Guidance, Outlines)
├── 17-observability/            (2 skills ✓ - LangSmith, Phoenix)
├── 18-multimodal/               (7 skills ✓ - CLIP, Whisper, LLaVA, Stable Diffusion, SAM, BLIP-2, AudioCraft)
├── 19-emerging-techniques/      (6 skills ✓ - MoE, Model Merging, Long Context, Speculative Decoding, Distillation, Pruning)
├── 20-ml-paper-writing/         (2 skills ✓ - ML Paper Writing with LaTeX templates, Academic Plotting)
├── 21-research-ideation/           (2 skills ✓ - Research Brainstorming, Creative Thinking)
├── 22-agent-native-research-artifact/ (3 skills ✓ - ARA Compiler, Research Manager, Rigor Reviewer)
└── packages/ai-research-skills/ (npm package for one-command installation)

Use Cases

For Researchers

"I need to fine-tune Llama 3 with custom data" → 03-fine-tuning/axolotl/ - YAML configs, 100+ model support

For ML Engineers

"How do I optimize inference latency?" → 12-inference-serving/vllm/ - PagedAttention, batching

For Students

"I want to learn how transformers work" → 01-model-architecture/litgpt/ - Clean implementations

For Teams

"We need to scale training to 100 GPUs" → 08-distributed-training/deepspeed/ - ZeRO stages, 3D parallelism

License

MIT License - See LICENSE for details.

Note: Individual skills may reference libraries with different licenses. Please check each project's license before use.

Citation

If you use AI Research Skills in your work or find it helpful for a publication, we'd appreciate a citation:

BibTeX

@software{ai_research_skills,
  title     = {AI Research Skills Library},
  author    = {{Orchestra Research}},
  year      = {2025},
  url       = {https://github.com/orchestra-research/AI-research-SKILLs},
  note      = {Open-source skills library enabling AI agents to autonomously conduct AI research}
}

APA

Orchestra Research. (2025). AI Research Skills Library [Computer software]. https://github.com/orchestra-research/AI-research-SKILLs

Chicago

Orchestra Research. "AI Research Skills Library." GitHub, 2025. https://github.com/orchestra-research/AI-research-SKILLs.

IEEE

Orchestra Research, "AI Research Skills Library," 2025. [Online]. Available: https://github.com/orchestra-research/AI-research-SKILLs

Tip: You can also click "Cite this repository" in the GitHub sidebar for auto-formatted citations.

Acknowledgments

Built with:

  • Claude Code - AI pair programming
  • Skill Seeker - Automated doc scraping
  • Open Source AI Community - For amazing tools and docs

Special thanks to:

  • EleutherAI, HuggingFace, NVIDIA, Lightning AI, Meta AI, Anthropic
  • All researchers who maintain excellent documentation

Contributors

Thanks to all the people who have contributed to the AI Research Skills Library:

We welcome contributions from the AI research community! See CONTRIBUTING.md for detailed guidelines on:

  • Adding new skills
  • Improving existing skills
  • Quality standards and best practices
  • Submission process

Recent Updates

  • 📊 Repo-wide inventory reconciled to 98 skills / 23 categories — corrected stale counts that had drifted apart across files: CLAUDE.md (said 90), CONTRIBUTING.md (said 86/22), the README sync line (said 87), WELCOME.md and the npm package README (said 86/22). Also fixed wrong per-category listings (TorchTitan, SwanLab, A-Evolve, ML Training Recipes, Cosmos Policy/OpenPI/OpenVLA-OFT, the paper-writing skills)
  • 🛡️ New CI drift guard — scripts/check-inventory.sh + check-inventory.yml fail CI whenever the documented skill/category counts diverge from the actual SKILL.md count on disk, so the inventory can't silently drift again
  • 📦 Marketplace sync hardening — sync-skills.yml now prunes build artifacts (node_modules/__pycache__/*.pyc/.ipynb_checkpoints) before zipping and fails loudly above 190 files instead of hitting the marketplace's 200-file rejection
  • 🔒 Security — pinned CLI dependencies (chalk/inquirer/ora) to exact, patched versions with a regenerated lockfile ([email protected] clears the tmp path-traversal advisory; npm audit now repo