• 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

🔬 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)

• 300KB+ documentation from official sources
• Real GitHub issues & solutions (when available)
• Code examples with language detection
• Version history & breaking changes
• Links to official docs

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.

• 📊 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 (inquirer@9.3.8 clears the tmp path-traversal advisory; npm audit now reports 0 vulnerabilities)
• 🧹 Full open PR/issue triage pass against scope + contribution standards

• 🧬 NEW CATEGORY: 22-agent-native-research-artifact/ (the 23rd category) — three skills that turn research outputs into a falsifiable, agent-traversable artifact:
  • 🛠️ ARA Compiler — compiles any input (PDF papers, GitHub repos, experiment logs, raw notes) into a structured ARA with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph (research DAG), and grounded evidence
  • 📋 ARA Research Manager — post-task epilogue that scans conversation history at session end and writes decisions, experiments, dead ends, claims, heuristics, and pivots into the ara/ directory with user / ai-suggested / ai-executed / user-revised provenance tags
  • 🔍 ARA Rigor Reviewer — Seal Level 2 semantic epistemic review scoring six dimensions of research rigor (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and emitting a severity-ranked report with a Strong Accept-to-Reject recommendation
• 🔗 Sourced from the Agent-Native-Research-Artifact-Init reference repo, restructured to AI-research-SKILLs standards (kebab-case names, third-person descriptions, Title-Case tags, one-level-deep references)
• 🧩 Plugin entry agent-native-research-artifact added to .claude-plugin/marketplace.json; CLI category registered as 22-agent-native-research-artifact with three individual skill entries in the npm installer
• 🔄 Auto-syncs to Orchestra marketplace via sync-skills.yml on push; npm package republished as @orchestra-research/ai-research-skills@1.6.0 via publish-npm.yml on version bump
• 📊 98 total skills across 23 categories — full lifecycle from idea → paper → falsifiable, auditable artifact

• 🔬 NEW SKILL: Autoresearch — autonomous research orchestration using a two-loop architecture (inner optimization loop + outer synthesis loop)
• 🧠 Manages the full research lifecycle: literature survey → ideation → experiments → synthesis → paper writing
• 🔄 Routes to all 86 domain skills automatically — agents don't need to know which skill to use
• ⏰ Mandatory /loop (Claude Code) and cron job (OpenClaw) for continuous autonomous operation
• 📊 Generates research presentations (HTML/PDF) with optimization trajectory plots for human review
• 📝 Findings.md as persistent project memory across sessions with "Lessons and Constraints" tracking
• 🗂️ Structured workspace: research-state.yaml, findings.md, research-log.md, literature/, experiments/, src/, data/, to_human/
• 📄 Two demo papers produced by autoresearch: Norm Heterogeneity → LoRA Brittleness and RL Algorithm Brain Scan
• 🚀 WELCOME.md for cold-start agent bootstrap — one URL to go from zero to autonomous research
• 📦 npm v1.4.x with Windows symlink fallback, all 22 categories installable
• 🤖 Supported agents: Claude Code, Hermes Agent, OpenCode, OpenClaw, Qoder, Cursor, Codex, Gemini CLI, Qwen Code
• 📊 87 total skills across 22 categories — complete research lifecycle coverage

• 🛡️ NEW SKILL: Prompt Guard - Meta's 86M prompt injection & jailbreak detector
• ⚡ 99%+ TPR, <1% FPR, <2ms GPU latency, multilingual (8 languages)
• 🔒 3 workflows: user input filtering, third-party data filtering, batch RAG processing
• 📊 83 total skills across 20 categories

• 📦 NEW: npx @orchestra-research/ai-research-skills - One-command installation for all coding agents
• 🤖 Supported agents: Claude Code, OpenCode, Cursor, Codex, Gemini CLI, Qwen Code
• ✨ Interactive installer with category/individual skill selection
• 🔄 Update installed skills, selective uninstall
• 📊 82 total skills (5 new post-training skills: verl, slime, miles, torchforge + TorchTitan)
• 🏗️ Megatron-Core moved to Distributed Training category

• 📝 NEW CATEGORY: ML Paper Writing (20th category, 77th skill)
• 🎯 Write publication-ready papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM
• 📚 Writing philosophy from top researchers (Neel Nanda, Farquhar, Gopen & Swan, Lipton, Perez)
• 🔬 Citation verification workflow - never hallucinate references
• 📄 LaTeX templates for 6 major conferences
• 🎪 NEW: Curated demos gallery (demos/) showcasing skills in action
• 🔗 Demo repos: NeMo Evaluator benchmark, LoRA Without Regret reproduction
• 📖 936-line comprehensive SKILL.md with 4 workflows

• 📊 NEW SKILL: NeMo Evaluator SDK for enterprise LLM benchmarking
• 🔧 NVIDIA's evaluation platform with 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM)
• ⚡ Multi-backend execution: local Docker, Slurm HPC, Lepton cloud
• 📦 Container-first architecture for reproducible evaluation
• 📝 454 lines SKILL.md + 4 comprehensive reference files (~48KB documentation)

• 🔬 NEW CATEGORY: Mechanistic Interpretability (4 skills)
• 🔍 TransformerLens skill: Neel Nanda's library for mech interp with HookPoints, activation caching, circuit analysis
• 🧠 SAELens skill: Sparse Autoencoder training and analysis for feature discovery, monosemanticity research
• ⚡ pyvene skill: Stanford's causal intervention library with declarative configs, DAS, activation patching
• 🌐 nnsight skill: Remote interpretability via NDIF, run experiments on 70B+ models without local GPUs
• 📝 ~6,500 new lines of documentation across 16 files
• 76 total skills (filling the missing 04 category slot)

• 🎉 ROADMAP COMPLETE: Reached 70-skill milestone!
• 🚀 Added 4 skills: Lambda Labs, Segment Anything (SAM), BLIP-2, AudioCraft
• ☁️ Lambda Labs skill: Reserved/on-demand GPU cloud with H100/A100, persistent filesystems, 1-Click Clusters
• 🖼️ SAM skill: Meta's Segment Anything for zero-shot image segmentation with points/boxes/masks
• 👁️ BLIP-2 skill: Vision-language pretraining with Q-Former, image captioning, VQA
• 🎵 AudioCraft skill: Meta's MusicGen/AudioGen for text-to-music and text-to-sound generation
• 📝 ~10,000 new lines of documentation across 12 files
• 70 total skills (100% roadmap complete!)

• 🚀 Added 2 infrastructure skills: Modal, SkyPilot
• ☁️ Modal skill: Serverless GPU cloud with Python-native API, T4-H200 on-demand, auto-scaling
• 🌐 SkyPilot skill: Multi-cloud orchestration across 20+ providers with spot recovery
• ✨ New Infrastructure category (2 skills - serverless GPU and multi-cloud orchestration)
• 📝 ~2,500 new lines of documentation across 6 files
• 66 total skills (94% towards 70-skill target)

• 🚀 Added 5 high-priority skills: HQQ, GGUF, Phoenix, AutoGPT, Stable Diffusion
• ⚡ HQQ skill: Half-Quadratic Quantization without calibration data, multi-backend support
• 📦 GGUF skill: llama.cpp quantization format, K-quant methods, CPU/Metal inference
• 👁️ Phoenix skill: Open-source AI observability with OpenTelemetry tracing and LLM evaluation
• 🤖 AutoGPT skill: Autonomous AI agent platform with visual workflow builder
• 🎨 Stable Diffusion skill: Text-to-image generation via Diffusers, SDXL, ControlNet, LoRA
• 📝 ~9,000 new lines of documentation across 15 files
• 64 total skills (91% towards 70-skill target)

• 🚀 Added 5 high-priority skills: PEFT, CrewAI, Qdrant, AWQ, LangSmith
• ✨ New Observability category with LangSmith for LLM tracing and evaluation
• 🎯 PEFT skill: Parameter-efficient fine-tuning with LoRA, QLoRA, DoRA, 25+ methods
• 🤖 CrewAI skill: Multi-agent orchestration with role-based collaboration
• 🔍 Qdrant skill: High-performance Rust vector search with hybrid filtering
• ⚡ AWQ skill: Activation-aware 4-bit quantization with minimal accuracy loss
• 📝 ~8,000 new lines of documentation across 15 files
• 59 total skills (84% towards 70-skill target)

• 📊 Added 3 comprehensive MLOps skills: Weights & Biases, MLflow, TensorBoard
• ✨ New MLOps category (3 skills - experiment tracking, model registry, visualization)
• 📝 ~10,000 new lines of documentation across 13 files
• 🔧 Comprehensive coverage: experiment tracking, hyperparameter sweeps, model registry, profiling, embeddings visualization
• 54 total skills (77% towards 70-skill target)

• 🎯 Added 4 comprehensive prompt engineering skills: DSPy, Instructor, Guidance, Outlines
• ✨ New Prompt Engineering category (4 skills - DSPy, Instructor, Guidance, Outlines)
• 📝 ~10,000 new lines of documentation across 16 files
• 🔧 Comprehensive coverage: declarative programming, structured outputs, constrained generation, FSM-based generation
• 47 total skills (67% towards 70-skill target)

• 🤖 Added 11 comprehensive skills: LangChain, LlamaIndex, Chroma, FAISS, Sentence Transformers, Pinecone, CLIP, Whisper, LLaVA
• ✨ New Agents category (2 skills - LangChain, LlamaIndex)
• 🔍 New RAG category (4 skills - Chroma, FAISS, Sentence Transformers, Pinecone)
• 🎨 New Multimodal category (3 skills - CLIP, Whisper, LLaVA)
• 📝 ~15,000 new lines of documentation
• 43 total skills (61% towards 70-skill target)

• 🚀 Added 8 comprehensive skills: TensorRT-LLM, llama.cpp, SGLang, GPTQ, HuggingFace Tokenizers, SentencePiece, Ray Data, NeMo Curator
• ⚡ Completed Inference & Serving category (4/4 skills)
• 🔤 New Tokenization category (2 skills)
• 📊 New Data Processing category (2 skills)
• 📝 9,617 new lines of documentation across 30 files
• 32 total skills (45% towards 70-skill target)

• Added 10 skills from GitHub (Megatron-Core, Lightning, Ray Train, etc.)
• Improved skill structure with comprehensive references
• Created strategic roadmap to 70 skills
• Added contribution guidelines

• 🎉 Initial release with 5 fine-tuning skills