Comprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent with full horsepower.
git clone https://github.com/zechenzhangAGI/AI-research-SKILLs ~/.claude/skills/ai-research# AI Research Engineering `Skills` Library > **The most comprehensive open-source library of AI research engineering skills for AI agents** [](https://opensource.org/licenses/MIT) [](https://www.orchestra-research.com/perspectives/ai-research-skills) [](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra) ## Table of Contents - [Our Mission](#our-mission) - [Path Towards AI Research Agent](#path-towards-ai-research-agent) - [Available AI Research Engineering Skills](#available-ai-research-engineering-skills) - [Demo](#demo) - [Skill Structure](#skill-structure) - [Roadmap](#roadmap) - [Repository Structure](#repository-structure) - [Use Cases](#use-cases) ## Our Mission We provide the layer of **Engineering Ability** that **enable your coding agent to write conduct AI research experiments**, including preparing datasets, executing training pipelines, deploying models, and validating scientific hypotheses. <p align="center"> <img src="docs/skills.png" alt="AI Research Agent System" width="50%"> <br> <em>System diagram of an AI research agent</em> </p> ## 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 library of expert-level research engineering skills that enable AI agents to autonomously implement and execute different stages of AI research experiments—from data preparation and model training to evaluation and deployment. - Specialized Expertise - Each skill provides deep, production-ready knowledge of a specific framework (Megatron-LM, vLLM, TRL, etc.) - End-to-End Coverage - 70 skills spanning model architecture, tokenization, fine-tuning, data processing, post-training, distributed training, optimization, inference, infrastructure, agents, RAG, multimodal, prompt engineering, MLOps, observability, and emerging techniques - 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. ### 📦 Install from Marketplace Install individual skills directly from the marketplace using the Claude Code CLI: ```bash # Install a single skill /plugin install skill-name@ai-research-skills # Examples: /plugin install serving-llms-vllm@ai-research-skills /plugin install grpo-rl-training@ai-research-skills /plugin install langchain@ai-research-skills ``` ### 🏗️ Model Architecture (5 skills) - **[LitGPT](01-model-architecture/litgpt/)** - Lightning AI's 20+ clean LLM implementations with production training recipes (462 lines + 4 refs) - **[Mamba](01-model-architecture/mamba/)** - State-space models with O(n) complexity, 5× faster than Transformers (253 lines + 3 refs) - **[RWKV](01-model-architecture/rwkv/)** - RNN+Transformer hybrid, infinite context, Linux Foundation project (253 lines + 3 refs) - **[NanoGPT](01-model-architecture/nanogpt/)** - Educational GPT in ~300 lines by Karpathy (283 lines + 3 refs) ### 🔤 Tokenization (2 skills) - **[HuggingFace Tokenizers](02-tokenization/huggingface-tokenizers/)** - Rust-based, <20s/GB, BPE/WordPiece/Unigram algorithms (486 lines + 4 refs) - **[SentencePiece](02-tokenization/sentencepiece/)** - Language-independent, 50k sentences/sec, used by T5/ALBERT (228 lines + 2 refs) ### 🎯 Fine-Tuning (4 skills) - **[Axolotl](03-fine-tuning/axolotl/)** - YAML-based fine-tuning with 100+ models (156 lines + 4 refs) - **[LLaMA-Factory](03-fine-tuning/llama-factory/)** - WebUI no-code fine-tuning (78 lines + 5 refs) - **[Unsloth](03-fine-tuning/unsloth/)** - 2x faster QLoRA fine-tuning (75 lines + 4 refs) - **[PEFT](03-fine-tuning/peft/)** - Parameter-efficient fine-tuning with LoRA, QLoRA, DoRA, 25+ methods (431 lines + 2 refs) ### 📊 Data Processing (2 skills) - **[Ray Data](05-data-processing/ray-data/)** - Distributed ML data processing, streaming execution, GPU support (318 lines + 2 refs) - **[NeMo Curator](05-data-processing/nemo-curator/)** - GPU-accelerated data curation, 16× faster deduplication (375 lines + 2 refs) ### 🎓 Post-Training (4 skills) - **[TRL Fine-Tuning](06-post-training/trl-fine-tuning/)** - Transformer Reinforcement Learning (447 lines + 4 refs) - **[GRPO-RL-Training](06-post-training/grpo-rl-training/)** (TRL) - Group Relative Policy Optimization with TRL (569 lines, **gold standard**) - **[OpenRLHF](06-post-training/openrlhf/)** - Full RLHF pipeline with Ray + vLLM (241 lines + 4 refs) - **[SimPO](06-post-training/simpo/)** - Simple Preference Optimization, no reference model needed (211 lines + 3 refs) ### 🛡️ Safety & Alignment (3 skills) - **[Constitutional AI](07-safety-alignment/constitutional-ai/)** - AI-driven self-improvement via principles (282 lines) - **[LlamaGuard](07-safety-alignment/llamaguard/)** - Safety classifier for LLM inputs/outputs (329 lines) - **[NeMo Guardrails](07-safety-alignment/nemo-guardrails/)** - Programmable guardrails with Colang (289 lines) ### ⚡ Distributed Training (5 skills) - **[Megatron-Core](01-model-architecture/megatron-core/)** - NVIDIA's framework for training 2B-462B param models with 47% MFU on H100 (359 lines + 4 refs) - **[DeepSpeed](08-distributed-training/deepspeed/)** - Microsoft's ZeRO optimization (137 lines + 9 refs) - **[PyTorch FSDP](08-distributed-training/pytorch-fsdp/)** - Fully Sharded Data Parallel (124 lines + 2 refs) - **[Accelerate](08-distributed-training/accelerate/)** - HuggingFace's 4-line distributed training API (324 lines + 3 refs) - **[PyTorch Lightning](08-distributed-training/pytorch-lightning/)** - High-level training framework with Trainer class (339 lines + 3 refs) - **[Ray Train](08-distributed-training/ray-train/)** - Multi-node orchestration and hyperparameter tuning (399 lines + 1 ref) ### 🚀 Optimization (6 skills) - **[Flash Attention](10-optimization/flash-attention/)** - 2-4x faster attention with memory efficiency (359 lines + 2 refs) - **[bitsandbytes](10-optimization/bitsandbytes/)** - 8-bit/4-bit quantization for 50-75% memory reduction (403 lines + 3 refs) - **[GPTQ](10-optimization/gptq/)** - 4-bit post-training quantization, 4× memory reduction, <2% accuracy loss (443 lines + 3 refs) - **[AWQ](10-optimization/awq/)** - Activation-aware weight quantization, 4-bit with minimal accuracy loss (310 lines + 2 refs) - **[HQQ](10-optimization/hqq/)** - Half-Quadratic Quantization, no calibration data needed, multi-backend (370 lines + 2 refs) - **[GGUF](10-optimization/gguf/)** - llama.cpp quantization format, K-quant methods, CPU/Metal inference (380 lines + 2 refs) ### 📊 Evaluation (1 skill) - **[lm-evaluation-harness](11-evaluation/lm-evaluation-harness/)** - EleutherAI's standard for benchmarking LLMs across 60+ tasks (482 lines + 4 refs) ### ☁️ Infrastructure (3 skills) - **[Modal](09-infrastructure/modal/)** - Serverless GPU cloud with Python-native API, T4-H200 on-demand (342 lines + 2 refs) - **[SkyPilot](09-infrastructure/skypilot/)** - Multi-cloud orchestration across 20+ providers with spot recovery (390 lines + 2 refs) - **[Lambda Labs](09-infrastructure/lambda-labs/)** - Reserved/on-demand GPU cloud with H100/A100, persistent filesystems (390 lines + 2 refs) ### 🔥 Inference & Serving (4 skills) - **[vLLM](12-inference-serving/vllm/)** - High-throughput LLM serving with PagedAttention (356 lines + 4 refs, **production-ready**) - **[TensorRT-LLM](12-inference-serving/tensorrt-llm/)** - NVIDIA's fastest inference, 24k tok/s, FP8/INT4 quantization (180 lines + 3 refs) - **[llama.cpp](12-inference-serving/llama-cpp/)** - CPU/Apple Silicon inference, GGUF quantization (251 lines + 3 refs) - **[SGLang](12-inference-serving/sglang/)** - Structured generation with RadixAttention, 5-10× faster for agents (435 lines + 3 refs) ### 🤖 Agents (4 skills) - **[LangChain](14-agents/langchain/)** - Most popular agent framework, 500+ integrations, ReAct pattern (658 lines + 3 refs, **production-ready**) - **[LlamaIndex](14-agents/llamaindex/)** - Data framework for LLM apps, 300+ connectors, RAG-focused (535 lines + 3 refs) - **[CrewAI](14-agents/crewai/)** - Multi-agent orchestration, role-based collaboration, autonomous workflows (498 lines + 3 refs) - **[AutoGPT](14-agents/autogpt/)** - Autonomous AI agent platform, visual workflow builder, continuous execution (400 lines + 2 refs) ### 🔍 RAG (5 skills) - **[Chroma](15-rag/chroma/)** - Open-source embedding database, local/cloud, 24k stars (385 lines + 1 ref) - **[FAISS](15-rag/faiss/)** - Facebook's similarity search, billion-scale, GPU acceleration (295 lines) - **[Sentence Transformers](15-rag/sentence-transformers/)** - 5000+ embedding models, multilingual, 15k stars (370 lines) - **[Pinecone](15-rag/pinecone/)** - Managed vector database, auto-scaling, <100ms latency (410 lines) - **[Qdrant](15-rag/qdrant/)** - High-performance vector search, Rust-powered, hybrid search with filtering (493 lines + 2 refs) ### 🎨 Multimodal (7 skills) - **[CLIP](18-multimodal/clip/)** - OpenAI's vision-language model, zero-shot classification, 25k stars (320 lines) - **[Whisper](18-multimodal/whisper/)** - Robust speech recognition, 99 languages, 73k stars (395 lines) - **[LLaVA](18-multimodal/llava/)** - Vision-language assistant, image chat, GPT-4V level (360 lines) - **[Stable Diffusion](18-multimodal/stable-diffusion/)** - Text-to-image generation via HuggingFace Diffusers, SDXL, ControlNet (380 lines + 2 refs) - **[Segment Anything](18-multimodal/segment-anything/)** - Meta's SAM for zero-shot image segmentation with points/boxes (500 lines + 2 refs) - **[BLIP-2](18-multimodal/blip-2/)** - Vision-language pretraining with Q-Former, image captioning, VQA (500 lines + 2 refs) - **[AudioCraft](18-multimodal/audiocraft/)** - Meta's MusicGen/AudioGen for text-to-music and text-to-sound (470 lines + 2 refs) ### 🎯 Prompt Engineering (4 skills) - **[DSPy](16-prompt-engineering/dspy/)** - Declarative prompt programming with optimizers, Stanford NLP, 22k stars (438 lines + 3 refs) - **[Instructor](16-prompt-engineering/instructor/)** - Structured LLM outputs with Pydantic validation, 15k stars (726 lines + 3 refs) - **[Guidance](16-prompt-engineering/guidance/)** - Constrained generation with regex/grammars, Microsoft Research, 18k stars (485 lines + 3 refs) - **[Outlines](16-prompt-engineering/outlines/)** - Structured text with FSM, zero-overhead, 8k stars (601 lines + 3 refs) ### 📊 MLOps (3 skills) - **[Weights & Biases](13-mlops/weights-and-biases/)** - Experiment tracking, sweeps, artifacts, model registry (427 lines + 3 refs) - **[MLflow](13-mlops/mlflow/)** - Model registry, tracking, deployment, autologging (514 lines + 3 refs) - **[TensorBoard](13-mlops/tensorboard/)** - Visualization, profiling, embeddings, scalars/images (538 lines + 3 refs) ### 👁️ Observability (2 skills) - **[LangSmith](17-observability/langsmith/)** - LLM observability, tracing, evaluation, monitoring for AI apps (422 lines + 2 refs) - **[Phoenix](17-observability/phoenix/)** - Open-source AI observability with OpenTelemetry tracing and LLM evaluation (380 lines + 2 refs) ### 🔬 Emerging Techniques (6 skills) - **[MoE Training](19-emerging-techniques/moe-training/)** - Mixture of Experts training with DeepSpeed, Mixtral 8x7B, 5× cost reduction (515 lines + 3 refs) - **[Model Merging](19-emerging-techniques/model-merging/)** - Combine models with TIES, DARE, SLERP using mergekit (528 lines + 3 refs) - **[Long Context](19-emerging-techniques/long-context/)** - Extend context windows with RoPE, YaRN, ALiBi, 32k-128k tokens (624 lines + 3 refs) - **[Speculative Decoding](19-emerging-techniques/speculative-decoding/)** - 1.5-3.6× faster inference with Medusa, Lookahead (379 lines) - **[Knowledge Distillation](19-emerging-techniques/knowledge-distillation/)** - Compress models 70B→7B with MiniLLM, temperature scaling (424 lines) - **[Model Pruning](19-emerging-techniques/model-pruning/)** - 50% sparsity with Wanda, SparseGPT, <1% accuracy loss (417 lines) **Available skills in Claude marketplace** (70 total): | Category | Skills | |----------|--------| | Model Architecture | `implementing-llms-litgpt`, `mamba-architecture`, `nanogpt`, `rwkv-architecture` | | Tokenization | `huggingface-tokenizers`, `sentencepiece` | | Fine-Tuning | `axolotl`, `llama-factory`, `peft-fine-tuning`, `unsloth` | | Data Processing | `nemo-curator`, `ray-data` | | Post-Training | `grpo-rl-training`, `openrlhf-training`, `simpo-training`, `fine-tuning-with-trl` | | Safety | `constitutional-ai`, `llamaguard`, `nemo-guardrails` | | Distributed Training | `huggingface-accelerate`, `deepspeed`, `training-llms-megatron`, `pytorch-fsdp`, `pytorch-lightning`, `ray-train` | | Infrastructure | `lambda-labs-gpu-cloud`, `modal-serverless-gpu`, `skypilot-multi-cloud-orchestration` | | Optimization | `awq-quantization`, `quantizing-models-bitsandbytes`, `optimizing-attention-flash`, `gguf-quantization`, `gptq`, `hqq-quantization` | | Evaluation | `evaluating-llms-harness` | | Inference | `llama-cpp`, `sglang`, `tensorrt-llm`, `serving-llms-vllm` | | MLOps | `mlflow`, `tensorboard`, `weights-and-biases` | | Agents | `autogpt-agents`, `crewai-multi-agent`, `langchain`, `llamaindex` | | RAG | `chroma`, `faiss`, `pinecone`, `qdrant-vector-search`, `sentence-transformers` | | Prompt Engineering | `dspy`, `guidance`, `instructor`, `outlines` | | Observability | `langsmith-observability`, `phoenix-observability` | | Multimodal | `audiocraft-audio-generation`, `blip-2-vision-language`, `clip`, `llava`, `segment-anything-model`, `stable-diffusion-image-generation`, `whisper` | | Emerging Techniques | `knowledge-distillation`, `long-context`, `model-merging`, `model-pruning`, `moe-training`, `speculative-decoding` | ## Demo All 70 skills in this repo are automatically synced to [Orchestra Research](https://www.orchestra-research.com/research-skills), where you can add them to your projects with one click and use them with AI research agents. **[Demo](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra)**: With this `skills`, a physics PhD is able to [reproduce](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra) Thinking Machines Lab's "LoRA Without Regret" findings. The Orchestra agent autonomously wrote training code using TRL, provisioned H100 GPUs, ran GRPO experiments overnight, and generated publication-ready analysis, successfully validating that rank=16 LoRA achieves 99.4% of rank=256's SFT performance and that rank=1 LoRA outperforms full fine-tuning on RL tasks (52.1% vs 33.3% on GSM8k math reasoning). ([Video demo](https://www.youtube.com/watch?v=X0DoLYfXl5I)) **Note**: When you contribute a skill to this repo via PR, it automatically syncs to the Orchestra marketplace after merge. ### 🛠️ Alternative Usage Methods **For Claude Users** (Claude.ai, Claude Code): ```bash # 1. Download skill folder cd 01-model-architecture/litgpt # 2. Use directly in Claude Code workspace # OR zip and upload to Claude.ai Projects zip -r litgpt-skill.zip SKILL.md references/ ``` **For Other AI Coding Assistants**: - **Gemini CLI**: Point to skill directory in your workspace - **Grok Code**: Use skill folder as context - **Cursor/Windsurf**: Add skill folder to project knowledge **For Custom RAG/Agent Systems**: - Ingest `SKILL.md` + `references/` into your knowledge base - Use as retrieval context for specialized queries - Build domain-specific agents with curated skill subsets ### 👨💻 For Skill Creators **Your contributions power the entire ecosystem!** When you contribute a skill to this repo: 1. It automatically syncs to [Orchestra marketplace](https://www.orchestra-research.com/research-skills) 2. Thousands of researchers can use your expertise 3. AI agents become more capable at conducting research **Getting started**: 1. **Read [CONTRIBUTING.md](CONTRIBUTING.md)** - Step-by-step guide 2. **Use [SKILL_TEMPLATE.md](SKILL_TEMPLATE.md)** - Copy-paste scaffold 3. **Run validation**: `python scripts/validate_skill.py your-skill/` 4. **Submit PR** - We review within 48 hours, auto-publish to Orchestra on merge ## 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) ``` <details> <summary><b>Quality Standards</b></summary> - 300KB+ documentation from official sources - Real GitHub issues & solutions (when available) - Code examples with language detection - Version history & breaking changes - Links to official docs </details> ## Roadmap We're building towards 70 comprehensive skills across the full AI research lifecycle. See our [detailed roadmap](ROADMAP.md) for the complete development plan. [View Full Roadmap →](ROADMAP.md) <details> <summary><b>View Detailed Statistics</b></summary> | Metric | Current | Target | |--------|---------|--------| | **Skills** | **70** (high-quality, standardized YAML) | 70 ✅ | | **Avg Lines/Skill** | **420 lines** (focused + progressive disclosure) | 200-600 lines | | **Documentation** | **~115,000 lines** total (SKILL.md + references) | 100,000+ lines | | **Gold Standard Skills** | **58** with comprehensive references | 50+ | | **Contributors** | 1 | 100+ | | **Coverage** | Architecture, Tokenization, Fine-Tuning, Data Processing, Post-Training, Safety, Distributed, Optimization, Evaluation, Infrastructure, Inference, Agents, RAG, Multimodal, Prompt Engineering, MLOps, Observability | Full Lifecycle ✅ | **Recent Progress**: +4 skills (Lambda Labs, SAM, BLIP-2, AudioCraft) completing the 70-skill roadmap with GPU cloud and extended multimodal capabilities **Philosophy**: Quality > Quantity. Following [Anthropic official best practices](anthropic_official_docs/best_practices.md) - each skill provides 200-500 lines of focused, actionable guidance with progressive disclosure. </details> ## Repository Structure ``` claude-ai-research-skills/ ├── README.md ← You are here ├── CONTRIBUTING.md ← Contribution guide ├── SKILL_TEMPLATE.md ← Skill scaffold ├── ROADMAP.md ← Development roadmap │ ├── 01-model-architecture/ (5 skills ✓ - Megatron, LitGPT, Mamba, RWKV, NanoGPT) ├── 02-tokenization/ (2 skills ✓ - HuggingFace Tokenizers, SentencePiece) ├── 03-fine-tuning/ (4 skills ✓ - Axolotl, LLaMA-Factory, Unsloth, PEFT) ├── 05-data-processing/ (2 skills ✓ - Ray Data, NeMo Curator) ├── 06-post-training/ (4 skills ✓ - TRL, GRPO, OpenRLHF, SimPO) ├── 07-safety-alignment/ (3 skills ✓ - Constitutional AI, LlamaGuard, NeMo Guardrails) ├── 08-distributed-training/ (5 skills ✓ - 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/ (1 skill ✓ - lm-evaluation-harness) ├── 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) ``` ## 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](LICENSE) for details. **Note**: Individual skills may reference libraries with different licenses. Please check each project's license before use. ## Acknowledgments Built with: - **[Claude Code](https://www.claude.com/product/claude-code)** - AI pair programming - **[Skill Seeker](https://github.com/yusufkaraaslan/Skill_Seekers)** - 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 ## Contributing We welcome contributions from the AI research community! See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines on: - Adding new skills - Improving existing skills - Quality standards and best practices - Submission process All contributors are featured in our [Contributors Hall of Fame](CONTRIBUTORS.md) 🌟 ## Recent Updates <details> <summary><b>November 25, 2025 - v0.10.0 🎉 70 Skills Complete!</b></summary> - 🎉 **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!) </details> <details> <summary><b>November 25, 2025 - v0.9.0</b></summary> - 🚀 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) </details> <details> <summary><b>November 25, 2025 - v0.8.0</b></summary> - 🚀 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) </details> <details> <summary><b>November 25, 2025 - v0.7.0</b></summary> - 🚀 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) </details> <details> <summary><b>November 15, 2025 - v0.6.0</b></summary> - 📊 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) </details> <details> <summary><b>November 12, 2025 - v0.5.0</b></summary> - 🎯 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) </details> <details> <summary><b>November 9, 2025 - v0.4.0</b></summary> - 🤖 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) </details> <details> <summary><b>November 8, 2025 - v0.3.0</b></summary> - 🚀 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) </details> <details> <summary><b>November 6, 2025 - v0.2.0</b></summary> - 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 </details> <details> <summary><b>November 3, 2025 - v0.1.0</b></summary> - 🎉 Initial release with 5 fine-tuning skills </details> ## Star History <a href="https://star-history.com/#zechenzhangAGI/AI-research-SKILLs&Date"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=zechenzhangAGI/AI-research-SKILLs&type=Date&theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=zechenzhangAGI/AI-research-SKILLs&type=Date" /> <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=zechenzhangAGI/AI-research-SKILLs&type=Date" /> </picture> </a>