brycewang-stanford/ai-ml-skills

AI & Machine Learning — 27 Skills

Select the skill matching the user's need, then read its SKILL.md.

| Skill | Description | |-------|-------------| | ai-agent-papers-guide | Curated 2024-2026 AI agent research papers collection | | ai-model-benchmarking | Benchmark AI models across 60+ academic evaluation suites and metrics | | anomaly-detection-papers-guide | Industrial anomaly detection methods and benchmark papers | | autonomous-agents-papers-guide | Daily-updated collection of autonomous AI agent papers | | computer-vision-guide | Apply computer vision research methods, models, and evaluation tools | | deep-learning-papers-guide | Annotated deep learning paper implementations with code walkthroughs | | dl-transformer-finetune | Build transformer fine-tuning plans for classification and generation | | domain-adaptation-papers-guide | Comprehensive collection of domain adaptation research papers | | generative-ai-guide | Curated guide to generative AI covering LLMs and diffusion models | | graph-learning-papers-guide | Conference papers on graph neural networks and graph learning | | huggingface-api | Search and discover ML models, datasets, and Spaces on Hugging Face | | huggingface-inference-guide | Run NLP and CV model inference via Hugging Face free-tier API | | keras-deep-learning | Build and debug deep learning models with Keras and TensorFlow backend | | kolmogorov-arnold-networks-guide | Papers and tutorials on KAN learnable activation networks | | llm-evaluation-guide | Evaluate and benchmark large language models for research applications | | llm-from-scratch-guide | Build a ChatGPT-like LLM from scratch using PyTorch step by step | | ml-pipeline-guide | Build and deploy reproducible production ML pipelines for research | | nlp-toolkit-guide | NLP analysis with perplexity scoring, burstiness, and entropy metrics | | npcpy-research-guide | All-in-one Python library for NLP, agents, and knowledge graphs | | prompt-engineering-research | Systematic prompt engineering methods for AI-assisted academic research workf... | | pytorch-guide | Avoid common PyTorch mistakes and apply robust training patterns | | pytorch-lightning-guide | PyTorch Lightning framework for scalable model training and research | | reinforcement-learning-guide | Reinforcement learning fundamentals, algorithms, and research | | responsible-ai-guide | Resources for trustworthy, fair, and ethical AI research | | tensorflow-guide | TensorFlow best practices for tf.function, GPU memory, and deployment | | transformer-architecture-guide | Guide to Transformer architectures for NLP and computer vision | | vmas-simulator-guide | Vectorized multi-agent reinforcement learning simulator |