Awesome-Agent-Skills-for-Empirical-Research ai-ml-skills
27 ai & machine learning skills. Trigger: ML experiments, model training, deep learning, NLP, computer vision. Design: covers frameworks, benchmarks, paper reproduction, and AI research workflows.
install
source · Clone the upstream repo
git clone https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/43-wentorai-research-plugins/skills/domains/ai-ml" ~/.claude/skills/brycewang-stanford-awesome-agent-skills-for-empirical-research-ai-ml-skills && rm -rf "$T"
manifest:
skills/43-wentorai-research-plugins/skills/domains/ai-ml/SKILL.mdsource content
AI & Machine Learning — 27 Skills
Select the skill matching the user's need, then
read| 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 |