by affaan-m
pytorch-patterns helps you write, review, and debug PyTorch code with device-agnostic patterns, reproducible experiments, and explicit tensor handling. Use the pytorch-patterns skill for cleaner training loops, model refactors, and practical PyTorch guidance.
by K-Dense-AI
torchdrug is a PyTorch-native toolkit for molecular and protein machine learning. Use the torchdrug skill to choose tasks, datasets, and modular models for graph neural networks, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis. It is best for custom model development and reproducible configs, not just canned demos.
by K-Dense-AI
torch-geometric skill guide for PyTorch Geometric graph neural networks. Use it for torch-geometric install help, torch-geometric usage, graph classification, node classification, link prediction, heterogeneous graphs, custom MessagePassing layers, and scaling GNNs for Machine Learning workflows.
by huggingface
huggingface-vision-trainer helps you install and use a Hugging Face skill for vision training jobs: object detection, image classification, and SAM/SAM2 segmentation. It covers dataset prep, cloud GPU setup, evaluation, Trackio logging, and pushing results to the Hub. Ideal for backend automation and repeatable training workflows.
by huggingface
huggingface-llm-trainer helps you train or fine-tune language and vision models on Hugging Face Jobs with TRL or Unsloth. Use this huggingface-llm-trainer skill for SFT, DPO, GRPO, reward modeling, dataset checks, GPU selection, Hub saving, Trackio monitoring, and GGUF export for backend development workflows.
by huggingface
The huggingface-best skill helps you find the best model for a task by checking Hugging Face benchmark leaderboards and filtering by device limits and model size. Use it for model recommendations in coding, reasoning, chat, OCR, RAG, speech, vision, or multimodal work when you need a practical shortlist, not a generic model list.
by K-Dense-AI
The transformers skill helps you use Hugging Face Transformers for model loading, inference, tokenization, and fine-tuning. It is a practical transformers guide for Machine Learning tasks across text, vision, audio, and multimodal workflows, with clear paths for quick baselines and custom training.
by K-Dense-AI
scvi-tools is a Python framework for probabilistic single-cell analysis. Use this scvi-tools skill for batch correction, latent embeddings, differential expression with uncertainty, transfer learning, and multimodal integration. It is a strong fit for single-cell RNA-seq, ATAC, CITE-seq, multiome, and spatial workflows, especially for advanced Machine Learning use cases.
by K-Dense-AI
pytorch-lightning skill for organizing PyTorch projects with LightningModules and Trainers. Use this pytorch-lightning guide for install, training, validation, logging, checkpointing, and distributed execution across multi-GPU or TPU workflows.
by K-Dense-AI
pyhealth helps you build clinical and healthcare deep-learning pipelines with a Dataset → Task → Model → Trainer → Metrics workflow. Use this pyhealth skill for MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot, prediction, drug recommendation, sleep staging, ICD coding, EEG events, and medical code mapping.
by K-Dense-AI
pufferlib is a high-performance reinforcement learning skill for fast parallel simulation, vectorized rollouts, and multi-agent training. Use this pufferlib guide to install, understand pufferlib usage, and adapt RL pipelines with Gymnasium, PettingZoo, Atari, Procgen, or NetHack-style environments. Ideal for code generation focused on throughput and scalable PPO workflows.
