skills/claude-scientific-skills
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Timothy Kassis
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scientific-packages/torch_geometric/SKILL.md
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name: torch-geometric
description: PyTorch Geometric (PyG) skill for building and training Graph Neural Networks (GNNs) on structured data including graphs, 3D meshes, and point clouds. Use this skill when working with graph-based machine learning tasks such as node classification, graph classification, link prediction, or geometric deep learning on irregular structures. Applies to molecular property prediction, social network analysis, citation networks, 3D vision, and any domain involving relational or geometric data.
description: PyTorch Geometric (PyG) skill for building, training, and deploying Graph Neural Networks (GNNs) on structured data including graphs, 3D meshes, point clouds, and heterogeneous networks. Use this skill for graph-based machine learning tasks such as node classification, graph classification, link prediction, graph generation, geometric deep learning, and message passing on irregular structures. Essential for molecular property prediction, drug discovery, chemical informatics, social network analysis, citation networks, recommendation systems, 3D computer vision, protein structure analysis, knowledge graphs, fraud detection, traffic prediction, and any domain involving relational, geometric, or topological data. Supports large-scale graph processing, multi-GPU training, neighbor sampling, heterogeneous graphs, graph transforms, model explainability, and custom message passing layers. Includes comprehensive datasets, pre-built GNN architectures (GCN, GAT, GraphSAGE, etc.), and utilities for graph visualization and benchmarking.
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# PyTorch Geometric (PyG)