Data & AI skills

Use the fair-esm repository skill for protein language-model embeddings, ESMFold structure prediction, ESM-IF1 inverse folding, and zero-shot variant-effect scoring.

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Route DoWhy causal inference, graphical causal model, and graph/data workflows to the right sub-skill and bundled references.

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Use DIG (Dive into Graphs) to load graph-learning datasets, run graph generation, self-supervised learning, GNN explainability, 3D graph learning, GOOD OOD datasets, graph augmentation, fair graph learning, and large-scale graph workflows.

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Routes DINO DETR object-detection setup, COCO data validation, CUDA deformable-attention preparation, training, evaluation, inference, visualization, and bounded benchmarking tasks.

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Use Diffusion Policy for robot imitation-learning configs, zarr replay data, policy/model interfaces, training/evaluation workflows, and safety-gated real robot operations.

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Use Diffusion Planner for nuPlan autonomous-driving trajectory generation: prepare model-ready data, train or resume the diffusion model, configure closed-loop planning, and add differentiable collision or classifier guidance.

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Route DGL graph learning tasks across graph APIs, datasets, message passing, GraphBolt, distributed tools, and DGL-Go workflows.

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Use DGL-LifeSci and dgllife for molecular graph learning, molecule featurization, datasets, property prediction, reaction prediction, binding affinity, model-zoo/pretrained models, and molecular generation workflows.

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Use Detectron2 for object detection, segmentation, configuration, datasets, training, inference, export, and extension workflows.

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Router for DeepAnalyze agentic data-science workflows.

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Use the DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic regression, scikit-learn Kaggle-style tabular classifiers, and Twitter JSONL extraction.

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Use the DALLE2-pytorch package for DALL-E 2 style text-to-image model APIs, diffusion prior/decoder training, WebDataset data layouts, and tracker/checkpoint workflows.

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Data-Juicer repo router for local recipes, Ray recovery, and service/MCP workflows.

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cuML operating skill for GPU-accelerated classical ML, cuml.accel, Dask multi-GPU workflows, data utilities, and native source-build guidance.

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Guide Det3D PyTorch 3D object-detection workflows across configuration, KITTI/nuScenes/Lyft data preparation, GPU training and evaluation, custom CUDA operations, and visualization.

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Use DeepXDE for scientific machine learning, PINNs, DeepONet/operator learning, backend selection, training, and troubleshooting.

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Guide DeepMedic 0.8.4 workflows for multi-modal 3D NIFTI medical-image segmentation, including data preparation, model configuration, training, checkpoint continuation, inference, and CPU/CUDA troubleshooting.

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Route DeePMD-kit package workflows for installation, data/config preparation, training, inference/model operations, integrations, and repository development.

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Route DeepKE knowledge extraction workflows across supervised extraction, data prep, triples, LLMs, and MCP tools.

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Use DeepFace for face recognition, verification, embeddings, face detection, demographic analysis, datastore search, API serving, model selection, and troubleshooting.

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Use DeepChem for molecular machine learning, data loading, featurization, model training, MoleculeNet, docking, and optional backend troubleshooting.

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Route Deepchecks package tasks for ML data/model validation across tabular, NLP, vision, result export, and integration workflows.

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Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation.

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Route DeepFilterNet speech enhancement, Python API/CLI, training data, model export/evaluation, and Rust/LADSPA realtime workflows.

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