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name: models-layers-and-operators description: "Routes CogDL model registry, layer, custom GNN, and sparse operator workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

CogDL Models, Layers, and Operators

Use this sub-skill when the user wants to pick a CogDL model name, inspect model or layer signatures, build a custom GNN, or understand sparse/message operator behavior.

Typical triggers:

• "Which CogDL model should I use for node classification?"
• "How do I write a custom GCN using CogDL layers?"
• "Why does an operator import or CUDA extension fail?"
• "What are the supported model names?"

Read references/model-registry-and-layers.md for verified model families, layer signatures, and the registry map. Read references/custom-gnn.md for the toy-graph custom GNN pattern and the Graph + BaseModel + GCNLayer recipe. Read references/operator-compatibility.md when sparse operators, optional CUDA/C++ kernels, or third-party graph-library examples are involved. Read references/troubleshooting.md for unknown model names, argument registration problems, layer shape issues, and backend/build failures.

Run scripts/inspect_model_registry.py to print or filter the supported model registry without touching datasets. Run scripts/custom_gnn_smoke.py to build a toy Graph and exercise a GCNLayer or GATLayer forward pass on CPU.

Route these elsewhere:

• ../graph-data-and-datasets/SKILL.md for dataset schemas, masks, and custom fixtures.
• ../experiments-and-cli/SKILL.md for experiment(...), CLI flags, and AutoML orchestration.
• ../training-wrappers-and-customization/SKILL.md for wrapper matching, Trainer, checkpoints, and logging.
• ../pipelines-and-applications/SKILL.md for pipeline() apps such as embedding generation or OAG-BERT.

What this sub-skill covers

• SUPPORTED_MODELS inspection and model-family selection.
• build_model(args) and try_adding_model_args(model, parser) behavior.
• BaseModel patterns for custom graph models.
• GCNLayer, GATLayer, and the BaseLayer.forward(graph, x) convention.
• Sparse/message operators in cogdl.operators and their CPU/GPU caveats.
• Optional dependency surfaces such as PyG, Jittor, DGL, or compiled operators when they affect model/layer use.

Decision rules

• Choose a model family before thinking about wrappers or training config.
• If the user only needs a model list, use the registry script first and keep the response lightweight.
• If the user wants to add a new GNN, keep the focus on Graph, layers, and a BaseModel forward pass; leave data selection to the dataset sub-skill.
• Treat CUDA acceleration as optional unless the user explicitly asks for a GPU/operator workflow.