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name: model-authoring description: "Guides ONNX model construction, loading, saving, serialization, composition, extraction, external data, and large-model utilities." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ONNX Model Authoring

Use this sub-skill when a task needs to create or transform an ONNX ModelProto, GraphProto, NodeProto, or TensorProto; load or save model files; inspect model IO; compose or extract graphs; or manage external/large tensor data.

Workflow

1. Choose an in-memory representation: onnx.helper for graphs/nodes/types, onnx.numpy_helper for NumPy and container values, or onnx.parser when a compact text fixture is easier.
2. Set explicit model inputs, outputs, initializers, and opset_imports. Do not rely on a guessed opset when a model will be shared.
3. Run onnx.checker.check_model(model) before saving or handing the model to another sub-skill.
4. Save with onnx.save_model/onnx.save; choose the format explicitly when using file-like objects or nonstandard extensions.
5. For a large initializer, use external data APIs and keep locations relative to the model file. Do not load huge external tensors into memory merely to inspect graph structure.
6. After composition, extraction, dimension updates, or constant replacement, re-run checker and, when useful, shape inference.

Route to References and Helpers

• Read references/api-reference.md for verified signatures and parameter relationships.
• Read references/workflows.md for tiny model, format round-trip, compose/extract, symbolic-dimension, and external-data recipes.
• Read references/data-formats.md for protobuf/textproto/JSON/onnxtxt, graph naming, types, and external-data conventions.
• Read references/troubleshooting.md before retrying an IO, dtype, path, or large-model failure.
• Run scripts/create_tiny_model.py --help to create a deterministic fixture without a source checkout.
• Run scripts/inspect_model_io.py --help to inspect inputs, outputs, initializers, nodes, and optional inferred value information.

Scope Boundaries

• Use validation-and-conversion for checker details, parser/printer syntax, shape/type inference, inlining, and opset conversion.
• Use reference-and-backend-tests when the question is whether a model computes expected values.
• Use operator-spec-maintenance when the task changes ONNX schemas, operator definitions, function bodies, or generated spec artifacts.