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name: model-io-and-prediction description: "Load, save, inspect, edit, package, and predict with Core ML model artifacts using MLModel and spec utilities." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Model I/O and Prediction

Use this sub-skill when the task is about operating on existing Core ML artifacts: .mlmodel, .mlpackage, specs, metadata, feature descriptions, prediction dictionaries, compute-unit selection, compiled models, or stateful prediction boundaries.

Start here:

• Use references/api-reference.md for the exact API contracts and platform gates.
• Use references/workflows.md for copyable load/edit/save/package/predict workflows.
• Use references/troubleshooting.md when artifact loading, prediction, packaging, compute units, or states fail.
• Use scripts/inspect_mlmodel.py to inspect a .mlmodel or .mlpackage spec without invoking Core ML prediction.

Operating rules:

1. Prefer spec-only inspection with coremltools.models.utils.load_spec when prediction is not required, especially on Linux.
2. Treat MLModel.predict, CompiledMLModel, compute devices, compute plans, and stateful runtime calls as macOS Core ML runtime features unless the environment proves otherwise.
3. Preserve artifact form: neural-network-style specs may be saved as .mlmodel; mlprogram models with external weights normally need .mlpackage and a valid weights_dir.
4. Prediction inputs are dictionaries keyed by Core ML feature names. Use NumPy arrays for multi-array inputs and PIL images for image inputs.
5. Route source model conversion to ../convert-models/, compression and weight optimization to ../optimize-models/, and MIL/debug utilities/custom passes to ../mil-and-debugging/.