Automatic repair

coreai-onnx convert --repair (and convert_model(repair=True) over MCP, or coreai_onnx.convert(model, repair=True) in Python) applies known-safe rewrites for documented Core AI runtime limitations, then re-verifies parity against ONNX Runtime before trusting the result.

What it guarantees

• Only documented, known-safe rewrites. Each repair is a semantics-preserving ONNX → ONNX transform tied to a confirmed Core AI runtime limitation.

• No silently-wrong models. After repairing, the precision check compares the .aimodel against ONNX Runtime. A rewrite that would change results beyond the original precision is rejected, not shipped.

• Opt-in and additive. Without --repair, conversion behaves exactly as before. With it, result.repairs lists every fix applied.

What it fixes today

Repair	Trigger	Rewrite
promote_float16_to_float32	float16 graph inputs	Promote all float16 tensors (inputs, initializers, outputs) to float32. The Core AI runtime cannot load most float16 programs (Program load failure 0x10004); float32 is a strict superset, so results match within float16 precision. Inputs and outputs become float32.

More strategies are added as Core AI runtime limitations are confirmed — each ships with its own parity test. As CoreAI fixes a limitation upstream, the corresponding repair is retired. The repair registry lives in coreai_onnx._repair.

Example

coreai-onnx convert model.onnx -o model.aimodel --repair --json

{
  "status": "ok",
  "result": {
    "output_path": "model.aimodel",
    "repairs": [
      {
        "name": "promote_float16_to_float32",
        "summary": "promote float16 tensors to float32 ...",
        "details": {"inputs": ["x"]}
      }
    ],
    "precision": {"passed": true}
  }
}

Using it from an agent

The skills/onnx-to-coreai skill packages this workflow as portable instructions any AI agent can follow — it simply drives the CLI shown above and branches on the JSON envelope. See the agent playbook.