conversion-issues

Testing & Quality

Investigate and fix model conversion issues in OpenVINO Frontends (ONNX, PyTorch) — triage, debugging, accuracy comparison, and pre-submission verification.

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Source SKILL.md: https://github.com/openvinotoolkit/openvino/blob/HEAD/.github/agents-prototype/skills/conversion-issues/SKILL.md

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Agent Skill: Investigate and Fix Frontend Conversion Issues

Goal

Diagnose and fix issues where models fail to convert to OpenVINO IR or produce incorrect inference results through an OpenVINO frontend.

Framework-Specific Workflows

Each frontend has its own detailed investigation workflow. Read the one matching the target framework:

Frontend	Skill file	What it covers
ONNX	onnx.md	Triage (unsupported op / conversion bug / shape-type / opset gap), ORT baseline comparison, translator debugging, `.prototxt` test models, C++ GTest, pre-submission checklist
PyTorch	pytorch.md	Triage (unsupported op / tracing mode / inplace / normalize-step), TorchScript vs torch.export identification, layer test debugging, pre-submission checklist

Related Skills (adding new ops)

Frontend	Skill file	When to use
ONNX	add-fe-op/onnx.md	Implementing a new ONNX op translator from scratch
PyTorch	add-fe-op/pytorch.md	Implementing a new PyTorch op translator from scratch

Notes

Always verify the model works with the framework's reference runtime (ONNX Runtime / PyTorch) before investigating OpenVINO code.
Prefer minimal, root-cause fixes over broad refactors.
Every fix needs a test and must pass the full frontend test suite.