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name: conversion-and-deployment description: "Plan PINTO_model_zoo model conversion, quantization, and backend deployment workflows from bundled catalog evidence and local script inspection." disable-model-invocation: true metadata: disco-role: operating license: MIT

conversion-and-deployment

Use this sub-skill when the user asks how to convert, quantize, or deploy a PINTO_model_zoo model artifact across ONNX, TensorFlow/SavedModel, TFLite, OpenVINO, CoreML, TensorFlow.js, TF-TRT, or EdgeTPU-oriented backends.

Typical triggers:

• "convert this ONNX model to TFLite/OpenVINO/CoreML/TFJS"
• "make an INT8/full-integer/FP16/TFLite/EdgeTPU artifact"
• "understand this quantization or conversion script"
• "deploy this model on Raspberry Pi, OpenVINO CPU, browser TFJS, TF-TRT, or EdgeTPU"
• "why do I need calibration data or a representative dataset?"

Route before planning

1. If the user has not selected a model, route model search and format availability to model-catalog and the bundled catalog at ../../references/model-catalog.json.
2. If a needed artifact is not present locally, route download/script safety and license checks to model-acquisition before proposing commands that fetch or unpack files.
3. If the user wants to run inference, benchmark, or smoke-test an artifact, route execution planning to inference-demos after this skill identifies the target backend and artifact constraints.
4. Stay in this skill for conversion-chain selection, quantization requirements, calibration gates, backend deployment preflight, and conversion-script diagnosis.

Operating workflow

1. Pin the source and target. Record current artifact family, desired target family, precision goal, input shape/layout/color order, deployment hardware, and whether network/dataset/hardware use is approved.
2. Prefer an existing zoo artifact. If the catalog already lists the target format flag for the selected model, advise using that artifact before rebuilding it. Treat catalog flags as availability evidence, not proof that a shallow checkout already contains the files.
3. Inspect local scripts without executing them. Use scripts/classify_conversion_script.py on candidate convert*.sh, *quant*.py, *tflite*.py, *openvino*.py, *coreml*.py, or similar files in the selected model directory. The helper reports conversion families, representative-dataset requirements, and dataset/network/hardware risks.
4. Choose a recipe. Use references/conversion-recipes.md for format-family constraints, TFLite quantization modes, ONNX/TensorFlow/OpenVINO/CoreML/TFJS/ TF-TRT chains, and calibration stop conditions.
5. Preflight the backend. Use references/deployment-backends.md for Raspberry Pi/TFLite, EdgeTPU, OpenVINO CPU, browser TFJS/WebGL, TF-TRT/GPU, and CoreML deployment gates.
6. Stop instead of guessing when required calibration data, network access, large datasets, proprietary hardware, GPU/EdgeTPU devices, or license approval is absent. Provide the smallest safe next check and mark unverified hardware claims explicitly.

Expected answer shape

For user-facing plans, return:

• selected source artifact and target artifact family;
• whether a catalog artifact already appears available;
• conversion chain with required tools/environments named only at the family level unless the selected folder supplies exact scripts;
• calibration or representative-dataset requirements;
• backend preflight and stop gates;
• smoke-test handoff to inference-demos when execution is requested;
• open risks from references/troubleshooting.md.

Owned failure modes

This sub-skill owns troubleshooting for conversion, quantization, calibration, and backend deployment errors. Use references/troubleshooting.md when symptoms include missing converter imports, unsupported TFLite/EdgeTPU ops, representative sample mismatch, OpenVINO XML/BIN mismatch, browser TFJS loading issues, TF-TRT GPU/runtime mismatch, or hardware-only verification claims.