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name: inference-demos description: "Plan and troubleshoot PINTO_model_zoo inference/demo scripts without blindly running hardware- or network-heavy examples." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference Demos

Use this sub-skill when a user wants to understand, plan, or troubleshoot a selected PINTO_model_zoo demo script.

Route here for

• "run this TFLite/ONNX/OpenVINO demo"
• "what runtime deps do I need for this script?"
• "can I turn this webcam sample into a deterministic CI fixture?"
• "the model file, label file, or test image is missing"
• "the script imports OpenCV, onnxruntime, tensorflow, openvino, tfjs, or edge hardware helpers"

If the request is really about fetching artifacts, route to ../model-acquisition/. If it is about conversion, quantization, or export, route to ../conversion-and-deployment/. If the user has not chosen a model folder yet, start from the bundled catalog at ../../references/model-catalog.json.

Operating pattern

1. Classify the script with scripts/classify_runtime_script.py.
2. Read the script's --help, default model path, and nearby asset names before planning execution.
3. Separate the inference backend from the input/output wrapper:
   • inference backend: TensorFlow/TFLite, ONNX Runtime, OpenVINO, TFJS/browser, or a closely related wrapper path;
   • wrapper/support: OpenCV camera/video, MediaPipe, Raspberry Pi/edge, or accelerator-specific flags.
4. Preflight local assets and optional dependencies before any runtime claim.
5. Replace live camera/video inputs with a fixed image, pinned clip, or saved frame set when possible.
6. Stop and hand off instead of guessing when the selected backend, artifact, or hardware is unavailable.

What this skill returns

• backend family and the import/file clues behind it
• likely model file extensions and other asset types
• missing model, label, image, or video fixtures
• camera/video/display or edge-hardware risks
• a deterministic fixture plan when live input is unnecessary
• the next owner when this is really an acquisition or conversion task

Owned failure modes

• missing or mismatched model/test assets
• webcam or video demos that fail in CI or headless environments
• import errors for optional backend runtimes
• browser/TFJS samples that are not Python-runnable
• Raspberry Pi / EdgeTPU / Myriad / CUDA / TensorRT paths that need concrete hardware
• scripts that are actually conversion/export helpers in disguise

Bundled resources

• references/inference-workflows.md
• references/runtime-dependencies.md
• references/troubleshooting.md
• scripts/classify_runtime_script.py

This sub-skill plans inference/demo work. It does not claim native backend verification in Creator.