---

name: pinto-model-zoo description: "Use PINTO_model_zoo for model catalog search, artifact acquisition, conversion planning, inference demo preparation, and edge/backend troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

PINTO_model_zoo

Use this repo skill when a user asks about PINTO_model_zoo models, directories, model-format availability, download scripts, inference demos, conversion/quantization recipes, or edge/backend deployment planning.

PINTO_model_zoo is a large model artifact and conversion-script zoo, not an installable Python package. Its main operating surface is the model catalog, numbered model folders, per-folder licenses, artifact/download scripts, and heterogeneous demo/conversion scripts for TensorFlow, TFLite, ONNX, OpenVINO, TFJS, TF-TRT, CoreML, EdgeTPU, and related runtimes.

Always apply these gates

1. License gate: check the selected model folder's license before use, publication, packaging, or redistribution. The conversion scripts and upstream model artifacts may have different licenses.
2. Acquisition gate: do not run download*.sh, curl/wget, Google Drive, large artifact downloads, or archive extraction without explicit user approval.
3. Backend gate: do not claim TensorFlow Lite, EdgeTPU, OpenVINO, TFJS, TF-TRT, CoreML, GPU, Raspberry Pi, camera, or browser behavior is verified until that concrete runtime/hardware case has actually run.
4. Self-containment gate: use bundled references/scripts in this skill for selection, inspection, and planning. Treat a user checkout or model folder as input data, not as documentation the skill depends on.

Route map

User intent	Use
Find models by task, model id/name, directory, format flag, or remarks.	sub-skills/model-catalog/SKILL.md
Understand format flags and rank candidates for a deployment target.	sub-skills/model-catalog/SKILL.md plus sub-skills/model-catalog/references/catalog-selection.md
Inspect a selected model folder, review download scripts, or diagnose Google Drive/network acquisition.	sub-skills/model-acquisition/SKILL.md
Plan or debug an inference/demo script, runtime imports, missing assets, camera/video replacement, or CI smoke test.	sub-skills/inference-demos/SKILL.md
Plan conversion, quantization, OpenVINO/TFLite/ONNX/CoreML/TFJS/TF-TRT/EdgeTPU deployment, or calibration requirements.	sub-skills/conversion-and-deployment/SKILL.md
Cross-cutting license, artifact, optional dependency, hardware, or staleness problems.	references/troubleshooting.md

Bundled references and scripts

• references/model-zoo-overview.md summarizes repository structure, format families, and operating boundaries.
• references/model-catalog.json is the self-contained parsed catalog snapshot used by scripts/query_model_catalog.py.
• references/repo-provenance.md records the source commit, dirty state, and evidence paths used to build this skill. Read it before deciding whether a checkout needs refresh-repo-skill.
• references/repo-routing-metadata.json is consumed by DisCo's managed repo-skills importer when import is later approved.
• scripts/query_model_catalog.py searches the bundled catalog by name, category, id, directory, format, or remarks.
• scripts/check_model_folder.py inspects a user-supplied model folder for licenses, notes, artifacts, scripts, and backend hints without executing anything.

Minimal verification commands

These commands validate the bundled skill helpers; they do not prove any model backend runtime:

python scripts/query_model_catalog.py --query YOLOX --format ONNX --limit 5
python scripts/query_model_catalog.py --list-formats
python scripts/check_model_folder.py /path/to/a/selected/PINTO_model_zoo/model-folder --json

Use sub-skill helpers for script classification:

python sub-skills/model-acquisition/scripts/inspect_download_plan.py /path/to/model-folder --json
python sub-skills/inference-demos/scripts/classify_runtime_script.py /path/to/demo.py --json
python sub-skills/conversion-and-deployment/scripts/classify_conversion_script.py /path/to/convert_or_quantize.py --json

Typical operating sequence

1. Use model-catalog to select candidate model folders and format flags.
2. Use model-acquisition to check license status and artifact/download availability.
3. Use inference-demos to plan a safe runtime smoke check or adapt a demo.
4. Use conversion-and-deployment only when an existing catalog artifact is missing or the user requires a changed precision, shape, target backend, or deployment package.
5. Report what is actually verified and what remains blocked by network, license, assets, optional dependencies, datasets, or hardware.