---

name: cli-and-model-registry description: "Guides Autodistill CLI runs, model registry inspection, plugin selection, and safe dry-run troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Autodistill CLI and Model Registry

Use this sub-skill when a task asks for an autodistill ... command, model alias selection, plugin package requirements, model support matrix interpretation, registry inspection, or a safe dry run before downloads/training.

The core CLI orchestrates plugin base models and target models. It can import or install plugin packages, label an image folder, train a target model, and optionally upload to Roboflow. Treat full CLI runs as side-effectful unless the user explicitly approves plugin installs, model downloads, training time, hardware use, output paths, and credentials.

Quick Route

• Build or explain CLI commands: read CLI reference for verified options, boolean value quirks, JSON ontology quoting, and safe examples.
• Choose base/target aliases: read model registry for supported aliases, package naming, default target checkpoints, and model matrix boundaries.
• Debug CLI failures: read troubleshooting for invalid ontology JSON, unsupported aliases, missing plugins, --model_type mismatch, Roboflow credentials, and training side effects.
• Check CLI help safely: run scripts/autodistill_cli_smoke.py. It runs help and optional ontology JSON validation only.
• Inspect registry safely: run scripts/inspect_model_registry.py. It lists aliases and installed/missing plugin modules without installing anything.

Minimal Safe Checks

python scripts/autodistill_cli_smoke.py \
  --ontology-json '{"milk bottle": "bottle"}'

python scripts/inspect_model_registry.py

If these pass, the core CLI/registry is importable. They do not prove that a concrete plugin can download weights, run inference, train, or upload.

Full CLI Pattern

autodistill images \
  --base grounding_dino \
  --target yolov8 \
  --ontology '{"milk bottle": "bottle"}' \
  --output ./dataset \
  --epochs 200 \
  --upload-to-roboflow false

Before running a full command, verify the input image folder, ontology JSON, selected plugin packages, target model training requirements, and output directory. Avoid -y true until plugin installation side effects are approved.

Important Snapshot Caveats

• --models, --upload-to-roboflow, -y, and --test are Click boolean options that expect explicit values in this snapshot's help output, for example --upload-to-roboflow false.
• Source verification shows SUPPORTED_MODEL_TYPES is ['detection', 'segmentationclassification'] because "segmentation" "classification" is missing a comma. The docs mention detection, segmentation, and classification, but --model_type classification may fail in this snapshot.
• import_requisite_module may call pip install autodistill_<alias> when noninteractive install is enabled. That package naming does not match every published plugin's hyphenated distribution spelling in documentation, so inspect before relying on auto-install.

When to Route Elsewhere

• To validate the dataset files generated by the CLI, use dataset labeling.
• To implement or debug a plugin class, use ontologies and model interfaces.
• To debug image input conversion or visualization, use utilities.