---

name: auto-labeling-models description: "Use and troubleshoot X-AnyLabeling AI-assisted model configs, registry, custom models, downloads, and optional inference backends." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Auto-labeling Models

Use this sub-skill when the task involves X-AnyLabeling's AI-assisted model registry, built-in model configuration YAMLs, custom model loading, download source selection, remote inference hooks, or CPU/GPU/TensorRT backend choices.

Start here

1. Identify the target model family in references/model-overview.md. Confirm whether the user needs a local built-in/adapted model, a remote server/API model, or a code-level adapter for an unadapted model.

2. For built-in or adapted custom models, use references/custom-models.md to check the YAML schema, required fields, custom-name rules, and model-specific config fields.

3. For downloads, cache layout, ModelScope selection, ONNX Runtime, GPU extras, or TensorRT, use references/backend-and-downloads.md.

4. If loading fails, route by symptom through references/troubleshooting.md.

5. For safe registry inspection without model downloads or weight loading, run the bundled helper:

   python sub-skills/auto-labeling-models/scripts/inspect_model_configs.py --show-types
   python sub-skills/auto-labeling-models/scripts/inspect_model_configs.py --custom-config <config.yaml> --json
   

What this sub-skill owns

• Built-in model family selection for classification, detection, segmentation, pose/face, tracking, OBB, depth, SAM, matting, RAM/tagging, OCR/layout/document parsing, VLM, grounding, counting, and lane workflows.
• Loading built-in model configs and adapted custom configs through the UI's model selector / custom model loader.
• Model cache and work-directory behavior, including default download locations and ModelScope-vs-default URL routing.
• YAML config schema: type, name, display_name, model path fields, classes, thresholds, provider, config_file, and YOLO-family engine values ort, dnn, and trt.
• ModelManager registry behavior: 204 built-in configs in the verified package environment, custom-name validation, five-custom-model limit, and _custom_ prefixing.
• Backend selection and troubleshooting for verified CPU ONNX Runtime, optional unverified CUDA/GPU extras, optional TensorRT, remote-server tokens, and API tokens.
• Code-level integration checklist for unadapted models: config entry, model registry branch, UI model-type lists, and a Model subclass implementing predict_shapes and unload.

Route elsewhere

• Manual annotation editing, shape semantics, XLABEL JSON schema, GUI review, or image/video/classifier panel operations: use ../annotation-ui/SKILL.md.
• Dataset import/export and xanylabeling convert tasks: use ../conversion-cli/SKILL.md.
• Training new models, exporting checkpoints, PyInstaller builds, localization, or maintainer workflows: use ../developer-workflows/SKILL.md.

Verified limits to preserve

The generated skill is grounded in package x-anylabeling-cvhub 4.0.2, Python >=3.11 with Python 3.12 recommended, and a CPU inspection environment where ONNX Runtime's CPU provider was verified. CUDA, TensorRT, remote server operation, model downloads, and training/export workflows were documented from repo evidence but were not executed during construction.