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name: x-anylabeling description: "Use X-AnyLabeling for AI-assisted visual and multimodal annotation, XLABEL conversion, model configuration, training, packaging, and repository workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

X-AnyLabeling

Use this repo skill when a task names X-AnyLabeling, x-anylabeling-cvhub, anylabeling, xanylabeling, XLABEL JSON, AI-assisted annotation, annotation format conversion, built-in/custom auto-labeling model configuration, or X-AnyLabeling development workflows.

Operating assumptions

• Public package: x-anylabeling-cvhub.
• Import package: anylabeling.
• CLI entry point: xanylabeling.
• Skill baseline version: 4.0.2; read references/repo-provenance.md before deciding whether this skill is stale for a newer checkout or release.
• Python support: package metadata requires Python >=3.11; Python 3.12 is the recommended runtime in the project docs and was used for verification.
• Runtime extras are mutually exclusive by backend family: cpu, gpu, gpu-cu11, and gpu-cu13. Install only one ONNX Runtime backend variant in a single environment.
• Construction verified CPU package import, CLI/version/help, conversion registry, ONNX Runtime CPU provider, and no-download model-registry inspection. CUDA, TensorRT, model downloads, remote servers, builds, and training are documented but not verified by this skill.

First install and smoke check

For ordinary CPU annotation/conversion use:

python -m pip install "x-anylabeling-cvhub[cpu]"
xanylabeling version
xanylabeling convert

For a local development checkout, install the matching backend extra in editable mode instead of installing all extras:

python -m pip install -e ".[cpu]"
# or exactly one of: .[gpu], .[gpu-cu11], .[gpu-cu13]

Run the bundled environment checker when a future task needs to confirm package identity, CLI availability, ONNX Runtime providers, or model-registry access:

python scripts/check_xanylabeling_env.py --show-model-registry --json

Read references/install-and-runtime.md for backend extra selection, CLI launch behavior, config/work-dir behavior, and headless/runtime notes. Use references/troubleshooting.md for cross-cutting install/import/Qt/backend failures before drilling into sub-skill-specific troubleshooting.

Route by task

User intent	Load
Launch the GUI, open image/video data, configure labels/flags, edit shapes, review quality, understand XLABEL JSON, or preview labels without opening the GUI	sub-skills/annotation-ui/SKILL.md
Convert between XLABEL and YOLO/VOC/COCO/DOTA/MASK/MOT/MOTS/PPOCR/ODVG/VLM-R1-OVD, or use LabelConverter APIs	sub-skills/conversion-cli/SKILL.md
Choose/load built-in auto-labeling models, write custom model configs/adapters, troubleshoot downloads, ModelScope, ONNX Runtime, GPU extras, TensorRT, or remote/API models	sub-skills/auto-labeling-models/SKILL.md
Use Ultralytics training integration, inspect the hidden training worker, plan PyInstaller builds, refresh translations/resources, interpret ONNX exporter utilities, or follow contribution/test hygiene	sub-skills/developer-workflows/SKILL.md

Common cross-skill workflows

• Create an annotated dataset for YOLO training: use annotation-ui for manual/AI-assisted labels, conversion-cli to export YOLO/pose/segmentation labels, then developer-workflows to preflight Ultralytics training.
• Use a custom detector in the GUI: use developer-workflows only if you still need to export/train the model, then use auto-labeling-models to write the custom config and load it, and annotation-ui to validate predictions in XLABEL.
• Repair a failed conversion: use conversion-cli for command/API errors; if the source XLABEL is malformed, cross-load annotation-ui for schema and shape/group semantics.
• Diagnose model loading: use auto-labeling-models for config/download/ backend errors, then references/troubleshooting.md for cross-cutting install or Qt runtime failures.

Included root references and script

• references/repo-provenance.md — source snapshot, package version, evidence paths, and refresh checks.
• references/install-and-runtime.md — install modes, backend extras, CLI launch/config behavior, work directories, and safe smoke checks.
• references/troubleshooting.md — cross-cutting install/import, Qt/display, ONNX Runtime, model-cache, and optional-backend symptoms.
• references/repo-routing-metadata.json — structured router metadata for the managed DisCo repo-skills router.
• scripts/check_xanylabeling_env.py — safe package/CLI/backend/model-registry inspection helper that does not download models or launch the GUI.

Avoid this skill when

• The task is only about a different annotation platform, a generic YOLO/SAM library, or an unrelated model zoo with no X-AnyLabeling integration.
• The task asks to reproduce research results, train a model at scale, or run a remote service without X-AnyLabeling-specific data/config/GUI involvement.
• The task requires executing GPU/TensorRT/model-download/training/build flows as verification; this skill can plan and troubleshoot them, but those local executions need task-specific approval, hardware, data, and acceptance gates.