AnyLabeling repo skill

Use this repo skill when a task involves AnyLabeling, the anylabeling or anylabeling-gpu Python packages, the desktop image-annotation app, AnyLabeling label JSON, auto-labeling with YOLO/SAM-family ONNX or CoreML models, or package build/release maintenance.

Start here

• Install/use the app from PyPI with pip install anylabeling for the CPU package. Use anylabeling-gpu only when the task explicitly targets the published GPU variant on Linux/Windows.
• Development installs use pip install -e .; add only the focused extra needed by the task, such as .[gpu], .[macos], or .[dev].
• Run the desktop app with anylabeling or python -m anylabeling.app when working from an installed package. For headless import checks, set QT_QPA_PLATFORM=offscreen.
• Minimal import smoke:

  python -c "import anylabeling; print(anylabeling.__version__)"
  

• Startup smoke for UI dependencies:

  QT_QPA_PLATFORM=offscreen python -c "from anylabeling.views.labeling import label_widget; from anylabeling import app; print('startup imports OK')"
  

Route by task

Task signal	Read
Manual annotation, opening images/folders, label JSON, flags/labels validation, canvas shape editing, output path behavior, or YOLO/VOC/COCO/CreateML export	sub-skills/annotation-ui-and-data/SKILL.md
Built-in model catalog, custom model YAML, model downloads/cache, ModelRegistry, ModelManager, YOLOv5/v8, Segment Anything, MobileSAM, SAM2, SAM3 text prompts, CoreML, or real-inference diagnostics	sub-skills/auto-labeling-models/SKILL.md
Fresh install gates, Python version support, CPU/GPU/macOS package variants, PyPI wheels, PyInstaller executables, translations/resources, CI, or release preparation	sub-skills/packaging-release/SKILL.md

Shared references and helpers

• references/installation-and-cli.md summarizes install variants, CLI flags, config file behavior, and safe smoke checks.
• references/troubleshooting.md covers cross-cutting install/import, Qt, dependency, config, and backend triage before routing to a focused sub-skill.
• references/repo-provenance.md records the source snapshot used to build this skill; read it before deciding whether to refresh the skill for a newer checkout.
• scripts/check_anylabeling_env.py performs a safe installed-package, CLI, catalog, optional Qt startup, and optional backend/model-cache inspection.

Decision rules

• Prefer package-level checks before GUI actions: import, CLI help, config load, then startup smoke.
• Do not install broad extras by default. GPU, CoreML, developer tooling, and real-model downloads are optional unless the user explicitly asks for those workflows.
• Treat external model weights as user cache artifacts. Built-in auto-labeling models can download on first use, but diagnostic scripts in this skill avoid downloads unless a task explicitly requests them.
• For repository maintenance tasks, run focused native tests after reading the matching sub-skill; for ordinary package use, rely on smoke checks and bundled helpers.
• If a task asks for generic computer vision modeling, model training, or non-AnyLabeling annotation tools without AnyLabeling/API/config signals, use a more specific vision or annotation-platform skill instead.