---

name: developer-workflows description: "Operate X-AnyLabeling developer workflows for training, packaging, localization, exporters, and contribution validation." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Developer Workflows

Use this sub-skill when the task is about X-AnyLabeling development rather than ordinary annotation use: Ultralytics training integration, training-worker diagnosis, PyInstaller build planning, translation/resource regeneration, ONNX exporter interpretation, contribution hygiene, or safe validation before an expensive local action.

Route first

• Dataset format conversion details belong in ../conversion-cli/SKILL.md.
• Loading an exported/custom model into the auto-labeling panel belongs in ../auto-labeling-models/SKILL.md.
• Annotation UI, XLABEL JSON editing, and manual labeling workflows belong in ../annotation-ui/SKILL.md.
• Release publishing and release-note generation are maintainer-only, side-effectful workflows; do not perform them from this sub-skill unless the user explicitly asks for release maintenance and provides the required release context.

Capability map

User intent	Use
Configure or diagnose GUI Ultralytics training	references/training-workflows.md and the bundled safe validator scripts/check_training_config.py
Inspect hidden training-worker payload/event behavior	references/training-workflows.md and references/troubleshooting.md
Plan a PyInstaller executable build	references/packaging-and-localization.md
Refresh translations or Qt resources	references/packaging-and-localization.md
Interpret selected ONNX exporter utility behavior	references/model-exporters.md
Prepare a contribution or focused developer check	references/contributor-guidance.md
Diagnose common failures	references/troubleshooting.md

Verified scope and safety limits

• Package identity used for this skill: x-anylabeling-cvhub version 4.0.2, import package anylabeling, CLI entry point xanylabeling.
• Runtime baseline: Python >=3.11 with Python 3.12 recommended. Optional package extras are cpu, gpu, gpu-cu11, and gpu-cu13; install only one runtime extra in a single environment.
• Adjacent verified routing facts: the conversion registry contained 19 tasks, and ModelManager loaded 204 configs after configuration initialization. Use the sibling conversion and auto-labeling sub-skills for those surfaces.
• ONNX Runtime CPU provider was verified during construction. CUDA, TensorRT, model downloads, and actual model training were not verified here and must be treated as optional local capabilities.
• Ultralytics training dependencies are optional. A normal annotation/runtime environment may not include ultralytics or a usable torch installation.
• The bundled validator only checks configuration shape and local device availability; it never launches training, downloads weights, writes datasets, or builds executables.

Safe operating pattern

1. Classify the requested action as training, packaging/localization, exporter interpretation, or contribution hygiene.

2. Use the matching bundled reference to collect required fields and prerequisites.

3. For training requests, run the safe validator before launching anything expensive:

   python sub-skills/developer-workflows/scripts/check_training_config.py \
     --task-type Detect \
     --label-count 20 \
     --model yolov8n.pt \
     --data data.yaml \
     --project runs \
     --name exp \
     --device cpu
   

4. If validation passes but the task would train, build, mutate translations, or export a heavyweight model, tell the user what side effects will occur before proceeding.

5. Preserve license and dependency boundaries. Ultralytics is AGPL-3.0; using the training feature as a network service can carry source-disclosure obligations distinct from X-AnyLabeling's GPL-3.0-only project license.