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name: apps-auto3dseg description: "Use high-level MONAI application workflows, especially Auto3DSeg orchestration, DataAnalyzer statistics, BundleGen, EnsembleRunner, HPO generators, nnU-Net bridging, and built-in app dataset helpers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MONAI Apps and Auto3DSeg

Use this sub-skill when a task asks for high-level MONAI application helpers instead of hand-written model, transform, training-loop, or bundle mechanics.

Route Here For

• Planning an Auto3DSeg run with monai.apps.auto3dseg.AutoRunner, including safe configuration review before analysis, algorithm generation, training, HPO, or ensemble inference.
• Running or troubleshooting DataAnalyzer, BundleGen, EnsembleRunner, NNIGen, OptunaGen, nnUNetV2Runner, or the python -m monai.apps.auto3dseg / python -m monai.apps.nnunet CLIs.
• Choosing built-in app dataset helpers such as MedNISTDataset, DecathlonDataset, TciaDataset, and CrossValidation while documenting download/cache behavior.
• Handing Auto3DSeg-generated bundles to the Bundle route, or deciding whether a request belongs to raw data/transforms, primitive modeling/inference, or custom training loops.

Do Not Use For

• General MONAI Bundle config syntax or python -m monai.bundle execution; use ../bundle-config/SKILL.md for generated bundle internals and CLI overrides.
• Raw datalist loading, transforms, metadata, image readers, and caching primitives; use ../data-transforms/SKILL.md.
• Network/loss/metric/inferer selection and prediction primitives; use ../modeling-inference/SKILL.md.
• Custom Ignite or PyTorch training/evaluation loops; use ../training-evaluation/SKILL.md.

References and Scripts

• Read references/workflows.md when designing an Auto3DSeg, DataAnalyzer, BundleGen, ensemble, HPO, nnU-Net, or app-dataset workflow and deciding what is safe to inspect versus expensive to execute.
• Read references/api-reference.md when you need concise constructor signatures, required input schemas, output artifacts, and CLI routing notes.
• Read references/troubleshooting.md when diagnosing YAML/datalist errors, image-label layout mismatches, optional dependency failures, multi-GPU assumptions, generated bundle handoff, or dataset downloads.
• Run scripts/auto3dseg_smoke.py when you need a no-training, no-download import/CLI/config inspection check for the installed MONAI Auto3DSeg surfaces.

Safe Operating Rules

1. Treat AutoRunner.run(), DataAnalyzer.get_all_case_stats(), BundleGen.generate(), EnsembleRunner.run(), HPO trials, nnU-Net conversion/training, and dataset download=True as potentially expensive or side-effecting.
2. Prefer config review, import/signature inspection, CLI help, and tiny synthetic file plans before launching analysis, downloads, GPU jobs, multi-fold training, or HPO.
3. Confirm that optional packages are installed before claiming support for NNI, Optuna, nnU-Net, TensorBoard tracking, image readers beyond core formats, or TCIA/medical image downloads.
4. When Auto3DSeg produces bundle folders, route edits to ../bundle-config/SKILL.md; when it needs datalist or image layout fixes, route to ../data-transforms/SKILL.md.