---

name: monai description: "Use MONAI, the PyTorch-based medical imaging AI toolkit, for data/transforms, modeling/inference, training/evaluation, Bundle configs, and Auto3DSeg/app workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MONAI

Use this repo skill when a task asks an agent to build, inspect, debug, or explain MONAI workflows for healthcare imaging with PyTorch. MONAI covers dictionary transforms, medical-image data loading, metadata-aware tensors, segmentation networks, losses, metrics, sliding-window inference, Ignite-style training/evaluation engines, Bundle configuration/CLI workflows, and high-level Auto3DSeg applications.

Start Here

1. Confirm the requested workflow is MONAI-specific rather than plain PyTorch, generic DICOM/NIfTI processing, or another MONAI project such as Label or Deploy.
2. Check the environment with scripts/check_monai_environment.py --help, then scripts/check_monai_environment.py when you need import, version, optional dependency, and CLI signals.
3. Route to the narrowest sub-skill below before writing code, commands, configs, or troubleshooting steps.
4. Use root references for cross-cutting install, dependency, provenance, and troubleshooting guidance; use sub-skill references for concrete APIs and workflows.

Route by Task

• Use sub-skills/data-transforms/SKILL.md for monai.transforms, monai.data, medical-image IO, dictionary pipelines, MetaTensor, datalists, datasets, caches, dataloaders, inverse transforms, lazy resampling, and collation.
• Use sub-skills/modeling-inference/SKILL.md for monai.networks, losses, metrics, sliding-window inference, postprocessing, visualization, shape/channel contracts, and model primitive smoke checks.
• Use sub-skills/training-evaluation/SKILL.md for MONAI engines, Ignite handlers, validation, checkpointing, logging, optimizers, schedulers, AMP/distributed caveats, and trainer/evaluator debugging.
• Use sub-skills/bundle-config/SKILL.md for MONAI Bundle config syntax, ConfigParser, ConfigWorkflow, python -m monai.bundle, metadata/spec validation, CLI overrides, export commands, and copyable tiny bundle templates.
• Use sub-skills/apps-auto3dseg/SKILL.md for Auto3DSeg, DataAnalyzer, AutoRunner, BundleGen, ensemble/HPO generators, nnU-Net bridge guidance, and high-level app dataset helpers.

Cross-Cutting References

• Read references/installation-and-optional-dependencies.md before choosing install commands, extras, image-reader dependencies, Ignite, Bundle CLI dependencies, export packages, HPO packages, or CUDA/TensorRT paths.
• Read references/troubleshooting.md for package import failures, optional dependency errors, CPU/GPU mismatches, data/config validation failures, and when to route failures to a sub-skill.
• Read references/repo-provenance.md when deciding whether this skill still matches the MONAI checkout or package version.
• Run scripts/check_monai_environment.py to collect safe environment facts without downloads, training, credentials, or destructive writes.

Minimal Install and Import Check

For normal users, start with the released package unless the task explicitly targets a local checkout:

pip install monai
python - <<'PY'
import monai, torch, numpy
print(monai.__version__)
print(torch.__version__)
print(numpy.__version__)
PY

Install optional extras only for the route that needs them. Examples: image readers may need nibabel, pydicom, pynrrd, pillow, itk, tifffile, or openslide; training engines need pytorch-ignite; Bundle CLIs need fire; export workflows may need onnx, onnxruntime, TensorRT/polygraphy, or Hugging Face packages; Auto3DSeg HPO may need nni or optuna.

Safe Operating Rules

• Do not run downloads, dataset constructors with download=True, Auto3DSeg training, HPO, nnU-Net jobs, TensorRT export, Hub push, or multi-GPU workflows unless the user explicitly asks and the environment/data are ready.
• Prefer tiny CPU smoke checks, parser/help commands, and synthetic arrays before launching medical-image IO, training, inference over full volumes, or external services.
• Keep tensor layout explicit. MONAI networks and losses generally expect channel-first tensors such as (B, C, H, W) or (B, C, H, W, D).
• Treat MetaTensor metadata, affine, decollation, and inverse transforms as first-class debugging signals rather than incidental details.
• When a workflow spans multiple routes, build in this order: data/transforms, model/loss/metric/inferer, training/evaluation or Bundle config, then app-level orchestration.

Verification Mindset

• For data pipelines, validate one transformed sample, one batch, and any inverse/lazy path before training.
• For modeling, run a tiny forward pass and match logits, labels, postprocessing, loss, and metric channel semantics.
• For training engines, verify prepare_batch, event handlers, metric reset/aggregate, checkpoint paths, and optional Ignite availability.
• For bundles, validate config parsing and metadata before running a workflow or export command.
• For Auto3DSeg, inspect task config and data schema before analysis, bundle generation, training, ensemble, or HPO.