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name: data-transforms description: "Build MONAI preprocessing, IO, metadata, caching, dataset, dataloader, decollation, inverse-transform, and lazy-transform workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MONAI Data and Transform Workflows

Use this sub-skill when an agent needs to load medical images or arrays, assemble dictionary transform pipelines, preserve MetaTensor metadata, choose MONAI dataset/cache classes, debug collation, or validate inverse/lazy transform behavior.

Route Here

• Build monai.transforms.Compose pipelines for image/label dictionaries, arrays, random crops, intensity transforms, spatial transforms, or post-load channel handling.
• Load file-backed data with LoadImaged, create datalists with load_decathlon_datalist, and wrap items in Dataset, CacheDataset, PersistentDataset, SmartCacheDataset, or DataLoader.
• Preserve, inspect, or troubleshoot MetaTensor metadata, affine, applied_operations, batching, decollate_batch, lazy resampling, and inverse transforms.
• Diagnose transform errors: missing keys, channel-first mistakes, optional reader dependencies, cache staleness, multiprocessing/collate issues, and inverse failures.

Route Elsewhere

• Route model architecture, losses, metrics, sliding-window inference, and prediction postprocessing to modeling-inference.
• Route training/evaluation loops, MONAI engines, handlers, checkpointing, AMP, and trainer input batches to training-evaluation.
• Route Bundle config syntax, ConfigParser, and python -m monai.bundle usage to bundle-config.
• Route Auto3DSeg, DataAnalyzer, generated bundle orchestration, and app-level dataset analysis to apps-auto3dseg.

Required References and Script

• Read references/workflows.md when designing end-to-end data loading, transform, caching, metadata, lazy, inverse, or validation workflows.
• Read references/api-reference.md when checking signatures, key arguments, data contracts, shape conventions, and dataset/cache selection notes.
• Read references/troubleshooting.md when a pipeline fails from optional readers, missing keys, channel layout, metadata, lazy resampling, cache, collation, or inverse-transform issues.
• Run scripts/check_data_pipeline.py --help to inspect the bundled safe validation script, and run the script to verify that installed MONAI can build a tiny dictionary transform Dataset returning MetaTensor outputs.

Fast Operating Pattern

1. Start with dictionary items using stable keys such as "image" and "label"; add optional keys only if transforms set allow_missing_keys=True or the data always contains them.
2. Compose deterministic loading and normalization before random augmentation; cache only deterministic work before the first Randomizable transform.
3. Prefer monai.data.DataLoader over PyTorch's raw dataloader for MONAI random state handling, patch-list collation, and metadata-aware batches.
4. Validate one sample, then one batch, then any inverse/lazy path before embedding the pipeline in a trainer, bundle, or Auto3DSeg workflow.