Anomalib Models and Data Review

Use this skill when reviewing changes under models, data, callbacks, metrics, pipelines, deployment, or CLI integration.

Purpose and scope

Use this skill for architectural fit. It is most useful when a change affects how anomalib models are built, configured, loaded, or connected to data, callbacks, metrics, or CLI/config entrypoints.

Request changes when

• a model does not fit the established AnomalibModule-based architecture;
• structured data is replaced with ad hoc dictionaries where anomalib already has typed item/batch dataclasses;
• callback or engine behavior bypasses existing Lightning or anomalib extension points;
• public metrics, models, or CLI components are added without matching exports, docs, or config compatibility;
• user-facing constructor/config surfaces become opaque or harder to serialize.

Models

• New trainable models should fit the existing AnomalibModule-based architecture.
• Review whether the model integrates cleanly with existing model discovery and loading patterns in src/anomalib/models/init.py.
• Prefer Lightning hooks and existing framework extension points over ad hoc training side effects inside model bodies.
• Check that configurable constructor arguments are explicit and compatible with the repo's jsonargparse-driven config flow.

Data and dataclasses

• Data should move through anomalib's typed dataclass system rather than ad hoc dictionaries where the library already has structured item/batch types.
• Changes to shared dataclasses should preserve validation and batching behavior.
• src/anomalib/data/dataclasses/generic.py is the main reference for FieldDescriptor, typed fields, update behavior, and batch/item patterns.
• If a dataclass surface changes, review both runtime behavior and the corresponding public documentation.

Callbacks and engine integration

• Training side effects such as checkpointing, timing, compression, or visualization should follow the callback-based patterns already present in src/anomalib/callbacks/.
• New callback-style behavior should align with Lightning callback usage instead of bypassing the engine lifecycle.
• Do not approve model/callback changes that tightly couple to trainer internals when a documented hook already exists.

Metrics

• Metrics should align with the torchmetrics-based patterns in src/anomalib/metrics/base.py.
• Review whether image-level and pixel-level metric handling remains clear and consistent.
• If a metric becomes part of the public API, verify that exports and docs are updated too.

CLI and config compatibility

• Review whether user-configurable types remain compatible with jsonargparse and the existing CLI/config structure.
• Prefer explicit constructor parameters over opaque config plumbing when the component is user-facing.
• If a new public component is configurable, ask whether config-driven usage and import paths are documented and tested.

Repo-grounded review anchors

• src/anomalib/models/init.py
• src/anomalib/data/dataclasses/generic.py
• src/anomalib/callbacks/init.py
• src/anomalib/metrics/base.py
• src/anomalib/cli/cli.py

Review prompts

• Does the change fit anomalib's module, data, and callback architecture?
• Is structured data still flowing through the established dataclass/batch system?
• Will this remain usable from config files and CLI entrypoints?
• Are public exports, docs, and integration points updated alongside the code?

Reviewer checklist

• Check model architecture fit.
• Check typed data flow.
• Check callback and metric integration.
• Check CLI/config compatibility.
• Check exports and docs for new public surfaces.