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Adds a new dataset/datamodule to anomalib under src/anomalib/data/. Use when wiring a new data source into the AnomalibDataset/AnomalibDataModule base classes, exporting it so anomalib.data.<Name> and the CLI/config (jsonargparse) can discover it, and adding…
Reviews anomalib model, data, callback, metric, and CLI integration conventions
Use when writing or updating Anomalib Studio UI component or hook tests that need the shared render/renderHook helpers, React Router paths or parameters, React Query, theme, URL-query, stream, suspense, or toast providers.
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD…
Adds a new anomaly-detection model to anomalib under src/anomalib/models/. Use when implementing a new model architecture (image or video), wiring it into the AnomalibModule base class, registering it so get_model() and the CLI/config (jsonargparse) can…
Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV. Use when comparing multiple models/datasets/categories in one sweep, or authoring/editing a benchmark config…
Runs and configures the anomalib tiled-ensemble pipeline, which trains/evaluates one model per image tile and merges results (with optional seam smoothing) for high-resolution anomaly detection. Use when the user wants to train with image tiling, mentions…
Trains an anomalib model on a dataset via the Python API or CLI, including training on a custom folder-structured dataset with the Folder datamodule. Use when writing or debugging an anomalib training script/command, choosing Engine/Trainer arguments, or…
Designs and reviews REST APIs for FastAPI services using consistent resource naming, HTTP semantics, validation, security, and error handling patterns. Use for backend API tasks, endpoint design/refactors, or API review requests in FastAPI/Python projects.
Run or continue model benchmarks, collect measured results, and refresh README/docs benchmark sections from generated artifacts. Use when benchmark tables in model docs need to be created, updated, or corrected.
Reviews anomalib docstrings, documentation updates, and changelog expectations
Keep anomalib model READMEs, docs pages, image assets, and benchmark/result references in sync
Export, validate, and publish model sample-result images into docs/source/images and reference them from README/docs pages. Use when model sample images are missing, outdated, or suspected to be invalid.
Reviews anomalib contributor workflow, PR title, branch naming, and quality gate expectations
Enforces Google-style Python docstrings for Python code
Reviews anomalib Python style, typing, imports, and public API conventions
Review/generate unit, integration, and regression test expectations
Review/generate third-party code attribution, licensing, and notice requirements
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