Model Sample Image Export
Documentsmodel-sample-image-exportModel Sample Image Export is an Agent Skill for documents published in the open-edge-platform/anomalib repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
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 Use when model sample images are missing, outdated, or suspected to be invalid.
How to use this skill
Bring this guide into your coding agent with a prompt tailored to the tool you use.
- Open your project in Codex.
- Copy and paste the prompt below.
- Review files and risks before approving any action.
Inspect the Agent Skill at https://github.com/open-edge-platform/anomalib/blob/HEAD/.agents/skills/model-sample-image-export/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/model-sample-image-export, but wait for my approval before writing files or running scripts.
Copying this prompt does not install or run the skill. Review third-party files before use.
Model Sample Image Export
Use this skill to create or refresh sample-result images for model documentation.
Scope
This skill focuses on:
- selecting completed trained checkpoints or finished benchmark runs
- exporting prediction/sample images
- copying or saving them into
docs/source/images/<model>/results/ - updating README/docs sample-result references
- rejecting broken or misleading outputs
It does not own benchmark table maintenance. Use benchmark-and-docs-refresh for that.
Request changes when
- sample images come from incomplete or untrusted runs;
- published outputs are clearly degenerate or misleading;
- README or docs references point to missing image files;
- the docs surface implies three valid examples when fewer trustworthy outputs exist.
Required Source Quality
Only use sample images from:
- completed trained checkpoints
- completed benchmark runs with valid prediction outputs
- finished model outputs that can be traced back to a real run artifact
- if no suitable completed checkpoint, benchmark output, or other traceable run artifact exists, schedule a few runs to generate trustworthy sample images
Do not use:
- incomplete runs
- partially written checkpoints
- outputs with empty/degenerate masks
- outputs driven by NaNs or obviously broken predictions
Required Workflow
- Identify candidate checkpoints/runs in
results/. - Verify the run is complete enough to trust.
- If verification fails, schedule a few runs to train the model on a few categories.
- Generate predictions from the checkpoint/run.
- Inspect output quality before publishing images.
- Save the selected images into
docs/source/images/<model>/results/. - Update README/docs references.
Preferred Output Layout
docs/source/images/<model>/results/0.pngdocs/source/images/<model>/results/1.pngdocs/source/images/<model>/results/2.png
If you have fewer than 3 trustworthy images, train the model on a few more categories to generate more sample images.
README Update Pattern
Preferred pattern:
### Sample Results

Repeat for additional images.
Docs Update Pattern
Preferred docs-page pattern:
## Sample Results
```{eval-rst}
.. image:: ../../../../../images/<model>/results/0.png
```
Validation Rules
Before publishing an image:
- Check that the referenced file exists.
- Check that the image is visually plausible.
- Check that the mask/anomaly region is not obviously wrong.
- Check that the sample came from a trained or otherwise valid completed run.
- If a model/category output is degenerate, exclude it and say so explicitly.
Reviewer checklist
- Check run completeness.
- Check image quality.
- Check exported file existence.
- Check README and docs references.
Repo-Specific Notes
- In this repo, some completed checkpoints can still produce bad masks.
- If generic visualization helpers fail, derive a narrow exporter for the specific model/run.
- Keep exporter scripts focused and traceable to the chosen checkpoints.
- When in doubt, prefer fewer trustworthy sample images over a full set of misleading ones.