| name | review-figures |
| description | Send paper figures to vision-capable models for feedback on clarity, design, and presentation |
| allowed-tools | Read, Bash, Glob, Grep |
| user-invocable | true |
Review Figures
Sends each figure from the paper to vision-capable models via EDSL for feedback on clarity, labeling, design quality, and caption adequacy. Results are recorded as eval responses under the "figures" category.
Usage
/review-figures
Prerequisites
- The paper must be registered in katz with images copied to
.katz/versions/<commit>/paper/.
(This happens automatically when registering with katz paper register — sibling image files
are copied alongside the manuscript.)
edsl must be installed.
Workflow
1. Validate
Run katz paper status to confirm registration. Check that images exist:
ls .katz/versions/$(katz paper status | python3 -c "import sys,json; print(json.load(sys.stdin)['commit'])")/paper/*.png
2. Run the figure review
python <katz-skills-path>/review-figures/scripts/edsl_review_figures.py
python <katz-skills-path>/review-figures/scripts/edsl_review_figures.py --dry-run
python <katz-skills-path>/review-figures/scripts/edsl_review_figures.py --models 1
The script:
- Loads all images from the paper directory
- Extracts surrounding caption/notes from the manuscript for context
- Sends each figure + context to vision models
- Records results as eval responses with grades under the "figures" category
3. View results
Results appear in the HTML report under the Evaluations section (category: "figures"), or via:
katz eval results --category figures
4. What the models evaluate
Each figure is assessed on five dimensions:
- Self-explanatory: Can it be understood standalone? Axes labeled? Legend present?
- Takeaway: Is the main message immediately apparent?
- Design quality: Color choices, clutter, data-ink ratio, font sizes
- Caption adequacy: Does the caption explain what's shown and how to read it?
- Suggestions: Specific improvements
