scientific-visualization
Category: visualization-and-presentation · License: MIT · Version: 1.2 · Includes scripts
Tags: scientific figures, data visualization, publication figures, accessibility, plotting
When to use it
Create and audit truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.Example requests
Ask your agent in plain language, for example:- “Can you check if my figure palette is colorblind-safe and has enough contrast?”
- “I have fig3.pdf for my Nature Communications revision with 4 panels made in Matplotlib (results/fig3_raw.csv has the data). The editor says the figure is too low resolution and fonts aren’t embedded. How do I check what’s wrong and re-export it at 180 mm width?”
- “My growth-curve data has a few timepoints where the plate reader failed, and I want a clean figure showing treated vs control with error bars for a paper. The lines currently look smooth and complete, which feels a bit too good. What should I change?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole visualization-and-presentation category with /plugin install visualization-and-presentation@research-agent-skills.
Related
academic-plottingacm-sigconfbulk-rnaseqcell-pressieee-transactionsmatplotlibnature-portfoliopathway-enrichment