Beautiful data viz

Skill BioTender-max/awesome-bio-agent-skills/skills/omics/beautiful-data-viz

Create publication-quality matplotlib/seaborn charts with readable axes, tight layout, and curated palettes.From its SKILL.md

Install

npx -y skills add BioTender-max/awesome-bio-agent-skills --skill beautiful-data-viz

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SKILL.md

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Beautiful Data Viz

Create polished, publication-ready visualizations in Python/Jupyter with strong typography, clean layout, and accessible color choices.

Instructions

Clarify the message, audience, and medium (notebook/paper/slides).
Choose the simplest chart type that answers the question.
Select an appropriate palette type (categorical/sequential/diverging).
Apply the shared style helpers, then build the plot.
Validate readability at target size and export with tight bounds.

Quick Reference

Task	Action
Apply style	Use `assets/beautiful_style.py` helpers
Pick palette	See `references/palettes.md`
QA checklist	See `references/checklist.md`
Plot recipes	See `examples/recipes.md`

Input Requirements

Data in a tabular form (pandas DataFrame or similar)
Clear statement of the primary message
Target medium and background preference

Output

Publication-ready figure(s) (PNG/SVG/PDF)
Consistent styling and labeling

Quality Gates

Message is clear in 3 seconds at target size
Labels and units are readable and accurate
Color choice is colorblind-safe and grayscale-tolerant
Layout is tight with minimal whitespace

Examples

Example 1: Apply the shared style helper

from assets.beautiful_style import set_beautiful_style, finalize_axes
set_beautiful_style(medium="notebook", background="light")
# build plot here
finalize_axes(ax, title="Example", subtitle="", tight=True)

Troubleshooting

Issue: Labels overlap or are unreadable Solution: Reduce tick count, rotate labels, or increase figure width.

Issue: Colors are hard to distinguish Solution: Use a colorblind-safe categorical palette and limit categories.

What ships with it: 6 files

15.9 KB alongside SKILL.md, 1 of them executable