beautiful-data-viz
Scannednpx machina-cli add skill fmschulz/omics-skills/beautiful-data-viz --openclawBeautiful 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.
Source
git clone https://github.com/fmschulz/omics-skills/blob/main/skills/beautiful-data-viz/SKILL.mdView on GitHub Overview
Beautiful Data Viz helps you craft publication-ready visualizations in Python and Jupyter with strong typography, clean layout, and accessible color choices. It guides you from message clarification to exporting tight, publication-grade figures that work in notebook, paper, and slides.
How This Skill Works
Start by clarifying the primary message, audience, and medium (notebook, paper, or slides). Then choose the simplest chart type that answers the question and pick an appropriate palette (categorical, sequential, or diverging). Apply the shared style helpers (assets/beautiful_style.py), build the plot, and finally validate readability at the target size before exporting with tight bounds.
When to Use It
- Creating quick, reusable figures in a notebook for exploratory work.
- Preparing figures for a journal or conference paper.
- Building slides with consistent visuals for talks.
- Producing thesis or report figures with publication-grade styling.
- Generating tutorial/demo visuals with accessible palettes and tight layouts.
Quick Start
- Step 1: Clarify the message, audience, and medium (notebook/paper/slides) and choose the simplest chart.
- Step 2: Select a palette type (categorical/sequential/diverging) and apply the shared style helpers.
- Step 3: Build the plot, validate readability at the target size, and export with tight bounds (PNG/SVG/PDF).
Best Practices
- Clarify the core message, audience, and target medium before plotting.
- Use the simplest chart type that answers the question.
- Choose a palette type (categorical/sequential/diverging) suitable for the data.
- Apply the beautiful_style helpers first, then finalize axes.
- Validate readability at the target size and export with tight bounds.
Example Use Cases
- Line plot with strong typography and tight layout for a methods paper.
- Scatter plot with colorblind-safe categorical palette to show group differences.
- Heatmap with readable axis labels and compact spacing for a publication figure.
- Bar chart created in notebook with consistent styling for slides.
- Publication-ready figure exported as PNG/SVG/PDF at target resolution.
