Agent skill · Data & Analytics

beautiful-data-viz

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

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill beautiful-data-viz --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 7
SKILL.md size: 2 KB
Bundled scripts: yes
Path: skills/omics/beautiful-data-viz/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Beautiful Data Viz Create polished, publication-ready visualizations in Python/Jupyter with strong typography, clean layout, and accessible color choices. ## Instructions 1. Clarify the message, audience, and medium (notebook/paper/slides). 2. Choose the simplest chart type that answers the question. 3. Select an appropriate palette type (categorical/sequential/diverging). 4. Apply the shared style helpers, then build the plot. 5. 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

What's inside
Steps it walks through
  1. Instructions
  2. Quick Reference
  3. Input Requirements
  4. Output
  5. Quality Gates
  6. Examples
  7. Example 1: Apply the shared style helper
  8. Troubleshooting
Ships with 6 files
  • assets/beautiful_style.py
  • eval.yaml
  • examples/recipes.md
  • fixtures/growth_curve.csv
  • references/checklist.md
  • references/palettes.md
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About this skill
What does the beautiful-data-viz skill do?

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

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill beautiful-data-viz --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going