scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scientific-visualization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill instructs an agent to generate publication-quality scientific figures suitable for manuscript submissions. It emphasizes multi-panel layouts, inclusion of significance markers, error bars, and colorblind-friendly palettes. It supports exporting figures in PDF, EPS, TIFF, SVG, and other formats, and provides steps to apply journal-specific styles (Nature, Science, Cell) via pre-configured style files and helper functions. It also covers ensuring correct dimensions, labeling with units, and grayscale readability. It references using style presets, color palettes, and a figure_export workflow to save figures in required formats and resolutions.
How it works
- Apply publication-style presets before plotting (e.g., apply_publication_style and configure_for_journal).
- Create figures with appropriate size and panel layout (single/multi-panel) using GridSpec or seaborn/matplotlib defaults.
- Use colorblind-safe palettes (e.g., OKABE_ITO_LIST) and set perceptually uniform colormaps for heatmaps.
- Add error bars, significance markers, and individual data points as appropriate; ensure axes labels include units and follow sentence case.
- Remove nonessential styling (unnecessary spines, grids) and ensure labels are readable at final print size.
- Export figures via specialized functions (save_publication_figure, save_for_journal) in required formats and DPI, matching journal guidelines.
- When using seaborn, apply publication style first, then configure seaborn (sns.set_theme, sns.set_palette) and generate plots with emphasis on publication-ready appearance.
When to use it
Use this skill when creating figures for scientific manuscripts destined for journal submissions (Nature, Science, Cell, PLOS, etc.), especially if you need multi-panel figures with consistent styling, colorblind accessibility, and correct export settings.
What it can touch
- Tools: claude-code
- Code patterns shown include: applying publication style, using style_presets, configuring for journals, and saving figures via save_publication_figure or save_for_journal. Examples reference modules such as style_presets, assets/color_palettes.py, figures_export.py, and references to journal requirement checks.
Caveats
- The skill assumes the presence of publication-style presets, color palettes, and figure export utilities as described (e.g., save_publication_figure, save_for_journal).
- It emphasizes specific journals and styles; results depend on correct environment setup with the referenced files and styles. No guarantees about exact visual outcomes beyond adhering to described steps and formats.
# Scientific Visualization ## Overview Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts. ## When to Use This Skill This skill should be used when: - Creating plots or visualizations for scientific manuscripts - Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.) - Ensuring figures are colorblind-friendly and accessible - Making multi-panel figures with consistent styling - Exporting figures at correct resolution and format - Following specific publication guidelines - Improving existing figures to meet publication standards - Creating figures that need to work in both color and grayscale ## Quick Start Guide ### Basic Publication-Quality Figure ```python import matplotlib.pyplot as plt import numpy as np # Apply publication style (from scripts/style_presets.py) from style_presets import apply_publication_style apply_publication_style('default') # Create figure with appropriate size (single column = 3.5 inches) fig, ax = plt.subpl
- Overview
- When to Use This Skill
- Quick Start Guide
- Basic Publication-Quality Figure
- Using Pre-configured Styles
- Quick Start with Seaborn
- Core Principles and Best Practices
- 1. Resolution and File Format
- 2. Color Selection - Colorblind Accessibility
- 3. Typography and Text
- 4. Figure Dimensions
- 5. Multi-Panel Figures
- Common Tasks
- Task 1: Create a Publication-Ready Line Plot
What does the scientific-visualization skill do?
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scientific-visualization --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.