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 LeonChaoX/qinyan-academic-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 the agent to transform data into publication-ready figures, including multi-panel layouts, error bars, significance markers, and colorblind-friendly palettes, following journal guidelines (Nature, Science, Cell, etc.). It emphasizes exporting in PDF, EPS, TIFF, SVG, and vector formats using matplotlib, seaborn, and plotly, and provides preset styles and helper scripts for applying publication styles and saving figures.
How it works
- Apply publication styling using provided helpers (e.g., apply_publication_style, configure_for_journal).
- Create figures with appropriate sizes for target journals ( Nature, Science, Cell ) and multi-panel layouts via GridSpec or seaborn faceting.
- Use colorblind-friendly palettes (e.g., OKABE_ITO_LIST) and recommended colormaps (viridis, cividis) with grayscale testing.
- Add axis labels with units, remove unnecessary spines, and ensure typography meets final-size requirements.
- Generate statistical visuals with error bars (SEM/SD/CI) and include significance markers as needed.
- Save/export figures using scripts like save_publication_figure or save_for_journal in formats such as pdf, png, tif, eps, svg, ensuring proper DPI (e.g., 300) for publication.
- For multi-panel figures, use GridSpec and add bold panel labels (A, B, C, ...).
When to use it
- When creating figures for scientific manuscripts and journal submissions (Nature, Science, Cell, PLOS, etc.).
- When figures must be colorblind-friendly, multi-panel, or grayscale-friendly.
- When exporting figures at correct resolution and format for publication.
- When aligning figures to journal-specific requirements and ensuring consistency across panels.
What it can touch
- Matplotlib figures and seaborn/plotly plots integrated via publication styles.
- Scripts: style_presets, figure_export, and pre-configured style files located under assets/ and references/ (as described in the skill).
Caveats
- License: MIT; tool usage constrained to the defined scripting environment.
- The guidance relies on the presence of helper scripts (e.g., apply_publication_style, save_publication_figure, configure_for_journal) and style assets in the repository.
- It assumes the user will provide appropriate data in DataFrames compatible with seaborn/matplotlib calls.
# 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 LeonChaoX/qinyan-academic-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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.