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LeonChaoX
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qinyan-nature-figures

Designs and validates publication-ready scientific figures with Python or R plotting code.

863|74|Updated Feb 27, 2026
Search Tags:#Research#publication#ggplot2#Matplotlib#scientific figures#figure validation#Data Visualization
One-click install
npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill qinyan-nature-figures
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qinyan-nature-figures
Source: https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/%E6%B2%81%E8%A8%80%E5%AD%A6%E6%9C%AFskills/qinyan-nature-figures
Command: npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill qinyan-nature-figures

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Creating journal-grade scientific figures requires aligning chart types with data structure, enforcing statistical and visual standards, and producing reproducible exports—work that is error-prone when done ad hoc.

Core Features & Use Cases

  • Figure Contract Planning: Defines the conclusion, evidence hierarchy, panel map, data contract, statistics contract, and export contract before any plotting code is written.
  • Chart Selection & Visual Standards: Reference guides map scientific questions (distributions, paired changes, time courses, heatmaps, forest plots) to appropriate chart types with colorblind-safe, perceptually uniform palettes.
  • Automated Preflight Validation: A dependency-free Python script checks plotting source for syntax, font strategy, vector/raster exports, 300 dpi resolution, risky colormaps, data exclusions, and random seeds.
  • Use Case: Given a CSV of experimental results, produce a multi-panel figure with editable SVG/PDF vector exports, 600 dpi TIFF rasters, a standalone figure legend, and a full integrity log of exclusions and transformations.

Quick Start

Use the qinyan-nature-figures skill to design a submission-ready multi-panel figure from my experiment data and run the preflight checks on the plotting code.

Frequently Asked Questions about qinyan-nature-figures

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a publication-ready scientific figure from my data?▼

Start by writing a figure contract defining the conclusion, evidence hierarchy, panel map, and data/statistics rules. Then generate plotting code in Python (matplotlib/seaborn) or R (ggplot2), export SVG/PDF vectors plus high-resolution rasters, and run the preflight script to validate the output.

What chart type should I use for comparing distributions across groups?▼

Use dot plots or box/violin plots overlaid with raw data points rather than bar charts alone. This preserves sample size visibility and individual-level information, avoiding hidden pseudoreplication and misleading summary-only displays.

Does the figure preflight script require any Python packages?▼

No, the preflight script is dependency-free and uses only the Python standard library. It checks syntax, font strategy, export formats, raster resolution, colormap risks, data exclusions, and random seed usage via static source analysis.

Can I use R ggplot2 instead of Python matplotlib for figures?▼

Yes, both Python (matplotlib/seaborn) and R (ggplot2/patchwork/ComplexHeatmap) are supported. The preflight script auto-detects the backend from the file extension and applies appropriate checks for each language.

Why does the preflight check warn about jet or rainbow colormaps?▼

Jet and rainbow colormaps are not perceptually uniform and can distort continuous data interpretation. The workflow recommends perceptually uniform, colorblind-friendly palettes and redundant encodings like shape or line style instead.

What resolution is required for raster figure exports?▼

The preflight check enforces a minimum of 300 dpi for raster exports such as TIFF and PNG, treating anything below as a failure. Higher resolutions are recommended for line art or when the target journal explicitly requires them.

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