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nature-figure

Generate publication-grade multi-panel scientific figures with Python or R backends.

Updated May 21, 2026
Search Tags:#matplotlib#panel design#ggplot2#scientific figures#SVG export#Nature style#figure QA
One-click install
npx skills add https://github.com/Liangxianguang/helpfulskills --skill nature-figure-liangxianguang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nature-figure
Source: https://github.com/Liangxianguang/helpfulskills/tree/main/nature-skills/skills/nature-figure
Command: npx skills add https://github.com/Liangxianguang/helpfulskills --skill nature-figure-liangxianguang

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill prevents manuscript figures from becoming “pretty but unconvincing” by forcing a clear scientific claim, evidence hierarchy, and journal-grade export/QA workflow before any plotting happens.

Core Features & Use Cases

  • Figure contract first: Builds a claim + archetype + panel map (what each panel proves) and enforces reviewer-risk checks before styling or code generation.
  • Backend-exclusive plotting: Works with either Python (matplotlib/seaborn) or R (ggplot2 + patchwork + ComplexHeatmap) and prohibits cross-rendering between languages.
  • Publication-grade outputs: Produces editable SVG as the primary artifact (plus optional PNG/PDF/TIFF) with strict font/SVG settings to keep text editable.
  • Nature/NMI visual discipline: Enforces minimalist spines/legends, cohesive palettes across panels, panel-label conventions, and export bundle completeness (script + source data + QA notes when needed).

Quick Start

Ask the assistant to generate a Nature-style multi-panel figure and include “Backend: Python or R”, the one-sentence core conclusion, the panel map (a/b/c…), and the required output formats (editable SVG plus optional PNG/PDF/TIFF).

Frequently Asked Questions about nature-figure

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

FAQPage Schema
How do I create submission-ready scientific figures for high-impact journals?▼

To create submission-ready scientific figures, you need a claim-driven panel map, minimalist journal styling, and backend-exclusive plotting using Python or R to export editable SVG files.

How do I export editable SVG files from matplotlib or ggplot2 for Nature submission?▼

Exporting editable SVG files from matplotlib or ggplot2 requires strict font and SVG settings during generation to ensure all text remains editable for final Nature publication preparation.

Can I mix Python and R code when building multi-panel scientific figures?▼

Mixing Python and R for multi-panel scientific figures is prohibited; you must explicitly select one backend, either Python with matplotlib/seaborn or R with ggplot2 and patchwork, to guarantee rendering consistency.

What is a figure contract and why is it needed before generating scientific charts?▼

A figure contract defines the core scientific claim, evidence hierarchy, and panel map to prevent unconvincing visuals, ensuring reviewer-risk checks pass before any journal-ready styling or code generation begins.

Does this figure generation workflow support microscopy image composites and heatmaps?▼

Yes, the figure generation workflow supports microscopy image-plate style composites and scientific heatmaps, applying cohesive palettes and panel-label conventions across all multi-panel chart types.

What is included in the export bundle for a Nature-style multi-panel figure?▼

The export bundle for a Nature-style figure includes the primary editable SVG artifact, optional PNG/PDF/TIFF formats, the plotting script, source data, and QA reproduction notes when needed.

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