# Create journal-ready scientific figures for Nature submissions

Researchers struggle to produce submission-quality figures that meet Nature and high-impact journal standards. This skill provides a backend-gated workflow with Python or R, ensuring figures are scientifically clear, aesthetically polished, and export-ready.

## Install

```bash
npx skillstore add community contribution, refactored into static/dynamic layers/yuan1z0825-nature-figure
```

## Metadata

- - Slug: yuan1z0825-nature-figure
- - Version: 2.0.0
- - Author: Community contribution, refactored into static/dynamic layers
- - GitHub username: Yuan1z0825
- - License: MIT
- - Repository: https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-figure
- - Ref: main
- - Supported tools: Claude, Codex, Claude Code
- - Risk level: low
- - Risk factors: filesystem, external\_commands, network
- - Quality score: 77
- - Quality tier: bronze
- - Public page: https://skillstore.pages.dev/skills/yuan1z0825-nature-figure
- - Manifest: https://skillstore.pages.dev/api/skills/yuan1z0825-nature-figure/manifest

## Capabilities

- Guides creation of multi-panel scientific figures for Nature and high-impact journals
- Supports matplotlib/seaborn for Python and ggplot2/patchwork/ComplexHeatmap for R backends
- Enforces a backend-selection gate, requiring explicit Python or R choice before proceeding
- Provides archetype-first composition with hero panels, restrained palettes, and clear conclusions
- Exports journal-ready SVG, PDF, and TIFF outputs at appropriate DPI
- Includes worked examples and a chart-atlas for reference across scientific domains

## Use Cases

- Preparing figures for a Nature manuscript submission: A researcher needs multi-panel figures with journal-compliant formatting, color schemes, and export resolution for Nature-family journal submission.
- Polishing existing scientific plots for publication: A PhD student has draft matplotlib or ggplot2 plots and needs them refined to publication standards with proper typography, spacing, and export settings.
- Creating reproducible figure workflows for a lab: A lab head wants a consistent, reproducible figure-creation pipeline that lab members can follow with either Python or R, ensuring visual consistency across papers.

## Prompt Templates

### Basic figure creation request

```
I need to create a 3-panel figure for my Nature submission showing gene expression across three conditions. Python or R?
```

### Figure refinement with existing data

```
Here's my data file and draft matplotlib script. Can you help me refine it to Nature publication standards with proper DPI, font choices, and panel arrangement?
```

### Specific chart type guidance

```
I need to make a complex heatmap with annotations for a Nature paper. My backend is R. Can you guide me through ComplexHeatmap best practices?
```

### QA and export verification

```
I've finished my figure and need to verify it meets submission standards. Can you run through the QA contract and help with final export to PDF and TIFF?
```

## Limitations

- Does not support Illustrator or Figma-first infographic workflows
- Requires explicit backend selection; will halt and ask rather than auto-choose
- Not designed for dashboards or interactive visualizations
- Reference material loads only on demand to keep context lean

## Best Practices

- Always confirm backend \(Python or R\) before generating any plotting code
- Define the figure's core conclusion and evidence chain before writing any plotting script
- Use vector formats \(PDF/SVG\) for figures with text or sharp lines, and raster formats \(PNG/TIFF\) at 300\+ DPI for image-heavy figures
- Apply archetype-first composition: identify the hero panel, use restrained palettes, and make statistics an integral part of the figure

## Anti Patterns

- Do not auto-select a backend; always ask the user to choose Python or R explicitly
- Do not use more than 5-7 distinct colors in a single figure; Nature style favors restrained palettes
- Do not include 3D effects, shadows, or decorative gridlines in publication figures
- Do not skip the figure contract step; every figure must have a stated conclusion and evidence logic

## Security Audit

- - Safe to publish: true
- - Audited at: 2026-06-24T05:59:14.883\+00:00
- - Summary: Static analysis flagged 783 potential issues, but evaluation confirms the vast majority are false positives. The skill is a legitimate scientific figure-generation toolkit for academic publishing. Flagged patterns are explainable: 'C2 keywords' are model names \(DCRNN, GTS\) in comparison charts, 'weak crypto' triggers are hex color codes, 'os file operations' are figure output directory creation, and 'shell backtick' matches are markdown inline code formatting. No malicious intent or data exfiltration patterns found.

## Stats

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- - Downloads: 0
- - Favorites: 0
- - Popularity score: 0