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Guide for creating publication-quality scientific figures that meet major journal standards (Nature, Science, ACS, RSC). Use this skill when users ask about scientific figure formatting, journal requirements, colorblind-safe palettes, resolution/DPI, file formats, fonts, or figure dimensions for publication.
How this skill is triggered — by the user, by Claude, or both
Slash command
/creator-skills:sci-figure-formatThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Guidelines for creating publication-quality figures that meet standards of Nature, Science, ACS, and RSC journals.
Guidelines for creating publication-quality figures that meet standards of Nature, Science, ACS, and RSC journals.
| Element | Requirement |
|---|---|
| Colors | Okabe-Ito palette (colorblind-safe) |
| Font | Helvetica or Arial, 6-8 pt |
| Resolution | 600 DPI minimum |
| Format | PDF (graphs) or TIFF (images) |
| Width | 3.5" (single column) or 7" (double column) |
| Line weight | ≥0.5 pt |
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When to use: Line plots, bar charts, scatter plots, any categorical data
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When to use: More than 8 categories needed
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When to use: Correlation matrices, positive vs negative values Why: Avoids red-green, colorblind-safe
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When to use: Temperature data, classic diverging scale
Avoid: Red-Green combinations (colorblind issue)
Font: Helvetica or Arial (sans-serif only)
Sizes:
Minimum: 5 pt (Nature/ACS requirement) Recommended: 6-8 pt for readability
Resolution:
File Formats:
Key principle: Use vector formats (PDF/EPS) for graphs - they scale without quality loss
Standard widths:
Journal-specific widths:
Design at final print size to ensure text legibility and correct proportions.
Do:
Avoid:
Format:
Typography:
Dimensions:
Colors:
Design:
When helping users format figures:
npx claudepluginhub aeghnnsw/cc-toolkit --plugin creator-skillsGuides chart type selection by data structure, accessible color palettes like viridis and Okabe-Ito, figure composition, and journal formatting for scientific publications and talks.
Creates publication-ready scientific figures with multi-panel layouts, error bars, significance annotations, and colorblind-safe palettes. Orchestrates matplotlib, seaborn, and plotly for journal submission (Nature, Science, Cell).
Uses matplotlib, seaborn, and plotly with publication styles to create journal-ready figures (Nature, Science, Cell). Supports multi-panel layouts, statistical annotations, error bars, and colorblind-safe palettes.