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scientific-figure-generator skill

/plugins/babysitter/skills/babysit/process/specializations/domains/science/scientific-discovery/skills/scientific-figure-generator

This skill helps generate publication-quality scientific figures by applying journal standards and accessibility best practices across multi-panel layouts.

npx playbooks add skill a5c-ai/babysitter --skill scientific-figure-generator

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: scientific-figure-generator
description: Skill for generating publication-quality scientific figures
allowed-tools:
  - Bash
  - Read
  - Write
metadata:
  specialization: scientific-discovery
  domain: science
  category: Visualization
  skill-id: SK-SCIDISC-021
---

# Scientific Figure Generator Skill

## Purpose

Generate publication-quality scientific figures following journal standards and best practices for data visualization.

## Capabilities

- Create statistical plots
- Generate multi-panel figures
- Apply journal formatting
- Export in multiple formats
- Ensure accessibility
- Add annotations

## Usage Guidelines

1. Prepare data
2. Select visualization type
3. Apply styling
4. Add annotations
5. Check accessibility
6. Export figure

## Process Integration

Works within scientific discovery workflows for:
- Manuscript preparation
- Presentation creation
- Data communication
- Results visualization

## Configuration

- Journal style guides
- Color schemes
- Resolution settings
- Export formats

## Output Artifacts

- Publication figures
- Multi-panel layouts
- Vector graphics
- Figure legends

Overview

This skill generates publication-quality scientific figures that meet journal standards and best practices for data visualization. It produces single and multi-panel layouts, applies consistent styling, and exports high-resolution raster and vector formats. The skill focuses on accessibility, clear annotations, and reproducible outputs for manuscript and presentation workflows.

How this skill works

You provide cleaned data and select visualization types; the skill then builds statistical plots, composes multi-panel figures, and applies journal-specific formatting. It supports configurable color schemes, resolution and export settings, and adds annotations and legends automatically. The output is validated for accessibility (contrast and labeling) and exported in requested formats such as SVG, PDF, and high-resolution PNG.

When to use it

  • Preparing figures for manuscript submission to journals with strict formatting requirements
  • Creating multi-panel figures for conference posters or slide decks
  • Converting exploratory plots into publication-ready graphics
  • Sharing figures with collaborators while keeping styling and colors consistent
  • Exporting vector graphics for print or high-resolution raster images for presentations

Best practices

  • Start with cleaned, well-structured data and include metadata for axes and units
  • Choose color palettes that are colorblind-safe and maintain sufficient contrast
  • Use vector formats (SVG/PDF) for line art and high-resolution PNG for images
  • Compose panels with consistent axis scales and aligned labels to aid comparison
  • Annotate results succinctly: callouts, scale bars, and statistical indicators only when necessary

Example use cases

  • Generate a 2x2 multi-panel figure showing different statistical comparisons with shared legend and aligned axes
  • Produce publication-ready line and scatter plots with confidence intervals and formatted axis labels
  • Export a clinical study figure in journal-compliant dimensions and resolution as PDF and high-res PNG
  • Create accessible colorblind-safe heatmaps and annotate significant regions
  • Build a composite figure combining microscopy images and quantitative plots with consistent scale bars

FAQ

What export formats are supported?

Common vector and raster formats are supported, including SVG, PDF, and high-resolution PNG.

How does the skill ensure accessibility?

It checks color contrast, recommends colorblind-safe palettes, enforces readable font sizes, and validates axis labels and legends.