Skip to content
Agentmart
Submit a tool

scientific-visualization

Publication-ready scientific figure design with matplotlib and seaborn.

Skill by researchclaw

IndexedMIT
Write a review

Chalk Labs

Need help deploying scientific-visualization?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/raja21068-scientific-visualization

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.

Use cases: design, data, research

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/scientific-visualization

What's inside

Skill name
scientific-visualization
Description
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
License
MIT
Metadata
author: researchclaw · version: 1.0 · category: writing · priority: 3 · references: adapted from K-Dense-AI/claude-scientific-skills · trigger-keywords: figure,plot,chart,visualization,matplotlib,seaborn,colorblind,publication · applicable-stages: 14,17,22
Instructions
45 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

## Scientific Visualization Best Practice

### Figure Design Principles
1. Every figure must have a clear, self-contained message
2. Minimize chartjunk: remove gridlines, background shading, and 3D effects
3. Use direct labeling instead of legends when possible
4. Remove top and right spines for cleaner appearance
5. Ensure all text is readable at final print size (minimum 6pt font)

### Journal Figure Sizing
1. **Single column**: 3.3-3.5 inches (85-89 mm) wide
2. **1.5 column**: 4.5-5.5 inches (114-140 mm) wide
3. **Double column / full width**: 6.5-7.1 inches (165-180 mm) wide
4. Resolution: 300 DPI minimum for raster; prefer vector formats (PDF, EPS, SVG)
5. Check target journal author guidelines for exact specifications

### Colorblind-Safe Design
1. Use colorblind-friendly palettes: seaborn "colorblind", Okabe-Ito, viridis, cividis
2. NEVER rely on color alone — combine with shape, pattern, or line style
3. Avoid red-green combinations; prefer blue-orange or blue-yellow contrasts
4. Test figures with a colorblind simulator before submission
5. Ensure figures work in grayscale for print journals

### Multi-Panel Layouts
1. Label panels with uppercase letters: (A), (B), (C) in bold, top-left corner
2. Use consistent axis scales across panels when comparing related data
3. Share axes where appropriate to reduce redundancy
4. Maintain consistent font sizes and line widths across all panels
5. Use `plt.subplots()` with `constrained_layout=True` for automatic spacing

### Statistical Annotations on Figures
1. Show individual data points alongside summary statistics (box + strip plots)
2. Always include error bars; specify type in caption (SEM, SD, 95% CI)
3. Use significance brackets with stars: * p<.05, ** p<.01, *** p<.001
4. Annotate effect sizes or key statistics directly on the figure when helpful
5. Never use bar charts for small-n data — use dot plots or box plots instead

### Export and Quality Checklist
1. Save in vector format (PDF/SVG) for line art; TIFF/PNG for photographs
2. Embed fonts or convert text to outlines for cross-platform consistency
3. Verify axis labels include units in parentheses: "Time (s)", "Force (N)"
4. Ensure figure caption fully explains all symbols, abbreviations, and panels
5. Check that color-coded elements match between figure and caption

From raja21068/AutoScholar, MIT.

More from raja21068/AutoScholar

Signals

Stars
0
Forks
0
Open issues
0
Last commit (this folder)
Apr 12, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.0
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from researchclaw

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get scientific-visualization running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying scientific-visualization?

CallGet help
Cite this page

scientific-visualization skill. Agent Mart. https://agentmart.dev/skills/raja21068-scientific-visualization (last updated 2026-10-24)