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research-figures

Audit matplotlib figures for publication readiness and styling compliance.

Updated Jul 22, 2026
Search Tags:#accessibility#visualization#matplotlib#data-science#publication#academic-writing
One-click install
npx skills add https://github.com/narenp12/figure-gate --skill research-figures
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-figures
Source: https://github.com/narenp12/figure-gate/tree/main/skill
Command: npx skills add https://github.com/narenp12/figure-gate --skill research-figures

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, pytest, griffe, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the common failure of generated figures to meet publication standards, preventing amateurish aesthetics, illegible typography, and inaccessible color palettes in academic and professional documents.

Core Features & Use Cases

  • Mechanical Validation: Automatically audits figures for colorblind-safe palettes, line weights, type legibility, and composition rules.
  • Publication-Ready Styling: Provides a bundled matplotlib style sheet that enforces journal-standard aesthetics.
  • Use Case: When preparing a figure for a paper or thesis, use this Skill to ensure your plot is colorblind-safe, the text is readable at the final print size, and the composition avoids common pitfalls like truncated baselines or overplotting.

Quick Start

Use the research-figures skill to audit the current matplotlib figure for publication readiness and suggest necessary fixes.

Frequently Asked Questions about research-figures

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

FAQPage Schema
How do I make matplotlib figures publication-ready for academic journals?▼

Publication-ready matplotlib figures require applying journal-standard style sheets and passing mechanical validation gates that enforce colorblind-safe palettes, typography legibility, and composition rules to prevent amateurish aesthetics in academic documents.

How do I check if my matplotlib plot is colorblind-safe?▼

Checking matplotlib plots for colorblind safety involves running validation scripts that audit rendered figure objects against accessibility rules, enforcing palettes and line weights that meet publication and accessibility standards automatically.

Can I use this to fix typography legibility in data science visualizations?▼

Fixing typography legibility in data science visualizations is achieved by applying matplotlib style sheets that enforce readable text at final print size, combined with mechanical auditing of type legibility and composition.

What's the best way to audit composition rules like truncated baselines in matplotlib?▼

Auditing composition rules like truncated baselines in matplotlib is best done by running mechanical validation scripts against rendered figure objects to detect overplotting and composition pitfalls before final document export.

Do I need a standard scientific computing environment to run matplotlib validation scripts?▼

Running matplotlib validation scripts requires a standard scientific computing environment with matplotlib installed to execute validation scripts against rendered figure objects and audit colorblind-safe palettes, line weights, and type legibility.

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