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figure-hygiene

Validates scientific figure correctness and legibility using Python QA scripts.

14|6|Updated Feb 24, 2026
Search Tags:#data visualization#research workflow#scientific publication#figure validation#figure quality
One-click install
npx skills add https://github.com/fkguo/nullius --skill figure-hygiene
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: figure-hygiene
Source: https://github.com/fkguo/nullius/tree/main/skills/figure-hygiene
Command: npx skills add https://github.com/fkguo/nullius --skill figure-hygiene

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps researchers and scientists ensure the correctness and legibility of their data/results figures in scientific publications, reducing errors and improving the quality of visual communication.

Core Features & Use Cases

  • Data Fidelity: Checks for excluded rows, comparable conditions, connected series, and self-consistency in figure data.
  • Label Economy: Ensures minimal yet informative labeling, adhering to color threading and typography guidelines.
  • Color Threading: Provides a CVD-safe color palette and ensures one color per entity.
  • Typography: Defines clear and consistent typography rules for figure elements.
  • Chart Choice: Offers guidance on choosing the appropriate chart type based on data shape.
  • Anti-Patterns: Alerts on common graphical mistakes that can compromise figure correctness.
  • Render-Then-Verify: Ensures figures are correctly rendered and legible.
  • Figure Reproduction Provenance: Ensures figures can be re-rendered from recorded inputs.
  • Review Gate: Suggests a review process for submission-bound figures.
  • Use Case: Before submitting a manuscript, use this Skill to check all figures for adherence to scientific publication standards.

Quick Start

Run the figure-hygiene skill on your figure 'results_figure.pdf' to ensure it meets publication standards.

Frequently Asked Questions about figure-hygiene

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

FAQPage Schema
How do I validate scientific publication figures for correctness and legibility?▼

To validate scientific publication figures, you can run Python QA scripts to check data fidelity, label economy, and color threading. This ensures proper data representation and adherence to publication standards before manuscript submission.

What is figure reproduction provenance in a research workflow?▼

Figure reproduction provenance ensures that your scientific figures can be re-rendered from recorded inputs. By verifying this during figure validation, you guarantee that your visual data results remain reproducible and traceable.

How do I check if my data visualization uses CVD-safe color palettes?▼

You can check your data visualization for CVD-safe color palettes by applying color threading rules. This validation process ensures one color per entity and confirms that your figure meets proper color standards.

Does this figure validation process work with PDF files?▼

Yes, the figure validation process works with PDF files like 'results_figure.pdf'. Running the QA scripts on your PDF ensures it meets publication standards for correctness, legibility, and proper data representation.

What are common graphical errors in scientific data visualization?▼

Common graphical errors in scientific data visualization include excluded data rows, inconsistent conditions, and poor typography. The validation process alerts you to these anti-patterns to ensure proper representation of data.

When should I perform a figure quality check for my research manuscript?▼

You should perform a figure quality check right before submitting your research manuscript. This review gate process verifies that all figures adhere to scientific publication standards and are correctly rendered.

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