figure-evaluation

Evaluate scientific figures against academic standards using a VLM-as-a-judge protocol.

Updated Jun 4, 2026
One-click install
npx skills add https://github.com/zc6600/aura --skill figure-evaluation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: figure-evaluation
Source: https://github.com/zc6600/aura/tree/main/skills/figure-evaluation
Command: npx skills add https://github.com/zc6600/aura --skill figure-evaluation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill addresses the subjectivity and inconsistency in evaluating scientific illustrations, ensuring that AI-generated figures meet rigorous academic standards before submission.

Core Features & Use Cases

  • Multi-Dimensional Scoring: Evaluates figures based on content fidelity, visual design, and communication effectiveness.
  • Blind Pairwise Comparison: Uses a judge persona to perform A/B testing on generated results to determine the superior visual output.
  • Use Case: Researchers can use this to automatically critique draft figures against ground truth data to ensure logical topology and professional aesthetics are maintained.

Quick Start

Use the figure-evaluation skill to critique the uploaded image against the provided research paper text.

Frequently Asked Questions about figure-evaluation

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

FAQPage Schema
How do I evaluate scientific figures for publication quality?▼

To evaluate scientific figures for publication quality, use a VLM-as-a-judge protocol that applies multi-dimensional scoring to content fidelity, visual design, and communication effectiveness against rigorous academic standards.

Can I use VLM to critique AI-generated illustrations against research paper text?▼

Yes, you can use a VLM judge persona to critique uploaded AI-generated illustrations against provided research paper text, ensuring logical topology and professional aesthetics are maintained for academic submission.

What is blind pairwise comparison in visual verification?▼

Blind pairwise comparison is an A/B testing mechanism where a judge persona evaluates generated visual outputs to determine the superior illustration, reducing subjectivity in academic visual verification workflows.

Does the figure evaluation skill require a subagent persona?▼

Yes, the figure evaluation process requires a subagent persona to perform multi-dimensional scoring and blind pairwise comparisons based on defined academic standards for rigorous visual verification.

When do I need multi-dimensional scoring for academic research workflows?▼

You need multi-dimensional scoring for academic research workflows when automatically critiquing draft figures against ground truth data to ensure AI-generated illustrations meet professional publication standards before submission.