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visual-engine

Interpret scientific figures, generate publication-quality visuals, and auto-generate captions.

Updated Feb 23, 2026
Search Tags:#python#r#latex#interpretation#captioning#figure-generation#multimodal-vision
One-click install
npx skills add https://github.com/olaTechie/scientific-paper-writer --skill visual-engine-olatechie
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: visual-engine
Source: https://github.com/olaTechie/scientific-paper-writer/tree/main/skills/visual
Command: npx skills add https://github.com/olaTechie/scientific-paper-writer --skill visual-engine-olatechie

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates the labor-intensive tasks of interpreting scientific figures, generating publication-quality visuals from data, and writing structured captions, reducing manual effort and improving consistency.

Core Features & Use Cases

  • Figure interpretation: Uses Claude's multimodal vision to extract data and insights from uploaded figures (e.g., extracting axis values, data points, and legend labels).
  • Figure generation: Produces publication-ready figures from data using R or Python templates, enabling quick iteration and customization.
  • Caption auto-generation: Creates standalone, journal-ready captions that follow standard formatting and include abbreviations definitions.
  • Use Case: A researcher uploads a forest plot image and automatically obtains a clean data table, a new figure generated from dataset, and a caption ready for manuscript submission.

Quick Start

Provide an image of a figure and request either interpretation, generation, or captioning to receive ready-to-use outputs.

Frequently Asked Questions about visual-engine

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

FAQPage Schema
How do I interpret data from a scientific figure image like a forest plot?▼

To interpret a scientific figure, the Skill uses Claude's multimodal vision to extract axis values, data points, and legend labels from uploaded images like forest plots, funnel plots, and Kaplan-Meier curves, outputting a clean data table.

Can I generate publication-quality figures from data using Python or R?▼

Yes, you can generate publication-quality figures from data using Python or R templates. The Skill produces ready-to-use visual outputs, enabling quick iteration and customization for scientific manuscripts.

How do I auto-generate structured captions for scientific manuscript figures?▼

You auto-generate structured captions by providing a figure image or data. The Skill creates standalone, journal-ready captions that follow standard formatting and include definitions for any abbreviations used.

What is the best way to extract data points from a Kaplan-Meier curve for analysis?▼

The best way to extract data points from a Kaplan-Meier curve is uploading the image for multimodal vision interpretation. The Skill identifies axis values and survival data, converting the visual plot into a clean dataset.

Do I need to install R or Python environments locally to generate figures from my datasets?▼

No local environment setup is required. The Skill uses available scripts and templates to generate publication-ready figures directly from your provided datasets, handling the R or Python execution internally.

Why does my generated figure need a standalone caption for journal submission?▼

Your generated figure needs a standalone caption because journals require standard formatting and clear definitions of abbreviations. The Skill automates this by writing structured, publication-ready captions that contextualize the visual.

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