image-understand

Name: image-understand
Availability: InStock
Author: fatby

Identify and describe semantic content in images using vision-enabled LLMs.

2|Updated Feb 13, 2026

Search Tags:#multimodal #design analysis #vision #image understanding #ui analysis #screenshot analysis #chart interpretation

Ready to run

npx skills add https://github.com/FatBy/DunCrew --skill image-understand-fatby

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: image-understand
Source: https://github.com/FatBy/DunCrew/tree/main/skills/image-understand
Command: npx skills add https://github.com/FatBy/DunCrew --skill image-understand-fatby

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Multimodal AI image understanding enables accurate semantic interpretation of images (beyond OCR), revealing layout, relationships, and meaning in UI designs, charts, screenshots, and diagrams.

Core Features & Use Cases

Semantic understanding: analyzes images to describe layout, elements, and relationships.
UI/design interpretation: explains design intent, color usage, and element hierarchy.
Chart/screenshot analysis: interprets data visuals and error screens to extract actionable insights.
Code screenshot analysis: reasons about code snippets and UI code structure from images.

Quick Start

Analyze the image at the given path and return a detailed semantic description with layout, elements, and inferred meaning.

Frequently Asked Questions about image-understand

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

FAQPage Schema

How do I analyze a UI design mockup and extract its layout and element hierarchy?▼

To analyze a UI design mockup, this skill uses vision-enabled LLMs to identify layout, element relationships, and inferred design intent. It evaluates color usage and hierarchy to reveal the underlying meaning of design mockups.

Can I interpret data charts and error screenshots without relying solely on OCR?▼

Yes, analyzing charts and screenshots uses multimodal vision models to interpret data visuals and error screens, optionally falling back to OCR. This extracts actionable semantic insights rather than just transcribing visible text.

Does this image understanding approach work for analyzing code screenshots?▼

Yes, code screenshot analysis works by using vision-enabled models to reason about code snippets and UI code structure directly from images. It identifies structural relationships and semantic meaning within the captured code.

Do I need a specific model to perform multimodal image analysis?▼

Multimodal image analysis requires a vision-enabled LLM to process image inputs and extract semantic content. An OCR fallback is optionally supported if a dedicated vision model is unavailable.

What is the best way to refine the semantic description returned for an image?▼

To refine the semantic description, you can use the supported prompt and detail parameters during image analysis. These inputs allow you to specify the level of granularity and guide the vision model's output.