VLM

Implement vision-based AI chat with image, video, and document analysis.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/UholySmokes/Voidverse-alt --skill vlm-uholysmokes
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: VLM
Source: https://github.com/UholySmokes/Voidverse-alt/tree/main/skills/VLM
Command: npx skills add https://github.com/UholySmokes/Voidverse-alt --skill vlm-uholysmokes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires z-ai-web-dev-sdk, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables AI models to understand and respond to images and videos combined with text prompts, allowing for visual content analysis and multimodal AI applications.

Core Features & Use Cases

  • Image Analysis: Describe, classify, and extract information from images.
  • Video Analysis: Understand and respond to video content.
  • Document Analysis: Extract text and data from documents.
  • Use Case: Upload a photo of a complex diagram and ask the AI to explain its components and their relationships.

Quick Start

Use the VLM skill to describe the image located at https://example.com/photo.jpg.

Frequently Asked Questions about VLM

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

FAQPage Schema
How do I use multimodal AI for image and video analysis?

Multimodal AI combines visual understanding with conversational AI to perform image and video analysis. You can pass visual content via URLs or base64 encoding alongside text prompts to extract information, describe scenes, or compare content.

Can I extract text and data from documents using vision-based AI?

Yes, vision-based AI supports document analysis to extract text and data. By processing the document as an image or via a URL, the AI can recognize structures and extract the necessary information for your applications.

Does the z-ai-web-dev-sdk support base64 encoding for visual content?

Yes, the z-ai-web-dev-sdk supports base64 encoding for passing visual content. This allows you to process images and videos directly from binary data streams without needing to host them at a public URL.

What is the best way to analyze a complex diagram with AI?

The best way to analyze a complex diagram is using a multimodal AI approach. You upload the diagram photo and use a text prompt to ask the AI to explain its components and their relationships, leveraging visual understanding.

How do I implement conversational AI that understands visual content?

You can implement conversational AI with visual understanding by using the z-ai-web-dev-sdk. It enables applications that combine image and video inputs with text prompts for tasks like description and data extraction.

Are there limitations when processing videos with multimodal AI?

Processing videos with multimodal AI requires passing content through URLs or base64 encoding. While it enables video understanding for conversational AI, large video files may present processing constraints depending on the implementation environment.