VLM

Name: VLM
Availability: InStock
Author: samuelrm25

Analyze images and describe visual content in multimodal chat conversations.

Updated Apr 19, 2025

Search Tags:#backend #multimodal #vision #chat #conversational-ai #image-analysis #z-ai-web-dev-sdk

Ready to run

npx skills add https://github.com/SamuelRM25/app-visitador --skill vlm-samuelrm25

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: VLM
Source: https://github.com/SamuelRM25/app-visitador/tree/main/skills/VLM
Command: npx skills add https://github.com/SamuelRM25/app-visitador --skill vlm-samuelrm25

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables developers to build vision-based conversational AI that can analyze images and describe visuals within a chat flow, without requiring separate vision tooling.

Core Features & Use Cases

Vision Chat: Describe images and answer questions about visual content in multi-turn conversations.
Multimodal Interactions: Combine text prompts with image inputs (via URLs or base64) for richer responses.
Use Case: Narrate visuals for accessibility or enrich product support bots with image insights.

Quick Start

Run the vlm.ts script with an image URL and a prompt to generate a description of the image and answer questions in a chat.

Frequently Asked Questions about VLM

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

FAQPage Schema

How do I add image analysis to a conversational AI chatbot?▼

To add image analysis to a conversational AI chatbot, use a multimodal vision API that processes image inputs alongside text prompts. The VLM Skill enables this by passing image URLs or base64 data through the z-ai-web-dev-sdk to generate descriptive responses within chat workflows.

Can I use base64 image inputs for vision-driven chat responses?▼

Yes, you can use base64 image inputs for vision-driven chat responses. The VLM Skill supports both image URLs and base64 encoded images, allowing backend systems to assemble multimodal prompts and receive descriptive text outputs via the z-ai-web-dev-sdk.

What is the best way to build an accessibility assistant that narrates visual content?▼

The best way to build an accessibility assistant that narrates visual content is integrating vision-based AI chat. The VLM Skill processes image inputs and generates descriptive text answers, enabling multi-turn conversations that narrate visuals for accessibility without separate vision tooling.

Does the z-ai-web-dev-sdk support multimodal interactions with image URLs?▼

Yes, the z-ai-web-dev-sdk supports multimodal interactions with image URLs. The VLM Skill leverages this dependency on the backend to combine text prompts with image URL inputs, routing them to a Vision API to generate contextual descriptions.

How do I implement image-question answering workflows for a product support bot?▼

To implement image-question answering workflows for a product support bot, integrate a multimodal chat interface that accepts image inputs. The VLM Skill uses the z-ai-web-dev-sdk to analyze provided images and answer specific user questions about the visual content in conversation.

Do I need separate vision tooling to describe images in a multi-turn conversation?▼

No, you do not need separate vision tooling to describe images in a multi-turn conversation. The VLM Skill consolidates this capability within the z-ai-web-dev-sdk, allowing developers to run a backend script that handles visual analysis and conversational context together.