VLM

Analyze images and answer visual questions within conversational workflows.

Updated Feb 13, 2026
One-click install
npx skills add https://github.com/Munreader/M-nreader --skill vlm-munreader
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: VLM
Source: https://github.com/Munreader/M-nreader/tree/main/skills/VLM
Command: npx skills add https://github.com/Munreader/M-nreader --skill vlm-munreader

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Facilitates multimodal AI interactions by analyzing images, describing visual content, and integrating image understanding into conversational workflows, thereby bridging the gap between visual data and textual AI responses.

Core Features & Use Cases

  • Image Content Analysis: Describe and interpret visual information within images for applications like product descriptions, content moderation, or accessibility.
  • Visual Question Answering: Respond to user queries related to images, such as identifying objects, summarizing scenes, or detecting anomalies.
  • Multimodal Conversation: Support multi-turn AI chats that incorporate images, enabling complex workflows such as comparing visual inputs or providing detailed explanations.

Quick Start

Use the VLM skill to analyze an image URL and get a descriptive response with a simple command.

Frequently Asked Questions about VLM

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

FAQPage Schema
Can I use multimodal AI to answer questions about objects in an image?

Visual question answering allows AI to respond to user queries related to images by identifying objects, summarizing scenes, or detecting anomalies. It integrates vision models with text prompts to answer specific visual questions accurately.

What is the best way to support multi-turn conversations involving images?

Supporting multi-turn conversations involving images requires integrating multimodal AI capabilities that seamlessly fuse visual and textual data. This allows the system to recall previous visual inputs and provide detailed explanations across multiple chat turns.

Does the VLM skill work with web dev SDKs for visual content moderation?

Yes, the VLM skill works with the z-ai-web-dev-sdk to facilitate visual content moderation by describing and interpreting visual information within images. This integration bridges visual data with textual AI responses for moderation workflows.

What are the limitations of using image understanding for conversational AI?

Limitations of image understanding in conversational AI include the complexity of seamlessly fusing visual and textual data across multiple turns. While it supports complex workflows like comparing visual inputs, highly dynamic visual anomalies may challenge accurate interpretation.