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VLM

Extract insights and structured information from images, videos, and documents.

1|Updated May 1, 2026
Search Tags:#ocr#vision ai#image analysis#backend integration#image comparison#multimodal chat#z-ai sdk
One-click install
npx skills add https://github.com/e2662020/QuickMovie --skill vlm-e2662020
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: VLM
Source: https://github.com/e2662020/QuickMovie/tree/main/skills/VLM
Command: npx skills add https://github.com/e2662020/QuickMovie --skill vlm-e2662020

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill eliminates the need for manual visual content analysis, enabling users to quickly extract insights, descriptions, and structured information from images, videos, and documents via natural language conversational AI.

Core Features & Use Cases

  • Multimodal Visual Analysis: Supports analyzing single or multiple images, videos, and documents via URL or base64 encoding, with capabilities for image comparison, OCR text extraction, and content classification.
  • Conversational Vision Workflows: Enables multi-turn chat sessions with visual content for follow-up questions, and can be integrated into backend APIs for production use cases like product defect detection, content moderation, and accessibility alt text generation.
  • Use Case Example: An e-commerce team can use this Skill to automatically analyze product images to identify visual defects, or a content team can generate accurate alt text for website images to improve accessibility.

Quick Start

Use the VLM skill to analyze the provided product image and generate a detailed description of its features, condition, and any visible defects.

Frequently Asked Questions about VLM

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

FAQPage Schema
How do I extract text from images using OCR for backend processing?▼

OCR text extraction from receipts and forms is handled by sending image URLs, base64 encoded media, or file URLs to the skill for structured information retrieval via conversational AI.

Can I use the z-ai-web-dev-sdk to generate accessibility alt text for web images?▼

Yes, automated accessibility alt text generation is supported by processing visual content inputs through the z-ai-web-dev-sdk backend library to produce accurate descriptive outputs.

What is the best way to perform image comparison for product defect analysis?▼

Image difference comparison for product defect analysis is performed by submitting multiple images to the multimodal chat interface, which identifies visual discrepancies and generates detailed condition reports.

Does this visual content analysis approach support multi-turn conversational workflows?▼

Yes, conversational vision workflows support multi-turn chat sessions with visual content, allowing follow-up questions to refine extracted insights, descriptions, and structured information.

How do I integrate content moderation into my backend API using multimodal AI?▼

Content moderation is integrated into backend APIs by passing visual content through the z-ai-web-dev-sdk, which classifies materials and returns moderation insights for production use cases.

Do I need base64 encoding to analyze visual content through the z-ai-web-dev-sdk?▼

No, while base64 encoded media is supported, you can also analyze visual content by providing standard image URLs, video URLs, or document file URLs as inputs to the processing library.

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