VLM

Analyze images in conversations using image_url or base64 inputs.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Enables developers to inject visual understanding into conversational flows by analyzing images and generating contextual descriptions or insights.

Core Features & Use Cases

  • Vision-enabled chat: analyze an image and respond with natural language descriptions.
  • Multimodal conversations: combine text prompts with image inputs for richer interactions.
  • Use Case: build accessibility features by describing images for visually impaired users.

Quick Start

Describe an image by providing its URL and a prompt to start a vision chat session.

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 chat application backend?

To add image analysis to a chat application backend, you inject visual understanding into conversations by processing image_url or base64 inputs to generate natural language descriptions and contextual insights. This enables multimodal interactions combining text prompts with images.

Can I use base64 image inputs for multimodal chat?

Yes, you can use base64 image inputs for multimodal chat sessions. The system supports both base64 and image_url formats, allowing developers to pass encoded image data directly within text prompts for contextual vision analysis.

Does vision chat require the z-ai-web-dev-sdk integration?

Yes, vision chat requires the z-ai-web-dev-sdk backend integration. This SDK dependency provides the underlying multimodal processing logic needed to evaluate images and generate descriptive, contextual responses within conversational flows.

What is the best way to build accessibility features for visually impaired users?

The best way to build accessibility features for visually impaired users is implementing vision-enabled chat to analyze images and respond with natural language descriptions. This approach translates visual content into accessible, conversational text summaries.

How do multi-turn vision-enabled interactions handle contextual image reasoning?

Multi-turn vision-enabled interactions handle contextual image reasoning by maintaining conversational state while analyzing new images. This allows the backend to generate descriptive responses that account for previous text prompts and visual inputs.