Analyze images with image_url or base64 inputs for descriptive chat responses.
npx skills add https://github.com/Kin3Xc/promoly --skill vlm-kin3xc
Please help me install this Agent Skill. Skill: VLM Source: https://github.com/Kin3Xc/promoly/tree/main/skills/VLM Command: npx skills add https://github.com/Kin3Xc/promoly --skill vlm-kin3xc
This Skill enables developers to add vision-based conversational capabilities to applications by enabling AI models to analyze images and respond with descriptive insights and contextual information.
Describe this image: https://example.com/photo.jpg
High-intent search queries and answers about installing and using this skill.
You can add image analysis to your backend javascript application using the z-ai-web-dev-sdk to process image_url or base64 inputs. This enables multimodal Q&A workflows by generating descriptive or analytical text responses from image content.
Yes, you can use base64 image inputs for vision-based chat alongside image_url formats. The Skill processes both input types via the z-ai-web-dev-sdk to generate rich text responses for multimodal interactions and product analysis workflows.
Multimodal Q&A combines text prompts with image inputs to generate descriptive insights and contextual information. It works by passing image_url or base64 data to the AI model, which analyzes the visual content and produces analytical responses for applications like accessibility tools or media tagging.
Yes, z-ai-web-dev-sdk supports analyzing single or multiple images to generate a single rich text response. This allows for complex vision-enabled chat interactions where multiple visual inputs are combined with text prompts.
Use vision-based chat for product analysis or accessibility tools when you need to automate image QA in e-commerce or describe scenes for visually impaired users. It processes image content to deliver contextual information and descriptive insights automatically.
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