VLM
Implement vision-based AI chat capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze images, describe visual content, or create applications that combine image understanding with conversational AI. Supports image URLs and base64 encoded images for multimodal interactions.
npx skills add jjyaoao/HelloAgents --skill VLM --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# VLM(Vision Chat) Skill This skill guides the implementation of vision chat functionality using the z-ai-web-dev-sdk package, enabling AI models to understand and respond to images combined with text prompts. ## Skills Path **Skill Location**: `{project_path}/skills/VLM` this skill is located at above path in your project. **Reference Scripts**: Example test scripts are available in the `{Skill Location}/scripts/` directory for quick testing and reference. See `{Skill Location}/scripts/vlm.ts` for a working example. ## Overview Vision Chat allows you to build applications that can analyze images, extract information from visual content, and answer questions about images through natural language conversation. **IMPORTANT**: z-ai-web-dev-sdk MUST be used in backend code only. Never use it in client-side code. ## Prerequisites The z-ai-web-dev-sdk package is already installed. Import it as shown in the examples below. ## CLI Usage (For Simple Tasks) For simple image analysis tasks, you can use the z-ai CLI instead of writing code. This is ideal for quick image descriptions, testing vision capabilities, or simple automation. ### Basic Image Analysis ```bash # Describe an image from UR
- Skills Path
- Overview
- Prerequisites
- CLI Usage (For Simple Tasks)
- Basic Image Analysis
- Analyze Local Images
- Multiple Images
- With Thinking (Chain of Thought)
- Streaming Output
- CLI Parameters
- Supported Image Formats
- When to Use CLI vs SDK
- Recommended Approach
- Supported Content Types
Describe an image from URL z-ai vision --prompt "What's in this image?" --image "https://example.com/photo.jpg" Using short options z-ai vision -p "Describe this image" -i "https://example.com/image.png" Analyze a local image file z-ai vision -p "What objects are in this photo?" -i "./photo.jpg" Save response to file z-ai vision -p "Describe the scene" -i "./landscape.png" -o description.json Analyze multiple images at once z-ai vision \
What does the VLM skill do?
Implement vision-based AI chat capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze images, describe visual content, or create applications that combine image understanding with conversational AI. Supports image URLs and base64 encoded images for multimodal interactions.
How do I install it?
Run `npx skills add jjyaoao/HelloAgents --skill VLM --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jjyaoao/HelloAgents, a repository with 2,615 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.