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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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.
license
MIT
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
# Describe an image from URL
z-ai vision --prompt --image
z-ai vision -p -i
"What's in this image?"
"https://example.com/photo.jpg"
# Using short options
"Describe this image"
"https://example.com/image.png"
Analyze Local Images
# 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
Multiple Images
# Analyze multiple images at once
z-ai vision \
-p "Compare these two images" \
-i "./photo1.jpg" \
-i "./photo2.jpg" \
-o comparison.json
# Multiple images with detailed analysis
z-ai vision \
--prompt "What are the differences between these images?" \
--image "https://example.com/before.jpg" \
--image "https://example.com/after.jpg"
With Thinking (Chain of Thought)
# Enable thinking for complex visual reasoning
z-ai vision \
-p "Count the number of people in this image and describe their activities" \
-i "./crowd.jpg" \
--thinking \
-o analysis.json
Streaming Output
# Stream the vision analysis
z-ai vision -p "Describe this image in detail" -i "./photo.jpg" --stream
CLI Parameters
--prompt, -p <text>: Required - Question or instruction about the image(s)
--image, -i <URL or path>: Optional - Image URL or local file path (can be used multiple times)
Note: You can combine multiple content types in a single message. For example, you can include both text and multiple images, or text with both an image and a document.
