| name | zai-vision |
| description | Dynamic access to zai-vision MCP server (8 tools, transport: stdio) |
zai-vision Skill
This skill provides dynamic access to the zai-vision MCP server with progressive disclosure loading.
Transport Protocol
Protocol: Standard Input/Output (stdio)
Context Efficiency
Traditional MCP approach:
- All 8 tools loaded at startup
- Estimated context: 4000 tokens
This skill approach:
- Metadata only: ~150 tokens
- Full instructions (when used): ~5k tokens
- Tool execution: 0 tokens (runs externally)
Available Tools
ui_to_artifact - Convert UI screenshots into various artifacts: code, prompts, design specifications, or descriptions.
extract_text_from_screenshot - Extract and recognize text from screenshots using advanced OCR capabilities.
diagnose_error_screenshot - Diagnose and analyze error messages, stack traces, and exception screenshots.
understand_technical_diagram - Analyze and explain technical diagrams including architecture diagrams, flowcharts, UML, ER diagrams, and system design diagrams.
analyze_data_visualization - Analyze data visualizations, charts, graphs, and dashboards to extract insights and trends.
ui_diff_check - Compare two UI screenshots to identify visual differences and implementation discrepancies.
analyze_image - General-purpose image analysis for scenarios not covered by specialized tools.
analyze_video - Analyze video content using advanced AI vision models.
Usage Pattern
When the user's request matches this skill's capabilities:
Step 1: Identify the right tool from the list above
Step 2: Generate a tool call in this JSON format:
{
"tool": "tool_name",
"arguments": {
"param1": "value1",
"param2": "value2"
}
}
Step 3: Execute via bash:
cd $SKILL_DIR
python3 executor.py --call 'YOUR_JSON_HERE'
⚠️ 重要: Replace $SKILL_DIR with the actual discovered path of this skill directory.
Getting Tool Details
If you need detailed information about a specific tool's parameters:
cd $SKILL_DIR
python3 executor.py --describe tool_name
Examples
Example 1: List all tools
cd $SKILL_DIR
python3 executor.py --list
Example 2: Describe a tool
cd $SKILL_DIR
python3 executor.py --describe tool_name
Example 3: Call a tool
cd $SKILL_DIR
python3 executor.py --call '{"tool": "tool_name", "arguments": {"param1": "value"}}'
Example 4: Call a tool with parameters
cd $SKILL_DIR
python3 executor.py --call '{
"tool": "ui_to_artifact",
"arguments": {
"image_source": "/path/to/image.png",
"output_type": "code",
"prompt": "Generate React code"
}
}'
Error Handling
If the executor returns an error:
- Check the tool name is correct
- Verify required arguments are provided
- Ensure the MCP server is accessible
- Check API keys in mcp-config.json
Performance Notes
Context usage comparison:
| Scenario | MCP (preload) | Skill (dynamic) |
|---|
| Idle | 4000 tokens | 150 tokens |
| Active | 4000 tokens | 5k tokens |
| Executing | 4000 tokens | 0 tokens |
Savings: ~96% reduction in typical usage
This skill was auto-generated from MCP server configuration
Generator: mcp-to-skill (simplified)
