zai-vision

# 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: ```json { "tool": "tool_name", "arguments": { "param1": "value1", "param2": "value2" } } ``` **Step 3: Execute via bash:** ```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: ```bash cd $SKILL_DIR python3 executor.py --describe tool_name ``` ## Examples ### Example 1: List all tools ```bash cd $SKILL_DIR python3 executor.py --list ``` ### Example 2: Describe a tool ```bash cd $SKILL_DIR python3 executor.py --describe tool_name ``` ### Example 3: Call a tool ```bash cd $SKILL_DIR python3 executor.py --call '{"tool": "tool_name", "arguments": {"param1": "value"}}' ``` ### Example 4: Call a tool with parameters ```bash 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)*