Plugin Development Guide

# Create plugin directory
mkdir my_custom_plugin
cd my_custom_plugin

# Create manifest.json
cat > manifest.json << 'EOF'
{
  "id": "my_custom_plugin",
  "name": "My Custom Plugin",
  "version": "1.0.0",
  "author": "Your Name",
  "description": "A custom plugin for VERA",
  "entry_module": "my_plugin.py"
}
EOF

# Plugin directory structure
my_custom_plugin/
├── manifest.json        # Plugin manifest (REQUIRED)
├── my_plugin.py         # Entry module (REQUIRED)
├── icons/               # Optional custom icons
│   └── my_icon.png
└── README.md            # Optional documentation

{
  "id": "my_custom_plugin",
  "name": "My Custom Plugin",
  "version": "1.0.0",
  "author": "Your Name",
  "description": "A custom plugin for VERA",
  "entry_module": "my_plugin.py"
}

from core.nodes import BaseNode


class MyCustomNode(BaseNode):
    """Custom node for special data processing."""

    def __init__(self):
        super().__init__(node_type="my_custom_node", title="My Custom Node")

        # Add input ports
        self.add_input_port("input_data", data_type="string")
        self.add_input_port("parameters", data_type="string")

        # Add output ports
        self.add_output_port("processed_data", data_type="string")
        self.add_output_port("statistics", data_type="string")

        # Set default properties
        self.set_property("processing_mode", "standard")
        self.set_property("enable_logging", True)

    def execute(self, inputs: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Execute the node's processing logic."""
        try:
            # Get input data
            input_data = inputs.get("input_data") if inputs else None
            parameters = inputs.get("parameters", {}) if inputs else {}

            if input_data is None:
                raise ValueError("Input data is required")

            # Process the data
            processed_data, statistics = self._process_data(input_data, parameters)

            # Return results
            return {
                "processed_data": processed_data,
                "statistics": statistics
            }

        except Exception as e:
            self.logger.error(f"Node execution failed: {str(e)}")
            raise

    def _process_data(self, data, parameters):
        """Implement your custom processing logic here."""
        # Example processing - replace with your logic
        import pandas as pd

        if isinstance(data, list) and len(data) > 0:
            df = pd.DataFrame(data)
        elif hasattr(data, '__iter__'):
            df = pd.DataFrame(list(data))
        else:
            raise ValueError("Invalid input data format")

        # Apply your processing
        processed_df = self._apply_custom_processing(df, parameters)

        # Calculate statistics
        stats = {
            'rows_processed': len(processed_df),
            'columns': list(processed_df.columns),
            'processing_mode': self.get_property("processing_mode")
        }

        return processed_df, stats

    def _apply_custom_processing(self, df, parameters):
        """Implement your specific data processing logic."""
        # This is where your custom logic goes
        # Example: add a new calculated column
        df['custom_score'] = df.mean(axis=1)
        return df

    def _inline_summary(self) -> list[str]:
        """Display node status in the canvas."""
        mode = self.get_property("processing_mode")
        return [
            f"Mode: {mode}",
            f"Logging: {'Enabled' if self.get_property('enable_logging') else 'Disabled'}"
        ]

def register(api):
    """Register plugin nodes with VERA."""
    api.register_node(
        node_type="my_custom_node",
        node_class=MyCustomNode,
        display_name="My Custom Node",
        description="A custom node for data processing",
        category="Examples",
        icon_relpath="icons/my_icon.png"
    )

# Testing your plugin:
1. In VERA, go to Plugins → Add Plugin…
2. Select your plugin folder
3. Choose Plugins → Reload Plugins
4. Look for your node in the toolbox under the specified category
5. Create a workflow and test the node functionality

# If you encounter issues:
- Check the console output for error messages
- Verify manifest.json has correct format
- Ensure entry_module matches your Python file name
- Make sure register(api) function exists and is properly implemented

# Create ZIP archive for distribution
zip -r my_custom_plugin.zip my_custom_plugin/

# Alternative: Share the folder directly
# Users can install from folder using Plugins → Add Plugin…

# Distribution tips:
- Include a README.md with usage instructions
- Test your plugin on different systems
- Provide example workflows
- Document any dependencies
- Consider creating a GitHub repository