name: node-creator
description: Create custom Genfeed nodes using the SDK. Triggers on "create a new node", "add a custom node type", "build a node for X".
license: MIT
metadata:
author: genfeedai
version: "1.0.0"

Node Creator

You are an expert at creating custom nodes for Genfeed using the SDK. When the user describes a node they want to create, you generate the complete TypeScript code for the node definition.

SDK Overview

The Genfeed SDK provides a fluent builder API for creating custom nodes:

import { createNode, registerNode } from '@genfeedai/sdk';

const myNode = createNode('myOrg/customNode')
  .name('My Custom Node')
  .description('Does something useful')
  .category('processing')
  .input('image', 'image', 'Input Image', { required: true })
  .output('image', 'image', 'Processed Image')
  .config({ key: 'intensity', type: 'slider', label: 'Intensity', min: 0, max: 100 })
  .process(async (data, ctx) => {
    // Processing logic
    return { outputs: { image: processedImageUrl } };
  })
  .build();

registerNode(myNode);

Core Interfaces

CustomNodeDefinition

interface CustomNodeDefinition<TData extends CustomNodeData = CustomNodeData> {
  /** Unique identifier for the node type (e.g., 'myOrg/customNode') */
  type: string;

  /** Human-readable name displayed in the UI */
  name: string;

  /** Short description of what the node does */
  description: string;

  /** Category for organizing in the node palette */
  category: NodeCategory | 'custom';

  /** Icon name (Lucide icon names) */
  icon?: string;

  /** Input handles (data the node receives) */
  inputs: HandleDefinition[];

  /** Output handles (data the node produces) */
  outputs: HandleDefinition[];

  /** Default data values when creating a new instance */
  defaultData: Partial<TData>;

  /** Configuration schema for the node's settings panel */
  configSchema?: ConfigField[];

  /** Processing function executed when the node runs */
  process: NodeProcessor<TData>;

  /** Optional validation function */
  validate?: NodeValidator<TData>;

  /** Optional cost estimator for workflow cost calculations */
  estimateCost?: CostEstimator<TData>;
}

HandleDefinition

interface HandleDefinition {
  /** Unique ID within the node */
  id: string;

  /** Data type this handle accepts/produces */
  type: HandleType; // 'image' | 'text' | 'video' | 'number' | 'audio'

  /** Human-readable label */
  label: string;

  /** Can accept multiple connections (for inputs) */
  multiple?: boolean;

  /** Is this handle required? */
  required?: boolean;
}

ConfigField

interface ConfigField {
  /** Field key in node data */
  key: string;

  /** Field type */
  type: 'text' | 'number' | 'select' | 'checkbox' | 'slider' | 'textarea' | 'color';

  /** Human-readable label */
  label: string;

  /** Optional description/help text */
  description?: string;

  /** Default value */
  defaultValue?: unknown;

  /** Is this field required? */
  required?: boolean;

  /** Options for select fields */
  options?: Array<{ value: string; label: string }>;

  /** Min/max for number/slider fields */
  min?: number;
  max?: number;
  step?: number;

  /** Placeholder text */
  placeholder?: string;

  /** Conditional display based on other field values */
  showWhen?: Record<string, unknown>;
}

ProcessorContext

interface ProcessorContext {
  /** Node instance ID */
  nodeId: string;

  /** Current execution ID */
  executionId: string;

  /** Workflow ID */
  workflowId: string;

  /** Input values resolved from connected nodes */
  inputs: Record<string, unknown>;

  /** Update progress (0-100) */
  updateProgress: (percent: number, message?: string) => Promise<void>;

  /** Log a message */
  log: (message: string) => Promise<void>;

  /** Abort signal for cancellation */
  signal: AbortSignal;
}

ProcessorResult

interface ProcessorResult {
  /** Output values keyed by output handle ID */
  outputs: Record<string, unknown>;

  /** Optional metadata about the processing */
  metadata?: Record<string, unknown>;
}

Handle Types

Node Categories

Common Icons (Lucide)

Image, Video, AudioLines, MessageSquare, FileText, Sparkles, Brain, Mic,
Wand2, Layers, Scissors, Film, Crop, Maximize, Grid3X3, Pencil, Subtitles,
CheckCircle, GitBranch, ArrowRightToLine, Navigation

NodeBuilder Methods

Example Nodes

Image Filter Node

import { createNode, registerNode } from '@genfeedai/sdk';

interface ImageFilterData {
  label: string;
  status: 'idle' | 'pending' | 'processing' | 'complete' | 'error';
  inputImage: string | null;
  outputImage: string | null;
  filterType: 'blur' | 'sharpen' | 'grayscale' | 'sepia';
  intensity: number;
}

const imageFilterNode = createNode<ImageFilterData>('custom/imageFilter')
  .name('Image Filter')
  .description('Apply visual filters to images')
  .category('processing')
  .icon('Wand2')
  .input('image', 'image', 'Input Image', { required: true })
  .output('image', 'image', 'Filtered Image')
  .config({
    key: 'filterType',
    type: 'select',
    label: 'Filter Type',
    defaultValue: 'blur',
    options: [
      { value: 'blur', label: 'Blur' },
      { value: 'sharpen', label: 'Sharpen' },
      { value: 'grayscale', label: 'Grayscale' },
      { value: 'sepia', label: 'Sepia' },
    ],
  })
  .config({
    key: 'intensity',
    type: 'slider',
    label: 'Intensity',
    defaultValue: 50,
    min: 0,
    max: 100,
    step: 1,
    showWhen: { filterType: ['blur', 'sharpen'] },
  })
  .defaults({
    label: 'Image Filter',
    status: 'idle',
    inputImage: null,
    outputImage: null,
    filterType: 'blur',
    intensity: 50,
  })
  .process(async (data, ctx) => {
    const { inputImage, filterType, intensity } = data;
    const imageUrl = ctx.inputs.image as string;

    await ctx.updateProgress(10, 'Applying filter...');

    // Call your filter API
    const response = await fetch('https://your-api.com/filter', {
      method: 'POST',
      body: JSON.stringify({ imageUrl, filterType, intensity }),
      signal: ctx.signal,
    });

    const result = await response.json();

    await ctx.updateProgress(100, 'Complete');

    return {
      outputs: { image: result.filteredImageUrl },
      metadata: { filterApplied: filterType },
    };
  })
  .validate((data, inputs) => {
    if (!inputs.image) {
      return { valid: false, errors: ['Input image is required'] };
    }
    return { valid: true };
  })
  .cost((data) => ({
    estimated: 0.01,
    description: 'Image filter processing',
  }))
  .build();

registerNode(imageFilterNode);

Text Summarizer Node

import { createNode, registerNode } from '@genfeedai/sdk';

interface TextSummarizerData {
  label: string;
  status: 'idle' | 'pending' | 'processing' | 'complete' | 'error';
  inputText: string | null;
  outputText: string | null;
  maxLength: number;
  style: 'bullet' | 'paragraph' | 'tldr';
}

const textSummarizerNode = createNode<TextSummarizerData>('custom/textSummarizer')
  .name('Text Summarizer')
  .description('Summarize long text into concise summaries')
  .category('ai')
  .icon('FileText')
  .input('text', 'text', 'Input Text', { required: true })
  .output('text', 'text', 'Summary')
  .config({
    key: 'style',
    type: 'select',
    label: 'Summary Style',
    defaultValue: 'paragraph',
    options: [
      { value: 'bullet', label: 'Bullet Points' },
      { value: 'paragraph', label: 'Paragraph' },
      { value: 'tldr', label: 'TL;DR' },
    ],
  })
  .config({
    key: 'maxLength',
    type: 'number',
    label: 'Max Length (words)',
    defaultValue: 100,
    min: 10,
    max: 500,
  })
  .defaults({
    label: 'Text Summarizer',
    status: 'idle',
    inputText: null,
    outputText: null,
    maxLength: 100,
    style: 'paragraph',
  })
  .process(async (data, ctx) => {
    const inputText = ctx.inputs.text as string;
    const { maxLength, style } = data;

    await ctx.updateProgress(20, 'Analyzing text...');

    // Call summarization API
    const summary = await summarizeText(inputText, { maxLength, style });

    await ctx.updateProgress(100, 'Complete');

    return {
      outputs: { text: summary },
    };
  })
  .build();

registerNode(textSummarizerNode);

Video Watermark Node

import { createNode, registerNode } from '@genfeedai/sdk';

interface VideoWatermarkData {
  label: string;
  status: 'idle' | 'pending' | 'processing' | 'complete' | 'error';
  inputVideo: string | null;
  inputImage: string | null;
  outputVideo: string | null;
  position: 'top-left' | 'top-right' | 'bottom-left' | 'bottom-right' | 'center';
  opacity: number;
  scale: number;
}

const videoWatermarkNode = createNode<VideoWatermarkData>('custom/videoWatermark')
  .name('Video Watermark')
  .description('Add a watermark image overlay to videos')
  .category('processing')
  .icon('Layers')
  .input('video', 'video', 'Input Video', { required: true })
  .input('image', 'image', 'Watermark Image', { required: true })
  .output('video', 'video', 'Watermarked Video')
  .config({
    key: 'position',
    type: 'select',
    label: 'Position',
    defaultValue: 'bottom-right',
    options: [
      { value: 'top-left', label: 'Top Left' },
      { value: 'top-right', label: 'Top Right' },
      { value: 'bottom-left', label: 'Bottom Left' },
      { value: 'bottom-right', label: 'Bottom Right' },
      { value: 'center', label: 'Center' },
    ],
  })
  .config({
    key: 'opacity',
    type: 'slider',
    label: 'Opacity',
    defaultValue: 80,
    min: 10,
    max: 100,
    step: 5,
  })
  .config({
    key: 'scale',
    type: 'slider',
    label: 'Scale (%)',
    defaultValue: 20,
    min: 5,
    max: 50,
    step: 5,
  })
  .defaults({
    label: 'Video Watermark',
    status: 'idle',
    inputVideo: null,
    inputImage: null,
    outputVideo: null,
    position: 'bottom-right',
    opacity: 80,
    scale: 20,
  })
  .process(async (data, ctx) => {
    const videoUrl = ctx.inputs.video as string;
    const watermarkUrl = ctx.inputs.image as string;
    const { position, opacity, scale } = data;

    await ctx.updateProgress(10, 'Processing video...');

    // Call watermark API (FFmpeg-based)
    const result = await addWatermark(videoUrl, watermarkUrl, {
      position,
      opacity: opacity / 100,
      scale: scale / 100,
    });

    await ctx.updateProgress(100, 'Complete');

    return {
      outputs: { video: result.outputUrl },
    };
  })
  .build();

registerNode(videoWatermarkNode);

Multi-Input Combiner Node

import { createNode, registerNode } from '@genfeedai/sdk';

interface ImageCombinerData {
  label: string;
  status: 'idle' | 'pending' | 'processing' | 'complete' | 'error';
  inputImages: string[];
  outputImage: string | null;
  layout: 'grid' | 'horizontal' | 'vertical';
  gap: number;
}

const imageCombinerNode = createNode<ImageCombinerData>('custom/imageCombiner')
  .name('Image Combiner')
  .description('Combine multiple images into a single image')
  .category('processing')
  .icon('Grid3X3')
  .input('images', 'image', 'Input Images', { multiple: true, required: true })
  .output('image', 'image', 'Combined Image')
  .config({
    key: 'layout',
    type: 'select',
    label: 'Layout',
    defaultValue: 'grid',
    options: [
      { value: 'grid', label: 'Grid' },
      { value: 'horizontal', label: 'Horizontal Strip' },
      { value: 'vertical', label: 'Vertical Strip' },
    ],
  })
  .config({
    key: 'gap',
    type: 'slider',
    label: 'Gap (pixels)',
    defaultValue: 10,
    min: 0,
    max: 50,
    step: 2,
  })
  .defaults({
    label: 'Image Combiner',
    status: 'idle',
    inputImages: [],
    outputImage: null,
    layout: 'grid',
    gap: 10,
  })
  .process(async (data, ctx) => {
    const images = ctx.inputs.images as string[];
    const { layout, gap } = data;

    if (images.length < 2) {
      throw new Error('At least 2 images required');
    }

    await ctx.updateProgress(20, `Combining ${images.length} images...`);

    const result = await combineImages(images, { layout, gap });

    await ctx.updateProgress(100, 'Complete');

    return {
      outputs: { image: result.combinedImageUrl },
    };
  })
  .build();

registerNode(imageCombinerNode);

Backend Processor Pattern (BullMQ)

For nodes that require long-running jobs:

// In your NestJS processor
import { Processor, WorkerHost } from '@nestjs/bullmq';
import { Job } from 'bullmq';

@Processor('custom-node-queue')
export class CustomNodeProcessor extends WorkerHost {
  async process(job: Job<CustomNodeJobData>) {
    const { nodeId, executionId, data, inputs } = job.data;

    // Update progress
    await job.updateProgress(10);

    // Do work...
    const result = await this.processNode(data, inputs);

    // Update progress
    await job.updateProgress(100);

    return result;
  }
}

Registration Pattern

// nodes/index.ts - Export all custom nodes
import { imageFilterNode } from './imageFilter';
import { textSummarizerNode } from './textSummarizer';
import { videoWatermarkNode } from './videoWatermark';

export const customNodes = [
  imageFilterNode,
  textSummarizerNode,
  videoWatermarkNode,
];

// Register all at once
import { nodeRegistry } from '@genfeedai/sdk';

nodeRegistry.registerAll(customNodes);

Plugin Manifest

For distributing as a plugin:

import { PluginManifest } from '@genfeedai/sdk';

export const manifest: PluginManifest = {
  name: '@myorg/genfeed-plugin-filters',
  version: '1.0.0',
  description: 'Image and video filter nodes for Genfeed',
  author: 'My Organization',
  license: 'MIT',
  nodes: ['custom/imageFilter', 'custom/videoWatermark'],
  minGenfeedVersion: '1.0.0',
  homepage: 'https://github.com/myorg/genfeed-plugin-filters',
};

Instructions

When the user describes a custom node they want to create:

1. Understand the purpose: What does the node do? What inputs/outputs does it need?
2. Define the data interface: Create a typed interface for the node's data
3. Choose appropriate handles: Select input/output types based on data flow
4. Design configuration: Add config fields for user-adjustable settings
5. Implement processing: Write the async process function with progress updates
6. Add validation: Include input validation where appropriate
7. Estimate costs: Add cost estimation if the node uses paid APIs

Always output complete, runnable TypeScript code that follows the SDK patterns.