Slash Command

/n8n-builder

Generates complete, production-ready n8n workflow JSON files from natural language requirements. Includes templates for common patterns like AI email responders and data pipelines.

N8n

OpenAI

automation

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How this command is triggered — by the user, by Claude, or both

Slash command

/n8n-workflow-designer:n8n-builder

Model invocable

No pre-commands

Referenced Files

Files this command reads when invoked

n8n/n8n-nodes-langchain.lmChatOpenAi

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# n8n Workflow Builder

Generate production-ready n8n workflow JSON files.

## Usage

When the user requests an n8n workflow, analyze their requirements and create a complete, importable workflow JSON file.

## Workflow Templates

### 1. AI Email Responder


### 2. Data Processing Pipeline


### 3. Content Pipeline


### 4. Lead Qualification


## Features

- Complex branching logic
- Loops and iterations
- Error handling and retries
- Custom code nodes
- 200+ integrations
- Self-hostable
- AI model integration
- Database operations
- API calls and webhooks

## Best Practices

1. **Always a...

Command Content

519 lines · ~2.9k tokens

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LanguagePython

Parent stars1

MaintenancePoor

Last CommitMar 22, 2026

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n8n Workflow Builder

Generate production-ready n8n workflow JSON files.

Usage

When the user requests an n8n workflow, analyze their requirements and create a complete, importable workflow JSON file.

Workflow Templates

1. AI Email Responder

{
  "name": "AI Email Auto-Responder",
  "nodes": [
    {
      "parameters": {
        "pollTimes": {
          "item": [
            {
              "mode": "everyMinute"
            }
          ]
        },
        "simple": true,
        "filters": {
          "labelIds": ["INBOX"]
        }
      },
      "id": "1",
      "name": "Gmail Trigger",
      "type": "n8n-nodes-base.gmailTrigger",
      "typeVersion": 1,
      "position": [250, 300]
    },
    {
      "parameters": {
        "model": "gpt-4",
        "messages": {
          "values": [
            {
              "content": "=Draft a professional response to this email:\n\nFrom: {{ $json.from }}\nSubject: {{ $json.subject }}\nBody: {{ $json.body }}\n\nWrite a helpful, friendly response."
            }
          ]
        }
      },
      "id": "2",
      "name": "OpenAI",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "typeVersion": 1,
      "position": [450, 300],
      "credentials": {
        "openAiApi": {
          "id": "1",
          "name": "OpenAI API"
        }
      }
    },
    {
      "parameters": {
        "sendTo": "={{ $('Gmail Trigger').item.json.from }}",
        "subject": "=Re: {{ $('Gmail Trigger').item.json.subject }}",
        "message": "={{ $json.output }}",
        "options": {}
      },
      "id": "3",
      "name": "Gmail Send",
      "type": "n8n-nodes-base.gmail",
      "typeVersion": 2,
      "position": [650, 300],
      "credentials": {
        "gmailOAuth2": {
          "id": "1",
          "name": "Gmail OAuth2"
        }
      }
    }
  ],
  "connections": {
    "Gmail Trigger": {
      "main": [
        [
          {
            "node": "OpenAI",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI": {
      "main": [
        [
          {
            "node": "Gmail Send",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

2. Data Processing Pipeline

{
  "name": "CSV to Database with AI Enhancement",
  "nodes": [
    {
      "parameters": {
        "fileFormat": "csv",
        "options": {}
      },
      "id": "1",
      "name": "Read CSV",
      "type": "n8n-nodes-base.spreadsheetFile",
      "typeVersion": 2,
      "position": [250, 300]
    },
    {
      "parameters": {
        "batchSize": 10,
        "options": {}
      },
      "id": "2",
      "name": "Loop Over Items",
      "type": "n8n-nodes-base.splitInBatches",
      "typeVersion": 3,
      "position": [450, 300]
    },
    {
      "parameters": {
        "model": "gpt-4",
        "messages": {
          "values": [
            {
              "content": "=Enhance this data record with additional context:\n{{ JSON.stringify($json) }}"
            }
          ]
        }
      },
      "id": "3",
      "name": "AI Enhancement",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "typeVersion": 1,
      "position": [650, 300]
    },
    {
      "parameters": {
        "operation": "insert",
        "schema": {
          "__rl": true,
          "value": "public",
          "mode": "list"
        },
        "table": {
          "__rl": true,
          "value": "records",
          "mode": "list"
        },
        "columns": {
          "mappingMode": "autoMapInputData",
          "value": {}
        },
        "options": {}
      },
      "id": "4",
      "name": "PostgreSQL Insert",
      "type": "n8n-nodes-base.postgres",
      "typeVersion": 2.4,
      "position": [850, 300]
    }
  ],
  "connections": {
    "Read CSV": {
      "main": [
        [
          {
            "node": "Loop Over Items",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Loop Over Items": {
      "main": [
        [
          {
            "node": "AI Enhancement",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "AI Enhancement": {
      "main": [
        [
          {
            "node": "PostgreSQL Insert",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

3. Content Pipeline

{
  "name": "RSS to Social Media with AI",
  "nodes": [
    {
      "parameters": {
        "url": "https://example.com/feed.xml",
        "options": {}
      },
      "id": "1",
      "name": "RSS Feed",
      "type": "n8n-nodes-base.rssFeedRead",
      "typeVersion": 1,
      "position": [250, 300]
    },
    {
      "parameters": {
        "conditions": {
          "dateTime": [
            {
              "value1": "={{ $json.pubDate }}",
              "operation": "after",
              "value2": "={{ $now.minus({ hours: 24 }) }}"
            }
          ]
        }
      },
      "id": "2",
      "name": "Filter New Items",
      "type": "n8n-nodes-base.filter",
      "typeVersion": 2,
      "position": [450, 300]
    },
    {
      "parameters": {
        "model": "gpt-4",
        "messages": {
          "values": [
            {
              "content": "=Create a compelling social media post from this article:\n\nTitle: {{ $json.title }}\nSummary: {{ $json.contentSnippet }}\n\nMake it engaging and include relevant hashtags."
            }
          ]
        }
      },
      "id": "3",
      "name": "Generate Post",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "typeVersion": 1,
      "position": [650, 300]
    },
    {
      "parameters": {
        "text": "={{ $json.output }}",
        "additionalFields": {}
      },
      "id": "4",
      "name": "Twitter Post",
      "type": "n8n-nodes-base.twitter",
      "typeVersion": 2,
      "position": [850, 300]
    }
  ],
  "connections": {
    "RSS Feed": {
      "main": [
        [
          {
            "node": "Filter New Items",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Filter New Items": {
      "main": [
        [
          {
            "node": "Generate Post",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Generate Post": {
      "main": [
        [
          {
            "node": "Twitter Post",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

4. Lead Qualification

{
  "name": "Lead Scoring and Routing",
  "nodes": [
    {
      "parameters": {
        "path": "lead-webhook",
        "options": {}
      },
      "id": "1",
      "name": "Webhook",
      "type": "n8n-nodes-base.webhook",
      "typeVersion": 2,
      "position": [250, 300]
    },
    {
      "parameters": {
        "model": "gpt-4",
        "messages": {
          "values": [
            {
              "content": "=Score this lead from 0-100 based on fit:\n\nCompany: {{ $json.company }}\nRole: {{ $json.role }}\nIndustry: {{ $json.industry }}\nBudget: {{ $json.budget }}\n\nProvide score and reasoning."
            }
          ]
        }
      },
      "id": "2",
      "name": "AI Score",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "typeVersion": 1,
      "position": [450, 300]
    },
    {
      "parameters": {
        "conditions": {
          "number": [
            {
              "value1": "={{ $json.score }}",
              "operation": "larger",
              "value2": 70
            }
          ]
        }
      },
      "id": "3",
      "name": "IF High Score",
      "type": "n8n-nodes-base.if",
      "typeVersion": 2,
      "position": [650, 300]
    },
    {
      "parameters": {
        "resource": "deal",
        "operation": "create"
      },
      "id": "4",
      "name": "Create CRM Deal",
      "type": "n8n-nodes-base.hubspot",
      "typeVersion": 2,
      "position": [850, 250]
    },
    {
      "parameters": {
        "resource": "email",
        "operation": "send",
        "subject": "New Lead Follow Up",
        "message": "={{ $json.reasoning }}"
      },
      "id": "5",
      "name": "Nurture Email",
      "type": "n8n-nodes-base.gmail",
      "typeVersion": 2,
      "position": [850, 350]
    }
  ],
  "connections": {
    "Webhook": {
      "main": [
        [
          {
            "node": "AI Score",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "AI Score": {
      "main": [
        [
          {
            "node": "IF High Score",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "IF High Score": {
      "main": [
        [
          {
            "node": "Create CRM Deal",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Nurture Email",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

Features

Complex branching logic
Loops and iterations
Error handling and retries
Custom code nodes
200+ integrations
Self-hostable
AI model integration
Database operations
API calls and webhooks

Best Practices

Always add error handling - Use error workflows or try-catch nodes
Test with small datasets first - Validate before processing large volumes
Use environment variables for secrets - Never hardcode API keys
Implement logging for debugging - Add notes or database logging
Version control your workflows - Export and commit to git
Monitor resource usage - Watch execution times and API costs
Use descriptive node names - Make workflows self-documenting
Implement rate limiting - Respect API limits with Wait nodes
Batch processing for scale - Use Split in Batches for large datasets
Regular backups - Export workflows regularly

Output Format

When generating a workflow, provide:

Workflow Name - Clear, descriptive name
Architecture Diagram - Visual flow of nodes
Complete JSON - Importable workflow file
Setup Instructions - Credentials, settings needed
Testing Steps - How to validate the workflow
Deployment Notes - Self-hosted vs cloud considerations
Cost Estimation - Expected API and resource costs

Example Response

# Workflow: AI Customer Support Automation

## Architecture

Ticket Created → Classify (AI) → Route by Priority → Draft Response (AI) → Human Review → Send


## Nodes
1. Webhook trigger for new tickets
2. OpenAI classification
3. IF node for routing
4. OpenAI response generation
5. Slack notification for review
6. Email send on approval

## Setup
1. Create webhook in your ticketing system
2. Add OpenAI API credentials
3. Configure Slack webhook
4. Set up email credentials
5. Test with sample ticket

## JSON
[Complete importable JSON here]

## Testing
- Send test ticket through webhook
- Verify classification accuracy
- Check routing logic
- Review AI-generated responses
- Test Slack notifications

## Cost Estimate
- ~$0.01 per ticket (GPT-4 usage)
- Self-hosted n8n: Free (Docker)
- Cloud n8n: $20/month (standard plan)

This workflow builder helps users create production-ready n8n automations with AI integration, error handling, and best practices built in.

/n8n-builder

Popularity

Invocation

Referenced Files

Context Preview

Command Content

/n8n-builder

Popularity

Invocation

Referenced Files

Context Preview

Command Content

n8n Workflow Builder

Usage

Workflow Templates

1. AI Email Responder

2. Data Processing Pipeline

3. Content Pipeline

4. Lead Qualification

Features

Best Practices

Output Format

Example Response

Reused across plugins

Other plugins with /n8n-builder

n8n Workflow Builder

Usage

Workflow Templates

1. AI Email Responder

2. Data Processing Pipeline

3. Content Pipeline

4. Lead Qualification

Features

Best Practices

Output Format

Example Response

Reused across plugins

Other plugins with /n8n-builder