# templates

name: Generate AI Music with the ElevenLabs API and upload to Google Drive
author_name: phil
author_username: phil
categories: []
created_at: 2025-08-24T16:11:09.275Z
updated_at: 2025-08-24T16:11:09.275Z
url: https://n8n.io/workflows/7793
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  This generate unique AI-powered music tracks using the ElevenLabs Music API. 

  Enter a text description of the music you envision, and the workflow will compose it, save the MP3 file to your Google Drive, and instantly provide a link to listen to your creation. 

  It is a powerful tool for quickly producing background music, soundscapes, or musical ideas without any complex software.

  Who's it for

  This template is ideal for:
  Content Creators: Generate royalty-free background music for videos, podcasts, and streams on the fly.
  Musicians & Producers: Quickly brainstorm musical themes and ideas from a simple text prompt.
  Developers & Hobbyists: Integrate AI music generation into projects or simply experiment with the capabilities of the ElevenLabs API.

  How to set up

  Configure API Key:
     Sign up for an ElevenLabs account and get your API key.
     In the "API Key" node, replace the placeholder value with your actual ElevenLabs API key.
  Connect Google Drive:
     Select the "Upload mp3" node.
     Create new credentials to connect your Google Drive account.
  Activate the Workflow:
     Save and activate the workflow.
     Use the Form Trigger's production URL to access the AI Music Generator web form.

  Requirements

  An active n8n instance.
  An ElevenLabs account for the API key.
  A Google Drive account.

  How to customize this workflow

  Change Storage: Replace the Google Drive node with another storage service node like Dropbox, AWS S3, or an FTP server to save your music elsewhere.
  Modify Music Quality: In the "elevenlabs_api" node, you can change the output_format in the body to adjust the MP3 quality. Refer to the ElevenLabs API documentation for available options.
  Customize Confirmation Page: Edit the "prepare reponse" node to change the design and text of the final page shown to the user.





  .

  Phil | Inforeole | Linkedin

  🇫🇷 Contactez nous pour automatiser vos processus

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.html","n8n-nodes-base.form","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Multimedia"],"complexity":"medium","use_cases":["generate AI music","create soundscapes","upload audio to cloud","battery art production"],"estimated_setup_minutes":15,"required_services":["ElevenLabs","Google Drive"],"key_features":["automatic music composition","API-driven content generation","direct cloud storage","prompt-based creation"],"target_audience":["content creators","independent musicians","video producers"]}


name: Auto-Document and Backup Workflows with GPT-4 and Airtable
author_name: Guillaume Duvernay
author_username: duv
categories: []
created_at: 2025-08-24T20:56:38.794Z
updated_at: 2025-08-24T20:56:38.794Z
url: https://n8n.io/workflows/7799
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Never worry about losing your n8n workflows again. This template provides a powerful, automated backup system that gives you the peace of mind of version control without the complexity of Git. 

  On a schedule you define, it intelligently scans your n8n instance for new workflow versions and saves them as downloadable snapshots in a clean and organized Airtable base.

  But it’s more than just a backup. This workflow uses AI to automatically generate a concise summary of what each workflow does and even documents the changes between versions. The result is a fully searchable, self-documenting library of all your automations, making it the perfect "single source of truth" for your team or personal projects.

  Who is this for?

  Self-hosted n8n users:** This is an essential insurance policy to protect your critical automations from server issues or data loss.
  n8n developers & freelancers:** Maintain a complete version history for client projects, allowing you to easily review changes and restore previous versions.
  Teams using n8n:** Create a central, browseable, and documented repository of all team workflows, making collaboration and handovers seamless.
  Any n8n user who values their work:** Protect your time and effort with an easy-to-use, "set it and forget it" backup solution.

  What problem does this solve?

  Prevents catastrophic data loss:** Provides a simple, automated way to back up your most critical assets—your workflows.
  Creates "no-code" version control:** Offers the benefits of version history (like Git) but in a user-friendly Airtable interface, allowing you to browse and download any previous snapshot.
  Automates documentation:** Who has time to document every change? The AI summary and changelog features mean you always have up-to-date documentation, even if you forget to write it yourself.
  Improves workflow discovery:** Your Airtable base becomes a searchable and browseable library of all your workflows and their purposes, complete with AI-generated summaries.

  How it works

  Scheduled check: On a recurring schedule (e.g., daily), the workflow fetches a list of all workflows from your n8n instance.
  Detect new versions: It compares the current version ID of each workflow with the snapshot IDs already saved in your Airtable base. It only proceeds with new, unsaved versions.
  Generate AI documentation: For each new snapshot, the workflow performs two smart actions:
      AI Changelog: It compares the new workflow JSON with the previously saved version and uses AI to generate a one-sentence summary of what’s changed.
      AI Summary: It periodically re-analyzes the entire workflow to generate a fresh, high-level summary of its purpose, ensuring the main description stays up-to-date.
  Store in Airtable: It saves everything neatly in the provided two-table Airtable base:
      A Workflows table holds the main record and the AI summary.
      A linked Snapshots table stores the version-specific details, the AI changelog, and the actual .json backup file as an attachment.

  Setup

  Duplicate the Airtable base: Before you start, click here to duplicate the Airtable Base template into your own Airtable account.
  Configure the workflow:
      Connect your n8n API credentials to the n8n nodes.
      Connect your Airtable credentials and map the nodes to the base you just duplicated.
      Connect your AI provider credentials to the OpenAI Chat Model nodes.
      Important: In the Store workflow file into Airtable (HTTP Request) node, you must replace &lt;AIRTABLE-BASE-ID&gt; in the URL with your own base ID (it starts with app...).
  Set your schedule: Configure the Schedule Trigger to your desired frequency (daily is a good start).
  Activate the workflow. Your automated, AI-powered backup system is now live!

  Taking it further

  Add notifications:* Add a *Slack* or *Email** node at the end of the workflow to send a summary of which workflows were backed up during each run.
  Use different storage:* While designed for Airtable, you could adapt the logic to store the JSON files in *Google Drive* or *Dropbox* and the metadata in *Google Sheets* or *Notion**.
  Optimize AI costs:* The *Check workflow status** (Code) node is set to regenerate the main AI summary for the first few snapshots and then every 5th snapshot. You can edit the code in this node to change this frequency and manage your token consumption.

nodes_used:
  ["n8n-nodes-base.moveBinaryData","n8n-nodes-base.splitInBatches","n8n-nodes-base.extractFromFile","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.airtable","n8n-nodes-base.n8n","n8n-nodes-base.filter","n8n-nodes-base.wait","n8n-nodes-base.n8n","n8n-nodes-base.merge","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.extractFromFile","n8n-nodes-base.airtable","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.airtable","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 9032 characters]

metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"medium","use_cases":["automated workflow backups","AI-generated documentation","version management","data archiving"],"estimated_setup_minutes":15,"required_services":["Airtable","OpenAI","n8n"],"key_features":["scheduled backups","AI-powered summaries","version tracking","organized storage","automated documentation"],"target_audience":["workflow administrators","IT operations","developers"]}


name: Automate Multi-step Onboarding with Google Sheets, Forms and Gmail Notifications
author_name: PollupAI
author_username: Pollup
categories: []
created_at: 2025-08-25T09:13:18.859Z
updated_at: 2025-08-25T09:13:18.859Z
url: https://n8n.io/workflows/7809
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  This n8n workflow enables teams to automate and standardize multi-step onboarding or messaging workflows using Google Sheets, Forms, Gmail, and dynamic logic powered by Code and Switch nodes. It ensures the right email is sent at the right step, tracks progress in Google Sheets, and handles errors or escalations.

  🧑‍💼 Who is this for?

  HR and PeopleOps teams managing candidate or employee onboarding processes
      
  Customer success or onboarding teams handling structured, step-based journeys
      
  Any team using Google Sheets as a lightweight CRM or workflow tracker
  🧩 What problem is this workflow solving?

    

  Coordinating multi-step onboarding or communication flows using spreadsheets and email can quickly become error-prone. This workflow:

  Dynamically detects the current step and status from a Google Sheet
      
  Sends the right message at the right time
      
  Updates statuses and creates next steps
      
  Handles errors with escalation logic
  ⚙️ What this workflow does

  Trigger via Form or Manual Execution
      
      Users can fill a web form or run the workflow manually.
          
      
  Read Google Sheets
      
      Retrieves step definitions and user progress from two sheets:
          
          One for message templates
              
          One for user step/status tracking
              
          
      
  Generate Message Table
      
      Uses custom JavaScript to parse and structure message templates based on step and message type (sent, completed, error).
          
      
  Determine User Step
      
      A Switch node checks the user’s current step and status.
          
      Logic branches into the correct flow for message sending and verification.
          
      
  Send Message
      
      Message variables are replaced using a Code node.
          
      Final message is sent via Gmail or another workflow (using Execute Workflow).
          
      
  Update Sheets
      
      Marks the step as completed or passed.
          
      Creates the next step in the sheet with updated status.
          
      
  Handle Errors
      
      If the step fails, an error message is sent to the responsible admin.
  🛠️ Setup

  Connect Google Sheets and Gmail:
      
      Ensure credentials are configured in n8n.
          
      
  Prepare Sheets:
      
      One sheet for messages with steps/types/subjects/content. You can copy the one from here
          
      One sheet for tracking users and their current step/status. You can copy the one from here
          
      
  Customize Webform:
      
      Use the provided form to capture First Name, Last Name, and Email.
          
      
  Connect to Your Email Templates:
      
      Ensure your message structure follows the expected JSON format by step and type.
  ✏️ How to customize this workflow to your needs

  Add More Steps**: Expand the Switch logic and template sheet for more steps.
      
  Use Different Channels**: Replace Gmail with Slack, Telegram, or another messaging node.
      
  Change Form Fields**: Adjust the FormTrigger node to match your onboarding fields.
      
  Custom Error Logic**: Customize escalation emails, including admin routing or Slack alerts.
      
  Integrate with Databases**: Swap Google Sheets with Airtable, PostgreSQL, or Notion.
      

  This workflow is ideal for automating repetitive, step-based processes involving human communication. It’s modular, extensible, and powered by spreadsheet-driven logic.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.form","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.googleSheets","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.gmail","n8n-nodes-base.executeWorkflow","n8n-nodes-base.executeWorkflow","n8n-nodes-base.executeWorkflow","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.formTrigger"]

workflow_json_compressed:
  [encoded data, 6148 characters]

metadata_json:
  {"categories":["HR & Recruitment","Communication","Workflow Automation"],"complexity":"medium","use_cases":["automate onboarding workflows","manage candidate pipelines","track employee status","send conditional emails","handle onboarding escalations"],"estimated_setup_minutes":20,"required_services":["Google Sheets","Google Forms","Gmail"],"key_features":["conditional email routing","progress tracking","error handling","dynamic workflow logic","notification management"],"target_audience":["HR managers","People Ops","Customer Success Managers"]}


name: Weather Forecast Email - Receive Automated Weather Forecasts Daily
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-08-25T10:32:43.035Z
updated_at: 2025-08-25T10:32:43.035Z
url: https://n8n.io/workflows/7815
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  Workflow Overview

  This workflow automatically delivers a daily weather forecast to your email inbox every morning at 7:00 AM. It demonstrates practical API-to-API integration by connecting the Meteosource weather API directly to Gmail using n8n's HTTP Request node, without requiring a pre-built Meteosource integration.

  Why This Workflow is Valuable

  Instead of manually checking weather forecasts each morning, this automation fetches current and next-day weather summaries from Meteosource and delivers them directly to your inbox. It's a perfect example of how direct API integration unlocks tools that don't have dedicated n8n nodes, giving you access to the full functionality of any service with an API.

  Key Features

  Scheduled daily execution at 7:00 AM (customizable)
  Fetches weather data using the Meteosource API with secure Query Auth credentials
  Sends formatted email with today's weather in the subject and tomorrow's forecast in the body
  Easy location and recipient customization through a Config node

  Setup Requirements

  Meteosource API Account: Sign up for a free account at Meteosource to get your API key (includes 400 free calls per day, more than enough for daily forecasts).

  Credentials needed:
  Meteosource credentials**: Create an HTTP Query Auth credential in n8n with the parameter name key and your Meteosource API key as the value
  Gmail OAuth2**: Connect your Gmail account to n8n for sending emails

  Configuration

  Open the Config node to personalize:
  place_id**: Change from "london" to your desired location (use Meteosource place ID format)
  send_to_email**: Update with your preferred recipient email address

  This workflow demonstrates the power of the HTTP Request node for connecting any API to your automation workflows.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Communication","Automation"],"complexity":"simple","use_cases":["daily weather updates","automated email delivery","API integration"],"estimated_setup_minutes":15,"required_services":["Meteosource","Gmail"],"key_features":["API-to-API integration","scheduled tasks","direct HTTP requests","email automation"],"target_audience":["end users","workflow testers"]}


name: Automated Recipe Finder with an API - n8n API Tutorial Material
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-08-25T12:29:05.450Z
updated_at: 2025-08-25T12:29:05.450Z
url: https://n8n.io/workflows/7835
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  Workflow Overview

  This workflow creates a recipe finder web application that connects to the API Ninjas Recipe API using n8n's HTTP Request node. Users submit search queries through a public web form, which triggers an API call to fetch matching recipes. The results—including recipe title, ingredients, and cooking instructions—are displayed in a formatted completion page.

  Why This Workflow Is Valuable

  This template demonstrates API-to-API integration, a crucial skill for connecting services that don't have pre-built n8n nodes. By using direct API calls, you gain access to the full functionality of external services. This pattern can be adapted to integrate virtually any tool with an API into your n8n workflows.

  Common Use Cases

  Create public-facing forms that query external databases
  Build custom search tools for specialized APIs
  Integrate niche services without dedicated n8n nodes
  Learn header authentication for secure API connections

  Setup & Configuration

  Register for API Ninjas: Sign up at API Ninjas to access their Recipe API
  Get your API Key: Locate your API key in the API Ninjas dashboard
  Configure Credentials: In n8n, create a new Header Auth credential
     Set the header name to match the API requirement (e.g., 'X-Api-Key')
     Paste your API key as the value
     Apply this credential to the HTTP Request node

  The Form Trigger generates a public URL for users to submit queries. Customize the form title, field labels, and completion message HTML to match your needs.

nodes_used:
  ["n8n-nodes-base.form","n8n-nodes-base.formTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Communication","Data Processing"],"complexity":"medium","use_cases":["search recipe data","build recipe web app","consume recipe API"],"estimated_setup_minutes":15,"required_services":["API Ninjas Recipe API","n8n HTTP Request Node"],"key_features":["HTTP Request node integration","API response parsing","form to API call automation"],"target_audience":["developers","n8n beginners"]}


name: Generate daily Bitcoin on-chain metrics PDF report with NASDAQ data
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-08-25T12:33:15.765Z
updated_at: 2025-08-25T12:33:15.765Z
url: https://n8n.io/workflows/7836
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Purpose and Audience
  This n8n automation workflow is designed to generate a Bitcoin Daily On-Chain Metrics report in PDF format by fetching and transforming real-time blockchain data. It is ideal for cryptocurrency analysts, traders, investors, and blockchain enthusiasts who want to monitor Bitcoin's network activity, market valuation, and security metrics efficiently.

  What It Does
  The workflow automates the retrieval of Bitcoin blockchain data via the Nasdaq API, processes key on-chain metrics such as transaction volume, fees, market cap, hash rate, and mining difficulty, and dynamically populates a visually appealing PDF report template. It provides a clear snapshot of Bitcoin’s network health, adoption levels, and market dynamics based on the latest available data.

  Who Is It For
  This workflow is perfect for crypto analysts, financial data scientists, blockchain researchers, and investors who require automated, up-to-date, and detailed Bitcoin network reports. It serves those looking to integrate real-time crypto data into their decision-making, reporting, or portfolio management processes with minimal manual intervention.

  How to Setup
  Import the provided n8n workflow into your n8n instance.
  Configure API keys for Nasdaq data access and APITemplate.io for PDF generation within the workflow nodes.
  Trigger the workflow manually or use schedule trigger node for automatic execution to fetch the previous day's Bitcoin on-chain metrics.
  Review or download the generated PDF report containing all key Bitcoin metrics, including transaction volumes, market cap, miner revenue, hash rate, and more.

  Note: Step by step setup guide is inlcuded with the workflow file.

  Report Snapshot:

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest"]

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metadata_json:
  {"categories":["Data Processing","Financial Analytics"],"complexity":"medium","use_cases":["Monitor Bitcoin on-chain activity","Generate daily PDF reports","Fetch real-time market data","Track cryptocurrency network metrics","Automate financial analysis"],"estimated_setup_minutes":20,"required_services":["Nasdaq API","NPM libraries","PDF engine"],"key_features":["Automated data scraping","Blockchain metrics processing","PDF Report generation","Scheduling support","Real-time data integration"],"target_audience":["Financial analysts","Cryptocurrency traders","Investors"]}


name: Find step-by-step recipes from API Ninjas Recipe API in an n8n AI agent
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-08-25T12:39:20.001Z
updated_at: 2025-08-25T12:39:20.001Z
url: https://n8n.io/workflows/7837
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  Workflow Overview

  This workflow creates an intelligent AI chatbot that retrieves recipes from an external API through natural conversation. When users ask for recipes, the AI agent automatically determines when to use the recipe lookup tool, fetches real-time data from the API Ninjas Recipe API, and provides helpful, conversational responses. This demonstrates the powerful capability of API-to-API integration within n8n, allowing AI agents to access external data sources on demand.

  Key Features

  Intelligent Tool Calling:** The AI agent automatically decides when to use the HTTP Request Tool based on user queries
  External API Integration:** Connects to API Ninjas Recipe API using Header Authentication for secure access
  Conversational Memory:** Maintains context across multiple turns for natural dialogue
  Dynamic Query Generation:** The AI model automatically generates the appropriate search query parameters based on user input

  Common Use Cases

  Build AI assistants that need access to real-time external data
  Create chatbots with specialized knowledge from third-party APIs
  Demonstrate API-to-API integration patterns for custom automation
  Prototype AI agents with tool-calling capabilities

  Setup & Configuration

  Required Credentials:

  OpenAI API: Sign up at OpenAI and obtain an API key for the language model. Configure this in n8n's credential manager.
  API Ninjas: Register at API Ninjas to get your free API key for the Recipe API (supports 400+ calls/day). This API uses Header Authentication with the header name "X-Api-Key".

  Agent Configuration:

  The AI Agent includes a system message instructing it to "Always use the recipe tool if i ask you for recipe." This ensures the agent leverages the external API when appropriate. The HTTP Request Tool is configured with the API endpoint (https://api.api-ninjas.com/v1/recipe) and set to accept query parameters automatically from the AI model. The tool description "Use the query parameter to specify the food, and it will return a recipe" helps the AI understand when and how to use it.

  Language Model: Currently configured to use OpenAI's gpt-5-mini, but you can change this to other compatible models based on your needs and budget.

  Memory: Uses a window buffer to maintain conversation context, enabling natural multi-turn conversations where users can ask follow-up questions.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"medium","use_cases":["Retrieve recipes from conversation","Build conversational AI agents","Integrate external recipe APIs","Automate cooking Assistant workflows"],"estimated_setup_minutes":15,"required_services":["API Ninjas Recipe API","n8n AI Agent"],"key_features":["Natural language recipe queries","Tool use in conversational contexts","Real-time API integration","Structured recipe data retrieval"],"target_audience":["AI developers","Workflow automation specialists","Conversational AI builders"]}


name: Automated Export of Quickbooks Online Data with n8n - With Tutorial
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-08-25T12:49:54.531Z
updated_at: 2025-08-25T12:49:54.531Z
url: https://n8n.io/workflows/7838
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  Overview

  This workflow automatically exports customer balance data from QuickBooks to Google Sheets on a monthly basis. It eliminates manual data entry and creates a historical record of customer balances that updates automatically, making it easy to track payment trends, identify outstanding balances, and monitor customer financial health over time.

  Key Features

  Automated Monthly Reporting**: Runs on the first day of each month to capture a snapshot of all customer balances
  Clean Data Structure**: Extracts only the essential fields (Customer ID, Balance, Email, and Period) for easy analysis
  Historical Tracking**: Each monthly run appends new data to your Google Sheet, building a timeline of customer balances
  No Manual Work**: Once configured, the workflow runs completely hands-free

  Common Use Cases

  Track customer payment patterns and identify accounts with growing balances
  Create monthly reports for management or finance teams
  Build dashboards and visualizations from historical QuickBooks data
  Monitor customer account health without logging into QuickBooks

  Setup Requirements

  QuickBooks Developer Account: Register at developer.intuit.com and create a new app in the App Dashboard. Select the 'Accounting' scope for permissions. You'll receive a Client ID and Client Secret to configure your n8n credentials.

  Credentials: Set up QuickBooks OAuth2 credentials in n8n using your app's Client ID and Client Secret. Use 'Sandbox' environment for testing or 'Production' for live data (requires Intuit app approval). Also connect your Google Sheets account.

  Google Sheet: Create a spreadsheet with column headers matching the workflow output: Period, Id, Balance, and Email.

  Configuration

  Schedule**: The workflow runs monthly on the first day at 8 AM. Modify the Schedule Trigger to change timing or frequency
  Spreadsheet URL**: Update the 'Export to Google Sheets' node with your destination spreadsheet URL
  Data Fields**: Customize the 'Prepare Customer Data' node to extract different customer fields if needed

nodes_used:
  ["n8n-nodes-base.quickbooks","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Finance","Data Processing"],"complexity":"medium","use_cases":["track customer balances","monitor payment trends","generate financial reports","automate monthly statements","maintain historical records"],"estimated_setup_minutes":20,"required_services":["QuickBooks","Google Sheets"],"key_features":["scheduled exports","historical data tracking","email notifications","customer financial health monitoring"],"target_audience":["Accountants","Financial Analysts","Small Business Owners"]}


name: QuickBooks Automated Invoicing - Sync Google Sheets with QBO
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-08-25T13:02:33.936Z
updated_at: 2025-08-25T13:02:33.936Z
url: https://n8n.io/workflows/7840
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  Overview

  This workflow automates invoice creation in QuickBooks Online by importing data directly from a Google Sheet. Instead of manually entering invoice details one by one, this template reads structured data from your spreadsheet and automatically generates corresponding invoices in QuickBooks, saving time and reducing data entry errors.

  Key Features

  Automatically reads invoice data from Google Sheets including customer IDs, descriptions, and amounts
  Creates properly formatted invoices in QuickBooks Online with line items
  Eliminates manual data entry and reduces human error
  Scalable solution for processing multiple invoices at once

  Common Use Cases

  Batch invoice generation from sales or order data
  Automated billing workflows for recurring services
  Syncing invoice data from external systems via Google Sheets
  Streamlining accounting processes for small businesses

  Setup and Configuration

  QuickBooks Developer Account:
  Register at developer.intuit.com and create a new app in the App dashboard
  Select 'Accounting' scope permissions for your application
  Copy your Client ID and Client Secret from the Keys & Credentials section
  Add the n8n OAuth redirect URL to your app's authorized redirect URIs
  In n8n, create a QuickBooks Online OAuth2 credential using your Client ID and Secret
  Set Environment to 'Sandbox' for testing or 'Production' for live data
  Click 'Connect my account' and authorize the connection

  Google Sheets Setup:
  Connect your Google Sheets account in n8n using OAuth2 authentication
  Update the 'Config - Sheet URL' node with your Google Sheets URL
  Your sheet must contain these columns: CustomerId (QuickBooks customer ID), Description (line item description), and Amount (invoice amount)

  Invoice Customization:
  In the 'Create Invoice in QuickBooks' node, adjust the itemId and Qty fields to match your QuickBooks accounting setup and product catalog.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.quickbooks","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Finance"],"complexity":"medium","use_cases":["create_invoice","sync_spreadsheet","automate_billing"],"estimated_setup_minutes":15,"required_services":["QuickBooks Online","Google Sheets"],"key_features":["reads_invoice_data_from_spreadsheet","generates_quickbooks_invoices_automatically"],"target_audience":["accountants","finance_managers","business_owners"]}


name: QuickBooks Online AI Agent - Chat with Quickbooks Data with GPT Agent
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-08-25T13:06:33.029Z
updated_at: 2025-08-25T13:06:33.029Z
url: https://n8n.io/workflows/7841
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  What This Workflow Does

  This workflow creates an AI-powered chatbot that answers natural language questions about your QuickBooks data in real-time. Users can interact with a public chat interface to query customer information, balances, invoices, and other accounting data without needing to navigate the QuickBooks interface directly. The AI agent uses OpenAI's language model to understand questions and automatically retrieves the relevant data from QuickBooks.

  Key Features

  Natural Language Interface**: Ask questions like "How many customers do we have?" or "What's the total balance across all customers?" and get instant answers
  Public Chat URL**: Share a link with team members or stakeholders so they can access QuickBooks insights without direct system access
  Customizable Data Access**: Configure the QuickBooks tool to retrieve different resources (customers, invoices, expenses, bills, etc.) based on your needs

  Common Use Cases

  Enable non-accounting staff to get quick answers about customer data
  Provide executives with on-demand financial insights without training them on QuickBooks
  Create a self-service interface for sales teams to check customer balances
  Build internal support bots that can answer accounting questions

  Setup Requirements

  QuickBooks Developer Account: Register at developer.intuit.com and create a new app with Accounting permissions
  Credentials: Configure QuickBooks OAuth2 API credentials (Client ID, Client Secret, and Redirect URI from the Intuit Developer dashboard) and OpenAI API credentials
  Environment: Start with Sandbox mode for testing, then switch to Production after getting your app approved by Intuit
  Tool Configuration: The QuickBooks tool is pre-configured to retrieve all customer data, but you can modify the operation and filters to access different QuickBooks resources based on your requirements

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.quickbooksTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Business Operations","Accounting"],"complexity":"medium","use_cases":["answer accounting queries","query financial data","generate insights from ledgers","chat with business records","extract customer balances"],"estimated_setup_minutes":15,"required_services":["QuickBooks Online","OpenAI / GPT API","hosted chat interface"],"key_features":["natural language querying","real-time data analysis"," integrates accounting database","transparent AI responses"],"target_audience":["accountants","finance managers","small business owners"]}


name: QuickBooks Online MCP Server - Add QuickBooks tool to any AI (Like Claude)
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-08-25T13:46:14.362Z
updated_at: 2025-08-25T13:46:14.362Z
url: https://n8n.io/workflows/7842
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  What This Workflow Does

  This workflow creates an AI-powered chatbot that can answer natural language questions about your QuickBooks Online data. Using OpenAI's GPT models and the Model Context Protocol (MCP), the agent can retrieve customer information, analyze balances, and provide insights through a conversational interface. Users can simply ask questions like "How many customers do we have?" or "What's our total customer balance?" and get instant answers from live QuickBooks data.

  Key Features

  Natural language queries**: Ask questions about your QuickBooks data in plain English
  MCP architecture**: Uses Model Context Protocol to manage tools efficiently, making it easy to expand with additional QuickBooks operations
  Public chat interface**: Share the chatbot URL with team members who need QuickBooks insights without direct access
  Real-time data**: Queries live QuickBooks data for up-to-date answers

  Common Use Cases

  Customer service teams checking account balances without logging into QuickBooks
  Sales teams quickly looking up customer information
  Finance teams getting quick answers about customer data
  Managers monitoring key metrics through conversational queries

  Setup Requirements

  QuickBooks Developer Account: Register at developer.intuit.com and create an app with Accounting scope permissions. You'll receive a Client ID and Client Secret.

  Configure OAuth: In your Intuit Developer dashboard, add the redirect URL provided by n8n when creating QuickBooks credentials. Set the environment to Sandbox for testing, or complete Intuit's app approval process for Production use.

  OpenAI API: Add your OpenAI API credentials to power the chat model. The workflow uses GPT-4.1-mini by default, but you can select other models based on your performance and cost requirements.

  Chat Access: The chat trigger is set to public by default. Configure access settings based on your security requirements before sharing the chat URL.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.quickbooksTool","@n8n/n8n-nodes-langchain.mcpClientTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Finance","AI/ML"],"complexity":"simple","use_cases":["AI chatbot for financial data","Natural language query response","Customer data analysis ],<key_features>: [ "],"estimated_setup_minutes":5,"required_services":["OpenAI","QuickBooks Online","MCP Server","Noran"],"key_features":["Natural Language Query Processing","Balances Analysis","Customer Information Retrieval","Conversational Interface Integration"],"target_audience":["Finance Admin","Finance Analyst"]}


name: 🍎 Update Grocery Lists in Asana via Telegram Voice & Text using GPT-4o mini
author_name: Zawanah
author_username: zawanah
categories: []
created_at: 2025-08-25T14:45:33.723Z
updated_at: 2025-08-25T14:45:33.723Z
url: https://n8n.io/workflows/7845
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  This n8n workflow demonstrates how to use AI to update your grocery list in Asana via Telegram chat or voice. 

  Use cases
  Update grocery list details in Asana eg. check or uncheck the items, update expiry dates, update quantities etc.

  How it works
  Instruct telegram bot (via chat or voice) to update a grocery item using natural language. For example, "we just bought 10 cartons of milk that expires in 6 months".
  If via text, just the text message will be sent to the Grocery Agent.
  If via voice, voice file will be downloaded then transcribed into text using OpenAI.
  Once Grocery agent receives the text, it will search the item in your grocery list in Asana. It will then check the item since it's bought, and update the quantity and expiry date accordingly.
  Once task is done, it will respond with the changes it made and insert a hyperlink to Asana if you want to see it.

  How to set up
  Set up Telegram bot via Botfather. See setup instructions here 
  Setup OpenAI API for transcription services (Credits required) here
  Set up Openrouter account. See details here
  Set up Asana API using the account where you have your grocery list set in. See details here

  Customization Options
  You can have other custom fields you use to track other than expiry dates or quantity. For example, food type, date purchased etc. 

  Requirements
  Asana account where you manage your grocery list
  Telegram bot
  Open AI account
  Open Router account

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.asanaTool","n8n-nodes-base.asanaTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.switch"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Communication","Productivity","E-commerce"],"complexity":"medium","use_cases":["manage grocery lists","update shopping tasks","process voice commands","automate list maintenance","get shopping inputs via n8n"],"estimated_setup_minutes":10,"required_services":["telegram","asana","openrouter"],"key_features":["voice message processing","natural language understanding","UI automation integration","grocery item management","expiry date tracking"],"target_audience":["project managers","shopping teams","grocery organizers"]}


name: Generate AI Newsletter Drafts from Google Sheets to Notion with GPT-5-mini
author_name: Snehasish Konger
author_username: snehasish
categories: []
created_at: 2025-08-25T15:59:55.535Z
updated_at: 2025-08-25T15:59:55.535Z
url: https://n8n.io/workflows/7848
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  How it works:

  This template turns rows in a Google Sheet into polished newsletter drafts in Notion using an AI writing agent. You click Execute workflow in n8n. It fetches all rows marked N8n Status = Pending, generates a draft from Newsletter Title and About the Newsletter, creates a Notion page, writes the full draft, then flips the sheet row to Done before moving to the next one.

  Before you start (use your own credentials):

  Create and select your credentials in n8n:

    Google Sheets (OAuth2 or Service Account) with access to the target spreadsheet.
    Notion (Internal Integration) with access to the target database.
    OpenAI (API Key) for the Chat Model.
  Replace any placeholders in nodes:

    Spreadsheet ID/URL and sheet/tab name (e.g., newsletter).
    Notion Database ID / Parent and any page or block IDs used by HTTP Request nodes.
    OpenAI model name if you prefer a different model.
  Give the Notion integration access to the database (Share → Invite the integration).
  Do not hard-code secrets in nodes. Store them in n8n Credentials.

  Step-by-step:

  Manual Trigger
     Start the run with When clicking ‘Execute workflow’.

  Fetch pending input (Google Sheets → Get row(s) in sheet)
     Read the newsletter tab and pull only rows where N8n Status = Pending.

  Iterate (Split In Batches → Loop Over Items)
     Process one sheet row at a time for stable memory use and pacing.

  Generate the newsletter (AI Agent + OpenAI Chat Model)

     AI Agent loads the “System Role Instructions” that define style, sections, and format.
     Pass Newsletter Title and About the Newsletter to the OpenAI Chat Model to produce the draft.

  Create a Notion page (Notion → Create Page)
     Create a page in your Newsletter Automation database with the page title set from Newsletter Title.

  Prepare long content for Notion (Code)
     Split the AI output into \~1,800-character chunks and wrap as Notion paragraph blocks to avoid payload limits.

  Write content blocks to Notion (HTTP Request → UpdateNotionBlock)
     Send a PATCH request to append all generated blocks so the full draft appears on the page.

  Mark the sheet row as done (Google Sheets → Update row in sheet)
     Update N8n Status = Done for the processed Newsletter Title.

  Continue the loop
     Return to Split In Batches for the next pending row until none remain.

  Tools integration:

  Google Sheets** — input queue and status tracking (Pending → Done)
  OpenAI** — LLM that writes the draft from provided fields
  Notion** — destination database for each draft page
  n8n Code + HTTP Request** — chunking and Notion API block updates

  Want auto-runs? Add a Cron trigger before step 2 and keep the flow unchanged.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.notion","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches"]

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metadata_json:
  {"categories":["Marketing"],"complexity":"medium","use_cases":["Generate newsletter drafts","Automate content creation","Sync Google Sheets to Notion"],"estimated_setup_minutes":15,"required_services":["Google Sheets","Notion","OpenAI (GPT)","n8n"],"key_features":["Batch processing","AI content generation","Status tracking","Automatic state management"],"target_audience":["Content creators","Marketers","Newsletter administrators"]}


name: Extract and Process Healthcare Claims with VLM Run, Google Drive and Sheets
author_name: Shahrear
author_username: shahrear
categories: []
created_at: 2025-08-25T17:39:34.472Z
updated_at: 2025-08-25T17:39:34.472Z
url: https://n8n.io/workflows/7849
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  Automatically process healthcare claims into structured Google Sheets entries with VLM Run extraction

  What this workflow does

  Monitors Google Drive for new files in a target folder
  Downloads the file inside n8n for processing
  Sends the file to VLM Run for AI transcription or analysis
  Fetches extra details from the healthcare.claims-processing domain as JSON
  Appends normalized fields to a Google Sheet as a new row

  Setup

  Prerequisites: Google account, VLM Run API credentials, Google Sheets access, n8n. Install the verified VLM Run node by searching for VLM Run in the node list, then click Install. Once installed, you can start using it in your workflows.

  Quick Setup:

  Create the Drive folder you want to watch and copy its Folder ID
  Create a Google Sheet with headers like: timestamp, file\_name, file\_id, mime\_type, size\_bytes, uploader\_email, form\_type, carrier\_name, patient\_name, patient\_birth\_date, patient\_sex, patient\_address, insurance\_type, insurance\_id, insured\_name, total\_charge, amount\_due, amount\_paid, hospitalization\_from, hospitalization\_to, referring\_physician\_name, processing\_notes, …other claim fields as needed
  Configure Google Drive OAuth2 for the trigger and download nodes
  Add VLM Run API credentials from https://app.vlm.run/dashboard to the VLM Run node
  Configure Google Sheets OAuth2 and set Spreadsheet ID and target sheet tab
  Test by uploading a sample file to the watched Drive folder and activate

  Perfect for

  Centralized intake of healthcare claim documents with instant AI summaries
  Claims and operations teams collecting structured claim insights
  Customer support attachments that need quick triage to a Sheet
  Compliance and audit logs for claim documents

  Key Benefits

  End to end automation from Drive to Sheets
  Accurate AI output via VLM Run with optional timestamps
  Domain enrichment from healthcare.claims-processing JSON
  Clean, searchable logs in Google Sheets
  No manual steps after activation

  How to customize

  Extend by adding:

  OCR tuning and field validation for claim forms
  Per type routing for PDFs, images, or scanned forms
  Slack notifications on each new Sheet append
  Keyword extraction and auto tagging for claim categories
  Error branch that records failures to a second Sheet

nodes_used:
  ["@vlm-run/n8n-nodes-vlmrun.vlmRun","n8n-nodes-base.googleSheets","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Healthcare","Data Processing","Automation"],"complexity":"medium","use_cases":["process healthcare claim files","auto populate sheets from PDFs","extract data via VLM AI","automate document ingestion"],"estimated_setup_minutes":20,"required_services":["googleDrive","sheets","vlmRun"],"key_features":["multi-file folder monitoring","AI-driven transcription","structured data normalization","automatic spreadsheet updates"],"target_audience":["healthcare administrators","data entry analysts","reimbursement managers"]}


name: CYBERPULSE AI GRC: Vendor Risk Evaluator
author_name: Adnan Tariq
author_username: adnantariq
categories: []
created_at: 2025-08-26T01:06:25.458Z
updated_at: 2025-08-26T01:06:25.458Z
url: https://n8n.io/workflows/7860
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:22
description:
  Description

  Automates the intake, classification, and documentation of third-party vendors.
  Evaluates vendor function, data access level, certification status, and applies AI-driven risk tiering (Low, Standard, Critical).
  Ensures standardized onboarding and scalable due diligence.

  Who It’s For

  Compliance and procurement teams onboarding vendors

  GRC consultants managing supply chain risk

  Security leads needing traceable vendor evidence

  Audit teams validating third-party controls

  How It Works

  Trigger via intake form or webhook

  AI engine assigns vendor risk tier based on context (certification, access type, data sensitivity)

  If certifications (ISO 27001, SOC 2, IRAP, PCI DSS) are missing, status is flagged

  Scorecard is formatted automatically with vendor details, risk tier, and timestamp

  Logs results to Google Sheets for audit-ready evidence

  Sends instant alerts for high-risk or uncertified vendors

  Requirements

  Google Sheets (OAuth2) connected for logging

  Gmail/SMTP node for automated alerts (optional)

  Intake form/webhook for vendor submissions

  Mandatory fields in vendor intake:

  Vendor Name

  Function

  Data Access

  Access Type (Read/Write/API)

  Certification (if any)

  Owner (GRC)

  File Templates

  Vendor_Risk_Log.xlsx (auto-created sheet with audit-ready logs)

  Intake form (customizable, connects via webhook)

  Customization Tips

  Modify AI prompt logic to reflect internal risk appetite

  Add more certification checks (e.g., ISO 27701, FedRAMP)

  Adjust alert thresholds (e.g., auto-notify only for Critical tier)

  Extend integration to Slack/Teams or SIEM for escalation

  Link logs to a dashboard for executive reporting

  Compliance

  ISO/IEC 27001: Controls A.15.1.1, A.15.1.2 (supplier relationships and information security)

  SOC 2: Vendor management and risk classification

  NIST 800-53: SR-3, SR-5 (supply chain risk and contractual requirements)

  Essential Eight: Supports control validation via vendor documentation logging

  Provides timestamped, structured logs suitable for external audits

  Setup Instructions

  Deploy the provided intake form or connect your own vendor submission process to the webhook.

  Connect Google Sheets to store vendor logs (Vendor_Risk_Log.xlsx).

  Connect Gmail/SMTP node for notifications (optional).

  Customize AI prompt and certification logic if needed.

  Run intake tests to validate risk tier assignment.

  Confirm vendor logs and alerts are recorded correctly.

  Supports

  ISO 27001 Information Security

  SOC 2 Compliance

  PCI DSS v4.0

  NIST CSF / 800-53

  ACSC Essential Eight
  https://cyberpulsesolutions.com

nodes_used:
  ["n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Compliance","GRC","Data Processing","Security","Vendor Management"],"complexity":"simple","use_cases":["automate vendor onboarding","classify third-party vendors","evaluate vendor risk","document compliance evidence","tier vendor risk using AI"],"estimated_setup_minutes":10,"required_services":["Spreadsheets","Communication","AI/ML"],"key_features":["vendor risk tiering","AI-driven classification","standardized onboarding","scalable due diligence","compliance documentation"],"target_audience":["compliance teams","procurement staff","security leads"]}


name: Daily tech news curation with RSS, GPT-4o-Mini, and Gmail delivery
author_name: Alex Huy
author_username: alexhuy
categories: []
created_at: 2025-08-26T04:46:40.190Z
updated_at: 2025-08-26T04:46:40.190Z
url: https://n8n.io/workflows/7874
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:20
description:
  How it works

  This workflow automatically curates and sends a daily AI/Tech news digest by aggregating articles from premium tech publications and using AI to select the most relevant and trending stories.

  🔄 Automated News Pipeline
  RSS Feed Collection - Fetches articles from 14 premium tech news sources (TechCrunch, MIT Tech Review, The Verge, Wired, etc.)
  Smart Article Filtering - Limits articles per source to ensure diverse coverage and prevent single-source domination
  Data Standardization - Cleans and structures article data (title, summary, link, date) for AI processing
  AI-Powered Curation - Uses Google Vertex AI to analyze articles and select top 10 most relevant/trending stories
  Newsletter Generation - Creates professional HTML newsletter with summaries and direct links
  Email Delivery - Automatically sends formatted digest via Gmail

  🎯 Key Features
  Premium Sources** - Curates from 14 top-tier tech publications
  AI Quality Control** - Intelligent article selection and summarization
  Balanced Coverage** - Prevents source bias with smart filtering
  Professional Format** - Clean HTML newsletter design
  Scheduled Automation** - Daily delivery at customizable times
  Error Resilience** - Continues processing even if some feeds fail

  Setup Steps

  1. 🔑 Required API Access
  Google Cloud Project** with Vertex AI API enabled
  Google Service Account** with AI Platform Developer role
  Gmail API** enabled for email sending

  2. ☁️ Google Cloud Setup
  Create or select a Google Cloud Project
  Enable the Vertex AI API
  Create a service account with these permissions:
     AI Platform Developer
     Service Account User
  Download the service account JSON key
  Enable Gmail API for the same project

  3. 🔐 n8n Credentials Configuration
  Add these credentials to your n8n instance:

  Google Service Account (for Vertex AI):
  Upload your service account JSON key
  Name it descriptively (e.g., "Vertex AI Service Account")

  Gmail OAuth2:
  Use your Google account credentials
  Authorize Gmail API access
  Required scopes: gmail.send

  4. ⚙️ Workflow Configuration
  Import the workflow into your n8n instance
  Update node configurations:
     Google Vertex AI Model: Set your Google Cloud Project ID
     Send Newsletter Email: Update recipient email address
     Daily Newsletter Trigger: Adjust schedule time if needed
  Verify credentials are properly connected to respective nodes

  5. 📰 RSS Sources Customization (Optional)
  The workflow includes 14 premium tech news sources:
  TechCrunch (AI & Startups)
  The Verge (AI section)
  MIT Technology Review
  Wired (AI/Science)
  VentureBeat (AI)
  ZDNet (AI topics)
  AI Trends
  Nature (Machine Learning)
  Towards Data Science
  NY Times Technology
  The Guardian Technology
  BBC Technology
  Nikkei Asia Technology

  To customize sources:
  Edit the "Configure RSS Sources" node
  Add/remove RSS feed URLs as needed
  Ensure feeds are active and properly formatted

  6. 🚀 Testing & Deployment
  Manual Test: Execute the workflow manually to verify setup
  Check Email: Confirm newsletter arrives with proper formatting
  Verify AI Output: Ensure articles are relevant and well-summarized
  Schedule Activation: Enable the daily trigger for automated operation

  💡 Customization Options

  Newsletter Timing:
  Default: 8:00 AM UTC daily
  Modify "triggerAtHour" in the Schedule Trigger node
  Add multiple daily sends if desired

  Content Focus:
  Adjust the AI prompt in "AI Tech News Curator" node
  Specify different topics (e.g., focus on startups, enterprise AI, etc.)
  Change output language or format

  Email Recipients:
  Update single recipient in Gmail node
  Or modify to send to multiple addresses
  Integrate with mailing list services

  Article Limits:
  Current: Max 5 articles per source
  Modify the filtering logic in "Filter & Balance Articles" node
  Adjust total article count in AI prompt

  🔧 Troubleshooting

  Common Issues:
  RSS Feed Failures**: Individual feed failures won't stop the workflow
  AI Rate Limits**: Vertex AI has generous limits, but monitor usage
  Gmail Sending**: Ensure sender email is authorized in Gmail settings
  Missing Articles**: Some RSS feeds may be inactive - check source URLs

  Performance Tips:
  Monitor execution times during peak RSS activity
  Consider adding delays if hitting rate limits
  Archive old newsletters for reference

  This workflow transforms daily news consumption from manual browsing to curated, AI-powered intelligence delivered automatically to your inbox.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.rssFeedRead","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML"],"complexity":"medium","use_cases":["Automated content curation","Daily newsletter generation","RSS feed aggregation"],"estimated_setup_minutes":30,"required_services":["RSS Feed sources","LLM providers","Email providers"],"key_features":["Multi-source RSS aggregation","AI-powered content filtering","Automated email delivery","Smart scheduling","Data normalization"],"target_audience":["Content creators","Marketing teams","Tech news enthusiasts"]}


name: Automate YouTube Video SEO Tagging with GPT and Slack Notifications
author_name: Trung Tran
author_username: trungtran
categories: []
created_at: 2025-08-26T06:38:04.761Z
updated_at: 2025-08-26T06:38:04.761Z
url: https://n8n.io/workflows/7881
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:55
description:
  AI-Powered YouTube Auto-Tagging Workflow (SEO Automation)
  Watch the demo video below:

  &gt; Supercharge your YouTube SEO with this AI-powered workflow that automatically generates and applies smart, SEO friendly tags to your new videos every week. No more manual tagging, just better discoverability, improved reach, and consistent optimization. Plus, get instant Slack notifications so your team stays updated on every video’s SEO boost.
  Who’s it for
  YouTube creators, channel admins, and marketing teams who publish regularly and want consistent, SEO-friendly tags without manual effort.  
  Agencies managing multiple channels who need an auditable, automated tagging process with Slack notifications.

  How it works / What it does
  Weekly Schedule Trigger  
     Runs the workflow once per week.

  Get all videos uploaded last week  
     Queries YouTube for videos uploaded by the channel in the past 7 days.

  Get video detail  
     Retrieves each video’s title, description, and ID.

  YouTube Video Auto Tagging Agent (LLM)  
     Inputs: video.title, video.description, channelName.  
     Uses a SEO-specialist system prompt to generate 15–20 relevant, comma-separated tags.

  Update video with AI-generated tags  
     Writes the tags back to the video via YouTube Data API.

  Inform via Slack message  
     Posts a confirmation message (video title + ID + tags) to a chosen Slack channel for visibility.

  How to set up
  YouTube connection
     Create a Google Cloud project and enable YouTube Data API v3.
     Configure OAuth client (Web app / Desktop as required).
     Authorize with the Google account that manages the channel.
     In your automation platform, add the YouTube credential and grant scopes (see Requirements).

  Slack connection
     Create or use an existing Slack app/bot.
     Install to your workspace and capture the Bot Token.
     Add the Slack credential in your automation platform.

  LLM / Chat Model
     Select your model (e.g., OpenAI GPT).  
     Paste the System Prompt (SEO expert) and the User Prompt template:
       Inputs: {{video_title}}, {{video_description}}, {{channel_name}}.
       Output: comma-separated list of 15–20 tags (no #, no duplicates).

  Node configuration
     Weekly Schedule Trigger: choose day/time (e.g., Mondays 09:00 local).
     Get all videos uploaded last week: date filter = now() - 7 days.
     Get video detail: map each video ID from previous node.
     Agent node: map fields to the prompt variables.
     Update video: map the agent’s tag string to the YouTube tags field.
     Slack message:  
            The video "{{video_title}} - {{video_id}}" has been auto-tagged successfully.
       Tags: {{tags}}
       
  Test run
     Manually run the workflow with one recent video.
     Verify the tags appear in YouTube Studio and the Slack message posts.

  Requirements
  APIs & Scopes
  YouTube Data API v3**
    youtube.readonly (to list videos / details)
    youtube or youtube.force-ssl (to update video metadata incl. tags)
  Slack Bot Token Scopes**
    chat:write (post messages)
    channels:read or groups:read if selecting channels dynamically (optional)

  Platform
  Access to a chat/LLM provider (e.g., OpenAI).
  Outbound HTTPS allowed.

  Rate limits & quotas
  YouTube updates consume quota; tag updates are write operations—avoid re-writing unchanged tags.
  Add basic throttling (e.g., 1–2 updates/sec) if you process many videos.

  How to customize the workflow
  Schedule:** switch to daily, or run on publish events instead of weekly.
  Filtering:** process only videos matching rules (e.g., title contains “tutorial”, or missing tags).
  Prompt tuning:**  
    Add brand keywords to always include (e.g., “WiseStack AI”).  
    Constrain to language (e.g., “Vietnamese tags only”).  
    Enforce max 500 chars total for tags if you want a stricter cap.
  Safety guardrails:**  
    Validate model output: split by comma, trim whitespace, dedupe, drop empty/over-long tags.  
    If the agent fails, fall back to a heuristic generator (title/keywords extraction).
  Change log:** write a row per update to a sheet/DB (videoId, oldTags, newTags, timestamp, runId).
  Human-in-the-loop:** send tags to Slack as buttons (“Apply / Edit / Skip”) before updating YouTube.
  Multi-channel support:** loop through a list of channel credentials and repeat the pipeline.
  Notifications:** add error Slack messages for failed API calls; summarize weekly results.

  Tip: Keep a small allow/deny list (e.g., banned terms, mandatory brand terms) and run a quick sanitizer right after the agent node to maintain consistency across your channel.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.youTube","n8n-nodes-base.youTube","n8n-nodes-base.youTube","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["Marketing","Communication"],"complexity":"medium","use_cases":["generate SEO tags","notify on updates","schedule automation"],"estimated_setup_minutes":15,"required_services":["YouTube","Slack","GPT"],"key_features":["AI-powered tag generation","scheduled execution","real-time notifications"],"target_audience":["youtube creators","channel admins"]}


name: Index Legal Documents for Hybrid Search with Qdrant, OpenAI & BM25
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2025-08-27T11:48:31.351Z
updated_at: 2025-08-27T11:48:31.351Z
url: https://n8n.io/workflows/7945
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  Index Legal Dataset to Qdrant for Hybrid Retrieval
  This pipeline is the first part of *"Hybrid Search with Qdrant & n8n, Legal AI"**.  
  The second part, "Hybrid Search with Qdrant & n8n, Legal AI: Retrieval", covers retrieval and simple evaluation.* 

  Overview
  This pipeline transforms a Q&A legal corpus from Hugging Face (isaacus) into vector representations and indexes them to Qdrant, providing the foundation for running Hybrid Search, combining:

  Dense vectors (embeddings) for semantic similarity search;  
  Sparse vectors for keyword-based exact search.


  After running this pipeline, you will have a Qdrant collection with your legal dataset ready for hybrid retrieval on BM25 and dense embeddings: either mxbai-embed-large-v1 or text-embedding-3-small.

  Options for Embedding Inference
  This pipeline equips you with two approaches for generating dense vectors:

  Using Qdrant Cloud Inference, conversion to vectors handled directly in Qdrant;
  Using external provider, e.g. OpenAI for generating embeddings.

  Prerequisites
  A cluster on Qdrant Cloud  
    Paid cluster in the US region if you want to use Qdrant Cloud Inference  
    Free Tier Cluster if using an external provider (here OpenAI)  
  Qdrant Cluster credentials: 
    You'll be guided on how to obtain both the URL and API_KEY from the Qdrant Cloud UI when setting up your cluster;  
  An OpenAI API key (if you’re not using Qdrant’s Cloud Inference);  

  P.S.
  To ask retrieval in Qdrant-related questions, join the Qdrant Discord.  
  Star Qdrant n8n community node repo &lt;3

nodes_used:
  ["n8n-nodes-qdrant.qdrant","n8n-nodes-qdrant.qdrant","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.aggregate","n8n-nodes-qdrant.qdrant","n8n-nodes-base.limit","n8n-nodes-base.merge","n8n-nodes-base.summarize","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-qdrant.qdrant","n8n-nodes-qdrant.qdrant","n8n-nodes-qdrant.qdrant","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate"]

workflow_json_compressed:
  [encoded data, 8512 characters]

metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"medium","use_cases":["index legal documents","create vector embeddings","prepare hybrid search dataset"],"estimated_setup_minutes":45,"required_services":["Qdrant","OpenAI"],"key_features":["processes Q&A legal corpus","generates semantic embeddings","indexes data for hybrid retrieval","handles batch processing"],"target_audience":["developers","legal tech engineers"]}


name: Evaluate Hybrid Search for Legal Question-Answering using Qdrant & BM25/mxbai
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2025-08-27T11:50:13.629Z
updated_at: 2025-08-27T11:50:13.629Z
url: https://n8n.io/workflows/7946
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  Evaluate Hybrid Search on Legal Dataset
  This is the second part of *"Hybrid Search with Qdrant & n8n, Legal AI."**
  The first part, "Indexing", covers preparing and uploading the dataset to Qdrant.*

  Overview
  This pipeline demonstrates how to perform Hybrid Search on a Qdrant collection using questions and text chunks (containing answers) from the  
  LegalQAEval dataset (isaacus).

  On a small subset of questions, it shows:  
  How to set up hybrid retrieval in Qdrant with:  
    BM25-based keyword retrieval;
    mxbai-embed-large-v1 semantic retrieval;  
    Reciprocal Rank Fusion (RRF), a simple zero-shot fusion of the two searches;
  How to run a basic evaluation:  
    Calculate hits@1 — the percentage of evaluation questions where the top-1 retrieved text chunk contains the correct answer  


  After running this pipeline, you will have a quality estimate of a simple hybrid retrieval setup.  
  From there, you can reuse Qdrant’s Query Points node to build a legal RAG chatbot.  

  Embedding Inference
  By default, this pipeline uses Qdrant Cloud Inference to convert questions to embeddings.  
  You can also use an external embedding provider (e.g. OpenAI).  
    In that case, minimally update the pipeline, similar to the adjustments showed in Part 1: Indexing.  

  Prerequisites
  Completed Part 1 pipeline*, *"Hybrid Search with Qdrant & n8n, Legal AI: Indexing", and the collection created in it;
  All the requirements of Part 1 pipeline;

  Hybrid Search
  The example here is a basic hybrid query. You can extend/enhance it with:
  Reranking strategies;  
  Different fusion techniques;
  Score boosting based on metadata;
  ...  

  More details: Hybrid Queries in Qdrant.  

  P.S.
  To ask retrieval in Qdrant-related questions, join the Qdrant Discord.  
  Star Qdrant n8n community node repo &lt;3

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.httpRequest","n8n-nodes-qdrant.qdrant","n8n-nodes-base.merge","n8n-nodes-base.splitInBatches","n8n-nodes-base.filter","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

workflow_json_compressed:
  [encoded data, 5688 characters]

metadata_json:
  {"categories":["Legal Tech","AI/ML"],"complexity":"medium","use_cases":["compare search results","test retrieval accuracy","evaluate hybrid models","benchmark vector search"],"estimated_setup_minutes":15,"required_services":["Qdrant","LegalQAEval"],"key_features":["hybrid search","BM25 keyword retrieval","vector similarity search","result comparison","dataset evaluation"],"target_audience":["developers","AI researchers","legal tech engineers"]}


name: Kubernetes RCA and alerting using Gemini, Loki, Prometheus, Slack
author_name: John Pranay Kumar Reddy
author_username: johnpranay
categories: []
created_at: 2025-08-27T14:41:00.607Z
updated_at: 2025-08-27T14:41:00.607Z
url: https://n8n.io/workflows/7953
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Summary
  This n8n workflow automates Kubernetes root cause analysis (RCA) and incident alerting by integrating with Loki, Prometheus, and Slack. It streamlines log collection, cluster health monitoring, and AI-driven RCA with Gemini, saving DevOps teams hours of manual troubleshooting. Designed for production-grade Kubernetes environments, this plug-and-play workflow delivers actionable insights directly to your Slack channels.

  Who’s It For

  🛠️ DevOps Engineers automating Kubernetes monitoring and incident response.

  🔍 Site Reliability Engineers (SREs) aiming to reduce mean time to resolution (MTTR).

  🚀 Teams using n8n, Slack, Loki, and Prometheus for observability and automation.

  What It Does

  Pulls logs from Loki, filtering for critical errors.
  Monitors cluster health, checking pending, not ready, or restarting pods.
  Fetches Prometheus metrics for nodes, namespaces, and services.
  Generates AI-driven RCA using Google Gemini to analyze logs and metrics.
  Sends formatted Slack alerts with deduplication and batching for readability.
  Tracks pod lifecycle events, including crashloop states and restarts.

  How It Works

  Log Collection: The workflow uses an n8n HTTP Request node to pull logs from Loki, filtering for critical errors.
  Cluster Monitoring: A Kubernetes SSH node checks pod states (terminated or running). If terminated ignore those pods for RCA.
  Metrics Fetching: Prometheus metrics are retrieved via HTTP Request for nodes and services.
  RCA Generation: Logs and metrics are merged into structured prompts for Google Gemini AI to produce actionable RCA insights.
  Kubernetes Enrichment: RCA from Gemini will goes to this node and enriches with Kubernetes documentation and provide the documentation links in RCA
  Slack Notifications: Alerts are batched, deduplicated, and sent to Slack with clear formatting.

  How to Set Up

  Import the Workflow: Download the .json file and import it into your n8n instance.

  Configure Credentials:

  Add your Loki, Prometheus, and Slack API credentials in the respective nodes (use n8n Credentials Manager if required).
  Set up Google Gemini AI credentials for RCA generation.
  Test the Workflow: Run the workflow manually to ensure logs, metrics, and alerts are processed correctly.
  Schedule Execution: Use n8n’s Schedule Trigger to run the workflow at desired intervals (e.g., every 5 minutes).

  Requirements

  🌐 n8 K8s node installed (self-hosted only, see n8n documentation).

  🔑 Access to Kubernetes clusters and API.

  📊 Loki and Prometheus set up for log and metrics collection.

  💬 Slack workspace with webhook access for notifications.

  🤖 Google Gemini AI API key for RCA generation.

  How to Customize the Workflow

  Adjust Filters: Modify the Loki query in the HTTP Request node to focus on specific error patterns.
  Change Alert Frequency: Update the Schedule Trigger node for different intervals.
  Add More Metrics: Extend the Prometheus node to include additional metrics like CPU or memory usage.
  Customize Slack Alerts: Edit the Slack node to change message formatting or add more channels.
  Enhance RCA: Adjust the Gemini AI prompt in the Set Fields node for more specific insights.

  🗂️ Category
  DevOps / Monitoring & Observability / Kubernetes/ AI

  🏷️ Tags
  kubernetes, prometheus, slack, alerting, sre, ops, kube-state-metrics, Gemini, AI

  Slack Output

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.ssh","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code"]

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metadata_json:
  {"categories":["DevOps"],"complexity":"complex","use_cases":["automate Kubernetes root cause analysis","streamline incident alerting","collect cluster logs","monitor cluster health","generate actionable insights"],"estimated_setup_minutes":60,"required_services":["Kubernetes","Loki","Prometheus","Slack","Google Gemini"],"key_features":["AI-driven root cause analysis","plugg-and-play alerting","automated log collection","actionable Slack notifications"],"target_audience":["DevOps Engineers","SREs","Platform Engineers"]}


name: Conversational Meta Ads Reporting & Management with GPT-5
author_name: Konrad Roziewski
author_username: kroziewski
categories: []
created_at: 2025-08-27T16:01:05.882Z
updated_at: 2025-08-27T16:01:05.882Z
url: https://n8n.io/workflows/7957
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  Currently Work-In-Progress

  This n8n template creates an intelligent AI assistant that responds to chat messages, providing conversational access to your Meta Ads data. Powered by an OpenAI GPT-5 model and equipped with memory to maintain context, this agent can interact with your Meta Ads accounts via the Facebook Graph API.

  Users can ask it to:
  List all connected ad accounts.**
  Retrieve detailed information** for a specific ad account, including active campaigns, ad sets, and individual ads.
  Fetch performance insights** (e.g., spend, impressions, conversions, CPC, CPM, CTR, ROAS) for a given account and time range.
   
  Ideal for marketers, advertisers, or anyone needing quick, conversational access to their Meta Ads performance data and campaign structure without logging into the Ad Manager directly.

  Requires: OpenAI and Facebook Graph API credentials.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.noOp","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.noOp","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.code"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Marketing","Data Processing"],"complexity":"medium","use_cases":["conversational ad reporting","real-time ad account retrieval","automated client communication","campaign performance analysis"],"estimated_setup_minutes":30,"required_services":["OpenAI","Facebook Graph API"],"key_features":["natural language query processing","ad account inventory listing","hierarchical campaign data retrieval","context-aware conversation","memory-based state management"],"target_audience":["marketing managers","social media admins","ad optimization specialists"]}


name: Automated SEO Blog Publishing with WordPress, OpenAI & Perplexity
author_name: LukaszB
author_username: lukaszb
categories: []
created_at: 2025-08-27T23:34:53.567Z
updated_at: 2025-08-27T23:34:53.567Z
url: https://n8n.io/workflows/7965
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Automated Blog n8n System – Professional Articles in Minutes  

  Most blogs publish words.  
  This system publishes experiences.  

  With one simple row in Google Sheets, your blog comes alive with SEO & RAO-friendly WordPress articles — written, illustrated, and published without lifting a finger.  

  What makes it different? Not just automation. Not just speed. It’s the way images are woven between paragraphs, giving your readers a richer, more engaging journey. Instead of walls of text, every article feels alive — easier to read, easier to share, and impossible to ignore.  

  ✨ What this workflow delivers  
  End-to-end automation**: idea → research → writing → visuals → publishing.  
  SEO & RAO optimized*: content built to rank in Google *and surface in AI systems.  
  Built-in storytelling with visuals**: Perfect Images embedded directly inside the article body, making every post more compelling.  
  Seamless WordPress publishing**: titles, excerpts, slugs, body, and featured image in one click.  
  Effortless setup**: designed so anyone can run it, even without technical skills.  

  🔧 Requirements  
  Google Sheets (title, short_description, created)  
  WordPress site with REST API + credentials  
  OpenAI API key (text + image generation)  
  Perplexity API key (research)  
  Cloudinary account with unsigned upload preset  
  (Optional) Pexels API key for stock header images  

  ✅ Perfect for  
  Bloggers who want their content to stand out visually.  
  Agencies managing client blogs and SEO projects.  
  Businesses that need engaging, living content on autopilot.  
  Anyone ready to turn a silent blog into a beautiful, automated publishing machine.  

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.googleGemini","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","n8n-nodes-base.limit","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.perplexityTool","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleSheets","n8n-nodes-base.wordpress","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Marketing","Content Creation","AI/ML"],"complexity":"medium","use_cases":["Automate blog publishing","Generate SEO articles","Create illustrated content","Write with AI","Publish to WordPress"],"estimated_setup_minutes":45,"required_services":["WordPress","OpenAI","Perplexity","Google Sheets","Google Gemini"],"key_features":["Multi-AI assistance","SEO optimization","SEO/RAO friendly content","Automatic image integration","Batch processing"],"target_audience":["content creators","marketing teams","bloggers"]}


name: Automated Patient Response System with GPT-3.5 and Google Sheets for Dental Clinics
author_name: Sana Fatima
author_username: iamsanafatimachattha
categories: []
created_at: 2025-08-28T04:57:02.618Z
updated_at: 2025-08-28T04:57:02.618Z
url: https://n8n.io/workflows/7971
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  From Leads to Smiles – Automated Patient Engagement______Turn every lead into a booked appointment with AI
  💡 Why this workflow?
  Dental clinics lose 50%+ of leads due to slow response.

  This template ensures instant engagement with AI-powered personalization.

  Works for  dentists, beauty clinics, physiotherapists, or any appointment-based business.
  Description:

  When a new patient submits a form (e.g., teeth whitening, Invisalign, implants), the workflow:
  Captures and stores the lead in Google Sheets.
  2.Uses an AI Agent to generate a friendly, personalized welcome message.
  Instantly sends tailored notifications to both the patient and the dentist.

  No more generic thank-you emails → engage leads before they go cold! 🚀

  Category:

  Marketing & Sales

  Customer Engagement

  Healthcare Automation

  📖 Step-by-Step Guide (for beginners)
  Trigger (Google Sheets):

  Every time a new patient submits a Google Form, the lead is automatically captured in Google Sheets.

  AI Agent (OpenAI Chat Model):

  Generates a personalized welcome message 

  Patient Notification (Email):

  Sends a warm, engaging welcome message with appointment details instantly.

  Dentist Notification (Email):

  Alerts the dentist with lead details so they can follow up quickly.

  Execution:

  Just hit Execute Workflow → watch as your clinic never loses another lead.

  👉 Perfect for beginners in AI + n8n automation. You can duplicate this template, connect your Google Sheet + Gmail + OpenAI API key, and start converting leads today.

  Tags: lead generation dentist crm google sheets ai agent chatgpt email automation

nodes_used:
  ["n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolThink","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Healthcare","Data Processing","Marketing"],"complexity":"medium","use_cases":["lead management","appointment booking","patient engagement","automated communication","data storage"],"estimated_setup_minutes":30,"required_services":["Google Sheets","GPT","Google Form"],"key_features":["AI-powered personalization","instant lead response","automated patient engagement","data capture and storage"],"target_audience":["dentists","clinic administrators","marketing managers"]}


name: LinkedIn content using viral post analysis with Gemini & image generation
author_name: Roshan Ramani
author_username: rawsun007
categories: []
created_at: 2025-08-28T06:47:01.985Z
updated_at: 2025-08-28T06:47:01.985Z
url: https://n8n.io/workflows/7973
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  LinkedIn Viral Post Scraper + AI Content Generator with Auto Publishing

  Who's it for

  Content creators, social media managers, marketing agencies, and LinkedIn growth hackers who want to automate their content strategy by analyzing viral posts and generating original, high-engagement content at scale.

  What it does

  This comprehensive automation pipeline combines LinkedIn data intelligence with AI-powered content creation to build a complete content marketing system:

  Data Collection Phase:
  Automatically scrapes high-engagement posts from target LinkedIn profiles
  Filters content based on viral metrics (20+ likes, comments, or reposts)
  Stores trending content data in Google Sheets for analysis
  Runs on a 12-hour schedule with built-in rate limiting

  Content Generation Phase:
  AI analyzes trending patterns and engagement triggers from collected data
  Generates original LinkedIn posts using Google Gemini's advanced language model
  Creates custom visuals using AI image generation (HuggingFace Flux model)
  Automatically publishes complete posts with images to your LinkedIn company page
  Tracks processed content to prevent duplicate analysis

  How it works

  The workflow operates in two connected phases. The scraping phase monitors specified LinkedIn profiles, collecting high-performing posts into a centralized database. When new viral content is detected, the AI analysis phase triggers automatically, identifying trending topics and creating original content that leverages proven engagement patterns while maintaining authenticity.

  Requirements

  APIs and Credentials:
  Google Sheets API (for data storage and profile management)
  Apify API key (for LinkedIn post scraping)
  Google Gemini API access (for content generation)
  HuggingFace API token (for image generation)
  LinkedIn API access (for publishing)

  Setup Prerequisites:
  Google Sheets document with LinkedIn profile URLs
  LinkedIn company page for publishing
  Self-hosted n8n instance (uses community nodes)

  How to set up

  Configure Data Sources: Create a Google Sheet with two tabs - one for target LinkedIn profile URLs and another for storing scraped data
  API Integration: Add all required API credentials to n8n credential manager
  Customize Filters: Adjust engagement thresholds (currently 20+ for each metric) based on your niche
  AI Prompt Tuning: Modify the content generation prompt to match your brand voice and industry focus
  Publishing Settings: Configure your LinkedIn organization ID and posting preferences
  Schedule Optimization: Set scraping frequency based on your content needs and API limits

  How to customize the workflow

  Content Targeting: Modify the profile list in Google Sheets to focus on specific industries, thought leaders, or competitors in your niche.

  Engagement Thresholds: Adjust the filter conditions to be more or less selective about which posts qualify as "viral" content.

  AI Voice Customization: Edit the comprehensive system prompt in the LinkedIn Content Strategy AI node to match your brand personality, industry terminology, and content style preferences.

  Visual Branding: Customize image generation prompts to include your brand colors, logo elements, or specific visual styles.

  Publishing Schedule: Modify the trigger frequency and add conditional logic to publish at optimal times for your audience.

  Data Retention: Add cleanup nodes to manage historical data and prevent Google Sheets from becoming unwieldy over time.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.filter","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.filter","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.linkedIn","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Marketing","Communication"],"complexity":"medium","use_cases":["Generate viral LinkedIn content","Scrape and analyze competitors","Automate social media publishing","Create engaging visual assets"],"estimated_setup_minutes":25,"required_services":["LinkedIn","Google Gemini API","DALL-E 3","Slack Business"],"key_features":["Competitor content analysis","AI-driven copy generation","Automated image creation","Scheduled publishing"],"target_audience":["Content creators","Social media managers","Marketing agencies"]}


name: Chat with Google Drive Documents using GPT, Pinecone, and RAG
author_name: Marko
author_username: perspectalog
categories: []
created_at: 2025-08-28T08:46:43.250Z
updated_at: 2025-08-28T08:46:43.250Z
url: https://n8n.io/workflows/7979
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  📌 Short Overview

  Automatically sync files from Google Drive into a searchable AI knowledge base with Pinecone, and answer user queries using GPT-4o with conversational memory.

  ⸻

  🛠️ Workflow Usage Steps

  1.	Watch Google Drive for file changes
  Trigger the workflow when a new file is uploaded or an existing file is updated in a specific Google Drive folder.
  2.	Download and process the file
  Retrieve the file, split it into smaller text chunks with a Recursive Character Text Splitter, and generate vector embeddings using OpenAI.
  3.	Store embeddings in Pinecone
  Save the embeddings in a Pinecone vector database to keep your knowledge base continuously updated and searchable.
  4.	Search context for chat queries
  When a user asks a question, query Pinecone for relevant context, combine results with conversational memory, and process them with GPT-4o.
  	5.	Respond with AI-powered answers
  Provide a concise response (100–200 words) that blends knowledge from your documents with the conversation history.

  ⸻

  ✅ Use Cases
  •	Keep a live, AI-ready knowledge base from your Google Drive files.
  	•	Enable team members to query company documents instantly.
  	•	Build a personal assistant that stays up to date with your latest uploads.

  ⚙️ Setup Steps
  Google Drive
  	•	Create a Google Cloud project.
  	•	Enable the Google Drive API.
  	•	Generate OAuth credentials and connect them in n8n.
  OpenAI
  	•	Sign up at OpenAI.
  	•	Copy your API key from the dashboard.
  	•	Add it to n8n under Credentials → OpenAI API.
  Pinecone
  	•	Create an account at Pinecone.
  	•	Create a new index (e.g., docs-embeddings).
  	•	Copy your API key and environment, then add them to n8n under Credentials → Pinecone API.
  Workflow Configuration
  	•	Import this workflow into your n8n instance.
  	•	Select the Google Drive folder you want to monitor.
  	•	Set the Pinecone index name in the workflow.
  	•	Adjust chunk size / overlap in the text splitter if needed.
  Test the Workflow
  	•	Upload a new document to your Google Drive folder.
  	•	Run the workflow to confirm embeddings are created and stored in Pinecone.
  	•	Ask a sample query and verify the AI returns a context-aware answer.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.vectorStorePinecone","n8n-nodes-base.googleDrive"]

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metadata_json:
  {"categories":["AI/ML","Data Processing","Communication"],"complexity":"medium","use_cases":["Automated document indexing","AI-powered document Q&A","Knowledge base management"],"estimated_setup_minutes":15,"required_services":["Google Drive","Pinecone","OpenAI GPT-4o"],"key_features":["Automatic file sync to vector store","RAG-based document retrieval","Conversational memory for chat context"],"target_audience":["developers","data scientists","knowledge workers"]}


name: Automate Support Ticket Classification & Routing from HubSpot to Jira with GPT
author_name: PollupAI
author_username: Pollup
categories: []
created_at: 2025-08-28T09:20:20.187Z
updated_at: 2025-08-28T09:20:20.187Z
url: https://n8n.io/workflows/7981
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Who is this for?

    

  This n8n workflow template is designed for customer support, CX, and ops teams that manage customer messages through HubSpot and use Jira for internal task management. It is especially useful for SaaS companies aiming to automate ticket triage, sentiment detection, and team assignment using AI agents.

  🧩 What problem is this workflow solving?

    

  Customer service teams often struggle with manual message classification, delayed reactions to churn signals, and inefficiencies in routing support issues to the right internal teams. This workflow uses LLMs and automated profiling to:

  Detect churn risk or intent in customer messages
      
  Summarize issues
      
  Classify tickets into categories (e.g. fulfillment, technical, invoicing)
      
  Automatically create Jira tickets based on enriched insights
  🤖 What this workflow does

    

  This AI-powered workflow processes HubSpot support tickets and routes them to Jira based on sentiment and topic. Here’s the full breakdown:

  Triggers: Either manually or on a schedule (via cron).
      
  Fetch HubSpot tickets: Retrieves new messages and their metadata.
      
  Run Orchestration Agent:
      
      Uses Sentinel Agent to detect emotional tone, churn risk, and purchase intent.
          
      Calls Profiler Agent to enrich customer profiles from HubSpot.
          
      Summarizes the message using OpenAI.
          
      Classifies the ticket using a custom classifier (technical, fulfillment, etc.).
          
      
  Generate a Jira ticket:
      
      Title and description are generated using GPT.
          
      The assignee and project are predefined.
          
      
  AI agents can be expanded (e.g. add Guide or Facilitator agents).
  ⚙️ Setup

    

  To use this template, you’ll need:

  HubSpot account** with OAuth2 credentials in n8n
      
  Jira Software Cloud account** and project ID
      
  OpenAI credentials** for GPT-based nodes
      
  Optional: Create sub-workflows for additional AI agents
      

    

  Steps:

  Clone the workflow in your n8n instance.
      
  Replace placeholder credentials for HubSpot, OpenAI, and Jira.
      
  Adjust Jira project/issue type IDs to match your setup.
      
  Test the workflow using the manual trigger or scheduled trigger node.
  🛠️ How to customize this workflow to your needs

  1. Edit category logic

    

  In the “Category Classifier” node, modify categories and prompt structure to match your internal team structures (e.g., billing, account management, tech support).

  2. Refine AI prompts

    

  Customize the agent prompt definitions in:

  Sentinel_agent
      
  Profiler_agent
      
  Orchestrator
      
      to better align with your brand tone or service goals.
  3. Update Jira integration

    

  You can route tickets to different projects or team leads by adjusting the “Create an issue in Jira” node based on classification output.

  4. Add escalation paths


  Insert Slack, email, or webhook notifications for specific risk levels or customer segments.

  This workflow empowers your team with real-time message triage, automated decision-making, and AI-enhanced customer insight, turning every inbound ticket into a data-driven action item.

nodes_used:
  ["n8n-nodes-base.hubspot","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.hubspot","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","n8n-nodes-base.jira","n8n-nodes-base.switch","n8n-nodes-base.hubspotTool","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.chainSummarization","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7152 characters]

metadata_json:
  {"categories":["Customer Support","AI/ML","Data Processing"],"complexity":"medium","use_cases":["automatic event creation","event classification","ticket summarization","sentiment detection"],"estimated_setup_minutes":45,"required_services":["HubSpot","Jira"],"key_features":["AI-powered content extraction","automatic ticket assignment","churn signal detection","integration with CRM"],"target_audience":["customer support","ops engineers"]}


name: Receipt scanning & analysis workflow
author_name: Aemal Sayer
author_username: aemal
categories: []
created_at: 2025-08-28T10:47:11.676Z
updated_at: 2025-08-28T10:47:11.676Z
url: https://n8n.io/workflows/7990
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:19
description:
  How it works:
  Automatically detects when a new receipt is uploaded to Google Drive.
  Extracts text from the receipt using OCR.
  Uses an AI Agent to analyze the extracted data and structure it (e.g., vendor, date, total, tax).
  Saves the organized receipt data into a Google Sheet for easy tracking.

  Set up steps:
  Setup takes around 15–20 minutes.
  You'll need a Google Drive folder for receipts and a Google Sheet to store results.
  Configure your Google Drive Trigger, OCR extraction, AI Agent, and Google Sheets connection.

  Detailed instructions and explanations are included in this n8n Starter Session tutorial series.

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.mistralAi","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Data Processing","AI/ML","Finance"],"complexity":"medium","use_cases":["Digitize receipts","Extract vendor data","Organize expense records","Automated expense tracking","OCR text extraction"],"estimated_setup_minutes":15,"required_services":["Google Drive","Mistral AI","Google Sheets"],"key_features":["Automatic file detection","OCR-based text extraction","AI-powered data structuring","Automated spreadsheet updates","Expense categorization"],"target_audience":["finance managers","accountants","business owners"]}


name: Complete WooCommerce to Odoo Integration: Sync Orders, Products & Customers
author_name: Evozard
author_username: evozardconsulting
categories: []
created_at: 2025-08-28T11:09:55.601Z
updated_at: 2025-08-28T11:09:55.601Z
url: https://n8n.io/workflows/7993
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  📖 Description (WooCommerce Version)

  This workflow functions like the integration of WooCommerce products with Odoo products, WooCommerce customers with Odoo customers, and WooCommerce orders with Odoo sales orders containing multiple products.

  🔔 Trigger (WooCommerce – Order Created)

  The workflow starts when a new order is created in WooCommerce.

  🔄 Loop Through Each Line Item in the Order
  For each product in the WooCommerce order:
  Check if Product Exists in Odoo (via Internal Reference/Default Code):
  If the product exists, proceed.

  If the product does not exist, create it in Odoo with:
  Product Name
  Description
  Price
  Internal Reference (Default Code)

  👤 Check if Customer Contact Exists in Odoo (via Email)

  If the contact exists, proceed.

  If the contact does not exist, create a new contact in Odoo with:
  Full Name
  Email Address
  Phone Number

  Shipping or Billing Address (as available)

  📝 Create a Sale Order in Odoo

  Create a new Sale Order in Odoo using:
  The matched or newly created Customer Contact
  All the matched or newly created Products from the WooCommerce order (with correct quantities and pricing)
  Each WooCommerce product in the order is matched to the corresponding Odoo product using the Internal Reference field.
  👉 This ensures a seamless sync between WooCommerce and Odoo, keeping your orders, products, and customers aligned automatically.

  Setup referecence:
  https://docs.n8n.io/integrations/builtin/credentials/woocommerce/
  https://docs.n8n.io/integrations/builtin/credentials/odoo/

nodes_used:
  ["n8n-nodes-base.wooCommerceTrigger","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.odoo","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.odoo","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.odoo","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.odoo","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.odoo","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.odoo","n8n-nodes-base.odoo","n8n-nodes-base.odoo","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.code"]

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metadata_json:
  {"categories":["E-commerce","Data Processing","DevOps"],"complexity":"complex","use_cases":["syncing wocommerce and odoo","synchronizing orders and products","integrating customer data"],"estimated_setup_minutes":15,"required_services":["WooCommerce","Odoo"],"key_features":["multi-product order sync","bidirectional data validation","batch processing logic","product existence checking"],"target_audience":["technical implementers","e-commerce developers","system administrators"]}


name: Crypto RSI Alert System with EODHD, Telegram and TradingView Charts
author_name: Kevin Meneses
author_username: pythonia-kevin
categories: []
created_at: 2025-08-28T13:22:22.827Z
updated_at: 2025-08-28T13:22:22.827Z
url: https://n8n.io/workflows/7997
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  How it works

  Runs on a schedule and iterates a watchlist of symbols (e.g., BTC/ETH/SOL).

  For each symbol, request intraday 1h OHLCV from EODHD.

  A Code node computes Wilder’s RSI(14) and detects 30/70 crossings.

  When a signal appears, the bot sends a Telegram alert (HTML message) with price, RSI (prev → now), timestamp, and a “View chart” button that opens the pair on TradingView (BINANCE/USD).

  Set up steps (≈10–15 min)

  Prereqs: n8n (cloud or self-hosted), EODHD API key, Telegram bot + your chat_id.

  Env vars: set EODHD_TOKEN and TELEGRAM_CHAT_ID on your n8n instance.

  Credentials: add your Telegram credential (bot token).

  Import the workflow JSON.

  Edit Fields node: adjust the symbol array to your watchlist.

  Schedule Trigger: choose how often to run (e.g., every 5–10 min).

  Test: temporarily flip the Code node’s FORCE_ALERT flag to true to verify Telegram delivery, then set it back to false.

  EODHD API Access the EODHD API with a 10% discount

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5196 characters]

metadata_json:
  {"categories":["Fintech"],"complexity":"medium","use_cases":["monitor crypto prices","generate RSI alerts","send Telegram notifications","auto-trade signals"],"estimated_setup_minutes":15,"required_services":["EODHD","Telegram","TradingView"],"key_features":["scheduled execution","market data extraction","indicator calculation","crossing detection","real-time alerts"],"target_audience":["traders","finops","developers"]}


name: Generate AI Sales Pitches from Website URLs with GPT-4o and Google Sheets
author_name: Zach @BrightWayAI
author_username: ajenta
categories: []
created_at: 2025-08-28T16:59:53.716Z
updated_at: 2025-08-28T16:59:53.716Z
url: https://n8n.io/workflows/8000
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Overview

  This n8n workflow converts a simple list of website URLs in Google Sheets into a concise, consultative Context + AI pitch for each organization. It scrapes real page content (markdown preferred), feeds it to OpenAI with tight prompts to avoid fluff, and writes the result back to the same sheet row. You can run it on demand or schedule it, and it’s resume-safe—rows with existing messages are skipped. The net effect: faster, higher-quality prospect research and consistent outbound messaging without leaving your sheet.

  How it works

  Reads rows from Google Sheets with headers Website and Personalized Message.
  Skips rows where Personalized Message already has content.
  Scrapes each URL to get clean page content (prefer markdown from a scraper like Firecrawl).
  Calls OpenAI with strict System/User prompts to produce Context + Pitch grounded in the site.
  Writes the generated message back to the same row (upsert by Website).
  (Optional) Adds a short delay between rows to respect rate limits.

  Setup:

  Sheet: Create headers Website, Personalized Message (row 1).
  Credentials: Add Google Sheets OAuth2 and OpenAI in n8n.
  Read node (Sheets): Select your doc/tab → Get All.
  Scrape step: Use Firecrawl → Scrape (markdown), or fallback to HTTP Request → HTML extract.
  OpenAI node: Model like gpt-4o-mini; paste your System/User prompts; enable Simplify Output if available.
  Write node (Sheets): Append or Update → Column to match on = Website; map
  Website → original URL
  Personalized Message → OpenAI output ({{$json.content}}
  (Optional) Add a Wait node (500–2000 ms) for throttling and stability.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.wait","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","@mendable/n8n-nodes-firecrawl.firecrawl","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Sales & Marketing","AI/ML","Data Processing"],"complexity":"medium","use_cases":["Automate sales outreach research using website scraping and AI to generate personalized pitches and brief profile summaries for prospects.","Transform raw website URLs into concise, AI-summarized client profiles for sales teams","Scale outbound sales communication by automatically gathering context and generating pitches from website content"],"estimated_setup_minutes":45,"required_services":["OpenAI (GPT-4o)","Google Sheets","Firecrawl (Web Scraper)"],"key_features":["Website content scraping to extract relevant business context","AI-powered personalization to generate consultative sales pitches","Batch processing with efficient sheet updates","Resume-safe workflow that skips previously processed records","Scheduling or on-demand execution support"],"target_audience":["Sales Development Representatives","B2B Marketers","Business Developers"]}


name: Google Maps to Airtable Lead Scraper with GPT Contact Extraction from Impressum
author_name: SuS
author_username: sulieman-said
categories: []
created_at: 2025-08-28T19:30:14.552Z
updated_at: 2025-08-28T19:30:14.552Z
url: https://n8n.io/workflows/8003
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  How it Works  
  This workflow automates the process of discovering companies in different cities, extracting their contact data, and storing it in Airtable.  

  City Loop (Airtable → Google Maps API)  
     Reads a list of cities from Airtable.  
     Uses each city combined with a search term (e.g., SEO Agency, Berlin) to query Google Maps.  
     Marks processed cities as “checked” to allow safe restarts if interrupted.  

  Business Discovery & Deduplication  
     Searches for businesses via Google Maps Text Search.  
     Checks Airtable to avoid scraping the same company multiple times.  
     Fetches detailed info for each business via Google Maps Place Details API.  

  Impressum Extraction (Website → HTML Parsing)  
     Builds an Impressum page URL for each business.  
     Requests the HTML and cleans out ads, headers, footers, etc.  
     Extracts relevant contact info using an AI extractor (OpenAI node).  

  Contact Information Extraction  
     Pulls out:  
       Decision Maker (Name + Position in one string, if available).  
       Email address (must be valid, containing @).  
       Phone number (international format if possible).  
     Filters out incomplete results (e.g., empty email).  

  Database Storage  
     Writes company data back into Airtable:  
       Company name  
       Address  
       Website  
       Email  
       Phone number  
       Decision Maker (Name + Position)  
       Search term & city used  

  Setup Steps  

  1. Prerequisites  
  Google Maps API Key with access to:  
    Places API → Text Search + Place Details  
  Airtable base with at least two tables:  
    Cities (with columns: ID, City, Country, Status)  
    Companies (for scraped results)  
  OpenAI API key (for decision maker + contact extraction).  

  2. Authentication  
  Configure your Airtable API credentials in n8n.  
  Set up HTTP Query Auth with your Google Maps API key.  
  Add your OpenAI API key in the OpenAI Chat node.  

  3. Configuration  
  In the Airtable “Cities” table, list all cities you want to scrape.  
  Define your search term in the “Execute Workflow” node (e.g., SEO Agency).  
  Adjust the batch sizes and wait intervals if you want faster/slower scraping (Google API has strict rate limits).  

  4. Execution  
  Start manually or from another workflow.  
  The workflow will scrape all companies in each city step by step.  
  It can be safely stopped and resumed — cities already marked as processed will be skipped.  

  5. Results  
  Enriched company dataset stored in Airtable, ready for CRM import, lead generation, or further automation.  

  Tips & Notes  
  Always respect GDPR and local laws when handling scraped data.  
  The workflow is modular → you can swap Airtable with Google Sheets, Notion, or a database of your choice.  
  Add custom filters to limit results (e.g., only companies with websites).  
  Use sticky notes inside the workflow to understand each step (mandatory for template publishing).  
  Keep an eye on Google Places API costs** — queries are billed after the free quota. If you are still within the first 2 months of the Google Cloud Developer free trial, you can benefit from free credits.  


  **

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metadata_json:
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name: Generate & Publish AI Cinematic Videos to YouTube Shorts using VEO3 & Gemini
author_name: SuS
author_username: sulieman-said
categories: []
created_at: 2025-08-28T20:07:27.635Z
updated_at: 2025-08-28T20:07:27.635Z
url: https://n8n.io/workflows/8004
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:19
description:
  How it Works
  This workflow uses VEO3 to generate surreal cinematic AI videos at a fraction of the cost compared to Google’s standard video generation APIs.  
  With it, you can automatically run a YouTube Shorts channel (and optionally Instagram uploads) that posts new AI videos every day  completely automated.  

  Workflow Overview  
  Schedule Trigger  
     Runs automatically at your chosen times (e.g., twice per day).  

  AI Prompt Generator  
     Creates a detailed video idea based on your chosen niche.  
     You only need to adapt the system prompt to your theme (e.g., Travel Facts, Tech Visuals, Mini-Facts, Impossible Realities, Food Illusions).  
     Output includes:  
       Video Prompt (for VEO3 API)  
       Social Caption with hashtags  
       YouTube Title  
       YouTube Description  

  VEO3 Video Generation  
     Sends the prompt to KIE AI VEO3 (model: veo3_fast).  
     Produces 9:16 vertical short-form videos optimized for YouTube Shorts, TikTok, Instagram Reels.  
     Significantly cheaper than Google’s Video AI while delivering cinematic quality.  

  Publishing  
     Uploads automatically to YouTube Shorts (with AI-generated titles, descriptions, tags).  
     In parallel, posts the same video to a Telegram channel (Instagram upload possible with a small modification).  

  Why Use This Workflow?  
  Cost Advantage:**  
    Google’s video generation is very expensive.  
    VEO3 costs are much lower, making it possible to publish multiple Shorts every day without breaking your budget.  

  Automated Channel Growth:**  
    Build a fully automated YouTube Shorts channel.  
    Post fresh videos daily → more reach, more chances to go viral.  

  Flexible Niches:**  
    Simply edit the system prompt to match your niche.  
    Examples:  
      Mini Facts Channel  
      Surreal Visual Loops  
      Tech Explainers in Visual Form  
      Food or Nature Illusions  
    The AI adapts automatically to your theme.  

  Cross-Platform:**  
    Out of the box: YouTube + Telegram.  
    With small adjustments: Instagram Reels, TikTok uploads possible.  

  Setup Steps  
  Get API Keys:  
     KIE AI VEO3 API key.  
     YouTube API credentials (OAuth2).  
     (Optional) Telegram Bot API or Instagram Graph API.  

  Configure Schedule:  
     Define how often you want new videos (e.g., 1–5 per day).  

  Adapt Prompts:  
     Change the AI Agent system prompt to your target niche.  

  Run & Scale:  
     Let the workflow run automatically.  
     Watch your channel fill with new daily Shorts.  

  Tips & Notes  
  With VEO3 you can experiment with higher volume since costs are predictable and far lower than Google.  
  Start with one niche channel → once it works, clone the workflow to build multiple parallel AI-driven channels.  
  Always respect YouTube/Instagram guidelines when posting automated content.  
  Adding background music layers or captions can further increase virality.  

  ⚡ Summary:  
  With this template you can create an AI-powered YouTube Shorts machine that runs on autopilot.  
  Just choose your niche, adjust the prompt, and let VEO3 + n8n handle video creation, uploading, and scaling — cheaper, faster, and smarter than traditional Google Video AI.


  Questions or custom requests?  
  📩 suliemansaid.business@gmail.com

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metadata_json:
  {"categories":["Content Creation","Marketing","Video Production"],"complexity":"medium","use_cases":["Automate YouTube Shorts","Generate AI videos","Schedule social media posts","Integrate video generation with publishing"],"estimated_setup_minutes":20,"required_services":["Google Gemini","VEO3","YouTube API"],"key_features":["Scheduled triggers","AI prompt generation","Automatic video creation","Multi-platform posting"],"target_audience":["Content creators","Social media managers","Video marketers"]}


name: Generate Professional Zoom Meeting Summaries with GPT-4O and Google Docs
author_name: inderjeet Bhambra
author_username: idsinghbhambra
categories: []
created_at: 2025-08-29T01:13:25.636Z
updated_at: 2025-08-29T01:13:25.636Z
url: https://n8n.io/workflows/8009
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it Works

  This automated workflow transforms Zoom meeting emails into professional summaries and Google Docs. It monitors your Gmail for Zoom meeting notification emails, extracts meeting content using AI-powered analysis, and generates both email-safe HTML summaries and Google Docs-compatible text. 

  The workflow intelligently parse meeting transcripts, creating structured summaries with attendee lists, key discussion points, action items, and next steps. 

  It automatically creates Google Docs for record-keeping and sends formatted email summaries to meeting participants.

  Who is it For

  Perfect for project managers, team leads, executives, and meeting coordinators who regularly conduct Zoom meetings and need consistent, professional documentation. Ideal for organizations that require structured meeting records, action item tracking, and seamless integration between communication tools. Especially valuable for distributed teams, consulting firms, and corporate environments where meeting accountability and follow-up are critical.

  Benefits for Using this Workflow

  Time Savings**: Eliminates 30-60 minutes of manual meeting summary creation per meeting.
  Professional Consistency**: Ensures all meeting summaries follow the same structured format with headers, attendees, discussion points, and action items. 
  Automated Documentation**: Creates searchable Google Docs archives and distributes summaries without manual intervention.
  Enhanced Accountability**: Clear action item tracking with assignees and deadlines improves follow-through. 
  Multi-Format Output**: Provides both email-friendly HTML and plain text formats for maximum compatibility across different platforms and systems.

  Setup Requirements
  Prerequisites**: Active n8n instance, Gmail account, Google Docs access, OpenAI API key. 
  Required Credentials**: Configure Gmail OAuth2 for both trigger and sending, Google Docs OAuth2 for document creation, OpenAI API for GPT-4 processing. 
  Configuration Steps**: 
  		1) Import workflow and activate Gmail trigger with filter for Zoom meeting emails, 
  		2) Set up Google Drive folder permissions for document creation, 
  		3) Configure OpenAI credentials and verify AI agent tools connection, 
  		4) Test workflow with a sample Zoom email to ensure proper formatting and delivery. 
  Optional Customization**: Modify Gmail search filters, adjust Google Docs folder location, customize email templates, or fine-tune AI prompt for specific meeting formats.

nodes_used:
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metadata_json:
  {"categories":["Communication","Document Automation","AI/ML"],"complexity":"medium","use_cases":["Automate meeting summary creation","Transform emails into documents","Extract action items from transcripts","Generate Google Docs from notifications"],"estimated_setup_minutes":15,"required_services":["Gmail","Zoom","Google Docs","GPT-4"],"key_features":["Parse Zoom meeting emails","AI-powered transcript analysis","Structured output generation","Automatic document creation"],"target_audience":["office managers","administrative staff","team leads"]}


name: Automate Twitter posting with GPT-4 content generation & Google Sheets tracking
author_name: Intuz
author_username: intuz
categories: []
created_at: 2025-08-29T04:24:04.290Z
updated_at: 2025-08-29T04:24:04.290Z
url: https://n8n.io/workflows/8010
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:20
description:
  This n8n template from Intuz provides a complete and automated solution for creating an autonomous social media manager. 
  This workflow uses an AI agent to intelligently generate unique, high-quality content, check for duplicates, and post it on a consistent schedule to automate your entire Twitter presence.

  Who's this workflow for?
  Social Media Managers
  Marketing Teams & Agencies
  Startup Founders & Solopreneurs
  Content Creators

  How it works
  1. Runs on a Schedule: The workflow automatically starts at a set interval (e.g., every 6 hours), ensuring a consistent posting schedule.

  2. AI Generates a New Tweet: An advanced AI Agent, powered by OpenAI, uses a detailed prompt to craft a new, engaging tweet. The prompt defines the tone, topics, character limits, and hashtags.

  3. Checks for Duplicates: Before finalizing the tweet, the AI Agent is equipped with a tool to read a Google Sheet containing a log of all previously published posts. This allows it to ensure the new content is always unique.

  4. Posts to Twitter (X): The final, unique tweet is automatically posted to your connected Twitter account.

  5. Logs the New Post: After posting, the workflow logs the new tweet back into the Google Sheet, updating the history for the next run. This completes the autonomous loop.

  Setup Instructions
  Schedule Your Posts: In the Start Workflow (Schedule Trigger) node, set the frequency you want the workflow to run (e.g., every 6 hours).

  Connect OpenAI:
  Add your OpenAI API key in the OpenAI Chat Model node.
  Customize the prompt in the AI Agent node to match your brand's voice, target keywords, and specific URLs.

  Configure Google Sheets:
  Connect your Google Sheets account.
  Create a sheet with two columns: Tweet Content and Status.
  In both the Get Data from Google Sheet and Add new Tweet to Google sheet nodes, select your credentials and specify the Document ID and Sheet Name.

  Connect Twitter (X):
  In the Create Tweet node, connect the Twitter account where you want to post.

  Activate Workflow:
  Save the workflow and toggle the "Active" switch to ON. Your AI social media manager is now live!

  Key Requirements to Use This Template
  Before you start, please ensure you have the following accounts and assets ready:

  An n8n Instance: An active n8n account (Cloud or self-hosted) where you can import and run this workflow.

  OpenAI Account: An active OpenAI account with an API Key. You will need to have billing enabled to use the language models for tweet generation.

  Google Account & Sheet: A Google account and a pre-made Google Sheet. The sheet must have two specific columns: Tweet Content and Status.

  Twitter (X) Developer Account: A Twitter (X) account with an approved Developer profile. You need an App created within the Developer Portal with the necessary permissions (v2 API access with Write scopes) to post tweets automatically.

  Connect with us
  Website: https://www.intuz.com/n8n-workflow-automation-templates
  Email: getstarted@intuz.com
  LinkedIn: https://www.linkedin.com/company/intuz
  Get Started: https://n8n.partnerlinks.io/intuz

  For Custom Worflow Automation
  Click here- Get Started

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.twitter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Marketing","AI/ML","Social Media Management"],"complexity":"medium","use_cases":["generate social media content","automate scheduling posts","track links to spreadsheet","avoid duplicate content"],"estimated_setup_minutes":10,"required_services":["Twitter","Google Sheets","OpenAI (GPT-4)"],"key_features":["AI content generation","automated scheduling","link tracking","duplicate check","scheduled execution"],"target_audience":["social media managers","marketing teams","content creators"]}


name: Automate LinkedIn profile research & email outreach with Apify, Gemini & Sheets
author_name: Intuz
author_username: intuz
categories: []
created_at: 2025-08-29T06:19:47.212Z
updated_at: 2025-08-29T06:19:47.212Z
url: https://n8n.io/workflows/8012
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:20
description:
  This n8n template from Intuz provides a complete and automated solution for deep-dive lead research and hyper-personalized email generation. 

  It transforms a basic list of LinkedIn profiles into a campaign-ready database by first enriching contacts with detailed career data and then using AI to craft unique, context-aware emails based on each individual's professional journey.

  Who's this workflow for?
  Sales Development Representatives (SDRs)
  Account Executives (AEs)
  B2B Marketers & Growth Hackers
  Recruiters & Talent Acquisition Specialists
  Startup Founders

  How it works
  1. Scheduled Data Fetch: The workflow runs automatically on a schedule, fetching a list of leads (containing LinkedIn URLs) from a Google Sheet.

  2. Enrich Profiles with Apify: For each lead that hasn't been processed, it uses an Apify actor to scrape their LinkedIn profile, extracting key information like their "About" section and detailed work experience.

  3. Update Central Database: The scraped career history is saved back into a "Profile Data" column in the original Google Sheet, creating a rich, centralized lead profile.

  4. AI Email Personalization: The workflow sends the complete, enriched profile data to a Google Gemini AI model via LangChain, using a sophisticated prompt that instructs it to act as an expert B2B copywriter.

  5. Craft a Unique Hook: The AI analyzes the lead's entire career journey to find unique "nuggets"—like long tenure, specific achievements, or unusual career paths—and uses them to write a compelling opening line.

  6. Save the Final Email Draft: The AI-generated subject line and personalized email body are saved back into the Google Sheet, leaving you with a ready-to-send, hyper-personalized outreach campaign.

  Setup Instructions
  1. Google Sheets Setup:
  Connect your Google Sheets account to n8n.
  In all three Google Sheets nodes, select your credentials and update the Document ID and Sheet Name to match your lead list.
  Populate your sheet with initial lead data, including at least their LinkedIn URL.

  2. Apify Connection:
  Connect your Apify account in the Runs Profile Extraction Actor node.

  3. Google Gemini AI Connection:
  Connect your Google Gemini (PaLM) API account in the Google Gemini Chat Model node.

  4. Configure the Schedule:
  In the Schedule Trigger node, set the interval for how often you want the workflow to run and process new leads.

  5. Activate Workflow:
  Save the workflow and toggle the "Active" switch to ON. Your automated research and personalization engine is now active.

  Key Requirements to Use This Template
  n8n Instance: An active n8n account (Cloud or self-hosted).
  Google Account & Sheet: A pre-made Google Sheet with columns for First Name, Last Name, LinkedIn, Profile Data, Subject, and Email Body.
  Apify Account: An active Apify account with a plan that supports the LinkedIn Profile Scraper actor.
  Google Gemini AI Account: A Google Cloud account with the Vertex AI API (for Gemini models) enabled and an associated API Key.

  Connect with us
  Website: https://www.intuz.com/n8n-workflow-automation-templates
  Email: getstarted@intuz.com
  LinkedIn: https://www.linkedin.com/company/intuz
  Get Started: https://n8n.partnerlinks.io/intuz

  For Custom Worflow Automation
  Click here- Get Started

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.limit","@apify/n8n-nodes-apify.apify","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","@apify/n8n-nodes-apify.apify","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Sales","Marketing","Data Processing"],"complexity":"medium","use_cases":["LinkedIn lead enrichment","Email campaign generation","Account research automation","Prospect outreach"],"estimated_setup_minutes":45,"required_services":["Apify","Google Gemini","Google Sheets","LinkedIn"],"key_features":["AI-driven email personalization","Deep profile enrichment","Data pipeline from scraper to spreadsheet","Context-aware content generation"],"target_audience":["Sales Development Representatives","Account Executives","B2B Marketers"]}


name: Automate scraping Y Combinator startups with Apify & Google Sheets
author_name: Intuz
author_username: intuz
categories: []
created_at: 2025-08-29T06:50:56.787Z
updated_at: 2025-08-29T06:50:56.787Z
url: https://n8n.io/workflows/8013
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Disclaimer
  Community nodes are used, and template can only be used on self-hosted n8n instances.

  This n8n template from Intuz provides a complete solution to automate the process of scraping company and founder data from Y Combinator. 

  It systematically extracts valuable information from your target search and organizes it directly into a Google Sheet, building a powerful prospecting list with minimal effort.

  Who's this workflow for?
  Sales Teams & SDRs
  Venture Capitalists 
  Angel Investors
  Market Researchers
  Startup Founders

  How it works
  1. Trigger the Scrape: You start the workflow manually whenever you want to gather new company data.

  2. Scrape Y Combinator: An Apify actor automatically visits your specified Y Combinator search URL (e.g., filtered by batch, industry, or region) and scrapes the details of each company listed.

  3. Retrieve Structured Data: The workflow fetches the neatly structured data from Apify, including company names, descriptions, websites, founder details, and more.

  4. Log to Google Sheets: All the scraped information is added or updated as new rows in your designated Google Sheet, creating an organized and actionable database.

  Setup Instructions
  1. Apify Configuration:
  In the Run an Actor node, connect your Apify account.
  Select the Y Combinator Directory Scraper actor.
  In the Custom Body field, replace {YOUR_Y_COMBINATOR_SEARCH_URL} with the actual URL from the Y Combinator website after you've applied your desired filters.
  You can adjust the maxCompanies value to control how many companies are scraped per run.

  2. Google Sheets Configuration:
  In the Add data to Google Sheet node, connect your Google Sheets account.
  Select the Document (spreadsheet) and Sheet where you want to save the data. Make sure the sheet has the required columns listed in the "Key Requirements" section.

  3. Execute the Workflow:
  Click the "Execute workflow" button to run the scraper and populate your Google Sheet.

  Key Requirements to Use This Template
  n8n Instance: An active n8n account (Cloud or self-hosted).
  Apify Account: An active Apify account with an API key. You will also need sufficient credits or a subscription plan to run the Y Combinator Directory Scraper actor.
  Google Account & Sheet: A Google account and a pre-made Google Sheet. The sheet must have the following columns created in advance: Company, Location, Website, LinkedIn, Founded, Description, Industry Tags, Founder 1 Name, Founder 1 LinkedIn, Founder 2 Name, and Founder 2 LinkedIn.

  Connect with us
  Website: https://www.intuz.com/n8n-workflow-automation-templates
  Email: getstarted@intuz.com
  LinkedIn: https://www.linkedin.com/company/intuz
  Get Started: https://n8n.partnerlinks.io/intuz

  For Custom Worflow Automation
  Click here- Get Started

nodes_used:
  ["@apify/n8n-nodes-apify.apify","@apify/n8n-nodes-apify.apify","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Marketing","Data Processing"],"complexity":"medium","use_cases":["prospecting","lead generation","company research","data collection"],"estimated_setup_minutes":10,"required_services":["Apify","Google Sheets"],"key_features":["web scraping automation","data export to spreadsheets","search filtering"],"target_audience":["sales teams","venture capitalists","sdrs"]}


name: Automate AI Upwork proposal generation with Apify, Google Gemini & sheets
author_name: Intuz
author_username: intuz
categories: []
created_at: 2025-08-29T07:27:56.439Z
updated_at: 2025-08-29T07:27:56.439Z
url: https://n8n.io/workflows/8015
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Disclaimer
  Community nodes are used, and template can only be used on self-hosted n8n instances.

  This n8n template from Intuz provides a complete and automated solution for finding relevant Upwork jobs and generating highly-personalized, knowledge-base-driven proposals using AI. 

  This workflow acts as an autonomous agent, handling everything from job discovery to drafting the final proposal text, ready for you to review and submit.

  Who's this workflow for?
  Freelancers & Consultants
  Digital Agencies
  Business Development Teams
  Sales Professionals

  How it works
  1. Scrape Upwork for New Jobs: The workflow starts by running an Apify actor to scrape Upwork based on your defined criteria (keywords, categories, payment verification, etc.).

  2. Log Jobs to Google Sheets: The newly found job opportunities are automatically saved as new rows in a Google Sheet, creating a central dashboard for tracking. A notification is sent confirming the new jobs found.

  3. Trigger AI Proposal Generation: The workflow then reads the new jobs from the sheet and, for each one that hasn't been applied for, triggers the AI.

  4. Craft a Knowledge-Based Proposal: It sends the job details to a Google Gemini AI model. The AI uses a detailed, built-in "Company Knowledge Base" (which you can customize) to write a comprehensive and relevant proposal. It addresses client questions, highlights relevant experience, and suggests a timeline.

  5. Save the Proposal Draft: The AI-generated proposal is saved back into the Google Sheet in the corresponding job row.

  6. Final Notification: A final email is sent to notify you that the proposals are ready, including a link to the sheet for final review and submission on Upwork.

  Setup Instructions
  1. Apify Configuration:
  In the Run Upwork Actor... node, connect your Apify account.
  We have used this apify scrapper- Upwork Extractor
  In the Custom Body field, customize the search parameters (like category, search.any keywords) to match the jobs you are looking for.

  2. Google Gemini AI Configuration:
  In the Google Gemini Chat Model node, connect your Google Gemini (PaLM) API account.
  Crucially: In the Proposal Crafting node, edit the prompt to replace the "Company Knowledge Base" with your own company's details, services, and project examples. This is essential for personalizing the proposals.

  3. Google Sheets Setup:
  Connect your Google Sheets account.
  In all three Google Sheets nodes, select your credentials and update the Document ID and Sheet Name to point to your Upwork tracking sheet.

  4. Gmail Configuration:
  In both Gmail nodes, connect your account and update the sendTo email address to your own.

  5. Execute the Workflow:
  Click "Execute workflow" to run the entire job scraping and proposal generation process.

  Key Requirements to Use This Template
  n8n Instance: An active n8n account (Cloud or self-hosted).
  Apify Account: An active Apify account with an API key and sufficient credits/subscription to run the "Upwork Extractor" actor.
  Google Account & Sheet: A pre-made Google Sheet with columns for Title, URL, Description, Skills, Questions, Applied, and Proposal Template.
  Google Gemini AI Account: A Google Cloud account with the Vertex AI API (for Gemini models) enabled and an associated API Key.
  Gmail Account: A Gmail account to send and receive notifications about the workflow's progress.

  Connect with us
  Website: https://www.intuz.com/n8n-workflow-automation-templates
  Email: getstarted@intuz.com
  LinkedIn: https://www.linkedin.com/company/intuz
  Get Started: https://n8n.partnerlinks.io/intuz

  For Custom Workflow Automation
  Click here- Get Started

nodes_used:
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metadata_json:
  {"categories":["AI/ML","Communication","Data Processing"],"complexity":"medium","use_cases":["generate freelance proposals","discover jobs automatically","research job requirements"],"estimated_setup_minutes":15,"required_services":["Apify","Google Gemini","Google Sheets"],"key_features":["autonomous job discovery","AI-powered proposal drafting","knowledge-based personalization","automatic proposal summarization"],"target_audience":["freelancers","consultants","web self-hosters"]}


name: LeadChat Booker — conversational lead capture that schedules
author_name: Le Nguyen
author_username: leeseifer
categories: []
created_at: 2025-08-29T08:31:42.598Z
updated_at: 2025-08-29T08:31:42.598Z
url: https://n8n.io/workflows/8018
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:22
description:
  Real Working Demo

  https://crmaiinsight.com/leadbot
  How It Works (High-Level)

  1) Chat &gt; Capture  
     LeadBot greets, collects Full Name → Email → Mobile (optional) → Product interest via friendly chat (no forms).  
     Validates email/phone, confirms inputs, supports corrections.

  2) De-dupe  
     Checks Salesforce for an existing lead by Email.  
     If found → update_lead. If not → create_lead.

  3) Product Selection  
     Pulls your Feature_Product__c list (Name + one-line Description) and lets the user choose by number or name.

  4) Demo Booking (Optional)  
     If the user says Yes:  
     Detect/ask Time Zone (IANA).  
     Reads Salesforce Events for the next 14 days.  
     Computes free 30-min weekday slots (09:00–17:00 UTC) strictly after now.  
     User picks a slot → creates a Salesforce Event (30 min) linked to the Lead.

  5) Notifications & Follow-ups  
     Slack DM to your team (concise summary).  
     Email confirmation to the prospect (local date/time included).  
     Ends with a polite confirmation message.

  Setup at a Glance

  Effort:* Most teams finish the base setup in *~45–90 minutes** (credentials, field mapping, test).  
  What you’ll configure:** Salesforce OAuth, SMTP (or your ESP), Slack, OpenAI key, and the web chat widget.

  &gt; Tip: Start with the provided Feature_Product__c object. You can switch to your own product catalog with a few tweaks (see below).

  Prerequisites

  Salesforce**  
    Connected App + OAuth (API enabled).  
    Lead fields: FirstName, LastName, Email, MobilePhone, and one product lookup/text field (e.g., Interested_Product__c or Feature_Product__c).  
    (Recommended) Custom object Feature_Product__c with fields: Name, Description__c, Features__c, Tags__c.

  n8n Credentials**  
    salesforceOAuth2Api (to read/write Leads & Events).  
    smtp (or use your email provider node).  
    slackApi (for internal notifications).  
    openAiApi (for the chat agent).

  Widget Embed (optional)**  
    A place on your site/app to embed the chat launcher.

  Step-by-Step Setup

  1) Import the flow into n8n  
     Verify nodes: Chat Trigger → AI Agent → check_duplicate_lead → update/create lead → get_products → get_scheduled_events → create_demo_event → Slack/Email.

  2) Add/Bind Credentials
     Salesforce OAuth: authorize org with API scope.  
     SMTP: sender, domain, and authentication (verify SPF/DKIM).  
     Slack: ensure bot can DM users/channels.  
     OpenAI: drop in your API key.

  3) Map Product Field on Lead
     Pick one field to store the selection (e.g., Interested_Product__c).  
     Ensure AI Agent and Salesforce nodes reference the same field.

  4) (Option A) Use Feature_Product__c
     Confirm fields exist: Name, Description__c (used in the numbered list).  
     In get_products, make sure Id is returned; pass it as customFieldsValues0_Value.

  5) (Option B) Use Your Own Catalog
     Swap customObject: Feature_Product__c → Product2 (or your object).  
     Update fields to at least: Id, Name, and a short description field.  
     Keep the variable contract the same (AI expects to store Id, name, description).

  6) Configure Demo Booking
     get_scheduled_events uses a SOQL search window (today → +14 days).  
     Ensure your Event permissions allow read/write.  
     Confirm create_demo_event URL points to your Salesforce domain & API version.

  7) Customize Widget (optional)
     Set title, brand colors, and initial message in the Chat Trigger options.  
     Limit reply length (≤100 words) is already set.

  8) Test End-to-End
     Run a full path: enter name/email/phone, pick a product, select a time slot.  
     Verify: Lead/Update in Salesforce, Event creation, Slack DM, Prospect Email.

  Switching to Your Own Products (Quick Tweaks)

  Replace Data Source:**  
    In get_products:  
      customObject: Product2 (or your object API name).  
      Fields: ensure Id, Name, and a short description are returned.  
  Update Lead Field Mapping:**  
    Set Interested_Product__c (or your chosen field) to the returned Product Id.  
  Copy Text Logic:**  
    Keep the AI’s “numbered list” output; it supports both number and name replies.

  What Users Experience

  No forms. A friendly AI asks one thing at a time, confirms details, and offers a live calendar of the next available 30-minute demo slots.  
  The moment they choose, a Salesforce Event is created and they get a confirmation email.  
  Your team receives a Slack heads-up with who/what/when.

  Troubleshooting Quick Checks

  No product list?* Ensure Id, Name, Description__c (or equivalent) are returned by *get_products**.  
  No free slots shown?** Confirm Event query window (+14 days), timezone handling, and that “current time” filter excludes past times.  
  Lead not updated?* Check the *duplicate search by Email** and that update_lead receives Lead_ID.  
  Event fails to create?** Verify Salesforce API endpoint, OAuth scope, and required Event fields.

nodes_used:
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metadata_json:
  {"categories":["Salesforce Integration","Lead Generation","Customer Engagement"],"complexity":"medium","use_cases":["capture leads via chat","schedule appointments","validate contact details","duplicate lead checks","product interest collection"],"estimated_setup_minutes":30,"required_services":["Salesforce"],"key_features":["contextual chatbot interactions","form-free lead capture","duplicate lead detection","product catalog integration","integration via https"],"target_audience":["sales teams","marketing managers","customer support"]}


name: Auto-Generate Virtual AI Try-On Images for WooCommerce with Gemini Nano Banana
author_name: Davide Boizza
author_username: n3witalia
categories: []
created_at: 2025-08-29T10:05:54.420Z
updated_at: 2025-08-29T10:05:54.420Z
url: https://n8n.io/workflows/8022
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  This workflow automates the creation of AI-generated virtual try-on images for fashion eCommerce stores. Instead of relying on expensive and time-consuming photoshoots, the system uses AI to generate realistic images of models wearing selected clothing items.

  This n8n workflow automates the process of generating AI-powered virtual try-on images for a WooCommerce store. It fetches product data from a Google Sheet, uses the Fal.ai Nano Banana model to create an image of a model wearing the clothing item, and then updates both the Google Sheet and the WooCommerce product with the final generated image.

  Advantages

  ✅ Cost Reduction: Eliminates the need for professional photo shoots, saving on models, photographers, and studio expenses.
  ✅ Time Efficiency: Automates the entire workflow—from data input to product update—minimizing manual work.
  ✅ Scalability: Works seamlessly across large product catalogs, making it easy to update hundreds of products quickly.
  ✅ Enhanced eCommerce Experience: Provides shoppers with realistic previews of clothing on models, boosting trust and conversion rates.
  ✅ Marketing Flexibility: The generated images can also be repurposed for ads, social media, and promotional campaigns.
  ✅ Centralized Management: Google Sheets acts as the control center, making it easy to manage inputs and track results.

  How It Works

  The workflow operates in a sequential, loop-based manner to process multiple products from a spreadsheet. Here is the logical flow:

  Manual Trigger & Data Fetch: The workflow starts manually (e.g., by clicking "Test workflow"). It first reads data from a specified Google Sheet, looking for rows where the "IMAGE RESULT" column is empty.

  Loop Processing: It loops over each row of data fetched from the sheet. Each row should contain URLs for a model image and a product image, along with a WooCommerce product ID.

  API Request to Generate Image: For each item in the loop, the workflow sends a POST request to the Fal.ai Nano Banana API. The request includes the two image URLs and a prompt instructing the AI to create a photo of the model wearing the submitted clothing item.

  Polling for Completion: The AI processing is asynchronous. The workflow enters a polling loop: it waits for 60 seconds and then checks the status of the processing request. If the status is not COMPLETED, it waits and checks again. This loop continues until the image is ready.

  Fetching and Storing the Result: Once the status is COMPLETED, the workflow retrieves the URL of the generated image, downloads the image file, and uploads it to a designated folder in Google Drive.

  Updating Systems: The workflow then performs two crucial update steps:
      It updates the original Google Sheet row, writing the URL of the final generated image into the "IMAGE RESULT" column.
      It updates the corresponding product in WooCommerce, adding the generated image to the product's gallery.

  Loop Continuation: After processing one item, the workflow loops back to process the next row in the Google Sheet until all items are complete.

  * Set Up Steps*

  To make this workflow functional, you need to configure three main connections:

  Step 1: Prepare the Google Sheet
  Create a Google Sheet with the following columns: IMAGE MODEL, IMAGE PRODUCT, PRODUCT ID, and IMAGE RESULT.
  Populate the first three columns for each product. The IMAGE RESULT column must be left blank; the workflow will fill it automatically.
  In the n8n workflow, configure the "Google Sheets" node to point to your specific Google Sheet and worksheet.

  Step 2: Configure the Fal.ai API Key
  Create an account at fal.ai and obtain your API key.
  In the n8n workflow, locate the three "HTTP Request" nodes named "Get Url image", "Get status", and "Create Image".
  Edit the credentials for these nodes (named "Fal.run API") and update the Value field in the Header Auth to be Key YOURAPIKEY (replacing YOURAPIKEY with your actual key).

  Step 3: Set Up WooCommerce API
  Ensure you have the API keys (Consumer Key and Consumer Secret) for your WooCommerce store's REST API.
  In the n8n workflow, locate the "WooCommerce" node.
  Edit its credentials and provide the required information: your store's URL and the API keys. This allows the workflow to authenticate and update your products.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wooCommerce","n8n-nodes-base.googleSheets","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["E-Commerce","AI/ML","Marketing"],"complexity":"medium","use_cases":["Generate virtual fashion images","Automate product listings","Reduce photography costs"],"estimated_setup_minutes":30,"required_services":["WooCommerce","Google Sheets","Google Drive","Fal.ai"],"key_features":["Automatic image generation","WooCommerce API integration","Batch processing logic","Dynamic data fetching"],"target_audience":["eCommerce owners","Fashion marketers","Product managers"]}


name: Fully Automate Blog Creation with GPT & Publish to WordPress via Google Sheets
author_name: Zakwan
author_username: zakwanzahid
categories: []
created_at: 2025-08-29T13:00:44.830Z
updated_at: 2025-08-29T13:00:44.830Z
url: https://n8n.io/workflows/8027
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  Creating high-quality, SEO-friendly blog posts consistently can be time-consuming. This template helps content creators, bloggers, SEO specialists, and agencies fully automate their blogging workflow. By combining AI content generation (GPT), Google Sheets for keyword management, and WordPress for direct publishing, this workflow saves hours of manual work and ensures professional results.

  ⚡ Use Cases

  Automate content creation for niche blogs.

  Generate SEO-optimized articles from keyword lists.

  Keep a consistent publishing schedule without manual effort.

  Scale content production for agencies or affiliate sites.

  ✅ Pre-requirements

  Before using this template, you will need:

  Google Sheets API credentials (for managing topics & keywords).

  AI API key (e.g., OpenAI, LM Studio, Ollama, or any connected model).

  WordPress credentials with API access.

  Basic understanding of n8n workflow editor.

  🔧 Step-by-Step Setup

  Connect Google Sheets:
  Add your API credentials.-
  Use the sheet to store keywords, titles, and categories.
  Integrate AI Model (GPT or others):
  Insert your API key into the AI node.
  Customize the SEO writing prompt for Rank Math 90+ score.

  Content Processing:

  The workflow will fetch one keyword at a time.

  AI will generate a 1200–1500+ word SEO blog post.

  Output is cleaned into proper HTML.

  Publish to WordPress:

  Configure the WordPress node with your site credentials.

  Automatically post as Draft or Published.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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

metadata_json:
  {"categories":["Content Creation","Marketing","Automation"],"complexity":"medium","use_cases":["Automate blog post creation","Generate SEO articles","Publish to WordPress"],"estimated_setup_minutes":30,"required_services":["OpenAI (GPT)","Google Sheets","WordPress API"],"key_features":["Keyword-to-article generation","Automated publishing","SEO optimization integration"],"target_audience":["Content Creators","Bloggers","Marketing Teams"]}


name: Automate HS Code Lookup & Enrichment with Google Sheets and Customs API (beta)
author_name: Thomas
author_username: thomas-z
categories: []
created_at: 2025-08-29T16:38:21.850Z
updated_at: 2025-08-29T16:38:21.850Z
url: https://n8n.io/workflows/8034
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:22
description:
  "I used to spend hours every week just copy-pasting product descriptions to find the right tariff codes for our international shipments. It was tedious and prone to errors." - Accounting specialist.


  This workflow eliminates that manual work entirely. It automatically finds customs tariff numbers (also known as HS Codes or "Zolltarifnummern") for your products and enriches your data in Google Sheets. It offers two powerful modes: bulk processing for entire product lists and an on-demand chat interface for quick single lookups.

  new features
  added the API score in percentage (80 to 100% is perfect, 70-80% still good)
  added description of the found HS Code to better verify the accuracy
  -please keep in mind, that is still a beta
  https://www.zolltarifnummern.de/services/api 

  What this workflow does
  Bulk Enrichment from Google Sheets:** Reads a list of product descriptions from a specified Google Sheet.
  External API Lookup:** For each product, it queries the zolltarifnummern.de API to find the most relevant customs tariff number.
  Automated Data Update:** Writes the found tariff numbers back into the correct row in your Google Sheet.
  On-Demand Single Lookup:** Use the integrated Chat Trigger to instantly look up a tariff number for a single product description without leaving n8n.
  Completion Notification:** Sends a confirmation email via Gmail once the bulk processing job is finished.

  Nodes Used
  Google Sheets
  HTTP Request
  Loop Over Items (Split in Batches)
  Set
  Gmail
  Chat Trigger
  Manual Trigger

  Preparation
  A Google Sheet prepared with at least two columns: one for your product descriptions (e.g., ProductDescription) and an empty one for the results (e.g., TariffCode).
  How to set up this workflow
  Configure Google Sheets (Read):

  Open the "Read Item Descriptions" node.
  Select your Google Sheets credentials.
  Enter your Spreadsheet ID and the name of the sheet containing your product data.
  Make sure the "Columns to Read" field includes the name of your product description column.
  Configure Google Sheets (Update):

  Open the "Write Customs Tariff to Sheet"  node.
  Select the same Google Sheets credentials.
  Enter the same Spreadsheet ID and Sheet Name.
  Under Columns, set Matching Columns to your product description column name. This is crucial for updating the correct rows.
  Configure Email Notification:

  Open the "Send Completion Email" (Gmail) node.
  Select your Gmail credentials.
  In the Send To field, enter the email address where you want to receive the completion notification.

  Run the Workflow:

  For Bulk Processing: Activate the workflow and execute the "Abfrage starten" (Start Query) Manual Trigger.
  For a Single Lookup: Use the Chat Trigger. Open the chat pane, type a product description, and hit send. The workflow will return the suggested tariff number.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["Finance","Data Processing","Logistics"],"complexity":"medium","use_cases":["automate tariff code lookup","enrich product data","process shipments"],"estimated_setup_minutes":24,"required_services":["Google Sheets","Customs API","Data Normalization"],"key_features":["bulk mode","manual entry fallback","data enrichment"],"target_audience":["accountants","logistics managers","finance teams"]}


name: Version Control n8n Workflows in GitLab with Customer Tag Organization
author_name: Omar Kennouche
author_username: kennouche-omar
categories: []
created_at: 2025-08-29T20:58:27.777Z
updated_at: 2025-08-29T20:58:27.777Z
url: https://n8n.io/workflows/8042
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it works
  Triggers manually or on schedule (03:00 daily by default)  
  Fetches workflows tagged backup-workflows via n8n API  
  Normalizes workflow names and applies [client: NAME] tag convention  
  Prepares JSON in the same structure as an n8n UI export  
  Checks GitLab repository:  
    Create new file if missing  
    Update file if content differs  
    Skip if unchanged  
  Logs results with recap (created, updated, unchanged, total)  

  Set up steps
  Configure your GitLab credentials in n8n  
  Create a repository and branch for workflow backups  
  Set global variables (owner, project, branch, backup path)  
  Tag workflows to include with backup-workflows  
  Run manually once to test, then enable the schedule  

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.n8n","n8n-nodes-base.gitlab","n8n-nodes-base.gitlab","n8n-nodes-base.gitlab","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.merge"]

workflow_json_compressed:
  [encoded data, 10760 characters]

metadata_json:
  {"categories":["DevOps","Version Control"],"complexity":"medium","use_cases":["backup worklflows to git","organize workflows by client","automate version control"],"estimated_setup_minutes":30,"required_services":["GitLab","n8n API"],"key_features":["automatic scheduling","workflow name normalization","tag-based organization","GitLab file synchronization","change detection tracking"],"target_audience":["devops engineers","workflow admins","n8n users"]}


name: Personalized Evening Wind-Down System with Mood Tracking via Telegram, Notion & Email
author_name: Shelly-Ann Davy
author_username: SheCodesFlow
categories: []
created_at: 2025-08-30T16:30:27.279Z
updated_at: 2025-08-30T16:30:27.279Z
url: https://n8n.io/workflows/8067
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:22
description:
  🌸 The Quiet Evening Ritual — Wind-Down Automation (Telegram + Notion + Email)

  Create a calming 9PM evening routine that runs itself. This n8n template checks in via Telegram with mood buttons, delivers personalized support (meditation when you’re tired, celebration when you’re thriving), logs reflections to a Notion “Evening Reflection Log,” and sends a gentle goodnight email with tomorrow’s affirmation.

  Who it’s for

  Homemakers, moms, and creators who feel “always on” and want a graceful, one-tap transition into rest.

  What it does

  9:00 PM Telegram mood check (I’m Tired 💤 / Felt Great ✨)

  Personalized content (5-min meditation or celebration prompt)

  Automatic Notion journaling (mood, reflection, wins, date, affirmation)

  Goodnight email with a fresh morning affirmation

  Optional: GPT-4o mini for gentle reflection prompts

  Why it’s different

  Not just logging—this is a ritualized, emotionally intelligent handoff from work to rest that protects your peace and builds consistency.

  Stack & Nodes

  Cron → Telegram → IF → Notion → Email (+ optional GPT)

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.cron","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.openAi","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.notion","n8n-nodes-base.emailSend","n8n-nodes-base.telegram","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.notion","n8n-nodes-base.emailSend"]

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metadata_json:
  {"categories":["Health & Wellness","Communication","Personal Productivity"],"complexity":"simple","use_cases":["automate evening routine","track daily mood","send personalized notifications","log reflection notes","deliver wellness tips"],"estimated_setup_minutes":10,"required_services":["Telegram","Notion","Email/SendGrid"],"key_features":["user interaction via chatbot buttons","conditional AI-based content delivery","timestamped logging to knowledge base","scheduled workflow trigger","personalized daily messages"],"target_audience":["health-conscious users","mental health advocates","productivity enthusiasts"]}


name: Find Valid Vouchers and Promo Codes with SerpAPI, Decodo, and GPT-5 Mini
author_name: Khaisa Studio
author_username: khaisastudio
categories: []
created_at: 2025-08-31T07:30:33.648Z
updated_at: 2025-08-31T07:30:33.648Z
url: https://n8n.io/workflows/8075
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Promo Seeker finds fresh, working promo codes and vouchers on the web so your team never misses a deal. 

  This n8n workflow uses SerpAPI and Decodo Scrapper for real-time search, an agent powered by GPT-5 Mini for filtering and validation, and Chat Memory to keep context—saving time, reducing manual checks, and helping marketing or customer support teams deliver discounts faster to customers (and yes, it's better at hunting promos than your inbox).

  💡 Why Use Promo Seeker?

  Speed: Saves hours per week by automatically finding and validating current promo codes, so you can publish deals faster.  
  Simplicity: Eliminates manual searching across sites, no more copy-paste scavenger hunts.  
  Accuracy: Reduces false positives by cross-checking results and keeping only working vouchers—fewer embarrassed "expired code" moments.  
  Edge: Combine search APIs with an AI agent to surface hard-to-find, recently-live offers—win over competitors who still rely on manual scraping.

  ⚡ Perfect For

  Marketing teams: Quickly populate newsletters, landing pages, or ads with valid promos.  
  Customer support: Give verified discount codes to users without ping-ponging between tabs.  
  Deal aggregators & affiliates: Discover fresh vouchers faster and boost conversion rates.

  🔧 How It Works

  ⏱ Trigger: A user message via the chat webhook starts the search (Message node).  
  📎 Process: The agent queries SerpAPI and Decodo Scrapper to collect potential promo codes and voucher pages.  
  🤖 Smart Logic: The Promo Seeker Agent uses GPT-5 Mini with Chat Memory to filter for fresh, working promos and to verify validity and relevance.  
  💌 Output: Results are returned to the chat with clear, copy-ready promo codes and source links.  
  🗂 Storage: Chat Memory stores context and recent searches so the agent avoids repeating old results and can follow up with improved queries.

  🔐 Quick Setup

  Import JSON file to your n8n instances  
  Add credentials: SerpAPI, Azure OpenAI (Gpt 5 Mini), Decodo API  
  Customize: Search parameters (brands, regions, validity window), agent system message, and result formatting  
  Update: Azure OpenAI endpoint and API key in the Gpt 5 Mini credentials; add your SerpAPI key and Decodo key  
  Test: Run a few queries like "latest Amazon promo" or "food delivery voucher" and confirm returned codes are valid

  🧩 You'll Need

  Active n8n instances  
  SerpAPI account and API key  
  Azure OpenAI (for GPT-5 Mini) with key and endpoint  
  Decodo account/API key  

  🛠️ Level Up Ideas

  Push verified promos to a Slack channel or email digest for the team.  
  Add scheduled scans to detect newly expired codes and remove them from lists.  
  Integrate with a CMS to auto-post verified deals to landing pages.

  Made by: khaisa Studio  
  Tags: promo, vouchers, discounts  
  Category: Marketing Automation  
  Need custom work? Contact Us

nodes_used:
  ["@n8n/n8n-nodes-langchain.toolSerpApi","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@decodo/n8n-nodes-decodo.decodoTool"]

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metadata_json:
  {"categories":["Marketing","Data Processing","E-commerce"],"complexity":"medium","use_cases":["search for promo codes","validate discount voucher","automate deal hunting"],"estimated_setup_minutes":15,"required_services":["SerpAPI","Decodo","GPT-5 Mini","chat memory system"],"key_features":["real-time search","AI-driven validation","automatic filtering","context retention"],"target_audience":["marketers","customer support specialists","sales operations"]}


name: Generate Student Course Schedules Based on Prerequisites with GPT and Google Sheets
author_name: Robert Breen
author_username: rbreen
categories: []
created_at: 2025-08-31T20:28:14.026Z
updated_at: 2025-08-31T20:28:14.026Z
url: https://n8n.io/workflows/8090
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  Create a Fall 2025 course schedule for each student based on what they’ve already completed, catalog prerequisites, and term availability (Fall/Both). Reads students from Google Sheets → asks an AI agent to select exactly 5 courses (target 15–17 credits, no duplicates, prereqs enforced) → appends each student’s schedule to a schedule tab.

  🧠 Summary
  Trigger:* Manual — *“When clicking ‘Execute workflow’”
  I/O:** Google Sheets in → OpenAI decisioning → Google Sheets out
  Ideal for:** Registrars, advisors, degree-planning prototypes

  ✅ What this template does
  Reads: StudentID, Name, Program, Year, CompletedCourses (pipe-separated CourseIDs) from **Sheet1  
  Decides: AI **Scheduling Agent chooses 5 courses per student following catalog rules and prerequisites  
  Writes: Appends StudentID + Schedule strings to **schedule worksheet  
  Credits target**: 15–17 total per term  
  Catalog rules** (enforced in the agent prompt):
    Use Fall or Both courses for Fall 2025
    Enforce AND prereqs (e.g., CS-102|CS-103 means both)
    Priority: Major Core → Major Elective → Gen Ed (include Gen Ed if needed)
    No duplicates or already-completed courses
    Prefer 200-level progression when prereqs allow

  ⚙️ Setup (only 2 steps)

  1) Connect Google Sheets (OAuth2)
  In n8n → Credentials → New → Google Sheets (OAuth2), sign in and grant access  
  In the Google Sheets nodes, select your spreadsheet and these tabs:
    Sheet1 (input students)
    schedule (output)

  &gt; Example spreadsheet (replace with your own):  
  &gt; - Input: .../edit#gid=0  
  &gt; - Output: .../edit#gid=572766543

  2) Connect OpenAI (API Key)
  In n8n → Credentials → New → OpenAI API, paste your key  
  In the OpenAI Chat Model node, select that credential and a chat model (e.g., gpt-4o)

  📥 Required input (Sheet1)
  Columns**: StudentID, Name, Program, Year, CompletedCourses  
  CompletedCourses**: pipe-separated IDs (e.g., GEN-101|GEN-103|CS-101)  
  Program* names should match those referenced in the embedded catalog (e.g., *Computer Science BS, Business Administration BBA, etc.)

  📤 Output (schedule tab)
  Columns**:
    StudentID
    Schedule → a selected course string (written one row per course after splitting)

  🧩 Nodes in this template
  Manual Trigger* → *Get Student Data (Google Sheets)* → *Scheduling Agent (OpenAI)**  
    → Split Schedule → Set Fields → Clear sheet → Append Schedule (Google Sheets)

  🛠 Configuration tips
  If you rename tabs, update both Get Student Data and Append Schedule nodes  
  Keep CompletedCourses consistent (use | as the delimiter)  
  To store rationale as well, add a column to the output and map it from the agent’s JSON

  🧪 Test quickly
  1) Add 2–3 sample student rows with realistic CompletedCourses  
  2) Run the workflow and verify:
     5 course rows per student in schedule
     Course IDs respect prereqs & Fall/Both availability
     Credits sum ~15–17

  🧯 Troubleshooting
  Sheets OAuth error:** Reconnect “Google Sheets (OAuth2)” and re-select the spreadsheet & tabs  
  Empty schedules:** Ensure CompletedCourses uses | and that programs/courses align with the provided catalog names  
  Prereq violations:** Check that students actually have all AND-prereqs in CompletedCourses  
  OpenAI errors (401/429):** Verify API key, billing, and rate-limit → retry with lower concurrency

  🔒 Privacy & data handling
  Student rows are sent to OpenAI for decisioning. Remove or mask any fields you don’t want shared.  
  Google Sheets retains input/output. Use spreadsheet sharing controls to limit access.

  💸 Cost & performance
  OpenAI**: Billed per token; cost scales with student count and prompt size  
  Google Sheets**: Free within normal usage limits  
  Runtime**: Typically seconds to a minute depending on rows and rate limits

  🧱 Limitations & assumptions
  Works for Fall 2025 only (as written). For Spring, update availability rules in the agent prompt  
  Assumes catalog in the agent system message is your source of truth  
  Assumes Program names match catalog variants (case/spacing matter for clarity)

  🧩 Customization ideas
  Add a Max Credits column to cap term credits per student  
  Include Rationale in the sheet for advisor review  
  Add a “Gen Ed needed?” flag per student to force at least one Gen Ed selection  
  Export to PDF or email the schedules to advisors/students

  🧾 Version & maintenance
  n8n version:** Tested on recent n8n Cloud builds (2025)  
  Community nodes:** Not required  
  Maintenance:** Update the embedded catalog and offerings each term; keep prerequisites accurate

  🗂 Tags & category
  Category:** Education / Student Information Systems  
  Tags:** scheduling, registrar, google-sheets, openai, prerequisites, degree-planning, catalog, fall-term

  🗒 Changelog
  v1.0.0** — Initial release: Sheets in/out, Fall 2025 catalog rules, prereq enforcement, 5-course selection, credits target

  📬 Contact
  Need help customizing this (e.g., cohort logic, program-specific rules, adding rationale to the sheet, or emailing PDFs)?

  📧 rbreen@ynteractive.com  
  🔗 Robert Breen — https://www.linkedin.com/in/robert-breen-29429625/  
  🌐 ynteractive.com — https://ynteractive.com

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheets"]

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metadata_json:
  {"categories":["Education","Workflow Automation"],"complexity":"medium","use_cases":["Generate student course schedules","Assign courses with prerequisites","Personalize class registration"],"estimated_setup_minutes":15,"required_services":["Google Sheets","OpenAI"],"key_features":["Prerequisite enforcement","Credit hour calculation","Duplicate course avoidance"],"target_audience":["Registrars","Advisors"]}


name: AI-Powered Degree Audit System with Google Sheets and GPT-5
author_name: Robert Breen
author_username: rbreen
categories: []
created_at: 2025-08-31T20:38:34.540Z
updated_at: 2025-08-31T20:38:34.540Z
url: https://n8n.io/workflows/8093
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  Run an AI-powered degree audit for each senior student. This template reads student rows from Google Sheets, evaluates completed courses against hard-coded program requirements, and writes back an AI Degree Summary of what's still missing (major core, Gen Eds, major electives, and upper-division credits). It's designed for quick advisor/registrar review and SIS prototypes.

  Trigger: Manual — When clicking "Execute workflow"  
  Core nodes: Google Sheets, OpenAI Chat Model, (optional) Structured Output Parser  
  Programs included: Computer Science BS, Business Administration BBA, Psychology BA, Mechanical Engineering BS, Biology BS (Pre-Med), English Literature BA, Data Science BS, Nursing BSN, Economics BA, Graphic Design BFA

  Who's it for
  Registrars & advisors** who need fast, consistent degree checks  
  Student success teams** building prototype dashboards  
  SIS/EdTech builders** exploring AI-assisted auditing

  How it works
  Read seniors from Google Sheets (Senior_data) with: StudentID, Name, Program, Year, CompletedCourses.  
  AI Agent compares CompletedCourses to built-in requirements (per program) and computes Missing items + a short Summary.  
  Write back to the same sheet using "Append or update" by StudentID (updates AI Degree Summary; you can also map the raw Missing array to a column if desired).

  Example JSON (for one student):
  {
   "StudentID": "S001",
   "Program": "Computer Science BS",
   "Missing": [
     "GEN-REMAIN | General Education credits remaining | 6",
     "CS-EL-REM | CS Major Electives (200+ level) | 6",
     "UPPER-DIV | Additional Upper-Division (200+ level) credits needed | 18",
     "FREE-EL | Free Electives to reach 120 total credits | 54"
   ],
   "Summary": "All core CS courses are complete. Still need 6 Gen Ed credits, 6 CS electives, and 66 total credits overall, including 18 upper-division credits — prioritize 200/300-level CS electives."
  }

  Setup (2 steps)

  1) Connect Google Sheets (OAuth2)
  In n8n → Credentials → New → Google Sheets (OAuth2) and sign in.

  In the Google Sheets nodes, select your spreadsheet and the Senior_data tab.

  Ensure your input sheet has at least: StudentID, Name, Program, Year, CompletedCourses.

  2) Connect OpenAI (API Key)
  In n8n → Credentials → New → OpenAI API, paste your key.

  In the OpenAI Chat Model node, select that credential and a model (e.g., gpt-4o or gpt-5).

  Requirements
  Sheet columns:** StudentID, Name, Program, Year, CompletedCourses
  CompletedCourses format:** pipe-separated IDs (e.g., GEN-101|GEN-103|CS-101).
  Program labels:** should match the built-in list (e.g., Computer Science BS).
  Credits/levels:** Template assumes upper-division ≥ 200-level (adjust the prompt if your policy differs).

  Customization
  Change requirements:** Edit the Agent's system message to update totals, core lists, elective credit rules, or level thresholds.
  Store more output:** Map Missing to a new column (e.g., AI Missing List) or write rows to a separate sheet for dashboards.
  Distribute results:** Email summaries to advisors/students (Gmail/Outlook), or generate PDFs for advising folders.
  Add guardrails:** Extend the prompt to enforce residency, capstone, minor/cognate constraints, or per-college Gen Ed variations.

  Best practices (per n8n guidelines)
  Sticky notes are mandatory:** Include a yellow sticky note that contains this description and quick setup steps; add neutral sticky notes for per-step tips.
  Rename nodes clearly:** e.g., "Get Seniors," "Degree Audit Agent," "Update Summary."
  No hardcoded secrets:** Use credentials—not inline keys in HTTP or Code nodes.
  Sanitize identifiers:** Don't ship personal spreadsheet IDs or private links in the published version.
  Use a Set node for config:** Centralize user-tunable values (e.g., column names, tab names).

  Troubleshooting
  OpenAI 401/429:** Verify API key/billing; slow concurrency if rate-limited.
  Empty summaries:** Check column names and that CompletedCourses uses |.
  Program mismatch:** Align Program labels to those in the prompt (exact naming recommended).
  Sheets auth errors:** Reconnect Google Sheets OAuth2 and re-select spreadsheet/tab.

  Limitations
  Not an official audit:** It infers gaps from the listed completions; registrar rules can be more nuanced.
  Catalog drift:** Requirements are hard-coded in the prompt—update them each term/year.
  Upper-division heuristic:** Adjust the level threshold if your institution defines it differently.

  Tags & category
  Category: Education / Student Information Systems  
  Tags: degree-audit, registrar, google-sheets, openai, electives, upper-division, graduation-readiness

  Changelog
  v1.0.0 — Initial release: Senior_data in/out, 10 programs, AI Degree Summary output, append/update by StudentID.

  Contact
  Need help tailoring this to your catalog (e.g., per-college Gen Eds, capstones, minors, PDFs/email)?

  📧 rbreen@ynteractive.com  
  📧 robert@ynteractive.com  
  🔗 Robert Breen — https://www.linkedin.com/in/robert-breen-29429625/  
  🌐 ynteractive.com — https://ynteractive.com

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
  [encoded data, 4620 characters]

metadata_json:
  {"categories":["Academic Tools","Data Processing","Communication"],"complexity":"medium","use_cases":["student degree audit","course requirement analysis"," advisor review preparation"],"estimated_setup_minutes":15,"required_services":["Google Sheets","OpenAI API"],"key_features":["automated degree audit","missing credits identification","advisory summary generation"],"target_audience":["registrars","academic advisors","institutional IT"]}


name: Email new leads from Google Sheets via Outlook on a schedule
author_name: Robert Breen
author_username: rbreen
categories: []
created_at: 2025-08-31T21:09:22.082Z
updated_at: 2025-08-31T21:09:22.082Z
url: https://n8n.io/workflows/8095
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  Send a templated outreach email to new leads in a Google Sheet on a daily schedule, then mark each lead as contacted so they won’t be emailed twice. Built with: Schedule Trigger → Google Sheets → Filter → Outlook Send Email → Google Sheets (append/update).

  Who’s it for
  Teams that collect leads in Sheets and want automatic, once-per-lead outreach  
  Solo founders/consultants running simple top-of-funnel follow-ups  
  Anyone prototyping CRM-lite flows in n8n

  How it works / What it does
  Schedule Trigger runs daily (e.g., 09:00).
  Get rows (Google Sheets) pulls leads from your sheet.
  Filter keeps only rows where Contacted is empty.
  Outlook – Send a message emails each remaining lead (to = {{$json.Email}}).
  Google Sheets (appendOrUpdate) writes back Contacted = Yes (match on Email) to prevent re-sending.

  How to set up
  1) Google Sheets (OAuth2)
  In n8n → Credentials → New → Google Sheets (OAuth2), sign in and grant access.  
  In Get rows: select your Lead Source sheet (e.g., “New Leads”).  
  In Append/Update: select the sheet you want to mark as contacted (can be the same sheet or a CRM sheet).  
  Make sure your sheet has at least: Email, Contacted (blank for new).

  2) Outlook (Microsoft Graph) OAuth2
  n8n Cloud (quick connect):**  
    In Credentials → New → Microsoft Outlook OAuth2, choose Connect, sign in with your Microsoft account, and accept permissions.
  Self-hosted (Azure App Registration):**  
    1) Azure Portal → App registrations → New registration.  
    2) Add redirect URL: https://YOUR_N8N_URL/rest/oauth2-credential/callback.  
    3) API permissions (Delegated): offline_access, Mail.Send, User.Read. Grant admin consent if required.  
    4) Create a Client secret; copy Application (client) ID and Directory (tenant) ID.  
    5) In n8n, create Microsoft Outlook OAuth2 credential with those values and scopes: offline_access Mail.Send openid email profile.  
  In the Send a message node, select that credential and keep To mapped to {{$json.Email}}.  
  Customize Subject and Body to your brand (default provided).

  Requirements
  A Google Sheet of leads with an Email column (and an optional Contacted column).  
  A Microsoft account with permission to send mail via Microsoft Graph (Outlook).

  How to customize the workflow
  Email content:** Edit subject/body (add variables like {{$json.FirstName}}).  
  Send window:** Change Schedule Trigger time/interval.  
  Idempotency:** Use the same sheet for read+write (match on Email) or separate source/CRM sheets.  
  Logging:** Add a Sheet or Notion append step after send for an audit trail.

  Security & best practices
  No hardcoded secrets:* Always use *OAuth2 credentials** (Sheets & Outlook).  
  Sanitize PII:* If publishing, *remove real spreadsheet IDs/URLs** and personal emails/phone numbers.  
  Sticky notes are mandatory:* Add a *yellow sticky note** in the workflow with this description and a short setup checklist. Use neutral sticky notes for per-node tips.

  Troubleshooting
  No emails sent:** Ensure Contacted is blank for new rows and the Filter checks “is empty.”  
  Auth errors (Outlook):** Reconnect credential; confirm Graph scopes and tenant consent.  
  Sheets errors:** Re-select spreadsheet/tab; verify headers (Email, Contacted).  
  Duplicates:* Confirm *Append/Update* uses *matching column = Email**.

  Tags & category
  Category: Sales & CRM  
  Tags: outreach, follow-up, google-sheets, outlook, schedule, dedupe, cold-email

  Changelog
  v1.0.0 — Initial release: scheduled outreach, Outlook email, de-dupe via Contacted = Yes.

  Contact
  Need help customizing (personalization, rate-limits, CRM sync, bounce checks)?

  📧 rbreen@ynteractive.com  
  🔗 Robert Breen — https://www.linkedin.com/in/robert-breen-29429625/  
  🌐 ynteractive.com — https://ynteractive.com

nodes_used:
  ["n8n-nodes-base.microsoftOutlook","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.filter","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Marketing","Communication","Data Processing"],"complexity":"medium","use_cases":["automated lead outreach","prevent duplicate emailing","schedule daily data sync","mark leads as contacted"],"estimated_setup_minutes":10,"required_services":["Google Sheets","Outlook","n8n Computer Vision API"],"key_features":["Schedule-based triggers","Duplicate prevention logic","Data state tracking updates"],"target_audience":["Sales teams","Solo founders","Consultants"]}


name: Analyze Google Business Reviews & Send Sentiment Reports to Slack with Gemini
author_name: SayOne Technologies
author_username: sayonetech
categories: []
created_at: 2025-09-01T08:17:37.468Z
updated_at: 2025-09-01T08:17:37.468Z
url: https://n8n.io/workflows/8111
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:22
description:
  ⭐ Google Review Sentiment Analysis & Slack Notification Workflow  

  This workflow automates the process of collecting Google Business Profile reviews 🏪, analyzing customer sentiment with Google Gemini 🤖✨, and sending structured reports to Slack 💬.  

  🔑 Key Advantages  
  📥 Fetches Google Business Profile reviews for a given business and time period  
  🧠 Runs sentiment analysis using Gemini AI  
  📊 Consolidates comments, ratings, and trends into a JSON-based summary  
  🧩 Restructures results into Slack Block Kit format for easy readability  
  🚀 Sends automated sentiment reports directly to a Slack channel  

  ⚙️ Set Up Essentials You’ll Need  
  🔑 Google Business Profile API access with project approval  
  ✅ Enabled Google Business Profile API service  
  🔐 Gemini API credentials  
  💬 Slack workspace & channel for receiving reports  

  🚀 How to Get Started  
  🔧 Configure your Google Business Profile API and enable access  
  👤 Set the owner name and 📍 location to fetch reviews  
  ⏳ Define the review time period using the Set Time Period node  
  🔗 Connect your Slack account and select a channel for notifications  
  🕒 Deploy and let the workflow run on schedule for automated insights  

nodes_used:
  ["n8n-nodes-base.googleBusinessProfile","@n8n/n8n-nodes-langchain.sentimentAnalysis","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.filter","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.slack"]

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metadata_json:
  {"categories":["AI/ML","Communication","Customer Relations"],"complexity":"medium","use_cases":["Monitor business reviews","Automate sentiment analysis","Notify teams on feedback"],"estimated_setup_minutes":15,"required_services":["Google Business Profile","Google Gemini","Slack"],"key_features":["Automatic review collection","AI sentiment analysis","Scheduled reporting","Structured Slack notifications"],"target_audience":["Customer experience managers","Marketing teams","Business owners"]}


