Workflow Templates

Verified Parent Consent Form Automation for School Trips 🎯 Description This workflow automates the entire parent consent process for school field trips, replacing manual paper forms with a secure, verified, and legally compliant digital system. When a parent submits consent data via POST request (from Postman or any form), the workflow: Receives parent & trip details through a Webhook trigger. Verifies the parent’s email using the VerifiEmail API to prevent fake or disposable entries. Generates a unique Consent ID and timestamps for tracking and legal validation. Creates a professional HTML-based digital consent form, including child details, trip information, and a parent signature section. Converts the HTML document to a PDF using the HTMLCSSToPDF API (ready for printing or archiving). Uploads the PDF to Google Drive automatically, organizing it under a designated folder (e.g., /School_Consents/2025/November). Sends an automated Gmail notification to the respective class teacher, including all verified details and the Drive reference. Responds instantly to the original POST request with a success confirmation and all metadata (Consent ID, verification status, storage location, timestamp). Handles invalid emails gracefully, returning a 400 error response for unauthorized or unverified submissions. Provides complete traceability, digital audit, and tamper-proof documentation for school compliance. Use Case: Ideal for schools, institutions, or organizations that need paperless consent workflows with email verification, cloud storage, and automated staff alerts — ensuring authenticity, security, and compliance. 🧩 Features Email verification (VerifiEmail) Auto-generated unique Consent ID HTML-to-PDF conversion (HTMLCSSToPDF) Google Drive cloud storage integration Automated Gmail teacher notification API-friendly POST-based trigger Real-time error handling & response Legally formatted consent slip 🧪 Test Input Example (Postman) Use this JSON in Postman when testing the webhook: { "parent_name": "Ritu Sharma", "parent_email": "ritu.sharma@gmail.com", "child_name": "Aarav Sharma", "child_class": "Grade 5-A", "trip_name": "Science Museum Visit", "trip_date": "2025-11-10", "teacher_email": "teacher@school.edu" } ✅ Expected Output Success Response (200): { "status": "success", "message": "Parent consent form verified and stored successfully", "data": { "consent_id": "CONSENT-1699123456789", "child_name": "Aarav Sharma", "trip_name": "Science Museum Visit", "email_verified": true, "stored_at": "Google Drive", "teacher_notified": true } } Error Response (400): { "status": "error", "message": "Invalid email address provided. Please use a valid email.", "reason": "Email verification failed" } 🌐 Workflow Tags for n8n Creators #Education #Automation #EmailVerification #PDFGeneration #GoogleDrive #Webhook #SchoolAdmin #PaperlessWorkflow #Compliance

Solar Energy Production Monitoring Alert Workflow This workflow automatically monitors solar energy production every 2 hours by fetching data from the Energidataservice API. If the energy output falls below a predefined threshold, it instantly notifies users via email. Otherwise, it logs the data into a Google Sheet and posts a daily summary to Slack. Who’s It For Renewable energy teams monitoring solar output. Facility managers and power plant supervisors. ESG compliance officers tracking sustainability metrics. Developers or analysts automating solar energy reporting. How It Works Trigger: The workflow starts every 2 hours using a Schedule Trigger. Data Fetch: An HTTP Request node fetches solar energy production data from the Energidataservice API. Processing: A Code node filters out entries with production below the minimum threshold. Decision Making: An If node checks whether any low-production entries are present. Alerts: If low-production is detected, an email is sent via the Gmail node. Logging: If all entries are valid, they are logged into a Google Sheet. Slack Summary: A Slack node posts the summary sheet data for end-of-day visibility. How to Set Up Schedule Trigger: Configure to run every 2 hours. HTTP Request Node: Method: GET URL: https://api.energidataservice.dk/dataset/YourDatasetHere Add necessary headers and params as required by the API. Code Node: Define logic to filter entries where solar_energy_production < required_threshold. If Node: Use items.length > 0 to check for low-production entries. Gmail Node: Auth with Gmail credentials. Customize recipient and message template. Google Sheets Node: Connect to a spreadsheet. Map appropriate columns. Slack Node: Use Slack OAuth2 credentials. Specify channel and message content. Requirements n8n Cloud or Self-hosted instance. Access to Energidataservice API. Gmail account (with n8n OAuth2 integration). Google Sheets account & sheet ID. Slack workspace and app with appropriate permissions. How to Customize Change Frequency:** Adjust the Schedule Trigger interval (e.g., every hour or 4x per day). Threshold Tuning:** Modify the value in the Code node to change the minimum acceptable solar production. Alert Routing:** Update Gmail recipients or replace Gmail with Microsoft Outlook/SendGrid. Sheet Format:** Add or remove columns in the Google Sheet based on extra metrics (e.g., wind or nuclear data). Slack Posting:** Customize Slack messages using Markdown for improved readability. Add‑ons Telegram Node:** Send alerts to a Telegram group instead of email. Discord Webhook:** Push updates to a Discord channel. n8n Webhook Trigger:** Extend it to receive external production update notifications. Integromat/Make or Zapier:** For multi-platform integration with CRMs or ticketing tools. Use Case Examples Utility Companies:** Automatically detect and act on solar underperformance to maintain grid stability. Solar Farm Operators:** Log clean production data for auditing and compliance reports. Sustainability Teams:** Track daily performance and anomalies without manual checks. Home Solar System Owners:** Get notified if solar generation drops below expected. Common Troubleshooting | Issue | Possible Cause | Solution | | -------------------------------------- | -------------------------------------- | ------------------------------------------------------------------- | | HTTP Request fails | API key missing or URL is incorrect | Check API endpoint, parameters, and authentication headers | | Gmail not sending alerts | Missing or invalid Gmail credentials | Re-authenticate Gmail OAuth2 in n8n credentials | | No data getting logged in Google Sheet | Incorrect mapping or sheet permissions | Ensure the sheet exists, columns match, and credentials are correct | | Slack node fails | Invalid token or missing channel ID | Reconnect Slack credentials and check permissions | | Code node returns empty | Filter logic may be too strict | Validate data format and relax the threshold condition | Need Help? Need help setting this up or customizing it for your own solar or energy monitoring use case? ✅ Set it up on your n8n Cloud or self-hosted instance ✅ Customize it for your own API or data source ✅ Modify alerts to suit your internal tools (Teams, Discord, SMS, etc.) 👉 Just reach out to our n8n automation team at WeblineIndia, we'll be happy to help.

What this workflow does This workflow creates an automated web scraper that accepts form submissions, extracts specific data from any website using AI, and emails the results back to you. Step by step: Web Scraper Form Submission provides a web form interface where users submit a URL and specify what data to extract Get HTML from Source URL fetches the complete HTML content from the provided website HTML Extractor processes the raw HTML and extracts the body content for analysis Data Extractor LLM Chain uses Google Gemini AI to intelligently analyze the content and extract only the specific data requested by the user Structured Output Parser formats the AI response into clean JSON structure with standardized format Gmail Send Result delivers the extraction results via email including the source URL, extraction request details, and clean extracted results How to set up Connect your Google Gemini API to the Google Gemini Chat Model node for AI-powered data extraction Connect your Gmail account to the Gmail node for sending result emails Update the recipient email in the Gmail node Customize the extraction prompt in the Data Extractor LLM Chain node based on your specific requirements How to customize this workflow to your needs Switch AI models**: Replace Google Gemini with OpenAI, Claude, or other LLM providers in the Chat Model node based on your accuracy requirements and budget preferences Change result delivery**: Replace Gmail with Google Sheets for data storage, Outlook for corporate email, Slack for team notifications, or webhook integrations for custom applications Customize extraction prompts**: Modify the LLM prompt in the Data Extractor Chain to handle specific data types, extraction formats, or industry-specific terminology for your use case Need help customizing? Contact me for consulting and support: 📧 billychartanto@gmail.com

The workflow is triggered manually with user input, searches LinkedIn profiles, processes the results using AI, generates connection recommendations, and delivers them via email. It leverages AI to enhance networking opportunities based on insights from profiles. Good to Know Each email is personalized with the user’s name and recommended connections. Recommendations are based on LinkedIn search results and AI analysis. The system ensures data privacy by processing inputs securely. Email notifications include a curated list of potential connections. How it Works Profile Analysis Workflow Get User Data from Email**: Manually inputs user email and profile information to initiate the workflow. Your Profile Information**: Provides initial user data for LinkedIn search. Search LinkedIn Profiles**: Queries LinkedIn via an API (e.g., SerpAPI) to gather profile data. Process LinkedIn Search Results**: Extracts relevant details from search results. AI Recommendation Workflow AI Profile Analysis**: Uses an AI model (e.g., Ollama Model) to analyze profile data and suggest connections. Create Recommendations**: Generates a curated list of potential connections. Create Final Recommendations**: Refines and formats the recommendation list. Create Email**: Prepares a personalized email with the connection list. Send Email**: Delivers the email to the user. Excel Sheet Structure No persistent Excel sheet is required**; data is processed in-memory and emailed directly. However, optional logging can be set up: Optional Log Sheet (Recommendations): Timestamp: Date and time of recommendation generation. User Email: User’s email address. Profile Name: User’s LinkedIn profile name. Industry: User’s industry. Recommended Connections: List of suggested connections. Sent Status: Whether the email was sent successfully. How to Use Import the Workflow into your n8n instance and configure email integration. Provide User Data: Manually enter the user’s email and profile information in the "Get User Data from Email" node. Configure API Credentials: Set up SerpAPI for LinkedIn searches and email service (e.g., SMTP). Run the Workflow: Execute manually to test the process. Monitor Emails: Check the user’s inbox for the curated connection list. Optional Logging: Set up a Google Sheet to log recommendations if desired. Requirements SerpAPI**: For LinkedIn profile searches. Email Service Integration**: Gmail, SMTP, or similar for email delivery. Ollama Model**: For AI-based profile analysis. n8n Instance**: With SerpAPI, email, and function nodes. Customizing this Workflow Expand Data Sources**: Integrate additional platforms (e.g., Xing) for broader searches. Enhance AI**: Train the Ollama Model for more specific connection criteria (e.g., job role, location). Add Notifications**: Include Slack or SMS alerts for admin tracking. Customize Email**: Adjust the email template for branding or additional details. Automate Trigger**: Replace manual input with a scheduled trigger or webhook.

This n8n workflow enables an AI-powered symptom checker where users input symptoms via a form or chat, analyzes them using an AI model, matches possible conditions, and suggests relevant doctors with contact details via WhatsApp or email, enhancing healthcare accessibility. Why Use It This workflow improves healthcare outreach by providing quick, AI-driven symptom analysis and doctor recommendations, reducing the burden on medical staff, empowering users with informed choices, and streamlining appointment scheduling. How to Import It Download the Workflow JSON: Obtain the workflow file from the n8n template or create it based on this document. Import into n8n: In your n8n instance, go to "Workflows," click the three dots, select "Import from File," and upload the JSON. Configure Credentials: Set up form/webhook, AI model, WhatsApp, email (e.g., SMTP), and optional doctor database API credentials in n8n. Run the Workflow: Test with a sample symptom input and verify responses. System Architecture Symptom Input Pipeline**: Form/Chat Trigger: Initiates the workflow when a user submits symptoms. Extract Symptom Data: Processes the input from the form or chat. AI Analysis Flow**: Send to AI Model: Analyzes symptoms using an AI model. Match Possible Conditions: Identifies potential health conditions. Doctor Suggestion Flow**: Retrieve Doctor Details: Fetches relevant doctor data from a Google Sheet or API. Prepare Suggestion Message: Formats the suggestion with doctor names and contacts. Send WhatsApp Suggestion: Delivers the suggestion via WhatsApp. Send Email Suggestion: Delivers the suggestion via email. Update Log: Logs the request and response in a Google Sheet. Google Sheet File Structure Columns**: timestamp: Date and time of the symptom submission. user_id: Unique identifier for the user (e.g., form ID or chat handle). symptoms: List of symptoms entered by the user. condition: AI-identified possible condition. doctor_name: Name of the suggested doctor. contact: Doctor’s contact (phone or email). sent_via: Channel used for delivery (e.g., WhatsApp, Email). Customization Ideas Add More Channels**: Integrate SMS or Slack for additional notifications. Enhance AI**: Train the AI model with more medical data for better accuracy. Include Appointment Booking**: Add a node to schedule appointments with suggested doctors. Multilingual Support**: Adapt responses for different languages. Severity Alerts**: Flag critical conditions for immediate medical attention. Requirements to Run This Workflow Google Sheets Account**: For logging symptom data and doctor details. AI Model**: Ollama or similar for symptom analysis (requires API access). Form/Chat Service**: Google Forms, WhatsApp webhook, or similar for user input. WhatsApp Business API**: For sending WhatsApp messages (requires token and phone number). Email Service**: Gmail, SMTP, or similar for email delivery. n8n Instance**: With Google Sheets, AI, WhatsApp, and email connectors configured. Internet Connection**: To access APIs and services. Want a tailored workflow for your business? Our experts can craft it quickly Contact our team

This n8n workflow sends a real-time Slack notification when a user submits a contact form on your website. It also optionally sends a confirmation email via either Outlook or Gmail. You can embed the form directly into your website and route all submissions to your preferred Slack channel for quick follow-up. ✅ Key Features 🌐 Website Form Trigger**: A no-code form that can be embedded directly on your website. ⚡ Slack Integration**: Instantly posts lead details to a designated Slack channel. 📧 Email Follow-Up (Optional)**: Sends a thank-you email to the user via either Gmail or Microsoft Outlook. 🔗 Easy to Connect**: Requires minimal setup with OAuth for Slack and email APIs. 🧰 What You'll Need Slack Bot Token & Permissions** Gmail or Outlook account (optional)** n8n instance (cloud or self-hosted)** 🛠️ Step-by-Step Setup 1️⃣ Embed Contact Form on Website Node**: Form Submission on Website What it does**: Captures user inputs — Name, Email, and Phone. How to use**: Grab the public webhook URL from this node. Embed it as a form action on your website (HTML form or no-code builder). API**: None required. Webhook-based. 2️⃣ Send to Slack Node**: Slack What it does**: Posts a message to your Slack #leads channel with submission details. Message format**: You have a form submission with these details. Name: {{ Name }} Email: {{ Email }} Phone: {{ Phone }} API**: Slack Bot Token (OAuth2) 3️⃣ Optional: Send Confirmation Email Node**: Send Email - Gmail Sends email using Gmail API. Node**: Send Email - Outlook Sends email using Microsoft Outlook API. Message**: Hi {{ Name }}, Thank you for reaching out on our website. We'll be in touch soon! API**: Gmail or Microsoft Outlook OAuth2 👤 Created By Robert Breen Automation Consultant | AI Workflow Designer | n8n Expert 📧 rbreen@ynteractive.com 🌐 ynteractive.com 🔗 LinkedIn 🏷️ Tags slack lead alerts website form n8n email automation real-time notification lead generation form trigger webhook

This automated n8n workflow enables AI-powered responses across multiple social media platforms, including Instagram DMs, Facebook messages, and WhatsApp chats using Meta's APIs. The system provides intelligent customer support, lead generation, and smart engagement at scale through AI-driven conversation management and automated response routing. Good to Know Supports multi-platform messaging across Instagram, Facebook, and WhatsApp Uses AI Travel Agent and Ollama Chat Model for intelligent response generation Includes platform memory for maintaining conversation context and history Automatic message processing and routing based on platform and content type Real-time webhook integration for instant message detection and response How It Works WhatsApp Trigger** - Monitors incoming WhatsApp messages and initiates automated response workflow Instagram Webhook** - Captures Instagram DM notifications and processes them for AI analysis Facebook Webhook** - Detects Facebook Messenger interactions and routes them through the system Message Processor** - Analyzes incoming messages from all platforms and prepares them for AI processing AI Travel Agent** - Processes messages using intelligent AI model to generate contextually appropriate responses Ollama Chat Model** - Provides advanced language processing for complex conversation scenarios Platform Memory** - Maintains conversation history and context across multiple interactions for personalized responses Response Router** - Determines optimal response strategy and routes messages to appropriate sending mechanisms Instagram Sender** - Delivers AI-generated responses back to Instagram DM conversations Facebook Sender** - Sends automated replies through Facebook Messenger API Send Message (WhatsApp)** - Delivers personalized responses to WhatsApp chat conversations How to Use Import workflow into n8n Configure Meta's Instagram Graph API, Facebook Messenger API, and WhatsApp Business Cloud API Set up approved Meta Developer App with required permissions Configure webhook endpoints for real-time message detection Set up Ollama Chat Model for AI response generation Test with sample messages across all three platforms Monitor response accuracy and adjust AI parameters as needed Requirements Access to Meta's Instagram Graph API, Facebook Messenger API, and WhatsApp Business Cloud API Approved Meta Developer App Webhook setup and persistent token management for real-time messaging Ollama Chat Model integration AI Travel Agent configuration Customizing This Workflow Modify AI prompts for different business contexts (customer service, sales, support) Adjust response routing logic based on message content or user behavior Configure platform-specific message templates and formatting Set up custom memory storage for enhanced conversation tracking Integrate additional AI models for specialized response scenarios Add message filtering and content moderation capabilities

YouTube Chapter Auto-Description with AI This n8n template automatically adds structured timestamp chapters to your latest YouTube video’s description using your RSS feed, SupaData for transcript extraction, and an AI tool for chapter generation. Ideal for creators who want every video to include chapter markers without doing it manually. Good to Know SupaData extracts full transcripts from YouTube videos via URL. The AI chapter generator converts long transcripts into formatted timestamps with short titles. This workflow edits the existing video description and appends the chapters to the bottom. How It Works The RSS Feed Trigger detects new uploads from your YouTube channel. The workflow checks Airtable to prevent duplicate processing. Transcript is fetched using SupaData API. The total video duration is extracted from the transcript. AI is prompted to generate well-formatted chapter timestamps. The existing description is fetched from YouTube. The chapters are appended and pushed back via the YouTube API. How to Use Start with the Manual Trigger to test the setup. Replace it with the RSS Trigger once you're ready for automation. Chapters are added only if the video hasn't been processed before. Requirements YouTube OAuth2** credentials in n8n SupaData API Key** Airtable account** (for optional deduplication logic) Customizing This Workflow Change the chapter format, or instruct the AI to use emojis, bold titles, or include sections like "sponsor" or "Q&A". Replace the RSS Trigger with a webhook if using a different publishing process.

🧠 Automated Resume & Cover Letter Generator This project is an automation workflow that generates a personalized resume and cover letter for each job listing. 🚀 Features Automated Resume Crafting Generates an HTML resume from your data. Hosts it live on GitHub Pages. Converts it to PDF using Gotenberg and saves it to Google Drive. Automated Cover Letter Generation Uses an LLM to create a tailored cover letter for each job listing. Simple Input Database Agent Stores your experience in an n8n Data Table with the following fields: role, summary, task, skills, tools, industry. The main agent pulls this data using RAG (Retrieval-Augmented Generation) to personalize the outputs. One-Time GitHub Setup Initializes a blank GitHub repository to host HTML files online, allowing Gotenberg to access and convert them. 🧩 Tech Stack Gotenberg** – Converts HTML to PDF GitHub Pages** – Hosts live HTML files n8n** – Handles data tables and workflow automation LLM (OpenAI / Cohere / etc.)** – Generates cover letters Google Drive** – Stores the final PDFs ⚙️ Installation & Setup 1. Create a GitHub Repository This repo will host your HTML resume through GitHub Pages. 2. Set the Webhook URL In the notify-n8n.yml file, replace: role | summary | task | skills | tools | industry 3. Create the n8n Data Table Add the following columns: role | summary | task | skills | tools | industry 4. Create a Google Spreadsheet Add these columns: company | cover_letter | resume 5. Install Gotenberg Follow the installation instructions on the Gotenberg GitHub repository: https://github.com/thecodingmachine/gotenberg 6. Customize the HTML Template Modify the HTML resume to your liking. You can use an LLM to locate and edit specific sections. 7. Add Authentication and Link Your GitHub Repo Ensure your workflow has permission to push updates to your GitHub Pages branch. 8. Run the Workflow Once everything is connected, trigger the workflow to automatically generate and save personalized resumes and cover letters. 📝 How to Use Copy and paste the job listing description into the Telegram bot. Wait for the "Done" notification before submitting another job. Do not use the bot again until the notification appears. The process usually takes a few minutes to complete. ✅ Notes This workflow is designed to save time and personalize your job applications efficiently. By combining n8n automation, LLMs, and open-source tools like Gotenberg, you can maintain full control over your data while generating high-quality resumes and cover letters for every job opportunity.

Recruiter Mirror is a proof‑of‑concept ATS analysis tool for SDRs/BDRs. Compare your LinkedIn or CV to job descriptions and get recruiter‑ready insights. By comparing candidate profiles against job descriptions, it highlights strengths, flags missing keywords, and generates actionable optimization tips. Designed as a practical proof of concept for breaking into tech sales, it shows how automation and AI prompts can turn LinkedIn into a recruiter‑ready magnet. Got it ✅ — based on your workflow (Webhook → LinkedIn CV/JD fetch → GhostGenius API → n8n parsing/transform → Groq LLM → Output to Webhook), here’s a clear list of tools & APIs required to set up your Recruiter Mirror (Proof of Concept) project: 🔧 Tools & APIs Required 1. n8n (Automation Platform) Either n8n Cloud or self‑hosted n8n instance. Used to orchestrate the workflow, manage nodes, and handle credentials securely. 2. Webhook Node (Form Intake) Captures LinkedIn profile (LinkedIn_CV) and job posting (LinkedIn_JD) links submitted by the user. Acts as the starting point for the workflow. 3. GhostGenius API Endpoints Used: /v2/profile → Scrapes and returns structured CV/LinkedIn data. /v2/job → Scrapes and returns structured job description data. Auth**: Requires valid credentials (e.g., API key / header auth). 4. Groq LLM API (via n8n node) Model Used: moonshotai/kimi-k2-instruct (via Groq Chat Model node). Purpose: Runs the ATS Recruiter Check, comparing CV JSON vs JD JSON, then outputs a structured JSON per the ATS schema. Auth**: Groq account + saved API credentials in n8n. 5. Code Node (JavaScript Transformation) Parses Groq’s JSON output safely (JSON.parse). Generates clean, recruiter‑ready HTML summaries with structured sections: Status Reasoning Recommendation Matched keywords / Missing keywords Optimization tips 6. n8n Native Nodes Set & Aggregate Nodes** → Rebuild structured CV & JD objects. Merge Node** → Combine CV data with job description for comparison. If Node** → Validates LinkedIn URL before processing (fallback to error messaging). Respond to Webhook Node** → Sends back the final recruiter‑ready insights in JSON (or HTML). ⚠️ Important Notes Credentials**: Store API keys & auth headers securely inside n8n Credentials Manager (never hardcode inside nodes). Proof of Concept: This workflow demonstrates feasibility but is **not production‑ready (scraping stability, LinkedIn terms of use, and API limits should be considered before real deployments).

Overview This n8n template demonstrates how you can generate an AI-produced weather analysis of your local radar loop and home assistant precipitation sensor(s) to keep your family informed of National Weather Service Alerts. With as crazy as things have been lately in the open world, how will you and your family know when a severe or extreme alert impacts your area? How it Works This workflow is triggered by a webhook which takes a latitude and longitude json payload to identify the area for monitoring. Then, it fetches the National Weather Service Alerts and filters them down to alerts which are currently active and their severity. Next, it fetches the local precipitation value from your Home Assistant instance (a value like "Light Rain" or "No Rain", etc) coupled with your respective weather.gov radar loop image. It then submits this data to OpenAI and produces an output regarding the image analysis. Finally, it takes this analysis and uses OpenAI to again generate a short summary. How to Use Import the workflow into your n8n instance Update the credentials in the problematic nodes Make sure you adjust the radar loop image that is being used Requirements A Home Assistant Instance (you could remove this data if you wanted) An OpenAI account for LLM and image analysis

WooCommerce New Category Alert via WhatsApp Using Rapiwa API This n8n automation listens for the creation of a new WooCommerce product category, fetches all WooCommerce customers, cleans and formats their phone numbers, verifies them using the Rapiwa WhatsApp validation API, sends a WhatsApp message to verified numbers with the new category info, and logs each interaction into a Google Sheet (separately for verified and unverified customers). Who this is for You have a WooCommerce store and want to: Send a promotional message when a new product category is added, Verify customer WhatsApp numbers in bulk, Keep a clear log in Google Sheets of which numbers are verified or not. What it does (high level) Webhook is triggered when a new WooCommerce category is created. Fetches all WooCommerce customers via API. Limits processing to the first 10 customers (for performance/testing). Cleans phone numbers (removes +, spaces, and non-digits). Verifies each number via Rapiwa WhatsApp Verify API. If verified: sends WhatsApp message with new category info, logs as Verification = verified, Status = sent. If not verified: logs as Verification = unverified, Status = not sent. Processes users in batches with delays to avoid rate limiting. How it works (step-by-step) Trigger**: Webhook node is triggered by WooCommerce category creation. Format Data**: Category details (name, slug, description) are parsed. Get Customers**: Fetch all WooCommerce customers using the WooCommerce API. Limit**: Only the first 10 are processed. Loop & Clean**: Loop over each customer, clean phone numbers and extract info. Verify Number**: Send HTTP POST to https://app.rapiwa.com/api/verify-whatsapp. Decision Node**: Use If node to check if exists == true. Send Message**: If verified, send WhatsApp message with category details. Append to Sheet**: Log verified and unverified customers separately in Google Sheets. Wait + Batch Control**: Use Wait and SplitInBatches nodes to control flow and prevent throttling. Example verify body (HTTP Request node): { "number": "{{ $json['WhatsApp No'] }}" } Customization ideas Send images, videos, or template messages if supported by Rapiwa. Personalize messages using name or category data. Increase delay or reduce batch size to minimize risk of rate limits. Add a second sheet to log full API responses for debugging and auditing. Best practices Test on small batches before scaling. Only send messages to users who opted in. Store API credentials securely using n8n’s credentials manager. Ensure your Google Sheet column headers match exactly with what's expected. Key Improvements Made Clarified the trigger source as a Webhook from WooCommerce category creation. Fixed inconsistency in the "What it does" section (originally referenced reading from Google Sheets, but your workflow starts from WooCommerce, not Sheets). Standardized terminology to match n8n nodes: Webhook, Loop, HTTP Request, etc. Aligned the flow exactly with your nodes: Webhook → Format → Get Customers → Limit → Loop → Clean → Verify → If → Send/Log → Wait → Repeat Useful Links Dashboard:** https://app.rapiwa.com Official Website:** https://rapiwa.com Documentation:** https://docs.rapiwa.com Support WhatsApp Support: Chat Now Discord: Join SpaGreen Community Facebook Group: SpaGreen Support Website: https://spagreen.net Developer Portfolio: Codecanyon SpaGreen