Workflow Templates

This n8n template lets you instantly serve batches of inspirational quotes via a webhook using the free ZenQuotes API. It’s perfect for developers, content creators, community managers, or educators who want to add dynamic, uplifting content to websites, chatbots, or internal tools—without writing custom backend code. 🔧 How it works A Webhook node listens for incoming HTTP requests on your chosen path. Get Random Quote from ZenQuotes sends an HTTP Request to https://zenquotes.io/api/random?count=5 and retrieves five random quotes. Format data uses a Set node to combine each quote (q) and author (a) into a single string: "“quote” – author". Send response returns a JSON array of objects { quote, author } back to the caller. 👤 Who is it for? This workflow is ideal for: Developers building motivational Slack or Discord bots. Website owners adding on-demand quote widgets. Educators or trainers sharing daily inspiration via webhooks. Anyone learning webhook handling and API integration in n8n. 🗂️ Response Structure Your webhook response will be a JSON array, for example: [ { "quote": "Life is what happens when you're busy making other plans.", "author": "John Lennon" }, { "quote": "Be yourself; everyone else is already taken.", "author": "Oscar Wilde" } ] ⚙️ Setup Instructions Import the workflow JSON into your n8n instance. In the Webhook node, set your desired path (e.g., /inspire). (Optional) Change the count parameter in the HTTP Request node to fetch more or fewer quotes. Activate the workflow. Test by sending an HTTP GET or POST to https://<your-n8n-domain>/webhook/<path>.

Extract Title tag and meta description from url for SEO analysis. How it works The workflows takes records from Airtable, get the url in the records and extract from the related webpage the title tag (<title>) and meta description (<meta name="description" content="Some content">). If title tag and/or meta description tag isn't available on the webpage, the result will be empty. Setup Set a Base in Airtable with a table with the following structure: url (field type url), title tag (field type text string), meta desc (field type text field) Minimum suggested table structure is: url (https://example.com), title (Title example), meta desc* (This is the meta description of the example page) Connect Airtable to both Airtable nodes in the template and, with the following formula, get all the records that miss title tag and meta desc. Formula: AND(url != "", {title tag} = "", {meta desc} = "") Insert the url to be analyzed in the table in the field url and let the workflow do the rest. Extra You can also calculate the length for title tag and meta desc using formula field inside Airtable. This is the formula: LEN({title tag}) or LEN({meta desc}) You can automate the process calling a Webhook from Airtable. For this, you need an Airtable paid plan.

Real-time Weather Forecasts with MCP Tools This n8n workflow demonstrates how to integrate real-time weather intelligence into any automation using the Model Context Protocol (MCP). Get current conditions and 5-day forecasts with natural language queries like "What's the weather like in Miami?" or "Will it rain next Tuesday in Seattle?" - all powered by live weather data and AI. Good to know No API keys required - uses hosted MCP weather server with built-in WorldWeatherOnline integration Provides current conditions and detailed 5-day forecasts Natural language queries work for any location worldwide Powered by WorldWeatherOnline - the world's most accurate weather system Fully preconfigured and ready to run out-of-the-box Enterprise-ready with error handling and rate limiting How it works Natural Language Input**: Receives weather queries via webhook, chat, email, or voice AI Agent Processing**: n8n Agent node interprets requests and determines: Location extraction from natural language Weather data type needed (current or 5-day forecast) Response formatting preferences MCP Weather Tool**: Live hosted server provides: Real-time current conditions (temperature, humidity, wind, conditions) 5-day detailed forecasts with daily highs/lows Weather descriptions and condition codes Powered by WorldWeatherOnline's premium data Intelligent Responses**: AI formats weather data into: Conversational natural language responses Structured data for downstream automation Action-triggering data for workflows How to use Import the workflow into n8n from the template Add your preferred AI model API key to the Agent node Customize the system prompt for your specific use case Connect to your preferred input/output channels Run and start querying weather with natural language Use Cases Smart Home Automation**: "Turn on sprinklers if no rain forecast for 3 days" Travel Planning**: "Check weather for my Paris trip next week" Event Management**: "Will outdoor wedding conditions be good Saturday?" Agriculture/Farming**: "Check 5-day forecast for planting schedule" Logistics**: "Delay shipping if severe weather forecast in delivery zone" Personal Assistant**: "Should I wear a jacket today in Chicago?" Sports/Recreation**: "Surf conditions and wind forecast for weekend" Construction**: "Safe working conditions for outdoor project this week" Requirements n8n instance (cloud or self-hosted) AI model provider account (OpenAI, Anthropic, Google, etc.) Internet connection for MCP weather server access Optional: Webhook endpoints for external integrations Customizing this workflow Location Intelligence**: Add geocoding for address-to-coordinates conversion Data Storage**: Save weather history to databases for trend analysis Dashboard Integration**: Connect to Grafana, Tableau, or custom visualizations Voice Integration**: Add speech-to-text for voice weather queries Scheduling**: Set up automated daily/weekly weather briefings Conditional Logic**: Trigger different actions based on weather conditions Sample Input/Output Natural Language Queries: "What's the weather like in Miami?" "Will it rain next Tuesday in Seattle?" "5-day forecast for London" "Temperature in Tokyo tomorrow" "Weather conditions for outdoor event Saturday" Rich Responses: { "location": "Miami, FL", "current": { "temperature": "78°F", "condition": "Partly Cloudy", "humidity": "65%", "wind": "10 mph SE" }, "forecast": { "today": "High 82°F, Low 71°F, 20% rain", "tomorrow": "High 85°F, Low 73°F, Sunny" }, "ai_summary": "Perfect beach weather in Miami today! Partly cloudy with comfortable temperatures and light winds." } Why This Workflow is Unique Zero Setup Weather Data**: No API key management - MCP server handles everything World-Class Accuracy**: Powered by WorldWeatherOnline's premium weather data AI-Powered Intelligence**: Natural language understanding of complex weather queries Enterprise Ready**: Built-in error handling, rate limiting, and reliability Global Coverage**: Worldwide weather data with location intelligence Action-Oriented**: Designed for automation decisions, not just information display Transform your automations with intelligent weather awareness powered by the world's most accurate weather system! 🧪 Setup Steps ✅ The Agent node is already configured: The system prompt is included The tool endpoint is pre-set All you need to do is: Add your AI model API key to the existing Agent credential Hit run and you're done ✅ 🔗 Full project link: Github: weathertrax-mcp-agent-demo

This n8n template demonstrates how to create a comprehensive voice-powered restaurant assistant that handles table reservations, food orders, and restaurant information requests through natural language processing. The system uses VAPI for voice interaction and PostgreSQL for data management, making it perfect for restaurants looking to automate customer service with voice AI technology. Good to know Voice processing requires active VAPI subscription with per-minute billing Database operations are handled in real-time with immediate confirmations The system can handle multiple simultaneous voice requests All customer data is stored securely in PostgreSQL with proper indexing How it works Table Booking & Order Handling Workflow Voice requests are captured through VAPI triggers when customers make booking or ordering requests The system processes natural language commands and extracts relevant details (party size, time, food items) Customer data is immediately saved to the bookings and orders tables in PostgreSQL Voice confirmations are sent back through VAPI with booking details and estimated wait times All transactions are logged with timestamps for restaurant management tracking Restaurant Info Provider Workflow Info requests trigger when customers ask about hours, menu, location, or services Restaurant details are retrieved from the restaurant_info table containing current information Wait nodes ensure proper data loading before voice response generation Structured restaurant information is delivered via VAPI in natural, conversational format Database Schema Bookings Table booking_id (PRIMARY KEY) - Unique identifier for each reservation customer_name - Customer's full name phone_number - Contact number for confirmation party_size - Number of guests booking_date - Requested reservation date booking_time - Requested time slot special_requests - Dietary restrictions or special occasions status - Booking status (confirmed, pending, cancelled) created_at - Timestamp of booking creation Orders Table order_id (PRIMARY KEY) - Unique order identifier customer_name - Customer's name phone_number - Contact for order updates order_items - JSON array of food items and quantities total_amount - Calculated order total order_type - Delivery, pickup, or dine-in special_instructions - Cooking preferences or allergies status - Order status (received, preparing, ready, delivered) created_at - Order timestamp Restaurant_Info Table info_id (PRIMARY KEY) - Information entry identifier category - Type of info (hours, menu, location, contact) title - Information title description - Detailed information content is_active - Whether info is currently valid updated_at - Last modification timestamp How to use The manual trigger can be replaced with webhook triggers for integration with existing restaurant systems Import the workflow into your n8n instance and configure VAPI credentials Set up PostgreSQL database with the required tables using the schema provided above Configure restaurant information in the restaurant_info table Test voice commands such as "Book a table for 4 people at 7 PM" or "What are your opening hours?" Customize voice responses in VAPI nodes to match your restaurant's tone and branding The system can handle multiple concurrent voice requests and scales with your restaurant's needs Requirements VAPI account for voice processing and natural language understanding PostgreSQL database for storing booking, order, and restaurant information n8n instance with database and VAPI integrations enabled Customising this workflow Voice AI automation can be adapted for various restaurant types - from quick service to fine dining establishments Try popular use-cases such as multi-location booking management, dietary restriction handling, or integration with existing POS systems The workflow can be extended to include payment processing, SMS notifications, and third-party delivery platform integration

Encode JSON to Base64 String in n8n This example workflow demonstrates how to convert a JSON object into a base64-encoded string using n8n’s built-in file processing capabilities. This is a common requirement when working with APIs, webhooks, or SaaS integrations that expect payloads to be base64-encoded. > Tip: The three green-highlighted nodes (Stringify → Convert to File → Extract from File) can be wrapped in a Subworkflow to create a reusable Base64 encoder in your own projects. 🔧 Requirements Any running n8n instance (local or cloud) No credentials or external services required What This Workflow Does Generates example JSON data Converts the JSON to a string Saves the string as a binary file Extracts the file’s contents as a base64 string Outputs the base64 string on the final node Step-by-Step Setup Manual Trigger Start the workflow using the Manual Execution node. This is useful for testing and development. Create JSON Data The Create Json Data node uses raw mode to construct a sample object with all major JSON types: strings, numbers, booleans, nulls, arrays, nested objects, etc. Convert to String The Convert to String node uses the expression ={{ JSON.stringify($json) }} to flatten the object into a single string field named json_text. Convert to File The Convert to File node takes the json_text value and saves it to a UTF-8 encoded binary file in the property encoded_text. Extract from File This node takes the binary file and extracts its contents as a base64-encoded string. The result is saved in the base64_text field. Customization Tips Replace the sample JSON in the Create Json Data node with your own payload structure. To make this reusable, extract the three core nodes into a Subworkflow or wrap them in a custom Function. Use the base64_text output field to post to APIs, store in databases, or include in webhook responses.

Google Sheets to MailChimp Auto-Importer Overview This n8n workflow automatically imports contacts from Google Sheets into your MailChimp mailing list. Perfect for businesses collecting leads through Google Forms, event registrations, or maintaining contact lists in spreadsheets. Key Features 📊 Bulk Import: Process entire Google Sheets at once 🔄 Smart Name Parsing: Automatically splits full names into first and last names 📱 Phone Number Support: Includes phone numbers as merge fields ⚡ Error Resilience: Continues processing even if individual contacts fail 📝 Import Summary: Generates a summary of processed contacts Prerequisites Before using this workflow, ensure you have: An active n8n instance (self-hosted or cloud) A Google account with access to Google Sheets A MailChimp account with at least one audience/list created Basic understanding of n8n workflows Initial Setup Step 1: Import the Workflow Copy the workflow JSON In n8n, click "Import from File" or paste the JSON Save the workflow with a meaningful name Step 2: Configure Google Sheets Connection Click on the "Get Google Sheet Data" node Click on "Credential to connect with" Select "Create New" and choose "Google Sheets OAuth2" Follow the OAuth flow to authenticate your Google account Save the credentials Step 3: Configure MailChimp Connection Click on the "Add to MailChimp" node Click on "Credential to connect with" Select "Create New" and choose "MailChimp OAuth2" or "MailChimp API" For API method: Log into MailChimp Go to Account → Extras → API keys Generate a new API key Copy and paste it into n8n Save the credentials Step 4: Configure Your Specific Settings Google Sheets Settings: Open the "Get Google Sheet Data" node Replace YOUR_GOOGLE_SHEET_ID with your actual sheet ID Find this in your Google Sheets URL: https://docs.google.com/spreadsheets/d/[SHEET_ID]/edit Replace YOUR_SHEET_NAME with your worksheet name (e.g., "Sheet1" or "Form Responses 1") MailChimp Settings: Open the "Add to MailChimp" node Replace YOUR_MAILCHIMP_LIST_ID with your audience ID Find this in MailChimp: Audience → Settings → Audience name and defaults Verify the status is set to "subscribed" Google Sheets Format Requirements Your Google Sheet must have the following columns (exact names): Names**: Full name of the contact (e.g., "John Doe") Email address**: Valid email address Phone Number**: Contact phone number (optional) Example: | Names | Email address | Phone Number | |-------|--------------|--------------| | John Doe | john@example.com | +1234567890 | | Jane Smith | jane@example.com | +0987654321 | How to Use Manual Execution: Open the workflow in n8n Click "Execute Workflow" Monitor the execution progress Check the output of "Create Import Summary" for results Scheduling (Optional): To run this automatically: Replace the "Manual Trigger" node with a "Schedule Trigger" node Set your desired schedule (e.g., daily at 9 AM) Activate the workflow Customization Options Adding More Fields: To include additional fields like company name or address: Add columns to your Google Sheet Modify the "Edit Fields" node to include new fields Update the "Format Subscriber Data" code to map new fields Add corresponding merge fields in the MailChimp node Handling Duplicates: The workflow uses "continueRegularOutput" error handling, which means: Existing subscribers will be skipped New subscribers will be added The workflow continues processing Adding Email Notifications: To receive import summaries via email: Add a Gmail or Email node after "Create Import Summary" Configure with your email settings Use the import summary data in the email body Troubleshooting Common Issues: "Invalid API Key" (MailChimp) Verify your API key is correct Check that your MailChimp account is active "Sheet not found" (Google Sheets) Verify the sheet ID is correct Ensure the service account has access to the sheet "Email already exists" errors This is normal for existing subscribers The workflow will continue processing other contacts Missing data in MailChimp Check that column names match exactly (case-sensitive) Verify data exists in the Google Sheet Best Practices Test First: Always test with a small dataset first Backup Data: Export your MailChimp list before large imports Clean Data: Ensure email addresses are valid before importing Monitor Regularly: Check import summaries for any issues Respect Privacy: Only import contacts who have consented to receive emails Support For issues specific to: n8n platform: Visit n8n Community Forum Google Sheets API: Check Google Developers Documentation MailChimp API: See MailChimp API Documentation Need help customizing? Contact me for consulting and support or add me on LinkedIn - https://www.linkedin.com/in/femi-adedayo-h44/ License This workflow template is provided free for personal and commercial use. Feel free to modify and share!

This n8n template demonstrates how to create an automated customer feedback collection system for restaurants. The workflow triggers when new customer emails are added to an Excel sheet, automatically sends personalized feedback forms, and stores all responses in a separate Excel tracking sheet. Perfect for restaurants wanting to systematically gather customer insights and improve service quality. Good to know Each feedback form is personalized with the customer's name and email All responses are automatically timestamped and organized in Excel sheets The system handles form validation and ensures complete data capture Email notifications keep your team updated on new feedback submissions How it works Email Distribution Workflow New customer entries are detected in Excel Sheet-1 (customer database) containing customer names and email addresses The system automatically generates personalized feedback forms for each new customer Customized feedback emails are sent with embedded forms tailored to restaurant experience evaluation Wait nodes ensure proper processing timing before sending emails Feedback Collection Workflow Customer form submissions trigger the data collection process All feedback responses are captured including ratings, comments, and contact information Data is automatically appended to Excel Sheet-2 (feedback responses) with complete timestamps The system handles multiple concurrent submissions without data loss Excel Sheet Structure Sheet-1 (Customer Database) Name - Customer's full name Email - Customer's email address for form distribution Sheet-2 (Feedback Responses) Timestamp - Date and time of form submission Name - Customer's full name E-Mail - Customer's email address Contact Number - Customer's phone number How was the cleanliness of the dining area? - Cleanliness rating/feedback Did you like the taste of the food? - Food taste evaluation What dish did you enjoy the most? - Favorite dish identification Was your order accurate and timely? - Service accuracy rating Was our staff polite and helpful? - Staff service evaluation Was the food presentation appealing? - Food presentation rating How would you rate your overall dining experience? - Overall experience score Any additional comments or suggestions? - Open-ended feedback field How to use Import the workflow into your n8n instance and configure Excel integration Set up Sheet-1 with customer names and emails for feedback distribution Configure the feedback form with your restaurant's specific questions and branding Add new customer entries to Sheet-1 to automatically trigger feedback emails Monitor Sheet-2 for incoming responses and analyze customer satisfaction trends The system scales automatically with your customer database growth Requirements Google Sheets account for data storage and management Email service integration (Gmail, SMTP, or similar) n8n instance with Google Sheets and email connectors Customising this workflow Customer feedback automation can be adapted for different restaurant types and service models Try popular use-cases such as post-dining follow-ups, seasonal menu feedback, or special event evaluations The workflow can be extended to include automated response analysis, sentiment scoring, and management dashboard integration

Supercharge Your Security Operations for Free Stop wasting time manually investigating suspicious IP addresses. This workflow template is your launchpad to automating real-time IP cybersecurity analysis using the NixGuard platform, which you can use for free. This is the first of a two-part system designed to integrate seamlessly into your existing security stack, especially with Wazuh. It calls our main workflow, Automate IP Reputation Checks and Get AI Risk Summaries from NixGuard, to do the heavy lifting. What This Workflow Unlocks for You Free AI-Powered Risk Summaries:** Don't just get data; get answers. NixGuard provides a clear, human-readable summary of why an IP is considered risky. Automated IP Reputation Checks:** Programmatically check any IP against a vast array of threat intelligence sources. A Foundation for Your SOC Automation:** Use the results to trigger your incident response process. The template includes a pre-built example of how to send a detailed alert to Slack, which you can easily adapt for Jira, TheHive, or any other tool. How the Two-Workflow System Works This "Dispatcher" workflow is designed for flexibility. It holds your API key and input, then calls the main analysis workflow. This allows you to easily create multiple triggers (e.g., one for Slack bots, one for webhooks) without duplicating the core logic. Critical Setup Instructions Get the Main Workflow: First, add the main analysis engine to your n8n instance from the community page: NixGuard Analysis Workflow. Add Your Free API Key: In this workflow, click the blue Set API Key & Initial Prompt node. Paste your free NixGuard API key into the apiKey value field. Connect The Workflows: Click the purple Execute NixGuard & Wazuh Workflow node. In the parameters, use the dropdown to select the main analysis workflow you added in Step 1. Ready to automate your threat intelligence? Get your free API key and learn more at; 🔗 Learn more about NixGuard: [thenex.world](thenex.world )🔗 Get started with a free security subscription: thenex.world/security/subscribe Tags: Free, IP Analysis, NixGuard, Wazuh, Security, Automation, AI, Cybersecurity, Threat Intelligence, SOC, Incident Response, IP Reputation, DevSecOps, API

Who is this template for? This template is for any Microsoft Outlook user who wants a trained AI agent to reason and reply on their behalf. Teach your agent tone and writing style to replicate your own, or develop a persona for a shared inbox. Requirements Outlook with authentication credentials OpenAI account with authentication credentials A few sample email replies of various lengths and topics How it works: Connect your Outlook account. Select (filter) which email sender(s) your trained AI agent will reply to. [Tip: pick a sender that has some repeatability either with a topic (ie. sales) or an individual (coworker@yourcompany.com)] Connect your OpenAI account. Choose your AI model (ie. gpt-4o-mini) Add Prompt (User Message) and select "system message" from the option below Update the instructions by filling in your name (or persona), response style, and add full email replies from the topic or individual you want the AI agent to emulate. [Tip: Add actual replies from your email sent folder, including your greeting and sign off. Paste each email sample between a set of <example> .... </example> tags] Configure the reply (or reply all) to remain within the original email string Test it! Send an email from the address to which your agent wants to respond. Check your sent (or draft) folder for the result. Enjoy all the free time you now have!! If you have questions or need assistance, email us at: support@teambisonandbird.com ++This template does not include retrieving email addresses out of the message or body of the email.++

This workflow restores all n8n instance workflows from GitHub backups using the n8n API node. It complements the Backup Your Workflows to GitHub template by allowing users to seamlessly restore previously saved workflows. How It Works The workflow fetches workflows stored in a GitHub repository and imports them into your n8n instance. Setup Instructions To configure the workflow, update the Globals node with the following values: repo.owner** – Your GitHub username repo.name** – The name of your GitHub repository storing the workflows repo.path** – The folder path within the repository where workflows are stored For example, if your GitHub username is john-doe, your repository is named n8n-backups, and workflows are stored in a workflows/ folder, you would set: repo.owner → john-doe repo.name → n8n-backups repo.path → workflows/ Required Credentials GitHub API** – Access to your repository n8n API** – To import workflows into your n8n instance Who Is This For? This template is ideal for users who want to restore their workflows from GitHub backups, ensuring easy migration and recovery in case of data loss. Check out my other templates: 👉 My n8n Templates

Intro This template is for project managers, team leads, or anyone who wants to automatically remind teammates of tasks due today—no manual copy‑and‑paste required. How it works Schedule Trigger runs every morning at 8 AM. Google Sheets node reads your “Tasks” sheet. If node filters rows where Due Date = today. Summarize (ChatGPT HTTP Request) generates a friendly reminder per person. Message a model sends the prompt to your ChatGPT Assistant and returns the AI response. Send a message (Gmail) emails each assignee their personalized reminder. Required Google Sheet Structure | Column Name | Type | Example | Notes | |-------------|--------|---------------------------|-------------------------| | Name | string | Alice Johnson | Person to remind | | Email | string | user@example.com | Recipient email address | | Task | string | Submit quarterly report | Task description | | Due Date | date | 2025‑07‑29 | Format: YYYY‑MM‑DD | Detailed Setup Steps Google Sheets Create your sheet with the columns above. In n8n → Credentials, add Google Sheets API (do not include real sheet IDs in the name). ChatGPT Assistant In the OpenAI Dashboard → Assistants, click Create Assistant. Choose a model (e.g., gpt-4), copy the Assistant ID. In n8n → Credentials → OpenAI, add your API Key and Assistant ID. Gmail In n8n → Credentials → Gmail (OAuth2 or SMTP), connect your account without embedding your real address in the credential name. Import & Configure Export this workflow’s JSON (three‑dot menu → Export). Paste it under Template Code in the Creator form. In each node, select your Google Sheets, OpenAI, and Gmail credentials. Sticky Notes A note on the Schedule node: “Set your desired run time.” A note on the ChatGPT node: “Customizes reminder text.” A note on the Gmail node: “Sends reminder email.” Customization Guidance Change schedule: edit the Cron expression in **Schedule Trigger. Adjust tone**: modify the system prompt in your ChatGPT Assistant. Email format: update **Subject and Body in the Gmail node. Batch processing: insert a **SplitInBatches node before Summarize for large sheets. Troubleshooting Ensure your Google Sheet is shared with the connected service account. Verify Due Date format (YYYY‑MM‑DD). If ChatGPT fails, check your API key and quota. Security & Best Practices Do not** hard‑code API keys, sheet IDs, or real emails. Use n8n Credentials or environment variables only. Remove any private information before submitting.

Who Is This For? This workflow is perfect for content creators, marketers, and business professionals who receive regular newsletters and want to effortlessly convert them into engaging LinkedIn posts. By automating the extraction and repurposing process, you can save time and consistently share thoughtful updates with your network. What Problem Does This Workflow Solve? Manually reading newsletters, extracting the key points, and then formatting that content into professional, engaging LinkedIn posts can be time-consuming and error-prone. This workflow automates those steps by: Filtering Emails:** Uses the Gmail node to process only those emails from a specific sender (e.g., newsletter@example.com). Extracting Content:** Leverages OpenAI to identify and summarize the top news items in your newsletter. Generating Posts:** Crafts concise, insightful LinkedIn posts in a smart, deadpan style with a touch of subtle humor. Publishing:** Posts the generated content directly to LinkedIn. What This Workflow Does Filter Newsletters:** The Gmail node is set up to only handle emails from your chosen sender, ensuring that only relevant newsletters are processed. Extract Key Content:** An OpenAI node analyzes the newsletter text to pull out the most important news items, including headlines and summaries. Split Content:** A Split Out node divides the extracted content so each news item is processed on its own. Generate LinkedIn Posts:** Another OpenAI node takes each news item's details and produces a well-structured LinkedIn post that delivers practical insights and ends with a reflective observation or question. Publish to LinkedIn:** The LinkedIn node publishes the crafted posts directly to your account. Setup Gmail Node: Rename it to “Filter Gmail Newsletter” and configure it to filter emails by your newsletter sender. OpenAI Nodes: Ensure your OpenAI API credentials are set up correctly. Customize the prompt if needed to match your desired tone. LinkedIn Node: Rename it to “Post to LinkedIn” and confirm that your LinkedIn OAuth2 credentials are properly configured. How to Customize OpenAI Prompts:** Adjust the prompts in the OpenAI nodes to fine-tune the post tone and output formatting. Email Filter:** Change the Gmail filter to match the sender of your newsletters. Post Processing:** Optionally, add extra formatting (using Function nodes) to further enhance the readability of the generated LinkedIn posts. This template offers an automated, hands-off solution to transform your newsletter content into engaging LinkedIn updates, keeping your audience informed and inspired with minimal effort.