Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Automatically transform your video uploads into AI-powered summaries with key topic extraction and instant team notifications. What this workflow does Monitors Google Drive for new video uploads Downloads and processes videos using VLM Run AI Generates intelligent summaries with key topics extracted Posts results to Slack for immediate team access Setup Prerequisites: Google Drive account, VLM Run API credentials, Slack workspace, self-hosted n8n. You need to install VLM Run community node Quick Setup: Configure Google Drive OAuth2 and create video upload folder Add VLM Run API credentials Set up Slack integration for notifications Update folder/channel IDs in workflow nodes Test and activate Perfect for Meeting recordings and training videos Webinar summaries and educational content Content analysis and team collaboration Any video content requiring quick insights Key Benefits Asynchronous processing** handles large files without timeouts Multi-format support** for MP4, AVI, MOV, WebM, MKV Instant team updates** via Slack notifications Saves hours** of manual video review time How to customize Extend by adding: Video categorization and tagging Integration with project management tools Email notifications alongside Slack Searchable video databases with summaries This workflow transforms lengthy videos into actionable insights, making your content instantly accessible and shareable with your team.

This n8n template allows you to monitor hourly weather conditions in a specific city using OpenWeatherMap and log the results to a Google Sheet. It’s perfect for anyone needing periodic weather tracking—whether you're managing logistics, travel planning, or environmental monitoring. 🔧 How it works A Schedule Trigger activates the workflow every hour. The Get Weather Data from OpenWeatherMap node fetches real-time weather details using the city name you specify. An IF node checks if the weather description contains "rain" or the temperature is below a set threshold. If the condition is true, the data is formatted with city, temperature, humidity, and conditions. The Google Sheets node appends this formatted information to your designated sheet. 👤 Who is it for? This workflow is ideal for: Operations teams monitoring weather-sensitive logistics Researchers collecting climate data Developers and hobbyists learning how to connect APIs with Google Sheets 🗂️ Google Sheet Structure Your Google Sheet should have the following columns: city (string) temperature (K) (number) humidity (number) conditions (string) status (string) ⚙️ Setup Instructions Create a Google Sheet with the above columns. Set up your Google Service Account credentials in n8n. Replace the API key in the HTTP Request node with your own OpenWeatherMap credential. Specify your target city and ensure your OpenWeatherMap account is active. Adjust the frequency in the Schedule Trigger as needed (default: every hour).

Scrape Latest 20 TechCrunch Articles Who is this for? This workflow is designed for developers, researchers, and data analysts who need to track the latest trending repositories on GitHub. It is useful for anyone who wants to stay updated on popular open-source projects without manually browsing GitHub’s trending page. What problem is this workflow solving? Manually checking GitHub’s trending repositories daily can be time-consuming and inefficient. This workflow automates the extraction of trending repositories, providing structured data including repository name, author, description, programming language, and direct repository links. What this workflow does This workflow scrapes the trending repositories from GitHub’s trending page and extracts essential metadata such as repository names, languages, descriptions, and URLs. It processes the extracted data and structures it into an easy-to-use format. Setup Ensure you have n8n installed and configured. Import this workflow into your n8n instance. Run the workflow manually or schedule it to execute at regular intervals. (Optional) Customize the extracted data or integrate it with other systems. How to customize this workflow to your needs Modify the HTTP request node to target different GitHub trending categories (e.g., specific programming languages). Add further processing steps such as filtering repositories by stars, forks, or specific keywords. Integrate this workflow with Slack, email, or a database to store or notify about trending repositories. Workflow Steps Trigger execution manually using the "When clicking ‘Test workflow’" node. Send an HTTP request to fetch GitHub’s trending page using "Request to Github Trend". Extract the trending repositories box from the HTML response using "Extract Box". Extract all repository data including names, authors, descriptions, and languages using "Extract all repositories". Convert extracted data into a structured list for easier processing using "Turn to a list". Extract detailed repository information using "Extract repository data". Format and set variables to ensure clean and structured data output using "Set Result Variables". Note: Since GitHub’s trending page updates dynamically, ensure you run this workflow periodically to capture the latest trends.

Monitor Postgres Data Freshness and Email Alert If Stale This template monitors a set of tables inside a Postgres database to ensure they're getting updated. If the table hasn't been updated in 3 days (configurable), an email alert is sent containing the tables that are stale. Requirements You must have a Postgres database containing one or more tables that you'd like to monitor. Each table to monitor must have a date or timestamp column that tracks when data was pushed. For example, this might be: A timestamp column if your table holds event/timeseries data A last_updated column if your rows are expected to be modified Usage Use this template Add your Postgres and email credentials Adjust the Produce tables + date columns node to produce pairs of [table, date_column] that should be monitored for freshness 💁‍♂️ Note that a timestamp column also works (Optional) Adjust the Remove fresh tables node for your desired staleness window (default is 3 days, but you can adjust as you please) (Optional) Customize the Send alerts node to call whichever alerting workflow you please (I recommend my alerting workflow for easiest plug-and-play) How it works This template works by: Pulling the most recent row for each table Calculating how out-of-date each table is, in days Dropping fresh tables that have been updated within the past 3 days Sending an email alert with the stale tables that haven't been updated within the past 3 days

Overview This workflow allows users to generate AI music using the KIE. ai API integrated with the Suno V3.5 model. It provides a simple form interface for inputting parameters like music prompts, styles, and titles. The system automatically submits the request to the API, monitors the generation status in real time until completion, and retrieves the final music output. This is perfect for musicians, content creators, or developers looking to automate custom music creation with support for various modes and intelligent generation. Prerequisites A KIE. ai account and API key: Create an account at KIE.ai and obtain your API key. An active n8n instance (self-hosted or cloud-based) with support for HTTP requests and form submissions. Familiarity with AI music prompts to optimize results, such as describing mood, instruments, and rhythm. Setup Instructions Get API Key: Sign up at KIE. ai and generate your API key. Keep it secure and input it in the form—do not disclose it to others. Import Workflow: Copy the JSON from this template and import it into your n8n editor. Configure the Form: In the form node, set fields for: prompt: Describe the music content (e.g., "A calm and relaxing piano track with soft melodies"). style: Specify the genre (e.g., "Classical", "Jazz", "Pop"). title: Provide a title for the generated music (max 80 characters). api_key: Your KIE. ai key. Test the Workflow: Click "Execute Workflow" in n8n to activate the form. Access the form URL, fill in the parameters, and submit. The workflow will send a POST request to the API, wait and poll every 10 seconds for status updates, and display the music file once ready. View Results: The output node formats the results, showing playable music files. Customization Guidance Refine Prompts**: For better results, include detailed descriptions like emotions, rhythm, instruments, or lyrics. Example: "A peaceful piano meditation track with gentle waves in the background."

How it works This workflow is a hands-on tutorial for the Code node in n8n, covering both basic and advanced concepts through a simple data processing task. Provides Sample Data: The workflow begins with a sample list of users. Processes Each Item (Run Once for Each Item): The first Code node iterates through each user to calculate their fullName and age. This demonstrates basic item-by-item data manipulation using $input.item.json. Fetches External Data (Advanced): The second Code node showcases a more advanced feature. For each user, it uses the built-in this.helpers.httpRequest function to call an external API (genderize.io) to enrich the data with a predicted gender. Processes All Items at Once (Run Once for All Items): The third Code node receives the fully enriched list of users and runs only once. It uses $items() to access the entire list and calculate the averageAge, returning a single summary item. Create a Binary File: The final Code node gets the fully enriched list of users once again and creates a binary CSV file to show how to use binary data Buffer in JavaScript. Set up steps Setup time: < 1 minute This workflow is a self-contained tutorial and requires no setup. Explore the Nodes: Click on each of the Code nodes to read the code and the comments explaining each step, from basic to advanced. Run the Workflow: Click "Execute Workflow" to see it in action. Check the Output: Click on each node after the execution to see how the data is transformed at each stage. Notice how the data is progressively enriched. Experiment! Try changing the data in the 1. Sample Data node, or modify the code in the Code nodes to see what happens.

What this workflow does This n8n workflow connects to Google Search Console to fetch SEO performance data (clicks, impressions, CTR, and average position) for the last 7 days. It formats the results into a clean weekly summary and automatically sends it to your email inbox every Monday morning. Ideal for: Website owners Bloggers SEO consultants who want to track site performance over time without manual reporting. Setup steps Replace YOUR_SITE_URL in the HTTP Request node with your verified domain from Google Search Console. Connect your Google OAuth2 credentials to the HTTP Request node. Set up your SMTP credentials in the "Send Email" node. Adjust the recipient email address and subject line if necessary. (Optional) Customize the Function node to include more queries or format the report as a PDF. Estimated setup time: ~10 minutes Sticky notes are included in the workflow canvas to guide you step-by-step. Technologies used Google Search Console API SMTP Email Node n8n Function Node n8n HTTP Request Node n8n Sticky Notes

This workflow is perfect for technical writers, content creators, marketers, and developers who write in Markdown but need to collaborate or publish using Google Docs format. Ideal for teams that want to streamline their content creation and review process. What problem does this workflow solve? Manual conversion from Markdown to Google Docs is time-consuming and often loses formatting. This workflow eliminates the tedious copy-paste process, automatically preserves formatting, and creates organized, timestamped documents in your Google Drive. Perfect for content teams who write in Markdown but need Google Docs for collaboration and review. What this workflow does Converts Markdown to HTML** with proper formatting preservation (headers, lists, links, tables) Creates timestamped Google Docs** documents with automatic naming Adds Drive location metadata** for better organization and reference Maintains document structure** including emojis, tables, and text formatting Automates file creation** in specified Google Drive folders Setup Google Drive OAuth2 credentials configured in n8n Target Google Drive folder URL Input your content title and Markdown text in the "Set Input Data" node How to customize this workflow to your needs Modify HTML formatting options** in the Markdown conversion node Change file naming patterns** to match your organization system Adjust Drive folder structure** and metadata inclusion Update MIME type handling** for different output requirements Add additional processing steps** like notifications or integrations Perfect for technical documentation workflows, content publishing pipelines, blog preparation, and automated report generation. Setup Instructions - Markdown to Google Docs Converter Prerequisites n8n instance** (local or cloud) Google account** with Google Drive access Basic understanding** of n8n workflow configuration Step 1: Import the Workflow Open n8n and navigate to Workflows Click "Add workflow" → "Import from JSON" Upload the Export_Markdown_Content_do_Google_Docs_Document.json file Save the workflow with a descriptive name Step 2: Configure Google Drive Credentials Create Google Drive OAuth2 Credentials In n8n, go to Settings → Credentials Click "Add credential" → "Google Drive OAuth2 API" Follow the OAuth setup to authorize n8n access to Google Drive: Visit Google Cloud Console Create or select a project Enable Google Drive API Create OAuth2 credentials Add authorized redirect URI for your n8n instance Name the credential (e.g., "Google Drive - Markdown Converter") Configure Google Drive Nodes Update these nodes with your Google Drive credentials: Create Empty File Update Document with Correct HTML Formatting In each node: Select your Google Drive credential from the dropdown Test the connection to ensure it works properly Step 3: Prepare Your Google Drive Create Target Folder Go to Google Drive (drive.google.com) Create a new folder for your converted documents Copy the folder URL (will look like: https://drive.google.com/drive/folders/FOLDER_ID) Ensure the folder has proper permissions for your Google account Step 4: Configure Input Data Set Your Default Values Open the "Set Input Data" node Update the assignments with your preferences: Google Drive URL: Replace the example URL with your target folder URL Format: https://drive.google.com/drive/folders/YOUR_FOLDER_ID Content Title: Set a default title or leave placeholder text This will be used in the document filename Content in Markdown: Add your Markdown content or keep example for testing Supports standard Markdown syntax (headers, lists, links, tables) Step 5: Test the Workflow Initial Test Run Ensure all credentials are configured Click the "Test workflow" button on the Manual Trigger node Monitor the execution - check for any errors in node outputs Verify the result: Check your Google Drive folder Look for a new document with timestamp in the name Open the document to verify formatting Troubleshooting Common Issues Google Drive Permission Errors: Verify OAuth2 credentials are properly configured Check that the target folder exists and is accessible Ensure Google Drive API is enabled in Google Cloud Console Markdown Conversion Issues: Check that your Markdown syntax is valid Test with simple content first (headers, paragraphs, lists) Verify the "Change Markdown To HTML" node settings File Creation Problems: Confirm the Google Drive folder URL format is correct Check that the folder ID in the URL is valid Ensure your Google account has write permissions to the folder Step 6: Customize for Your Needs Modify HTML Formatting Options In the "Change Markdown To HTML" node: Enable/disable emoji support** (currently enabled) Adjust table formatting** (currently enabled) Modify header ID generation** (currently disabled) Configure space requirements** for headers Update File Naming Pattern In the "Create Empty File" node: Change the naming convention**: Currently uses _PUB {Content Title} {timestamp} Modify timestamp format**: Currently yyyy-MM-dd HH:mm:ss Add prefixes or suffixes** as needed for your organization Step 7: Production Usage Regular Workflow Execution Update the "Set Input Data" node with new content Execute the workflow manually or set up triggers Monitor execution logs for any issues Check Google Drive for generated documents Integration Options Webhook Integration: Add a Webhook trigger to accept external Markdown content Useful for automated content publishing workflows Email Integration: Add email notifications when documents are created Include links to generated Google Docs Advanced Configuration Error Handling Add error handling nodes after critical operations Implement retry logic for API failures Set up notifications for failed executions Performance Optimization Adjust the "Wait for Document Creation" timing if needed Consider file size limits for Google Docs Support and Troubleshooting Common Solutions Timeout errors**: Increase wait time in "Wait for Document Creation" Authentication failures**: Refresh Google OAuth2 credentials Formatting issues**: Test with simpler Markdown first Getting Help Check n8n community forums for Google Drive integration issues Review Google Drive API documentation for rate limits Test with minimal Markdown content to isolate problems Total setup time: ~15-20 minutes Difficulty level: Intermediate Requirements: Google account, n8n instance, basic OAuth2 setup knowledge

Description Turn your Telegram bot into a powerful OCR (Optical Character Recognition) tool. This workflow allows you to send any image (like a screenshot, a photo of a document, or a picture of a sign) to your bot, and it will instantly extract and send back the text from that image. Powered by Google's advanced Gemini AI, this automation is perfect for quickly digitizing notes, saving important snippets, or avoiding manual typing. How it works This workflow performs a few high-level steps: It triggers when a new image is sent to your Telegram bot. It sends the image to the Google Gemini Vision API to be analyzed. It extracts the text found in the image. It sends the extracted text back to you as a message in Telegram. Set up steps Estimated set up time: Less than 5 minutes. The setup is straightforward. You only need to configure two credentials: Telegram Bot Credentials: To connect your bot. Google Gemini API Credentials: To use the OCR feature. You can get a free API key from Google AI Studio.

This workflow automatically adds a new lead to Pipedrive once someone forks your GitHub repository. Prerequisites Pipedrive account and Pipedrive credentials GitHub account and GitHub credentials How it works GitHub Trigger node starts the workflow once someone forks your GitHub repository. HTTP Request node gets user's data from GitHub and sends it further. Pipedrive node searches forkee's data in Pipedrive by email. IF node decides whether to create a new person in Pipedrive in case contact doesn't exist yet or update an existing contact in Pipedrive. In case there's no contact existing yet, the Pipedrive node creates a lead and adds a note with GitHub URL.

This workflow automates the process of publishing PR News articles to the WordPress website. 🔧 How it works: Uses an RSS Feed Trigger to monitor new PR News articles. Extracts the article content and parses the featured image URL. Uploads the image to WordPress as a media item. Creates a new draft post on the WordPress site using the article's content and sets the uploaded image as the featured image. ✅ Features: Polls RSS feed every minute. Automatically extracts and sets featured images. Posts are created as drafts for editorial review. 📝 Requirements: WordPress REST API access with media upload permission. Active WordPress credentials in n8n. Perfect for teams who want to streamline PR content publishing without manual effort.

AI LinkedIn Outreach Automation with Apollo, OpenAI & PhantomBuster Categories:* Sales Automation Lead Generation AI Personalization This workflow creates a complete LinkedIn outreach automation system that generates targeted lead lists from Apollo using natural language, enriches profiles with AI-personalized icebreakers, and automatically sends connection requests through PhantomBuster. Built by someone who's made over $1 million with AI automation, this system demonstrates the real-world approach to building profitable automation workflows. Benefits* Natural Language Lead Targeting - Describe your ideal prospects in plain English and automatically generate Apollo search URLs AI-Powered Personalization - Creates custom icebreakers based on LinkedIn profile data, employment history, and professional background Complete Outreach Pipeline - From lead discovery to personalized connection requests, fully automated end-to-end Smart Data Management - Automatically tracks all prospects in Google Sheets with deduplication and status tracking Cost-Effective Scraping - Uses Apify to extract Apollo data without expensive subscription costs Scalable Architecture - Processes hundreds of leads while respecting LinkedIn's connection limits How It Works* Natural Language Lead Generation: Form input accepts audience descriptions in plain English AI converts descriptions into properly formatted Apollo search URLs Automatically includes location, company size, job titles, and keyword filters Apollo Data Extraction: Uses Apify actor to scrape targeted lead lists from Apollo Extracts LinkedIn URLs, email addresses, employment history, and profile data Processes 500+ leads per run with detailed professional information AI Personalization Engine: Analyzes LinkedIn profile data including job history and company information Generates personalized icebreakers using proven connection request templates Creates human-like messages that reference specific career details and achievements Google Sheets Integration: Automatically stores all lead data in organized spreadsheet format Tracks prospect information, contact details, and generated icebreakers Provides easy data management and campaign tracking PhantomBuster Automation: Connects to PhantomBuster API to trigger LinkedIn connection campaigns Sends personalized connection requests with custom icebreakers Respects LinkedIn's daily limits and mimics human behavior patterns Business Use Cases* Sales Teams - Automate prospecting for B2B outreach campaigns Agencies - Scale client acquisition through targeted LinkedIn outreach Recruiters - Find and connect with qualified candidates efficiently Entrepreneurs - Build professional networks in specific industries Business Development - Generate qualified leads for partnership opportunities Revenue Potential This system can replace expensive LinkedIn outreach tools that cost $200-500/month. Users typically see: 400% improvement in response rates through personalization 10x faster lead generation compared to manual prospecting Ability to process 500+ leads per hour vs. 10-20 manually Difficulty Level: Intermediate Estimated Build Time: 1-2 hours Monthly Operating Cost: ~$50 (Apollo + PhantomBuster + AI APIs) Watch My Complete 1-Hour Build* Want to see exactly how I built this system from scratch? I walk through the entire development process live, including all the debugging, API integrations, and real-world testing that goes into building profitable automation systems. 🎥 See My Live Build Process: "Build This Automated AI LinkedIn DM System in 1 Hour (N8N)" This comprehensive tutorial shows my actual development approach - including the detours, problem-solving, and iterative testing that real automation building involves. Required Google Sheets Setup* Create a Google Sheet with these exact column headers: Essential Lead Columns: id - Unique prospect identifier first_name - Contact's first name last_name - Contact's last name name - Full name linkedin_url - LinkedIn profile URL title - Current job title email_status - Email verification status photo_url - Profile photo URL icebreaker - AI-generated personalized message Setup Instructions: Create Google Sheet with these headers in row 1 Connect Google Sheets OAuth in n8n Update the document ID in the "Add to Google Sheet" node PhantomBuster will read from this sheet for automated outreach Set Up Steps* Apollo & Apify Configuration: Set up Apify account and obtain API credentials Configure Apollo scraper actor with proper parameters Test lead extraction with sample audience descriptions AI Personalization Setup: Configure OpenAI API for natural language processing and personalization Set up prompt templates for audience targeting and icebreaker generation Test personalization quality with sample LinkedIn profiles Google Sheets Integration: Create lead tracking spreadsheet with proper column structure Configure Google Sheets API credentials and permissions Set up data mapping for automatic lead storage PhantomBuster Connection: Set up PhantomBuster account and LinkedIn connection Configure LinkedIn auto-connect agent with custom message templates Connect API for automated campaign triggering Form and Workflow Setup: Configure form trigger for audience input collection Set up data flow between all components Add proper error handling and rate limiting Testing and Optimization: Start with small batches (5-10 connections daily) Monitor LinkedIn account health and response rates Optimize icebreaker templates based on performance data Important Compliance Notes* LinkedIn Limits: Respect 100 connection requests per week limit Account Safety: Use PhantomBuster's human-like behavior patterns Message Quality: Regularly update templates to avoid automation detection Response Management: Monitor and respond to replies within 24 hours Advanced Extensions* This system can be enhanced with: Multi-channel Outreach: Add email sequences for comprehensive campaigns A/B Testing: Test different icebreaker templates automatically CRM Integration: Connect to Salesforce, HubSpot, or other sales systems Response Tracking: Monitor reply rates and optimize messaging Explore My Channel* For more advanced automation systems that generate real business results, check out my YouTube channel where I share the exact strategies I've used to make over $1 million with AI automation.