Browse 12,955 Workflows

By Michael Yang

Who is this template for? This workflow is perfect for competitive‑intel analysts, product managers, content marketers, and anyone who tracks multiple company blogs or news sources. If you need a weekly snapshot of fresh, on‑topic articles—without wading through dozens of tabs—this template is for you. What does it do? The workflow reads a curated list of candidate URLs from Google Sheets, filters out duplicates and off‑topic pages with an AI agent, scrapes the surviving links, generates three‑sentence summaries, logs the results back to Sheets, and delivers a polished HTML digest to your inbox every week. Why is it useful? Instead of manually opening competitor links, checking for relevance, copying highlights, and pasting them into reports, this automation does the grunt work for you. It turns scattered URLs into a searchable knowledge base and a ready‑to‑share email, freeing you to focus on insights and strategy—not housekeeping. How does it work? A Sunday‑morning cron trigger kicks things off. The workflow pulls links from the Input Links tab, compares them to the existing Summary tab, and passes fresh candidates to an AI “bouncer” that keeps only blog posts, tutorials, news, and product updates. Firecrawl then scrapes each page; Gemini 2.5‑Flash and OpenAI condense the content into title, author, date, and summary. The structured data is appended to your Summary sheet and formatted into a company‑grouped HTML digest, which lands in your email before the workweek starts. Set up steps Clone the workflow Import the JSON into your n8n Cloud workspace. Create the Google Sheet Make a new spreadsheet with two tabs: Input Links and Summary (names must match). In Input Links, add columns Company, Page Type, and Link (or rename to match the node mapping). Leave Summary blank—the workflow will populate it. Copy the Sheet URL; you’ll paste it into two Google Sheets nodes. Add credentials (n8n ▸ Credentials) Google Sheets OAuth2 – Authorise with the Google account that owns the spreadsheet. Gmail OAuth2 – Authorise the Gmail account that should send the digest. Firecrawl HTTP Header Auth – Set Authorization: Bearer <YOUR_FIRECRAWL_API_KEY>. Point nodes to your Sheet Open each Google Sheets node (Input Links, Read_Url_Summary_Tool, Append row in sheet, Get row(s) in sheet). Paste the Document ID (found in the Sheet URL) and select the correct tab (Input Links or Summary). Update email recipients In the Send a message (Gmail) node, replace the sample addresses with your own distribution list. Adjust scheduling (optional) Double‑click the Schedule Trigger node and change the cron expression if you prefer a different day/time. Tune AI models (optional) OpenAI o4‑mini and Gemini 2.5‑Flash nodes default to cost‑efficient settings. Feel free to switch models or tweak temperature to suit your tone. Test with a single URL Add one row in Input Links, then execute the workflow manually (▶ Run). Verify that a new row appears in Summary and an email lands in your inbox. Go live Activate the workflow (toggle in top bar). Confirm the green status badge and wait for the next scheduled run. Tip: The Firecrawl Free tier limits you to ~10 requests/min. If you scale beyond that, raise the batching interval in both Firecrawl nodes or upgrade your Firecrawl plan. An n8n automation workflow template by Michael Yang.

By Oneclick AI Squad

Description Automates stock maintenance for real estate (e.g., construction materials, office supplies). Monitors stock levels, processes additions/deductions, and sends low-stock alerts via email. Uses Google Sheets for data storage and n8n for workflow automation. Essential Information Daily workflow to check and update stock levels. Stores data in a Google Sheet for easy access and analysis. Sends email notifications for low-stock items based on predefined thresholds. Supports dynamic stock updates via API or form input (configurable). System Architecture Stock Monitoring Pipeline**: Daily Stock Check: Triggers daily to initiate stock monitoring. Fetch Stock Data: Retrieves current stock levels from Google Sheet. Update Stock Levels: Processes stock additions or deductions. Alert Generation Flow**: Check Low Stock: Identifies items below threshold. Send Email Alert: Notifies stakeholders of low-stock items. Data Management**: Update Google Sheet: Saves updated stock levels and timestamps. Implementation Guide Import the workflow JSON into n8n. Configure Google Sheets credentials and specify sheet ID. Set up SMTP credentials for email alerts. Test stock update and low-stock alert processes. Monitor email delivery and adjust thresholds as needed. Technical Dependencies Google Sheets API for stock data storage and retrieval. SMTP service for sending low-stock email alerts. n8n for workflow automation and orchestration. Optional: Web form or API for dynamic stock updates. Database & Sheet Structure Stock Inventory Sheet** (StockInventory): Columns: item, quantity, threshold, last_updated, unit Example: Cement, 100, 20, 2025-07-29T08:00:00Z, Bags Customization Possibilities Modify Update Stock Levels node to integrate with a form or API for real-time updates. Adjust Check Low Stock node to set custom thresholds per item. Customize email alert format in Send Email Alert node. Add error-handling nodes for invalid stock updates. Integrate with a dashboard tool for visual stock monitoring. An n8n automation workflow template by Oneclick AI Squad.

By Dhruv from Saleshandy

This n8n template captures every “Request a Demo” booking in Calendly, uses OpenAI to score and qualify leads in real time, routes them into the correct Saleshandy sequence, and logs all data in Google Sheets for full GTM visibility. Use cases include: Empowering SDR teams to focus on high-value demos Providing growth marketers with reliable funnel metrics Automating triage for B2B AE teams overwhelmed by demo requests Good to know OpenAI GPT-4 calls cost based on token usage—you can expect ~1,200 tokens per lead. Calendly API rate-limits at 180 requests/min; consider batching if volume spikes. Google Sheets writes are single-threaded; high-volume users may opt for Airtable or BigQuery. How it works Capture – Webhook node listens for every new “Request a Demo” form submission in Calendly. Score – AI Agent node sends job title, company size, domain quality, and custom questions to OpenAI; returns a 1–10 score plus label (Qualified/Semi-qualified/Unqualified). Verify meeting – HTTP Request node confirms via the Calendly API that a slot was actually scheduled. Route – Switch node selects the appropriate Saleshandy sequence ID (Qualified, Nurture, Disqualify). Send – HTTP Request nodes add each prospect to the chosen Saleshandy sequence. Log – Google Sheets nodes write to three tabs (Qualified, Semi-qualified, Unqualified) with lead data, score, routing path, and timestamp. Prerequisites n8n workspace Accounts & API credentials for: Calendly OpenAI (GPT-4 or GPT-3.5) Google Sheets Saleshandy Step-by-Step Setup 1. Import the n8n Template Upload the JSON file into your n8n workspace. 2. Add Required Credentials In n8n → Credentials, add: Calendly: Personal Access Token (PAT) OpenAI: API Key Google Sheets: OAuth2 connection Saleshandy: API Key 3. Calendly Setup Go to Calendly Webhook Docs Create a Routing Form in Calendly. Generate your access token. Use Postman or any API client to: Make a POST request to create a webhook subscription. Use your n8n webhook URL in the url field. Add your Authorization token and extract the Organization ID. Paste the webhook URL into the Calendly Routing Form. 4. Set Your Saleshandy Sequences In n8n, locate the Set: Sequence IDs node. Replace the placeholder text with: Your actual Qualified Semi-qualified and Unqualified Saleshandy sequence step IDs. 5. Configure Google Sheets Create a spreadsheet with the following tabs: Qualified Semi-qualified Unqualified In n8n, connect the three Google Sheets nodes to this file. Customising this workflow Adjust scoring logic – Modify the OpenAI prompt in the AI Agent node to weight ARR, industry, or headcount differently. Refine thresholds – Change the Switch node rules for score ranges (e.g., Qualified ≥8, Semi-qualified 5–7). Swap destinations – Edit HTTP Request nodes to integrate with your CRM or email platform instead of Saleshandy. Enhance logging – Replace Google Sheets with Airtable, BigQuery, or another analytics store. Add notifications – Insert Slack or Microsoft Teams nodes after routing to alert reps instantly. An n8n automation workflow template by Dhruv from Saleshandy.

By Bedrijfautomatiseren.nl

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. ## Scrape Google Maps leads to Google Sheets via Apify Who's it for This workflow is ideal for marketers, sales professionals, and solo entrepreneurs who want to collect local leads based on Google Maps search terms. For example, restaurants in North Holland. What it does This workflow uses an Apify actor to scrape business details from Google Maps and automatically appends them to a Google Sheet with the following fields: Business name Street Postal code City Website Phone number How it works The workflow starts with a manual trigger (you can replace it with any other trigger). The Google Maps Scraper Apify actor is launched. A short wait ensures the actor completes the task (you can increase the wait time for more extensive outputs). The resulting dataset is retrieved from Apify. Data is mapped and added into a connected Google Sheet. How to set up Step 1: Configure the “Run Apify scraper” node Go to the Google Maps Scraper actor on Apify. Create a new Task with your search term. Find your actor from list. Step 2: Edit the “Find your last run” node Select the correct actor (Google Maps Scraper) from the dropdown in this node. Step 3: Fetch the dataset In the “Get the data from Apify” node, use {{$json.defaultDatasetId}} to dynamically pull the correct dataset. Step 4: Connect your Google Sheet Link your Google account and select the desired spreadsheet and tab (for example, Leads). Make sure the column headers match the data fields. Current column headers: Title Street Postcode City Website Phone If you have any questions, feel free to reach out to info@ai-amigos.com. An n8n automation workflow template by Bedrijfautomatiseren.nl.

By Oneclick AI Squad

This automated n8n workflow processes any inquiry emails using AI-powered intelligence to determine customer intent and provide appropriate responses. The system analyzes incoming emails, performs availability checks or direct booking processing, and sends personalized responses based on the customer's specific requirements across any industry vertical. Good to Know Uses Google Gemini Chat Model for intelligent email analysis and response generation Automatically detects customer intent (availability check vs direct booking request) Includes conditional routing for different response types based on AI analysis Integrates with external booking systems through HTTP requests Provides seamless email automation with personalized customer communication How It Works Gmail Trigger: Initiates the workflow upon receiving a new email. AI Agent: Analyzes the email content to determine the customer's intent (availability check or direct booking). Code: Parses the JSON output from the AI Agent. Wait for Data - Ensures proper data synchronization before proceeding with conditional logic If: Routes the workflow based on the detected intent. Gmail Nodes: Sends appropriate responses or forwards booking details. How to Use Import workflow into n8n Configure Gmail API credentials for email monitoring and sending Set up Google Gemini Chat Model API access Customize AI prompts based on your industry and booking requirements Test with sample inquiry emails to verify intent detection accuracy Configure external booking system integration if needed Monitor email processing and response quality Requirements Gmail API credentials Google Gemini Chat Model API access Email monitoring and sending permissions Optional: External booking system API integration Customizing This Workflow Modify AI prompts for different industries (hotels, restaurants, services, appointments) Adjust conditional logic based on specific business requirements Configure custom email templates for various response scenarios Add integration with CRM or booking management systems Set up additional data processing nodes for complex booking workflows Implement custom validation rules for booking requests. An n8n automation workflow template by Oneclick AI Squad.

By Oneclick AI Squad

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. An n8n automation workflow template by Oneclick AI Squad.

By Gaurav Ramse

Automate your entire guest communication journey from booking to post-stay with personalized welcome emails, review requests, and daily operational reports. Perfect for hotels, B&Bs, and short-term rental properties looking to enhance guest experience while reducing manual work and improving operational efficiency. How it works Pre-arrival welcome emails - Automatically sends personalized welcome emails 1-2 days before guest check-in with reservation details, hotel amenities, and contact information Post-stay review requests - Sends automated review request emails 24 hours after checkout with Google Reviews links and return guest discount codes Daily staff reports - Generates comprehensive arrival/departure reports every morning at 6 AM for front desk, housekeeping, and management teams Smart tracking - Prevents duplicate emails by automatically updating tracking status in your Google Sheets database Professional templates - Uses responsive HTML email templates that work across all devices and email clients Set up steps Connect Google Sheets - Link your hotel reservation spreadsheet (must include columns for guest details, check-in/out dates, and email tracking) Configure Gmail account - Set up Gmail credentials for sending automated emails Customize hotel information - Update hotel name, contact details, and branding in the "Edit Fields" nodes Set staff email addresses - Configure recipient addresses for daily operational reports Adjust timing - Modify schedule triggers if you want different timing for emails and reports (currently set to every 6 hours for guest emails and 6 AM daily for staff reports) Time investment: ~30 minutes for initial setup, then fully automated operation. An n8n automation workflow template by Gaurav Ramse.

By Oneclick AI Squad

This automated n8n workflow streamlines real estate marketing by combining voice campaigns and email outreach with AI-powered lead generation. The system monitors real estate offers, generates personalized promotional content using AI, creates targeted email campaigns, and manages lead follow-up through automated voice calls and CRM integration. Good to Know Integrates voice campaign automation with email marketing for multi-channel outreach Uses Llama 3.2 AI model for generating personalized promotional content Automatically syncs lead data with CRM systems for comprehensive tracking Includes delay mechanisms to ensure proper data synchronization Supports both email and voice-based lead nurturing strategies How It Works Watch Real Estate Offer** - Monitors incoming real estate listings and opportunities to trigger marketing campaigns Get Client Contact List** - Fetches targeted client information and contact details from CRM or database systems Generate Promo Content with Llama** - Uses AI to create personalized marketing content based on property details and client preferences Trigger Voice Campaign via VAPI** - Initiates automated voice calls to prospects using personalized messaging Create Personalized Email Template** - Generates custom HTML email templates with property information and promotional content Email Promo to Clients (Gmail)** - Sends targeted email campaigns to segmented client lists through Gmail integration Delay to Sync Data** - Ensures proper data synchronization between systems before processing leads Receive Lead Data from VAPI** - Captures lead information and responses from voice campaign interactions Save Lead to CRM Sheet** - Logs all lead data and campaign results to spreadsheet for tracking and analysis Send Acknowledgment to VAPI** - Confirms successful lead processing and maintains system synchronization How to Use Import workflow into n8n Configure VAPI credentials for voice campaign automation Set up Gmail API for email marketing integration Connect CRM or Google Sheets for lead management Configure Llama 3.2 AI model access Test with sample real estate data Monitor campaign performance and lead conversion rates Requirements VAPI account for voice campaigns Gmail API credentials Llama 3.2 AI model access Google Sheets or CRM integration Real estate data source Customizing This Workflow Adjust AI prompts for different property types or market segments Modify email templates for various campaign styles Configure voice campaign scripts based on target audience Set up custom lead scoring and qualification criteria Integrate additional CRM systems or marketing platforms. An n8n automation workflow template by Oneclick AI Squad.

By vinci-king-01

How it works This workflow automatically processes bank statements from various formats and extracts structured transaction data with intelligent categorization using AI. Key Steps File Upload - Accepts bank statements via webhook upload (PDF, Excel, CSV formats). Smart Format Detection - Automatically routes files to appropriate processors (PDF text extraction or spreadsheet parsing). AI-Powered Extraction - Uses GPT-4 to extract account details, transactions, and balances from statement data. Data Processing & Categorization - Cleans, validates, and automatically categorizes transactions into expense categories. Database Storage - Saves processed data to PostgreSQL database for analysis and reporting. API Response - Returns structured summary with transaction counts, expense totals, and category breakdowns. Set up steps Setup time: 8-12 minutes Configure OpenAI credentials - Add your OpenAI API key for AI-powered data extraction. Set up PostgreSQL database - Connect your PostgreSQL database and create the required table structure. Configure webhook endpoint - The workflow provides a /upload-statement endpoint for file uploads. Customize transaction categories - Modify the AI prompt to include your preferred expense categories. Test the workflow - Upload a sample bank statement to verify the extraction and categorization process. Set up database table - Ensure your PostgreSQL database has a bank_statements table with appropriate columns. Features Multi-format support**: PDF, Excel, CSV bank statements AI-powered extraction**: GPT-4 extracts account details and transactions Automatic categorization**: Expenses categorized as groceries, dining, gas, shopping, utilities, healthcare, entertainment, income, fees, or other Data validation**: Cleans and validates transaction data with error handling Database storage**: PostgreSQL integration for data persistence API responses**: Clean JSON responses with transaction summaries and category breakdowns Smart routing**: Automatic format detection and appropriate processing paths. An n8n automation workflow template by vinci-king-01.

By Gerald Denor

Reddit Sentiment Analysis with AI-Powered Insights Automatically analyze Reddit posts and comments to extract sentiment, emotional tone, and actionable community insights using AI. This powerful n8n workflow combines Reddit's API with advanced AI sentiment analysis to help community managers, researchers, and businesses understand public opinion and engagement patterns on Reddit. Get structured insights including sentiment scores, toxicity levels, trending concerns, and moderation recommendations. Features Comprehensive Sentiment Analysis**: Categorizes content as Positive, Negative, or Neutral with confidence scores Emotional Intelligence**: Detects emotional tones like excitement, frustration, concern, or sarcasm Content Categorization**: Identifies discussion types (questions, complaints, praise, debates) Toxicity Detection**: Flags potentially harmful content with severity levels Community Insights**: Analyzes engagement quality and trending concerns Actionable Intelligence**: Provides moderation recommendations and response urgency levels Batch Processing**: Efficiently processes multiple posts and their comments Structured JSON Output**: Returns organized data ready for further analysis or integration How It Works The workflow follows a two-stage process: Data Collection: Fetches recent posts from specified subreddits along with their comments AI Analysis: Processes content through DeepSeek AI to generate comprehensive sentiment and contextual insights Use Cases Community Management**: Monitor sentiment trends and identify posts requiring moderator attention Brand Monitoring**: Track public opinion about your products or services on Reddit Market Research**: Understand customer sentiment and concerns in relevant communities Content Strategy**: Identify what type of content resonates positively with your audience Crisis Management**: Quickly detect and respond to negative sentiment spikes Required Credentials Before setting up this workflow, you'll need to obtain the following credentials: Reddit OAuth2 API Go to Reddit App Preferences Click "Create App" or "Create Another App" Choose "web app" as the app type Fill in the required fields: Name: Your app name Description: Brief description of your app Redirect URI: http://localhost:8080/oauth/callback (or your n8n instance URL + /oauth/callback) Note down your Client ID and Client Secret OpenRouter API Visit OpenRouter Sign up for an account Navigate to your API Keys section Generate a new API key Copy the API key for use in n8n Step-by-Step Setup Instructions Step 1: Import the Workflow Copy the workflow JSON from this template In your n8n instance, click the "+" button to create a new workflow Select "Import from URL" or "Import from Clipboard" Paste the workflow JSON and click "Import" Step 2: Configure Reddit Credentials Click on any Reddit node (e.g., "Get many posts") In the credentials dropdown, click "Create New" Select "Reddit OAuth2 API" Enter your Reddit app credentials: Client ID: Your Reddit app client ID Client Secret: Your Reddit app client secret Auth URI: https://www.reddit.com/api/v1/authorize Access Token URI: https://www.reddit.com/api/v1/access_token Click "Connect my account" and authorize the app Save the credentials Step 3: Configure OpenRouter Credentials Click on the "OpenRouter Chat Model1" node In the credentials dropdown, click "Create New" Select "OpenRouter API" Enter your OpenRouter API key Save the credentials Step 4: Test the Webhook Click on the "Webhook" node Copy the webhook URL (it will look like: https://your-n8n-instance.com/webhook/reddit-sentiment) Test the webhook using a tool like Postman or curl with this sample payload: { "subreddit": "technology", "query": "AI", "limit": 5 } Step 5: Customize the Analysis Modify the Structured Data Generator prompt: Edit the prompt in the "Structured Data Generator" node to adjust the analysis criteria or output format Change the AI model: In the "OpenRouter Chat Model1" node, you can switch to different models like anthropic/claude-3-haiku or openai/gpt-4 based on your preferences and budget Adjust post limits: Modify the limit parameter in the "Get many posts" and "Get many comments in a post" nodes to control how much data you process Usage Instructions Making API Calls Send a POST request to your webhook URL with the following parameters: Required Parameters: subreddit: The subreddit name (without r/) limit: Number of posts to analyze (recommended: 5-15) Optional Parameters: query: Search term to filter posts (optional) Example Request: curl -X POST https://your-n8n-instance.com/webhook/reddit-sentiment \ -H "Content-Type: application/json" \ -d '{ "subreddit": "CustomerService", "limit": 10 }' Understanding the Output The workflow returns a JSON array with detailed analysis for each post: [ { "sentiment_analysis": { "overall_sentiment": { "category": "Negative", "confidence_score": 8 }, "emotional_tone": ["frustrated", "concerned"], "intensity_level": "High" }, "content_categorization": { "discussion_type": "Complaint", "key_themes": ["billing issues", "customer support"], "toxicity_level": { "level": "Low", "indicators": "No offensive language detected" } }, "contextual_insights": { "community_engagement_quality": "Constructive", "potential_issues_flagged": ["service disruption"], "trending_concerns": ["response time", "resolution process"] }, "actionable_intelligence": { "moderator_attention_needed": { "required": "Yes", "reason": "Customer complaint requiring company response" }, "response_urgency": "High", "suggested_follow_up_actions": [ "Escalate to customer service team", "Monitor for similar complaints" ] } } ] Workflow Nodes Explanation Data Collection Nodes Webhook**: Receives API requests with subreddit and analysis parameters Get many posts**: Fetches recent posts from the specified subreddit Split Out**: Processes individual posts for analysis Get many comments in a post**: Retrieves comments for each post Processing Nodes Loop Over Items**: Manages batch processing of multiple posts Sentiment Analyzer**: Primary AI analysis node that processes content Structured Data Generator**: Formats AI output into structured JSON Code**: Parses and cleans the AI response Respond to Webhook**: Returns the final analysis results Customization Options Adjusting Analysis Depth Modify the limit parameters to analyze more or fewer posts/comments Update the AI prompts to focus on specific aspects (e.g., product mentions, competitor analysis) Adding Data Storage Connect database nodes to store analysis results for historical tracking Add email notifications for high-priority findings Integrating with Other Tools Connect to Slack/Discord for real-time alerts Link to Google Sheets for easy data visualization Integrate with CRM systems for customer feedback tracking Tips for Best Results Choose Relevant Subreddits: Focus on communities where your target audience is active Monitor Regularly: Set up scheduled executions to track sentiment trends over time Customize Prompts: Tailor the AI prompts to your specific industry or use case Respect Rate Limits: Reddit API has rate limits, so avoid excessive requests Review AI Output: Periodically check the AI analysis accuracy and adjust prompts as needed Troubleshooting Common Issues "Reddit API Authentication Failed" Verify your Reddit app credentials are correct Ensure your redirect URI matches your n8n instance Check that your Reddit app is set as "web app" type "OpenRouter API Error" Confirm your API key is valid and has sufficient credits Check that the selected model is available Verify your account has access to the chosen model "Webhook Not Responding" Ensure the workflow is activated Check that the webhook URL is correct Verify the request payload format matches the expected structure "AI Analysis Returns Errors" Review the prompt formatting in the Structured Data Generator Check if the selected AI model supports the required features Ensure the input data is not empty or malformed Performance Considerations Rate Limits**: Reddit allows 60 requests per minute for OAuth applications AI Costs**: Monitor your OpenRouter usage to manage costs Processing Time**: Larger batches will take longer to process Memory Usage**: Consider n8n instance resources when processing large datasets Contributing This workflow can be extended and improved. Consider adding: Support for multiple subreddits in a single request Historical sentiment tracking and trend analysis Integration with visualization tools Custom classification models for industry-specific analysis Ready to start analyzing Reddit sentiment? Import this workflow and start gaining valuable insights into online community discussions!. An n8n automation workflow template by Gerald Denor.

By vinci-king-01

How it works This workflow automatically extracts data from invoice documents (PDFs and images) and processes them through a comprehensive validation and approval system. Key Steps Multi-Input Triggers - Accepts invoices via email attachments or direct file uploads through webhook. AI-Powered Extraction - Uses ScrapeGraphAI to extract structured data from invoice documents. Data Cleaning & Validation - Processes and validates extracted data against business rules. Approval Workflow - Routes invoices requiring approval through a multi-stage approval process. System Integration - Automatically sends validated invoices to your accounting system. Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for invoice data extraction. Set up Telegram connection - Connect your Telegram account for approval notifications. Configure email trigger - Set up IMAP connection for processing emailed invoices. Customize validation rules - Adjust business rules, amount thresholds, and vendor lists. Set up accounting system integration - Configure the HTTP request node with your accounting system's API endpoint. Test the workflow - Upload a sample invoice to verify the extraction and approval process. Features Multi-format support**: PDF, PNG, JPG, JPEG, TIFF, BMP Intelligent validation**: Business rules, duplicate detection, amount thresholds Approval automation**: Multi-stage approval workflow with role-based routing Data quality scoring**: Confidence levels and completeness analysis Audit trail**: Complete processing history and metadata tracking. An n8n automation workflow template by vinci-king-01.

By vinci-king-01

How it works This workflow automatically analyzes real estate market sentiment by scraping investment forums and news sources, then provides AI-powered market predictions and investment recommendations. Key Steps Scheduled Trigger - Runs on a cron schedule to regularly monitor market sentiment. Multi-Source Scraping - Uses ScrapeGraphAI to extract discussions from BiggerPockets forums and real estate news articles. Sentiment Analysis - JavaScript nodes analyze text content for bullish/bearish keywords and calculate sentiment scores. Market Prediction - Generates investment recommendations (buy/sell/hold) based on sentiment analysis with confidence levels. Timing Optimization - Provides optimal timing recommendations considering seasonal factors and market urgency. Investment Advisor Alerts - Formats comprehensive reports with actionable investment advice. Telegram Notifications - Sends formatted alerts directly to your Telegram channel for instant access. Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for web scraping. Set up Telegram bot - Create a bot via @BotFather and add your bot token and chat ID. Customize data sources - Update the URLs to target specific real estate forums or news sources. Adjust schedule frequency - Modify the cron expression based on how often you want sentiment updates. Test sentiment analysis - Run manually first to ensure the analysis logic works for your market. Configure alert preferences - Customize the alert formatting and priority levels as needed. Technologies Used ScrapeGraphAI** - For extracting structured data from real estate forums and news sites JavaScript Code Nodes** - For sentiment analysis, market prediction, and timing optimization Schedule Trigger** - For automated execution using cron expressions Telegram Integration** - For instant mobile notifications and team alerts JSON Data Processing** - For structured sentiment analysis and market intelligence. An n8n automation workflow template by vinci-king-01.

By vinci-king-01

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This workflow automatically analyzes property maintenance costs by scraping contractor websites and provides comprehensive budget planning and recommendations. Key Steps Scheduled Trigger - Runs weekly to update maintenance cost data from multiple sources. Multi-Source Scraping - Uses ScrapeGraphAI to extract service data from plumbing, electrical, and HVAC contractor websites. Cost Analysis - JavaScript nodes process and categorize services by price level and urgency. Service Comparison - Compares providers within each category to find best-rated and most cost-effective options. Budget Planning - Creates annual budget with quarterly breakdown and service scheduling recommendations. Property Manager Alerts - Formats comprehensive reports with budget summaries and actionable recommendations. Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for web scraping. Customize contractor websites - Update the URLs in the scraping nodes to target specific local contractor directories. Adjust schedule frequency - Modify the trigger timing based on how often you want cost updates. Review budget parameters - Customize the budget planning logic in the JavaScript nodes if needed. Test the workflow - Run manually first to ensure all scraping and analysis nodes work correctly. Technologies Used ScrapeGraphAI** - For extracting structured data from contractor websites JavaScript Code Nodes** - For data processing, cost analysis, and budget planning Schedule Trigger** - For automated weekly execution JSON Data Processing** - For structured data handling and analysis. An n8n automation workflow template by vinci-king-01.

By vinci-king-01

How it works This workflow automatically scrapes commercial real estate listings from LoopNet and sends opportunity alerts to Telegram while logging data to Google Sheets. Key Steps Scheduled Trigger - Runs every 24 hours to collect fresh CRE market data AI-Powered Scraping - Uses ScrapeGraphAI to extract property information from LoopNet Market Analysis - Analyzes listings for opportunities and generates market insights Smart Notifications - Sends Telegram alerts only when investment opportunities are found Data Logging - Stores daily market metrics in Google Sheets for trend analysis Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for web scraping Set up Telegram connection - Connect your Telegram bot and specify the target channel Configure Google Sheets - Set up Google Sheets integration for data logging Customize the LoopNet URL - Update the URL to target specific CRE markets or property types Adjust schedule - Modify the trigger timing based on your market monitoring needs Keep detailed configuration notes in sticky notes inside your workflow. An n8n automation workflow template by vinci-king-01.

By Gegenfeld

Build a Website Customer Support Chatbot with Groq AI and Google Sheets as its Knowledge Base. Setup Instructions Prerequisites API Credentials Required: Groq API credentials - You'll need a valid API key from Groq Google Sheets credentials - OAuth authentication required to access your knowledge base sheets Step-by-Step Setup Add Required Credentials: Click on the Credentials menu and add your Groq API credentials Set up Google OAuth credentials for Google Sheets access Configure the Groq Chat Model: Click on the "Groq Chat Model" node Select your preferred Groq model (e.g., Llama-3-70b or Mixtral-8x7b) Set token limits and other parameters as needed Set Up Your Knowledge Base: Create a Google Sheet with your support information (example structure below) Note the Google Sheet ID from the URL Configure the Google Sheets Node: Click on the "Google Sheets" node Select your document ID from the dropdown Select the specific sheet name containing your knowledge base Customize the AI Agent: Modify the system message to match your brand's tone and support style Adjust the context window length in the "Chat History" node based on your needs Test the Chatbot: Click the "Chat" button to test with sample customer questions Verify the AI retrieves correct information from your knowledge base Deploy to Your Website: Click "Make Public" to generate an embed code Add the embed code to your website HTML Knowledge Base Structure Example Your Google Sheet should be structured with clear headers and organized data. Example format: | Question | Answer | Category | Keywords | |----------|--------|----------|----------| | How do I reset my password? | To reset your password, click the "Forgot Password" link on the login page and follow the instructions sent to your email. | Account | password, reset, forgot, login | | What are your shipping rates? | Standard shipping is $5.99. Express shipping is $12.99. Orders over $50 qualify for free standard shipping. | Shipping | rates, costs, delivery, free shipping | | How do I return an item? | Returns can be initiated within 30 days of purchase by logging into your account and selecting "Start a Return" in your order history. | Returns | return policy, exchange, refund | Each row should contain a complete customer query and response pair, with optional categorization and keywords to help the AI find relevant information quickly. Website Embedding You can use and replace the placeholders in the following code: View on Codepen (external link) Put the customized Code inside your website's head element (before the </head> tag). An n8n automation workflow template by Gegenfeld.

By Gegenfeld

This workflow creates an intelligent chatbot that uses your Microsoft Excel workbooks as a knowledge base. The AI agent can automatically query your Excel spreadsheets to provide accurate, contextual responses based on your stored data and information. Who's it for This template is perfect for: Business teams who maintain their knowledge base in Excel spreadsheets Organizations with existing data and processes in Microsoft 365 Small businesses using Excel for inventory, customer data, or documentation Teams wanting to make their Excel data conversationally accessible without complex database setup Companies looking to leverage familiar Excel workflows with AI capabilities How it works The workflow combines OpenAI's language model with Microsoft Excel's spreadsheet capabilities to create a smart chatbot. When users ask questions, the AI agent automatically determines which Excel worksheets and data ranges are relevant and uses that information to generate helpful responses. The system maintains conversation history for natural, contextual interactions. How to set up Add your credentials: Configure your Microsoft Excel (Office 365) credentials in the Get Excel Data node Set up your OpenAI API credentials in the OpenAI Chat Model node Configure your Excel connection: Click the Get Excel Data node Select your Excel workbook containing your knowledge base data The AI will automatically determine relevant worksheets and data ranges Customize the AI model: Open the OpenAI Chat Model node Choose your preferred model (GPT-4, GPT-3.5-turbo, etc.) Adjust token limits if needed Test the chatbot: Click the Chat button to start a conversation Ask questions related to your Excel data Optional - Make it public: Enable public access in the Chat Trigger node Embed the provided code into your website Requirements n8n instance (cloud or self-hosted) Microsoft 365 account with Excel Online access Excel workbooks with data you want to query OpenAI API key with available credits Microsoft Graph API permissions for Excel access How to customize the workflow Change the AI Provider: You can replace the OpenAI Chat Model with other providers like Anthropic Claude, Google Gemini, or local models by swapping the language model node. Adjust Context Window: Modify the "Remember Chat History" node to increase or decrease how many previous messages the AI remembers (default is 10 interactions). Update System Instructions: Edit the Smart AI Agent's system message to change how the assistant behaves or add specific instructions for your use case. Connect Multiple Workbooks: Add additional Get Excel Data nodes to give the AI access to multiple Excel workbooks within your Microsoft 365 environment. Add Data Validation: Include nodes to validate Excel data format and structure before processing to ensure consistent AI responses. Add More Tools: Extend the AI agent with additional tools like web search, email sending, or integration with other Microsoft 365 services. Workflow Structure Chat Trigger → Smart AI Agent ← OpenAI Chat Model ↓ Get Excel Data ↑ Remember Chat History The Smart AI Agent orchestrates the conversation, deciding when to query Excel and how to use the retrieved data in responses. The memory buffer ensures natural conversation flow by maintaining context across interactions. An n8n automation workflow template by Gegenfeld.

By Gegenfeld

This workflow creates an intelligent chatbot that uses your MongoDB database as a knowledge base. The AI agent can automatically query your MongoDB collections to provide accurate, contextual responses based on your stored documents and data. Who's it for This template is perfect for: Developers using MongoDB for document-based data storage Organizations with complex, nested data structures in MongoDB Teams managing large-scale applications with MongoDB Atlas Businesses wanting to leverage NoSQL flexibility for AI chatbots Companies with existing MongoDB infrastructure and expertise How it works The workflow combines OpenAI's language model with MongoDB's document database capabilities to create a smart chatbot. When users ask questions, the AI agent automatically constructs MongoDB queries to find relevant documents and uses that data to generate helpful responses. The system maintains conversation history for natural, contextual interactions. How to set up Add your credentials: Configure your MongoDB connection string and credentials in the MongoDB Database Lookup node Set up your OpenAI API credentials in the OpenAI Chat Model node Configure your MongoDB connection: Click the MongoDB Database Lookup node Specify your MongoDB collection containing your knowledge base data The AI will automatically construct queries to find relevant documents Customize the AI model: Open the OpenAI Chat Model node Choose your preferred model (GPT-4, GPT-3.5-turbo, etc.) Adjust token limits if needed Test the chatbot: Click the Chat button to start a conversation Ask questions related to your MongoDB data Optional - Make it public: Enable public access in the Chat Trigger node Embed the provided code into your website Requirements n8n instance (cloud or self-hosted) MongoDB instance (self-hosted, MongoDB Atlas, or other MongoDB service) OpenAI API key with available credits MongoDB user credentials with read permissions on target collections How to customize the workflow Change the AI Provider: You can replace the OpenAI Chat Model with other providers like Anthropic Claude, Google Gemini, or local models by swapping the language model node. Adjust Context Window: Modify the "Remember Chat History" node to increase or decrease how many previous messages the AI remembers (default is 10 interactions). Update System Instructions: Edit the Smart AI Agent's system message to change how the assistant behaves or add specific instructions for your use case. Connect Multiple Collections: Add additional MongoDB Database Lookup nodes to give the AI access to multiple collections within your MongoDB database. Optimize Query Performance: Create appropriate indexes on your MongoDB collections to improve query performance for frequently accessed data. Add More Tools: Extend the AI agent with additional tools like web search, email sending, or integration with other services. Workflow Structure Chat Trigger → Smart AI Agent ← OpenAI Chat Model ↓ MongoDB Database Lookup ↑ Remember Chat History The Smart AI Agent orchestrates the conversation, deciding when to query MongoDB and how to use the retrieved documents in responses. The memory buffer ensures natural conversation flow by maintaining context across interactions. An n8n automation workflow template by Gegenfeld.

By Yaron Been

This workflow automatically identifies and qualifies Instagram influencers based on your marketing criteria. It saves you hours of manual research by automatically filtering profiles that meet specific engagement, follower, and verification requirements, then storing qualified leads directly in Google Sheets. Overview This workflow uses Bright Data to scrape Instagram profile data, then applies smart filters to identify high-quality influencers or brand accounts. Only profiles that meet all your criteria (verified status, follower count, engagement rate, and account type) are saved to your lead database, keeping your list clean and actionable. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping Instagram profile data without restrictions Google Sheets**: For storing qualified influencer leads and profile data How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the Instagram scraping node Configure Google Sheets: Connect your Google Sheets account and copy the template spreadsheet Customize Filters: Adjust the criteria (followers, engagement rate, etc.) to match your needs Run: Simply paste any Instagram profile URL and execute the workflow Use Cases Influencer Marketing**: Build a database of qualified influencers for campaigns Brand Partnerships**: Identify potential brand collaboration opportunities Competitor Analysis**: Track competitor accounts and their engagement metrics Lead Generation**: Find business accounts in your niche for B2B outreach Market Research**: Analyze account types and engagement patterns in your industry Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #influencermarketing #instagram #brightdata #webscraping #leadgeneration #n8nworkflow #workflow #nocode #instagrammarketing #influenceroutreach #socialmediastrategy #brandpartnerships #marketingautomation #instagramanalytics #influencerdatabase #contentcreators #digitalmarketing #socialmediatools #influencerresearch #instagramleads #marketingtools #influenceridentification #instagramscraping #leadqualification #influencerengagement #brandcollaboration #instagramautomation #marketingdatabase. An n8n automation workflow template by Yaron Been.

By Yaron Been

markdownThis workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically analyzes competitor LinkedIn posts to extract strategic insights and engagement patterns. It saves you time by eliminating manual competitive analysis and provides actionable marketing intelligence from your competitors' social media activity. Overview This workflow automatically scrapes LinkedIn post data including engagement metrics, comments, and content details, then uses AI to analyze the post's intent, effectiveness, and key marketing takeaways. It transforms raw LinkedIn data into structured competitive intelligence stored in Google Sheets. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping LinkedIn post data without restrictions Google Gemini**: AI agent for intelligent post analysis and insight extraction Google Sheets**: For storing structured competitive intelligence data How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the scraping node Set Up Google Gemini: Configure your Google Gemini API credentials Configure Google Sheets: Connect your Google Sheets account and copy the template spreadsheet Customize: Simply paste any LinkedIn post URL and run the workflow Use Cases Marketing Teams**: Understand what content drives engagement for competitors Content Strategists**: Identify successful post formats and messaging strategies Social Media Managers**: Benchmark your content performance against industry leaders Agencies/Consultants**: Offer LinkedIn competitive analysis as a service to clients Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #linkedinanalytics #competitiveintelligence #brightdata #webscraping #marketinga. An n8n automation workflow template by Yaron Been.

By Yaron Been

This workflow automatically analyzes Reddit comments to understand public sentiment and community reactions. It saves you hours of manual reading by using AI to classify comments as positive, negative, or neutral, providing instant insights into how people feel about any Reddit post. Overview This workflow scrapes Reddit post comments using Bright Data's web scraping capabilities, then uses Google Gemini AI to analyze the sentiment of each comment. The results are automatically saved to Google Sheets with the comment text, sentiment classification, and reasoning behind each classification. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping Reddit comments without restrictions or rate limits Google Gemini**: AI model for intelligent sentiment analysis Google Sheets**: For storing and tracking sentiment analysis results How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the scraping nodes Set Up Google Gemini: Configure your Google Gemini API credentials Configure Google Sheets: Connect your Google Sheets account and copy the template spreadsheet Customize: Simply paste any Reddit post URL and run the workflow Use Cases Brand Monitoring**: Track sentiment around your brand or products on Reddit Product Managers**: Understand user feedback and pain points from Reddit discussions Market Research**: Analyze community reactions to news, launches, or announcements Community Managers**: Monitor sentiment trends and identify issues early Content Creators**: Gauge audience reactions to topics before creating content Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #sentimentanalysis #reddit #brightdata #webscraping #marketresearch #n8nworkflow #workflow #nocode #brandmonitoring #communityanalysis #redditanalytics #customersentiment #sociallistening #aianalysis #publicsentiment #marketintelligence #userresearch #communityinsights #redditmonitoring #sentimenttracking #customervoice #brandreputation #socialmediaanalysis #consumerinsights #feedbackan. An n8n automation workflow template by Yaron Been.

By Daniel Rosehill

This workflow provides a mechanism for using AI transcribed voice notes using Voicenotes AI and then running them into an AI agent as prompts. On the "collection" end of the workflow, we gather the output (with the recorded prompt) and do two things: 1) It is saved into NocoDB as a new row on a database table recording AI outputs and prompts. 2) The prompt gets sent to an AI agent and the output gets returned to the user's email Who Is It For? If you like using voice AI tools to write detailed prompts for AI, then this workflow helps to remove the points of friction in getting from A to B! How Does It Work? Simply tag your voice note in Voicenotes with your preferred tag (I'm using 'prompt'). Then, provide the N8N webhook as the URL for a webhook that will trigger whenever a new note is created with this tag (and this tag only). Now, whenever you wish to use a voice note as a prompt, just add the 'tag.' This will trigger the webhook which, in turn, will trigger this workflow - sending the prompt to an AI agent of your choosing (configure within the workflow) and then saving the output into a database and returning it by email. Note: The AI agent system prompt is written to define a structured output to provide Gmail-safe HTML. This is thin injected into a template. You can use a Google Group to gather together the output runs or just receive them at your main address (if you don't use Gmail, just swap out for any other email node or your preferred delivery channel). How To Set It Up You'll need a Voicenotes account in order to use the service! Once you have one, you'll next want to create the tag and the webhook. In N8N, create your webhook node and then provide that to Voicenotes: Create a note. Then assign it a new tag: "Prompts" (or as you prefer). The webhook is matched to the tag. Requirements Voicenotes AI account Customisation The delivery mechanism can be customized to your preferences. If you're not a Google user, substitute the template and sending mechanism for your preferred delivery provider You could for example collect the outputs to a Slack channel or Telegram bot. You may omit the collector in NocoDB or substitute it for another wiki or knowledge management platform such as Notion or Nuclino. An n8n automation workflow template by Daniel Rosehill.

By Léo

How it works This workflow automates a full weekly monitoring and reporting cycle using AI. Scheduled Trigger** Every Monday at 9 AM, the workflow starts automatically. AI Agent Configuration** A system prompt defines the role, objectives, and behavior of your AI agent for web monitoring or research tasks. Language Model via OpenRouter** The agent uses a powerful model (e.g., DeepSeek from Perplexity) to generate relevant insights. Data Storage in Notion** The results are saved and updated directly in a connected Notion database. Email Dispatch** A summary report is automatically sent by email to predefined recipients. Customizable elements: - AI prompt and objectives - Trigger schedule - Target database and report structure - Email recipients and message content. An n8n automation workflow template by Léo.

By Alexandra Spalato

Who's it for This workflow is for community builders, marketers, consultants, coaches, and thought leaders who want to grow their presence in Skool communities through strategic, value-driven engagement. It's especially useful for professionals who want to: Build authority in their niche by providing helpful insights Scale their community engagement without spending hours manually browsing posts Identify high-value conversation opportunities that align with their expertise Maintain authentic, helpful presence across multiple Skool communities What problem is this workflow solving Many professionals struggle to consistently engage meaningfully in online communities due to: Time constraints**: Manually browsing multiple communities daily is time-consuming Missed opportunities**: Important discussions happen when you're not online Inconsistent engagement**: Sporadic participation reduces visibility and relationship building Generic responses**: Quick replies often lack the depth needed to showcase expertise This workflow solves these problems by automatically monitoring your target Skool communities, using AI to identify posts where your expertise could add genuine value, generating thoughtful contextual comment suggestions, and organizing opportunities for efficient manual review and engagement. How it works Scheduled Community Monitoring Runs daily at 7 PM to scan your configured Skool communities for new posts and discussions from the last 24 hours. Intelligent Configuration Management Pulls settings from Airtable including target communities, your domain expertise, and preferred tools Possibility to add several configurations Filters for active configurations only Processes multiple community URLs efficiently Comprehensive Data Extraction Uses Apify Skool Scraper to collect: Post content and metadata Comments over 50 characters (quality filter) Direct links for easy access AI-Powered Opportunity Analysis Leverages OpenAI GPT-4.1 to: Analyze each post for engagement opportunities based on your expertise Identify specific trigger sentences that indicate a need you can address Generate contextual, helpful comment suggestions Maintain authentic tone without being promotional Smart Filtering and Organization Only surfaces genuine opportunities where you can add value Stores results in Airtable with detailed reasoning Provides suggested comments ready for review and posting Tracks engagement history to avoid duplicate responses Quality Control and Review All opportunities are saved to Airtable where you can: Review AI reasoning and suggested responses Edit comments before posting Track which opportunities you've acted on Monitor success patterns over time How to set up Required credentials OpenAI API key** - For GPT-4.1 powered opportunity analysis Airtable Personal Access Token** - For configuration and results storage Apify API token** - For Skool community scraping Airtable base setup Create an Airtable base with two tables: Config Table (config): Name (Single line text): Your configuration name Skool URLs (Long text): Comma-separated list of Skool community URLs cookies (Long text): Your Skool session cookies for authenticated access Domain of Activity (Single line text): Your area of expertise (e.g., "AI automation", "Digital marketing") Tools Used (Single line text): Your preferred tools to recommend (e.g., "n8n", "Zapier") active (Checkbox): Whether this configuration is currently active Results Table (Table 1): title (Single line text): Post title/author url (URL): Direct link to the post reason (Long text): AI's reasoning for the opportunity trigger (Long text): Specific sentence that triggered the opportunity suggested answer (Long text): AI-generated comment suggestion config (Link to another record): Reference to the config used date (Date): When the opportunity was found Select (Single select): Status tracking (not commented/commented) Skool cookies setup To access private Skool communities, you'll need to: Install Cookie Editor: Go to Chrome Web Store and install the "Cookie Editor" extension Login to Skool: Navigate to any Skool community you want to monitor and log in Open Cookie Editor: Click the Cookie Editor extension icon in your browser toolbar Export cookies: Click "Export" button in the extension Copy the exported text Add to Airtable: Paste the cookie string into the cookies field in your Airtable config Trigger configuration Ensure the Schedule Trigger is set to your preferred monitoring time Default is 7 PM daily, but adjust based on your target communities' peak activity Requirements Self-hosted n8n or n8n Cloud account** Active Skool community memberships** - You must be a legitimate member of communities you want to monitor OpenAI API credits** Apify subscription** - For reliable Skool data scraping (free tier available) Airtable account** - Free tier sufficient for most use cases How to customize the workflow Modify AI analysis criteria Edit the EvaluateOpportunities And Generate Comments node to: Adjust the opportunity detection sensitivity Modify the comment tone and style Add industry-specific keywords or phrases Change monitoring frequency Update the Schedule Trigger to: Multiple times per day for highly active communities Weekly for slower-moving professional groups Custom intervals based on community activity patterns Customize data collection Modify the Apify scraper settings to: Adjust the time window (currently 24 hours) Change comment length filters (currently >50 characters) Include/exclude media content Modify the number of comments per post Add additional filters Insert filter nodes to: Skip posts from specific users Focus on posts with minimum engagement levels Exclude certain post types or keywords Prioritize posts from influential community members Enhance output options Add nodes after Record Results to: Send Slack/Discord notifications for high-priority opportunities Create calendar events for engagement tasks Export daily summaries to Google Sheets Integrate with CRM systems for lead tracking Example outputs Opportunity analysis result { "opportunity": true, "reason": "The user is struggling with manual social media management tasks that could be automated using n8n workflows.", "trigger_sentence": "I'm spending 3+ hours daily just scheduling posts and responding to comments across all my social accounts.", "suggested_comment": "That sounds exhausting! Have you considered setting up automation workflows? Tools like n8n can handle the scheduling and even help with response suggestions, potentially saving you 80% of that time. The initial setup takes a day but pays dividends long-term." } Airtable record example Title: "Sarah Johnson - Social Media Burnout" URL: https://www.skool.com/community/post/123456 Reason: "User expressing pain point with manual social media management - perfect fit for automation solutions" Trigger: "I'm spending 3+ hours daily just scheduling posts..." Suggested Answer: "That sounds exhausting! Have you considered setting up automation workflows?..." Config: [Your Config Name] Date: 2024-12-09 19:00:00 Status: "not commented" Best practices Authentic engagement Always review and personalize AI suggestions before posting Focus on being genuinely helpful rather than promotional Share experiences and ask follow-up questions Engage in subsequent conversation when people respond Community guidelines Respect each community's rules and culture Avoid over-promotion of your tools or services Build relationships before introducing solutions Contribute value consistently, not just when selling Optimization tips Monitor which types of opportunities convert best A/B test different comment styles and approaches Track engagement metrics on your actual comments Adjust AI prompts based on community feedback. An n8n automation workflow template by Alexandra Spalato.

By WeblineIndia

Extract a key–value pair by index from JSON to fields in n8n This template takes a JSON object and a row index and returns exactly one key–value pair at that index. It’s a handy helper when you only need a single entry from a structured JSON payload (e.g., picking one form field for downstream logic). Who’s it for Makers who want a quick JSON picker without writing full parsing logic. Developers testing API payloads or building proofs of concept. Ops/analysts who need to pluck a single field for emails, documents or notifications. How it works Manual Trigger (When clicking ‘Test workflow’) starts the flow. Set → Input JSON Node holds your sample payload with: myData: an object of key → value pairs. rowIndex: a 0‑based index indicating which pair to extract. Code (Python) → Find Key‑Value Pair iterates myData and returns [key, value] at rowIndex as result. Set → Key maps result[0] to a field named result. Set → Value maps result[1] to a field named result[1]. The selected key and value are then available to any downstream nodes. How to set up Open the workflow and select Input JSON Node. Replace the sample with your own JSON: { "myData": { "name": "Alice", "age": "30", "city": "Paris" }, "rowIndex": "1" } Click Execute Workflow. Check the Key and Value nodes for the outputs. Requirements n8n running (cloud or self‑hosted). Code node (Python)** enabled in your n8n version. Input payload structure: myData: object with keys/values rowIndex: integer (0‑based) How to customize Pick by key name** (instead of index): adjust the Python code to look up a specific key. Handle nested objects/arrays**: walk or flatten the structure before selecting. Change output shape**: return { "key": ..., "value": ... } or write directly to next‑node fields. Validate inputs**: add checks for out‑of‑range rowIndex, non‑object myData, or empty objects. Add‑ons Webhook intake**: Replace Manual Trigger with a Webhook to accept live JSON. Schema guard**: Add an If/Function step to ensure myData is an object and rowIndex is numeric. Audit log**: Append the selected key/value to Google Sheets or a database. Use Case Examples Pull one field from a large API response to include in an email. Extract a specific answer from a form submission for conditional routing. Read a configuration pair from a settings object to control a downstream step. Common troubleshooting | Issue | Possible Cause | Solution | |---|---|---| | “Index out of range” | rowIndex is larger than the number of keys | Use a valid 0‑based index; add a guard in the Code node to clamp or default. | | Wrong key returned | Object key order differs from expectations | Object key order isn’t guaranteed across sources—prefer pick by key name for reliability. | | Empty/invalid output | myData is not an object or is empty | Ensure myData is a flat object with at least one key. | | Python errors | Code node’s Python runtime not available | Enable Python in the Code node or convert the snippet to JavaScript. | | Value type mismatch | Value isn’t a string | Cast as needed in the Set node or normalize types in the Code node. | Need Help? If you’d like this to pick by key, handle nested JSON, accept data via Webhook or fully customized to your needs, write to us and we’ll adapt the template to your exact use case. An n8n automation workflow template by WeblineIndia.

By Sarry

What does this workflow do? This workflow acts as the backend "brain" for a sophisticated AI Voice Interviewer. It receives a user's resume text and a target job description, then uses a Large Language Model (LLM) to conduct a realistic, voice-based interview. The workflow maintains conversation history to ask relevant follow-up questions, creating a dynamic and personalized interview practice experience. This template is designed to work with a simple HTML frontend that handles the voice-to-text and text-to-speech functionality. What services does this workflow use? Google Gemini:** This is the LLM used to generate intelligent interview questions. You can easily swap this out for other models like OpenAI. What credentials do you need to have? You will need one credential: A Google Gemini API Key. You can get one for free from the Google AI Studio. How to use this workflow This workflow is the backend and requires a frontend to interact with. Set up the Frontend: You can find the complete frontend code and setup instructions in this GitHub repository. Configure Credentials: In this n8n workflow, click on the "Google Gemini Chat Model" node and add your own Gemini API credential. Activate the Workflow: Make sure the workflow is saved and active. Connect Frontend to Backend: Click on the "Webhook" node and copy the Production URL. Paste this URL into the voice-interview.html page as instructed in the GitHub repository's README.md file. Start Interviewing: Fill out the form on the web page to begin your voice interview!. An n8n automation workflow template by Sarry.

By Marth - Business Automation

Okay, here are the "How It Works" and "Setup Steps" for your "Automated Social Media Content Distribution System," presented clearly in Markdown. How It Works (Workflow Stages) ⚙️ This system transforms manual, repetitive tasks into a smooth, automated content distribution pipeline: Content Submission & Trigger: You add a new row to your designated Google Sheet with all the content details (Title, URL, Short_Description, Image_URL, Hashtags, and boolean flags for which platforms to post to). The Google Sheets Trigger node immediately detects this new entry, initiating the workflow. Content Preparation: The Set node takes the raw data from your Google Sheet and formats it into a cohesive text string (social_media_text_core) that is suitable for posting across different social media platforms. Conditional Social Media Posting: A series of If nodes (Check Facebook Post, Check Twitter Post, Check LinkedIn Post) sequentially check your preferences (based on the Post_to_Facebook, Post_to_Twitter, Post_to_LinkedIn columns in your sheet). If a platform is marked TRUE, the corresponding social media node (Facebook, Twitter, LinkedIn) is activated to publish your content. If FALSE, that platform is skipped, and the workflow moves to the next check. Status Update & Notification: After attempting to post to all selected platforms, the Google Sheets (Update) node updates the Publication_Status column of your original row to "Published." This prevents re-posting and provides a clear record. Finally, the Slack (Notification) node sends an alert to your chosen Slack channel, confirming that the content has been successfully distributed. Setup Steps 🛠️ (Build It Yourself!) Follow these detailed steps to build and implement this workflow in your n8n instance: Prepare Your Google Sheet: Create a new Google Sheet (e.g., named "Social Media Posts"). Set up the following exact column headers in the first row: Title, URL, Short_Description, Image_URL, Hashtags, Post_to_Facebook, Post_to_Twitter, Post_to_LinkedIn, Publication_Status Fill in a test row with some sample data, ensuring TRUE/FALSE values for the posting flags. Gather Your API Keys & Credentials: Google Sheets Credential: You'll need an OAuth2 credential for Google Sheets in n8n to allow read/write access to your sheet. Facebook Credential: An OAuth2 credential for Facebook with permissions to post to your selected Page. Twitter Credential: A Twitter API credential (API Key, API Secret, Access Token, Access Token Secret) from your Twitter Developer App. LinkedIn Credential: An OAuth2 credential for LinkedIn with permissions to share updates to your profile or organization page. Slack Credential: A Slack API token (Bot User OAuth Token) for sending messages to your channel. Build the n8n Workflow Manually (10 Nodes): Start a new workflow in n8n. Drag and drop each of the following nodes onto the canvas and connect them as described below: Google Sheets Trigger Name: Google Sheets Trigger Parameters: Authentication: Select your Google Sheets credential. Spreadsheet ID: [Copy the ID from your Google Sheet's URL] Sheet Name: [Your Sheet Name, e.g., 'Sheet1' or 'Content'] Watch For: Rows Events: Added Connections: Output to Set Content Parameters. Set Name: Set Content Parameters Parameters: Values to Set: Add a new value: Type: String Name: social_media_text_core Value: ={{ $json.Title }} - {{ $json.Short_Description }}\nRead more: {{ $json.URL }}\n{{ $json.Hashtags }} Connections: Output to Check Facebook Post. If Name: Check Facebook Post Parameters: Value 1: ={{ $json.Post_to_Facebook }} Operation: is true Connections: True output to Post Facebook Message. False output to Check Twitter Post. Facebook Name: Post Facebook Message Parameters: Authentication: Select your Facebook credential. Page ID: [YOUR_FACEBOOK_PAGE_ID] Message: ={{ $json.social_media_text_core }} Link: ={{ $json.URL }} Picture: ={{ $json.Image_URL }} Options: Published (checked) Connections: Output to Check Twitter Post. If Name: Check Twitter Post Parameters: Value 1: ={{ $json.Post_to_Twitter }} Operation: is true Connections: True output to Create Tweet. False output to Check LinkedIn Post. Twitter Name: Create Tweet Parameters: Authentication: Select your Twitter credential. Tweet: ={{ $json.social_media_text_core }} Image URL: ={{ $json.Image_URL }} Connections: Output to Check LinkedIn Post. If Name: Check LinkedIn Post Parameters: Value 1: ={{ $json.Post_to_LinkedIn }} Operation: is true Connections: True output to Share LinkedIn Update. False output to Update Publication Status. LinkedIn Name: Share LinkedIn Update Parameters: Authentication: Select your LinkedIn credential. Resource: Share Update Type: Organization or Personal (Choose as appropriate) Organization ID: [YOUR_LINKEDIN_ORG_ID] (If Organization type selected) Content: ={{ $json.social_media_text_core }} Content URL: ={{ $json.URL }} Image URL: ={{ $json.Image_URL }} Connections: Output to Update Publication Status. Google Sheets Name: Update Publication Status Parameters: Authentication: Select your Google Sheets credential. Spreadsheet ID: [YOUR_GOOGLE_SHEET_CONTENT_ID] Sheet Name: [Your Sheet Name, e.g., 'Sheet1' or 'Content'] Operation: Update Row Key Column: URL Key Value: ={{ $json.URL }} Values: Add a new value: Column: Publication_Status Value: Published Connections: Receives connections from both Share LinkedIn Update and the False branch of Check LinkedIn Post. Slack Name: Send Slack Notification Parameters: Authentication: Select your Slack credential. Chat ID: [YOUR_SLACK_CHANNEL_ID] Text: New content "{{ $json.Title }}" successfully published to social media! 🎉 Check: {{ $json.URL }} Connections: Output to Update Publication Status. Final Steps & Activation: Test the Workflow: Before activating, manually add a new row to your Google Sheet or use n8n's "Execute Workflow" button (if available for triggers). Observe the flow through each node to ensure it behaves as expected and posts to your social media accounts. Activate Workflow: Once you are confident it's working correctly, turn the workflow "Active" in the top right corner of your n8n canvas. An n8n automation workflow template by Marth - Business Automation.

By Trung Tran

Try It Out, HireMind – AI-Driven Resume Intelligence Pipeline! This n8n template demonstrates how to automate resume screening and evaluation using AI to improve candidate processing and reduce manual HR effort. A smart and reliable resume screening pipeline for modern HR teams. This workflow combines Google Drive (JD & CV storage), OpenAI (GPT-4-based evaluation), Google Sheets (position mapping + result log), and Slack/SendGrid integrations for real-time communication. Automatically extract, evaluate, and track candidate applications with clarity and consistency. How it works A candidate submits their application using a form that includes name, email, CV (PDF), and a selected job role. The CV is uploaded to Google Drive for record-keeping and later reference. The Profile Analyzer Agent reads the uploaded resume, extracts structured candidate information, and transforms it into a standardized JSON format using GPT-4 and a custom output parser. The corresponding job description PDF file is automatically retrieved from a Google Sheet based on the selected job role. The HR Expert Agent evaluates the candidate profile against the job description using another GPT-4 model, generating a structured assessment that includes strengths, gaps, and an overall recommendation. The evaluation result is parsed and formatted for output. The evaluation score will be used to mark candidate as qualified or unqualified, based on that an email will be sent to applicant or the message will be send to hiring team for the next process The final evaluation result will be stored in a Google Sheet for long-term tracking and reporting. Google drive structure ├── jd # Google drive folder to store your JD (pdf) │ ├── Backend_Engineer.pdf │ ├── Azure_DevOps_Lead.pdf │ └── ... │ ├── cv # Google drive folder, where workflow upload candidate resume │ ├── John_Doe_DevOps.pdf │ ├── Jane_Smith_FullStack.pdf │ └── ... │ ├── Positions (Sample: https://docs.google.com/spreadsheets/d/1pW0muHp1NXwh2GiRvGVwGGRYCkcMR7z8NyS9wvSPYjs/edit?usp=sharing) # 📋 Mapping Table: Job Role ↔ Job Description (Link) │ └── Columns: │ - Job Role │ - Job Description File URL (PDF in jd/) │ └── Evaluation form (Google Sheet) # ✅ Final AI Evaluation Results How to use Set up credentials and integrations: Connect your OpenAI account (GPT-4 API). Enable Google Cloud APIs: Google Sheets API (for reading job roles and saving evaluation results) Google Drive API (for storing CVs and job descriptions) Set up SendGrid (to send email responses to candidates) Connect Slack (to send messages to the hiring team) Prepare your Google Drive structure: Create a root folder, then inside it create: /jd → Store all job descriptions in PDF format /cv → This is where candidate CVs will be uploaded automatically Create a Google Sheet named Positions with the following structure: | Job Role | Job Description Link | |------------------------------|----------------------------------------| | Azure DevOps Engineer | https://drive.google.com/xxx/jd1.pdf | | Full-Stack Developer (.NET) | https://drive.google.com/xxx/jd2.pdf | Update your application form: Use the built-in form, or connect your own (e.g., Typeform, Tally, Webflow, etc.) Ensure the Job Role dropdown matches exactly the roles in the Positions sheet Run the AI workflow: When a candidate submits the form: Their CV is uploaded to the /cv folder The job role is used to match the JD from /jd The Profile Analyzer Agent extracts candidate info from the CV The HR Expert Agent evaluates the candidate against the matched JD using GPT-4 Distribute and store results: Store the evaluation results in the Evaluation form Google Sheet Optionally notify your team: ✉️ Send an email to the candidate using SendGrid 💬 Send a Slack message to the hiring team with a summary and next steps Requirements OpenAI GPT-4 account for both Profile Analyzer and HR Expert Agents Google Drive account (for storing CVs and evaluation sheet) Google Sheets API credentials (for JD source and evaluation results) Need Help? Join the n8n Discord or ask in the n8n Forum! Happy Hiring! 🚀. An n8n automation workflow template by Trung Tran.

By Marth - Business Automation

How It Works ⚙️ Imagine your recruitment process transformed into a sleek, efficient, AI-powered assembly line for talent. That's exactly what this system creates. It automates the heavy lifting, allowing your human recruiters to focus on strategic decisions and personal connections. Candidate Arrival & Smart Parsing (The Intake Officer): What happens: A new candidate applies (via your job board, application form, or email). The system instantly captures their resume and core details. Our intelligent parser then reads the resume, extracting all the crucial information like skills, experience, and education, turning it into structured data. Data In: Resume files (PDF/DOCX), basic candidate contact info. Output: Structured candidate data (skills, experience, contact), raw resume text. AI-Powered Screening & Qualification (The Expert Reviewer Team): What happens: This is where the magic of AI takes over. AI Agent 1 (The Matchmaker): Compares the candidate's extracted skills and experience against your specific job description and requirements. It provides a precise match score (e.g., 1-100) and highlights their strongest alignments and any potential gaps. AI Agent 2 (The Insight Investigator): Dives deeper into the raw resume text to uncover nuances. It identifies potential red flags (like very short tenures) or strong indicators of cultural fit. AI Agent 3 (The Interview Question Generator): Based on the candidate's profile and any identified gaps or areas of interest, it automatically crafts 3-5 tailored initial screening questions. Data In: Structured candidate data, raw resume text, job description details. Output: Match score, strengths/gaps, cultural fit notes, red flags, customized interview questions. Automated Initial Engagement & Scheduling (The Personalized Outreach Specialist): What happens: The system evaluates the AI match score. If the candidate meets your minimum threshold, AI Agent 4 (The Persuasion Pro) crafts a highly personalized email invitation for a screening call, directly referencing their strong fit and specific strengths. This email is then automatically sent, and a placeholder event can even be added to your calendar. Data In: Qualified candidate data, AI match score, AI-generated email content. Output: Personalized invitation emails sent, calendar event details. Smart Follow-up & CRM Integration (The Nurturer & Notifier): What happens: The system waits for a few days for a response. If no reply is received, AI Agent 5 (The Gentle Follow-up Generator) creates a polite, yet persistent, follow-up email, ensuring promising candidates don't fall through the cracks. All candidate status updates (e.g., "Invited for Screening," "Follow-up Sent," "Interview Scheduled") are seamlessly logged in your central candidate database. Finally, your recruiters get instant Slack notifications for highly qualified candidates or when someone responds positively, keeping them informed and agile. Data In: Candidate status, AI-generated follow-up content. Output: Follow-up emails sent, updated candidate status in your database, recruiter notifications. Setup Steps 🛠️ (Detailed & Easy to Follow) Building this powerful system will give you a significant edge in talent acquisition. Follow these steps meticulously: Prepare Your Digital Recruitment Backbone: Candidate Database (Google Sheets or Airtable): Create a dedicated spreadsheet (e.g., "Talent Pipeline") to serve as your central candidate database. Set up comprehensive columns: ID (for unique tracking), Name, Email, Phone, Resume URL (if applicable), Raw Resume Text, Education, Experience, Skills, Status (e.g., "New," "AI Screened," "Invited," "Interview Scheduled"), Match Score, Cultural Fit Notes, Red Flags, Interview Questions, Last Contacted. Job Descriptions Database (Google Sheets or Airtable): Create a separate sheet (e.g., "Job Openings") to store your active job descriptions. Include columns like JobID, Title, Description (full text of the job description), Required Skills (list key skills), Experience Level, Department. This will be the reference for AI screening. (Optional) Recruitment Templates (Google Docs): While AI generates most content, having a simple Google Doc template can be useful for any internal reports or structured notes you might want the system to generate in the future (though not strictly required for the core flow described). Gather Your Essential API Keys & Credentials: OpenAI API Key: This is the brain power for all your AI agents. Get it from your OpenAI account dashboard. Google Sheets Credential: Allows n8n to read from and write to your Candidate and Job Descriptions databases. Set up an OAuth2 credential in n8n. Gmail Credential: For sending personalized outreach and follow-up emails. Set up an OAuth2 credential. Google Calendar Credential: (Optional but recommended) For creating placeholder interview events on your recruiters' calendars. Set up an OAuth2 credential. Slack Credential: For sending internal notifications to your recruitment team (e.g., new qualified candidates, anomalies). Get a Bot Token from your Slack app settings. (Optional) PDF Parser API Key: If n8n's native PDF Read (Node 2) isn't robust enough for varied resume formats, you might consider an external service like Affinda or Textract and their corresponding API key. You would then use an HTTP Request node to call their API. Build the n8n Workflow Manually: Start a new workflow in n8n. Add each node one by one, according to the detailed explanation provided in our previous conversation (e.g., Webhook, PDF Read, Function, OpenAI, Google Sheets, Gmail, If, Wait, Slack). Crucially, connect the nodes by dragging lines between their output and input ports, following the logical flow of the system. Connect Your Tools (Credentialing & Linking): Click on each node that connects to an external service (e.g., OpenAI, Google Sheets, Gmail, Slack, Google Calendar). In the node's settings panel, you'll find an "Authentication" section. Select the specific credential you created in Step 2 from the dropdown list. This links n8n to your external accounts. Customize Workflow Nodes (The Tailoring Phase – This is Key!): Webhook Node (Node 1): Once you save this node, n8n will provide a unique Webhook URL. Copy this URL and configure your application forms (e.g., Google Forms, Typeform, your career page's backend) or job board integrations to send candidate application data (including resume file/URL) to this address via a POST request. PDF Read Node (Node 2): Ensure the Binary Property or PDF URL setting correctly points to where the resume file or its URL is located in the incoming data from your Webhook. Function Nodes (Nodes 3, 8, 12): Carefully review and, if necessary, adjust the JavaScript code inside these nodes. This code is responsible for parsing raw text, consolidating data, and preparing prompts. You might need to tweak regex patterns in Node 3 if your resumes have unique layouts. Google Sheets Nodes (Nodes 4, 5, 6, 7, 13, 18, 20, 24): For each of these nodes, accurately paste the Spreadsheet ID for your Candidate and Job Descriptions sheets. Enter the exact Sheet Name (e.g., "Candidates," "Job Descriptions"). CRITICAL MAPPING: In nodes that Append or Update rows, meticulously map the Values from previous nodes' outputs to the correct column headers in your Google Sheet (e.g., Name mapped to ={{ $json.parsed_candidate_name }}). OpenAI Nodes (Nodes 9, 10, 11, 15, 22): This is your AI's personality and intelligence! Carefully review and refine the Prompts within these nodes. For Agent 1 (Matcher), clarify what makes a "strong match" for your roles. For Agent 2 (Cultural Fit), specify cultural values important to your company and define "red flags." For Agent 4 (Outreach), customize the tone, call-to-action, and what strengths to highlight. TEST THESE PROMPTS RIGOROUSLY with various candidate profiles and job descriptions to ensure they deliver accurate, relevant, and desired outputs. If Nodes (Nodes 14, 21): Adjust the match score threshold in Node 14 (e.g., 70 for 70%) to control which candidates get automated outreach. In Node 21, confirm the Status values that indicate a positive response (e.g., "Interview Scheduled"). Gmail Nodes (Nodes 16, 23): Verify the To email address is dynamically pulled from the candidate data (={{ $json.candidate_email }}). Double-check that the Subject and Body expressions correctly extract the email content generated by the AI agent. Google Calendar Node (Node 17): Set your Calendar ID (typically your recruiter's calendar ID). Adjust the Start Date/Time and End Date/Time to suggest appropriate screening call durations. Wait Node (Node 19): Configure the Amount and Unit (e.g., 3 Days) for how long the system should wait before sending a follow-up. Slack Node (Node 25): Enter the specific Chat ID (the channel ID) where you want recruitment notifications to appear (e.g., #hiring-alerts). Test Thoroughly & Activate! Manual Trigger & Inspection: Before going live, trigger the workflow manually several times using diverse test candidate data. Use the "Test Workflow" feature in n8n to inspect the output of each individual node. Check that data is flowing correctly, AI agents are producing accurate results, and emails/notifications are formatted as you expect. Activate: Once you're completely confident in its performance, click the "Inactive" toggle in the top-right corner of your workflow to switch it to "Active." You've now successfully built and launched a cutting-edge, AI-powered recruitment and candidate engagement system. Get ready to transform your hiring process!. An n8n automation workflow template by Marth - Business Automation.

By InfyOm Technologies

✅ What problem does this workflow solve? Sending a plain PDF resume doesn’t stand out anymore. This workflow allows candidates to convert their resume and photo into a personalized video resume. Recruiters get a more engaging first impression, while candidates showcase their profile in a modern, impactful way. ⚙️ What does this workflow do? Presents a form for uploading: 📄 Resume (PDF) 🖼 Photo (headshot) Extracts key details from the resume (education, experience, skills). Detects gender from the photo to choose a suitable voice/avatar. Generates a script (spoken resume summary) based on the extracted information. Uploads the photo to HeyGen to create an avatar. Requests video generation on HeyGen: Uses the avatar photo Uses gender-specific settings Uses the generated script as narration Monitors video generation status until completion. Stores the final video URL in a Google Sheet for easy access and tracking. 🔧 Setup Instructions Google Services Connect Google Sheets to n8n to store records with: Candidate name Resume link Video link HeyGen Setup Get an API key from HeyGen. Configure: Avatar upload endpoint (image upload) Video generation endpoint (image ID + script) Form Setup Use the n8n Form Trigger to allow candidates to upload: Resume (PDF) Photo (JPEG/PNG) 🧠 How it Works – Step-by-Step 1. Candidate Submission A candidate fills out a form and uploads: Resume (PDF) Photo 2. Extract Resume Data The resume PDF is processed using OCR/AI to extract: Name Experience Skills Education highlights 3. Gender Detection The uploaded photo is analyzed to detect gender (used for voice/avatar selection). 4. Script Generation Based on the extracted resume info, a concise, natural script is generated automatically. 5. Avatar Upload & Video Creation The photo is uploaded to HeyGen to create a custom avatar. A video generation request is made using: The script The avatar (image ID) A matching voice for the detected gender 6. Video Status Monitoring The workflow polls HeyGen’s API until the video is ready. 7. Save Final Video URL Once complete, the video link is added to a Google Sheet alongside the candidate’s details. 👤 Who can use this? This workflow is ideal for: 🧑‍🎓 Students and job seekers looking to stand out 🧑‍💼 Recruitment agencies offering modern resume services 🏢 HR teams wanting engaging candidate submissions 🎥 Portfolio builders for professionals 🚀 Impact Instead of a static PDF, you can now send a dynamic video resume that captures attention, adds personality, and makes a lasting impression. An n8n automation workflow template by InfyOm Technologies.

By Sehar Nazeer

This AI-powered customer support automation built in n8n handles your e-commerce queries instantly — from order tracking to personalized product recommendations and support ticket management. Features: 🛒 Order Status Tracking: Instantly retrieve order details from your database. 🎯 AI Product Recommendations: GPT-powered suggestions based on customer preferences. 📩 Support Ticket Automation: Auto-create and manage tickets via email or CRM. 🔄 Context-Aware Conversations: AI agent with memory for smooth follow-ups. ⚡ Real-Time Webhook Responses: Instant, API-driven replies to customers. Tech Stack: n8n, OpenAI GPT, Webhooks, Supabase, Email API. Perfect for Shopify, WooCommerce, and custom e-commerce platforms looking to reduce response times and improve customer satisfaction. An n8n automation workflow template by Sehar Nazeer.

By Vlad Arbatov

Summary Every day at a set time, this workflow fetches yesterday’s newsletters from Gmail, summarizes each email into concise topics with an LLM, merges all topics, renders a clean HTML digest, and emails it to your inbox. What this workflow does Triggers on a daily schedule (default 16:00, server time) Fetches Gmail messages since yesterday using a custom search query with optional sender filters Retrieves and decodes each email’s HTML, subject, sender name, and date Prompts an LLM (GPT‑4.1‑mini) to produce a consistent JSON summary of topics per email Merges topics from all emails into a single list Renders a styled HTML email with enumerated items Sends the HTML digest to a specified recipient via Gmail Apps and credentials Gmail OAuth2: Gmail account (read and send) OpenAI: OpenAi account Typical use cases Daily/weekly newsletter rollups delivered as one email Curated digests from specific media or authors Team briefings that are easy to read and forward How it works (node-by-node) Schedule Trigger Fires at the configured hour (default 16:00). Get many messages (Gmail → getAll, returnAll: true) Uses a filter like: =(from:@.com) OR (from:@.com) OR (from:@.com -"__") after:{{ $now.minus({ days: 1 }).toFormat('yyyy/MM/dd') }} Returns a list of message IDs from the past day. Loop Over Items (Split in Batches) Iterates through each message ID. Get a message (Gmail → get) Retrieves the full message/payload for the current email. Get message data (Code) Extracts HTML from Gmail’s MIME parts. Normalizes the sender to just the display name. Formats the date as DD.MM.YYYY. Passes html, subject, from, date forward. Clean (Code) Converts DD.MM.YYYY → MM.DD (for prompt brevity). Passes html, subject, from, date to the LLM. Message a model (OpenAI, model: gpt‑4.1‑mini, JSON output) Prompt instructs: Produce JSON: { "topics": [ { "title", "descr", "subject", "from", "date" } ] } Split multi-news blocks into separate topics Combine or ignore specific blocks for particular senders (placeholders __) Keep subject untranslated; other values in __ language Injects subject/from/date/html from the current email Loop Over Items (continues) Processes all emails for the time window. Merge (Code) Flattens the topics arrays from all processed emails into one combined topics list. Create template (Code) Builds a complete HTML email: Enumerated items with title, one-line description Original subject and “from — date” Safely escapes HTML and preserves line breaks Inline, email-friendly styles Send a message (Gmail → send) Sends the final HTML to your recipient with a custom subject. Node map | Node | Type | Purpose | |---|---|---| | Schedule Trigger | Trigger | Run at a specific time each day | | Get many messages | Gmail (getAll) | Search emails since yesterday with filters | | Loop Over Items | Split in Batches | Iterate messages one-by-one | | Get a message | Gmail (get) | Fetch full message payload | | Get message data | Code | Extract HTML/subject/from/date; normalize sender and date | | Clean | Code | Reformat date and forward fields to LLM | | Message a model | OpenAI | Summarize email into JSON topics | | Merge | Code | Merge topics from all emails | | Create template | Code | Render a styled HTML email digest | | Send a message | Gmail (send) | Deliver the digest email | Before you start Connect Gmail OAuth2 in n8n (ensure it has both read and send permissions) Add your OpenAI API key Import the provided workflow JSON into n8n Setup instructions 1) Schedule Schedule Trigger node: Set your preferred hour (server time). Default is 16:00. 2) Gmail Get many messages: Adjust filters.q to your senders/labels and window: Example: =(from:news@publisher.com) OR (from:briefs@media.com -"promo") after:{{ $now.minus({ days: 1 }).toFormat('yyyy/MM/dd') }} You can use label: or category: to narrow scope. Send a message: sendTo = your email subject = your subject line message = set to {{ $json.htmlBody }} (already produced by Create template) The HTML body uses inline styles for broad email client support. 3) OpenAI Message a model: Model: gpt‑4.1‑mini (swap to gpt‑4o‑mini or your preferred) Update prompt placeholders: __ language → your target language __ sender rules → special cases (combine blocks, ignore sections) How to use The workflow runs daily at the scheduled time, compiling a digest from yesterday’s emails. You’ll receive one HTML email with all topics neatly listed. Adjust the time window or filters to change what gets included. Customization ideas Time window control: after: {{ $now.minus({ days: X }) }} and/or add before: Filter by labels: q = label:Newsletters after:{{ $now.minus({ days: 1 }).toFormat('yyyy/MM/dd') }} Language: Set the __ language in the LLM prompt Template: Edit “Create template” to add a header, footer, hero section, logo/branding Include links parsed from HTML (add an HTML parser step in “Get message data”) Subject line: Make dynamic, e.g., “Digest for {{ $now.toFormat('dd.MM.yyyy') }}” Sender: Use a dedicated Gmail account or alias for deliverability and separation Limits and notes Gmail size limit for outgoing emails is ~25 MB; large digests may need pruning LLM usage incurs cost and latency proportional to email size and count HTML rendering varies across clients; inline styles are used for compatibility Schedule uses the n8n server’s timezone; adjust if your server runs in a different TZ Privacy and safety Emails are sent to OpenAI for summarization—ensure this aligns with your data policies Limit the Gmail search scope to only the newsletters you want processed Avoid including sensitive emails in the search window Sample output (email body) Title 1 One-sentence description Original Subject → Sender — DD.MM.YYYY Title 2 One-sentence description Original Subject → Sender — DD.MM.YYYY Tips and troubleshooting No emails found? Check filters.q and the time window (after:) Model returns empty JSON? Simplify the prompt or try another model Odd characters in output? The template escapes HTML and preserves line breaks; verify your input encoding Delivery issues? Use a verified sender, set a clear subject, and avoid spammy keywords Tags gmail, openai, llm, newsletters, digest, summarization, email, automation Changelog v1: Initial release with scheduled time window, sender filters, LLM summarization, topic merging, and HTML email template rendering. An n8n automation workflow template by Vlad Arbatov.

By TheStock

MLB "Hits" Workflow — Overview • Pulls today's MLB schedule incl. probablePitcher + lineups (statsapi.mlb.com) • Batches season stats for all involved players • Builds pitcher vs. batter matchup rows Setup Steps: 1) Google Sheets OAuth2 → point to your Google Sheet → Tab name 2) Create Telegram Bot cred + chatId 3) Optional: tweak ERA/OPS thresholds or Top N in nodes 6 or 8. Optional Setup: Conditional Formatting in the Sheet for ColorScale on best stat in the column, as well as Lefty vs Right highlighting. COMING SOON! Results Tracker tab and Apps Scripts and formulas.**. An n8n automation workflow template by TheStock.

By Thomas Heal

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Overview This workflow is designed for inspiring teams or individuals who need to quickly and efficiently serve files, content, or documents via the web. It offers a straightforward approach while still being flexible and adaptable for different branding needs. The HTML branding can be easily updated through an external LLM, making it possible to fully customize the look and feel without complex coding. You simply prompt the AI (using the JSON as a guide) to output your desired design. This template makes use of a powerful community node, which brings in the benefits of shared knowledge and collective improvement. Setup Instructions Copy the JSON Preset into your AI model or use your own, along with your custom branding requirements. Ask the model for an HTML response, then paste the output into the HTML Preset. Next, connect the JSON inputs into the relevant locations from the structured output parser. Once complete, the static HTML can be served via AWS or another web server using HTTPS, ensuring secure and reliable delivery. Workflow Explanation This AI Agent takes a simple user input and transforms it into dynamic HTML. The structured JSON output forces consistent formatting, while giving you the creative flexibility to adjust visuals on demand. Since the output is relatively consistent the workflow can produce repitive business documents with consistency and accuracy. Requirements LLM account access AWS Account (S3) or HTTPs equivalent Basic HTML/JSON knowledge PDF.co Account. An n8n automation workflow template by Thomas Heal.

By Oneclick AI Squad

This automated n8n workflow manages AWS IAM users (create, delete, update, assign to groups) directly from email commands with automatic confirmation responses. Good to Know The workflow processes email requests via a GET Email Request node. Data extraction from emails is handled to identify user management commands. Error handling is included for invalid or missing email data. Responses are sent via email for each action performed. How It Works GET Email Request** - Captures incoming email requests. Extract Data from Email** - Parses email content to extract user management commands. Check Type of Task** - Validates the type of task (e.g., create, delete, update). Get User** - Retrieves user details from AWS IAM. Get Many Users** - Fetches multiple user details if required. Create User** - Creates a new IAM user. Delete User** - Deletes an existing IAM user. Add to Group** - Assigns a user to a group. Remove from Group** - Removes a user from a group. Update User** - Updates user details. Make Message for Email** - Prepares a confirmation email. Send Email Response** - Sends the confirmation email. How to Use Import the workflow into n8n. Configure the GET Email Request node to receive email commands. Test the workflow with sample email commands (e.g., "create user: john_doe", "add to group: admins"). Monitor email responses and adjust command parsing if needed. Requirements AWS IAM credentials configured in n8n. Email service integration (e.g., SMTP settings). n8n environment with workflow execution permissions. Customizing This Workflow Adjust the Extract Data from Email node to support additional command formats. Modify the Make Message for Email node to customize confirmation messages. Update the AWS IAM nodes to include additional user attributes or group policies. An n8n automation workflow template by Oneclick AI Squad.

By Paul

📊 AI-Powered Stock Analysis & Auto-Trading Workflow Supercharge your trading decisions with this end-to-end AI automation that connects market intelligence, technical analysis, and automated trade execution — all without manual intervention. My results so far: (100k paper trading account with the current template strategy) 🚀 What This Workflow Does Live AI-Driven Market Scanning** Integrates with Danelfin’s AI scoring system to identify top stocks daily based on technical, fundamental, sentiment, and risk scores. Advanced Technical & Trend Analysis** Combines chart patterns, Fibonacci retracements, Bollinger Bands, MACD, RSI, EMA trends, and support/resistance detection with real-time news sentiment to produce clear, professional-grade analysis reports. Chart Image AI Analysis** Uses LLM-powered vision models to interpret candlestick charts visually and extract pattern, trend, and indicator insights. Automated Trade Execution** Integrates with Alpaca Paper Trading API for secure, rule-based buy/sell execution. Includes: Risk management (position sizing, stop-loss/take-profit) Account balance & buying power checks No-repeat-loss policy Data Storage & Strategy Memory** Logs trades, PnL, and objectives in PostgreSQL for ongoing strategy refinement. Automated Reporting** Sends deep-dive market and trade reports directly to your email. 🔗 Integrated Services Danelfin API** – AI-based stock ranking Supabase Vector Store** – Strategy and knowledge retrieval TwelveData API** – Market prices & indicators Chart-img API** – TradingView chart generation Alphavantage** – News sentiment feed Alpaca API** – Automated order execution OpenAI, Anthropic, Cohere, OpenRouter** – Multi-model AI reasoning 📥 Perfect For Quantitative analysts testing strategies Investors looking for data-backed, automated execution Educational environments for learning AI-based market strategies People that want to know Real results Results 💼 What You Get Full Setup Pre-configured n8n workflow with all nodes and logic ready to run Step-by-step API key integration guide for Danelfin, Alpaca, TwelveData, Alphavantage, Chart-img Database logging setup with PostgreSQL schema Automated email reporting template Detailed Description Explanation of every sub-agent and AI integration How the strategy agent selects stocks based on AI scores and past trades Deep technical indicators breakdown (EMA, RSI, MACD, Fibonacci, Bollinger, Support/Resistance) Risk management methodology and allocation rules Examples Daily Automated Analysis:** Every morning the system emails you the top 3 stocks to watch, with price, chart, and sentiment score Trade Execution:** System buys AAPL with a defined stop-loss and take-profit based on technical setup Chatbot Mode:** Ask “What’s the trend on TSLA?” and get a concise, professional-grade market report instantly 💡 Why You’ll Love It This isn’t just an automation — it’s a full-stack AI trading assistant that thinks, analyzes, and executes while keeping risk in check. From sourcing the idea to placing the trade, it’s all covered. 🔑 Get Started Replace the placeholder API keys, set your trading preferences, and let the automation do the heavy lifting. An n8n automation workflow template by Paul.

By WeblineIndia

Smart Document Parser for Invoices, Logs or Sensor Reports (PDF/Image to Google Sheets) This n8n workflow automatically parses documents such as invoices, sensor logs or structured PDFs/images (including scanned docs or CSVs), extracts key fields like totals, dates and customer/vendor info using OCR and AI, and writes the structured output into Google Sheets. Who’s it for Finance or Ops teams automating invoice processing. SaaS platforms parsing uploaded reports or documents. Anyone needing a no-code backend for PDF/image/CSV document parsing. AI-powered data capture pipelines. How it works Webhook Trigger receives file uploads (/uploadDoc) Switch Node checks the file type: If image → Use Tesseract OCR If PDF → Use PDF parser If CSV → Extract as-is Extracted text is passed to: Google Gemini or Gemini Flash AI model Prompt extracts fields like invoice_id, total, customer_name, etc. JSON string is parsed and cleaned Data is appended to Google Sheets using appendOrUpdate How to set up Create a Google Sheet with columns like: invoice_id, invoice_date, due_date, customer_name, vendor_name, subtotal, tax_total, total, currency Connect: Google Sheets OAuth Google Gemini (PaLM API key) for LLM parsing Deploy the webhook endpoint: /uploadDoc Upload sample files (PDFs, images, CSVs) to test Review and map sheet columns in the Invoice Data node Requirements | Tool | Purpose | | ------------- | --------------------------------- | | n8n | Automation framework | | Google Sheets | To store structured output | | Tesseract OCR | For scanned image text extraction | | Google Gemini | For natural language parsing | How to customize Add extraction for line items using structured prompts. Change prompt to extract sensor readings, log types, or custom keys. Add support for other file types (e.g., XLSX, DOCX). Add Slack/Email notifications on success/failure. Swap Gemini with OpenAI or Hugging Face if preferred. Add‑ons Save uploaded files to Google Drive or S3 Add auth for secure uploads Use charting/dashboard nodes to visualize extracted data Integrate with billing/accounting software Use Case Examples | Scenario | What Happens | | ----------------------- | ------------------------------------------------------- | | Invoice Upload (PDF) | Extracts totals, customer, tax data into a Google Sheet | | Scanned Receipt (Image) | OCR + LLM extracts structured data | | Log File (CSV) | Parses and logs entries into Sheets | Common troubleshooting | Issue | Possible Cause | Solution | | --------------------------------- | ----------------------- | ------------------------------------------- | | Webhook not triggered | URL or method mismatch | Use correct POST URL /uploadDoc | | Text is blank | OCR failed | Check image quality or Tesseract config | | Gemini model not returning JSON | Prompt formatting issue | Ensure prompt ends with valid JSON schema | | Sheet not updated | Invalid Sheet ID or tab | Double-check sheet credentials and tab name | Need Help? Need help fine-tuning the Gemini prompt for better field accuracy? Want to extract full tables, multi-page invoices or convert PDFs to JSON lines? Our automation team at WeblineIndia can help you extend this into a full-blown document automation pipeline. An n8n automation workflow template by WeblineIndia.

By DataMinex

📊 Real-Time Flight Data Analytics Bot with Dynamic Chart Generation via Telegram 🚀 Template Overview This advanced n8n workflow creates an intelligent Telegram bot that transforms raw CSV flight data into stunning, interactive visualizations. Users can generate professional charts on-demand through a conversational interface, making data analytics accessible to anyone via messaging. Key Innovation: Combines real-time data processing, Chart.js visualization engine, and Telegram's messaging platform to deliver instant business intelligence insights. 🎯 What This Template Does Transform your flight booking data into actionable insights with four powerful visualization types: 📈 Bar Charts**: Top 10 busiest airlines by flight volume 🥧 Pie Charts**: Flight duration distribution (Short/Medium/Long-haul) 🍩 Doughnut Charts**: Price range segmentation with average pricing 📊 Line Charts**: Price trend analysis across flight durations Each chart includes auto-generated insights, percentages, and key business metrics delivered instantly to users' phones. 🏗️ Technical Architecture Core Components Telegram Webhook Trigger: Captures user interactions and button clicks Smart Routing Engine: Conditional logic for command detection and chart selection CSV Data Pipeline: File reading → parsing → JSON transformation Chart Generation Engine: JavaScript-powered data processing with Chart.js Image Rendering Service: QuickChart API for high-quality PNG generation Response Delivery: Binary image transmission back to Telegram Data Flow Architecture User Input → Command Detection → CSV Processing → Data Aggregation → Chart Configuration → Image Generation → Telegram Delivery 🛠️ Setup Requirements Prerequisites n8n instance** (self-hosted or cloud) Telegram Bot Token** from @BotFather CSV dataset** with flight information Internet connectivity** for QuickChart API Dataset Source This template uses the Airlines Flights Data dataset from GitHub: 🔗 Dataset: Airlines Flights Data by Rohit Grewal Required Data Schema Your CSV file should contain these columns: airline,flight,source_city,departure_time,arrival_time,duration,price,class,destination_city,stops File Structure /data/ └── flights.csv (download from GitHub dataset above) ⚙️ Configuration Steps 1. Telegram Bot Setup Create a new bot via @BotFather on Telegram Copy your bot token Configure the Telegram Trigger node with your token Set webhook URL in your n8n instance 2. Data Preparation Download the dataset from Airlines Flights Data Upload the CSV file to /data/flights.csv in your n8n instance Ensure UTF-8 encoding Verify column headers match the dataset schema Test file accessibility from n8n 3. Workflow Activation Import the workflow JSON Configure all Telegram nodes with your bot token Test the /start command Activate the workflow 🔧 Technical Implementation Details Chart Generation Process Bar Chart Logic: // Aggregate airline counts const airlineCounts = {}; flights.forEach(flight => { const airline = flight.airline || 'Unknown'; airlineCounts[airline] = (airlineCounts[airline] || 0) + 1; }); // Generate Chart.js configuration const chartConfig = { type: 'bar', data: { labels, datasets }, options: { responsive: true, plugins: {...} } }; Dynamic Color Schemes: Bar Charts: Professional blue gradient palette Pie Charts: Duration-based color coding (light→dark blue) Doughnut Charts: Price-tier specific colors (green→purple) Line Charts: Trend-focused red gradient with smooth curves Performance Optimizations Efficient Data Processing: Single-pass aggregations with O(n) complexity Smart Caching: QuickChart handles image caching automatically Minimal Memory Usage: Stream processing for large datasets Error Handling: Graceful fallbacks for missing data fields Advanced Features Auto-Generated Insights: Statistical calculations (percentages, averages, totals) Trend analysis and pattern detection Business intelligence summaries Contextual recommendations User Experience Enhancements: Reply keyboards for easy navigation Visual progress indicators Error recovery mechanisms Mobile-optimized chart dimensions (800x600px) 📈 Use Cases & Business Applications Airlines & Travel Companies Fleet Analysis**: Monitor airline performance and market share Pricing Strategy**: Analyze competitor pricing across routes Operational Insights**: Track duration patterns and efficiency Data Analytics Teams Self-Service BI**: Enable non-technical users to generate reports Mobile Dashboards**: Access insights anywhere via Telegram Rapid Prototyping**: Quick data exploration without complex tools Business Intelligence Executive Reporting**: Instant charts for presentations Market Research**: Compare industry trends and benchmarks Performance Monitoring**: Track KPIs in real-time 🎨 Customization Options Adding New Chart Types Create new Switch condition Add corresponding data processing node Configure Chart.js options Update user interface menu Data Source Extensions Replace CSV with database connections Add real-time API integrations Implement data refresh mechanisms Support multiple file formats Visual Customizations // Custom color palette backgroundColor: ['#your-colors'], // Advanced styling borderRadius: 8, borderSkipped: false, // Animation effects animation: { duration: 2000, easing: 'easeInOutQuart' } 🔒 Security & Best Practices Data Protection Validate CSV input format Sanitize user inputs Implement rate limiting Secure file access permissions Error Handling Graceful degradation for API failures User-friendly error messages Automatic retry mechanisms Comprehensive logging 📊 Expected Outputs Sample Generated Insights "✈️ Vistara leads with 350+ flights, capturing 23.4% market share" "📈 Long-haul flights dominate at 61.1% of total bookings" "💰 Budget category (₹0-10K) represents 47.5% of all bookings" "📊 Average prices peak at ₹14K for 6-8 hour duration flights" Performance Metrics Response Time**: <3 seconds for chart generation Image Quality**: 800x600px high-resolution PNG Data Capacity**: Handles 10K+ records efficiently Concurrent Users**: Scales with n8n instance capacity 🚀 Getting Started Download the workflow JSON Import into your n8n instance Configure Telegram bot credentials Upload your flight data CSV Test with /start command Deploy and share with your team 💡 Pro Tips Data Quality**: Clean data produces better insights Mobile First**: Charts are optimized for mobile viewing Batch Processing**: Handles large datasets efficiently Extensible Design**: Easy to add new visualization types Ready to transform your data into actionable insights? Import this template and start generating professional charts in minutes! 🚀. An n8n automation workflow template by DataMinex.

By Mohammadreza azari

Find Cannibalized Pages (Google Search Console) This n8n template helps you detect page cannibalization in Google Search Console (GSC): situations where multiple pages on your site rank for the same query and more than one page gets clicks. Use it to spot competing URLs, consolidate content, improve internal linking, and protect your CTR/rankings. Good to know Data source:** Google Search Console Search Analytics (Dimensions: query, page). Scope:* Defaults to *last 12 months* and up to *10,000 rows** per run (adjustable). Logic:* Keeps only queries with *>1 page* and where the *second page has clicks > 0** → higher confidence of true cannibalization. Privacy:** Template ships with a placeholder property (sc-domain:example.com) and a neutral credential name; replace both after import. Cost:** n8n nodes used here are free. GSC usage is also free (subject to Google limits). How it works Manual Start — run the workflow on demand. Google Search Console — fetch last 12 months of query–page rows. Summarize — group by query, building two arrays: appended_page[] → all pages seen for that query appended_clicks[] → clicks for each page-query row (aligned with appended_page) Filter — pass only queries where: count_query > 1 (more than one page involved), and appended_clicks[1] > 0 (the second page also received clicks) Output — list of cannibalized queries with the competing pages and their click counts. Example output { "query": "best running shoes", "appended_page": [ "https://example.com/blog/best-running-shoes", "https://example.com/guide/running-shoes-2025" ], "appended_clicks": [124, 37], "count_query": 3 } How to use Import the JSON into n8n. Open the Google Search Console node and: Connect your Google Search Console OAuth2 credential. Replace siteUrl with your property (sc-domain:your-domain.com). Press Execute Workflow on Manual Start. Review the output — focus on queries where the second page has meaningful clicks. 💡 Tip: If your site is large, start with a shorter date range (e.g., 90 days) or raise rowLimit. Requirements Access to the target property in Google Search Console. One Google Search Console OAuth2 credential in n8n. Customising this workflow More robust detection:* In the *Summarize* node, change clicks aggregation from append to sum. Then filter for “at least 2 pages with sum_clicks > 0*” to avoid any dependency on row order. Scoring & sorting:* Add a *Code/Function** node to sort competing pages by clicks or impressions and compute click-share per page. Deeper analysis:** Include impressions and position in the GSC node and extend the summary to prioritize fixes (e.g., high impressions + split clicks). Reporting:* Send results to *Google Sheets* or export a *CSV**; create a dashboard of top cannibalized queries. Thresholds:* Expose minimum click thresholds as *workflow variables** (e.g., second page clicks ≥ 3) to reduce noise. Troubleshooting Empty results:** Widen date range, increase rowLimit, or temporarily relax the filter (remove the second-page click condition to validate data flow). No property data:** Ensure you used sc-domain: vs. https:// property format correctly and that your user has GSC access. Credential issues:** Reconnect the OAuth2 credential and reauthorize if needed. An n8n automation workflow template by Mohammadreza azari.

By Aadarsh Jain

Who is this for? This workflow is designed for DevOps engineers, platform engineers, and Kubernetes administrators who want to interact with their Kubernetes clusters through natural language queries in n8n. It's perfect for teams who need quick cluster insights without memorizing complex kubectl commands or switching between multiple cluster contexts manually. How it works? The workflow operates in three intelligent stages: Cluster Discovery & Context Switching - Automatically lists available clusters from your kubeconfig and switches to the appropriate cluster based on your natural language query Command Generation - Uses GPT-4o to analyze your request and generate the correct kubectl command with proper flags, selectors, and output formatting Command Execution - Executes the generated kubectl command against your selected cluster and returns the results The workflow supports multi-cluster environments and can handle queries like: "Show me all pods in production cluster" "List failing deployments in production" "Get pod details in kube-system namespace" Setup Clone the MCP Server git clone https://github.com/aadarshjain/kubectl-mcp-server cd kubectl-mcp-server Configure your kubeconfig - Ensure your ~/.kube/config contains all the clusters you want to access Set up MCP STDIO credentials in n8n Command: /full/path/to/python-package Arguments: /full/path/to/kubectl-mcp-server/server.py Import the workflow into your n8n instance Configure OpenAI credentials for the GPT-4o models Test the workflow using the chat interface with queries like "show pods in [cluster-name]". An n8n automation workflow template by Aadarsh Jain.

By DataMinex

Dynamic Search Interface with Elasticsearch and Automated Report Generation 🎯 What this workflow does This template creates a comprehensive data search and reporting system that allows users to query large datasets through an intuitive web form interface. The system performs real-time searches against Elasticsearch, processes results, and automatically generates structured reports in multiple formats for data analysis and business intelligence. Key Features: 🔍 Interactive web form for dynamic data querying ⚡ Real-time Elasticsearch data retrieval with complex filtering 📊 Auto-generated reports (Text & CSV formats) with custom formatting 💾 Automatic file storage system for data persistence 🎯 Configurable search parameters (amounts, time ranges, entity filters) 🔧 Scalable architecture for handling large datasets 🛠️ Setup requirements Prerequisites Elasticsearch cluster** running on https://localhost:9220 Transaction dataset** indexed in bank_transactions index Sample dataset**: Download from Bank Transaction Dataset File system access** to /tmp/ directory for report storage HTTP Basic Authentication** credentials for Elasticsearch Required Elasticsearch Index Structure This template uses the Bank Transaction Dataset from GitHub: https://github.com/dataminexcode/n8n-workflow/blob/main/Dynamic%20Search%20Interface%20with%20Elasticsearch%20and%20Automated%20Report%20Generation/data You can use this python script for importing the csv file into elasticsearch: Python script for importing data Your bank_transactions index should contain documents with these fields: { "transaction_id": "TXN_123456789", "customer_id": "CUST_000001", "amount": 5000, "merchant_category": "grocery_net", "timestamp": "2025-08-10T15:30:00Z" } Dataset Info: This dataset contains realistic financial transaction data perfect for testing search algorithms and report generation, with over 1 million transaction records including various transaction patterns and data types. Credentials Setup Create HTTP Basic Auth credentials in n8n Configure with your Elasticsearch username/password Assign to the "Search Elasticsearch" node ⚙️ Configuration 1. Form Customization Webhook Path**: Update the webhook ID if needed Form Fields**: Modify amounts, time ranges, or add new filters Validation**: Adjust required fields based on your needs 2. Elasticsearch Configuration URL**: Change localhost:9220 to your ES cluster endpoint Index Name**: Update bank_transactions to your index name Query Logic**: Modify search criteria in "Build Search Query" node Result Limit**: Adjust the size: 100 parameter for more/fewer results 3. File Storage Directory**: Change /tmp/ to your preferred storage location Filename Pattern**: Modify fraud_report_YYYY-MM-DD.{ext} format Permissions**: Ensure n8n has write access to the target directory 4. Report Formatting CSV Headers**: Customize column names in the Format Report node Text Layout**: Modify the report template for your organization Data Fields**: Add/remove transaction fields as needed 🚀 How to use For Administrators: Import this workflow template Configure Elasticsearch credentials Activate the workflow Share the webhook URL with data analysts For Data Analysts: Access the search interface via the webhook URL Set parameters: Minimum amount, time range, entity filter Choose format: Text report or CSV export Submit form to generate instant data report Review results in the generated file Sample Use Cases: Data analysis**: Search for transactions > $10,000 in last 24 hours Entity investigation**: Filter all activity for specific customer ID Pattern analysis**: Quick analysis of transaction activity patterns Business reporting**: Generate CSV exports for business intelligence Dataset testing**: Perfect for testing with the transaction dataset 📊 Sample Output Text Report Format: DATA ANALYSIS REPORT Search Criteria: Minimum Amount: $10000 Time Range: Last 24 Hours Customer: All Results: 3 transactions found TRANSACTIONS: Transaction ID: TXN_123456789 Customer: CUST_000001 Amount: $15000 Merchant: grocery_net Time: 2025-08-10T15:30:00Z CSV Export Format: Transaction_ID,Customer_ID,Amount,Merchant_Category,Timestamp "TXN_123456789","CUST_000001",15000,"grocery_net","2025-08-10T15:30:00Z" 🔧 Customization ideas Enhanced Analytics Features: Add data validation and quality checks Implement statistical analysis (averages, trends, patterns) Include data visualization charts and graphs Generate summary metrics and KPIs Advanced Search Capabilities: Multi-field search with complex boolean logic Fuzzy search and text matching algorithms Date range filtering with custom periods Aggregation queries for data grouping Integration Options: Email notifications**: Alert teams of significant data findings Slack integration**: Post analytics results to team channels Dashboard updates**: Push metrics to business intelligence systems API endpoints**: Expose search functionality as REST API Report Enhancements: PDF generation**: Create formatted PDF analytics reports Data visualization**: Add charts, graphs, and trending analysis Executive summaries**: Include key metrics and business insights Export formats**: Support for Excel, JSON, and other data formats 🏷️ Tags elasticsearch, data-search, reporting, analytics, automation, business-intelligence, data-processing, csv-export 📈 Use cases Business Intelligence**: Organizations analyzing transaction patterns and trends E-commerce Analytics**: Detecting payment patterns and customer behavior analysis Data Science**: Real-time data exploration and pattern recognition systems Operations Teams**: Automated reporting and data monitoring workflows Research & Development**: Testing search algorithms and data processing techniques Training & Education**: Learning Elasticsearch integration with realistic datasets Financial Technology**: Transaction data analysis and business reporting systems ⚠️ Important notes Security Considerations: Never expose Elasticsearch credentials in logs or form data Implement proper access controls for the webhook URL Consider encryption for sensitive data processing Regular audit of generated reports and access logs Performance Tips: Index optimization improves search response times Consider pagination for large result sets Monitor Elasticsearch cluster performance under load Archive old reports to manage disk usage Data Management: Ensure data retention policies align with business requirements Implement audit trails for all search operations Consider data privacy requirements when processing datasets Document all configuration changes for maintenance This template provides a production-ready data search and reporting system that can be easily customized for various data analysis needs. The modular design allows for incremental enhancements while maintaining core search and reporting functionality. An n8n automation workflow template by DataMinex.

By Avkash Kakdiya

How it works This workflow enriches and personalizes your lead profiles by integrating HubSpot contact data, scraping social media information, and using AI to generate tailored outreach emails. It streamlines the process from contact capture to sending a personalized email — all automatically. The system fetches new or updated HubSpot contacts, verifies and enriches their Twitter/LinkedIn data via Phantombuster, merges the profile and engagement insights, and finally generates a customized email ready for outreach. Step-by-step 1. Trigger & Input HubSpot Contact Webhook: Fires when a contact is created or updated in HubSpot. Fetch Contact: Pulls the full contact details (email, name, company, and social profiles). Update Google Sheet: Logs Twitter/LinkedIn usernames and marks their tracking status. 2. Validation Validate Twitter/LinkedIn Exists: Checks if the contact has a valid social profile before proceeding to scraping. 3. Social Media Scraping (via Phantombuster) Launch Profile Scraper & 🎯 Launch Tweet Scraper: Triggers Phantombuster agents to fetch profile details and recent tweets. Wait Nodes: Ensures scraping completes (30–60 seconds). Fetch Profile/Tweet Results: Retrieves output files from Phantombuster. Extract URL: Parses the job output to extract the downloadable .json or .csv data file link. 4. Data Download & Parsing Download Profile/Tweet Data: Downloads scraped JSON files. Parse JSON: Converts the raw file into structured data for processing. 5. Data Structuring & Merging Format Profile Fields: Maps stats like bio, followers, verified status, likes, etc. Format Tweet Fields: Captures tweet data and associates it with the lead’s email. Merge Data Streams: Combines tweet and profile datasets. Combine All Data: Produces a single, clean object containing all relevant lead details. 6. AI Email Generation & Delivery Generate Personalized Email: Feeds the merged data into OpenAI GPT (via LangChain) to craft a custom HTML email using your brand details. Parse Email Content: Cleans AI output into structured subject and body fields. Sends Email: Automatically delivers the personalized email to the lead via Gmail. Benefits Automated Lead Enrichment — Combines CRM and real-time social media data with zero manual research. Personalized Outreach at Scale — AI crafts unique, relevant emails for each contact. Improved Engagement Rates — Targeted messages based on actual social activity and profile details. Seamless Integration — Works directly with HubSpot, Google Sheets, Gmail, and Phantombuster. Time & Effort Savings — Replaces hours of manual lookup and email drafting with an end-to-end automated flow. An n8n automation workflow template by Avkash Kakdiya.

By Intuz

This n8n template from Intuz provides a complete and automated solution for secure document archiving. It automatically saves new QuickBooks invoice PDFs directly into Google Drive, creating a reliable backup system. For perfect organization, the workflow uses keywords from the invoice, like the client name or invoice number, to dynamically name the PDF files, ensuring you have a complete and easily searchable financial record. Use Cases 1. Automated Document Archiving: Eliminate the manual work of downloading and saving invoices. Set it up once and let it run. 2. Compliance & Auditing: Maintain a clean, chronological, and separate record of all issued invoices for easy access during audits. 3. Secure Backup: Create a redundant, secure backup of your critical financial documents in your own cloud storage. 4. Enhanced Team Access: Share the Google Drive folder with accountants, bookkeepers, or team members who need access to invoices but not to your full QuickBooks account. How It Works: 1. Real-Time Invoice Trigger: The workflow starts the instant a new invoice is created in your QuickBooks account. A configured webhook sends a notification to n8n, kicking off the automation immediately. 2. Fetch Invoice Metadata: The workflow uses the invoice ID from the webhook to retrieve the full invoice details, such as the customer's name and the transaction date. This information is used in the next steps. 3. Generate the Invoice PDF: A crucial HTTP Request node makes a direct API call to QuickBooks, requesting a PDF version of the invoice. This ensures the archived document is the official, formatted PDF, exactly as it appears in QuickBooks. 4. Upload and Archive in Google Drive: The final node takes the binary PDF data and uploads it to your specified Google Drive folder. It dynamically names the file for easy identification (e.g., CustomerName_TransactionDate.pdf), creating a perfectly organized and searchable archive. Setup Instructions To get this workflow running, follow these key setup steps: 1. Credentials: QuickBooks: Connect your QuickBooks account credentials to n8n. Google: Connect your Google account using OAuth2 credentials and ensure the Google Drive API is enabled. 2. QuickBooks Webhook Configuration: First, activate this n8n workflow to make the webhook URL live. Copy the Production URL from the QuickBooks Webhook node. In your Intuit Developer Portal, go to the webhooks section for your app, paste the URL, and subscribe to Invoice creation events. 3. Node Configuration: Get an invoice & Generate PDF File: These nodes will use your configured QuickBooks credentials automatically. Upload file (Google Drive): In the parameters for this node, you must select the Folder ID where you want your invoices to be saved. Support If you need help setting up this workflow or require a custom version tailored to your specific use case, please feel free to reach out to the template author: 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. An n8n automation workflow template by Intuz.

By Hunyao

What it does Captures token usage and cost from your AI Agent/LLM. Logs model, tokens, cost, tool use, and conversation I/O to Google Sheets for simple observability and billing. Perfect for Developers adding usage monitoring to AI agents. Teams needing cost transparency in prototypes. How it works Chat Trigger collects user input for the AI Agent. A Set node injects metadata like workflow, execution, and client IDs. LangChain Code node returns a configured Chat model with a callback that reads usage metadata. The callback computes input, output, and total costs based on per‑million token prices you define. It appends token metrics to a Google Sheet via the Google Sheets Tool. The Agent records intermediate tool calls. An If node checks whether a tool was used. When tools are used, the workflow logs input, output, tool name, and metadata to an Observability sheet. How to use SELF-HOSTED N8N ONLY - the Langchain Code node is only available in the self-hosted version of n8n. It is not available in n8n cloud. Requirements Self-hosted version of n8n If you have any questions in running the workflow, see the attached video: https://youtu.be/JSulRS128MA. An n8n automation workflow template by Hunyao.

By Intuz

This n8n template from Intuz provides a complete and automated solution for real-time financial reporting. It instantly syncs new QuickBooks invoices to Google Sheets, using specific invoice data or keywords as triggers to ensure your financial records are always accurate and up-to-date. It uses a webhook to capture every new or updated invoice and logs the essential details into a designated Google Sheet. Perfect for creating custom reports, data backups, or a real-time dashboard of your accounts receivable. Use Cases Financial Reporting:** Create a simple, shareable Google Sheet for team members who don't have QuickBooks access. Data Backup:** Maintain a secure, independent log of all your invoices outside of the QuickBooks ecosystem. Custom Dashboards:** Use the Google Sheet as a data source for tools like Google Data Studio or Grafana to build custom financial dashboards. Auditing:** Easily track the history and status of all invoices in a simple, searchable spreadsheet format. How it Works 1. Instant Webhook Trigger: The workflow activates the moment an invoice is created or updated in QuickBooks. The QuickBooks webhook sends a notification to n8n, kicking off the process in real time. 2. Fetch Full Invoice Details: The initial webhook notification only contains the invoice ID. This node uses that ID to make a call back to the QuickBooks API and retrieve the complete invoice data, including customer name, due date, and more. 3. Format Key Data: A simple Code node cleans up the data fetched from QuickBooks. It extracts only the fields you need—ID, Domain, Customer Name, and Due Date—and structures them perfectly for the next step. 4. Append or Update in Google Sheets: The final node connects to your Google Sheet and uses the powerful "Append or Update" operation. If the ID of the invoice doesn't exist in the sheet, it adds a new row. If the ID already exists, it updates the existing row with the latest information. This ensures your Google Sheet is always a perfect mirror of your QuickBooks invoice data, preventing duplicates and keeping everything current. Setup Instructions For this workflow to run successfully, follow these setup steps: 1. Credentials: QuickBooks: Connect your QuickBooks account credentials to n8n. Google: Connect your Google account using OAuth2 credentials. Ensure the Google Sheets and Google Drive APIs are enabled. 2. QuickBooks Webhook Configuration: Activate the workflow. Copy the Production URL from the Webhook node. In your Intuit Developer Portal, go to the webhooks section for your app. Paste the URL and subscribe to Invoice events (e.g., Create, Update). 3. Google Sheet Setup: Create a Google Sheet for your invoice data. Crucially, create the following headers in the first row of your sheet: -ID -Domain -Customer Name -Due Date 4. Node Configuration: In the Append or update row in sheet node, select your Google Sheet document and the specific sheet name from the dropdown lists. The columns should map automatically if you've set up the headers correctly. 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. An n8n automation workflow template by Intuz.

By kote2

This workflow allows a LINE user to send either text or an image of food to a connected LINE bot. If text is sent, the AI agent responds directly via LINE. If an image is sent, the workflow downloads it from LINE’s API, analyzes it using OpenAI’s Vision model, estimates calories (only if the image contains food), and formats the result into JSON. Detected dishes and calories are appended to a Google Sheet, and a confirmation message is sent back to the user via LINE. Key Features: Integrates LINE Messaging API webhook with n8n Uses OpenAI Vision to detect food and estimate calories Automatically logs results into Google Sheets Sends real-time feedback to the LINE user How to use: Set up a LINE Messaging API channel and get your channel access token. Add your OpenAI API credentials in n8n. Replace placeholders for {channel access token}, {your id}, and Google Sheet IDs with your own. Activate the workflow and send a food image or text message to your LINE bot. An n8n automation workflow template by kote2.

By Robert Breen

This n8n training workflow demonstrates how to connect a sub-workflow as a tool to an AI Agent. In this example, the main workflow is a Website Chatbot that engages visitors, collects contact information, and sends that data to a CRM process. The CRM process itself is a separate sub-workflow, connected to the agent as a tool via the Tool Workflow node. Step-by-Step Setup Instructions 1. Create the Sub-Workflow (CRM Tool) This sub-workflow will be triggered by the AI agent to process collected information. It will: Receive inputs (email, description) from the main chatbot workflow. Format the data into a structured JSON format. Append the data to a Google Sheet (acting as the CRM database). Send a confirmation message back to the main workflow. Steps inside the sub-workflow: When Executed by Another Workflow** – Triggered by the main workflow’s tool node. Convert Conversation (Agent)** – Uses OpenAI to extract and format the input into a JSON structure: { "email": "jane.doe@example.com", "description": "Wants help automating lead intake and sending Slack notifications." } Structured Output Parser – Ensures the extracted data matches the expected JSON schema. Append row in sheet (Google Sheets) – Adds the new lead data to your CRM sheet. Code Node – Returns a simple text confirmation like "Thanks for the info, we will be in touch soon". Required setup for Google Sheets: Enable the Google Sheets API and connect your Google account in n8n. Create a sheet with at least the columns email and description. Use the sheet's Document ID and tab name in the Google Sheets node. 2. Create the Main Workflow (Website Chatbot) This workflow acts as the main AI Agent handling incoming chat messages. Steps in the main workflow: When chat message received – Starts the workflow whenever a visitor sends a message via your chatbot integration. Website Chatbot (Agent Node) – Configured with a System Message that: Briefly explains your services. Asks the visitor what processes they want to automate. Requests their name and email. Sends collected data to the CRM tool once email and description are available. OpenAI Chat Model – Connects to the AI agent as its language model. Simple Memory – Stores short-term context for the ongoing chat. CRM Tool (Tool Workflow Node) – Points to the sub-workflow created in Step 1, allowing the chatbot to trigger it directly. 3. Connecting the Sub-Workflow to the AI Agent Add a Tool Workflow node to the main workflow. Select "Parameter" as the source. Paste in your sub-workflow JSON or select it from your n8n workflows. Connect the Tool Workflow node to your AI Agent using the ai_tool connection. Give the tool a clear description (e.g., crm tool to store lead information) so the agent knows when to use it. 4. How It Works in Action A visitor sends a message through the chatbot. The AI Agent engages, asks questions, and collects their name, email, and request. Once collected, the agent triggers the CRM Tool. The sub-workflow formats the data, stores it in Google Sheets, and sends a confirmation. The chatbot confirms with the visitor that their request was received. 5. Customization Ideas Replace Google Sheets with your actual CRM API. Add validation to ensure the email format is correct before saving. Expand the CRM tool to send a Slack or email notification after storing the lead. Created by Robert A. – Ynteractive Website: https://ynteractive.com Email: robert@ynteractive.com. An n8n automation workflow template by Robert Breen.

By mariskarthick

🚀 Say Goodbye to Manual Rule Deployments in Wazuh! Just Commit— Let Your Pipeline Auto‑Deploy via GitHub + n8n 🎯 👨‍💻 Tired of This Endless Cycle? Create rule → Validate → Copy to server → Restart Wazuh → Notify team* Repeat that every week — you’re spending more time deploying than detecting. What if one GitHub commit could do it all automatically? **✅ Validate ✅ Deploy ✅ Restart ✅ Notify** — without touching the server. Well, this workflow does just that. **🔥 Presenting: ⚡️ Git‑Powered Wazuh Rule Deployment Using n8n** 🧠 What This Workflow Does in 10 Seconds — Automatically: ✅ Watches GitHub commits — triggers only if the message contains #deploy-wazuh ✅ Checks if commit author is allowed ✅ Sends contextual SOC notifications about deployment attempt 🧪 Downloads & validates rule XML using xmllint 📦 Uploads to Wazuh Manager node only if validation succeeds ♻️ Restarts Wazuh Manager and verifies loading 📢 Sends alert to your team on Telegram (or other medium) with result: success/failure & reasons 🧠 Why Detection Engineers Will Love This: ⏱️ Saves hours weekly — Just commit & chill 🕒 Zero‑delay deployments — Go live instantly 🧪 Stops bad rules before they crash your SIEM 🔁 Rapid iteration — build, commit, done 🧘 No babysitting — Pipeline handles everything 📊 Informative alerts like: "Rule custom_malware_alert.xml deployed by Mariskarthick – Validation ✅ – Restart 🔁 Completed" 📌 Perfect For: 🛡️ Detection Engineers deploying rules weekly 🏢 MSSPs with multiple Wazuh environments 🚨 Threat Intel teams needing rapid turnaround **💥 This Isn’t Just Automation — It’s Detection Engineering at Its Finest. Let your GitHub commits trigger real‑time rule deployment — with validation, restart, and SOC alerts built‑in.** Commit. Deploy. Detect.* Created by Mariskarthick M Senior Security Analyst | Detection Engineer | Threat Hunter | Open-Source Enthusiast. An n8n automation workflow template by mariskarthick.

By Tushar Mishra

This n8n workflow automatically monitors RSS feeds for the latest AI vulnerability news, extracts key threat details, and creates a corresponding Security Incident in ServiceNow for each item. Schedule Trigger – Runs at scheduled intervals to check for updates. RSS Read – Fetches the latest AI vulnerability entries from the RSS feed. Read URL Content – Retrieves the full article for detailed analysis. Information Extractor (OpenAI Chat Model) – Parses and summarizes critical security information. Split Out – Processes each vulnerability alert separately. Create Incident – Generates a ServiceNow Security Incident with the extracted details. Ideal for security teams to track and respond quickly to emerging AI-related threats without manual feed monitoring. An n8n automation workflow template by Tushar Mishra.