Rss feed news processing and distribution workflow
Who is this for? This workflow is designed for professionals and teams who need to monitor multiple RSS feeds, filter the latest content, and distribute actionable updates as a Trello comment. Ideal for content managers, marketers, and team leads managing news or content pipelines. What problem is this workflow solving? Manually monitoring RSS feeds and keeping track of the latest content can be time-consuming. This workflow automates the aggregation, filtering, and distribution of news, ensuring that only relevant and timely updates are shared with your team or audience. What this workflow does: Aggregates RSS Feeds: Pulls data from up to three RSS feeds simultaneously. Filters Content: Filters articles based on their publication date (default: last 7 days). Organizes and Sorts: Sorts filtered articles by date for clarity. Formats Updates: Transforms news items into Markdown format for better readability. Publishes and Notifies: Posts comments to Trello cards and sends an email to a moderator to check the comment. Setup: Connect your RSS feeds by configuring the RSS Read nodes. Link your Trello and Gmail accounts for seamless integration. Adjust the schedule trigger to set how often the workflow should run (e.g., daily, weekly). Test the workflow to ensure all connections and configurations are correct. How to customize this workflow to your needs: Change the Number of RSS Feeds: Add or remove RSS Read nodes and update the merge configuration accordingly. Adjust the Date Filter: Modify the date logic in the “Filter by date” node to include more or fewer days. Limit the Number of Articles: Adjust the limit in the “Limit news to x” node. Custom Formatting: Update the Transform node to format the news items differently. Alternative Notifications: Replace Trello and Gmail with other integrations, such as Slack or Microsoft Teams. This workflow ensures your team stays informed with minimal effort and delivers content updates in an organized and professional manner.
By PollupAIParse PDF with LlamaParse and save to Airtable
Video Guide I prepared a comprehensive guide detailing how to automate the parsing of invoices using n8n and LlamaParse, seamlessly capturing and storing vital billing information. [](https://youtu.be/E4I0nru-fa8) Youtube Link Who is this for? This workflow is ideal for finance teams, accountants, and business operations managers who need to streamline invoice processing. It is particularly helpful for organizations seeking to reduce manual entry errors and improve efficiency in managing billing information. What problem does this workflow solve? Manually processing invoices can be time-consuming and error-prone. This automation eliminates the need for manual data entry by capturing invoice details directly from uploaded documents and storing structured data efficiently. This enhances productivity and accuracy across financial operations. What this workflow does The workflow leverages n8n and LlamaParse to automatically detect new invoices in a designated Google Drive folder, parse essential billing details, and store the extracted data in a structured format. The key functionalities include: Real-time detection of new invoices via Google Drive triggers. Automated HTTP requests to initiate parsing through Lama Cloud. Structured storage of invoice details and line items in a database for future reference. Google Drive Integration: Monitors a specific folder in Google Drive for new invoice uploads. Parsing with LlamaParse: Automatically sends invoices for parsing and processes results through webhooks. Data Storage in Airtable: Creates records for invoices and their associated line items, allowing for detailed tracking. Setup N8N Workflow Google Drive Trigger: Set up a trigger to detect new files in a specified folder dedicated to invoices. File Upload to LlamaParse: Create an HTTP request that sends the invoice file to LlamaParse for parsing, including relevant header settings and webhook URL. Webhook Processing: Establish a webhook node to handle parsed results from LlamaParse, extracting needed invoice details effectively. Invoice Record Creation: Create initial records for invoices in your database using the parsed details received from the webhook. Line Item Processing: Transform string data into structured line item arrays and create individual records for each item linked to the main invoice.
By Mark ShcherbakovEnrich FAQ sections on your website pages at scale with AI
This n8n workflow template lets you easily generate comprehensive FAQ (Frequently Asked Questions) content for multiple services (or any items or pages you need to add the FAQs to). Simply provide the Google Sheets document containing the items to scrape, and the workflow automatically creates detailed, AI-enhanced FAQ documents. How it works The workflow reads data from a Google Sheets document containing information about different services and categories (again, in your case - whatever objects you need). For each service and category, it generates a set of standard questions and answers covering setup, permissions, integrations, use cases, and pricing benefits. An AI model (OpenAI's GPT) is used to enhance or complete some of the answers, making the content more comprehensive and natural-sounding. The workflow formats the Q&A pairs, combining AI-generated content with predefined answers where applicable. It creates a text file (JSON) for each service or category, containing the formatted Q&A pairs. The generated files are saved to specific folders in Google Drive, organized by the type of integration (native, credential-only, non-native) or category. After processing each service or category, it updates the status in the original Google Sheets document to mark it as completed. Ideal for: Marketing teams: Rapidly create comprehensive FAQ documents for multiple products or services. Customer support: Generate consistent and detailed answers for common customer queries. Product managers: Easily maintain up-to-date documentation as products evolve. Content creators: Streamline the process of creating informative content about various offerings. Accounts required Google account (for Google Sheets and Google Drive) OpenAI API account (for AI-enhanced content generation) n8n.io account (for workflow execution) Set up instructions Set up the required credentials for Google Sheets, Google Drive, and OpenAI when you first open the workflow. Prepare your Google Sheets document with the service/category information. Here's an example of Google Sheet. Fill the "Define Sheets" node with your sheets Adjust the folder IDs in the "Prepare Job" node to match your Google Drive structure. Configure the OpenAI model settings in the "OpenAI Chat Model" node if needed. Test the workflow with a small subset of data before running it on your entire dataset. Adjust the questions asked in the "Create your Q&A templates" section After testing, activate your workflow for automated FAQ generation. 🙏 Big, big kudos to Jim Le for his ideas, input and support when building this workflow. Your approach to AI workflows is always super helpful!
By Polina MedvedievaLinkedIn profile finder via form using Bright Data & GPT-4o-mini
This n8n workflow template automates the process of finding LinkedIn profiles for a person based on their name, and company. It scrapes Google search results via Bright Data, parses the results with GPT-4o-mini, and delivers a personalized follow-up email with insights and suggested outreach steps. 🚀 What It Does Accepts a user-submitted form with a person’s full name, and company. Performs a Google search using Bright Data to find LinkedIn profiles and company data. Uses GPT-4o-mini to parse HTML results and identify matching profiles. Filters and selects the most relevant LinkedIn entry. Analyzes the data to generate a buyer persona and follow-up strategy. Sends a styled email with insights and outreach steps. 🛠️ Step-by-Step Setup Deploy the form trigger to accept person data (name, position, company). Build a Google search query from user input. Scrape search results using Bright Data. Extract HTML content using the HTML node. Use GPT-4o-mini to parse LinkedIn entries and company insights. Filter for matches based on user input. Merge relevant data and generate personalized outreach content. Send email to a predefined address. Show a final confirmation message to the user. 🧠 How It Works: Workflow Overview Trigger: When User Completes Form Search: Edit Url LinkedIn, Get LinkedIn Entry on Google, Extract Body and Title, Parse Google Results Matching: Extract Parsed Results, Filter, Limit, IF LinkedIn Profile is Found? Fallback: Form Not Found if no match Company Lookup: Edit Company Search, Get Company on Google, Parse Results, Split Out Content Generation: Merge, Create a Followup for Company and Person Email Delivery: Send Email, Form Email Sent 📨 Final Output An HTML-styled email (using Tailwind CSS) with: Matched LinkedIn profile Company insights Persona-based outreach strategy 🔐 Credentials Used BrightData account for scraping Google search results OpenAI account for GPT-4o-mini-powered parsing and content generation SMTP account for sending follow-up emails ❓Questions? Template and node created by Miquel Colomer and n8nhackers. Need help customizing or deploying? Contact us for consulting and support.
By Miquel ColomerPush and update files in GitHub
This workflow performs various Git operations. It starts with a manual trigger, sets the local repository path, decodes a file and then updates a file's content, adds, commits, and pushes changes to a GitHub repository, and finally pulls changes. The upper branch of the workflow retrieves a specific file ("README.md") from a GitHub repository ("gitpusharticle") owned by "teds-tech-talks." It then decodes the file's binary data into readable text using a code node. The decoded content is used to update the file by adding a timestamp and data. Finally, the modified file is pushed back to the repository using a GitHub node, completing the process of editing and updating the file directly via the workflow. This bottom branch of the workflow makes changes to a local Git repository. It starts by updating the "README.md" file with a timestamp and some content. Then, it adds the modified files, commits the changes with a message, and pushes them to a remote GitHub repository owned by "teds-tech-talks." Additionally, the workflow allows pulling changes from the remote repository into the local repository. The goal is to demonstrate how to perform various Git operations using n8n nodes, including adding, committing, pushing, and pulling changes.
By n8n TeamBuild your own counseling chatbot on LINE to support mental health conversations
Are you looking to create a counseling chatbot that provides emotional support and mental health guidance through the LINE messaging platform ? This guide will walk you through connecting LINE with powerful AI language models like GPT-4 to build a chatbot that supports users in navigating their emotions, offering 24/7 conversational therapy and accessible mental health resources . By leveraging LINE's webhook integration and Azure OpenAI , this template allows you to design a chatbot that is both empathetic and efficient, ensuring users receive timely and professional responses. Whether you're a developer, counselor, or business owner, this guide will help you create a customizable counseling chatbot tailored to your audience's needs. Who Is This Template For? Developers who want to integrate AI-powered chatbots into the LINE platform for mental health applications. Counselors & Therapists looking to expand their reach and provide automated emotional support to clients outside of traditional sessions. Businesses & Organizations focused on improving mental health accessibility and offering innovative solutions to their users. Educators & Nonprofits seeking tools to provide free or low-cost counseling services to underserved communities. How this work? Line Webhook to receive new message Send loading animation in Line Check if the input is text or not Send the text as prompt in chat model (GPT 4o) Reply the message to user (you'll need 'edit field' to format it before reply) Pre-Requisites You have access to the LINE Developers Console. An Azure OpenAI account with necessary credentials. Set-up To receive messages from LINE, configure your webhook: Set up a webhook in LINE Developer Console. Copy the Webhook URL from the Line Chatbot node and paste it into the LINE Console. Ensure to remove any 'test' part when moving to production. The loading animation reassures users that the system is processing their request. Authorize using header authorization Message Handling Use the Check Message Type IsText? node to verify if the incoming message is text. If the message type is text, proceed with ChatGPT processing; otherwise, send a reply indicating non-text inputs are not supported. AI Agent Configuration Define the system message within the AI Agent node to guide the conversation based on desired interaction principles. Connect the Azure OpenAI Chat Model to the AI Agent. Formatting Responses Ensure responses are properly formatted before sending them back to the user. Reply Message Use the ReplyMessage - Line node to send the formatted response. Ensure proper header authorization using Bearer tokens.
By lin@davoy.techTrack investments using Baserow and n8n
This workflow uses a number of technologies to track the value of ETFs, stocks and other exchange-traded products: Baserow: To keep track of our investments n8n’s Cron node: To trigger the workflow compiling our daily morning briefing Webscraping: The HTTP Request & HTML Extract nodes to fetch up-to-date prices from the relevant stock exchange and structure this infromation Javascript: We’ll use the Function node to build a custom HTML body with all the relevant information Sendgrid: The Email Service Provider in this workflow to send out our email Thanks to n8n, the steps in this workflow can easily be changed. Not a Sendgrid user? Simply remove the Sendgrid node and add a Gmail node instead. The stock exchange has a REST API? Just throw away the HTML Extract node. Here’s how it works: Data Source In this scenario, our data source is Baserow. In our table, we’ll track all information needed to identify each investment product: We have two text type columns (Name and ISIN) as well as two number type columns (Count and Purchase Price). Workflow Nodes Cron The Cron node will trigger our workflow to run each work day in the morning hours. Baserow The Baserow node will fetch our investments from the database table shown above. HTTP Request Using the HTTP Request node we can fetch live data from the stock exchange of our choice based on the ISIN. This example uses Tradegate, which is used by many German fintechs. The basic approach should also work for other exchanges, as long as they provide the required data to the public. HTML Extract Since our HTTP Request node fetches full websites, we’re using the HTML Extract node to extract the information we’re looking for from each website. If an exchange other than Tradegate is used, the selectors used in this node will most likely need to be updated. + 6. Set The Set nodes helps with setting the exact columns we’ll use in our table. In this case we’re first formatting the results from our exchange, then calculate the changes based on the purchase price. Function Here were using a bit of Javascript magic to build an HTML email. This is where any changes to the email content would have to be made. Sendgrid Finally we send out the email built in the previous step. This is where you can configure sender and recipients. Result The basic email generated by this workflow will look like so:
By TomExtract pay slip data with Line Chatbot and Gemini to Google Sheets
Workflow Overview: Extract text from image using AI is worth because you need no code. It incorporates Google Gemini 2.0 Flash model for important text extraction from image. If you code without AI, you have to use multiple condition and may cause a lot of bug but with Google Gemini, you don't need any coding and if the Pay Slip is different, Gemini will extract it automatically. Workflow description: User uses Line Messaging API to send Pay Slip image or message to the chatbot, create Line Business ID from here: Line Business Classify the message which is image or text If the message is Pay Slip image, it will process using Gemini 2.0 Flash EXP and extract important information and response in JSON format without coding by using the following prompt: Analyze image and then return in JSON Response that has the only following value: Status, From, To, Date, Amount To get Google AI Studio API Key, you can find from the following link: Google AI Studio API Key Create Google Sheets which include the fileds (Status, From, To, Date, Amount) that we have created related to the AI prompt Google Sheets as the following example: If the message is text, it will process using Gemini 2.0 Flash EXP model as the AI Assistant else if the message is image, it will extract the important fields then reply to the User and insert into Google Sheets Key Features: Extract text from image with No Code Without N8N, we have to write code to extract text from image, but with N8N and Google Gemini 2.0 Flash EXP together, we don't need to code and it will process all slip vendors or other document vendors. Multipurpose Chatbot this chatbot accept both text and image so we don't have to create many chatbot accounts Reduce human error this workflow let any officer to verify document status when the job ends Note: You can change the information by changing your prompt and also Google Sheets Column names relatively.
By Sirisak ChantanatePersonalized LinkedIn connection requests with Apollo, GPT-4, Apify & PhantomBuster
AI LinkedIn Outreach Automation with Apollo, OpenAI & PhantomBuster Categories: Sales Automation Lead Generation AI Personalization This workflow creates a complete LinkedIn outreach automation system that generates targeted lead lists from Apollo using natural language, enriches profiles with AI-personalized icebreakers, and automatically sends connection requests through PhantomBuster. Built by someone who's made over $1 million with AI automation, this system demonstrates the real-world approach to building profitable automation workflows. Benefits Natural Language Lead Targeting - Describe your ideal prospects in plain English and automatically generate Apollo search URLs AI-Powered Personalization - Creates custom icebreakers based on LinkedIn profile data, employment history, and professional background Complete Outreach Pipeline - From lead discovery to personalized connection requests, fully automated end-to-end Smart Data Management - Automatically tracks all prospects in Google Sheets with deduplication and status tracking Cost-Effective Scraping - Uses Apify to extract Apollo data without expensive subscription costs Scalable Architecture - Processes hundreds of leads while respecting LinkedIn's connection limits How It Works Natural Language Lead Generation: Form input accepts audience descriptions in plain English AI converts descriptions into properly formatted Apollo search URLs Automatically includes location, company size, job titles, and keyword filters Apollo Data Extraction: Uses Apify actor to scrape targeted lead lists from Apollo Extracts LinkedIn URLs, email addresses, employment history, and profile data Processes 500+ leads per run with detailed professional information AI Personalization Engine: Analyzes LinkedIn profile data including job history and company information Generates personalized icebreakers using proven connection request templates Creates human-like messages that reference specific career details and achievements Google Sheets Integration: Automatically stores all lead data in organized spreadsheet format Tracks prospect information, contact details, and generated icebreakers Provides easy data management and campaign tracking PhantomBuster Automation: Connects to PhantomBuster API to trigger LinkedIn connection campaigns Sends personalized connection requests with custom icebreakers Respects LinkedIn's daily limits and mimics human behavior patterns Business Use Cases Sales Teams - Automate prospecting for B2B outreach campaigns Agencies - Scale client acquisition through targeted LinkedIn outreach Recruiters - Find and connect with qualified candidates efficiently Entrepreneurs - Build professional networks in specific industries Business Development - Generate qualified leads for partnership opportunities Revenue Potential This system can replace expensive LinkedIn outreach tools that cost $200-500/month. Users typically see: 400% improvement in response rates through personalization 10x faster lead generation compared to manual prospecting Ability to process 500+ leads per hour vs. 10-20 manually Difficulty Level: Intermediate Estimated Build Time: 1-2 hours Monthly Operating Cost: ~$50 (Apollo + PhantomBuster + AI APIs) Watch My Complete 1-Hour Build Want to see exactly how I built this system from scratch? I walk through the entire development process live, including all the debugging, API integrations, and real-world testing that goes into building profitable automation systems. 🎥 See My Live Build Process: "Build This Automated AI LinkedIn DM System in 1 Hour (N8N)" This comprehensive tutorial shows my actual development approach - including the detours, problem-solving, and iterative testing that real automation building involves. Required Google Sheets Setup Create a Google Sheet with these exact column headers: Essential Lead Columns: id - Unique prospect identifier first_name - Contact's first name last_name - Contact's last name name - Full name linkedin_url - LinkedIn profile URL title - Current job title email_status - Email verification status photo_url - Profile photo URL icebreaker - AI-generated personalized message Setup Instructions: Create Google Sheet with these headers in row 1 Connect Google Sheets OAuth in n8n Update the document ID in the "Add to Google Sheet" node PhantomBuster will read from this sheet for automated outreach Set Up Steps Apollo & Apify Configuration: Set up Apify account and obtain API credentials Configure Apollo scraper actor with proper parameters Test lead extraction with sample audience descriptions AI Personalization Setup: Configure OpenAI API for natural language processing and personalization Set up prompt templates for audience targeting and icebreaker generation Test personalization quality with sample LinkedIn profiles Google Sheets Integration: Create lead tracking spreadsheet with proper column structure Configure Google Sheets API credentials and permissions Set up data mapping for automatic lead storage PhantomBuster Connection: Set up PhantomBuster account and LinkedIn connection Configure LinkedIn auto-connect agent with custom message templates Connect API for automated campaign triggering Form and Workflow Setup: Configure form trigger for audience input collection Set up data flow between all components Add proper error handling and rate limiting Testing and Optimization: Start with small batches (5-10 connections daily) Monitor LinkedIn account health and response rates Optimize icebreaker templates based on performance data Important Compliance Notes LinkedIn Limits: Respect 100 connection requests per week limit Account Safety: Use PhantomBuster's human-like behavior patterns Message Quality: Regularly update templates to avoid automation detection Response Management: Monitor and respond to replies within 24 hours Advanced Extensions This system can be enhanced with: Multi-channel Outreach: Add email sequences for comprehensive campaigns A/B Testing: Test different icebreaker templates automatically CRM Integration: Connect to Salesforce, HubSpot, or other sales systems Response Tracking: Monitor reply rates and optimize messaging Explore My Channel For more advanced automation systems that generate real business results, check out my YouTube channel where I share the exact strategies I've used to make over $1 million with AI automation.
By Nick SaraevAi data extraction with dynamic prompts and Baserow
This n8n template introduces the Dynamic Prompts AI workflow pattern which are incredible for certain types of data extraction tasks where attributes are unknown or need to remain flexible. The general idea behind this pattern is that the prompts for requested attributes to be extracted live outside the template and so can be changed at any time - without needing to edit the template. This seriously cuts down on maintainance requirements and is reusable for any number of tables at little cost. Check out the n8n Studio Episode here: https://www.youtube.com/watch?v=_fNAD1u8BZw Community post here: https://community.n8n.io/t/dynamic-prompts-with-n8n-baserow-and-airtable/72052 Looking for the Airtable Version? https://n8n.io/workflows/2771-ai-data-extraction-with-dynamic-prompts-and-airtable/ How it works Given we have an "input" field for context and a number of fields for the data we want to extract, this template will run in the background to react to any changes to either the "input" or fields and automatically update the rows accordingly. The key is that Baserow fields have a special property called the "field description". In this pattern, we use this property to allow the user to store a simple prompt describing the data that should exist in the column. Our n8n template reads these column descriptions aka "prompts" to use as instructions to perform tasks on the "input". In this template, the "input" is a PDF of a resume/CV and the columns are attributes a HR person would want to extract from it - such as full name, address, last position, years of experience etc. How to use First publish this template and ensure it's accessible via webhook URL. You then have to complete the "create Baserow webhooks" steps to configure your baserow to send change events to the n8n template. Baserow webhooks are created in the Baserow web interface. Check the template for more instructions. Requirements Baserow for Tables/Database OpenAI for LLM and extraction. Feel free to choose another LLM if preferred. Customising this workflow If you're not using files, you can replace the "input" field with anything you like. For example, the "input" could be single line text.
By JimleukWrite a file to the host machine
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By tanaypantAutomate job search with LinkedIn, Google Sheets & AI
When I was applying for roles, I learned the hard way that a one-size-fits-all resume never wins. I obsessively tailored my resume for each posting, reflecting the exact scope, impact, and stack the team cared about. That’s when the callbacks really started landing from places like Google, Apple, Amazon, and other big tech teams. Personalization worked because recruiters saw immediate alignment: my bullets mirrored their needs. So I built a small automation that flips the script. Instead of you chasing listings, it collects them for you, scores the fit against your resume, drafts a tailored cover letter, and files everything neatly into a Google Sheet then pings you for the best ones. One daily run. No more “I’ll get to it later.” Just a short list of high-signal roles and a ready-to-edit draft. But tailoring takes time and the hardest part was upstream: finding roles that truly matched my skills and narrowing to the best-fit few worth customizing for. That’s why I built a lightweight n8n workflow that does the heavy lifting for me: it pulls fresh roles, scores each one against my resume, drafts a role-specific cover letter, and drops everything into a clean Google Sheet. Below is a quick guide to set it up end-to-end so you can spend less time hunting and more time sending targeted, high-quality applications. Tutorial - Full setup tutorial https://open.substack.com/pub/jugaldb/p/ultimate-job-search-workflow-with?r=18lxy5&utmcampaign=post&utmmedium=web&showWelcomeOnShare=false
By Jugal