Workflow Templates

How it works This workflow runs on a daily schedule. It starts by scraping real estate-related queries from Google using Apify. The organic search results are parsed and summarized into a single text block. That text is then sent to an AI model (GPT-4o) which extracts the top 3 pain points faced by real estate agents based on current online sentiment. The workflow compares today's insights with yesterday's data stored in Airtable to detect recurring or new pain points. Finally, it sends a summary notification via Telegram and stores the current day's insights into Airtable for trend tracking. How to set up Clone or import the workflow into your n8n instance. Get an Apify API token and insert it into the HTTP Request node. Create an Airtable base with a table containing two fields: "Date" (text) and "Summary" (long text). Copy the Base ID and Table ID into the Airtable nodes. Connect your Telegram bot and replace the chat ID in the Telegram node. Set up OpenAI credentials with GPT-4o or GPT-4o-mini for the LLM node. Run once manually to test, then activate the schedule trigger to run daily. (Optional) Extend the flow to generate cold outreach emails based on pain points, or sync to Notion/CRM.

Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs How the flow runs Fill the form with job position you're hunting for. Bright data's scraper will scrape Indeed based on your requirments. Workflow waits for the snapshot. Data returns as JSON. Jobs append to Google Sheets. Each row goes to an LLM to analyze if you're a good fit for the job (based on your prompts). The LLMswrites YES or NO next to each job opportunity, helping you find job posts that are relevant to you. What you need Google Sheets with our template. Bright Data dataset and API key. OpenAI key for GPT‑4o mini (or any other LLM). n8n with required nodes. Form fields To Fill Job Location** – city or region. Keyword** – role or skills. Country** – two‑letter code. Setup steps Copy the sheet template link. Import the JSON workflow. Add your credentials in nodes. Test the form manually. Add a schedule if desired. Bright Data filter example [ { "country": "US", "domain": "indeed.com", "keyword_search": "Growth Marketer", "location": "Miami", "date_posted": "Last 24 hours" } ] Tips -Choose Last 24 hours often. -Increase wait time for big snapshots. -Narrow keywords to save credits. **Need help? **Email me anytime: Yaron@nofluff.online YouTube: @YaronBeen LinkedIn: https://www.linkedin.com/in/yaronbeen/ Bright Data Docs: https://docs.brightdata.com/introduction

Youtube Video This n8n workflow is designed to assist YouTube content creators in identifying trending topics within a specific niche. By leveraging YouTube's search and data APIs, it gathers and analyzes video performance metrics from the past two days to provide insights into what content is gaining traction. Here's how the workflow operates: Trigger Setup: The workflow begins when a user sends a query through the chat_message_received node. If no niche is provided, the AI prompts the user to select or input one. AI Agent (Language Model): The central node utilizes a GPT-based AI agent to: Understand the user's niche or content preferences. Generate tailored search terms related to the niche. Process YouTube API responses and summarize trends using insights such as common themes, tags, and audience engagement metrics (views, likes, and comments). YouTube Search: The youtube_search node runs a secondary workflow to query YouTube for relevant videos published within the last two days. It retrieves basic video data such as video IDs, relevance scores, and publication dates. Video Details Retrieval: The workflow fetches additional details for each video: Video Snippet: Metadata like title, description, and tags. Video Statistics: Metrics such as views, likes, and comments. Content Details: Video duration, ensuring only content longer than 3 minutes and 30 seconds is analyzed. Data Processing: Video metadata is cleaned, sanitized, and stored in memory. Tags, titles, and descriptions are analyzed to identify patterns and trends across multiple videos. Output: The workflow compiles insights and presents them to the user, highlighting: The most common themes or patterns within the niche. URLs to trending videos and their respective channels. Engagement statistics, helping the user understand the popularity of the content. Key Notes for Setup: API Keys**: Ensure valid YouTube API credentials are configured in the get_videos, find_video_snippet, find_video_statistics, and find_video_data nodes. Memory Buffer**: The window_buffer_memory node ensures the AI agent retains context during analysis, enhancing the quality of the generated insights. Search Term Customization**: The AI agent dynamically creates search terms based on the user’s niche to improve search precision. Use Case: This workflow is ideal for YouTubers or marketers seeking data-driven inspiration for creating content that aligns with current trends, maximizing the potential to engage their audience. Example Output: For the niche "digital marketing": Trending Topic: Videos about "mental triggers" and "psychological marketing." Tags: "SEO," "Conversion Rates," "Social Proof." Engagement: Videos with over 200K views and high likes/comment ratios are leading trends. Video links: https://www.youtube.com/watch?v=video_id1 https://www.youtube.com/watch?v=video_id2

🔁 Loop & Optimize Meta Tags with Google Gemini This workflow automates the shortening of meta titles and descriptions for SEO—directly from your Google Sheet, row by row, using Google Gemini. ✅ What it does Reads rows from a Google Sheet (meta_title, meta_description, row_index) Loops through each row and checks if content exists Sends the data to Google Gemini for length-optimized output Cleans and parses the response Updates the original sheet with the shortened results 🛠️ Setup Requirements Google Sheets (OAuth2 credentials connected in n8n) Google Gemini API key (configured in n8n credentials) Sheet must contain: row_index meta_title meta_description Output will be written into: meta_titleFixed meta_descriptionFixed

🤖 AI-Powered Lead Enrichment with Explorium MCP & Telegram Who it's for Sales reps, agencies, and growth teams who want to turn basic company info into qualified leads with automated research . Perfect for B2B prospecting. What it does This workflow lets you send a company name or domain via Telegram, and instantly returns: ✅ Enriched company profile (industry, size, tech, pain points) ✅ A clean, structured JSON — ready for your CRM or sales tools How it works 💬 Send company info to your Telegram bot 🔎 Workflow pulls data from Explorium MCP + Tavily 🧠 AI analyzes model, tools, pain points & goals 📤 JSON response sent back via Telegram or logged to your database Requirements 🔐 OpenAI API (GPT-4) 🧠 Explorium MCP API 🌐 Tavily Web Search API 🤖 Telegram Bot API 🗃️ PostgreSQL (for memory/logging) How to set up Add API keys in n8n Connect Telegram bot to webhook Set up PostgreSQL for memory persistence Customize prompts (tone, niche, etc.) Test by sending a company name via Telegram Customization Options 🎯 Focus enrichment on specific industries or keywords 💬 Adjust the email sequence structure & style 🧩 Add extra data sources (e.g. Clearbit, Crunchbase) 🧾 Format JSON to match your CRM schema ⚙️ Add approval step before sending emails Highlights ✅ Uses multi-source enrichment ✅ Works 100% from Telegram ✅ Integrates into any sales pipeline

Who is this for? This workflow is ideal for: For freelancers, business owners, and finance teams who receive receipts via Gmail Automatically logs expenses for tax, bookkeeping and year-end audits What problem is this workflow solving? When tax season hits, missing receipts create panic. This workflow keeps everything in one place. It uses AI to extract details from Gmail attachments, logs them in a Google Sheet, and stores the PDFs in Google Drive. No digging. No copying. Just everything where it should be. How it works? Apply the label receipt to any incoming Gmail email. Do not mark it as read. On a schedule (e.g. daily at 8:00 AM), the workflow triggers. It searches for unread emails with the label receipt. For each matching email, it downloads the attached receipt file. It extracts text content from the receipt file. It uploads the original receipt file to a specified folder in Google Drive. It merges the extracted text with email metadata. It sends this combined data to OpenAI. OpenAI extracts structured fields: date merchant category description subtotal tax total The extracted data is appended as a new row in the specific Google Sheet. Finally, the email is marked as read to prevent it from being processed again. How to set up? Connect these services in your n8n credentials: Gmail (OAuth2) Google Drive Google Sheets OpenAI Configure the Google Drive upload: In the “Upload File” node, select the target folder where you want receipt PDFs stored. Set your execution schedule: Open the “Schedule Trigger” node and choose when it should run (default is once daily at 8:00 AM). Choose your Google Sheet and tab: In the “Append to Google Sheet” node, select your document and tab Ensure the sheet contains these columns: Date, Merchant, Category, Description, Subtotal, Tax, Total. How to customize this workflow to your needs? Change the Gmail label or search filter** to match your needs. Modify the OpenAI schema** to extract additional fields like currency, project, or notes.

This workflow is designed to automate the generation and updating of SEO meta titles and descriptions for WooCommerce products using n8n. It leverages Google Sheets for data input, a FREE language model (Gemini 2.0 Flash Exp. via OpenRouter) for generating SEO-optimized meta tags, and WooCommerce for updating product details. How It Works: Trigger: The workflow can be triggered manually or on a schedule. The manual trigger allows for testing, while the schedule trigger can be set to run at regular intervals (e.g., every few minutes) to process new products. Data Retrieval: The workflow starts by retrieving product IDs from a Google Sheets document. It looks for products that do not yet have meta titles or descriptions. Using the retrieved product ID, the workflow fetches the corresponding product details from WooCommerce, including the product name, description, short description, and categories. Meta Tag Generation: The product details are passed to a language model (Gemini 2.0 Flash Exp) via OpenRouter. The model generates SEO-optimized meta titles and descriptions based on the provided content. The generated meta tags are structured and validated to ensure they meet SEO best practices, such as character limits and keyword inclusion. Update WooCommerce: The generated meta title and description are then updated in the WooCommerce product metadata using the Yoast SEO fields. Update Google Sheets: Finally, the workflow updates the Google Sheets document with the newly generated meta tags, along with the product URL, title, and the timestamp of the update. Set Up Steps: Google Sheets Setup: Create a copy of the provided Google Sheets template and insert WooCommerce product IDs in column "B". Ensure the Google Sheets document has columns for METATITLE, METADESCRIPTION, URL, TITLE POST, and DATA (timestamp). n8n Workflow Configuration: Google Sheets Node: Configure the "Get product ID" node to connect to your Google Sheets document. Use OAuth2 for authentication. WooCommerce Node: Set up the WooCommerce nodes to connect to your WooCommerce store using the WooCommerce API credentials. OpenRouter Node: Configure the "Gemini 2.0 Flash Exp" node with your OpenRouter API credentials to access the language model. Structured Output Parser: Ensure the output parser is set to handle the structured data format for meta titles and descriptions. Workflow Execution: Trigger the workflow manually to test the process or set up a schedule trigger to automate the workflow at regular intervals. Monitor the workflow execution to ensure that meta tags are generated and updated correctly in both WooCommerce and Google Sheets. Validation: After the workflow runs, verify that the meta titles and descriptions in WooCommerce are correctly updated and that the Google Sheets document reflects the changes. This workflow streamlines the process of optimizing WooCommerce product pages for SEO, saving time and ensuring consistency in meta tag generation. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This workflow automatically generates draft replies in Gmail. It's designed for anyone who manages a high volume of emails or often face writer's block when crafting responses. Since it doesn't send the generated message directly, you're still in charge of editing and approving emails before they go out. How It Works: Email Trigger: activates when new emails reach the Gmail inbox Assessment: uses OpenAI gpt-4o and a JSON parser to determine if a response is necessary. Reply Generation: crafts a reply with OpenAI GPT-4 Turbo Draft Integration: after converting the text to html, it places the draft into the Gmail thread as a reply to the first message Set Up Overview (~10 minutes): OAuth Configuration (follow n8n instructions here): Setup Google OAuth in Google Cloud console. Make sure to add Gmail API with the modify scope. Add Google OAuth credentials in n8n. Make sure to add the n8n redirect URI to the Google Cloud Console consent screen settings. OpenAI Configuration: add OpenAI API Key in the credentials Tweaking the prompt: edit the system prompt in the "Generate email reply" node to suit your needs Detailed Walkthrough Check out this blog post where I go into more details on how I built this workflow. Reach out to me here if you need help building automations for your business.

How it works Automatically generates trending LinkedIn content topics using AI Researches current industry angles and hooks Writes posts in your authentic voice using OpenAI Creates professional images with DALL-E Posts everything on schedule without manual intervention Set up steps Connect OpenAI API for content generation and image creation Link LinkedIn API for automated posting Configure scheduling triggers (daily/weekly posting) Customize prompts to match your writing style and industry Set up content approval workflows (optional) Results you can expect 400% increase in profile views within 3 weeks Generate 120+ posts per month vs manual 12 posts Free up 15+ hours weekly for revenue-generating activities Consistent posting schedule that builds audience engagement Professional content that converts followers to clients Time to set up: 30-45 minutes Technical level: Beginner to intermediate APIs required: OpenAI, LinkedIn API Cost: OpenAI usage fees only (approximately $5-15/month) This workflow transforms LinkedIn content creation from a time-consuming daily task into a fully automated system that works while you sleep. Perfect for entrepreneurs, marketers, and content creators who want consistent LinkedIn presence without the manual effort.

This n8n workflow automates posting Amazon affiliate products to Mastodon — complete with image upload, description, and a shortened tracking URL using Shlink. 🔧 How it works Input Source: The workflow starts by reading from a connected Google Sheet that contains: SHlink (Shortlink) Amazon Link Description (Optional) PicURL Send /NO or YES A Send column (used as a flag to check if the row was already posted) Image Upload: It fetches the product image via HTTP and uploads it directly to a Mastodon instance via the /media API endpoint. URL Shortening (Shlink): The original Amazon URL is shortened using your self-hosted or cloud-hosted Shlink instance to enable click tracking and better presentation. Text Generation: A two-line promotional text is automatically generated using a Language Model (LLM), based on the product description. Posting to Mastodon: The post is then published on Mastodon with: The image The generated text The shortened Shlink URL Row Update: Once published, the Send column in the Google Sheet is updated to "YES" to prevent duplicates. Requirements ✅ Shlink – Required for shortening and tracking Amazon URLs ✅ Google Sheet – Used as a product queue and post ✅ Google Sheet Example https://link.unixweb.home64.de/w7VqY ✅ Mastodon account – OAuth2 credentials with write scope ✅ Product image URL – Must be valid and accessible ✅ n8n credentials – Set up for Google Sheets, Mastodon, and optionally OpenRouter or other LLM providers This workflow is ideal for content creators, affiliate marketers, and automation fans who want to save time and optimize reach across the Fediverse. #affiliate #amazon #mastodon #advertisment

Analyze and Explore your ZenDesk Support Requests using AI-Powered Knowledge Graph This template helps you create an interactive InfraNodus knowledge graph for your ZenDesk tickets using any search criteria (e.g. after a certain date, specific status, sender, keyword) that will automatically be sent to a selected Slack channel. Here's an example of the InfraNodus graph that shows the main topics and gaps in ZenDesk support tickets: You can use the workflow to: Get an instant overview of the main topics your customers are talking about Generate business and product ideas based on the blind spots identified using the InfraNodus AI See which topics correlate to the negative / positive sentiment understanding the weak and strong sides of your product and support Receive daily notifications on the main topics your customers are talking about via Slack / Telegram / Email and other channels Perform detailed search using a password-protected web form for tickets filtered by a certain date, status, tag, sender, keyword. Use the interactive graph to explore specific topics and concepts your customers are talking about — a great way to engage with their concerns in a non-linear way, bypassing the boring tabular interface Use the graph to explore the support requests by specific segments — e.g. status, priority, sentiment, tags, urgency. Use the graph generated as an AI expert available to your AI agents in other n8n workflows via InfraNodus GraphRAG. For instance, you could connect your knowledge base to the support tickets graph and let the agent discover possible solutions to your customers' most typical problems. See an sample template here. How it works You can start this workflow manually, with a daily / weekly trigger, or via a password-protected web form, where you can provide search requests. Once started, it will perform a ZenDesk tickets search with the default or your custom criteria. Then it will use the search results to generate an InfraNodus graph (or add the new data to an existing one), and — finally — use the InfraNodus AI endpoints to generate a topical summary and a product business idea based on the blind spots identified. The results are delivered a channel of your choice. Here's a description step by step: Start the workflow (manually or on schedule) Assign values to variables (search criteria, graph name) Perform ZenDesk support tickets search Convert the data received and submit it to InfraNodus to generate a knowledge graph Generate topical summary with InfraNodus Generate a business idea with InfraNodus (you can also change the setting to generate a question instead) Send a notification via Slack / Telegram / Email or back to the webform How to use You need an InfraNodus API account and key to use this workflow. You also need a ZenDesk account. It takes about 5 minutes to set everything up. Create an InfraNodus account. Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Add the authorization key to all the InfraNodus HTTP nodes in the template (Steps 3, 5, and 6). Generate a ZenDesk authorization token following the instructions in n8n's ZenDesk node (Step 3). Optionally: connect your Slack or Telegram or Gmail account to receive automated notifications with the link to the graph, once the workflow is ready (it takes about 30 seconds to run). Run it with using the form to play around with the search criteria that works best for you (you can leave everything empty at first), then choose the parameters you like and activate the Daily Trigger node to receive executive summaries to a channel of your choice. Open the graph in InfraNodus and use our customer feedback analysis guide to explore the graph and generate new insights. Requirements An InfraNodus account and API key A ZenDesk API key (Optional) — a Slack / Telegram / Gmail connection for notifications FAQ 1. What are the best use cases to try? I love to set the graph to deliver me a daily visual briefing of what's happening in my support portal. It shows me the main topics and gaps and generates product ideas based on them. Great to keep the pulse on the business. I also really like generating a graph for the past week manually, using the form, and then exploring the graph in InfraNodus directly using the customer feedback analysis workflow to: discover main topics my customers are talking about? understand the topics that have the most negative connotation for them (using the sentiment filter)? discover some support tickets that need more attention or that talk about the topics I'm personally interested in and engage with the client identify the gaps in your customers' discourse based on the blind spots — useful for generating ideas, see the graph below with a demo of how it works: 2. Why use the graph and not just AI summary? AI summary will just give you generic results. You'll see what you already know. Using the graph helps you deconstruct the discourse and get a much more nuanced understanding of the main pain points and interests of your customers. The auto-generated InfraNodus summary and business ideas have a direct explainable connection to the discourse, so you can always see where they are coming from and maintain the focus on all the topics, rather than the most prominent ones. Additionally, having an interactive graph opens a possibility to explore your customers' concerns in a more engaging way, finding the topics and concepts that are relevant to your interests or to your agents' expertise, helping you find the conversations that you'd otherwise have missed. 3. Is my customers' data safe? Absolutely. InfraNodus' terms of use and privacy policy state that the customers' data and text graphs are not used in AI training and are not offered to any third parties. Its underlying API system uses the Open API which explicitly states that data is not used for training either. So all the customers' data are private and safe. As an extra precaution, you can always delete the graphs after you analyzed them, in which case there is no trace of this data left on the servers. Customizing this workflow Check out the complete setup guide for this workflow at https://support.noduslabs.com/hc/en-us/articles/20447530961308-Zendesk-Tickets-Summarization-Sentiment-Analysis-and-Slack-Integration-with-n8n-and-InfraNodus For support with this template, please, contact https://support.noduslabs.com For more InfraNodus n8n workflows, please, see our creators page: https://n8n.io/creators/infranodus/ To learn more about InfraNodus, GraphRAG, and knowledge graph analysis: https://infranodus.com

This workflow enables you to automate the daily monitoring of how an AI model (like ChatGPT) responds to specific queries relevant to your market. It identifies mentions of your brand and predefined competitors, logs detailed interactions in Google Sheets, and delivers a comprehensive email report. Main Use Cases Monitor how your brand is mentioned by AI in response to relevant user queries. Track mentions of key competitors to understand AI's comparative positioning. Gain insights into AI's current knowledge and portrayal of your brand and market landscape. Automate daily intelligence gathering on AI-driven brand perception. How it works The workflow operates as a scheduled process, organized into these stages: Configuration & Scheduling Triggers daily (or can be run manually). Key variables are defined within the workflow: your brand name (e.g., "YourBrandName"), a list of queries to ask the AI, and a list of competitor names to track in responses. AI Querying For each predefined query, the workflow sends a request to the OpenAI ChatGPT API (via an HTTP Request node). Response Analysis Each AI response is processed by a Code node to: Check if your brand name is mentioned (case-insensitive). Identify if any of the listed competitors are mentioned (case-insensitive). Extract the core AI response content (limited to 500 characters for brevity in logs/reports). Data Logging to Google Sheets Detailed results for each query—including timestamp, date, the query itself, query index, your brand name, the AI's response, whether your brand was mentioned, and any errors—are appended to a specified Google Sheet. Email Report Generation A comprehensive HTML email report is compiled. This report summarizes: Total queries processed, number of times your brand was mentioned, total competitor mentions, and any errors encountered. A summary of competitor mentions, listing each competitor and how many times they were mentioned. A detailed table listing each query, whether your brand was mentioned, and which competitors (if any) were mentioned in the AI's response. Automated Reporting The generated HTML email report is sent to specified recipients, providing a daily snapshot of AI interactions. Summary Flow: Schedule/Workflow Trigger → Initialize Brand, Queries, Competitors (in Code node) → For each Query: Query ChatGPT API → Process AI Response (Check for Brand & Competitor Mentions) → Log Results to Google Sheets → Generate Consolidated HTML Email Report → Send Email Notification Benefits: Fully automated daily monitoring of AI responses concerning your brand and competitors. Provides objective insights into how AI models are representing your brand in user interactions. Delivers actionable competitive intelligence by tracking competitor mentions. Centralized logging in Google Sheets for historical analysis and trend spotting. Easily customizable with your specific brand, queries, competitor list, and reporting recipients.