Workflow Templates

Who is this for? Marketing, content, and enablement teams that need a quick, human-readable summary of every new video published by the YouTube channels they care about—without leaving Slack. What problem does this workflow solve? Manually checking multiple channels, skimming long videos, and pasting the highlights into Slack wastes time. This template automates the whole loop: detect a fresh upload from your selected channels → pull subtitles → distill the key take-aways with GPT-4o-mini → drop a neatly-formatted digest in Slack. What this workflow does Schedule Trigger fires every 10 min, then grabs a list of YouTube RSS feeds from a Google Sheet. HTTP + XML fetch & parse each feed; only brand-new videos continue. YouTube API fetches title/description, RapidAPI grabs English subtitles. Code nodes build an AI payload; OpenAI returns a JSON summary + article. A formatter turns that JSON into Slack Block Kit, and Slack posts it. Processed links are appended back to the “Video Links” sheet to prevent dupes. Setup Make a copy of this Google Sheet and connect a Google Sheets OAuth2 credential with edit rights. Slack App: create → add chat:write, channels:read, app_mention; enable Event Subscriptions; install and store the Bot OAuth token in an n8n Slack credential. RapidAPI key for https://yt-api.p.rapidapi.com/subtitles (300 free calls/mo) → save as HTTP Header Auth. OpenAI key → save in an OpenAI credential. Add your RSS feed URLs to the “RSS Feed URLs” tab; press Execute Workflow. How to customise Adjust the schedule interval or freshness window in “If newly published”. Swap the OpenAI model or prompt for shorter/longer digests. Point the Slack node at a different channel or DM. Extend the AI payload to include thumbnails or engagement stats. Use-case ideas Product marketing**: Instantly brief sales & CS teams when a competitor uploads a feature demo. Internal learning hub**: Auto-summarise conference talks and share bullet-point notes with engineers. Social media managers**: Get ready-to-post captions and key moments for re-purposing across platforms.

🖼️ Text-to-Image Generator using n8n + Flux AI This n8n workflow automates image generation from text prompts using the Text-to-Image Flux AI API. It reads prompts from Google Sheets, generates images via API, uploads them to Google Drive, and logs the outcome. 🌟 Key Features Integrates with Text-to-Image Flux AI on RapidAPI Converts base64 image data to downloadable files Stores images on Google Drive Updates logs and errors back into Google Sheets Skips prompts already processed 📄 Google Sheet Column Structure Your source Google Sheet should include the following columns: | Column Name | Description | |-------------------|--------------------------------------------------| | Prompt | The text prompt to generate an image from | | drive path | (Optional) File path or URL of saved image | | Generated Date | Date/time the image was generated | | Base64 | Base64 string or error message (for logging) | Only rows with a non-empty Prompt and empty drive path will be processed. 📌 Use Case Perfect for: Bulk AI image generation for content marketing Creative automation with prompt-based image creation Building image assets based on structured datasets Any workflow where prompts are tracked via Google Sheets Uses the Text-to-Image Flux AI API to generate high-quality images on demand. 🔧 Workflow Summary | Step | Node | Description | |------|------|-------------| | 1 | Manual Trigger | Manually start the workflow | | 2 | Google Sheets2 | Reads prompts from Google Sheets | | 3 | Loop Over Items | Processes rows one by one | | 4 | If2 | Skips rows that already have images | | 5 | HTTP Request1 | Calls Text-to-Image Flux AI via RapidAPI | | 6 | Code1 | Converts base64 image to binary file | | 7 | Google Drive1 | Uploads the image file to a Drive folder | | 8 | Google Sheets1 | Logs base64 result and timestamp back | | 9 | If1 | Handles errors from the API | | 10 | Google Sheets4 | Logs errors to the sheet | | 11 | Wait | Adds delay between batches to prevent rate-limiting | 🚀 RapidAPI: Text-to-Image Flux AI This flow is powered by Text-to-Image Flux AI. Be sure to: Sign up at RapidAPI and subscribe to the API. Copy your API Key. Replace "your key" in the HTTP Request1 node’s x-rapidapi-key header. You can test the API directly here before connecting it to n8n. ✅ Tips for Setup Ensure you’ve set up a Google Service Account with access to both Sheets and Drive. Fill only the Prompt column — leave drive path and Base64 empty for new prompts. Monitor your RapidAPI dashboard for usage and quota. Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

This automated workflow scrapes and processes the monthly "Who is Hiring" thread from Hacker News, transforming raw job listings into structured data for analysis or integration with other systems. Perfect for job seekers, recruiters, or anyone looking to monitor tech job market trends. How it works Automatically fetches the latest "Who is Hiring" thread from Hacker News Extracts and cleans relevant job posting data using the HN API Splits and processes individual job listings into structured format Parses key information like location, role, requirements, and company details Outputs clean, structured data ready for analysis or export Set up steps Configure API access to [Hacker News](https://github.com/HackerNews/API ) (no authentication required) Follow the steps to get your cURL command from https://hn.algolia.com/ Set up desired output format (JSON structured data or custom format) Optional: Configure additional parsing rules for specific job listing information Optional: Set up integration with preferred storage or analysis tools The workflow transforms unstructured job listings into clean, structured data following this pattern: Input: Raw HN thread comments Process: Extract, clean, and parse text Output: Structured job listing data This template saves hours of manual work collecting and organizing job listings, making it easier to track and analyze tech job opportunities from Hacker News's popular monthly hiring threads.

Who is this workflow template for? This workflow template is perfect for freelancers, small business owners, accounting teams, or anyone responsible for managing and recording invoices regularly. If you deal with multiple invoices and spend considerable time manually entering invoice data into a database, this automation will significantly simplify your daily operations and reduce potential errors. What this workflow does The workflow automates the entire invoice logging process. It continuously monitors a designated Google Drive folder every minute for new PDF invoice uploads. Once a new invoice is detected, it is automatically converted from PDF to an image format using the ILovePDF API. After conversion, Google's Gemini AI analyzes the image, intelligently extracting essential details such as vendor name, item description, invoice amount, invoice date, payment date, and bank reference numbers. Finally, this structured data is automatically recorded in an Airtable database (or optionally in a Google Sheet), ensuring organized, accessible records. Detailed Workflow Explanation Step 1: Invoice Detection** Monitors Google Drive for newly uploaded PDF invoices. Step 2: PDF to Image Conversion** Converts PDFs into images using ILovePDF. Step 3: Data Extraction via Gemini AI** Uses Gemini AI to analyze the invoice image. Extracts data such as Vendor, Description, Amount, Invoice Date, Paid Date, and Bank Reference. Provides clear descriptions even when original invoice descriptions are vague or missing by analyzing vendor context. Step 4: Structured Data Storage** Automatically sends extracted data to Airtable or Google Sheets. Step 5: File Management** Moves processed PDF files into a separate "Done" folder to clearly differentiate between processed and unprocessed invoices. Step-by-Step Setup Instructions Set Up Google Drive: Log in to Google Drive and create two folders: One named Invoices (for incoming PDF files) One named Processed (for processed files) Obtain API Credentials: ILovePDF API: Sign up at ILovePDF Developers. Retrieve your API key from your account dashboard. Google Gemini AI API: Register at Google AI and generate an API key. Airtable Database Preparation: Create an Airtable base with the following columns: Vendor (Text) Description (Text) Amount (Number or Text) Invoice Date (Date) Paid Date (Date) Bank Reference (Text) Import and Configure Workflow in n8n: Import the provided workflow JSON file into your n8n instance. Connect your Google Drive, ILovePDF, Google Gemini AI, and Airtable accounts by entering your credentials in their respective nodes. Adjust Workflow Settings: In the Google Drive nodes, ensure your newly created Invoices and Processed folders are correctly selected. Update the ILovePDF public key in the appropriate HTTP Request node. Customize the Gemini AI prompt to refine or expand data extraction according to your specific needs. Testing Your Setup: Upload a sample PDF invoice into the Invoices folder. Execute the workflow by clicking Test Workflow in n8n and verify if data extraction and Airtable logging operate correctly. Airtable Column Specifications Ensure your Airtable includes the following structure: Vendor**: Single Line Text Description**: Single Line Text Amount**: Currency or Single Line Text Invoice Date**: Date (formatted as YYYY-MM-DD) Paid Date**: Date (formatted as YYYY-MM-DD) Bank Reference**: Single Line Text How to Customize the Workflow System Prompt:** Adjust the AI instructions by modifying the prompt text to focus on additional or fewer invoice details. Structured Output Parser:** Modify the JSON schema in the parser node to match the structure and data points your project specifically requires: By following these instructions, you’ll have a fully automated, reliable system for handling and logging invoice data, significantly enhancing your productivity.

Extract Local Business Contacts with Google Sheets, SerpAPI & GPT‑4o Status: Ready for Use ✅ Disclaimer: This workflow relies on community nodes that are not part of n8n’s core package. Install the following from n8n → Community Nodes before running: n8n-nodes-langchain** n8n-nodes-openai** (Structured Output Parser) n8n-nodes-apify** 📝 Description This n8n workflow automates discovery of local‑business contact details by search term and location, then enriches the results with publicly listed email addresses using GPT‑4o AI. 🔑 Key Features 🔗 Google Sheets Integration Reads search terms and locations from a Google Sheet. Processes only rows that are not marked Complete, preventing duplicates. 🗺️ Google Maps Search via SerpAPI Queries Google Maps through SerpAPI for every search‑term‑and‑location pair. Retrieves the following fields: business name, website, street address, and phone number. 🧠 Website Scraping & Email Extraction Scrapes the business homepage content with Apify’s Fast Website Content Crawler. Sends the scraped HTML to a GPT‑4o AI Agent. Extracts any publicly listed email address. Returns a clean, structured JSON object for downstream use. 💾 Data Storage & Tracking Writes every result to a Results tab in the same Google Sheet. Marks the corresponding row in the Searches tab as Complete once finished. 🧱 Extensible Design The workflow uses modular sub‑workflows and AI agents. You can easily extend it to add: Phone‑number verification with Twilio Social‑media enrichment with Clearbit Exports to HubSpot, Salesforce, Airtable, PostgreSQL, or CSV files 📄 Google Sheet Setup Create a Searches tab with these exact columns (one header row): Search | Area | Area Name | Complete Create a results tab with these columns title | website | address | phone | Search | Search Name | Area | email (Manual Entry) ⚙️ Prerequisites Google Cloud Project with Google Sheets API and Google Drive API enabled SerpAPI account (free trial or paid) – obtain an API key Apify account (free trial or paid) with the Fast Website Content Crawler actor installed OpenAI account with an API key that can access GPT‑4o models 🚀 Setup Instructions Copy the Google Sheet Make a personal copy of the template sheet. Ensure the tab names are Searches and Results. https://docs.google.com/spreadsheets/d/1QgcVMlXRlM_5ZFFUHr6bVK-93Tzia9XseTX03ZYnowI/edit?usp=sharing Configure Google Sheets nodes in n8n Open the workflow. Update the nodes Extract Search Terms and Save Emails to Sheet to point at your copied sheet. Authenticate using Google OAuth2 credentials that have access to the sheet. Add SerpAPI credentials Sign in at <https://serpapi.com>. Copy your API key. In the Search Google Maps node, create a new credential and paste the key. Set up Apify Sign up at <https://apify.com>. Add the Fast Website Content Crawler actor to your account. In the Scrape Web Page HTTP node, append ?token=YOUR_API_KEY to the actor URL. Add your OpenAI API key Go to <https://platform.openai.com>. Generate an API key. Add it to the AI Agent and OpenAI Chat Model node credentials. ✅ Running the Workflow Click Execute Workflow in n8n. For each unprocessed row in the Searches tab, the automation will: Retrieve business information from Google Maps via SerpAPI. Scrape the business website using Apify. Use GPT‑4o to extract a public email address. Write all collected data to the Results tab. Mark the original row as Complete. 🧩 Example Use Cases Build highly targeted lead lists for sales and marketing outreach. Compile local business directories for regional websites or apps. Automate contact‑information collection for lead‑generation campaigns and reduce manual data entry. 🤝 Connect with Me Description I’m Robert Breen, founder of Ynteractive — a consulting firm that helps businesses automate operations using n8n, AI agents, and custom workflows. I’ve helped clients build everything from intelligent chatbots to complex sales automations, and I’m always excited to collaborate or support new projects. If you found this workflow helpful or want to talk through an idea, I’d love to hear from you. Links 🌐 Website: https://www.ynteractive.com 📺 YouTube: @ynteractivetraining 💼 LinkedIn: https://www.linkedin.com/in/robert-breen 📬 Email: rbreen@ynteractive.com

Who is this for? This workflow is for everyone who wants to have easier access to their Odoo sales data without complex queries. Use Case To have a clear overview of your sales data in Odoo you typically needs to extract data from it manually to analyse it. This workflow uses OpenAI's language models to create an intelligent chatbot that provides conversational access to your Odoo sales opportunity data. How it works Creates a summary of all Odoo sales opportunities using OpenAI Uses that summary as context for the OpenAI chat model Keeps the summary up to date using a schedule trigger Set up steps: Configure the Odoo credentials Configure OpenAI credentials Toggle "Make Chat Publicly Available" from the Chat Trigger node.

Automated Multilingual Gmail Draft Reply with OpenAI GPT-4o in n8n Who is this for? This workflow is ideal for anyone who receives a high volume of Gmail inquiries, especially those providing multilingual customer support or handling diverse client communications. What problem is this workflow solving? Managing frequent emails in multiple languages can be overwhelming. This workflow reduces manual drafting by automatically generating context-aware replies using OpenAI GPT-4o, letting users focus on personalization and quality assurance. What this workflow does Monitors your Gmail inbox for new emails with a specific label (e.g., "Inquiry"). Uses OpenAI GPT-4o for message assessment and language detection. Parses information using a JSON parser. Generates an AI-powered draft reply in the detected language via OpenAI GPT-4o. Converts the reply to HTML and saves it as a draft in the original Gmail thread for your review. Setup Connect your Gmail account and set up relevant labels in both Gmail and the workflow. Integrate your OpenAI credentials in n8n. Configure the workflow trigger for your desired labels. How to customize this workflow to your needs Adjust label names in both Gmail and the workflow for different email categories. Define custom starting and ending phrases for draft replies per supported language. Expand supported languages or modify AI prompt instructions to suit your brand’s tone.

This workflow allows users to generate AI videos using Google Veo3, save them to Google Drive, generate optimized YouTube titles with GPT-4o, and automatically upload them to YouTube with Upload-Post. The entire process is triggered from a Google Sheet that acts as the central interface for input and output. IT automates video creation, uploading, and tracking, ensuring seamless integration between Google Sheets, Google Drive, Google Veo3, and YouTube. Benefits of this Workflow 💡 No Code Interface**: Trigger and control the video production pipeline from a simple Google Sheet. ⚙️ Full Automation**: Once set up, the entire video generation and publishing process runs hands-free. 🧠 AI-Powered Creativity**: Generates engaging YouTube titles using GPT-4o. Leverages advanced generative video AI from Google Veo3. 📁 Cloud Storage & Backup**: Stores all generated videos on Google Drive for safekeeping. 📈 YouTube Ready**: Automatically uploads to YouTube with correct metadata, saving time and boosting visibility. 🧪 Scalable**: Designed to process multiple video prompts by looping through new entries in Google Sheets. 🔒 API-First**: Utilizes secure API-based communication for all services. How It Works Trigger: The workflow can be started manually ("When clicking ‘Test workflow’") or scheduled ("Schedule Trigger") to run at regular intervals (e.g., every 5 minutes). Fetch Data: The "Get new video" node retrieves unfilled video requests from a Google Sheet (rows where the "VIDEO" column is empty). Video Creation: The "Set data" node formats the prompt and duration from the Google Sheet. The "Create Video" node sends a request to the Fal.run API (Google Veo3) to generate a video based on the prompt. Status Check: The "Wait 60 sec." node pauses execution for 60 seconds. The "Get status" node checks the video generation status. If the status is "COMPLETED," the workflow proceeds; otherwise, it waits again. Video Processing: The "Get Url Video" node fetches the video URL. The "Generate title" node uses OpenAI (GPT-4.1) to create an SEO-optimized YouTube title. The "Get File Video" node downloads the video file. Upload & Update: The "Upload Video" node saves the video to Google Drive. The "HTTP Request" node uploads the video to YouTube via the Upload-Post API. The "Update Youtube URL" and "Update result" nodes update the Google Sheet with the video URL and YouTube link. Set Up Steps Google Sheet Setup: Create a Google Sheet with columns: PROMPT, DURATION, VIDEO, and YOUTUBE_URL. Share the Sheet link in the "Get new video" node. API Keys: Obtain a Fal.run API key (for Veo3) and set it in the "Create Video" node (Header: Authorization: Key YOURAPIKEY). Get an Upload-Post API key (for YouTube uploads) and configure the "HTTP Request" node (Header: Authorization: Apikey YOUR_API_KEY). YouTube Upload Configuration: Replace YOUR_USERNAME in the "HTTP Request" node with your Upload-Post profile name. Schedule Trigger: Configure the "Schedule Trigger" node to run periodically (e.g., every 5 minutes). Need help customizing? Contact me for consulting and support or add me on Linkedin.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. AI Arena - Debate of AI Agents to Optimize Answers and Simulate Diverse Scenarios Overview Version: 1.0 The AI Arena Workflow is designed to facilitate a refined answer generation process by enabling a structured debate among multiple AI agents. This workflow allows for diverse perspectives to be considered before arriving at a final output, enhancing the quality and depth of the generated responses. ✨ Features Multi-Agent Debate Simulation**: Engage multiple AI agents in a debate to generate nuanced responses. Configurable Rounds and Agents**: Easily adjust the number of debate rounds and participating agents to fit your needs. Contextualized AI Responses**: Each agent operates based on predefined roles and characteristics, ensuring relevant and focused discussions. JSON Output**: The final output is structured in JSON format, making it easy to integrate with other systems or workflows. 👤 Who is this for? This workflow is ideal for developers, data scientists, content creators, and businesses looking to leverage AI for decision-making, content generation, or any scenario requiring diverse viewpoints. It is particularly useful for those who need to synthesize information from multiple personalities or perspectives. 💡 What problem does this solve? The workflow addresses the challenge of generating nuanced responses by simulating a debate among AI agents. This approach ensures that multiple perspectives are considered, reducing bias and enhancing the overall quality of the output. Use-Case examples: 🗓️ Meeting/Interview Simulation ✔️ Quality Assurance 📖 Storywriter Test Environment 🏛️ Forum/Conference/Symposium Simulation 🔍 What this workflow does The workflow orchestrates a debate among AI agents, allowing them to discuss, critique, and suggest rewrites for a given input based on their roles and predefined characteristics. This collaborative process leads to a more refined and comprehensive final output. 🔄 Workflow Steps Input & Setup: The initial input is provided, and the AI environment is configured with necessary parameters. Round Execution: AI agents execute their roles, providing replies and actions based on the input and their individual characteristics. Round Results: The results of each round are aggregated, and a summary is created to capture the key points discussed by the agents. Continue to Next Round: If more rounds are defined, the process repeats until the specified number of rounds is completed. Final Output: The final output is generated based on the agents' discussions and suggestions, providing a cohesive response. ⚡ How to Use/Setup 🔐 Credentials Obtain an API key for the Mistral API or another LLM API. This key is necessary for the AI agents to function properly. 🔧 Configuration Set up the workflow in n8n, ensuring all nodes are correctly configured according to the workflow requirements. This includes setting the appropriate input parameters and defining the roles of each AI agent. This workflow uses a custom node for Global Variables called 'n8n-nodes-globals.' Alternatively, you can use the 'Edit Field (Set)' node to achieve the same functionality. ✏️ Customizing this workflow To customize the workflow, adjust the AI agent parameters in the JSON configuration. This includes defining their roles, personalities, and preferences, which will influence how they interact during the debate. One of the notes includes a ready-to-use example of how to customize the agents and the environment. You can simply edit it and insert it as your credential in the Global Variables node. 📌 Example An example with both input and final output is provided in a note within the workflow. 🛠️ Tools Used n8n: A workflow automation tool that allows users to connect various applications and services. Mistral API: A powerful language model API used for generating AI responses. (You can replace it with any LLM API of your choice) Podman: A container management tool that allows users to create, manage, and run containers without requiring a daemon. (It serves as an alternative to Docker for container orchestration.) ⚙️ n8n Setup Used n8n Version**: 1.100.1 n8n-nodes-globals**: 1.1.0 Running n8n via**: Podman 4.3.1 Operating System**: Linux ⚠️ Notes, Assumptions & Warnings Ensure that the AI agents are configured with clear roles to maximize the effectiveness of the debate. Each agent's characteristics should align with the overall goals of the workflow. The workflow can be adapted for various use cases, including meeting simulations, content generation, and brainstorming sessions. This workflow assumes that users have a basic understanding of n8n and JSON configuration. This workflow assumes that users have access to the necessary API keys and permissions to utilize the Mistral API or other LLM APIs. Ensure that the input provided to the AI agents is clear and concise to avoid confusion in the debate process. Ambiguous inputs may lead to unclear or irrelevant outputs. Monitor the output for relevance and accuracy, as AI-generated content may require human oversight to ensure it meets standards and expectations before being used in production. ℹ️ About Us This workflow was developed by the Hybroht team of AI enthusiasts and developers dedicated to enhancing the capabilities of AI through collaborative processes. Our goal is to create tools that harness the possibilities of AI technology and more.

Automated Gmail Labeling and Brainstorming This template can be used to automatically label your incoming Gmail messages with AI and to build a knowledge graph from the emails tagged with a specific label to brainstorm new ideas based on them. You can also get notified about the emails with the most important labels via Telegram as well as receive new ideas as you are building a knowledge graph of incoming messages. The idea generation is based on the InfraNodus knowledge graph content gap detection algorithm, which builds a network from your content and then finds a blind spot and uses AI to generate an interesting research question or idea that can be used to bridge this gap. Why it works so well? Think of all the business emails you receive that bypass the spam filters. Probably, they are personalized to you already. Now imagine if you build a knowledge graph from them for over a month. You will then have a ideation device based on your interests and marketing profile. Now, if you identify the gaps inside and generate interesting research questions based on them, you will come up with new interesting ideas that will be relevant (because they touch on the topics that matter to you), but novel, because they bridge them in new ways. What is it useful for? Automate Gmail incoming message labeling** with the new Classifier n8n node — much more advanced than the default Gmail labeling rules. Get notified via Telegram (or a messenger of your choice) about the most important messages and be sure not to miss anything important. Keep the messages with a certain label saved into knowledge graph for brainstorming and ideation. Every time a new message of this category comes in, it's added into the graph, changing its structure, a new idea is generated. So instead of looking at each specific offer, you now use them to generate insights for you. How it works Step 1: This template can is triggered automatically when a new Gmail message arrives. Note: you need to connect your Gmail account here in this node Step 2: We use the new n8n AI Classifier Node to classify your email based on its content. You might need to update to n8n 1.94 version to make it work. Note: we like to use Gemini AI for that classifier as it's the same company as Gmail, so should be safe with data Step 3: After classifying the message, we label the message with the appropriate label. Note: you need to create the labels before in your Gmail account Step 4: For a certain category (e.g. "Business" you format the message and save it into your InfraNodus graph. *Note: specify your InfraNodus API here and choose the name of the graph. It will use the InfraNodus HTTP graphAndEntries endpoint and save your data to an InfraNodus graph. By default, we save the text knowledge graph using the contextSettings parameters (it will only build a text graph of the content), but you can take an alternative setting from this InfraNodus HTTP node's settings and create a social knowledge graph, that will also show email senders in the graph itself.* Step 5 (optional): Generate an interesting insight question with the graphAndAdvice endpoint) of InfraNodus. Step 6 (optional): Then send this insight via Telegram to a chat. Step 7 (optional): Link some important labels to the second Telegram notification node, so you receive important messages for specified labels. Step 8 (optional): Send a Telegram notification We use Telegram, because it takes only 30 seconds to set up a bot with an API (send /newbot to @botfather, unlike Discord or Slack, which is long and cumbersome to set up. You can also attach a Gmail send node and generate an email instead. How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account or log in. Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Add this Authorization code in Steps 4 and 5 of the workflow. Come up with the name of the graph and change it in the HTTP InfraNodus nodes in the steps 4 and 5 and also in the Telegram node in Step 6 that sends a link to the graph. For additional text processing / idea generation settings you can use in the HTTP InfraNodus nodes, see the InfraNodus access points page. For example, in Step 4 you can change the text processing settings to build a social knowledge graph (settings are available in the Node's Notes section) and in Step 5 you can change the requestMode from question to idea to receive business ideas instead. Authorize your Gmail account for Steps 2, 3, 7 and 8 Gmail nodes. The easiest way to set it up is to open a free Google Console API account and to create an OAuth access point for n8n. You can then reuse it with other Google services like Google Sheets, Drive, etc. So it's a useful thing to have in general. Set up the Gemini AI API key using the instructions in the Step 2 Gemini AI classification node. Set up the Telegram node bot for the Step 8. It takes only 30 seconds: just go to @botfather and type in /newbot and you'll have an API key ready. To get the conversation ID, follow the n8n / Telegram instructions in the node itself. Once everything is ready, try to run the default automated workflow to test if everything works well. Requirements An InfraNodus account and API key An Google Cloud API OAuth client and key for Gmail access A Gemini AI API key A Telegram bot API key n8n version 1.94 and higher (for Text Classification AI node to work) Customizing this workflow Check our other n8n workflows at https://n8n.io/creators/infranodus/ for useful content gap analysis, expert panel, and marketing, and research workflows that utilize GraphRAG for better AI generation. Finally, check out https://infranodus.com to learn more about our network analysis technology used to build knowledge graphs from text. For support, please, contact https://support.noduslabs.com

AI-Powered SEO Keyword Research Workflow with n8n > automates comprehensive keyword research for content creation Table of Contents Introduction Workflow Architecture NocoDB Integration Data Flow Core Components Setup Requirements Possible Improvements Introduction This n8n workflow automates SEO keyword research using AI and data-driven analytics. It combines OpenAI's language models with DataForSEO's analytics to generate comprehensive keyword strategies for content creation. The workflow is triggered by a webhook from NocoDB, processes the input data through multiple stages, and returns a detailed content brief with optimized keywords. Workflow Architecture The workflow follows a structured process: Input Collection: Receives data via webhook from NocoDB Topic Expansion: Generates keywords using AI Keyword Metrics Analysis: Gathers search volume, CPC, and difficulty metrics Competitor Analysis: Analyzes competitor content for ranking keywords Final Strategy Creation: Combines all data to generate a comprehensive keyword strategy Output Storage: Saves results back to NocoDB and sends notifications NocoDB Integration Database Structure The workflow integrates with two tables in NocoDB: Input Table Schema This table collects the input parameters for the keyword research: | Field Name | Type | Description | | --------------- | ------------- | --------------------------------------------------------------------------- | | ID | Auto Number | Unique identifier | | Primary Topic | Text | The main keyword/topic to research | | Competitor URLs | Text | Comma-separated list of competitor websites | | Target Audience | Single Select | Description of the target audience (Solopreneurs, Marketing Managers, etc.) | | Content Type | Single Select | Type of content (Blog, Product page, etc.) | | Location | Single Select | Target geographic location | | Language | Single Select | Target language for keywords | | Status | Single Select | Workflow status (Pending, Started, Done) | | Start Research | Checkbox | Active Workflow when you set this to true | Output Table Schema This table stores the generated keyword strategy: | Field Name | Type | Description | | ------------------ | ----------- | ------------------------------------------------ | | ID | Auto Number | Unique identifier | | primary_topic_used | Text | The topic that was researched | | report_content | Long Text | The complete keyword strategy in Markdown format | | generatedAt | Datetime | Automatically generated by NocoDb | Webhook Settings NocoDB Webhook Settings Data Flow The workflow handles data in the following sequence: Webhook Trigger: Receives input from NocoDB when a new keyword research request is created Field Extraction: Extracts primary topic, competitor URLs, audience, and other parameters AI Topic Expansion: Uses OpenAI to generate related keywords, categorized by type and intent Keyword Analysis: Sends primary keywords to DataForSEO to get search volume, CPC, and difficulty Competitor Research: Analyzes competitor pages to identify their keyword rankings Strategy Generation: Combines all data to create a comprehensive keyword strategy Storage & Notification: Saves the strategy to NocoDB and sends a notification to Slack Core Components 1. Topic Expansion This component uses OpenAI and a structured output parser to generate: 20 primary keywords 30 long-tail keywords with search intent 15 question-based keywords 10 related topics 2. DataForSEO Integration Two API endpoints are used: Search Volume & CPC**: Gets monthly search volume and cost-per-click data Keyword Difficulty**: Evaluates how difficult it would be to rank for each keyword 3. Competitor Analysis This component: Analyzes competitor URLs to identify which keywords they rank for Identifies content gaps or opportunities Determines the search intent their content targets 4. Final Keyword Strategy The AI-generated strategy includes: Top 10 primary keywords with metrics 15 long-tail opportunities with low competition 5 question-based keywords to address in content Content structure recommendations 3 potential content titles optimized for SEO Setup Requirements To use this workflow, you'll need: n8n Instance: Either cloud or self-hosted NocoDB Account: For data input and storage API Keys: OpenAI API key DataForSEO API credentials Slack API token (for notifications) Database Setup: Create the required tables in NocoDB as described above Possible Improvements The workflow could be enhanced with the following improvements: Enhanced Keyword Strategy Add topic clustering to group related keywords Enhance the final output with more specific content structure suggestions Include word count recommendations for each content section Additional Data Sources Integrate Google Search Console data for existing content optimization Add Google Trends data to identify rising topics Include sentiment analysis for different keyword groups Improved Competitor Analysis Analyze content length and structure from top-ranking pages Identify common backlink sources for competitor content Extract content headings to better understand content organization Automation Enhancements Add scheduling capabilities to run updates on existing content Implement content performance tracking over time Create alert thresholds for changes in keyword difficulty or search volume Example Output Here is an example Output the Workflow generated based on the following inputs. Inputs: Primary Topic: AI Automation Competitor URLs: n8n.io, zapier.com, make.com Target Audience: Small Business Owners Content Type: Landing Page Location: United States Language: English Output: Final Keyword Strategy The workflow provides a powerful automation for content marketers and SEO specialists to develop data-driven keyword strategies with minimal manual effort. > Original Workflow: AI-Powered SEO Keyword Research Automation - The vibe Marketer

This n8n template demonstrates how you can automate community moderation using human-in-the-loop functionality for Discord. The use-case is for detecting and dealing with spam messages in a predefined and consistent way. Human-in-the-loop allows for a balance between overly aggressive bots and time and effort from the moderation team. How it works A scheduled trigger is used to scan the most recent messages in a Discord Channel. Messages are tagged via the "Remove Duplicates" node so they don't get processed again in the future. Messages are grouped by user to allow for minimising of number of notifications sent. An AI text classifier node is then used to detect for spam in each user's message. When detected, a notification is sent to a moderation channel using the Send-and-wait mode for Discord. This notification comes with an n8n form and dropdown list of predefined actions to take in dealing with the spam messages. Once sent the workflow waits until a response is received. Once a moderator selects an action, the workflow continues and carries out a predefined moderation action. How to use Depending on how busy your community is and subject to spammers, you may need to increase the scheduled interval. Add as many or few moderation actions as required. Remember to activate the workflow to get it started. Requirements Discord channel for messages to moderate OpenAI for text classification Customising this template It is possible to cover multiple channels. Add as many as your community needs. Not using Discord. The template can also work in slack or other services which offer the same bot functionality.