Workflow Templates

This is a workflow that sends daily astronomy picture of the day using the NASA node to a channel on Telegram. Cron node: The Cron node triggers the workflow daily at 8 PM. You can update the time in the Cron node to trigger the workflow at your desired time. NASA node: After the Cron node triggers the workflow, the NASA node fetches the Astronomy Picture of the Day from the NASA API. You can also get the binary file of the image. Toggle Download Image to true to get the file. Telegram node: The Telegram node sends the image to a Telegram channel. If you want to share the image on another platform, you can replace the Telegram node with the node of that platform. For example, if you want to post the image on a channel on Slack, replace the Telegram node with the Slack node. You can learn to build this workflow on the documentation page of the NASA node.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically tracks key sales pipeline metrics—new leads, deal stages, win rates—and sends actionable insights to your team. Eliminate manual CRM exports and stay on top of revenue health. Overview The automation queries your CRM API (HubSpot, Salesforce, or Pipedrive) on a schedule, pulls pipeline data, and feeds it into OpenAI for anomaly detection (e.g., stalled deals). Summaries and alerts appear in Slack, while daily snapshots are archived in Google Sheets for trend analysis. Tools Used n8n** – Pipeline orchestration CRM API** – Connects to your chosen CRM OpenAI** – Detects anomalies and highlights risks Slack** – Notifies reps and managers in real time Google Sheets** – Stores historical pipeline data How to Install Import the Workflow into n8n. Connect Your CRM: Provide API credentials in the HTTP Request node. Set Up OpenAI: Add your API key. Authorize Slack & Google Sheets. Customize Thresholds: Adjust what constitutes a stalled deal or low conversion. Use Cases Sales Management**: Monitor pipeline health without dashboards. Revenue Operations**: Detect bottlenecks early. Forecasting**: Use historical snapshots to improve predictions. Rep Coaching**: Alert reps when deals stagnate. Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #salespipeline #crm #openai #slackalerts #n8nworkflow #nocode #revenueops

Description This n8n workflow acts as your personal AI speechwriting coach, directly accessible through Telegram. It listens to your spoken or typed drafts, provides insightful feedback on clarity, engagement, structure, and content, and iteratively refines your message based on your updates. Once you're ready, it synthesizes a brand-new speech or talk incorporating all the improvements and your accumulated ideas. This tool streamlines the speechwriting process, offering on-demand AI assistance to help you craft impactful and well-structured presentations. How it Works Input via Telegram: You interact with the workflow by sending your speech drafts or talking points directly to a designated Telegram bot. AI Feedback: The workflow processes your input using AI models (OpenAI and/or Google Gemini) to analyze various aspects of your speech and provides constructive feedback via Telegram. Iterative Refinement: You can then send updated versions of your speech to the bot, receiving further feedback to guide your revisions. Speech Synthesis: When you send the command to "generate speech," the workflow compiles all your previous input and the AI's feedback to synthesize a new, improved speech or talk, which is then sent back to you via Telegram. New Speech Cycle: By sending the command "new speech," the workflow clears its memory, allowing you to start the process anew for a different topic. Set Up Steps (Takes Approximatly 5 Minutes) Step 1: Create a Telegram Bot and Obtain its ID Open the Telegram application and search for "BotFather". Start a chat with BotFather by clicking "Start" or sending the /start command. Create a new bot by sending the command /newbot. Follow BotFather's instructions to choose a name and username for your bot. Once your bot is created, BotFather will provide you with an API token. Keep this token secure as it's required to connect your n8n workflow to your bot. Step 2: Obtain an OpenAI API Key Go to the OpenAI website (https://platform.openai.com/) and sign up for an account if you don't already have one. Navigate to the API keys section (usually under your profile settings or a "Developers" tab). Click on "Create new secret key". Copy the generated API key and store it securely. You will need to provide this key to your n8n workflow to access OpenAI's language models. Step 3: Obtain a Google Gemini LLM API Key Go to the Google Cloud AI Platform or Google AI Studio website (the specific platform may vary depending on the current Google AI offerings; search for "Google AI API"). Sign up or log in with your Google account. Follow the instructions to enable the Gemini API and create an API key. This might involve creating a project if you haven't already. Copy the generated API key and store it securely. You can then configure your n8n workflow to utilize Google Gemini's language models as well. Customization Options This n8n workflow offers significant flexibility, below are a few options: Modify AI prompts to tailor feedback and generation for presentations, storytelling, interviews, sales pitches, academic talks, and creative writing. Switch the interface from Telegram to Slack, WhatsApp, or even a web interface by replacing the relevant n8n nodes. Integrate analysis for sentiment, keyword density, pacing (with voice input), and filler word detection by adjusting the workflow. Connect to external data sources to provide context to the AI for more targeted feedback and generation. This adaptability allows you to re use this workflow for a wide range of specific use cases and communication environments.

💡🌐 Essential Multipage Website Scraper with Jina.ai Use responsibly and follow local rules and regulations This N8N workflow enables automated multi-page website scraping using Jina.ai's powerful web scraping capabilities, with seamless integration to Google Drive for content storage. Here's how it works: Main Features The workflow automatically scrapes multiple pages from a website's sitemap and saves each page's content as a separate Google Drive document. Key Components Input Configuration Starts with a sitemap URL (default: https://ai.pydantic.dev/sitemap.xml)** Processes the sitemap to extract individual page URLs Includes filtering options to target specific topics or pages Scraping Process Uses Jina.ai's web scraper to extract content from each URL Converts webpage content into clean markdown format Extracts page titles automatically for document naming Storage Integration Creates individual Google Drive documents for each scraped page Names documents using the format "URL - Page Title" Saves content in markdown format for better readability Usage Instructions Set your target website's sitemap URL in the "Set Website URL" node Configure the "Filter By Topics or Pages" node to select specific content Adjust the "Limit" node (default: 20 pages) to control batch size Connect your Google Drive account Run the workflow to begin automated scraping Additional Features Built-in rate limiting through the Wait node to prevent overloading servers Batch processing capability for handling large sitemaps The workflow requires no API key for Jina.ai, making it accessible for immediate use while maintaining responsible scraping practices.

Video explanation This n8n workflow helps you identify trending videos within your niche by detecting outlier videos that significantly outperform a channel's average views. It automates the process of monitoring competitor channels, saving time and streamlining content research. Included in the Workflow Automated Competitor Video Tracking Monitors videos from specified competitor channels, fetching data directly from the YouTube API. Outlier Detection Based on Channel Averages Compares each video’s performance against the channel’s historical average to identify significant spikes in viewership. Historical Video Data Management Stores video statistics in a PostgreSQL database, allowing the workflow to only fetch new videos and optimize API usage. Short Video Filtering Automatically removes short videos based on duration thresholds. Flexible Video Retrieval Fetches up to 3 months of historical data on the first run and only new videos on subsequent runs. PostgreSQL Database Integration Includes SQL queries for database setup, video insertion, and performance analysis. Configurable Outlier Threshold Focuses on videos published within the last two weeks with view counts at least twice the channel's average. Data Output for Analysis Outputs best-performing videos along with their engagement metrics, making it easier to identify trending topics. Requirements n8n installed on your machine or server A valid YouTube Data API key Access to a PostgreSQL database This workflow is intended for educational and research purposes, helping content creators gain insights into what topics resonate with audiences without manual daily monitoring.

This workflow automatically monitors competitor social media engagement on LinkedIn to track their content performance and posting strategies. It saves you time by eliminating the need to manually check competitor social media accounts and provides detailed analytics on their engagement metrics. Overview This workflow automatically scrapes LinkedIn company profiles to extract the latest 5 posts and analyzes their engagement metrics including likes, comments, and content performance. It uses Bright Data to access LinkedIn without being blocked and AI to intelligently parse post data, calculating average engagement rates and storing detailed post information. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping LinkedIn company profiles without being blocked OpenAI**: AI agent for intelligent post data extraction and analysis Google Sheets**: For storing engagement metrics and detailed post information How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the MCP Client node Set Up OpenAI: Configure your OpenAI API credentials Configure Google Sheets: Connect your Google Sheets account and set up your competitor tracking spreadsheets Customize: Enter target LinkedIn company URLs and adjust engagement tracking parameters Use Cases Social Media Marketing**: Analyze competitor content strategies and engagement patterns Competitive Intelligence**: Track competitor posting frequency and content performance Content Strategy**: Identify high-performing content types and messaging approaches Brand Monitoring**: Monitor competitor social media presence and audience engagement Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #socialmedia #competitoranalysis #linkedin #brightdata #webscraping #socialmonitoring #engagementtracking #n8nworkflow #workflow #nocode #socialautomation #competitormonitoring #contentanalysis #socialmediamonitoring #linkedinanalytics #engagementmetrics #competitorresearch #socialintelligence #contentperformance #socialmediaanalytics #brandmonitoring #competitortracking #socialmediastrategy #contentmarketing #socialmediadata #engagementanalysis #competitiveanalysis #linkedinscraping

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. This pipeline is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] KNN classification tool This tool takes any image URL, and as output, it returns a class of the object on the image based on the image uploaded to the Qdrant dataset (lands). An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage AI Multimodal Embeddings API to fetch its embedding. The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes. Majority voting is done for classes of neighbouring images. A loop is used to resolve scenarios where there is a tie in Majority Voting, and we increase the number of neighbours to retrieve. When the loop finally resolves, the identified class is returned to the calling workflow.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically monitors competitor product launches across news sites, press releases, and social channels. It saves you hours of manual tracking and ensures your team is instantly alerted when a rival announces something new. Overview The automation regularly scrapes predefined sources for mentions of your competitors combined with launch-related keywords. Bright Data provides reliable scraping, while OpenAI analyzes each article to extract key details (product name, features, launch date, pricing). Summaries are pushed to Slack and logged in Google Sheets so your marketing and product teams can react quickly. Tools Used n8n** – Orchestrates the entire workflow Bright Data** – Scrapes news, blogs, and social posts without blocks OpenAI** – Extracts and summarizes launch information Slack** – Sends real-time alerts to a chosen channel Google Sheets** – Creates a searchable launch database How to Install Import the Workflow: Upload the provided .json to your n8n instance. Configure Bright Data: Add your Bright Data credentials in the MCP Client node. Set Up OpenAI: Enter your OpenAI API key. Connect Slack & Google Sheets: Authorize both services and choose the target channel / spreadsheet. Customize Sources: Edit the list of competitor domains and launch keywords in the initial Set node. Use Cases Product Marketing**: Track rival announcements to refine positioning. Sales Enablement**: Equip reps with up-to-date competitive intel. Competitive Intelligence**: Maintain a historical log of launches for trend analysis. Investor Relations**: Stay informed of market movements that affect valuation. Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #competitiveintelligence #productlaunch #brightdata #webscraping #openai #slackalerts #n8nworkflow #nocode #marketintel

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically analyzes customer lifetime value (CLV) metrics to optimize customer acquisition and retention strategies. It saves you time by eliminating the need to manually calculate CLV and provides data-driven insights for maximizing customer profitability and improving business growth. Overview This workflow automatically scrapes customer data, purchase history, and engagement metrics to calculate and analyze customer lifetime value patterns. It uses Bright Data to access customer analytics platforms and AI to intelligently segment customers, predict CLV, and identify high-value customer characteristics. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping customer analytics and CRM platforms without being blocked OpenAI**: AI agent for intelligent CLV analysis and customer segmentation Google Sheets**: For storing CLV calculations and customer analysis data How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the MCP Client node Set Up OpenAI: Configure your OpenAI API credentials Configure Google Sheets: Connect your Google Sheets account and set up your CLV analysis spreadsheet Customize: Define customer data sources and CLV calculation parameters Use Cases Customer Success**: Focus retention efforts on high-value customers Marketing Strategy**: Optimize customer acquisition costs based on projected CLV Sales Teams**: Prioritize prospects with higher lifetime value potential Business Strategy**: Make data-driven decisions about customer investments Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #customerlifetimevalue #clv #customeranalytics #brightdata #webscraping #customerdata #n8nworkflow #workflow #nocode #customersegmentation #valueanalysis #customerinsights #revenueoptimization #customervalue #clvanalysis #customermetrics #customerprofitability #businessintelligence #customerretention #valueprediction #customeroptimization #revenueanalysis #customerstrategy #lifetimevalue #customerroi #valuedriven #customerworth #profitability

How it Works This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the key operational steps: Email Reception and Summarization: The workflow starts with an Email Trigger (IMAP) node that listens for new emails in a specified inbox. Once an email is received, its HTML content is processed by a Markdown node to convert it into plain text if necessary, followed by an Email Summarization Chain node which uses AI to create a concise summary of the email's content using prompts tailored for this purpose. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, utilizing predefined templates and guidelines such as keeping responses under 100 words and ensuring they're formatted correctly in HTML. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through a Gmail node configured with double-approval settings. If approved (Approve?), the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. Set Up Steps To replicate this workflow within your own n8n environment, follow these essential configuration steps: Configuration: Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site. Import the provided JSON configuration file into your workspace, making sure all required credentials like IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials section since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals. Customization: Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?. Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems like Google Sheets or similar platforms where records might need tracking post-interaction completion.

AI-Powered MCQ Quiz Generator from YouTube Videos Transform any YouTube video into an interactive MCQ quiz automatically! This workflow uses Google Gemini AI to analyze video content and generate comprehensive multiple-choice questions with automatic grading - perfect for educators, trainers, and content creators. Who is this For This workflow is perfect for: Educators** creating quizzes from educational YouTube content Corporate Trainers** developing assessments from training videos Content Creators** engaging their audience with interactive quizzes Students** testing their knowledge on video lectures Online Course Creators** building assessments from video content Features AI Video Analysis**: Google Gemini 2.5 Flash analyzes entire YouTube videos (up to 50 minutes) Dynamic Question Generation**: Creates up to 90 MCQ questions with 3 options each Automatic Form Creation**: Generates Google Forms with quiz functionality Smart Grading**: Built-in correct answer identification and scoring Error Handling**: Robust error management with user feedback How It Works User Input via n8n Web Form: Form Name (Quiz Title) Email Address YouTube Video URL Number of Questions (1-90) AI Processing Pipeline: Google Gemini analyzes the YouTube video content AI extracts key concepts and generates relevant questions Structured output parser formats questions into JSON Google Forms Integration: Automatically creates a new Google Form Adds all generated questions with multiple choice options Configures quiz settings with correct answers and scoring Completion & Access: User receives direct link to the generated quiz Form ready for immediate use or sharing Video Demo: See this youtube Video to explore "how it works". Set Up Steps Import the Workflow Create a new workflow in n8n Import the JSON file by clicking "three dots" (upper right corner) > "Import from file..." Configure Google Gemini API Get your Google AI Studio API key from Google AI Studio On “HTTP Request to Gemini” node replace the “API_KEY” from url with your API key. Create a "Google Gemini (PaLM) API" credential in n8n Add your API key to the credential Connect the credential to the "Google Gemini Chat Model" node Set Up Google Forms Integration Enable Google Forms API in Google Cloud Console Create a "Google OAuth2 API" credential in n8n Authorize the credential with Forms permissions Connect the credential to both HTTP Request nodes (“Create a Google Form” node and “Create MCQ Quizzes” node) Configure Form Trigger The workflow includes a built-in form trigger No additional setup needed - the form URL will be generated automatically Customize form fields if needed in the “Input YouTube URL" node Test the Workflow Activate the workflow Submit the form to generate a test quiz Verify the Google Form is created successfully Pre-requisites Necessary Accounts:** Google Account (for Forms API access) Google AI Studio Account (for Gemini API access) n8n Instance (cloud or self-hosted) API Access:** Google Forms API enabled Google drive API enabled Google Generative AI API access Valid API keys and OAuth credentials N8N Requirements:** n8n version 1.95.2 or higher LangChain nodes package installed Internet access for API calls Customization Guidance Question Generation Prompts: Modify the prompt in "Set Prompt and model" node for different question styles Adjust difficulty levels or focus areas Change question format (True/False, Fill-in-blanks, etc.) Form Customization: Update form title and description templates Add additional input fields (difficulty level, subject area) Customize success/error messages Advanced Features You Can Add: Email Notifications: Send quiz links via email Analytics Integration: Track quiz performance and completion rates Multi-language Support: Generate quizzes in different languages Question Bank Storage: Save generated questions to a database Batch Processing: Generate multiple quizzes from a YouTube playlist Error Handling Enhancements: Add retry logic for API failures Implement fallback question generation Create detailed error logging Technical Specifications Video Length**: Up to 50 minutes supported Question Limit**: 1-90 questions per quiz Processing Time**: 2-10 minutes depending on video length Supported Formats**: YouTube videos (public and unlisted) Output Format**: Google Forms with automatic grading Limitations & Considerations YouTube video must be publicly accessible or unlisted Processing time increases with video length and question count API rate limits may apply for high-volume usage Some complex visual content may not be fully analyzed Ready to Transform Videos into Quizzes? This workflow streamlines the entire process from video analysis to quiz deployment. Perfect for educators and trainers looking to create engaging assessments from video content quickly and efficiently.

This workflow enables seamless, bidirectional communication between WhatsApp and Slack using n8n. It automates the reception, processing, and forwarding of messages (text, media, and documents) between users on WhatsApp and private Slack channels. Key Features & Flow: 1. WhatsApp to Slack Flow Trigger: The workflow starts with a WhatsApp Trigger node that listens for new incoming messages via a webhook. Channel Handling: It checks if a Slack channel with the WhatsApp sender’s number exists If not, it creates a private Slack channel with the sender's number as the name. Message Type Routing: A Switch Node (Message Type) inspects the message type (text, image, audio, document). Based on type: Text: Sends the message directly to Slack. Image/Audio/Document: Retrieves media URL via WhatsApp API → downloads the media → uploads it to the appropriate Slack channel. 2. Slack to WhatsApp Flow Trigger: A Slack Trigger listens for new messages or file uploads in Slack. Message Type Routing: A second Switch Node (Checking Message Type) checks if the message is text or media. Routing Logic: Text Message: Extracts and forwards it to the WhatsApp contact (identified by the Slack channel name). Media/File Message: Retrieves media file URL from Slack → downloads it → sends it as a document via WhatsApp API. Key Integrations: WhatsApp Cloud API: For receiving messages, downloading media, and sending messages. Slack API: For creating/getting channels, posting messages, and uploading files. HTTP Request Node: Used to securely download media from Slack and WhatsApp servers with proper authentication. Automation Use Case: This workflow is ideal for businesses that handle customer support or conversations over WhatsApp and wish to log, respond, and collaborate using Slack as their internal communication tool.