Agents & Automations

This workflow automates email categorization in Gmail using the Gmail API and OpenAI's language model. It periodically checks for new emails, reads their content, and categorizes them based on existing Gmail labels. If no matching label is found, the workflow creates a new label and assigns it to the email. Key FeaturesPolling for Emails: The workflow triggers every 5 minutes to check for new emails using the Gmail Trigger node. Reading Labels: Existing Gmail labels are fetched to determine the most relevant match for email categorization. Dynamic Labeling: If no existing label matches, a new label is created dynamically based on the email's content. OpenAI Integration: The workflow uses OpenAI's Chat model to analyze email content and suggest or create appropriate labels. Email Categorization: Labels are applied to emails, ensuring they are organized in Gmail's structure. The workflow also removes less relevant emails (e.g., ads) from the inbox. Nodes in Use - Gmail Trigger: Polls Gmail every 5 minutes for new emails. - Gmail - Read Labels: Fetches all existing Gmail labels. - Gmail - Get Message: Retrieves the full content of a specific email. - Gmail - Add Label to Message: Assigns a chosen label to the email. - Gmail - Create Label: Creates a new label if necessary. - OpenAI Chat Model: Analyzes email content for categorization. - Memory Buffer: Retains context for email analysis across multiple iterations. - Wait Node: Adds a buffer period to manage email processing. Prerequisites - Gmail API Setup: Ensure Gmail OAuth2 credentials are configured in n8n. - OpenAI API Key: Configure OpenAI credentials for email analysis. - Labeling Standards: Maintain a consistent Gmail label structure for better organization. Instructions 1. Add your Gmail API credentials to the Gmail nodes. 2. Add your OpenAI API credentials to the OpenAI Chat Model node. 3. Activate the workflow. It will start polling for new emails every 5 minutes. 4. Monitor and refine the categorization logic if necessary to ensure alignment with Gmail's organizational needs. Use Case Ideal for individuals or teams handling high email volumes who want to maintain an organized inbox and automate repetitive categorization tasks. Note: You can improve the prompt to get better results from the agent by giving it more personal rules on how to categorize.

Create instant press releases using Airtable, Google Docs, and ChatGPT. A fast and easy solution for quality media communications without hassle.

Automatically generate engaging Instagram posts with AI-created images and custom mailhook (email) alerts using ChatGPT and Instagram Business.

Este template de n8n empodera a los equipos de soporte de TI al automatizar la ingestión de documentos y la resolución instantánea de consultas a través de una IA conversacional. Integra Google Drive, Pinecone y un agente de Chat AI (usando Google Gemini/OpenRouter) para transformar documentos de soporte estáticos en una base de conocimiento interactiva y buscable. Con dos flujos de trabajo interconectados—uno para procesar documentos de soporte y otro para manejar consultas de chat—los empleados reciben respuestas rápidas y contextuales directamente de su documentación de soporte. Flujo de trabajo de ingestión de documentos - Disparador de Google Drive: Monitorea una carpeta especificada para nuevas cargas de archivos (por ejemplo, documentos de soporte actualizados). - Descarga y extracción de archivos: Descarga automáticamente nuevos archivos y extrae el contenido de texto. - Limpieza de datos y división de texto: Utiliza un nodo de código para eliminar saltos de línea, recortar espacios adicionales y eliminar caracteres especiales, mientras que un separador de texto segmenta el contenido en fragmentos manejables. - Incorporación y almacenamiento: Genera incorporaciones de texto utilizando Google Gemini y las almacena en un almacén de vectores de Pinecone para una búsqueda rápida por similitud. Flujo de trabajo de consulta de chat - Disparador de chat: Se inicia cuando un empleado envía una consulta de soporte. - Búsqueda de vectores y recuperación de contexto: Recupera los segmentos de documento más relevantes de Pinecone basados en puntajes de similitud. - Construcción de prompt: Un nodo de código combina los fragmentos de documento recuperados con la consulta del usuario en un prompt detallado. - Respuesta del agente AI: El prompt construido se envía a un agente de IA (usando el modelo de chat de OpenRouter) para generar una solución clara y paso a paso. Beneficios clave y caso de uso Imagina una gran organización donde cada documento de soporte de TI—desde guías de solución de problemas hasta configuraciones del sistema—se almacena en una sola carpeta de Google Drive. Cuando un empleado encuentra un problema (por ejemplo, "¿Cómo restablezco mis credenciales de VPN?"), simplemente escribe la consulta en una interfaz de chat. Instantly, el flujo de trabajo recupera el contexto más relevante de los documentos ingeridos y proporciona una respuesta detallada y procesable. Este proceso reduce los tiempos de resolución, mejora la consistencia del soporte y aligera significativamente la carga del personal de TI. Requisitos previos - Una cuenta válida de Google Drive con acceso a la carpeta designada. - Una cuenta de Pinecone para almacenar y recuperar incorporaciones de texto. - Credenciales de Google Gemini (o OpenRouter) para alimentar el agente de Chat AI. - Una instancia de n8n en funcionamiento configurada con los nodos y credenciales necesarios. Detalles del flujo de trabajo 1. Flujo de trabajo de ingestión de documentos - Nodo de disparador de Google Drive: Escucha eventos de creación de archivos en la carpeta especificada. - Nodo de descarga de Google Drive: Descarga el archivo recién agregado. - Nodo de extracción de archivos: Extrae el contenido de texto del archivo descargado. - Nodo de código (limpieza de datos): Limpia el texto extraído eliminando saltos de línea, recortando espacios y eliminando caracteres especiales. - Nodo de separador de texto recursivo: Segmenta el texto limpio en fragmentos manejables. - Nodo de almacén de vectores de Pinecone: Genera incorporaciones (a través de Google Gemini) y carga los fragmentos en Pinecone. 2. Flujo de trabajo de consulta de chat - Nodo de disparador de chat: Recibe consultas de usuario entrantes. - Nodo de almacén de vectores de Pinecone (consulta): Busca fragmentos de documento relevantes basados en la consulta. - Nodo de código (constructor de contexto): Ordena los documentos recuperados por relevancia y construye un prompt que fusiona el contexto con la consulta. - Nodo de agente AI: Envía el prompt al agente de Chat AI, que devuelve una respuesta detallada. Cómo usar - Importar la plantilla: Importa la plantilla en tu instancia de n8n. - Configurar el disparador de Google Drive: Establece el ID de la carpeta y conecta tus credenciales de Google Drive. - Configurar los nodos de Pinecone: Ingresa los detalles de tu índice de Pinecone y credenciales. - Configurar el agente de Chat AI: Proporciona tus credenciales de API de Google Gemini (o OpenRouter). - Probar los flujos de trabajo: Valida el flujo de trabajo de ingestión de documentos cargando un documento de soporte de muestra. Valida el flujo de trabajo de consulta de chat enviando una consulta de prueba y verificando la información de soporte devuelta. Notas adicionales Asegúrate de que todas las credenciales (Google Drive, Pinecone y Chat AI) estén configuradas y probadas correctamente antes de implementar los flujos de trabajo en producción. Esta plantilla no solo mejora la eficiencia del soporte de TI, sino que también ofrece una solución escalable para gestionar y aprovechar volúmenes crecientes de contenido de soporte.

Who this is for? Extract Amazon Best Seller Electronic Info is an automated workflow that extracts best seller data from Amazon's Electronics section using Bright Data Web Unlocker, transforms it into structured JSON using Google Gemini's LLM, and forwards a fully structured JSON response to a specified webhook for downstream use. This workflow is tailored for: - eCommerce Analysts Who need to monitor Amazon best-seller trends in the Electronics category and track changes in real-time or on a schedule. - Product Intelligence Teams Who want structured insights on competitor offerings, including rankings, prices, ratings, and promotions. - AI-powered Chatbot Developers Who are building assistants capable of answering product-related queries with fresh, structured data from Amazon. - Growth Hackers & Marketers Looking to automate competitive research and surface trending product data to inform pricing strategies. - Data Aggregators and Price Trackers Who need reliable and smart scraping of Amazon data enriched with AI-driven parsing. What problem is this workflow solving? Keeping up with Amazon's best sellers in Electronics is a time-consuming, error-prone task when done manually. This workflow automates the process, ensuring: - Automating Data Extraction from Amazon Best Sellers using Bright Data, ensuring reliable access to real-time, structured data. - Enhancing Raw Data with Google Gemini, turning product lists into structured JSON using the Google Gemini LLM. - Sending Results to a Webhook, enabling seamless integration into dashboards, databases, or chatbots. What this workflow does The workflow performs the following steps: - Extracts Amazon Best Seller Electronics page info using Bright Data's Web Unlocker API. - Processes the unstructured content using Google Gemini's Flash Exp model to extract structured product data. - Sends the structured information to a webhook endpoint. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a market researcher, e-commerce entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: - Change the Amazon Category Update the Amazon URL with the topic of your interest such as Computers & Accessories, Home Audio, etc. - Customize the Gemini Prompt Update the Gemini prompt to get different styles of output — comparison tables, summaries, feature highlights, etc. - Send Output to Other Destinations Replace the Webhook URL to forward output to: - Google Sheets - Airtable - Slack or Discord - Custom API endpoints

Video Guide I prepared a detailed guide that demonstrates the complete process of building a trading agent automation using n8n and Telegram, seamlessly integrating various functions for stock analysis. Who is this for? This workflow is perfect for traders, financial analysts, and developers looking to automate stock analysis interactions via Telegram. It’s especially valuable for those who want to leverage AI tools for technical analysis without needing to write complex code. What problem does this workflow solve? Many traders desire real-time analysis of stock data but lack the technical expertise or tools to perform in-depth analysis. This workflow allows users to easily interact with an AI trading agent through Telegram for seamless stock analysis, chart generation, and technical evaluation, all while eliminating the need for manual interventions. What this workflow does This workflow utilizes n8n to construct an end-to-end automation process for stock analysis through Telegram communication. The setup involves: - Receiving messages via a Telegram bot. - Processing audio or text messages for trading queries. - Transcribing audio using OpenAI API for interpretation. - Gathering and displaying charts based on user-specified parameters. - Performing technical analysis on generated charts. - Sending back the analyzed results through Telegram. SetupPrepare Airtable: Create a simple table to store tickers. Prepare Telegram Bot: Ensure your Telegram bot is set up correctly and listening for new messages. Replace Credentials: Update all nodes with the correct credentials and API keys for services involved. Configure API Endpoints: Ensure chart service URLs are correctly set to interact with the corresponding APIs properly. Start Interaction: Message your bot to initiate analysis; specify ticker symbols and desired chart styles as required.

AI Content Generator Workflow This workflow automates the process of creating high-quality articles using AI, organizing them in Google Drive, and tracking their progress in Google Sheets. It's perfect for marketers, bloggers, and businesses looking to streamline content creation. With minimal setup, you can have a fully operational system to generate, save, and manage your articles in one cohesive workflow. How It Works 1. Collect Inputs: Users fill out a form with details like article title, keywords, and instructions. 2. Generate Content: AI creates an outline and writes the article based on user inputs. 3. Organize Files: Saves the outline and final article in Google Drive for easy access. 4. Track Progress: Updates Google Sheets with links to the generated content for tracking. Set Up Steps - Time Required: Approximately 15–20 minutes to connect all integrations and test the workflow. - Steps: 1. Connect Google Drive and Google Sheets: Authorize access to store files and update the spreadsheet. 2. Set Up OpenAI Integration: Add your OpenAI API key for generating the outline and article content. 3. Customize the Form: Modify the form fields to match the details you want to collect for each article. 4. Test the Workflow: Run the workflow with sample inputs to ensure everything works smoothly. This workflow not only simplifies the process of article creation but also sets a foundation for expanding into additional automations, like posting to social media platforms.

Automatically process invoice emails by saving attachments to Google Drive and extracting key invoice data to Google Sheets using AI. This workflow monitors your Gmail for unread emails with attachments, saves PDFs to a specified Google Drive folder, and uses OpenAI's GPT-4o to extract invoice details (date, description, amount) into a structured spreadsheet. Use cases - Invoice Management: Automatically organize and track invoices received via email. - Financial Record Keeping: Maintain a structured database of all invoice information. - Document Organization: Keep digital copies of invoices organized in Google Drive. - Automated Data Entry: Eliminate manual data entry for invoice processing. Resources - Gmail account - Google Drive account - Google Sheets account - OpenAI API key Setup instructionsPrerequisites - Active Gmail, Google Drive, and Google Sheets accounts - OpenAI API key (GPT-4o model access) - n8n instance with credentials manager StepsGmail and Google Drive Setup: - Connect your Gmail account in n8n credentials. - Connect your Google Drive account with appropriate permissions. - Create a destination folder in Google Drive for invoice storage. Google Sheets Setup: - Connect your Google Sheets account. - Create a spreadsheet with columns: Invoice date, Invoice Description, Total price, and Fichero. - Copy your spreadsheet ID for configuration. OpenAI Setup: - Add your OpenAI API key to n8n credentials. Configure Email Filter: - Update the email filter node to match your specific sender requirements. Benefits - Time Saving: Eliminates manual downloading, filing, and data entry. - Accuracy: AI-powered data extraction reduces human error. - Organization: Consistent file naming and storage structure. - Searchability: Creates a searchable database of all invoice information. - Automation: Runs every minute to process new emails as they arrive.

This n8n demonstrates how any organisation can quickly and easily build and offer MCP servers to their customers or internal staff to improve productivity. This MCP example uses PayCaptain.com as an example and shows how to create an MCP server which can search for and update employee data. How it works A MCP server trigger is used and connected to 3 custom workflow tools: Search Employee, Get Employee by ID, and Update Employee. Each tool makes calls to the PayCaptain API to perform their respective tasks. Extra care is performed to strip out sensitive data and ensure we're not sharing too much. The Update Employee tool also guards against updating fields which would preferably remain readonly. When you control the MCP server, you can determine the behavior of the tool. Finally, a Google Sheet node is used to log all operations for later audit. This will add a tiny bit of latency but is recommended if sensitive data is being accessed. How to use This MCP server allows any compatible MCP client to manage their PayCaptain employee database. You will need to have a PayCaptain account and developer key to use it. Connect your MCP client by following the n8n guidelines. Try the following queries in your MCP client: - "When did Sarah start her employment at the company?" - "Does Jack work Wednesdays or Fridays?" - "Please update Tracy's NI number to ABCD123456" Requirements - PayCaptain Account and Developer Key. - Google Sheets to log actions for later audit. - MCP Client or Agent for usage such as Claude Desktop. Customising this workflow Add or remove employee attributes as required for your use case. If Google Sheets is too slow, consider an API call to a faster service to log calls to the MCP server. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

Make.com automation that publishes to 9 social platforms daily! This workflow uses Blotato API to publish to social platforms and Google Drive/Sheets to grab the videos you want to post.

Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n. What It Does This workflow monitors a Google Drive folder for new files, supports multiple file types (PDF, TXT, JSON), and processes them into vector embeddings using OpenAI’s text-embedding-3-small model. These embeddings are stored in a Postgres database using the PGVector extension, making them query-ready for semantic search or RAG-based AI agents. After successful processing, files are moved to a separate “vectorized” folder to avoid duplication. Use Cases - Powering Retrieval-Augmented Generation (RAG) AI agents - Semantic search across private documents - AI assistant knowledge ingestion - Automated document pipelines for indexing or classification Workflow Highlights - Trigger Options: Manual or Scheduled (3 AM daily by default) - Supported File Types: PDF, TXT, JSON - Embedding Stack: LangChain Text Splitter, OpenAI Embeddings, PGVector - Deduplication: Files are moved after processing - License: CC BY-SA 4.0 What You’ll Need - Google Drive OAuth2 credentials (connected to Search Folder, Download File, and Move File nodes) - OpenAI API Key (used in the Embeddings OpenAI node) - Postgres + PGVector database (connected in the Postgres PGVector Store node) Step-by-Step Setup Instructions 1. Create Google OAuth2 credentials in n8n and connect them to all Google Drive nodes. 2. Set your source folder ID in the Search Folder node — this is where incoming files are placed. 3. Set your processed folder ID in the Move File node — files will be moved here after vectorization. 4. Ensure you have a PGVector-enabled Postgres instance and input the table name and collection in the Postgres PGVector Store node. 5. Add your OpenAI credentials to the Embeddings OpenAI node and select text-embedding-3-small. 6. Optional: Activate the Schedule Trigger node to run daily or configure your own schedule. 7. Run manually by triggering When clicking ‘Test workflow’ for on-demand ingestion. Customization Tips Want to support more file types or enhance the pipeline? - Add new extractors: Use Extract from File with other formats like DOCX, Markdown, or HTML. - Refine logic by file type: The Switch node routes files to the correct extraction method based on MIME type (application/pdf, text/plain, application/json). - Pre-process with OCR: Add an OCR step before extraction to handle scanned PDFs or images. - Add filters: Enhance the Search Folder or Switch node logic to skip specific files or folders. License Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) Use, adapt, and share - even commercially - as long as you give proper credit and share alike.

Time Logging on Clockify Using SlackHow it works This workflow simplifies time tracking for teams and agencies by integrating Slack with Clockify. It enables users to log, update, or delete time entries directly within Slack, leveraging an AI-powered assistant for seamless and conversational interactions. Key features include: - Effortless Time Logging: Create and manage time entries in Clockify without leaving Slack. - AI-Powered Assistant: Get step-by-step guidance to ensure accurate and efficient time logging. - Project and Client Management: Retrieve project and client information from Clockify effortlessly. - Overlap Prevention: Avoid overlapping entries with built-in time validation. - Automated Descriptions: Generate ethical, grammatically correct descriptions for time logs. Set up steps 1. Prepare your integrations Ensure you have active accounts for both Slack and Clockify. Generate your Clockify API credentials for integration. 2. Import the workflow Download and import the workflow template into your n8n instance. Configure the workflow to connect with your Slack and Clockify accounts. 3. Configure the workflow Add your Clockify API credentials in the workflow settings. Set up the Slack Trigger to listen for app mentions or specific commands. 4. Test the workflow Use Slack to create a time entry and verify it in Clockify. Test updating and deleting existing entries to ensure smooth functionality. Check for any overlapping time logs or incorrect data entries. Why use this workflow? - Efficiency: Eliminate the need to switch between tools for time tracking. - Accuracy: AI-driven validation ensures error-free entries. - Automation: Simplify repetitive tasks like updating or deleting time logs. - Proactive Guidance: Conversational assistant ensures smooth operations.

Avoid Asking Redundant Questions with Dynamically Generated Forms using OpenAITarget Audience This workflow has been built for those who require a form to capture as much data as possible, as well as the answers to predefined questions, whilst optimizing the user experience by avoiding redundant questions. Use Case When creating a form to capture information, it can be useful to give the user an opportunity to input a long answer to a large, open-ended question. We then want to drill down to answer specific questions that we require the answer to. When doing this, we don't want to ask duplicate questions. This particular scenario imagines an AI consultancy capturing leads. What it Does This workflow requires users to input basic information and then answer an open-ended question. The specific questions on the next page will only be those that weren't answered in the open-ended question. How it Works The open-ended question (and relevant basic information) is analyzed by an LLM to determine which specific questions have not been answered. Chain-of-thought reasoning is utilized and the output structure is specified with the Structured Output Parser. Those questions that have already been answered are filtered out nodes. The remaining items are then used to generate the last page of the form. Once the user has filled in the final page of the form, they are shown a form completion page. Next Steps - Add additional nodes to send an email to the form owner. - Add a subsequent LLM call to analyze the form response - those that are qualified should be given the opportunity to book an appointment.

Every time a new message is received in Facebook Messenger, Make will automatically create a personalized audio response to the sender via Google Text-to-Speech API.

Automatically respond to Telegram updates using insights from Google Docs and ChatGPT. Enhance communication by integrating Telegram, Google Docs, and OpenAI.

How it works 🗣️> 📖 I set up this workflow to convert any audio or video file into structured text using the new ElevenLabs Scribe model, one of the best Speech-to-Text AIs, available in 99+ languages. This workflow integrates seamlessly with n8n and leverages the ElevenLabs Scribe API to: ✅ Upload audio/video files automatically ✅ Transcribe them with industry-leading accuracy in any language ✅ Export the text for further processing (summaries, subtitles, SEO content, etc.) --- Business Cases 🔹 Podcast Transcriptions – Convert podcast episodes into blog posts for SEO and accessibility 🔹 YouTube Subtitles – Generate captions automatically for increased engagement 🔹 Legal & Compliance – Accurately transcribe meetings, interviews, or customer calls 🔹 E-learning – Turn lectures and webinars into structured course notes 🔹 SEO & Content Marketing – Repurpose videos into articles, quotes, and social media content 💡 Boost your productivity with the new Scribe model → Start with ElevenLabs Scribe --- Set up steps 🚀 Quick & simple setup in n8n – Upload your file, select the model (scribe_v1), and let the AI handle the rest via the ElevenLabs API. --- 📢 Why I Chose the New ElevenLabs Scribe Model? I wanted the most accurate and reliable transcription tool for my workflow. After testing different options, Scribe outperformed Google Gemini & OpenAI Whisper in independent benchmarks. It delivers high-quality transcriptions, even in underserved languages like Serbian, Mongolian, and many more. ✅ Transcribes in 99+ languages ✅ Fast, accurate, and easy to integrate ✅ Suitable for content creators, businesses, and professionals 🔗 Get started now and revolutionize your workflow with the new Scribe model → Try Scribe AI today 🚀

This n8n workflow demonstrates how you can summarise and automate post-meeting actions from video transcripts fed into an AI Agent. Save time between meetings by allowing AI to handle the chores of organising follow-up meetings and invites. How it works: This workflow scans for the calendar for client or team meetings which were held online. Attempts will be made to fetch any recorded transcripts, which are then sent to the AI agent. The AI agent summarises and identifies if any follow-on meetings are required. If found, the Agent will use its Calendar Tool to create the event for the time, date, and place for the next meeting, as well as add known attendees. Requirements: - Google Calendar and the ability to fetch Meeting Transcripts (There is a special OAuth permission for this action!) - OpenAI account for access to the LLM. Customising the workflow: This example only books follow-on meetings but could be extended to generate reports or send emails.

This workflow implements the Gemini AI chat model to summarize your daily meetings and send the summary to a Slack channel daily at 9 AM (or any other time you choose). It automatically retrieves your Google Calendar events and feeds them to the model. The workflow uses Google’s Gemini AI for response generation. How it works The workflow uses a Scheduled Trigger Node as the main trigger. The AI Agent Node uses the Google Calendar action to retrieve relevant meeting data. The AI Agent sends the retrieved information to the Google Gemini Chat Model (gemini-flash). The Google Gemini Chat Model generates a summary and informative response based on today’s meetings. Setup Steps 1. Google Cloud Project and Vertex AI API: - Create a Google Cloud project. - Enable the Vertex AI API for your project. 2. Google AI API Key: - Obtain a Google AI API key from Google AI Studio. 3. Credentials in n8n: - Configure credentials in your n8n environment for: - Google Gemini (PaLM) API (using your Google AI API key). 4. Import the Workflow: - Import this workflow into your n8n instance. 5. Configure the Workflow: - Update both Slack and Gemini nodes with your credentials.

LinkedIn Hiring Signal Scraper — Jobs & Prospecting Using Bright DataPurpose: Discover recent job posts from LinkedIn using Bright Data's Dataset API, clean the results, and log them into Google Sheets — for both job hunting and identifying high-intent B2B leads based on hiring activity. Use Cases:Job Seekers – Spot relevant openings filtered by role, city, and country.Sales & Prospecting – Use job posts as buying signals. If a company is hiring for a role you support (e.g., marketers, developers, ops) — it's the perfect time to reach out and offer your services. Tools Needed: - n8n Nodes: - Form Trigger - HTTP Request - Wait - If - Code - Google Sheets - Sticky Notes (for embedded guidance) - External Services: - Bright Data (Dataset API) - Google SheetsAPI Keys & Authentication Required: - Bright Data API Key → Add in the HTTP Request headers:Authorization: Bearer YOUR_BRIGHTDATA_API_KEY - Google Sheets OAuth2 → Connect your account in n8n to allow read/write access to the spreadsheet. General Guidelines: - Use descriptive names for all nodes. - Include retry logic in polling to avoid infinite loops. - Flatten nested fields (like job_poster and base_salary). - Strip out HTML tags from job descriptions for clean output. Things to be Aware Of: - Bright Data snapshots take ~1–3 minutes — use a Wait node and polling. - Form filters affect output significantly: 🔍 We recommend filtering by "Last 7 days" or "Past 24 hours" for fresher data. - Avoid hardcoding values in the form — leave optional filters empty if unsure. Post-Processing & Outreach: After data lands in Google Sheets, you can use it to: - Personalize cold emails based on job titles, locations, and hiring signals. - Send thoughtful LinkedIn messages (e.g., "Saw you're hiring a CMO...") - Prioritize outreach to companies actively growing in your niche. Additional Notes: 📄 Copy the Google Sheet Template: Click here to make your copy → Rename for each campaign or client. Form fields include: - Job Location (city or region) - Keyword (e.g., CMO, Backend Developer) - Country (2-letter code, e.g., US, UK) This workflow gives you a competitive edge — 📌 For candidates: Be first to apply. 📌 For sellers: Be first to pitch. All based on live hiring signals from LinkedIn. STEP-BY-STEP WALKTHROUGHStep 1: Set up your Google Sheet Open this template Go to File → Make a copy You'll use this copy as the destination for the scraped job posts. Step 2: Fill out the Input Form in n8n The form allows you to define what kind of job posts you want to scrape. Fields: - Job Location → e.g. New York, Berlin, Remote - Keyword → e.g. CMO, AI Architect, Ecommerce Manager - Country Code (2-letter) → e.g. US, UK, IL 💡 Pro Tip: For best results, set the filter inside the workflow to:time_range = "Past 24 hours" or "Last 7 days" This keeps results relevant and fresh. Step 3: Trigger Bright Data Snapshot The workflow sends a request to Bright Data with your input.Example API Call Body:json [ { "location": "New York", "keyword": "Marketing Manager", "country": "US", "time_range": "Past 24 hours", "job_type": "Part-time", "experience_level": "", "remote": "", "company": "" } ] Bright Data will start preparing the dataset in the background. Step 4: Wait for the Snapshot to Complete The workflow includes a Wait Node and Polling Loop that checks every few minutes until the data is ready. You don't need to do anything here — it's all automated. Step 5: Clean Up the Results Once Bright Data responds with the full job post list: ✔️ Nested fields like job_poster and base_salary are flattened ✔️ HTML in job descriptions is removed ✔️ Final data is formatted for export Step 6: Export to Google Sheets The final cleaned list is added to your Google Sheet (first tab). Each row = one job post, with columns like: job_title, company_name, location, salary_min, apply_link, job_description_plain. Step 7: Use the Data for Outreach or ResearchExample for Job Seekers: You search for: - Location: Berlin - Keyword: Product Designer - Country: DE - Time range: Past 7 days Now you've got a live list of roles — with salary, recruiter info, and apply links. → Use it to apply faster than others. Example for Prospecting (Sales / SDR): You search for: - Location: London - Keyword: Growth Marketing - Country: UK And find companies hiring growth marketers. → That's your signal to offer help with media buying, SEO, CRO, or your relevant service. Use the data to: - Write personalized cold emails ("Saw you're hiring a Growth Marketer…") - Start warm LinkedIn outreach - Build lead lists of companies actively expanding in your niche. Adjustments & Customization Tips: - Modify the HTTP Request body to add more filters (e.g. job_type, remote, company). - Increase or reduce polling wait time depending on Bright Data speed. - Add scoring logic to prioritize listings based on title or location. Final Notes: 📄 Google Sheet Template: Make your copy here ⚙️ Bright Data Dataset API: Visit BrightData.com 📬 Personalization works best when you act quickly. Use the freshest data to reach out with context — not generic pitches. This workflow turns LinkedIn job posts into sales insights and job leads. All in one click. Fully automated. Ready for your next move.

Hacker News to Video ContentOverview This workflow converts trending articles from Hacker News into engaging video content. It integrates AI-based tools to analyze, summarize, and generate multimedia content, making it ideal for content creators, educators, and marketers. FeaturesArticle Retrieval: - Pulls trending articles from Hacker News. - Limits the number of articles to process (configurable). Content Analysis: - Uses OpenAI's GPT model to: - Summarize articles. - Assess their relevance to specific topics like automation or AI. - Extract key image URLs. Image and Video Generation: - Leonardo.ai: Creates stunning AI-generated images based on extracted prompts. - RunwayML: Converts images into high-quality videos. Structured Content Creation: - Parses content into structured formats for easy reuse. - Generates newsletter-friendly blurbs and social media-ready captions. Cloud Integration: - Uploads generated assets to: - Dropbox - Google Drive - Microsoft OneDrive - MinIO Social Media Posting (Optional): - Supports posting to YouTube, X (Twitter), LinkedIn, and Instagram. Workflow Steps 1. Trigger - Initiated manually via the "Test Workflow" button. 2. Fetch Articles - Retrieves articles from Hacker News. - Limits the results to avoid processing overload. 3. Content Filtering - Evaluates if articles are related to AI/Automation using OpenAI's language model. 4. Image and Video Generation - Generates: - AI-driven image prompts via Leonardo.ai. - Videos using RunwayML. 5. Asset Management - Saves the output to cloud storage services or uploads directly to social media platforms. Prerequisites - API Keys: - Hacker News - OpenAI - Leonardo.ai - RunwayML - Creatomate - n8n Installation: - Ensure n8n is installed and configured locally or on a server. - Credentials: - Set up credentials in n8n for all external services used in the workflow. Customization - Replace Hacker News with any other data source node if needed. - Configure the "Article Analysis" node for different topics. - Adjust the cloud storage services or add custom storage options. Usage - Import this workflow into your n8n instance. - Configure your API credentials. - Trigger the workflow manually or schedule it as needed. - Check the outputs in your preferred cloud storage or social media platform. Notes - Extend this workflow further by automating social media posting or newsletter integration. - For any questions, refer to the official documentation or reach out to the creator. About the Creator This workflow was built by AlexK1919, an AI-native workflow automation architect. Check out the overview video for a quick demo. Tools Used - Leonardo.ai - RunwayML - Creatomate - Hacker News API - OpenAI GPT Feel free to adapt and extend this workflow to meet your specific needs! 🎉

This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links and HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.