Agents & Automations

LinkedIn lead generation workflow using HARPA and Make.com that collects leads from searches or company employees and enriches your database with profile information. All collected data is stored in a Google Sheet. You can connect it to your CRM, database, Airtable, or other systems.

Use Case Manually extracting images from PDF files for analysis is often slow and inefficient. Many users resort to taking screenshots of each page, uploading them to an AI tool like OpenAI for image analysis, and then manually copying the insights into a document. This manual process is time-consuming and prone to errors. This workflow streamlines the entire process by automatically extracting images from a PDF, analyzing them using the GPT-4o model, and saving the results in seconds—eliminating the need for manual effort.What This Workflow Does - Extracts all images from the uploaded PDF file automatically The workflow scans each page of the PDF and identifies embedded images without manual intervention. - Uses the GPT-4o model to analyze each extracted image Each image is processed through GPT-4o to generate descriptive insights, summaries, or context-specific analysis depending on the use case. - Saves the analysis results to a .txt file, including image URLs The final output is a plain text file containing both the image URLs (e.g., hosted on cloud storage) and the corresponding GPT-4o analysis, ready for further use or sharing.Setup 1. Set up your credentials when you first open the workflow. You’ll need accounts for OpenAI, Convert API, and Google Drive. 2. Convert API does not rate-limit your API; sometimes you may receive a 503 service unavailable error. Nevertheless, it doesn’t mean that you cannot convert your file. It simply means that you should retry the conversion in a few seconds. 3. Upload a PDF with images to Google Drive. 4. Remove unnecessary parts and retrieve image-related information. 5. Integrate image and image analysis information together. 6. Analyze each image using the OPENAI GPT-4o model. 7. Retrieve all image analysis content and image URLs. 8. Integrate multiple image URLs and analysis content. 9. Output content to a .txt file.How to Customize - Replace the manual trigger with a Google Drive trigger or other automation triggers. - Change the image analysis model (e.g., switch or fine-tune GPT-4o). - Send the results to other platforms (e.g., Slack, Telegram, LINE, etc.) instead of saving to a .txt file.

Who is this for This workflow is perfect for teams and individuals who manage extensive data in Notion and need a quick, AI-powered way to interact with their databases. If you're looking to streamline your knowledge management, automate searches, and get faster insights from your Notion databases, this workflow is for you. It’s ideal for support teams, project managers, or anyone who needs to query specific data across multiple records or within individual pages of their Notion setup. --- How it works The Notion Database Assistant uses an AI Agent built with Retrieval-Augmented Generation (RAG) to query this Knowledge Base style Notion database. The assistant can search across multiple properties like tags or questions and retrieves content from inside individual Notion pages for additional context. Key features include: - Querying the database with flexible filters. - Searching within individual Notion pages and extracting relevant blocks. - Providing a reference link to the exact Notion pages used to inform its responses, ensuring transparency and easy verification. This assistant uses two HTTP request tools—one for querying the Notion database and another for pulling data from within specific pages. It streamlines knowledge retrieval, offering a conversational, AI-driven way to interact with large datasets. --- Set up Find basic setup instructions inside the workflow itself or watch a quickstart video.

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries. How It WorksMessage Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input. Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text). Query Processing: The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings. These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation. The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response. Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio. Set Up StepsEstimated Time: 20–25 minutes Prerequisites: - Create an account with an AI provider (e.g., OpenAI). - Connect WhatsApp or Telegram credentials in n8n. - Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text). - Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation. - Import the workflow template into n8n and update environment variables for your credentials.

Streamline your analytics workflow with automated AI analysis of Google Analytics data, focusing on user and session metrics by country through ChatGPT. This setup enhances your decision-making by optimizing insights into key areas such as: - Traffic fluctuations - Variations in traffic by country - Forecasting future visitations The analyzed results are conveniently sent to stakeholders via a Google Doc. This scenario automatically executes on the 1st of every month at 9 AM, ensuring timely and impactful data delivery for strategic planning. - Countries with increased and decreased traffic - Forecast expected visitations for the upcoming weeks

Use Case When creating chatbots that interface through applications such as Telegram and WhatsApp, users can often send multiple shorter messages in quick succession, in place of a single, longer message. This workflow accounts for this behavior. What it Does This workflow allows users to send several messages in quick succession, treating them as one coherent conversation instead of separate messages requiring individual responses. How it Works When messages arrive, they are stored in a Supabase PostgreSQL table. The system waits briefly to see if additional messages arrive. If no new messages arrive within the waiting period, all queued messages are: - Combined and processed as a single conversation - Responded to with one unified reply - Deleted from the queue Setup Create a table in Supabase called message_queue. It needs to have the following columns: user_id (uint8), message (text), and message_id (uint8). Add your Telegram, Supabase, OpenAI, and PostgreSQL credentials. Activate the workflow and test by sending multiple messages to the Telegram bot in one go. Wait ten seconds after which you will receive a single reply to all of your messages. How to Modify it to Your Needs Change the value of Wait Amount in the Wait 10 Seconds node in order to modify the buffering window. Add a System Message to the AI Agent to tailor it to your specific use case. Replace the OpenAI sub-node to use a different language model.

Monthly Spotify Track Archiving and Playlist Classification This n8n workflow allows you to automatically archive your monthly Spotify liked tracks in a Google Sheet, along with playlist details and descriptions. Based on this data, Claude 3.5 is used to classify each track into multiple playlists and add them in bulk. Who is this template for? This workflow template is perfect for Spotify users who want to systematically archive their listening history and organize their tracks into custom playlists. What problem does this workflow solve? It automates the monthly process of tracking, storing, and categorizing Spotify tracks into relevant playlists, helping users maintain well-organized music collections and keep a historical record of their listening habits. Workflow Overview - Trigger Options: Can be initiated manually or on a set schedule. - Spotify Playlists Retrieval: Fetches the current playlists and filters them by owner. - Track Details Collection: Retrieves information such as track ID and popularity from the user’s library. - Audio Features Fetching: Uses Spotify's API to get audio features for each track. - Data Merging: Combines track information with their audio features. - Duplicate Checking: Filters out tracks that have already been logged in Google Sheets. - Data Logging: Archives new tracks into a Google Sheet. - AI Classification: Uses an AI model to classify tracks into suitable playlists. - Playlist Updates: Adds classified tracks to the corresponding playlists. Setup Instructions - Credentials Setup: Make sure you have valid Spotify OAuth2 and Google Sheets access credentials. - Trigger Configuration: Choose between manual or scheduled triggers to start the workflow. - Google Sheets Preparation: Set up a Google Sheet with the necessary structure for logging track details. - Spotify Playlists Setup: Have a diverse range of playlists and exhaustive description ready to accommodate different music genres and moods. Customization Options - Adjust Playlist Conditions: Modify the AI model’s classification criteria to align with your personal music preferences. - Enhance Track Analysis: Incorporate additional audio features or external data sources for more refined track categorization. - Personalize Data Logging: Customize which track attributes to log in Google Sheets based on your archival preferences. - Configure Scheduling: Set a preferred schedule for periodic track archiving, e.g., monthly or weekly. Cost Estimate For 300 tracks, the token usage amounts to approximately 60,000 tokens (58,000 for input and 2,000 for completion), costing around 20 cents with Claude 3.5 Sonnet (as of October 2024). Playlists' Description Examples - Classique: Indulge in the timeless beauty of classical music with this refined playlist. From baroque to romantic periods, this collection showcases renowned compositions. - Poi: Find your flow with this dynamic playlist tailored for poi, staff, and ball juggling. Featuring rhythmic tracks that complement your movements. - Pro Sound: Boost your productivity and focus with this carefully selected mix of concentration-enhancing music. Ideal for work or study sessions. - ChillySleep: Drift off to dreamland with this soothing playlist of sleep-inducing tracks. Gentle melodies and ambient sounds create a peaceful atmosphere for restful sleep. - To Sing: Warm up your vocal cords and sing your heart out with karaoke-friendly tracks. Featuring popular songs, perfect for solo performances or group sing-alongs. - 1990s: Relive the diverse musical landscape of the 90s with this eclectic mix. From grunge to pop, hip-hop to electronic, this playlist showcases defining genres. - 1980s: Take a nostalgic trip back to the era of big hair and neon with this 80s playlist. Packed with iconic hits and forgotten gems, capturing the energy of the decade. - Groove Up: Elevate your mood and energy with this upbeat playlist. Featuring a mix of feel-good tracks across various genres to lift your spirits and get you moving. - Reggae & Dub: Relax and unwind with the laid-back vibes of reggae and dub. This playlist combines classic reggae tunes with deep, spacious dub tracks for a chilled-out vibe. - Psytrance: Embark on a mind-bending journey with this collection of psychedelic trance tracks. Ideal for late-night dance sessions or intense focus. - Cumbia: Sway to the infectious rhythms of Cumbia with this lively playlist. Blending traditional Latin American sounds with modern interpretations for a danceable mix. - Funky Groove: Get your body moving with this collection of funk and disco tracks. Featuring irresistible basslines and catchy rhythms, perfect for dance parties. - French Chanson: Experience the romance and charm of France with this mix of classic and modern French songs, capturing the essence of French musical culture. - Workout Motivation: Push your limits and power through your exercise routine with this high-energy playlist. From warm-up to cool-down, these tracks will keep you motivated. - Cinematic Instrumentals: Immerse yourself in a world of atmospheric sounds with this collection of cinematic instrumental tracks, perfect for focus, relaxation, or contemplation.

Google Calendar AI Agent with Dynamic SchedulingVersion: 1.0.0n8n Version: 1.88.0+Author: KoresolucoesLicense: MIT An AI-powered workflow to automate Google Calendar operations using dynamic parameters and MCP (Model Control Plane) integration. Enables event creation, availability checks, updates, and deletions with timezone-aware scheduling. Key Features: 📅 Full Calendar CRUD: Create, read, update, and delete events in Google Calendar. ⏰ Availability Checks: Verify time slots using AVALIABILITY_CALENDAR node with timezone support (e.g., America/Sao_Paulo). 🤖 AI-Driven Parameters: Use $fromAI() to inject dynamic values like Start_Time, End_Time, and Description. 🔗 MCP Integration: Connects to an MCP server for centralized AI agent control.Use Cases: - Automated Scheduling: Book appointments based on AI-recommended time slots. - Meeting Coordination: Sync calendar events with CRM/task management systems. - Resource Management: Check room/equipment availability before event creation.Instructions: 1. Import Template: Go to n8n > Templates > Import from File and upload this workflow. 2. Configure Credentials: Add Google Calendar OAuth2 credentials under Settings > Credentials. Ensure the calendar ID matches your target (e.g., ODONTOLOGIA group calendar). 3. Set Up Dynamic Parameters: Use $fromAI('Parameter_Name') in nodes like CREATE_CALENDAR to inject AI-generated values (e.g., event descriptions). 4. Activate & Test: Enable the workflow and send test requests to the webhook path /mcp/:tool/calendar.Notes: Extend multi-tenancy by adding :userId to the webhook path (e.g., /mcp/:userId/calendar). For timezone accuracy, always specify options.timezone in availability checks. Refer to n8n’s Google Calendar docs for advanced field mappings.Tags: Google Calendar, Automation, MCP, AI Agent, Scheduling, CRUD

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks, which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submit photos or images that are required to meet certain criteria before being accepted. For example, a wine review website may require users to only submit photos of wine with labels, or a bank may require account holders to submit scanned documents for verification. In this demonstration, our scenario will be to analyze a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website. How it works: Our set of portraits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate whether the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object, which has the "is_valid" boolean property. This can be useful to further extend the workflow. Not using Gemini? n8n's LLM node works with any compatible multimodal LLM, so feel free to swap Gemini out for OpenAI's GPT-4 or Anthropic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos, and more.

This workflow was presented at the AI Developers meetup in San Francisco on 24 July, 2024. AI workflows: - Categorize incoming Gmail emails and assign custom Gmail labels. This example uses the Text Classifier node, simplifying this use case. - Ingest a PDF into a Pinecone vector store and chat with it (RAG example). - AI Agent example showcasing the HTTP Request tool. We teach the agent how to check availability on a Google Calendar and book an appointment.

Who is this for? Jira users who want to automate the generation of a Lessons Learned or Retrospective report after an Epic is Done. What problem is this workflow solving? / use case Lessons Learned / Retrospective reports are often omitted in Agile teams because they take time to write. With the use of n8n and AI, this process can be automated. What is this workflow doing? - Triggers automatically upon an Epic reaching the "Done" status in Jira. - Collects all related tasks and comments associated with the completed Epic. - Intelligently filters the gathered data to provide the LLM with the most relevant information. - Utilizes an LLM with a structured System Message to generate insightful reports. - Delivers the finalized report directly to your specified Google Docs document. How to customize this workflow to your needs Change the System Message in the AI Agent to fit your needs.

Who is this for? This workflow is for digital marketers, small business owners, lead generation agencies, and VAs who need a scalable way to find and store local business leads using AI. It’s especially useful for teams that want to enrich leads with real-time news insights and save the structured data to Airtable. What problem is this workflow solving? Manually researching local businesses and staying up to date with relevant news is time-consuming and inefficient. This automation eliminates that burden by using Dumpling AI chat agents to generate leads and context, GPT-4o to summarize, and Airtable to store everything in one place. What this workflow does This AI workflow listens for a manual trigger in n8n and executes the following steps: 1. Extracts local business leads using a Local Business Agent from Dumpling AI. 2. Pulls current news related to the business type or location using a News Agent from Dumpling AI. 3. Uses GPT-4o to combine both responses into a human-readable summary. 4. Extracts structured lead data like name, category, and city. 5. Saves the summary and lead data into Airtable for easy follow-up. Setup 1. Create AI Agents in Dumpling AI Sign in at Dumpling AI and create two separate agents: - Local Business Agent: Designed to respond with structured lists of businesses by location and category. - News Agent: Designed to fetch relevant recent news and summaries about a specific industry or region. After setting up each agent, copy the Agent Key from Dumpling AI. These keys will be required in the headers of your HTTP Request nodes in n8n. 2. Manual Trigger This workflow begins with a manual trigger inside n8n, which is when a chat message is received. This makes it easy to test and reuse, especially during setup. 3. Get Local Business Data from Dumpling AI The first HTTP Request node sends a prompt like "List 5 top real estate companies in Atlanta with full address and services." Include your Local Business Agent Key in the x-agent-key header. The response will return a structured list of business leads. 4. Get News Context from Dumpling AI The second HTTP Request node sends a prompt such as "Give me the latest news related to the real estate market in Atlanta." Use your News Agent Key in the header. This fetches a brief set of recent news summaries relevant to the businesses being researched. 5. Use GPT-4o to Merge and Summarize The GPT node combines the list of businesses and news into one coherent summary. You can modify the prompt to output in paragraph format, bullet points, or structured notes. 6. Save Lead to Airtable The Airtable node sends all structured fields into your selected base and table. Be sure to connect your Airtable account and confirm the columns match exactly. How to customize this workflow - Replace the prompt inside the HTTP node to focus on different types of businesses or cities. - Expand the GPT output to include additional lead info like websites, phone numbers, or emails if the agent includes them. - Add a webhook trigger to allow this flow to be run via a chatbot, external app, or button. - Link to HubSpot or another CRM to sync the leads automatically. - Duplicate the process to run for multiple industries in parallel. Final Notes You must create and configure your Dumpling AI agents first before running this workflow. The Agent Keys from Dumpling AI are required in both HTTP Request nodes. This flow is modular and flexible, ready for deeper CRM integrations. The manual trigger is great for testing, but you can add a Webhook node to automate it. This workflow helps you launch an intelligent lead gen process that combines location-targeted business discovery, AI-generated insights, and structured CRM-friendly output, all powered by Dumpling AI and OpenAI.

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 2) This is the second part of the Luma AI Dream Machine automation. It captures the webhook response from Luma AI after video generation is complete, processes the data, and automatically updates Airtable with the video and thumbnail URLs. This completes the end-to-end automation for video creation and tracking. 👉 Airtable Base Template 👉 Tutorial VideoSetup 1. Luma AI Setup Ensure you’ve created an account with Luma AI and generated an API key. Confirm that the API key has permission to manage video requests. 2. Airtable Setup Make sure your Airtable base includes the following fields (set up in Part 1): Use the Airtable Base Template linked above to simplify setup. - Generation ID – To match incoming webhook data. - Status – Workflow status (e.g., "Done"). - Video URL – Stores the generated video URL. - Thumbnail URL – Stores the thumbnail URL. 3. n8n Setup Ensure that the n8n workflow from Part 1 is set up and configured. Import this workflow and connect it to the webhook callback from Luma AI. How It Works 1. Webhook Trigger The Webhook node listens for a POST response from Luma AI once video generation is finished. The response includes: - Video URL – Direct link to the video. - Thumbnail URL – Link to the video thumbnail. - Generation ID – Used to match the record in Airtable. 2. Process Webhook Data The Set node extracts the video data from the webhook response. The If node checks if the video URL is valid before proceeding. 3. Store in Airtable The Airtable node updates the record with: - Video URL – Direct link to the video. - Thumbnail URL – Link to the video thumbnail. - Status – Marked as "Done." Uses the Generation ID to match and update the correct record. Why This Workflow is Useful ✅ Automates the completion step for video creation ✅ Ensures accurate record-keeping by matching generation IDs ✅ Simplifies the process of managing and organizing video content ✅ Reduces manual effort by automating the update process Next Steps Future Enhancements – Adding more complex post-processing, video trimming, and multi-platform publishing.

This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication. How It WorksEmail Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian. Email Classification: The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other". Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. The Review email node uses another AI model (DeepSeek) to review and format the drafted response. It ensures the response is professional and formatted in HTML (e.g., using <br>, <b>, <i>, <p> tags where necessary). Email Sending: The Send Email node sends the reviewed and formatted response back to the original sender. Vector Database Integration: The Qdrant Vector Store node retrieves relevant information from a vector database (Qdrant) to assist in generating accurate responses. This is particularly useful for emails classified as "Company info request". The Embeddings OpenAI node generates embeddings for the email content, which are used to query the vector database. Document Vectorization: The workflow includes steps to create and refresh a Qdrant collection (Create collection and Refresh collection nodes). Documents from Google Drive are downloaded (Get folder and Download Files nodes), processed into embeddings (Embeddings OpenAI1 node), and stored in the Qdrant vector store (Qdrant Vector Store1 node). Set Up StepsConfigure Email Trigger: Set up the Email Trigger (IMAP) node with the appropriate IMAP credentials to monitor the email inbox. Set Up AI Models: Configure the DeepSeek R1, OpenAI, and DeepSeek nodes with the appropriate API credentials for text summarization, response generation, and review. Set Up Email Classification: Define the categories in the Email Classifier node (e.g., "Company info request", "Other"). Ensure the OpenAI 4-o-mini node is configured to assist in classification. Set Up Vector Database: Configure the Qdrant Vector Store and Qdrant Vector Store1 nodes with the appropriate Qdrant API credentials and collection details. Set up the Embeddings OpenAI and Embeddings OpenAI1 nodes to generate embeddings for the email content and documents. Set Up Document Processing: Configure the Get folder and Download Files nodes to access and download documents from Google Drive. Use the Token Splitter and Default Data Loader nodes to process and split the documents into manageable chunks for vectorization. Set Up Email Sending: Configure the Send Email node with the appropriate SMTP credentials to send responses. Test the Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all steps execute correctly. Verify that emails are summarized, classified, and responded to accurately. Activate the Workflow: Once tested, activate the workflow to automate the process of handling incoming emails. Key Features - Automated Email Handling: Automatically processes incoming emails, summarizes them, and generates professional responses. - AI-Powered Classification: Uses AI to classify emails into relevant categories for targeted responses. - Vector Database Integration: Retrieves relevant information from a vector database to enhance response accuracy. - Document Vectorization: Processes and stores documents from Google Drive in a vector database for quick retrieval. - Professional Email Formatting: Ensures responses are professionally formatted and concise. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Monitor Google Sheets for new rows and automatically generate chat responses using Perplexity AI and ChatGPT's Assistant. Update Google Sheets with the AI-generated content.

Overview This workflow answers user requests sent via Mac Shortcuts. Several Shortcuts call the same webhook, with a query and a type of query. Types of query are: - Translate to English - Translate to Spanish - Correct grammar (without changing the actual content) - Make content shorter - Make content longerHow it works 1. Select a text you are writing. 2. Launch the shortcut. 3. The text is sent to the webhook. Depending on the type of request, a different prompt is used. Each request is sent to an OpenAI node. The workflow responds to the request with the response from GPT. The shortcut replaces the selected text with the new one. How to use it 1. Activate the workflow. 2. Download this Shortcut template. 3. Install the shortcut. 4. In step 2 of the shortcut, change the URL of the webhook. 5. In Shortcut details, "add Keyboard Shortcut" with the key you want to use to launch the shortcut. 6. Go to settings, advanced, check "Allow running scripts." You are ready to use the shortcut. Select a text and hit the keyboard shortcut you just defined.

How It Works This workflow automates the entire property lead generation process in a few simple steps: Property Search: Connects to BatchData's Property Search API with customizable parameters (location, property type, value range, equity percentage, etc.) Lead Filtering & Scoring: Processes results to identify the most promising leads based on criteria like absentee ownership, years owned, equity percentage, and tax status. Each property receives a lead score to prioritize follow-up. Skip Tracing: Automatically retrieves owner contact information (phone, email, mailing address) for each qualified property. Data Formatting: Structures all property and owner data into a clean, organized format ready for your systems. Multi-Channel Output: - Generates an Excel spreadsheet with all lead details - Pushes leads directly to your CRM (configurable for HubSpot, Salesforce, etc.) - Sends a summary email with the spreadsheet attached The workflow can run on a daily schedule or be triggered manually as needed. All parameters are easily configurable through dedicated nodes, requiring no coding knowledge. Who's It For This workflow is perfect for: - Real Estate Investors looking to find off-market properties with motivated sellers - Real Estate Agents who want to generate listing leads from distressed or high-equity properties - Investment Companies that need regular lead flow for acquisitions - Real Estate Marketers who run targeted campaigns to property owners - Wholesalers seeking to build a pipeline of potential deals - Property Service Providers (roof repair, renovation contractors, etc.) who target specific property types Anyone who needs reliable, consistent lead generation for real estate without the manual work of searching, filtering, and organizing property data will benefit from this automation. About BatchData BatchData is a comprehensive property data provider that offers access to nationwide property information, owner details, and skip tracing services. Key features include: - Extensive Database: Covers 150+ million properties across all 50 states - Rich Property Data: Includes ownership information, tax records, sales history, valuation estimates, equity positions, and more - Skip Tracing Services: Provides owner contact information including phone numbers, email addresses, and mailing addresses - Distressed Property Indicators: Flags for pre-foreclosure, tax delinquency, vacancy, and other motivation factors - RESTful API: Professional API for programmatic access to all property data services - Regular Updates: Continuously refreshed data for accurate information BatchData's services are designed for real estate professionals who need reliable property and owner information to power their marketing and acquisition strategies. Their API-first approach makes it ideal for workflow automation tools like N8N.

Introduction The "Automatic Weekly Digital PR Stories Suggestions" workflow is a sophisticated automated system designed to identify trending news stories on Reddit, analyze public sentiment through comment analysis, extract key information from source articles, and generate strategic angles for potential digital PR campaigns. This workflow leverages the power of social media trends, natural language processing, and AI-driven analysis to deliver curated, sentiment-analyzed news opportunities for PR professionals. Operating on a weekly schedule, the workflow searches Reddit for posts related to specified topics, filters them based on engagement metrics, and performs a deep analysis of both the content and public reaction. It then generates comprehensive reports that include story opportunities, audience insights, and strategic recommendations. These reports are automatically compiled, stored in Google Drive, and shared with team members via Mattermost for immediate collaboration. This workflow solves the time-consuming process of manually monitoring social media for trending stories, analyzing public sentiment, and identifying PR opportunities. By automating these tasks, PR professionals can focus on strategy development and execution rather than spending hours on research and analysis. Who is this for? This workflow is designed for digital PR professionals, content marketers, communications teams, and media relations specialists who need to stay on top of trending stories and public sentiment to develop timely and effective PR campaigns. It's particularly valuable for: - PR agencies managing multiple clients across different industries - In-house PR teams needing to identify media opportunities quickly - Content marketers looking for trending topics to create timely content - Communications professionals monitoring public perception of industry news Users should have basic familiarity with n8n workflows and the PR strategy development process. While technical knowledge of the integrated APIs is not required to use the workflow, some understanding of Reddit, sentiment analysis, and PR campaign development would be beneficial for interpreting and acting on the generated reports. What problem is this workflow solving? Digital PR professionals face several challenges that this workflow addresses: - Information Overload: Manually monitoring social media platforms for trending stories is time-consuming and often results in missed opportunities. - Sentiment Analysis Complexity: Understanding public perception of news stories requires reading through hundreds of comments and identifying patterns, which is labor-intensive and subjective. - Content Extraction: Visiting multiple news sources to read and analyze articles takes significant time. - Strategic Angle Development: Identifying unique PR angles that leverage trending stories and public sentiment requires synthesizing large amounts of information. - Team Collaboration: Sharing findings and insights with team members in a structured format can be cumbersome. By automating these processes, the workflow enables PR professionals to quickly identify trending stories with PR potential, understand public sentiment, and develop strategic angles based on comprehensive analysis, all while maintaining a structured approach to team collaboration. What this workflow doesOverview The workflow automatically identifies trending posts on Reddit related to specified topics, analyzes both the content of linked articles and public sentiment from comments, and generates comprehensive PR strategy reports. These reports include story opportunities, audience insights, and strategic recommendations based on the analysis. The final reports are compiled, stored in Google Drive, and shared with team members via Mattermost. Process - Topic Selection and Reddit Search: The workflow starts with a list of topics specified in the "Set Data" node. It searches Reddit for posts related to these topics. Posts are filtered based on upvotes and other criteria to focus on trending content. - Comment Analysis: For each post, the workflow retrieves comments. It extracts the top 30 comments based on score. Using Claude AI, it analyzes the comments to understand: - Overall sentiment - Dominant narratives - Audience insights - PR implications - Content Analysis: The workflow extracts the content of the linked article using Jina AI. It analyzes the content to identify: - Core story elements - Technical aspects - Narrative opportunities - Viral elements - PR Strategy Development: Based on the combined analysis of comments and content, the workflow generates: - First-mover story opportunities - Trend-amplifier story ideas - Priority rankings - Execution roadmap - Strategic recommendations - Report Generation and Distribution: The workflow compiles comprehensive reports for each post. Reports are converted to text files. All files are compressed into a ZIP archive. The archive is uploaded to Google Drive. A link to the archive is shared with team members via Mattermost. Setup To set up this workflow, follow these steps: 1. Import the Workflow: Download the workflow JSON file and import it into your n8n instance. 2. Configure API Credentials: - Reddit: Add a new credential "Reddit OAuth2 API" by following the guide. - Anthropic: Add a new credential "Anthropic Account" by following the guide. - Google Drive: Add a new credential "Google Drive OAuth2 API" by following the guide. 3. Configure the "Set Data" Node: Set your interested topics (one per line). Add your Jina API key. 4. Configure the Mattermost Node: Update your Mattermost instance URL. Set your Webhook ID and Channel. Follow the guide for webhook setup. 5. Adjust the Schedule (Optional): The workflow is set to run every Monday at 6 am. Modify the "Schedule Trigger" node if you need a different schedule. 6. Test the Workflow: Run the workflow manually to ensure all connections are working properly. Check the output to verify the reports are being generated correctly. How to customize this workflow to your needs This workflow can be customized in several ways to better suit your specific requirements: - Topic Selection: Modify the topics in the "Set Data" node to focus on industries or subjects relevant to your PR strategy. Add multiple topics to cover different client interests or market segments. - Filtering Criteria: Adjust the "Upvotes Requirement Filtering" node to change the minimum upvotes threshold. Modify the filtering conditions to include or exclude certain types of posts. - Analysis Parameters: Customize the prompts in the "Comments Analysis," "News Analysis," and "Stories Report" nodes to focus on specific aspects of the content or comments. Adjust the temperature settings in the Anthropic Chat Model nodes to control the creativity of the AI responses. - Report Format: Modify the "Set Final Report" node to change the structure or content of the final reports. Add or remove sections based on your specific reporting needs. - Distribution Method: Replace or supplement the Mattermost notification with email notifications, Slack messages, or other communication channels. Add additional storage options beyond Google Drive. - Schedule Frequency: Change the "Schedule Trigger" node to run the workflow more or less frequently. Set up multiple triggers for different topics or clients. - Integration with Other Systems: Add nodes to integrate with your CRM, content management system, or project management tools. Create connections to automatically populate content calendars or task management systems.

Easily condense crucial email information using Gmail and Eden AI integration, and share summarized data on Slack to enhance team communication and productivity.

A powerful RSS feed scraper that automatically finds trending news articles for your social media content! This AI-powered system helps you create viral content by scraping, summarizing, and storing current events in a searchable database.

This template can be set up at a regular interval (every month, week, hour, or on a specific date) to automatically save today's Fireflies.ai transcripts to Google Docs.