Workflow Templates

Tired of juggling maintenance calls, lost requests, and slow vendor responses? This workflow streamlines the entire property maintenance process — from tenant request to vendor dispatch — powered by AI categorization and automated communication. Cut resolution time from 5–7 days to under 24 hours and boost tenant satisfaction by 85% with zero manual follow-up. What This Workflow Does Transforms chaotic maintenance management into seamless automation: 📝 Captures Requests – Tenants submit issues via JotForm with unit number, issue description, urgency, and photos. 🤖 AI Categorization – OpenAI (GPT-4o-mini) analyzes and classifies issues (plumbing, HVAC, electrical, etc.). ⚙️ Smart Prioritization – Flags emergencies (leak, electrical failure) and assigns priority. 📬 Vendor Routing – Routes issue to the correct contractor or vendor based on AI category. 📧 Automated Communication – Sends acknowledgment to tenant and work order to vendor via Gmail. 📊 Audit Trail Logging – Optionally logs requests in Google Sheets for performance tracking and reporting. Key Features 🧠 AI-Powered Categorization – Intelligent issue type and priority detection. 🚨 Emergency Routing – Automatically escalates critical issues. 📤 Automated Work Orders – Sends detailed emails with property and tenant info. 📈 Google Sheets Logging – Transparent audit trail for compliance and analytics. 🔄 End-to-End Automation – From form submission to vendor dispatch in seconds. 💬 Sticky Notes Included – Every section annotated for easy understanding. Perfect For Property management companies Real estate agencies and facility teams Smart building operators Co-living and rental startups Maintenance coordinators managing 50–200+ requests monthly What You’ll Need Required Integrations: JotForm – Maintenance request form Create your form for free on JotForm using this link OpenAI (GPT-4o-mini) – Categorization and prioritization Gmail – Automated email notifications (Optional) Google Sheets – Logging and performance tracking Quick Start Import Template – Copy JSON into n8n and import. Create JotForm – Include fields: Tenant name, email, unit number, issue description, urgency, photo upload. Add Credentials – Configure JotForm, Gmail, and OpenAI credentials. Set Vendor Emails – Update “Send to Contractor” Gmail node with vendor email IDs. Test Workflow – Submit sample maintenance requests for AI categorization and routing. Activate Workflow – Go live and let your tenants submit maintenance issues. Expected Results ⏱️ 24-hour average resolution time (vs 5–7 days). 😀 85% higher tenant satisfaction with instant communication. 📉 Zero lost requests – every issue logged automatically. 🧠 AI-driven prioritization ensures critical issues handled first. 🕒 10+ hours saved weekly for property managers. Pro Tips 🧾 Add Google Sheets logging for a complete audit trail. 🔔 Include keywords like “leak,” “no power,” or “urgent” in AI prompts for faster emergency detection. 🧰 Expand vendor list dynamically using a Google Sheet lookup. 🧑‍🔧 Add follow-up automation to verify task completion from vendors. 📊 Create dashboards for monthly maintenance insights. Learning Resources This workflow demonstrates: AI categorization using OpenAI’s Chat Model (GPT-4o-mini) Multi-path routing logic (emergency vs. normal) Automated communication via Gmail Optional data logging in Google Sheets Annotated workflow with Sticky Notes for learning clarity

Grab our list of chats from Airtable to send a random recipe. If the chat ID isn't in our airtable, we add it. This is to send a new recipe daily. . https://spoonacular.com/food-api/docs. https://spoo...

Conference Synthetic Personas: Slack → Gemini → CRM Insights Who is this for? Event strategists, conference organizers, and marketing teams planning content/networking who want to interview realistic audience personas based on their participantants behavioural data before spending budget. What problem is this workflow solving? Event deisgn and management is guesswork: Content misses audience needs Networking formats flop No pre-validation of concepts This workflow creates interviewable synthetic personas from your real CRM data, test ideas pre-event. What this workflow does Trigger**: Slack /doppelganger "EventX" 5 hubspot CRM Pull**: HubSpot/Salesforce/Sheets attendee data Gemini Analysis**: Generates 5+ realistic personas per event Slack Cards**: Rich persona profiles + 14 auto-interview questions Thread Replies**: Team follow-ups in persona context Sheets Log**: Personas + conversations archived Setup (8 minutes) Slack**: OAuth2 + /doppelganger slash command Gemini**: Google API key (Flash/Pro) CRM**: HubSpot API / Salesforce OAuth / Google Sheets Sheets ID**: Personas + Conversations tabs Fully configurable, no code changes needed. How to customize to your needs CRMs**: HubSpot → Salesforce → Sheets CSV Personas**: Speakers/Exhibitors/Attendees Questions**: Edit 14 interview prompts (5 categories) Scale**: Multi-event batching Output**: Add Teams/Notion sync ROI: 40% better content relevance** (pre-validated) 25% lower no-show rates** (targeted comms) 2h → 2min** persona generation Need help customizing?: Contact me for consulting and support: LinkedIn / Message Keywords: event personas, synthetic audience, conference planning, attendee segmentation, event strategy automation

Generate and publish Instagram carousels automatically Turn a single topic into a published Instagram Carousel in minutes. Creating educational carousel posts usually takes hours: writing the script, designing the slides in Figma/Canva, exporting images, and scheduling. This workflow automates the entire pipeline using Gemini AI, Google Slides, and the Meta Graph API. It generates the content, designs the visuals by manipulating a template, and publishes the carousel directly to your Instagram Business account. 🚀 What this workflow does Script Generation: Runs daily (or on demand) to prompt Google Gemini to write a 6-slide educational script (Hook, Mistake, Why It Matters, Value, Tip, CTA). Design Automation: Copies a master Google Slides template. Uses a "Find & Replace" operation to insert the AI-generated text into the correct placeholders. Generates thumbnail images for each slide. Image Hosting: Uploads the slide images to ImgBB to get public URLs (required by Meta's API). Publishing: Creates a carousel container on Instagram using the Meta Graph API. Checks the container status until it is "FINISHED". Publishes the media to your feed. Logging: Records the post details, captions, and status in Google Sheets. 💡 Key Features True Design Automation:** Doesn't just overlay text on images; it uses real Google Slides templates, allowing for complex layouts and branding. Smart Polling:** Includes a "Wait & Check" loop to ensure the media container is fully processed by Facebook before attempting to publish (prevents API errors). Structured Content:** The AI is prompted to follow a proven "Viral Educational" framework (Hook -> Value -> Action). Asset Management:** Automatically organizes generated slide images and links in Google Sheets for your archives. 🛠️ Prerequisites Google Cloud:** Enabled APIs for Drive, Slides, Sheets, and Gemini. Meta Developer App:** An Instagram Business account connected to a Facebook Page, with a System User token (instagram_basic, instagram_content_publish, pages_read_engagement). ImgBB Account:** A free API key for temporary image hosting. Templates:** A Google Sheet and Google Slide template (links provided in the workflow sticky notes). 📝 Setup Instructions Resources: Copy the provided Google Sheet and Slide templates to your Drive. Credentials: Authenticate Google, Meta, and ImgBB in n8n. Configuration: Update the Google Drive node with your Slide Template ID. Update the Google Sheets nodes with your Sheet ID. Update the HTTP Request nodes with your ImgBB API Key and Instagram Account ID. Run: Activate the schedule or click "Execute" to generate your first post! Need help setting this up or want a custom automation for your agency? I specialize in building agentic workflows for consultants and agencies. 📧 Contact me: bakdaulet.mph@gmail.com

How it works This workflow takes a list of links from Google Sheets, visits each page, extracts the main text using Decodo, and creates a summary with the help of artificial intelligence. It helps you turn research articles or web pages into clear, structured insights you can reuse for your projects, content ideas, or newsletters. Input: A Google Sheet named input with one column called url. Output: Another Google Sheet named output, where all the processed data is stored: URL:** original article link Title:** article title Source:** website or domain Published Date:** publication date (if found) Main Topic:** main theme of the article Key Ideas:** three main takeaways or insights Summary:** short text summary Text Type:** type of content (e.g., article, blog, research paper) Setup steps Connect your Google Sheets account. Add your links to the input sheet. In the Decodo node, insert your API key. Configure the AI model (for example, Gemini). Run the workflow and check the results in the output sheet.

Generate and approve Twitter content from Google News using AI and Telegram This n8n template automates the creation and approval of Twitter (X) content using real-time news data, AI-generated text, and a human approval workflow. It is designed to streamline content production while maintaining quality control through manual review. Good to know Generates 5 tweet variations per execution in different styles Uses structured JSON output to ensure reliable parsing Prevents duplicate content by tracking previously used articles Requires an active workflow for Telegram approval callbacks Google Sheets acts as the central data store How it works A scheduled trigger initiates the workflow at a defined time. The workflow retrieves trending news from Google News RSS and extracts relevant details such as title, description, and publication date. Previously used articles are fetched from Google Sheets and filtered out to ensure fresh content selection. The selected article is passed to an AI model (Groq), which generates five tweet variations across different styles including informational, opinion-based, and engagement-focused formats. The AI response is cleaned and converted into structured JSON. Each tweet is enriched with metadata such as scheduled time, status, and a unique identifier. Tweets are sent to Telegram with inline approval buttons. User actions are captured through Telegram callbacks, and the workflow updates the corresponding status (Approved or Rejected) in Google Sheets. Finally, the processed article is logged to prevent reuse. How to use The workflow runs on a schedule by default, but this can be replaced with a manual trigger or webhook depending on your requirements. Each execution generates multiple tweets and sends them for review. You can approve or reject each tweet directly within Telegram. Requirements n8n (cloud or self-hosted) Groq account or compatible AI provider Google Sheets account Telegram bot created via BotFather Customising this workflow Adjust the AI prompt to modify tone, format, or writing style Change scheduling intervals to match your posting strategy Extend the workflow to publish approved tweets automatically Integrate additional platforms such as LinkedIn or Slack Add analytics or tracking for performance monitoring Notes Replace all placeholder values before activating the workflow Ensure Telegram callback format is configured as: approve::rowId reject::rowId

Who’s it for Social-media managers, growth hackers, and brands who want to keep their Instagram accounts active by auto-liking posts from specific profiles they track—without scrolling feeds manually. How it works / What it does Schedule Trigger runs every 2 h. Profile Post Extractor pulls up to 20 recent posts from each Instagram profile in your CSV. Select Cookie rotates Instagram session-cookies. Get Random Post picks one and checks against instagram_posts_already_liked.csv. Builds instagram_posts_to_like.csv, uploads to SharePoint. Phantombuster Autolike Agent likes the post. Liked URLs are appended to prevent duplicates. Wait nodes throttle activity (~12 likes/profile/day). How to set up Add credentials: Phantombuster API, SharePoint OAuth2. In SharePoint › “Phantombuster” folder create: • instagram_session_cookies.txt (one per line). • instagram_posts_already_liked.csv (header postUrl). • profiles_instagram.csv with profile URLs. Adjust schedule if needed. Activate the workflow—likes will run automatically. Requirements n8n 1.33+ Phantombuster Growth plan Microsoft 365 SharePoint tenant How to customize Add/remove tracked profiles in profiles_instagram.csv. Adjust throttle by changing Wait intervals. Swap SharePoint for Google Drive/Dropbox if needed.

🤖 AI Workflow Recommender (RAG + Qdrant + Gemini) This workflow helps users find the most relevant n8n templates using AI. It combines Retrieval-Augmented Generation (RAG), vector search (Qdrant), and Gemini to understand user intent and recommend workflows based on meaning, not just keywords. ⚙️ How it works Collect workflow templates from the n8n API using multiple search queries Process and clean the data (split, format, deduplicate) Convert workflows into embeddings using Gemini Store embeddings in a vector database (Qdrant) Accept user queries via chat interface Convert queries into embeddings Retrieve relevant workflows using semantic search Generate AI-powered recommendations with explanations and template links 🚀 What this workflow does Understands user intent (not just keywords) Finds relevant workflows using semantic similarity Recommends the best workflows with explanations Provides ready-to-use template links 🧩 Setup steps Set up Qdrant (Cloud or self-hosted) Add Google Gemini API credentials Run the Data Ingestion workflow to populate the database Activate the RAG chatbot workflow ⚠️ Important Make sure the vector database is populated before using the chatbot Ensure embedding model and vector dimension match Free-tier APIs may have rate limits 🎥 Tutorial @youtube

AI Editor-in-Chief: Trend Research to 3 Notion Blog Drafts This workflow acts as your personal "AI Editor-in-Chief," fully automating the process from trend research to content creation. It scrapes Google Search results and generates three distinct article drafts (with different angles) complete with AI-generated cover images, saving everything directly to Notion. 🎯 Target Audience Bloggers & Affiliate Marketers: Individuals who struggle with writer's block and want to maintain a consistent posting schedule. Content Marketers & Editors: Teams running owned media who need to efficiently generate high-volume article ideas and drafts based on trends. SEO Specialists: Professionals who need to quickly create content based on the latest search keywords. ⚙️ How it Works & Features This workflow automates the entire editorial process: Automated Research: Scrapes top Google Search results for a specific keyword (e.g., "2025 AI Tools") using Apify. Multi-Angle Planning: GPT-4o analyzes the research and brainstorms article concepts from 3 different perspectives (e.g., "Beginner's Guide," "Critical Review," "Business Use Case"). Writing & Visualizing: For each concept, the AI writes a full article body in Markdown and DALL-E 3 generates a matching cover image. CMS Entry: Automatically saves the Title, Body Text, and Cover Image URL into a Notion database as a draft. Notification: Sends a completion report with links to the created Notion pages via Slack. 🛠 Setup Instructions Import: Copy the workflow JSON and paste it into your n8n editor. Credentials: Set up credentials for the following nodes: Apify: Required for the Google Search Scraper actor. OpenAI: Required for GPT-4o and DALL-E 3. Notion: Connect your account to access your database. Slack: Connect your account for notifications. Configuration: Open the Workflow Configuration node and set your desired Search Keyword. Notion Setup: Create a database in Notion (with properties for Title, Content, etc.). Crucial Step: Go to the Notion database page menu ... > Connect to and select your n8n integration to grant permission. Select this database in the Save Article to Notion node. Slack Setup: In the Send Completion Notification to Slack node, specify the target channel name (e.g., general). 📦 Requirements n8n Version: v1.0 or higher (recommended). Apify Account: Access to the apify/google-search-scraper actor. OpenAI Account: API access to GPT-4o and DALL-E 3. Notion Account: A workspace with a database. Slack Account: A workspace for receiving notifications. 🔧 Customization Change Keywords: Simply update the searchKeyword value in the Workflow Configuration node to target any topic (e.g., "Keto Diet," "Tech Gadgets," "Investment Trends"). Adjust Angles: Modify the System Prompt in the AI Editorial Meeting node to change the persona or angles (e.g., "Pros & Cons," "Global Reaction," "Tutorial"). Change Destination: You can replace the Notion node with a WordPress node to draft articles directly into your CMS. Scheduling: Update the Schedule Trigger node to run daily, weekly, or on specific days as needed.

This Workflow auto-ingests Google Drive documents, parses them with LlamaIndex, and stores Azure OpenAI embeddings in an in-memory vector store—cutting manual update time from ~30 minutes to under 2 minutes per doc. Why Use This Workflow? Cost Reduction: Eliminates pays monthly fee on cloud just for store knowledge Ideal For Knowledge Managers / Documentation Teams:** Automatically keep product docs and SOPs in sync when source files change on Google Drive. Support Teams:** Ensure the searchable KB is always up-to-date after doc edits, speeding agent onboarding and resolution time. Developer / AI Teams:** Populate an in-memory vector store for experiments, rapid prototyping, or local RAG demos. How It Works Trigger: Google Drive Trigger watches a specific document or folder for updates. Data Collection: The updated file is downloaded from Google Drive. Processing: The file is uploaded to LlamaIndex cloud via an HTTP Request to create a parsing job. Intelligence Layer: Workflow polls LlamaIndex job status (Wait + Monitor loop). If parsing status equals SUCCESS, the result is retrieved as markdown. Output & Delivery: Parsed markdown is loaded into LangChain's Default Data Loader, passed to Azure OpenAI embeddings (deployment "3small"), then inserted into an in-memory vector store. Storage & Logging: Vector store holds embeddings in memory (good for prototyping). Optionally persist to an external vector DB for production. Setup Guide Prerequisites | Requirement | Type | Purpose | |-------------|------|---------| | n8n instance | Essential | Execute and import the workflow — use the n8n instance | | Google Drive OAuth2 | Essential | Watch and download documents from Google Drive | | LlamaIndex Cloud API | Essential | Parse and convert documents to structured markdown | | Azure OpenAI Account | Essential | Generate embeddings (deployment configured to model name "3small") | | Persistent Vector DB (e.g., Pinecone) | Optional | Persist embeddings for production-scale search | Installation Steps Import the workflow JSON into your n8n instance: open your n8n instance and import the file. Configure credentials: Azure OpenAI: Provide Endpoint, API Key and set deployment name. LlamaIndex API: Create an HTTP Header Auth credential in n8n. Header Name: Authorization. Header Value: Bearer YOUR_API_KEY. Google Drive OAuth2: Create OAuth 2.0 credentials in Google Cloud Console, enable Drive API, and configure the Google Drive OAuth2 credential in n8n. Update environment-specific values: Replace the workflow's Google Drive fileId with the GUID or folder ID you want to watch (do not commit public IDs). Customize settings: Polling interval (Wait node): adjust for faster or slower job status checks. Target file or folder: toggled on the Google Drive Trigger node. Embedding model: change Azure OpenAI deployment if needed. Test execution: Save changes and trigger a sample file update on Drive. Verify each node runs and the vector store receives embeddings. Technical Details Core Nodes | Node | Purpose | Key Configuration | |------|---------|-------------------| | Knowledge Base Updated Trigger (Google Drive Trigger) | Triggers on file/folder changes | Set trigger type to specific file or folder; configure OAuth2 credential | | Download Knowledge Document (Google Drive) | Downloads file binary | Operation: download; ensure OAuth2 credential is selected | | Parse Document via LlamaIndex (HTTP Request) | Uploads file to LlamaIndex parsing endpoint | POST multipart/form-data to /parsing/upload; use HTTP Header Auth credential | | Monitor Document Processing (HTTP Request) | Polls parsing job status | GET /parsing/job/{{jobId}}; check status field | | Check Parsing Completion (If) | Branches on job status | Condition: {{$json.status}} equals SUCCESS | | Retrieve Parsed Content (HTTP Request) | Fetches parsed markdown result | GET /parsing/job/{{jobId}}/result/markdown | | Default Data Loader (LangChain) | Loads parsed markdown into document format | Use as document source for embeddings | | Embeddings Azure OpenAI | Generates embeddings for documents | Credentials: Azure OpenAI; Model/Deployment: 3small | | Insert Data to Store (vectorStoreInMemory) | Stores documents + embeddings | Use memory store for prototyping; switch to DB for persistence | Workflow Logic On Drive change, the file binary is downloaded and sent to LlamaIndex. Workflow enters a monitor loop: Monitor Document Processing fetches job status, If node checks status. If not SUCCESS, Wait node delays before re-check. When parsing completes, the workflow retrieves markdown, loads documents, creates embeddings via Azure OpenAI, and inserts data into an in-memory vector store. Customization Options Basic Adjustments: Poll Delay: Set Wait node (default: every minute) to balance speed vs. API quota. Target Scope: Switch the trigger from a single file to a folder to auto-handle many docs. Embedding Model: Swap Azure deployment for a different model name as needed. Advanced Enhancements: Persistent Vector DB Integration: Replace vectorStoreInMemory with Pinecone or Milvus for production search. Notification: Add Slack or email nodes to notify when parsing completes or fails. Summarization: Add an LLM summarization step to generate chunk-level summaries. Scaling option: Batch uploads and chunking to reduce embedding calls; use a queue (Redis or n8n queue patterns) and horizontal workers for high throughput. Performance & Optimization | Metric | Expected Performance | Optimization Tips | |--------|----------------------|-------------------| | Execution time (per doc) | ~10s–2min (depends on file size & LlamaIndex processing) | Chunk large docs; run embeddings in batches | | API calls (per doc) | 3–8 (upload, poll(s), retrieve, embedding calls) | Increase poll interval; consolidate requests | | Error handling | Retries via Wait loop and If checks | Add exponential backoff, failure notifications, and retry limits | Troubleshooting | Problem | Cause | Solution | |---------|-------|----------| | Authentication errors | Invalid/missing credentials | Reconfigure n8n Credentials; do not paste API keys directly into nodes | | File not found | Incorrect fileId or permissions | Verify Drive fileId and OAuth scopes; share file with the service account if needed | | Parsing stuck in PENDING | LlamaIndex processing delay or rate limit | Increase Wait node interval, monitor LlamaIndex dashboard, add retry limits | | Embedding failures | Model/deployment mismatch or quota limits | Confirm Azure deployment name (3small) and subscription quotas | Created by: khmuhtadin Category: Knowledge Management Tags: google-drive, llamaindex, azure-openai, embeddings, knowledge-base, vector-store Need custom workflows? 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This n8n template demonstrates how to automatically process feedback on tasks and procedures using an AI agent. Employees provide feedback after completing a task, which is then analyzed by the AI to suggest improvements to the underlying procedures. Improvements can be to update how to execute a single tasks or to split or merge tasks within a procedure. The management reviews decides whether to implement those improvements. This makes it easy to close the loop between execution, feedback, and continuous process improvement. Use cases are many: Marketing (improve the process of approving advertising content) Finance (optimize the process of expense reimbursement) Operations (refine the process of equipment maintenance) Good to know The automation is based on the Baserow template for handling Standard Operating Procedures. However, it can also be implemented in other databases. Baserow authentication is done through a database token. Check the documentation on how to create such a token. Tasks are inserted using the HTTP request node instead of a dedicated Baserow node. This is to support batch import instead of importing records one by one. Requirements Baserow account (cloud or self-hosted) The Baserow template for handling Standard Operating Procedures or a similar database with the following tables and fields: Procedures table with general procedure information like to name or description . Procedures steps table with all the steps associated with a procedure. Tasks table that contains the actual tasks based on the procedure steps. must have a field to capture Feedback must have a boolean field to indicate if the feedback has been processed or not. This to avoid that the same feedback keeps getting used. Improvement suggestions table to store the suggestions that were made by the AI agent. How it works Set table and field ids** Stores the ids of the involved Baserow database and tables, together with the information to make requests to the Baserow API Feedback processing agent** The prompt contains a small instruction to check the feedback and suggest improvements to the procedures. The system message is much more extensive to provide as much details and guidance to the agent as possible. It contains the following sections: Role: giving the agent a clear professional perspective Goals: allowing the agent to focus on clarity, efficiency and actionable improvements. Instructions: guiding the agent to a step-by-step flow Output: showing the agent the expected format and details Notes: setting guardrails for the agent to make justified and practical suggestions. The agent uses the following nodes: OpenAI Chat Model (Model): the template uses by default the gpt-4.1 model from OpenAI. But you can replace this with any LLM. current_procedures (Tool): provides information about all available procedures to the agent current_procedure steps (Tool): provides information about every step in the procedures to the agent tasks_feedback (Tool): provides the feedback of the employees to the agent. Required output schema (Output parser): forces the agent to use a JSON schema that matches the Improvement suggestions table structure for the output. This allows to easily add them to the database in the next step. Create improvement suggestions** Calls the API endpoint /api/database/rows/table/{table_id}/batch/ to insert multiple records at once in the Improvement suggestions table. The inserted records is the output generated by the AI agent. Check the Baserow API documentation for further details. Get non-processed feedback** Gets all records from the Tasks table that contain feedback but that are not marked as processed yet. Set feedback to processed** Updates the boolean field for each task to true to indicate that the feedback has been processed Aggregate records for input** Aggregates the data from the previous nodes as an array in a property named items. This matches perfect with the Baserow API to insert new records in batch. Update tasks to processed feedback** Calls the API endpoint /api/database/rows/table/{table_id}/batch/ to update multiple records at once in the Tasks table. The updated records will have their processed field set to true. Check the Baserow API documentation for further details. How to use The Manual Trigger node is provided as an example, but you can replace it with other triggers such as a webhook The included Baserow SOP template works perfectly as a base schema to try out this workflow. Set the corresponding ids in the Configure settings and ids node. Check if the field names for the filters in the tasks_feedback tool node matches with the ones in your Tasks table. Check if the field names for the filters in the Get non-processed feedback node matches with the ones in your Tasks table. Check if the property name in the Set feedback to processed node matches with the ones in your Tasks table. Customising this workflow You can add a new workflow that updates the procedures based on the acceptance or rejection by the management There is a lot of customization possible in the system prompt. For example: change the goal to prioritize security, cost savings or customer experience

LinkedIn DM Automation Overview Effortlessly scale personalized LinkedIn outreach using a no-code automation stack. This template provides a powerful, user-friendly system for harvesting leads from LinkedIn posts and managing outreach—all within Airtable and n8n. Features & Highlights Actionable Input:** Simply enter a LinkedIn post URL to kickstart the engine—no browser scraping or manual work needed. Lead Harvesting:** Automatically scrape commenters, likers, and profile data using Unipile’s API access. Qualification Hub:** Easily review and qualify leads right inside Airtable using custom filters and statuses. Automated Campaign Flow:** n8n handles the sequence—from sending connection requests (adhering to LinkedIn limits) to delivering personalized DMs upon acceptance. Unified Dashboard:** Monitor campaign progress, connection status, and messaging performance in real time. Flexible & Reusable:** Fully customizable for your own messaging, filters, or UD campaigns—clone, adapt, and deploy. Why Use This Template? ++Zero-code friendly:++ Ideal for entrepreneurs, sales professionals, and growth teams looking for streamlined, scalable outreach. ++Transparent and compliant:++ Built with Airtable UI and compliant API integration—no reliance on browser automation or unofficial methods. ++Rapid Deployment:++ Clone and launch your automation in under 30 minutes—no dev setup required. Setup Instructions Import the template into your n8n workspace. Connect your Airtable and Unipile credentials. Configure LinkedIn post input, filters, and DM templates in Airtable. Run the workflow and monitor results directly from Airtable or n8n. Use Cases Capture inbound leads from your viral LinkedIn posts. Qualify and nurture prospects seamlessly without manual follow-ups. Scale outreach with precision and personalization. YouTube Explanation You can access the video explanation of how to use the workflow: Explanation Video