Generate personalized cold email icebreakers with GPT-4 Mini, Apify & LinkedIn
This n8n workflow scrapes LinkedIn data for your leads, feeds it into a GPT-4 AI agent, and generates laser-targeted, personalized icebreakers you can drop into your cold email campaigns. It automates the personalization process at scale — saving you hours of research while sounding human and thoughtful. Step-by-Step Setup (Beginner Friendly) Step 1: Prepare Your Leads (Input Sheet) Get your lead list based on your industry and niche from Apollo (free) Copy the entire link Go to Apify and use this Apollo Scraper to scrape the leads. Download the result as CSV and upload the CSV to Google Sheets Add a column at the end of the Sheet. Name this column as "status". Mark the entire column (every row) as "un-enriched" (this is important) Connect your Google Sheets account to n8n The workflow will pull leads from this sheet where status = un-enriched Step 2: Set Your Credentials Google Sheets: Connect your account to n8n using OAuth2 OpenAI: Add your OpenAI API credentials Apify: Visit Apify Console to get your Apify API key Use this Apify LinkedIn Profile Scraper and copy the actorID --> get it from the URL : https://console.apify.com/:actorID/input Paste both Apify API Key and ActorID into the “Set Apify Tokens” node Step 3: Customize the AI Agent In the node “Generate Personalized Icebreaker”, adjust the system prompt. Update it with your own niche, offer, tone, and insights Keep the JSON output format exactly as shown. The rest of the workflow depends on it Step 4: Run the Workflow Click "Execute Workflow" The system will: -- Pull all unenriched leads -- Filter out entries without email -- Scrape LinkedIn profiles using Apify -- Use GPT-4 to write a short, personalized icebreaker -- Save the result to a separate “Enriched” sheet -- Mark those leads as “enriched” in your original sheet How It Works Behind the Scenes Manual Trigger starts the workflow Get Raw Leads from a Google Sheet (filter = un-enriched) Filter for Valid Emails (hasEmail?) Loop Over Leads Set Apify API credentials Call Apify’s LinkedIn Scraper using each lead's LinkedIn URL Aggregate the scraped data Simplify fields for AI prompt Call OpenAI GPT-4.1 Mini with structured, data-rich prompt to generate icebreaker Append results to Enriched Sheet Update original list’s status to prevent reprocessing Loop continues to the next lead Best Practices for Successful Use Clean your leads: Remove unnecessary columns from your Google Sheet raw lead list Throttle large batches: The Apify actor and OpenAI calls may hit rate limits. Process in small batches. Customize prompt deeply: The better your AI instructions, the more believable your icebreakers will sound. Use shortened company names and local slang: The system prompt already does this — keep it. Avoid fluff: Keep the tone Spartan, specific, and real. Ideal Use Cases Cold email campaigns for SMB SaaS, agency offers, B2B sales Personalized intros for LinkedIn DMs Data enrichment for lead gen automation Integrating with tools like Instantly.ai, Smartlead, or Mailshake Demo Link Watch the full walkthrough and see it in action: 👉 Watch me build this LIVE on YouTube
By Erfan MostafizMonitor CISA critical vulnerability alerts with RSS feed & Slack notifications
--- How It Works: The 5-Node Monitoring Flow This concise workflow efficiently captures, filters, and delivers crucial cybersecurity-related mentions. Monitor: Cybersecurity Keywords (X/Twitter Trigger) This is the entry point of your workflow. It actively searches X (formerly Twitter) for tweets containing the specific keywords you define. Function: Continuously polls X for tweets that match your specified queries (e.g., your company name, "Log4j," "CVE-2024-XXXX," "ransomware"). Process: As soon as a matching tweet is found, it triggers the workflow to begin processing that information. Format Notification (Code Node) This node prepares the raw tweet data, transforming it into a clean, actionable message for your alerts. Function: Extracts key details from the raw tweet and structures them into a clear, concise message. Process: It pulls out the tweet's text, the user's handle (@screen_name), and the direct URL to the tweet. These pieces are then combined into a user-friendly notificationMessage. You can also include basic filtering logic here if needed. Valid Mention? (If Node) This node acts as a quick filter to help reduce noise and prevent irrelevant alerts from reaching your team. Function: Serves as a simple conditional check to validate the mention's relevance. Process: It evaluates the notificationMessage against specific criteria (e.g., ensuring it doesn't contain common spam words like "bot"). If the mention passes this basic validation, the workflow continues. Otherwise, it quietly ends for that particular tweet. Send Notification (Slack Node) This is the delivery mechanism for your alerts, ensuring your team receives instant, visible notifications. Function: Delivers the formatted alert message directly to your designated communication channel. Process: The notificationMessage is sent straight to your specified Slack channel (e.g., cyber-alerts or security-ops). End Workflow (No-Op Node) This node simply marks the successful completion of the workflow's execution path. Function: Indicates the end of the workflow's process for a given trigger. --- How to Set Up Implementing this simple cybersecurity monitor in your n8n instance is quick and straightforward. Prepare Your Credentials Before building the workflow, ensure all necessary accounts are set up and their respective credentials are ready for n8n. X (Twitter) API: You'll need an X (Twitter) developer account to create an application and obtain your Consumer Key/Secret and Access Token/Secret. Use these to set up your Twitter credential in n8n. Slack API: Set up your Slack credential in n8n. You'll also need the Channel ID of the Slack channel where you want your security alerts to be posted (e.g., security-alerts or it-ops). Import the Workflow JSON Get the workflow structure into your n8n instance. Import: In your n8n instance, go to the "Workflows" section. Click the "New" or "+" icon, then select "Import from JSON." Paste the provided JSON code (from the previous response) into the import dialog and import the workflow. Configure the Nodes Customize the imported workflow to fit your specific monitoring needs. Monitor: Cybersecurity Keywords (X/Twitter): Click on this node. Select your newly created Twitter Credential. CRITICAL: Modify the "Query" parameter to include your specific brand names, relevant CVEs, or general cybersecurity terms. For example: "YourCompany" OR "CVE-2024-1234" OR "phishing alert". Use OR to combine multiple terms. Send Notification (Slack): Click on this node. Select your Slack Credential. Replace "YOURSLACKCHANNEL_ID" with the actual Channel ID you noted earlier for your security alerts. (Optional: You can adjust the "Valid Mention?" node's condition if you find specific patterns of false positives in your search results that you want to filter out.)* Test and Activate Verify that your workflow is working correctly before setting it live. Manual Test: Click the "Test Workflow" button (usually in the top right corner of the n8n editor). This will execute the workflow once. Verify Output: Check your specified Slack channel to confirm that any detected mentions are sent as notifications in the correct format. If no matching tweets are found, you won't see a notification, which is expected. Activate: Once you're satisfied with the test results, toggle the "Active" switch (usually in the top right corner of the n8n editor) to ON. Your workflow will then automatically monitor X (Twitter) at the specified polling interval. ---
By MarthQuickBooks Online MCP Server - add QuickBooks tool to any AI (like Claude)
Video Introduction [](https://youtu.be/mprQ4CY3yn0) Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin --- What This Workflow Does This workflow creates an AI-powered chatbot that can answer natural language questions about your QuickBooks Online data. Using OpenAI's GPT models and the Model Context Protocol (MCP), the agent can retrieve customer information, analyze balances, and provide insights through a conversational interface. Users can simply ask questions like "How many customers do we have?" or "What's our total customer balance?" and get instant answers from live QuickBooks data. Key Features Natural language queries: Ask questions about your QuickBooks data in plain English MCP architecture: Uses Model Context Protocol to manage tools efficiently, making it easy to expand with additional QuickBooks operations Public chat interface: Share the chatbot URL with team members who need QuickBooks insights without direct access Real-time data: Queries live QuickBooks data for up-to-date answers Common Use Cases Customer service teams checking account balances without logging into QuickBooks Sales teams quickly looking up customer information Finance teams getting quick answers about customer data Managers monitoring key metrics through conversational queries Setup Requirements QuickBooks Developer Account: Register at developer.intuit.com and create an app with Accounting scope permissions. You'll receive a Client ID and Client Secret. Configure OAuth: In your Intuit Developer dashboard, add the redirect URL provided by n8n when creating QuickBooks credentials. Set the environment to Sandbox for testing, or complete Intuit's app approval process for Production use. OpenAI API: Add your OpenAI API credentials to power the chat model. The workflow uses GPT-4.1-mini by default, but you can select other models based on your performance and cost requirements. Chat Access: The chat trigger is set to public by default. Configure access settings based on your security requirements before sharing the chat URL.
By Milan Vasarhelyi - SmoothWorkPrioritize Todoist tasks with OpenRouter AI and send daily summaries to Slack
Title Prioritize Todoist tasks and send a daily summary to Slack Who’s it for Busy professionals, team leads, and freelancers who want a plug-and-play, AI-assisted morning briefing that turns messy task lists into a clear, actionable plan. What it does / How it works At 08:00 every morning, the workflow pulls open tasks from Todoist. An AI agent scores and ranks them by urgency, importance, dependencies, and effort, then produces a concise plan. You receive the summary in Slack (Markdown). Overdue or critical items are highlighted with warnings. How to set up Connect OAuth for Todoist and Slack. Select your posting channel in Send to Slack. Adjust the time in Morning Schedule Trigger (default 08:00). Run once to verify the parser output and Slack preview, then set the workflow Active. Requirements n8n (cloud or self-hosted) Todoist account / Slack workspace LLM provider connected in the AI node (do not hardcode keys in HTTP nodes) How to customize the workflow Edit the prompt in AI Task Analyzer to tweak prioritization rules. Adjust Format AI Summary to match your tone and structure. Add filters in the Todoist node (e.g., due today). (Optional) Log results to Google Sheets or a database for analytics. Disclaimer (community node) This template uses a community LangChain node for AI features and is intended for self-hosted n8n. Add a workflow image at the top of your submission page for a clearer preview.
By Toshiya Minami