Workflow Templates

🧾 Generate Project Summary from meeting transcript Who’s it for 🤝 Project managers looking to automate client meeting summaries Client success teams needing structured deliverables from transcripts Agencies and consultants who want consistent, repeatable documentation How it works / What it does ⚙️ Trigger: Manual or webhook trigger kicks off the workflow. Get meeting transcript: Reads the raw transcript from a specified Google Docs file. Generate summary: Sends transcript + instructions to OpenAI (gpt-4.1-mini) to produce a structured project summary. Convert to HTML: Transforms the LLM-generated Markdown into styled HTML. Prepare request: Wraps HTML and metadata into a multipart request body. Create Google Doc: Uploads the new “Project Summary” document into your Drive folder. How to set up 🛠️ Credentials Google Docs & Drive OAuth2 credentials OpenAI API key (gpt-4.1-mini) Nodes configuration Manual Trigger / webhook node Google Docs “Get meeting transcript” node: set documentURL AI Chat Model node: select gpt-4.1-mini Markdown node: enable tables & emoji Google Drive “CreateGoogleDoc” node: set target folder ID Paste in your IDs Update documentURL to your transcript doc Update google_drive_folder_id in the Set node Execute Click “Execute Workflow” or call via webhook Requirements 📋 n8n Google OAuth2 scopes for Docs & Drive OpenAI account with GPT-4.1-mini access A Google Drive folder to store summaries How to customize ✨ Output format**: Edit the Markdown prompt in the ChainLlm node to adjust headings or tone Timeline section**: Extend LLM prompt template with your own phase table Styling**: Tweak inline CSS in the Code node (Prepare_Request) for fonts or margins Trigger**: Swap Manual Trigger for HTTP/Webhook trigger to integrate with other tools Language model**: Upgrade to a different model by changing model.value in the AI node

How it Works This workflow fetches top news headlines every 10 minutes from NewsAPI, summarizes them using OpenAI's GPT-4o model, and sends a concise email digest to a list of recipients defined in a Google Spreadsheet. It's ideal for anyone who wants to stay updated with the latest news in a short, digestible format. 🎯 Use Case Professionals who want summarized daily news Newsletters or internal communication updates Teams that require contextual summaries of the latest events Setup Instructions 1. Upload the Spreadsheet File name: Emails Column: Email with recipient addresses 2. Configure Google Sheets Nodes Connect your Google account to: Email List Send Email 3. Add API Credentials NewsAPI Key** → for fetching top headlines OpenAI API Key** → for summarizing headlines Gmail Account** → for sending the email digest 4. Activate the Workflow Once active, the workflow runs every 10 minutes via a cron trigger Summarized news is sent to the list of emails in the spreadsheet 🔁 Workflow Logic Trigger: Every 10 minutes via Cron Fetch News: HTTP request to NewsAPI for top headlines Summarize: Headlines are passed to OpenAI's GPT-4o for 5-bullet summary Read Recipients: Google Sheet is used to collect email recipients Send Email: Summary is formatted and sent via Gmail 🧩 Node Descriptions | Node Name | Description | |-----------|-------------| | Cron | Triggers the workflow every 10 minutes. | | HTTP Request - NewsAPI | Fetches top news headlines using NewsAPI. | | Set | Formats or structures raw news data before processing. | | AI Agent | Summarizes the news content using OpenAI into 5 bullet points. | | Email List | Reads recipient email addresses from the 'Emails' Google Spreadsheet. | | Send Email | Sends the email digest to all recipients using Gmail. | 🛠️ Customization Tips Modify the AI prompt for tone, length, or content type Send summaries to Slack, Telegram, or Notion instead of Gmail Adjust cron interval for more/less frequent updates Change email formatting (HTML vs plain text) 📎 Required Files | File Name | Purpose | |-----------|---------| | Emails spreadsheet | Google Sheet containing the list of email recipients | | daily_news.json | Main n8n workflow file to automate daily news digest | 🧪 Testing Tips Add 1–2 test email addresses in your spreadsheet Temporarily change the Cron node to run every minute for testing Check email inbox for delivery and formatting Inspect the execution logs for API errors or formatting issues 🏷 Suggested Tags & Categories #News #OpenAI #Automation #Email #Digest #Marketing

Daily Email Pulse Summary: This agent summarizes a user's daily emails into a clean, actionable summary. It uses OpenAI to analyze content and sends a formatted "Daily Pulse" email at the end of each day. Main use cases: Keep track of open loops and next steps across all email conversations Identify high-potential leads and flag conversations going nowhere Eliminate the need to manually review your inbox at day’s end Build a smart summary layer using AI without hallucination or noise How it works This workflow can be divided into eight core nodes, each serving a distinct purpose in helping a user stay on top of their day. The result is a curated, AI-generated summary delivered to your inbox — crafted from real message content, not guesswork. Schedule Trigger (Trigger Node – Runs Daily at Set Time) Kicks off the workflow at a specific time each day (e.g. 6:00 PM). Ensures you receive your Daily Pulse consistently, without needing to run it manually. Date Transformer (Function Node – Define Today & Tomorrow Range) Uses JavaScript to calculate the current day’s date range: today: Start of day (00:00:00) tomorrow: Start of next day (used as a cutoff) This ensures only emails from today are analyzed, keeping the summary focused and current. Get All Messages (Gmail Node – Fetch Filtered Emails) Pulls in all Gmail messages with internalDate between today and tomorrow. Outputs structured data: from, subject, and body text of each email. This forms the raw data for the daily business pulse. Aggregator (Function or Item Lists Node – Combine Message Fields) Aggregates each message into a readable format: From: John@example.com Subject: Demo Follow-up Body: Let’s schedule a time this week... All messages are stitched together into a single combinedText string for analysis. This gives the AI model full context for the day in one unified document. Email Cleanup (Function Node – Remove Noise & Normalize Text) Cleans the combinedText blob to remove: HTML tags Marketing footers (e.g., unsubscribe links) Redundant whitespace or formatting artifacts Ensures GPT gets clean, relevant message content with no distractions. Agent (OpenAI Node – Generate Structured Summary) Uses a System Prompt to define its role as an AI Chief of Staff. Uses a User Prompt that instructs it to categorize messages into sections: 📝 Open Loops / Pending Follow-Up 🚀 Next Steps You’ve Committed To 🧲 Leads Worth Following Up On 🛑 Conversations That Aren’t Leading Anywhere 🧠 Strategy Notes ✅ Top 3 Tasks for Tomorrow Built-in guardrails ensure the model only uses real content (no hallucination). Sections with no relevant data are omitted to keep it concise. HTML Formatter (Function Node – Wrap Markdown in Email-Ready HTML) Wraps the GPT-generated markdown summary in a simple <html><body> structure. Applies white-space: pre-wrap to preserve formatting and spacing. The result is a clean, readable email that renders well across all inboxes (especially Gmail). Email Send (Email Node – Deliver the Final Pulse) Sends the formatted summary to your email inbox. Subject: Your Daily Business Pulse – {{today}} HTML body: Uses the formatted output from the previous step. Final output: a well-organized, scannable summary of the day’s communication — focused on what matters. Why It Works Automates the end-of-day review ritual without effort Prioritizes follow-ups, action items, and time-sensitive leads Filters out noise and low-value conversations Leverages GPT without risk of hallucination or irrelevant output Delivers clarity, helping you focus on tomorrow’s most important tasks

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 1) Description This workflow automates video creation using Luma AI Dream Machine and n8n. It generates dynamic videos based on custom prompts, random camera motion, and predefined settings, then stores the video and thumbnail URLs in Airtable for easy access and tracking. This automation makes it easy to create high-quality videos at scale with minimal effort. 👉 Airtable Base Template 🎥 Tutorial Video Setup 1. Luma AI Setup Create an account with Luma AI. Generate an API key from Luma AI for authentication. Ensure the API key has permission to create and manage video requests. 2. Airtable Setup Create an Airtable base with the following fields: 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. Prompt** – The video prompt used in the request. Aspect Ratio** – Defines the video format (e.g., 9:16). Duration** – Length of the video. 👉 Use the Airtable template linked above to simplify setup. 3. n8n Setup Install n8n (local or cloud). Set up Luma AI and Airtable credentials in n8n. Import the workflow and customize the settings based on your needs. How It Works 1. Global Settings Configuration The Set node defines key settings such as: Prompt** – Example: "A crocheted parrot in a crocheted pirate outfit swinging on a crocheted perch." Aspect Ratio** – Example: "9:16" Loop** – Example: "true" Duration** – Example: "5 seconds" Cluster ID** – Used to group related videos for easy tracking. Callback URL** - Used for the Webhook workflow in Part 2 2. Random Camera Motion The Code node randomly selects a camera motion (e.g., Zoom In, Pan Left, Crane Up) to create dynamic and visually engaging videos. 3. API Request to Luma AI The HTTP Request node sends a POST request to Luma AI’s API with the following parameters: Prompt – Uses the defined global settings. Aspect Ratio – Matches the target platform (e.g., TikTok or YouTube). Duration – Length of the video. Loop – Determines if the video should loop. Callback URL – Sends a POST response when the video is complete. 4. Capture API Response Luma AI sends a POST response to the callback URL once video generation is complete. 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. 5. Store in Airtable The Airtable node updates the record with the video and thumbnail URLs. Generation ID** is crucial for matching future webhook responses to the correct video record. Why This Workflow is Useful ✅ Automates high-quality video creation ✅ Reduces manual effort by handling prompt generation and API calls ✅ Random camera motion makes videos more dynamic ✅ Ensures organized tracking with Airtable ✅ Scalable – Ideal for automating large-scale content creation Next Steps Part 2** – Handling webhook responses and updating Airtable automatically. Future Enhancements** – Adding more camera motions, multi-platform support, and automated video editing.

AI-Agent Code Review for GitHub Pull Requests Description: This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet). It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI. 🔧 What It Does Triggered on PR creation Extracts code diffs from the PR Formats and feeds them into an OpenAI prompt Enriches the prompt using a Google Sheet of Swift best practices Posts an AI-generated review as a comment on the PR Applies a PR label to visually mark reviewed PRs ✅ Prerequisites Before deploying this workflow, ensure you have the following: n8n Instance (Self-hosted or Cloud) GitHub Repository with PR activity OpenAI API Key** for GPT-4o, GPT-4-turbo, or GPT-3.5 GitHub OAuth App** (or PAT) connected to n8n to post comments and access PR diffs (Optional) Google Sheets API credentials if using the code best practices lookup node. ⚙️ Setup Instructions 1. Import the Workflow in n8n, click on Workflows → Import from file or JSON Paste or upload the JSON code of this template 2. Configure Triggers and Connections 🔁 GitHub Trigger Node**: PR Trigger Repository**: Select the GitHub repo(s) to monitor Events**: Set to pull_request Auth**: Use GitHub OAuth2 credentials 📥 HTTP Request Node: Get file's Diffs from PR No authentication needed; it uses dynamic path from trigger 🧠 OpenAI Model Node**: OpenAI Chat Model Model**: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo Credential**: Provide your OpenAI API Key 🧑‍💻 Code Review Agent Node : Code Review Agent Connected to OpenAI and optionally to tools like Google Sheets 💬 GitHub Comment Poster Uses GitHub API to post review comments back on PR Node: GitHub Robot Credential: Use the agent Github account (OAuth or PAT) Repo : Pick your owen Github Repository 🏷️ PR Labeler (optional) Adds label ReviewedByAI after successful comment Node: Add Label to PR Label : you ca customize the label text of your owen tag. 📊 Google Sheet Best Practices config (optional) Connects to a Google Sheet for coding guideline lookups, we can replace Google sheet by another tool or data base First prepare your best practices list with the clear description and the code bad/good examples Add al the best practices in your Google Sheet Configure* the Code *Best Practices node** in the template : Credential : Use your Google Sheet account by OAuth2 URL : Add your Google Sheet document URL Sheet : Add the name of the best practices sheet

Idea The idea for app came since I wanted to build a unique gift for my niece because she gets excited for her birthday (which Im going to miss this year). The web app has a simple countdown (in html and JS) but more importantly, there is an AI agent that will answer some specific questions and know her preferences. How it works The questions from app are sent via web hook to N8N which has pulls preferences file (about her likes, dislikes, personality) from postgre and AI Agent that will answer questions/respond. The current status is stored back in postgre (especially about status of cat and universe happenings) before responding back. Features Integrated AI chatbot via N8N webhook Persistent conversation history Minimizable chat interface Fallback support for offline testing Features: -- Wheres Mittens - This is a query to track her lost cat in multiverse. -- Multiverse updates with recent update stored Pre Requisites Postgre SQL database is available. Alternatively, use any other database but change the N8N nodes. LLM Api Key. Step by Step Instructions Export this N8N Workflow. Modify LLM API Key, I used openAI, 4.1 For web app scofflding,you will need Node, HTML and Javascript. I've created a mini version using Node and JS with web app and N8N connection settings here: <https://github.com/productiser/FiBirthdayAgent> PostgreSQL Database Script (1 table for memory and context storage): CREATE TABLE fifi_world_context ( id TEXT PRIMARY KEY, -- e.g., 'agent_fifi' cat_location TEXT, -- e.g., "Bubble Nebula" cat_activity TEXT, -- e.g., "Playing laser tag with moon mice" fifi_preferences JSONB, -- e.g., likes/dislikes/foods/shows world_history TEXT, -- Summary of narrative events last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); 5.Modify system prompt as per your needs. Built With N8N Self hosted Self hosted web app Hosted on Vercel Total spend = <£1 (AI costs only) Total Time = <1 day Support Watch this video for web app overview and how it looks. <https://youtu.be/e7PlrTdvwoM> Contact me on info@pankstr.com/ superllmuser@gmail.com for any queries Hope you enjoy!!

This workflow sends an automatic daily summary of your Microsoft Outlook calendar events into a Microsoft Teams channel. Perfect for team visibility or personal reminders. This automation is ideal for team leads, project managers, and remote workers who need to keep stakeholders informed of scheduled events without manual effort. It saves time, ensures consistent communication, and reduces the chance of missing important meetings. How it works Scheduled Trigger The workflow runs automatically every midnight (00:00 UTC). Create filter for "TODAY" value (Code Node) The code node generates the date value for "today" Calculates UTC start and end of the current day Builds a Microsoft Graph-compatible filter string Microsoft Outlook Node: Get Today’s Events Resource : Event Operation : Get Many Uses {{ $json.filter }}, which is generated from today's date, to retrieve only relevant entries Format Events as HTML (Code Node) Code node transforms each event into a formatted HTML message Meeting Time: 2025-07-08T10:00:00Z Subject: Weekly Sync Summary: Discuss project milestones and blockers. Microsoft Teams Node: Send Summary Message Chat Message | Create | Selected Channel | HTML content Uses the htmlMessage field from the previous node as the message body How to Use Import the Workflow Load the .json file into your n8n instance via “Import from File” or directly via the workflow UI. Set Up Credentials Go to Credentials in n8n. Add or configure your Microsoft Outlook OAuth2 API credential. Add or configure your Microsoft Teams OAuth2 API credential. Assign these credentials to the corresponding nodes in the workflow. Adjust Timezone and Schedule Edit the Schedule Trigger node to reflect your local timezone or preferred time. Configure the Microsoft Outlook Node Ensure the correct Outlook calendar is targeted. Confirm the Get Many node includes this expression in the filter field: {{ $json.filter }} Customize the HTML Output (Optional) Open the “Format Events” Code node to: Add new fields like Location, Organizer, or Attendees. Adjust date formatting to local time if needed. Target the Correct Teams Channel Open the Microsoft Teams node, select the team and channel where messages should be posted. Message type must be set to HTML if sending formatted content. Test the Workflow Run it manually to verify: Events are fetched correctly. The message is well-formatted and appears in the correct Teams channel. If you see no events, double-check the date filter logic or ensure events exist for today. Example Use Cases Team Syncs**: Automatically notify your project channel every morning with today's meetings. Remote Work**: Help remote teams stay aligned on shared calendars. Personal Assistant**: Keep track of your own day’s agenda with an automatic Teams message. Requirements Microsoft Outlook** Account must have permission to access calendar events via Graph API. OAuth2 credential must be configured in n8n Credential Manager. Microsoft Teams** Requires permission to post messages to specific channels. OAuth2 credential must be configured and authorized.

Who is this template for? This workflow template is designed for business owners and HR professionals to automatically detect and structure unstructured job applications received through email. Additionally, other email categories can be added, each with it's own workflow. How it works Every time a new email is received, an OpenAI model classifies it into a predefined category by analyzing the plain text of the email and the extracted content from the attachment. If the email is classified as a job application, an OpenAI model uses the email’s plain text and extracted attachment content to populate predefined fields such as age and study. A relevant additional step would be to directly push the applicant and their structured job application into a CRM or ATS like Hubspot or Recruitee. Set up steps Configure your IMAP credentials to connect your email account. Use this n8n documentation page for quickstart guides for common email providers. Connect your OpenAI account in the 'Classify email' node. And add or remove any category for classification in this node. Make sure the description is clear and concise. Connect your OpenAI account in the 'Extract variables - email & attachment' node. And add or remove any predefined fields that should be populated for job applications in this node. Make sure the description is clear and concise.

n8n Workflow: Calculate the Centroid of a Set of Vectors Overview This workflow receives an array of vectors in JSON format, validates that all vectors have the same dimensions, and computes the centroid. It is designed to be reusable across different projects. Workflow Structure Nodes and Their Functions: Receive Vectors (Webhook): Accepts a GET request containing an array of vectors in the vectors parameter. Expected Input: vectors parameter in JSON format. Example Request: /webhook/centroid?vectors=[[2,3,4],[4,5,6],[6,7,8]] Output: Passes the received data to the next node. Extract & Parse Vectors (Set Node): Converts the input string into a proper JSON array for processing. Ensures vectors is a valid array. If the parameter is missing, it may generate an error. Expected Output Example: { "vectors": [[2,3,4],[4,5,6],[6,7,8]] } Validate & Compute Centroid (Code Node): Validates vector dimensions and calculates the centroid. Validation: Ensures all vectors have the same number of dimensions. Computation: Averages each dimension to determine the centroid. If validation fails: Returns an error message indicating inconsistent dimensions. Successful Output Example: { "centroid": [4,5,6] } Error Output Example: { "error": "Vectors have inconsistent dimensions." } Return Centroid Response (Respond to Webhook Node): Sends the final response back to the client. If the computation is successful, it returns the centroid. If an error occurs, it returns a descriptive error message. Example Response: { "centroid": [4, 5, 6] } Inputs JSON array of vectors, where each vector is an array of numerical values. Example Input { "vectors": [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ] } Setup Guide Create a new workflow in n8n. Add a Webhook node (Receive Vectors) to receive JSON input. Add a Set node (Extract & Parse Vectors) to extract and convert the data. Add a Code node (Validate & Compute Centroid) to: Validate dimensions. Compute the centroid. Add a Respond to Webhook node (Return Centroid Response) to return the result. Function Node Script Example const input = items[0].json; const vectors = input.vectors; if (!Array.isArray(vectors) || vectors.length === 0) { return [{ json: { error: "Invalid input: Expected an array of vectors." } }]; } const dimension = vectors[0].length; if (!vectors.every(v => v.length === dimension)) { return [{ json: { error: "Vectors have inconsistent dimensions." } }]; } const centroid = new Array(dimension).fill(0); vectors.forEach(vector => { vector.forEach((val, index) => { centroid[index] += val; }); }); for (let i = 0; i < dimension; i++) { centroid[i] /= vectors.length; } return [{ json: { centroid } }]; Testing Use a tool like Postman or the n8n UI to send sample inputs and verify the responses. Modify the input vectors to test different scenarios. This workflow provides a simple yet flexible solution for vector centroid computation, ensuring validation and reliability.

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response Listens for incoming user questions (can be extended to webhook). Converts questions to vector embeddings and performs similarity search on MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce direct, context-aware answers. Maintains short-term conversation context using a memory buffer node. Setup Setting up vector embeddings Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. Setting up chat Configure the AI system prompt in the “Knowledge Base Agent” node to reflect your company’s tone, answering style, and any business rules. Update the workflow description and instructions to help users understand the chat’s purpose and capabilities. Connect the MongoDB collection used for vector search in the chat workflow and update the vector search index if needed to match your setup. Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection, with: An embedding field storing vector data, Relevant metadata fields (e.g., document ID, source), and The same vector index name configured (e.g., data_index). Search Index Example: { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }

A lightweight no-code workflow that captures student check-in data via a mobile app or webhook, stores it in a Google Sheet, and instantly notifies the class teacher via email. 🎯 What This Does Students check in using a mobile app or QR code Their data is formatted and saved to a Google Sheet A notification email is sent to the class teacher in real time 🔧 Workflow Steps | Step | Description | | ------------------------------ | ----------------------------------------------------------- | | Student Check-in (Webhook) | Triggered via POST request from mobile app or QR scanner | | Format Data | Cleans and prepares incoming JSON into structured format | | Append or Update Row | Saves student check-in data into Google Sheets | | Email Teacher | Sends formatted check-in email to the class teacher | | Success Response | Returns a confirmation response to the mobile app or system | 📱 Example Check-in Input (Webhook Body) { "student_name": "Aarav Mehta", "student_id": "STU025", "class_name": "Grade 6B" } 📊 Google Sheets Format | Student Name | Student ID | Class | Date | Time | | ------------ | ---------- | -------- | ---------- | ----- | | Aarav Mehta | STU025 | Grade 6B | 2025-08-06 | 08:35 | Date and time are added dynamically in the workflow. ⚙️ Setup Requirements n8n Instance** – Deployed with public webhook support Google Sheets** – Sheet with columns as shown above Email SMTP Settings** – For sending teacher notification ✅ Quick Setup Instructions Import the workflow into your n8n instance Replace the webhook URL in your mobile app Set your Google Sheet ID and range Enter the teacher’s email in the “Email Teacher” node Test with mock data Deploy and use live!

This n8n workflow automates website security audits. It combines direct website scanning, threat intelligence from AlienVault OTX, and advanced analysis from an OpenAI large language model (LLM) to generate and email a comprehensive security report. How it Works (Workflow Flow): Input: A user provides a website URL via a simple web form. Data Collection: An HTTP Request node visits the provided URL to gather initial data (status code, headers). An AlienVault HTTP Request node queries AlienVault OTX for known threats associated with the website's hostname. Data Preparation (Prepare Data for AI): A custom code node consolidates the collected website data and AlienVault intelligence, performing initial checks for common issues (e.g., error codes, missing security headers, AlienVault warnings). AI Analysis (Security Configuration Audit): The prepared data is sent to an OpenAI Chat Model, which acts as a cybersecurity expert. The AI analyzes the data to identify vulnerabilities, explain their impact, suggest exploitation methods, and outline mitigation steps. Report Formatting (Format Report for Email): Another custom code node takes the AI's plain-text report and converts it into a structured HTML format suitable for email. Delivery (Send Security Report): The final HTML report is sent via Gmail to a specified email address. Setup Steps: To use this workflow, you'll need an n8n instance and the following credentials: n8n Instance: Ensure your n8n environment is running. OpenAI API Key: Generate a key from OpenAI. Add an "OpenAI API" credential in n8n (e.g., "OpenAI account"). AlienVault OTX API Key: Obtain a key from your AlienVault OTX profile. Add an "AlienVault OTX API" credential in n8n (e.g., "AlienVault account"). Gmail Account: Set up a "Gmail OAuth2" credential in n8n for sending emails (recommended for security; involves Google Cloud setup). Import Workflow: Copy the workflow's JSON code. In n8n, import the workflow via "Workflows" > "New" > "Import from JSON". Configure Recipient: In the "Send Security Report" node, specify the email address where reports should be sent. Activate: Enable the workflow to start processing submissions. Once activated, access the "On form submission" webhook URL to input a URL and trigger an audit.