Workflow Templates

Streamline your accounting by automatically creating QuickBooks Online customers and sales receipts whenever a successful Stripe payment is processed. Ideal for businesses looking to reduce manual data entry and improve accounting efficiency. How it works Trigger: The workflow is triggered when a new successful payment intent event is received from Stripe. Retrieve Customer Data: Fetches customer details from Stripe associated with the payment. Check QuickBooks Customer: Searches QuickBooks Online to see if the customer already exists using their email address. Create or Use Existing Customer: If the customer doesn't exist in QuickBooks, they are created; otherwise, the existing customer is used. Generate Sales Receipt: A sales receipt is created in QuickBooks Online with payment details, including item descriptions, amounts, and currency. Set up steps Connect Accounts: Authenticate both your QuickBooks Online and Stripe accounts in n8n. Webhook Setup: Configure the Stripe webhook to send payment_intent.succeeded events to this workflow. Test the Workflow: Trigger a test payment in Stripe to validate the integration. Customize Details: Adjust item descriptions or other fields in the QuickBooks sales receipt JSON body as needed. This workflow requires basic familiarity with n8n, but setup can be completed in under 15 minutes for most users.

🤖 AI Agent Web Search using SearchApi & LLM Who is this for? This workflow is ideal for anyone conducting online research, including students, researchers, content creators, and professionals looking for accurate, up-to-date, and verifiable information. It also serves as an excellent foundation for building more sophisticated AI-driven applications. What problem does this workflow solve? / Use case This workflow automates web searches by enabling an AI agent to efficiently retrieve and summarize external, verifiable information, ensuring accuracy through source citations. What this workflow does Connects an AI agent node to SearchApi.io as an integrated search tool. Empowers the AI agent to perform real-time web searches using various SearchApi engines (e.g., Google, Bing). Allows the AI agent to dynamically determine search parameters based on user interaction, delivering contextually relevant results. Ensures responses include clearly cited sources for validation and further exploration. Setup Install the SearchApi community node: Open Settings → Community Nodes inside your self‑hosted n8n instance. Fill npm Package Name with @searchapi/n8n-nodes-searchapi. Accept the risk prompt, and hit Install. It should now appear as a node when you search for it. API Configuration: Set up your SearchApi.io credentials in n8n. Add your preferred LLM provider credentials (e.g., OpenRouter API). Input Requirements: Provide the YouTube video ID (e.g., wBuULAoJxok). Connect LLM Integration: Configure the summarization chain with your chosen model and parameters for text splitting. How to customize this workflow to your needs Integrate additional nodes to structure or store search results (e.g., saving to databases, Notion, Google Sheets). Extend chatbot capabilities to integrate with messaging platforms (Slack, Discord) or email notifications. Adjust search parameters and filters within the AI agent node to tailor information retrieval. Example Usage Input**: User asks, "What are the latest developments in AI regulation?" Output**: AI retrieves, summarizes, and cites recent, authoritative articles and news sources from the web.

Enhance Query Resolution with the Knowledge Base Tool! Our KB Tool - Confluence KB is crafted to seamlessly integrate into the IT Ops AI SlackBot Workflow, enhancing the IT support process by enabling sophisticated search and response capabilities via Slack. Workflow Functionality: Receive Queries**: Directly accepts user queries from the main workflow, initiating a dynamic search process. AI-Powered Query Transformation**: Utilizes OpenAI's models or local ai to refine user queries into searchable keywords that are most likely to retrieve relevant information from the Knowledge Base. Confluence Integration**: Executes searches within Confluence using the refined keywords to find the most applicable articles and information. Deliver Accurate Responses**: Gathers essential details from the Confluence results, including article titles, links, and summaries, preparing them to be sent back to the parent workflow for final user response. To view a demo video of this workflow in action, click here. Quick Setup Guide: Ensure correct configurations are set for OpenAI and Confluence API integrations. Customize query transformation logic as per your specific Knowledge Base structure to improve search accuracy. Need Help? Dive into our Documentation or get support from the Community Forum! Deploy this tool to provide precise and informative responses, significantly boosting the efficiency and reliability of your IT support workflow.

Scoring LinkedIn Profiles Against Your ICP Use Case This automation scores individual LinkedIn profiles against your Ideal Customer Profile (ICP) based on interest in AI, technical depth, and seniority level. It's ideal for prioritizing leads and understanding how well a person fits your ICP criteria. What This Automation Does Given a LinkedIn profile and an Airtop profile, it: Extracts relevant data from the person's profile Determines levels of AI interest, seniority, and technical depth Calculates an ICP score based on weighted criteria Returns the full enriched profile with the score Input parameters: LinkedIn Profile URL** (e.g., https://linkedin.com/in/janedoe) Airtop Profile** connected to LinkedIn ICP scoring method** in the Airtop node prompt Output fields in JSON format: Full name, job title, employer, company LinkedIn URL, location, number of connections and followers, about section content and more Calculated ICP Score (out of 100) How It Works Form Trigger or Workflow Trigger: Accepts input from either a form or another workflow. Parameter Assignment: Ensures proper variable names for downstream nodes. Airtop Enrichment Tool: Extracts and scores the person based on a detailed prompt. Scoring: Uses this point system: AI Interest: beginner (5), intermediate (10), advanced (25), expert (35) Technical Depth: basic (5), intermediate (15), advanced (25), expert (35) Seniority Level: junior (5), mid-level (15), senior (25), executive (30) Output Formatting: Cleans and returns the result as JSON. Setup Requirements IMPORTANT: Enter your ICP scoring method in the prompt field of the Airtop node Airtop Profile connected to LinkedIn. Airtop API credentials configured in n8n. Optional: a front-end form to collect profile URLs and trigger the automation. Next Steps Embed in CRM**: Trigger this automation on new leads to auto-score them. Batch Process Leads**: Run it over a list of profile URLs for segmentation. Customize Scoring**: Adjust point weights based on your sales priorities. Read more about Scoring LinkedIn Profiles Against Your ICP

This template is for self-hosted n8n instances only. This n8n demonstrates how to build a simple FileSystem MCP server. Connecting to this server allows MCP clients and agents to list, read and create directories and files on the local machine or remote server. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem How it works A MCP server trigger is used and connected to 5 tools: 3 Execute Command tools and 2 custom workflow tools. The 3 Execute Command tools allow for listing, searching and creating directories. The 2 custom workflow tools are for reading and writing files to disk. Special care has been to not allow the MCP agent to execute arbitrary linux commands on the target server. This is achieved by only allowing the agent to provide parameters such as filenames and paths rather than raw commands. How to use This Filesystem MCP server will write to the server which hosts the n8n instance - this can be your local machine or a remove server. If your target filesystem is on neither, then modify the commands to connect to the desired server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please help me list all folders under the project directory." "Help me create a bash script to send a notification to Slack." "Search for the log file on the 22nd April and read its contents. What was the cause of the outage?" Requirements Linux file system for this example template. Feel free to modify if working on Windows. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Implement the moving and renaming of files by adding more custom workflow tools to the MCP server. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

Scrape Latest 20 TechCrunch Articles Who is this for? This workflow is designed for developers, researchers, and data analysts who need to track the latest trending repositories on GitHub. It is useful for anyone who wants to stay updated on popular open-source projects without manually browsing GitHub’s trending page. What problem is this workflow solving? Manually checking GitHub’s trending repositories daily can be time-consuming and inefficient. This workflow automates the extraction of trending repositories, providing structured data including repository name, author, description, programming language, and direct repository links. What this workflow does This workflow scrapes the trending repositories from GitHub’s trending page and extracts essential metadata such as repository names, languages, descriptions, and URLs. It processes the extracted data and structures it into an easy-to-use format. Setup Ensure you have n8n installed and configured. Import this workflow into your n8n instance. Run the workflow manually or schedule it to execute at regular intervals. (Optional) Customize the extracted data or integrate it with other systems. How to customize this workflow to your needs Modify the HTTP request node to target different GitHub trending categories (e.g., specific programming languages). Add further processing steps such as filtering repositories by stars, forks, or specific keywords. Integrate this workflow with Slack, email, or a database to store or notify about trending repositories. Workflow Steps Trigger execution manually using the "When clicking ‘Test workflow’" node. Send an HTTP request to fetch GitHub’s trending page using "Request to Github Trend". Extract the trending repositories box from the HTML response using "Extract Box". Extract all repository data including names, authors, descriptions, and languages using "Extract all repositories". Convert extracted data into a structured list for easier processing using "Turn to a list". Extract detailed repository information using "Extract repository data". Format and set variables to ensure clean and structured data output using "Set Result Variables". Note: Since GitHub’s trending page updates dynamically, ensure you run this workflow periodically to capture the latest trends.

🧠 Problem This Solves Managing credit card expenses can be tricky, especially when you want to stay transparent and keep your spouse in the loop. Most banks don't offer real-time notification sharing with family members, and manually updating expenses takes time and effort. This n8n workflow automates the entire process: tracking your HDFC credit card usage, logging it in Google Sheets, and sending an instant Telegram notification to your spouse. 👥 Who This Template Is For Couples who want shared visibility of credit card spending Individuals looking for automated personal finance tracking Anyone using HDFC Credit Card with email alerts enabled n8n users who want to integrate Gmail, Google Sheets, and Telegram ⚙️ Workflow Breakdown Here’s how the automation works: Gmail Trigger – Monitors your Gmail inbox for credit card transaction alerts from HDFC Bank. Email Parser – Extracts transaction details like amount, merchant name, date, and card type. Google Sheets Node – Logs the parsed transaction data into a structured Google Sheet for record-keeping. Telegram Node – Sends a message to your wife’s Telegram account with transaction details for instant notification. 🛾 Step-by-Step Setup Instructions Prerequisites An HDFC Credit Card with email alerts enabled A Gmail account connected to n8n A Google Sheet created with columns like Date, Amount, Merchant, Card, etc. A Telegram Bot and your wife’s Telegram Chat ID Set up Gmail Trigger Use the Gmail Trigger Node to monitor incoming emails from alerts@hdfcbank.net or similar. Filter emails with subject line containing keywords like Credit Card Transaction Alert. Extract Email Content Use the HTML Extract or Regex node to parse out transaction amount, merchant name, date, and card number from the email body. Log to Google Sheets Connect your Google Sheets account in n8n Use the Append Row node to add each transaction as a new row in your finance sheet. Send Telegram Message Set up a Telegram Bot and get the Chat ID of your wife’s Telegram account Format a message like: "💳 HDFC Transaction Alert: ₹5,000 at Amazon on 17 May via XXXX1234" Send it via the Telegram node 🛠️ Customization Tips 💡 Add Spending Limits: Add a condition node to alert only if the transaction exceeds a certain amount. 🧾 Category Mapping: Use additional logic to classify expenses (e.g., Shopping, Dining) based on keywords. 📊 Weekly Summary: Create another workflow that sends a weekly Telegram summary using data from Google Sheets. 🔐 Security Tip: Mask part of the card number before sending the Telegram message for added security.

This workflow automatically sends you a list of your daily meetings every morning via a Telegram bot. Use Cases: This workflow is useful for anyone who wants to be automatically informed of their daily meetings, especially for busy professionals, students, and anyone with a hectic schedule. Setup: Google Calendar connected to n8n A Telegram bot created and connected to n8n Your Telegram user ID specified Notes: You need to replace the placeholder in the Telegram node with your actual Telegram user ID. You can customize the formatting of the Telegram message in the JavaScript Code node.

🎤 Audio-to-Insights: Auto Meeting Summarizer Transform your meeting recordings into actionable insights automatically. This powerful n8n workflow monitors your Google Drive for new audio files, transcribes them using OpenAI's Whisper, generates intelligent summaries with ChatGPT, and logs everything in Google Sheets - all without lifting a finger. 🔄 How It Works This workflow operates as a seamless 6-step automation pipeline: Step 1: Smart Detection The workflow continuously monitors a designated Google Drive folder (polls every minute) for newly uploaded audio files. Step 2: Secure Download When a new audio file is detected, the system automatically downloads it from Google Drive for processing. Step 3: AI Transcription OpenAI's Whisper technology converts your audio recording into accurate text transcription, supporting multiple audio formats. Step 4: Intelligent Summarization ChatGPT processes the transcript using a specialized prompt that extracts: Key discussion points and decisions Action items with assigned persons and deadlines Priority levels and follow-up tasks Clean, professional formatting Step 5: Timestamp Generation The system automatically adds the current date and formats it consistently for tracking purposes. Step 6: Automated Logging The final summary is appended to your Google Sheets document with the date, creating a searchable archive of all meeting insights. ⚙️ Setup Steps Prerequisites Before setting up the workflow, ensure you have: Active Google Drive account OpenAI API key with credits Google Sheets access n8n instance (cloud or self-hosted) Configuration Steps 1. Credential Setup Google Drive OAuth2**: Required for folder monitoring and file downloads OpenAI API Key**: Needed for both transcription (Whisper) and summarization (ChatGPT) Google Sheets OAuth2**: Essential for writing summaries to your spreadsheet 2. Google Drive Configuration Create a dedicated folder in Google Drive for meeting recordings Copy the folder ID from the URL (the long string after /folders/) Update the folderToWatch parameter in the workflow 3. Google Sheets Preparation Create a new Google Sheet or use an existing one Ensure it has columns: Date and Meeting Summary Copy the spreadsheet ID from the URL Update the documentId parameter in the workflow 4. Audio Requirements Supported Formats**: MP3, WAV, M4A, MP4 Recommended Size**: Under 100MB for optimal processing Language**: Optimized for English (customizable for other languages) Quality**: Clear audio produces better transcriptions 5. Workflow Activation Import the workflow JSON into your n8n instance Configure all credential connections Test with a sample audio file Activate the workflow trigger 🚀 Use Cases Project Management Team Standup Summaries**: Convert daily standups into actionable task lists Sprint Retrospectives**: Extract improvement points and action items Stakeholder Updates**: Generate concise reports for leadership Sales & Customer Success Discovery Call Notes**: Capture prospect pain points and requirements Demo Follow-ups**: Track questions, objections, and next steps Customer Check-ins**: Monitor satisfaction and expansion opportunities Consulting & Professional Services Client Strategy Sessions**: Document recommendations and implementation plans Requirements Gathering**: Organize complex project specifications Progress Reviews**: Track deliverables and milestone achievements HR & Training Interview Debriefs**: Standardize candidate evaluation notes Training Sessions**: Create searchable knowledge bases Performance Reviews**: Document development plans and goals Research & Development Brainstorming Sessions**: Capture innovative ideas and concepts Technical Reviews**: Log decisions and architectural choices User Research**: Organize feedback and insights systematically 💡 Advanced Customization Options Enhanced Summarization Modify the ChatGPT prompt to focus on specific elements: Add speaker identification for multi-person meetings Include sentiment analysis for customer calls Generate department-specific summaries (technical, sales, legal) Extract financial figures and metrics automatically Integration Expansions Slack Integration**: Auto-post summaries to relevant channels Email Notifications**: Send summaries to meeting participants CRM Updates**: Push action items directly to Salesforce/HubSpot Calendar Integration**: Schedule follow-up meetings based on action items Quality Improvements Audio Preprocessing**: Add noise reduction before transcription Multi-language Support**: Configure for international teams Custom Templates**: Create industry-specific summary formats Approval Workflows**: Add human review before final storage 🛠️ Troubleshooting & Best Practices Common Issues Large File Processing**: Split recordings over 100MB into smaller segments Poor Audio Quality**: Use noise reduction tools before uploading API Rate Limits**: Implement delay nodes for high-volume usage Formatting Issues**: Adjust ChatGPT prompts for consistent output Optimization Tips Upload files in supported formats only Ensure stable internet connection for cloud processing Monitor OpenAI API usage and costs Regularly backup your Google Sheets data Test workflow changes with sample files first 📊 Expected Outputs Sample Summary Format: Meeting Summary - March 15, 2024 Key Discussion Points: Q1 budget review and allocation decisions New product launch timeline and milestones Team restructuring and role assignments Action Items: John: Finalize budget proposal by March 20th (High Priority) Sarah: Schedule product demo sessions for March 25th Team: Submit org chart feedback by March 18th Decisions Made: Approved additional marketing budget of $50K Delayed product launch to April 15th for quality assurance Promoted Lisa to Senior Developer role 📞 Questions & Support For any questions, customizations, or technical support regarding this workflow: 📧 Email Support Primary Contact**: Yaron@nofluff.online Response Time**: Within 24 hours on business days Best For**: Setup questions, customization requests, troubleshooting 🎥 Learning Resources YouTube Channel**: https://www.youtube.com/@YaronBeen/videos Step-by-step setup tutorials Advanced customization guides Workflow optimization tips 🔗 Professional Network LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Connect for ongoing support Share your workflow success stories Get updates on new automation ideas 💡 What to Include in Your Support Request Describe your specific use case Share any error messages or logs Mention your n8n version and setup type Include sample audio file characteristics (if relevant) Ready to transform your meeting chaos into organized insights? Download the workflow and start automating your meeting summaries today!

👤 Who is this for? This workflow is ideal for social media managers, personal brand strategists, ghostwriters, and founders who want to post regularly on LinkedIn without spending hours writing from scratch. It’s also useful for marketing agencies and assistants looking to automate consistent post creation using curated articles as source material. 🧩 What problem does this workflow solve? Manually reading multiple articles, extracting key insights, and writing a clean, professional LinkedIn post is a time-consuming process. This workflow automates everything: from pulling topics, finding related articles, summarizing them using AI, and even generating a matching image to accompany the post. It ensures faster content turnaround, more consistency, and less manual effort. 🔁 What this workflow does This workflow starts manually and retrieves one topic marked as “To do” from a Google Sheet. That topic is used as a search term for Dumpling AI’s search endpoint, which scrapes and returns the top three article contents related to the topic. These articles are sent to a LangChain agent powered by GPT-4o, which analyzes and summarizes the content into a LinkedIn post in a friendly, insightful tone. It also generates an image prompt for the post. After generating the post and image prompt, the data is extracted using a Set node. The prompt is sent to Dumpling AI’s image generation endpoint, which returns an image URL. Finally, the post text, image prompt, image URL, and status update (“created”) are saved back to the original row in Google Sheets. 🛠️ Workflow Breakdown Manual Trigger – Starts the automation. Google Sheets (Get Topic) – Searches for the first row in your content pipeline sheet where the “status” is “To do”. HTTP Request (Dumpling AI Search) – Uses the topic as a search query to pull 3 article contents using Dumpling AI’s API. Set LangChain GPT Model – Defines GPT-4o as the LLM for the LangChain Agent. LangChain Agent (Summarize & Generate) – Summarizes all 3 articles and generates a LinkedIn post and a related image prompt. Set (Extract Data) – Extracts postText and imagePrompt from the LangChain agent output. HTTP Request (Dumpling Image Gen) – Sends imagePrompt to Dumpling AI’s image generation endpoint. Update Google Sheets – Writes the post, image prompt, and image URL back to the sheet and changes the row status to “created”. ⚙️ Setup Instructions Dumpling AI Sign up at Dumpling AI Get your API key and connect it in the HTTP Request nodes (Search and Image endpoints) Use the /search endpoint to retrieve article content Use the /generate-image endpoint to create the image Google Sheets Create a spreadsheet with columns: topic, status, postText, imagePrompt, imageURL Add sample topics and set their status to To do LangChain (GPT-4o) Connect your OpenAI credentials to n8n Make sure GPT-4o is available in your OpenAI account Use the LangChain node to process multi-input summarization and generate a social media caption Customize the Prompt (Optional) Adjust the Set node to tweak the input format sent to the LangChain agent Add constraints like tone, hashtags, or emojis to fit your brand style 🧠 How to Customize This Workflow Change the content source (RSS feed, Notion DB, etc.) instead of Google Sheets Add a scheduler node to run this automatically every morning or weekly Use Airtable instead of Google Sheets for more control and filtering Send the final post to LinkedIn using the Buffer or LinkedIn API Add a Telegram or Slack notification when new content is ready for approval

Inquiry Form to Personalised WhatsApp Message Overview This workflow creates a smart, automated system for capturing leads from an inquiry form, initiating personalized WhatsApp message via Unipile API, and updating your Google Sheet CRM. It uses AI to craft initial outreach messages and logs the success or failure of each message sent, ensuring you track every lead effectively. This automation helps you engage leads quickly and efficiently, without manual effort. Use Case This workflow is ideal for: Sales Teams**: Automate the first touchpoint with new leads, qualifying them and initiating conversations. Small Businesses**: Provide immediate, personalized responses to inquiries, enhancing customer experience. Customer Support**: Quickly gather more context from users after they fill out a help form. Lead Generation**: Streamline the process from form submission to active lead engagement and CRM tracking. How It Works Form Submission Trigger: The workflow is activated when someone submits an "Inquiry Form." This form collects essential lead details such as: Full Name Email WhatsApp number Company Name "How can we help you?" (a notes field) AI Crafts Personalized Message: An OpenAI node, acting as "Alex" (a friendly, approachable human assistant), generates a short, personalized, and engaging opening message for the lead. This message directly addresses the lead by their first name and includes an open-ended question to encourage them to share more details about their needs. WhatsApp Outreach: The AI then uses the WhatsApp API (via Unipile) to send this personalized message directly to the lead's WhatsApp number. Unipile is key here, as it allows sending messages without prior chat history and can connect to your personal WhatsApp. Log Success or Failure: The AI checks the response from the WhatsApp API. If the WhatsApp message is sent successfully: The lead's details, along with the personalized message, WhatsApp chat ID, and message ID, are logged into a "Successful" sheet in your Google Sheet CRM. If the WhatsApp message fails to send: The lead's information, the attempted message, and the reason for failure are logged into a "Failed" sheet in your Google Sheet CRM. This helps you identify and follow up on problematic leads. How to Set It Up To set up your Lead Capture Agent, follow these steps: Google Sheet Setup: Copy the Template: Make a copy of the provided Google Sheet Template ("Sales Agent" with "Successful" and "Failed" sheets) into your own Google Drive. Connect Google Sheets: Ensure your Google Sheets OAuth2 API credentials are set up in n8n and linked to the "Google Sheets" and "Google Sheets3" nodes. Update Sheet IDs: In both "Google Sheets" and "Google Sheets3" nodes, update the documentId with the ID of your copied "Sales Agent" Google Sheet. Unipile (WhatsApp API) Credentials: Sign up for Unipile: Get your DSN and API key from Unipile (they offer a 7-day free trial). Replace Placeholders: In the "Whatsapp API" node, replace <YOUR_DSN>, <YOUR_API_KEY>, and <YOUR_ACCOUNT_ID> with your actual Unipile credentials. OpenAI API Key: Connect your OpenAI API key as an API credential in n8n and link it to the "OpenAI" node. Inquiry Form Setup: The "Enquiry Form" node generates a public webhook URL. You can embed this form on your website or share the URL directly. Alternatively, if you use your own form solution, configure it to send data via a webhook to the URL provided by the "Enquiry Form" node. Import the Workflow: Import the provided workflow JSON into your n8n instance. Activate and Test: Once all settings are complete, activate the workflow. Test it by submitting a new entry through the "Inquiry Form." Check your Google Sheet to see the lead captured and the message status. This workflow is designed to ensure no lead falls through the cracks, giving your sales or support team a powerful edge!

✨ What It Does Mello is a Claude-powered Slack assistant that helps you stay on top of unread messages across all your channels. It: Summarizes conversations contextually using Claude AI. Generates reply suggestions and sends them as private (ephemeral) Slack messages. Lets you respond instantly with one-click AI-suggested replies. Perfect for busy teams, founders, and anyone looking to reduce Slack noise and save hours each week. 🔧 Setup Instructions Create a Slack App Go to Slack API → Your Apps Click Create New App and set it up for your workspace Under OAuth & Permissions, add: Bot Token Scopes: commands, chat:write, channels:history, users:read User Token Scopes: channels:history, chat:write Enable Interactivity, and point the Request URL to your n8n webhook (e.g. /slash-summarize) Add Claude API Get an API key from Claude (Anthropic) In n8n, set up the Claude API credential (or switch to OpenAI) Import This Workflow Go to your n8n instance, click Import, and paste this template Update any placeholders (Slack app, Claude key, webhook URLs) Follow the inline sticky notes for guidance Test It Type /summarize in any Slack channel Mello will fetch unread messages, summarize them, and show reply buttons in a private message ⏱ Setup time: ~10 minutes 🛠 Workflow Highlights Slash command trigger (/summarize) Slack API integration to fetch messages Claude AI for contextual summaries Reply suggestions with smart buttons Private Slack delivery (ephemeral messages) Designed to be easily extended (e.g. add support for OpenAI, custom storage) 🔒 Note This is a lite preview of the full Mello workflow. ✅ The full version includes: Slack reply buttons with thread context Full OAuth flow with token storage MongoDB integration Custom Claude/OpenAI configuration Hosted version with onboarding, branding & support 💡 Want access to the complete version? 📩 Email nina@baloon.dev