Workflow Templates

Automated YouTube Shorts Creator with yt-dlp & FFmpeg Description How It Works • Downloads videos/music from YouTube using yt-dlp • Merges assets with dynamic text overlays • Automatically uploads to YouTube as Shorts (9:16 format) • Tracks everything in Google Sheets Set Up Steps (~10 minutes) Install yt-dlp and FFmpeg in your n8n environment Connect Google Sheets (for video/music pools) Set up YouTube OAuth credentials Configure text overlay font (NotoSerif included) Key Features Dual Pipeline System Video Downloader (MP4) + Music Downloader (MP3 with thumbnails) Random pairing for endless combinations Professional Text Overlays Dynamic line wrapping for perfect 9:16 formatting Customizable fonts/colors YouTube API Integration Automatic upload with metadata (titles/descriptions) Privacy/license controls Google Sheets Tracking Logs download paths, YouTube URLs, timestamps Prevents duplicate processing

This n8n template from Intuz provides a complete solution to automate your accounting by instantly creating QuickBooks sales receipts for every new Stripe payment. This workflow automates the process of recording successful payments from Stripe into QuickBooks by creating corresponding Sales Receipts. It ensures payment data is captured accurately, checks whether the customer exists in QuickBooks, and creates a new customer if necessary before generating the receipt. This integration streamlines bookkeeping by eliminating manual data entry and ensuring all payment records are synchronized between systems. Who's this workflow for? Accountants & Bookkeepers Small Business Owners E-commerce Managers Finance Teams How it works 1. Trigger on Successful Payment: The workflow starts instantly when a payment_intent.succeeded event is received from Stripe via a webhook. This means it only runs after a payment is confirmed. 2. Get Customer Details: It uses the customer ID from the payment to fetch the customer's full details (name and email) from Stripe. 3. Check for Customer in QuickBooks: The workflow then searches your QuickBooks account to see if a customer with that name already exists. 4. Create Customer if New: If the customer is not found in QuickBooks, a new customer record is automatically created using the information from Stripe. 5. Generate Sales Receipt: Finally, using the correct customer record (either existing or newly created) and the payment amount, the workflow creates and saves a new sales receipt in QuickBooks, perfectly matching the Stripe transaction. Key Requirements to Use This Template 1. n8n Instance: An active n8n account (Cloud or self-hosted). 2. Stripe Account: An active Stripe account with API access. You must be able to create and manage webhooks. 3. QuickBooks Online Account: An active QuickBooks Online account with API access to manage customers and sales receipts. Setup Instructions 1. Configure the Webhook Trigger: Copy the webhook URL from the Capture Payment (Webhook) node in n8n. In your Stripe dashboard, go to Developers > Webhooks and add a new endpoint. Paste the n8n webhook URL and have it listen for the payment_intent.succeeded event. 2. Connect Stripe: In the Get a customer node, connect your Stripe account credentials. 3. Connect QuickBooks: In all three QuickBooks nodes (Find Customer, Create a customer, and Create a payment), connect your QuickBooks Online account using OAuth2 credentials. 4. Activate Workflow: Save the workflow and toggle the "Active" switch to ON. Your accounting automation is now live! Connect with us Website: https://www.intuz.com/services Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz For Custom Worflow Automation Click here- Get Started

📄 What this workflow does This workflow transforms your n8n instance into a fully automated AI sales assistant for WooCommerce stores. It detects customer intent from chat, searches products, answers FAQs, generates Stripe payment links, captures leads into your CRM, and even escalates to human support when needed. It provides smooth conversational memory and syncs with your knowledge base to ensure accurate, human-like responses. 👤 Who is this for WooCommerce store owners who want to automate customer support and sales. Sales and marketing teams looking to scale personalized product recommendations. E-commerce managers who want to reduce manual chat handling. Anyone aiming to integrate AI assistants with payments, CRM, and FAQs. ✅ Requirements WooCommerce account with API access. Qdrant vector store (for FAQ and RAG retrieval). OpenAI/Gemini API credentials (for intent detection + message generation). Google Drive account (to sync and update knowledge base docs). Stripe account (to generate instant payment links). CRM account (HubSpot, Pipedrive, etc.) if lead capture is required. Telegram account for optional human escalation. ⚙️ How to set up Connect WooCommerce API credentials in n8n. Configure Gemini/OpenAI API for intent extraction and chat generation. Set up Qdrant for document retrieval, and link to your Google Drive Sales Docs folder. Configure Stripe API to enable instant payment link generation. Connect your CRM to capture new leads automatically. Add Telegram bot credentials for human escalation (optional). Enable conversational memory and test chat flows end-to-end. 🔁 How it works Intent Extraction → AI analyzes chat messages to detect Product Search, FAQ, Payment, or Lead Capture. Product Search → Queries WooCommerce catalog by keyword, SKU, or price range. FAQ Answering → Retrieves company policies/docs from Qdrant + Google Drive, answered via RAG. Payment Links → Stripe generates instant checkout links for customers ready to buy. Lead Capture → Name + email are auto-stored into CRM. Human Escalation → If intent is unclear, conversation is forwarded to Telegram. Conversational Memory → Maintains last 12 exchanges for natural dialogue. Knowledge Sync → Google Drive docs auto-update into Qdrant for live FAQ support. 💡 About Margin AI Margin AI is an AI-services agency that acts as your AI Service Companion. We design intelligent, human-centric automation solutions—turning your team’s best practices into scalable workflows and tools. Industries like marketing, sales, and operations benefit from our tailored AI consulting, automation tools, and chatbot development.

Automated WhatsApp Welcome Messages for Sales Leads with Google Sheets & Rapiwa Who is this for? This automation is ideal for sales teams, digital marketers, support agents, or small business owners who collect leads in Google Sheets and want to automatically send WhatsApp welcome messages. It's a cost-effective and easy-to-use solution built for those not using the official WhatsApp Business API but still looking to scale communication. What this Workflow Does This n8n automation reads leads from a connected Google Sheet, verifies if the provided WhatsApp numbers are valid using the Rapiwa API, and sends a personalized welcome message. It updates the sheet based on delivery success or failure, and continues this process every 5 minutes — ensuring new leads are automatically engaged. Key Features Automatic Scheduling**: Runs every 5 minutes (adjustable) Google Sheets Integration**: Reads and updates lead data WhatsApp Number Validation**: Confirms number validity via Rapiwa Personalized Messaging**: Uses lead name for custom messages Batch Processing**: Sends up to 60 messages per cycle Safe API Usage**: Adds 5-second delay between each message Error Handling**: Marks failed messages as not sent and unverified Live Status Updates**: Sheet columns are updated after each attempt Loop Logic**: Repeats continuously to catch new rows How to Use Step-by-step Setup Prepare Your Google Sheet Copy this Sample Sheet Ensure it includes the following columns: WhatsApp No name (note: trailing space is required) row_number status, check, validity Connect Google Sheets in n8n Use OAuth2 credentials to allow n8n access Set the workflow to fetch rows where check is not empty Get a Rapiwa Account Sign up at https://rapiwa.com Add your WhatsApp number Retrieve your Bearer Token from your Rapiwa dashboard Configure HTTP Request Nodes Use Rapiwa's API endpoints: Verify Number: https://app.rapiwa.com/api/verify-whatsapp Send Message: https://app.rapiwa.com/api/send-message Add your Bearer Token to the header Start Your Workflow Run the n8n automation It will read leads, clean phone numbers, verify WhatsApp validity, send messages, and update the sheet accordingly Requirements A Google Sheet with correctly formatted columns Active Rapiwa subscription (~$5/month) A valid Bearer Token from Rapiwa Your WhatsApp number connected to Rapiwa n8n instance with: Google Sheets integration (OAuth2 setup) HTTP Request capability Google Sheet Column Reference | name | number | email | time | check | validity | status | |-----------------|--------------|-------------------|-----------------------------|---------|------------|-----------| | Abdul Mannan | 8801322827799| contact@spagreen.net| September 14th 2025, 10:34 | checked | verified | sent | | Abdul Mannan | 8801322827798| contact@spagreen.net| September 14th 2025, 10:34 | checked | unverified | not sent | Workflow Logic Summary Trigger Every 5 Minutes Fetch All Rows with Pending Status Limit to 60 Rows per Execution Clean and Format Phone Numbers Check Number Validity via Rapiwa Condition Check: If valid → Send Message If invalid → Update status as not sent, unverified Send WhatsApp Message via Rapiwa Update Sheet Row On success: sent, verified, checked On failure: not sent, unverified Delay 5 seconds before next message Repeat for next lead Customization Ideas Add image or document sending support via Rapiwa Customize messages based on additional fields (e.g., product, service) Log failures to a separate sheet Send admin email for failed batches Add support for multilingual messages Notes & Warnings The column name "name " includes a space — do not remove or rename it. International number format is required for Rapiwa to work correctly. If you're sending many messages, increase the Wait node delay to prevent API throttling. Support WhatsApp Support: Chat Now Discord: Join SpaGreen Community Facebook Group: SpaGreen Support Website: https://spagreen.net Developer Portfolio: Codecanyon SpaGreen

📍Overview This no-code workflow is built for creators, agencies, and operators who want to automate the repurposing of Instagram Reels. It runs end-to-end and outputs structured insights and content-ready scripts—without touching a single tool manually. 🧰 What It Does Triggered simply by sending an Instagram Reel URL via Telegram. Downloads the Reel automatically. Converts video to audio using FreeConvert API. Transcribes speech to text using AssemblyAI. Analyzes both transcript and description using a connected LLM (OpenAI or Mistral). Extracts: Niche Core message 3 viral content hooks 3 ready-to-use short-form video scripts Saves all data to a Google Sheet for easy reuse by the creator or team. 🧪 APIs & Integrations Telegram Bot API (for triggering) FreeConvert API (MP4 to MP3 conversion) AssemblyAI (for transcription) OpenAI or Mistral (LLM for content analysis) Google Sheets API (for logging all outputs) ✅ Requirements An n8n instance (self-hosted or cloud) AssemblyAI API key FreeConvert API key Telegram Bot token Google service account credentials Your preferred LLM key (OpenAI or Mistral) 💡 Why Use This Workflow Runs entirely from Telegram—no dashboards required Helps you extract deep insights and reusable content from any Instagram Reel All tools used are free or very low cost Ideal for scaling personal brands or agency operations

HubSpot Lead Refinement 🚀 How it works Triggers: HubSpot Trigger: Fires when contacts are created/updated. Manual Trigger: Run on demand for testing or batch checks. Get Recently Created/Updated Contacts: Pulls fresh contacts from HubSpot. Edit Fields (Set): Maps key fields (First Name, Last Name, Email) for the Agent. AI Agent: First reads your Google Doc (via the Google Docs tool) to learn the research steps and output format. Then uses SerpAPI (Google engine) to locate the contact’s likely LinkedIn profile and produce a concise result. Code – Remove Think Part: Cleans the model output (removes hidden “think” blocks / formatting) so only the final answer remains. HubSpot Update: Writes the cleaned LinkedIn URL to the contact (via email match). 🔑 Required Credentials: HubSpot App Token (Private App) — for Get/Update contact nodes. HubSpot Developer OAuth (optional) — if you use the HubSpot * Trigger node for event-based runs. Google Service Account — for the Google Docs tool (share your * playbook doc with this service account). OpenRouter — for the OpenRouter Chat Model used by the AI Agent. SerpAPI — for targeted Google searches from within the Agent. 🛠️ Setup Instructions HubSpot Create a Private App and copy the Access Token. Add or confirm the contact property linkedinUrl (Text). Plug the token into the HubSpot nodes. If using HubSpot Trigger, connect your Developer OAuth app and subscribe to contact create/update events. Google Docs (Living Instructions) ➡️ Sample configuration doc file Copy the sample doc file and modify to your need. Share the doc with your Google Service Account (Viewer is fine). In the Read Google Docs node, paste the Document URL. OpenRouter & SerpAPI Add your OpenRouter key to the OpenRouter Chat Model credential. Add your SerpAPI key to the SerpAPI tool node. (Optional) In your Google Doc or Agent prompt, set sensible defaults for SerpAPI (engine=google, hl=en, gl=us, num=5, max 1–2 searches). ✨ What you get Auto-enriched contacts with a LinkedIn URL and profile insights (clean, validated output). A research process you can change anytime by editing the Google Doc—no workflow changes needed. Tight, low-noise searches via SerpAPI to keep costs down. And that’s it—publish and let the Agent enrich new leads automatically while you refine the rules in your doc. It allows handing off to a team who wouldn't necessarily tweak the automation nodes.

Overview When designing user interfaces, toolbar icons often get overlooked, even though their placement and grouping dramatically impact usability and user flow. This workflow leverages Gemini AI to automatically analyze UI screens, classify toolbar icons based on Apple’s Human Interface Guidelines (HIG), and suggest optimal placements. By combining AI analysis with structured placement logic, this workflow helps designers build more consistent, efficient, and user-friendly interfaces—without spending hours manually arranging icons. 🚀 Features AI Classification**: Uses Gemini AI to analyze screenshots and classify icons into roles like .primaryAction, .navigation, .confirmationAction, and more. HIG-Based Placement**: Automatically assigns icons to the correct toolbar areas—Leading (Left), Trailing (Right), Center, Bottom, or System-decided. Usage-Aware Reordering**: Reorders icons based on frequency of use so the most relevant actions appear where users expect them. JSON Output**: Delivers structured results for seamless integration into design tools or documentation. 🔧 Setup Instructions Install the Workflow: Import the workflow into your n8n instance. Configure Input: Upload a screenshot of your UI. Upload a set of icons you want to classify and place. Set Up Gemini AI Node: Add your Gemini AI API key in the node’s credentials. Run the Workflow: Submit the inputs and let the AI classify and assign placements. Export Results: Copy the JSON output or connect the workflow to your preferred design/documentation tools. ⚙️ How It Works Form Submission – Capture screenshot + icons. Gemini AI Agent – Interprets screen context and classifies each icon. Placement Logic – Maps icons to the correct toolbar areas. Reordering – Adjusts order based on relevance and HIG standards. Structured Output – Produces clean JSON for further use. 🎨 Customization Change AI Prompts**: Modify the Gemini AI node prompts to reflect your app’s design language. Adjust Placement Rules**: Update logic to follow custom guidelines beyond Apple HIG. Integrate with Design Tools**: Send the JSON output directly to tools like Figma, Sketch, or internal systems. 💡 Why This Matters Consistency**: Ensures toolbar designs always follow Apple’s HIG. Efficiency**: Saves designers hours of manual icon placement. Scalability**: Works across multiple screens, flows, and apps. AI-Assisted Design**: Augments designer decisions with structured insights instead of replacing them.

This workflow implements a Retrieval-Augmented Generation (RAG) system that integrates Google Drive and Qdrant. This setup creates a powerful, self-updating knowledge base that provides accurate, context-aware answers to user queries. Key Advantages Automated Knowledge Base Updates** No manual intervention is required—documents in Google Drive are automatically synchronized with Qdrant. Efficient Search and Retrieval** Vector embeddings enable fast and precise retrieval of relevant information. Scalable and Flexible** Works with multiple documents and supports continuous growth of your dataset. Seamless AI Integration** Combines OpenAI embeddings for vectorization and Google Gemini for high-quality natural language answers. Metadata-Enhanced Storage** Each document stores metadata (file ID and name), making it easy to manage and track document versions. End-to-End RAG Pipeline** From document ingestion to AI-powered Q\&A, everything is handled inside one n8n workflow. How It Works This workflow implements a Retrieval-Augmented Generation (RAG) system that automatically processes, stores, and retrieves document information for AI-powered question answering. Here’s how it functions: Document Processing & Vectorization: The system monitors a specified Google Drive folder for new or updated files. When a file is added or modified, it is downloaded and split into manageable chunks using a Recursive Character Text Splitter. Each chunk is converted into vector embeddings using OpenAI's embedding model. These vectors, along with metadata (file ID, file name), are stored in a Qdrant vector database. Automatic Updates: The workflow includes a mechanism to delete old vectors associated with an updated file before inserting the new ones, ensuring the knowledge base remains current. Query Handling & Response Generation: When a user sends a chat message (via a chat trigger), the system: Retrieves the most relevant document chunks from Qdrant based on the query's semantic similarity. Uses a Google Gemini language model to generate a context-aware answer grounded in the retrieved documents. This provides accurate, source-based responses instead of relying solely on the AI's internal knowledge. Initial Setup & Maintenance: The workflow can be triggered manually to create the Qdrant collection or clear all existing data. It processes all existing files in the Drive folder during initial setup, populating the vector store. Set Up Steps To configure this workflow, follow these steps: STEP 1: Create Qdrant Collection Replace QDRANTURL in the "Create collection" and "Clear collection" nodes with your Qdrant instance URL (e.g., http://your-qdrant-host:6333). Replace COLLECTION with your desired collection name. Ensure the Qdrant API credentials are correctly set in the respective HTTP Request nodes. STEP 2: Configure Google Drive Access Set up OAuth credentials for Google Drive to allow the workflow to: Read files from a specific folder . Download files for processing. Update the Folder ID in the "Search files" and "Update?" trigger nodes to point to your target Google Drive folder. STEP 3: Set Up AI Models Configure the OpenAI API credentials in the Embeddings nodes for generating text embeddings. Configure the Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node for generating answers. STEP 4: Configure Metadata The system automatically attaches metadata (file_id, file_name) to each document chunk. This is set in the Default Data Loader nodes. This metadata is crucial for identifying the source of information and for the update mechanism. STEP 5: Test the RAG System The workflow includes a chat trigger ("When chat message received") for testing. Send a query to test the retrieval and answer generation process. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Auto-Routing Nicereply Feedback to Microsoft Teams by Team and Sentiment Automatically collect client feedback from Nicereply, analyze sentiment, and send it to the right Microsoft Teams channels — smartly split by team, tone, and comment presence. About this Workflow This workflow pulls customer satisfaction feedback from Nicereply, filters out irrelevant or test entries, and evaluates each item based on the team it belongs to and the sentiment of the response (Great, OK, Bad). It automatically routes the feedback to Microsoft Teams — either as a summary in a channel or a direct message — depending on the team's role and whether a comment is included. Perfect for support, delivery, consulting, and documentation teams that want to stay in the loop with customer sentiment. It ensures that positive feedback reaches the teams who earned it, and that negative feedback is escalated quickly to leads or management. Use Cases Send daily customer feedback directly to the responsible teams in MS Teams Automatically escalate negative responses to leads or managers Avoid clutter by filtering out unimportant or test entries Keep internal teams motivated by sharing only the most relevant praise How it works Schedule Trigger Starts the workflow on a set schedule (e.g., daily at 7:00 AM) Get Feedback Pulls customer feedback from Nicereply using survey ID Split Out Processes each feedback entry separately Edit Feedbacks Renames or adjusts fields for easier filtering and readability Change Survey ID Maps internal survey identifiers for accurate team routing (Survey ID can be found in Nicereply: Settings > Surveys > [Survey] > ID) Filter Excludes old responses Code Node Tag unknown clients Change Happiness Value Converts score into “Great”, “OK”, or “Bad” for routing logic Without Comment Checks if feedback includes a text comment or not Send Feedback Without Comment Routes simple feedback (no comment) to MS Teams based on team + score Send Feedback With Comment Routes full feedback with comment to MS Teams for closer review Feedback Routing Logic Each team receives only what’s most relevant: Support, Docs, Consulting* get only *Great** feedback to boost morale Team Leads* receive *OK and Bad** feedback so they can follow up Management* is only alerted to *Bad** feedback for critical response These rules can be freely customized. For example, you may want Support to receive all responses, or Management only when multiple Bad entries are received. The structure is modular and easily adjustable. How to Use Import the workflow Load the .json file into your Easy Redmine automation workspace Set up connections Nicereply API key or integration setup Microsoft Teams integration (chat and/or channel posting) Insert your Survey ID(s) You’ll find these in the Nicereply admin panel under Survey settings Customize team logic Adjust survey-to-team mappings and message routing as needed Edit Teams message templates Modify message text or formatting based on internal tone or content policies Test with real data Run manually and verify correct delivery to MS Teams Deploy and schedule Let it run on its own to automate the feedback cycle Requirements Nicereply account with active surveys Microsoft Teams account with permissions to post to channels or send chats Optional Enhancements Add AI to summarize long comments Store feedback history in external DB Trigger follow-up tasks or alerts for repeated Bad scores Localize messages for multilingual feedback systems Integrate additional tools like Slack, Easy Redmine, etc. Tips for a Clean Setup Keep team routing logic in one place for easy updates Rename all nodes clearly to reflect their function (e.g., Change Happiness Value) Add logging or alerting in case of failed delivery or empty feedback pull Use environment variables for tokens and survey IDs where possible

Gets Google Calendar events for the day (12 hours from execution time), and filters out in-person meetings, Signal meetings, and meetings canceled by Calendly ("transparent").

Simplify event planning with this automated n8n workflow. Triggered by incoming requests, it fetches speaker and audience data from Google Sheets, analyzes profiles and preferences, and generates optimized session recommendations. The workflow delivers formatted voice responses and updates tracking data, ensuring organizers receive real-time, tailored suggestions. 🎙️📊 Key Features Real-time analysis of speaker and audience data for personalized recommendations. Generates optimized session lineups based on profiles and preferences. Delivers responses via voice agent for a seamless experience. Logs maintain a detailed recommendation history in Google Sheets. Workflow Process The Webhook Trigger node initiates the workflow upon receiving voice agent or external system requests. Parse Voice Request** processes incoming voice data into actionable parameters. Fetch Database** retrieves speaker ratings, past sessions, and audience ratings from Google Sheets. Calculate & Analyze** combines voice request data with speaker profiles and audience insights for comprehensive matching. AI Optimization Engine** analyzes speaker-audience fit and recommends optimal session lineups. Format Recommendations** structures the recommendations for voice agent response. Voice Agent Response** returns formatted recommendations to the user with natural language summary and structured data. Update Tracking Sheet** saves recommendation history and analytics to Google Sheets. If errors occur, the Check for Errors node branches to: Format Error Response prepares an error message. Send Error Response delivers the error notification. Setup Instructions Import the workflow into n8n and configure Google Sheets OAuth2 for data access. Set up the Webhook Trigger with your voice agent or external system's API credentials. Configure the AI Optimization Engine node with a suitable language model (e.g., Anthropic Chat Model). Test the workflow by sending sample voice requests and verifying recommendations. Adjust analysis parameters as needed for specific event requirements. Prerequisites Google Sheets OAuth2 credentials Voice agent API or integration service AI/LLM service for optimization (e.g., Anthropic) Structured speaker and audience data in a Google Sheet Google Sheet Structure: Create a sheet with columns: Speaker Name Rating Past Sessions Audience Rating Preferences Updated At Modification Options Customize the Calculate & Analyze node to include additional matching criteria (e.g., topic expertise). Adjust the AI Optimization Engine to prioritize specific session formats or durations. Modify voice response templates in the Voice Agent Response node with branded phrasing. Integrate with event management tools (e.g., Eventbrite) for live data feeds. Set custom error handling rules in the Check for Errors node. Discover more workflows – Get in touch with us

🔧 How It Works **This n8n workflow leverages an agentic AI solution, where multiple AI agents collaborate to process and generate tailored job application assets. ✅ Features Agent-based AI Coordination: Utilizes multiple AI agents working in sequence to analyze the job description and generate results. Outputs: A customized cover letter An optimized resume (CV) A list of interview preparation questions Automated Delivery: The final outputs are created as Google Docs and stored in your connected Google Drive folder. 🧾 Input Requirement Simply provide a LinkedIn job URL as the input. Example: https://www.linkedin.com/jobs/view/4184156975 ⚙️ Setup Instructions To deploy and run this workflow, you'll need to configure the following credentials: Google Cloud Platform (GCP) Enable the Google Drive API Set up OAuth credentials for n8n integration OpenAI API Key Needed for generating the content (cover letter, CV, and questions) BrightData (formerly Luminati) Used to scrape and extract job details from the LinkedIn job link ⚠️ Setup requires moderate technical familiarity with APIs and OAuth. A step-by-step configuration guide is recommended for beginners.