Workflow Templates

What this template does Receives meeting data via a webform, cleans/structures it, fills a Word docx template, uploads the file to SharePoint, appends a row to Excel 365, and sends an Outlook email with the document attached. Good to know Uses a community node: DocxTemplater to render the DOCX from a template. Install it from the Community Nodes catalog. The template context is the workflow item JSON. In your docx file, use placeholders. Includes a minimal HTML form snippet (outside n8n) you can host anywhere. Replace the placeholder WEBHOOK_URL with your Webhook URL before testing. Microsoft nodes require Azure app credentials with correct permissions (SharePoint, Excel/Graph, Outlook). How it works Webhook — Receives meeting form JSON (POST). Code (Parse Meeting Data) — Parses/normalizes fields, builds semicolon‑separated strings for attendees/absentees, and flattens discussion points / action items. SharePoint (Download) — Fetches the DOCX template (e.g., meeting_minutes_template.docx). Merge — Combines template binary + JSON context by position. DocxTemplater — Renders meeting_{{now:yyyy-MM-dd}}.docx using the JSON context. SharePoint (Upload) — Saves the generated DOCX to a target folder (e.g., /Meetings). Microsoft Excel 365 (Append) — Appends a row to your sheet (Date, Time, Attendees, etc.). Microsoft Outlook (Send message) — Emails the generated DOCX as an attachment. Requirements Community node DocxTemplater installed Microsoft 365 access with credentials for: SharePoint (download template + upload output) Excel 365 (append to table/worksheet) Outlook (send email) A Word template with placeholders matching the JSON keys Need Help 🔗 LinkedIn – Wessel Bulte

🔄 n8n Workflow: Check and Update n8n Version This workflow automatically checks if the local n8n version is outdated and, if so, creates a file to signal an update is needed. 🖥️ Working Environment Operating System:** Ubuntu 24.04 n8n Installation:** Docker container 📁 Project Directory Structure n8n/ ├── check_update.txt ├── check-update.sh ├── compose.yml ├── update_n8n.cron 🧾 File Descriptions check_update.txt Contains a single word: true: Update is needed false: No update required check-update.sh #!/bin/bash PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin if grep -q "true" /home/sysadmin/n8n/check_update.txt; then Place your update logic here echo "Update needed - please insert update logic" echo true > /home/sysadmin/n8n/check_update.txt fi Purpose: Checks the contents of check_update.txt If it contains true, executes update logic (currently a placeholder) Resets check_update.txt to true update_n8n.cron SHELL=/bin/sh 10 5 * * * /bin/sh /home/sysadmin/n8n/check-update.sh Purpose: Runs the check-update.sh script daily at 5:10 AM Uses /bin/sh as the shell environment 🧩 n8n Workflow Breakdown 1. Schedule Trigger 🕓 Purpose:** Triggers the workflow every day at 5:00 AM Node Type:** Schedule Trigger 2. Get the latest n8n version 🌐 Purpose:** Fetches the latest version of n8n from npm Endpoint:** https://registry.npmjs.org/n8n/latest Node Type:** HTTP Request 3. Get Local n8n version 🖥️ Purpose:** Retrieves the currently running n8n version Endpoint:** http://192.168.100.18:5678/rest/settings Node Type:** HTTP Request 4. If 🔍 Purpose:** Compares the local and latest versions Condition:** If not equal → update is needed 5. SSH1 🧾 Purpose:** Writes the result to a file on the host via SSH Logic:** echo "{{ $('If').params.conditions ? 'false' : 'true' }}" > check_update.txt Effect: Updates check_update.txt with "true" if an update is needed, "false" otherwise. 🛠️ Setting up Crontab on Ubuntu 1. Register the cron job with: crontab update_n8n.cron 2. Verify that your cron job is registered: crontab -l ✅ Result 5:00 AM** – n8n workflow checks versions and writes result to check_update.txt 5:10 AM** – Cron runs check-update.sh to respond to update flag

This workflow allows you to send SMS messages globally using API without needing a physical phone number. 1. How It Works Consists of three main nodes: Manual Trigger**: The workflow starts when you click the "Test workflow" button in n8n. Set SMS Data**: This node defines the SMS message content and the recipient's phone number (including the international prefix). Send SMS**: This node sends the SMS using the ClickSend API. It uses HTTP Basic Authentication with your ClickSend credentials and sends a POST request to the ClickSend API endpoint with the message and recipient details. The workflow is simple and efficient, making it easy to automate SMS sending for various use cases, such as notifications, alerts, or marketing campaigns. 2. Set Up Steps To set up and use this workflow in n8n, follow these steps: Register on ClickSend: Go to ClickSend and create an account. Obtain your API Key and take advantage of the 2 € free credits provided. Set Up Basic Authentication in n8n: In the "Send SMS" node, configure the HTTP Basic Auth credentials: Username: Use the username you registered with on ClickSend. Password: Use the API Key provided by ClickSend. Configure the SMS Data: In the "Set SMS data" node, define: The message content (e.g., "Hi, this is my first message"). The recipient's phone number, including the international prefix (e.g., +39xxxxxxxx). Test the Workflow: Click the "Test workflow" button in n8n to trigger the workflow. The workflow will send the SMS using the ClickSend API, and you should receive the message on the specified phone number. Optional Customization: You can modify the workflow to dynamically set the message content or recipient phone number using data from other nodes or external sources. This workflow is a quick and efficient way to send SMS messages programmatically. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This is an example that gets the logo, icon, and information of a company and stores it in Airtbale. You can set the values that you want to store in the Set node. If you want to store the data in a different database (Google Sheet, Postgres, MongoDB, etc.) replace the Airtable node with that node. You can refer to the documentation to learn how to build this workflow from scratch.

This example workflow allows you to create, update, and get a document in Google Cloud Firestore. The workflow uses the Set node to set the data, however, you might receive data from a different source. Add the node that receives the data before the Set node and set the values you want to insert in a document, in the Set node. Also, update the Columns/ attributes fields in the Google Cloud Firestore node.

This is the first of 4 workflows for a Mattermost Standup Bot. This workflow will create a default configuration file. You can set the default configuration in the Set node (Use Default Config) the values are: config.slashCmdToken - The token Mattermost provides when you make a new Slash Command config.mattermostBaseUrl - The base URL for your Mattermost instance config.botUserToken - The User token for your Mattermost bot config.n8nWebhookUrl - The URL for your "Action from MM" webhook in the "Standup Bot - Worker" workflow config.botUserId - The UserID for your Mattermost Bot user The config file is saved under /home/node/.n8n/standup-bot-config.json This workflow only needs to be run once manually as part of the setup .

This workflow ensures gender inclusive language in Mattermost channels. If someone addresses the group with “guys” or “gals”, a bot promptly replies with: "May I suggest “folks” or “y'all”? We use gender inclusive language here. 😄". Webhook node**: triggers the workflow when a new message is posted in Mattermost. IF node**: verifies if the message includes the words "guys" or "gals". If false, it does not take any action. If true, it triggers the Mattermost node. Mattermost node**: posts the language warning message in the Mattermost channel.

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. This pipeline is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] KNN classification tool This tool takes any image URL, and as output, it returns a class of the object on the image based on the image uploaded to the Qdrant dataset (lands). An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage AI Multimodal Embeddings API to fetch its embedding. The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes. Majority voting is done for classes of neighbouring images. A loop is used to resolve scenarios where there is a tie in Majority Voting, and we increase the number of neighbours to retrieve. When the loop finally resolves, the identified class is returned to the calling workflow.

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. 3. This is the third pipeline --- the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] Anomaly Detection Tool This is the tool that can be used directly for anomalous images (crops) detection. It takes as input (any) image URL and returns a text message telling if whatever this image depicts is anomalous to the crop dataset stored in Qdrant. An Image URL is received via the Execute Workflow Trigger, which is used to generate embedding vectors using the Voyage AI Embeddings API. The returned vectors are used to query the Qdrant collection to determine if the given crop is known by comparing it to threshold scores of each image class (crop type). If the image scores lower than all thresholds, then the image is considered an anomaly for the dataset.

Emailing Using Google Sheet, Google Docs, and SMTP Automate personalized email campaigns using a Google Sheets contact list, a Google Docs template, and SMTP delivery. How It Works Google Docs** is used as the email template with variables: {{firstname}}, {{lastname}}, {{company}}, {{email}}. Google Sheet** contains your list of recipients (one per row). For each contact, the workflow merges personal data into the Google Docs template. Email is sent to each recipient via SMTP (batch size: 1). Use the Wait node to respect provider quotas. After sending, the workflow updates the "process" column of the Google Sheet with the date/time. How to Use Copy Templates: Google Docs Template Google Sheet Template Find each document’s ID (the text after /d/ and before /edit in the URL). Configure Workflow: Enter your Google Docs and Google Sheets IDs in the settings node. Set your email subject in the appropriate parameter. Set Up Credentials: Connect your Google account. Configure the SMTP node with your mail server details. Update Data: Edit the Google Docs template with your message and variables. Prepare your Google Sheet with these columns: email, firstname, lastname, company. Deploy and Test: Connect all nodes. Test with a small contact batch. Troubleshoot any node errors (indicated in red in n8n). Requirements Google Credentials & permissions**: For Sheets and Docs access. SMTP Server**: For email delivery (adjust Wait node for rate limits). n8n Version**: Tested on 1.105.2 (Ubuntu). Need Help? Contact me on LinkedIn or ask in the n8n Community Forum!

This workflow automates AI-powered search insights by combining SE Ranking AI Search data with OpenAI summarization. It starts with a manual trigger and fetches the time-series AI visibility data via the SE Ranking API. The response is summarized using OpenAI to produce both detailed and concise insights. The workflow enriches the original metrics with these AI-generated summaries and exports the final structured JSON to disk, making it ready for reporting, analytics, or further automation. Who this is for This workflow is designed for: SEO professionals & growth marketers** tracking AI search visibility Content strategists** analyzing how brands appear in AI-powered search results Data & automation engineers** building SEO intelligence pipelines Agencies** producing automated search performance reports for clients What problem is this workflow solving? SE Ranking’s AI Search API provides rich but highly technical time-series data. While powerful, this data: Is difficult to interpret quickly Requires manual analysis to extract insights Is not presentation-ready for reports or stakeholders This workflow solves that by automatically transforming raw AI search metrics into clear, structured summaries, saving time and reducing analysis friction. What this workflow does At a high level, the workflow: Accepts input parameters such as target domain, AI engine, and region Fetches AI search visibility time-series data from SE Ranking Uses OpenAI GPT-4.1-mini to generate: A comprehensive summary A concise abstract summary Enriches the original dataset with AI-generated insights Exports the final structured JSON to disk for: Reporting Dashboards Further automation or analytics Setup Prerequisites n8n (self-hosted or cloud)** SE Ranking API access** OpenAI API key** Setup Steps If you are new to SE Ranking, please signup on seranking.com Import the workflow JSON into n8n Configure credentials: SE Ranking using HTTP Header Authentication. Please make sure to set the header authentication as below. The value should contain a Token followed by a space with the SE Ranking API Key. OpenAI for GPT-4.1-mini Open Set the Input Fields and update: target_site (e.g., your domain) engine (e.g., ai-overview) source (e.g., us, uk, in) Verify the file path in Write File to Disk Click Execute Workflow How to customize this workflow to your needs You can easily extend or tailor this workflow: Change analysis scope** Update domain, region, or AI engine Modify AI outputs** Adjust prompts or output schema for insights like trends, risks, or recommendations Replace storage** Send output to: Google Sheets Databases S3 / cloud storage Webhooks or BI tools Automate monitoring** Add a Cron trigger to run daily, weekly, or monthly Summary This workflow turns raw SE Ranking AI Search data into clear, executive-ready insights using OpenAI GPT-4.1-mini. By combining automated data collection with AI summarization, it enables faster decision-making, better reporting, and scalable SEO intelligence without manual analysis.

Yelp Business Scraper by URL via Scrape.do API with Google Sheets Storage Overview This n8n workflow automates the process of scraping comprehensive business information from Yelp using individual business URLs. It integrates with Scrape.do for professional web scraping with anti-bot bypass capabilities and Google Sheets for centralized data storage, providing detailed business intelligence for market research, competitor analysis, and lead generation. Workflow Components 1. 📥 Form Trigger | Property | Value | |----------|-------| | Type | Form Trigger | | Purpose | Initiates the workflow with user-submitted Yelp business URL | | Input Fields | Yelp Business URL | | Function | Captures target business URL to start the scraping process | 2. 🔍 Create Scrape.do Job | Property | Value | |----------|-------| | Type | HTTP Request (POST) | | Purpose | Creates an async scraping job via Scrape.do API | | Endpoint | https://q.scrape.do/api/v1/jobs | | Authentication | X-Token header | Request Parameters: Targets**: Array containing the Yelp business URL Super**: true (uses residential/mobile proxies for better success rate) GeoCode**: us (targets US-based content) Device**: desktop Render**: JavaScript rendering enabled with networkidle2 wait condition Function: Initiates comprehensive business data extraction from Yelp with headless browser rendering to handle dynamic content. 3. 🔧 Parse Yelp HTML | Property | Value | |----------|-------| | Type | Code Node (JavaScript) | | Purpose | Extracts structured business data from raw HTML | | Mode | Run once for each item | Function: Parses the scraped HTML content using regex patterns and JSON-LD extraction to retrieve: Business name Overall rating Review count Phone number Full address Price range Categories Website URL Business hours Image URLs 4. 📊 Store to Google Sheet | Property | Value | |----------|-------| | Type | Google Sheets Node | | Purpose | Stores scraped business data for analysis and storage | | Operation | Append rows | | Target | "Yelp Scraper Data - Scrape.do" sheet | Data Mapping: Business Name, Overall Rating, Reviews Count Business URL, Phone, Address Price Range, Categories, Website Hours, Images/Videos URLs, Scraped Timestamp Workflow Flow Form Input → Create Scrape.do Job → Parse Yelp HTML → Store to Google Sheet │ │ │ │ ▼ ▼ ▼ ▼ User submits API creates job JavaScript code Data appended Yelp URL with JS rendering extracts fields to spreadsheet Configuration Requirements API Keys & Credentials | Credential | Purpose | |------------|---------| | Scrape.do API Token | Required for Yelp business scraping with anti-bot bypass | | Google Sheets OAuth2 | For data storage and export access | | n8n Form Webhook | For user input collection | Setup Parameters | Parameter | Description | |-----------|-------------| | YOUR_SCRAPEDO_TOKEN | Your Scrape.do API token (appears in 3 places) | | YOUR_GOOGLE_SHEET_ID | Target spreadsheet identifier | | YOUR_GOOGLE_SHEETS_CREDENTIAL_ID | OAuth2 authentication reference | Key Features 🛡️ Anti-Bot Bypass Technology Residential Proxy Rotation**: 110M+ proxies across 150 countries WAF Bypass**: Handles Cloudflare, Akamai, DataDome, and PerimeterX Dynamic TLS Fingerprinting**: Authentic browser signatures CAPTCHA Handling**: Automatic bypass for uninterrupted scraping 🌐 JavaScript Rendering Full headless browser support for dynamic Yelp content networkidle2 wait condition ensures complete page load Custom wait times for complex page elements Real device fingerprints for detection avoidance 📊 Comprehensive Data Extraction | Field | Description | Example | |-------|-------------|---------| | name | Business name | "Joe's Pizza Restaurant" | | overall_rating | Average customer rating | "4.5" | | reviews_count | Total number of reviews | "247" | | url | Original Yelp business URL | "https://www.yelp.com/biz/..." | | phone | Business phone number | "(555) 123-4567" | | address | Full street address | "123 Main St, New York, NY 10001" | | price_range | Price indicator | "$$" | | categories | Business categories | "Pizza, Italian, Delivery" | | website | Business website URL | "https://joespizza.com" | | hours | Operating hours | "Mon-Fri 11:00-22:00" | | images_videos_urls | Media content links | "https://s3-media1.fl.yelpcdn.com/..." | | scraped_at | Extraction timestamp | "2025-01-15T10:30:00Z" | 🗂️ Centralized Data Storage Automatic Google Sheets export Organized business data format with 12 data fields Historical scraping records with timestamps Easy sharing and collaboration Use Cases 📈 Market Research Competitor business analysis Local market intelligence gathering Industry benchmark establishment Service offering comparison 🎯 Lead Generation Business contact information extraction Potential client identification Market opportunity assessment Sales prospect development 📊 Business Intelligence Customer sentiment analysis through ratings Competitor performance monitoring Market positioning research Brand reputation tracking 📍 Location Analysis Geographic business distribution Local competition assessment Market saturation evaluation Expansion opportunity identification Technical Notes | Specification | Value | |--------------|-------| | Processing Time | 15-45 seconds per business URL | | Data Accuracy | 95%+ for publicly available business information | | Success Rate | 99.98% (Scrape.do guarantee) | | Proxy Pool | 110M+ residential, mobile, and datacenter IPs | | JS Rendering | Full headless browser with networkidle2 wait | | Data Format | JSON with structured field mapping | | Storage Format | Structured Google Sheets with 12 predefined columns | Setup Instructions Step 1: Import Workflow Copy the JSON workflow configuration Import into n8n: Workflows → Import from JSON Paste configuration and save Step 2: Configure Scrape.do Get your API token: Sign up at Scrape.do Navigate to Dashboard → API Token Copy your token Update workflow references (3 places): 🔍 Create Scrape.do Job node → Headers → X-Token 📡 Check Job Status node → Headers → X-Token 📥 Fetch Task Results node → Headers → X-Token Replace YOUR_SCRAPEDO_TOKEN with your actual API token. Step 3: Configure Google Sheets Create target spreadsheet: Create new Google Sheet named "Yelp Business Data" or similar Add header row with columns: name | overall_rating | reviews_count | url | phone | address | price_range | categories | website | hours | images_videos_urls | scraped_at Copy the Sheet ID from URL (the long string between /d/ and /edit) Set up OAuth2 credentials: In n8n: Credentials → Add Credential → Google Sheets OAuth2 Complete the Google authentication process Grant access to Google Sheets Update workflow references: Replace YOUR_GOOGLE_SHEET_ID with your actual Sheet ID Update YOUR_GOOGLE_SHEETS_CREDENTIAL_ID with credential reference Step 4: Test and Activate Test with sample URL: Use a known Yelp business URL (e.g., https://www.yelp.com/biz/example-business-city) Submit through the form trigger Monitor execution progress in n8n Verify data appears in Google Sheet Activate workflow: Toggle workflow to "Active" Share form URL with users Sample Business Data The workflow captures comprehensive business information including: | Category | Data Points | |----------|-------------| | Basic Information | Name, category, location | | Performance Metrics | Ratings, review counts, popularity | | Contact Details | Phone, website, address | | Visual Content | Photos, videos, gallery URLs | | Operational Data | Hours, services, price range | Advanced Configuration Batch Processing Modify the input to accept multiple URLs by updating the job creation body: { "Targets": [ "https://www.yelp.com/biz/business-1", "https://www.yelp.com/biz/business-2", "https://www.yelp.com/biz/business-3" ], "Super": true, "GeoCode": "us", "Render": { "WaitUntil": "networkidle2", "CustomWait": 3000 } } Enhanced Rendering Options For complex Yelp pages, add browser interactions: { "Render": { "BlockResources": false, "WaitUntil": "networkidle2", "CustomWait": 5000, "WaitSelector": ".biz-page-header", "PlayWithBrowser": [ { "Action": "Scroll", "Direction": "down" }, { "Action": "Wait", "Timeout": 2000 } ] } } Notification Integration Add alert mechanisms: Email notifications for completed scrapes Slack messages for team updates Webhook triggers for external systems Error Handling Common Issues | Issue | Cause | Solution | |-------|-------|----------| | Invalid URL | URL is not a valid Yelp business page | Ensure URL format: https://www.yelp.com/biz/... | | 401 Unauthorized | Invalid or missing API token | Verify X-Token header value | | Job Timeout | Page too complex or slow | Increase CustomWait value | | Empty Data | HTML parsing failed | Check page structure, update regex patterns | | Rate Limiting | Too many concurrent requests | Reduce request frequency or upgrade plan | Troubleshooting Steps Verify URLs: Ensure Yelp business URLs are correctly formatted Check Credentials: Validate Scrape.do token and Google OAuth Monitor Logs: Review n8n execution logs for detailed errors Test Connectivity: Verify network access to all external services Check Job Status: Use Scrape.do dashboard to monitor job progress Performance Specifications | Metric | Value | |--------|-------| | Processing Time | 15-45 seconds per business URL | | Data Accuracy | 95%+ for publicly available information | | Success Rate | 99.98% (with Scrape.do anti-bot bypass) | | Concurrent Processing | Depends on Scrape.do plan limits | | Storage Capacity | Unlimited (Google Sheets based) | | Proxy Pool | 110M+ IPs across 150 countries | Scrape.do API Reference Async API Endpoints | Endpoint | Method | Purpose | |----------|--------|---------| | /api/v1/jobs | POST | Create new scraping job | | /api/v1/jobs/{jobID} | GET | Check job status | | /api/v1/jobs/{jobID}/{taskID} | GET | Retrieve task results | | /api/v1/me | GET | Get account information | Job Status Values | Status | Description | |--------|-------------| | queuing | Job is being prepared | | queued | Job is in queue waiting to be processed | | pending | Job is currently being processed | | rotating | Job is retrying with different proxies | | success | Job completed successfully | | error | Job failed | | canceled | Job was canceled by user | For complete API documentation, visit: Scrape.do Documentation Support & Resources Scrape.do Documentation**: https://scrape.do/documentation/ Scrape.do Dashboard**: https://dashboard.scrape.do/ n8n Documentation**: https://docs.n8n.io/ Google Sheets API**: https://developers.google.com/sheets/api This workflow is powered by Scrape.do - Reliable, Scalable, Unstoppable Web Scraping