Workflow Templates

✨ Try It Out! Sync your Linear issues to Todoist automatically with this n8n workflow. When an issue is created, updated, or completed in Linear, a corresponding task is created, updated, or closed in Todoist. ⚙️ How It Works Triggered by issue changes via linearTrigger Routes based on action (create, update, remove) Checks if a matching Todoist task already exists (via issue ID) If the issue has: A due date And is assigned to you (youremail@example.com) ➤ Then it creates or updates the task accordingly If the issue is marked Done, the Todoist task is closed If it's deleted in Linear, the Todoist task is also removed Sub-issues get enriched with their parent title for clarity 🛠️ Customization Replace youremail@example.com with your Linear email in the IF nodes Adjust which states are synced (e.g. “In Progress”, “Todo”...) Customize the Todoist project, labels, or title formatting ⚠️ Bi-directional Sync? This workflow is one-way (Linear ➜ Todoist). Bi-directional syncing might be possible but isn’t handled here—it would be a cool upgrade! ✅ Requirements n8n with OAuth2 access to Linear and Todoist Your Linear email set in the workflow for filtering A target Todoist project (default: Inbox) 💬 Need Help? Ask in the n8n Forum or join the Discord. Happy Automating! 🚀

How it works The workflow triggers on a new checkout event from Shopify and extracts all relevant cart data. It filters carts based on value and age to isolate qualified abandoned checkouts. For each qualified cart, it sends a follow-up email, updates or creates the corresponding HubSpot contact, and generates a CRM note linked to that contact. Finally, it logs the processed cart into Google Sheets for tracking and review. Step-by-step Trigger on new Shopify checkout** Shopify Trigger – Starts the workflow when a new checkout is created. Normalize and structure cart data** Parse Cart Data – Extracts email, customer name, items, cart totals, timestamps, and hours since creation. Filter carts that meet follow-up criteria** Filter Qualified Carts – Passes only carts older than 12 hours and valued above 50. Send follow-up message** Send a message – Delivers a reminder email to the customer about the pending checkout. Create or update CRM contact** Create or update a contact – Ensures the shopper exists as a HubSpot contact. Prepare CRM note details** Generates Note Data – Builds a structured note containing timing, cart details, and follow-up context. Create the HubSpot note** Create HubSpot Note – Submits the prepared note to HubSpot’s CRM. Associate note with the contact** Associate Note with Contact in HubSpot – Links the generated note to the correct HubSpot contact. Record activity in tracker sheet** Log to Google Sheets – Appends processed cart fields including items, totals, timestamps, and customer info. Why use this? Identifies high-value or long-abandoned carts automatically and follows up without manual effort. Keeps CRM records updated and adds contextual notes sales teams can act on. Maintains a structured audit trail of every abandoned cart interaction. Improves recovery chances by combining email outreach with CRM enrichment and logging. Enables analysis of abandoned checkout patterns directly from Google Sheets.

This template automates posting to Instagram Business and Facebook Pages using the Meta Graph API. It supports both short-lived and long-lived tokens, with a secure approach using System User tokens for reliable, ongoing automation. Includes detailed guidance for authentication, token refresh logic, and API use. Features: 📸 Publish to Instagram via /media + /media_publish 📘 Post to Facebook Pages via /photos 🔐 Long-lived token support via Meta Business System User ♻️ Token refresh support using staticData in n8n 🧠 In-line sticky note instructions Use Cases: Schedule and publish branded social media content Automate marketing flows with CRM + social sync Empower internal teams or clients to post without manual steps Tags: Instagram, Facebook, Meta Graph API, Social Media, Token Refresh, Long-Lived Token, Marketing Automation, System User

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.