Workflow Templates

This workflow allows you to receive updates about the positiong of the ISS and add it to a table in TimescaleDB. Cron node: The Cron node triggers the workflow every minute. You can configure the time based on your use-case. HTTP Request node: This node makes an HTTP Request to an API that returns the position of the ISS. Based on your use-case you may want to fetch data from a different URL. Enter the URL in the URL field. Set node: In the Set node we set the information that we need in the workflow. Since we only need the timestamp, latitude, and longitude we set this in the node. If you need other information, you can set them in this node. TimescaleDB node: This node stores the information in a table named iss. You can use a different table as well.

Who is this for? For developers and organizations that need to convert web page to PDF. What problem is this workflow solving? The web page conversion to PDF problem. What this workflow does Converts web page to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Change the parameter url to the webpage you want to convert to pdf Optionally, additional Body Parameters can be added for the converter.

Do you want to avoid communication problems when launching phone calls? This workflow verifies landline and mobile phone numbers using the uProc Get Parsed and validated phone tool with worldwide coverage. You need to add your credentials (Email and API Key - real -) located at Integration section to n8n. Node "Create Phone Item" can be replaced by any other supported service with phone values, like databases (MySQL, Postgres), or Typeform. The "uProc" node returns the next fields per every parsed and validated phone number: country_prefix: contains the international country phone prefix number. country_code: contains the 2-digit ISO country code of the phone number. local_number: contains the phone number without international prefix. formatted: contains a formatted version of the phone number, according to country detected. valid: detects if the phone number has a valid format and prefix. type: the phone number type (mobile, landline, or something else). "If" node checks if the phone number is valid. You can use the result to mark invalid phone numbers in your database or discard them from future telemarketing campaigns.

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 1. This is 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 1. This is 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] Batch Uploading Images Dataset to Qdrant This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant, also in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are). First, check for an existing Qdrant collection to use; otherwise, create it here. Additionally, when creating the collection, we'll create a payload index, which is required for a particular type of Qdrant requests we will use later. Next, import all (dataset) images from Google Cloud Storage but keep only non-tomato-related ones (for anomaly detection testing). Create (per batch) embeddings for all imported images using the Voyage AI multimodal embeddings API. Finally, upload the resulting embeddings and image descriptors to Qdrant via batch upload.

This workflow automates the process of fetching agricultural transaction data from the Taiwan Agricultural Products Open Data Platform and storing it in a Google Sheets document for further analysis. Key Features Manual Trigger: Allows manual execution of the workflow to control when data is fetched. HTTP Request: Sends a request to the Open Data Platform's API to retrieve detailed transaction data, including: Pricing (Upper, Middle, Lower, Average) Transaction quantities Crop and market details Split Out Node: Processes each record individually, ensuring accurate handling of every data entry. Google Sheets Integration: Appends the data into a structured Google Sheets document for easy access and analysis. Node Configurations 1. Manual Trigger Purpose**: Start the workflow manually. Configuration**: No setup needed. 2. HTTP Request Purpose**: Fetch agricultural data. Configuration**: URL: https://data.moa.gov.tw/api/v1/SheepQuotation Query Parameters: Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> Headers: accept: application/json 3. Split Out Purpose**: Split the API response data array into individual items. Configuration**: Field to Split Out: Data 4. Google Sheets Purpose**: Append the data to Google Sheets. Configuration**: Operation: Append Document ID: <your_document_id> Sheet Name: Sheet1 Mapped Fields: TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 此 Workflow 從 台灣農業產品開放資料平臺 獲取農產品交易數據，並將其儲存到 Google Sheets 文件 中進行進一步分析。 主要功能 Manual Trigger：允許手動執行工作流程，以控制數據獲取的時間。 HTTP Request：向開放資料平臺的 API 發送請求，獲取詳細的交易數據，包括： 價格 (Upper, Middle, Lower, Average) 交易數量 作物和市場詳細資料 Split Out Node：逐筆處理每一筆記錄，確保數據準確無誤。 Google Sheets Integration：將數據追加到結構化的 Google Sheets 文件中，方便存取和分析。 節點設定 1. Manual Trigger 用途**：手動啟動工作流程。 設定**：無需額外設定。 2. HTTP Request 用途**：抓取農產品數據。 設定**： URL: https://data.moa.gov.tw/api/v1/SheepQuotation 查詢參數 (Query Parameters)： Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> 標頭 (Headers)： accept: application/json 3. Split Out 用途**：將 API 回應的數據陣列分解為個別項目。 設定**： Field to Split Out: Data 4. Google Sheets 用途**：將數據追加至 Google Sheets。 設定**： Operation：Append Document ID：<your_document_id> Sheet Name：Sheet1 映射欄位 (Mapped Fields)： TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 請多利用 Curl Import 功能 例如 curl -X GET "https://data.moa.gov.tw/api/v1/AgriProductsTransType/?Start_time=114.01.01&End_time=114.01.01&MarketName=%E5%8F%B0%E5%8C%97%E4%BA%8C" -H "accept: application/json" 農業資料開放平台 文件

Having a seamless flow of customer data between your online store and your marketing platform is essential. By keeping your systems synchronized, you can ensure that your marketing campaigns are accurately targeted and effective. The integration between Shopify, a leading e-commerce platform, and Mautic, an open-source marketing automation system, is not available out-of-the-box. However, with a n8n workflow you can bridge this gap with. This template will help you: enhance accuracy in marketing lists by ensuring that subscription changes in Shopify are instantly updated in Mautic. improve compliance with data protection laws by respecting users' subscription preferences across platforms achieve integration without the need for additional plugins or software, minimizing complexity and potential points of failure. This template will demonstrate the follwing concepts in n8n: working with Shopify in n8n control flow with the IF node use Webhooks validate Webhooks with the Crypto node use the GraphQL node to call the Shopify Admin API The template consists of two parts: Sync Email Subscriptions from Shopify to Mautic Sync Email Subscriptions from Mautic to Shopify How to get started? Create a custom app in Shopify get the credentials needed to connect n8n to Shopify This is needed for the Shopify Trigger Create Shopify Acces Token API credentials n n8n for the Shopify trigger node Create Header Auth credentials: Use X-Shopify-Access-Token as the name and the Acces-Token from the Shopify App you created as the value. The Header Auth is neccessary for the GraphQL nodes. Enable the Mautic API under Configuration/API Settings, After the settings are saved you will have an additional entry in your settings menu to create API credentials for n8n Create Mautic credentials in n8n Please make sure to read the notes in the template. For a detailed explanation please check the corresponding video: https://youtu.be/x63rrh_yJzI

What it is- I wanted to create a simple, easy-to-use, MCP server for your Discord bot(s). How to set up- Literally all you do is select your bot auth (or crease a new Discord Bot auth if you havn't entered your key in n8n before) and that's IT! How to use it- You can now ask your bot to do things via any MCP client, including from within N8N workflows! Note: If you need an example, you can check out my simple quickstart Discord MCP Server that uses 4o to send messages to channels on your server and users who are members of the server the bot is in. Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community

About The ICP Company Scoring Automation Sorting through lists of potential leads manually to determine who's truly worth your sales team's time isn't just tedious, it's incredibly inefficient. Without proper qualification, your team might spend hours pursuing prospects who aren't the right fit for your product, while ideal customers slip through the cracks. How to Automate Identifying Your Ideal Customers With this automation, you'll learn how to automatically score and prioritize leads using data extracted directly from LinkedIn profiles via Airtop's integration with n8n. By the end, you'll have a fully automated workflow that analyzes prospects and calculates an Ideal Customer Profile (ICP) score, helping your sales team focus on high-potential opportunities. What You'll Need A free Airtop API key A copy of this Google Sheets Understanding the Process This automation transforms how you qualify and prioritize leads by extracting real-time, accurate information directly from LinkedIn profiles. Unlike static databases that quickly become outdated, this workflow taps into the most current professional information available. The workflow in this template: Uses Airtop to extract comprehensive LinkedIn profile data Analyzes the data to calculate an ICP score based on AI interest, technical depth, and seniority Updates your Google Sheet with the enriched data and the ICP Company score Company ICP Scoring Workflow Our company-focused workflow analyzes company LinkedIn profiles with a comprehensive set of criteria: Company Identity Extraction Company Scale Assessment Business Classification Technical Sophistication Assessment Investment Profile To then calculate the ICP Scoring, it will focus on: AI Implementation Level: Low-5 pts, Medium-10 pts, High-25 pts Technical Sophistication: Basic-5 pts, Intermediate-15 pts, Advanced-25 pts, Expert-35 pts Employee Count: 0-9 employees-5 pts, 10-150 employees-25 pts, 150+ employees-30 pts Automation Agency Status: True-20 pts, False-0 pts Geography: US/Europe Based-10 pts, Other-0 pts Setting Up Your Automation We've created ready-to-use templates for both person and company ICP scoring. Here's how to get started: Configure your connections Connect your Google Sheets account Add your Airtop API key (obtain from the Airtop dashboard) Set up your Google Sheet Ensure your Google Sheet has the necessary columns for input data and result fields Ensure that columns Linkedin_URL_Company and ICP_Score_Company exist at least Configure the Airtop module Set up the Airtop module to use the appropriate LinkedIn extraction prompt Use our provided prompt that extracts company profile data Customization Options While our templates work out of the box, you might want to customize them for your specific needs: Modify the ICP scoring criteria: Adjust the point values or add additional criteria specific to your business Add notification triggers: Set up Slack or email notifications for high-value leads that exceed a certain ICP threshold Implement batch processing: Modify the workflow to process leads in batches to optimize performance Add conditional logic: Create different scoring models for different industries or product lines Integrate with your CRM: Integrate this automation with your preferred CRM to get the details added automatically for you Real-World Applications Here's how businesses are using this automation: AI Sales Platform: A B2B AI company could implement this workflow to process their trade show lead list of contacts. Within hours, they can identify the top 50 prospects based on ICP score. SaaS Analytics Tool: A SaaS company could implement LinkedIn enrichment to identify which companies fit best. The automation processes weekly leads and categorizes them into high, medium, and low priority tiers, allowing their sales team to focus on the most promising opportunities first. Best Practices To get the most out of this automation: Review and refine your ICP criteria quarterly: What constitutes an ideal customer may evolve as your product and market develop Create tiered follow-up processes: Develop different outreach strategies based on ICP score ranges Perform regular data validation: Periodically check the accuracy of the automated scoring against your actual sales results What's Next? Now that you've automated your ICP scoring with LinkedIn data, you might be interested in: Setting up automated outreach sequences based on ICP score thresholds Creating custom reporting dashboards to track conversion rates by ICP segment Expanding your scoring model to include additional data sources Implementing lead assignment automation based on ICP scores Happy automating!

Drive-to-Store is a multi-channel marketing strategy that includes both the web and the physical context, with the aim of increasing the number of customers and sales in physical stores. This strategy guides potential customers from the online world to the physical point of sale through the provision of a coupon that can be spent in the store or on an e-commerce site. The basic idea is to have a landing page with a form and a series of unique coupons to assign to leads as a "reward" for filling out the form. This workflow is ideal for businesses looking to automate lead generation and management, especially when integrating with CRM systems like SuiteCRM and using Google Sheets for data tracking. How It Works Form Submission: The workflow starts with the On form submission node, which triggers when a user submits a form on a landing page. The form collects the user's name, surname, email, and phone number. Form Data Processing: The Form Fields node extracts and sets the form data (name, surname, email, and phone) for use in subsequent steps. Duplicate Lead Check: The Duplicate Lead? node checks if the submitted email already exists in a Google Sheets document. If the email is found, the workflow responds with a "duplicate lead" message (Respond KO node) and stops further processing. Coupon Retrieval: If the email is not a duplicate, the Get Coupon node retrieves a coupon code from the Google Sheets document based on the lead's email. Lead Creation in SuiteCRM: The Create Lead SuiteCRM node creates a new lead in SuiteCRM using the form data and the retrieved coupon code. The lead includes: First name, last name, email, phone number, and coupon code. Google Sheets Update: The Update Sheet node updates the Google Sheets document with the newly created lead's details, including: Name, surname, email, phone, coupon code, lead ID, and the current date and time. Response to Webhook: The Respond OK node sends a success response back to the webhook, indicating that the lead was created successfully. Set Up Steps Configure Form Trigger: Set up the On form submission node to collect user data (name, surname, email, and phone) via a web form. Set Up Google Sheets Integration: Configure the Duplicate Lead?, Get Coupon, and Update Sheet nodes to interact with the Google Sheets document. Ensure the document contains columns for email, coupon, lead ID, and other relevant fields. Set Up SuiteCRM Authentication: Configure the Token SuiteCRM node with the appropriate client credentials (client ID and client secret) to obtain an access token from SuiteCRM. Set Up Lead Creation in SuiteCRM: Configure the Create Lead SuiteCRM node to send a POST request to SuiteCRM's API to create a new lead. Include the form data and coupon code in the request body. Set Up Webhook Responses: Configure the Respond OK and Respond KO nodes to send appropriate JSON responses back to the webhook based on whether the lead was created or if it was a duplicate. Test the Workflow: Submit a test form to ensure the workflow correctly checks for duplicates, retrieves a coupon, creates a lead in SuiteCRM, and updates the Google Sheets document. Activate the Workflow: Once tested, activate the workflow to automate the process of handling form submissions and lead creation. Key Features Duplicate Lead Check**: Prevents duplicate leads by checking if the email already exists in the Google Sheets document. Coupon Assignment**: Retrieves a coupon code from Google Sheets and assigns it to the new lead. SuiteCRM Integration**: Automatically creates a new lead in SuiteCRM with the form data and coupon code. Data Logging**: Logs all lead details in a Google Sheets document for tracking and analysis. Webhook Responses**: Provides immediate feedback on whether the lead was created successfully or if it was a duplicate.

CallForge - AI Gong Sales Call Processing Workflow Automate your Gong.io sales call analysis with AI-driven insights, real-time tracking, and structured CRM integration. Who is This For? This workflow is designed for: ✅ Sales teams looking to automate sales call processing. ✅ Revenue operations (RevOps) professionals managing high volumes of call data. ✅ AI-driven sales intelligence teams using Gong.io for data-driven insights. What Problem Does This Workflow Solve? Manually managing and analyzing large volumes of Gong call data is time-consuming and error-prone. With CallForge, you can: ✔ Automate call processing to scale AI-driven insights. ✔ Integrate with Notion to track and organize sales call data efficiently. ✔ Get real-time Slack updates to stay informed on call processing progress. ✔ Handle API failures gracefully, allowing easy reruns if a rate limit is hit. ✔ Ensure AI-ready analysis, feeding structured call data into an AI-powered system. What This Workflow Does 1. Triggers on New Gong Calls Captures new Gong calls and retrieves metadata, call summaries, and participant details. 2. Compares Calls Against Notion Database Checks whether the call has already been processed and stored in Notion. Prevents duplicate entries** from being added. 3. Creates a Parent Notion Record for AI Processing Stores call details such as date, title, URL, company name, sales rep, and opportunity details in Notion. Links calls to Salesforce Opportunity (SF Opp) data. Assigns sales representatives and customer information to each call. 4. Loops Through Calls for Processing Ensures resilience* by allowing failed runs to *restart where they left off**. Processes calls one at a time to prevent Notion rate limits. 5. Sends Call Data to an AI Processor Extracts structured call details and sends them to an AI-powered analysis workflow. Allows multiple AI agents to process and extract structured data from calls. 6. Provides Real-Time Slack Alerts Posts a progress update in Slack when the queue starts processing. Sends real-time call progress notifications. Sends a completion alert once all calls are processed. How to Set Up This Workflow 1. Connect Your APIs 🔹 Gong API Credentials – Ensure you have valid Gong API credentials in n8n. 🔹 Notion Database – Provide access to a Notion database for storing call insights. 🔹 Slack Integration – Configure a Slack channel for progress alerts. 🔹 AI Processing Workflow – Connect an AI-powered call processing workflow for final analysis. CallForge - 01 - Filter Gong Calls Synced to Salesforce by Opportunity Stage CallForge - 02 - Prep Gong Calls with Sheets & Notion for AI Summarization CallForge - 03 - Gong Transcript Processor and Salesforce Enricher CallForge - 04 - AI Workflow for Gong.io Sales Calls CallForge - 05 - Gong.io Call Analysis with Azure AI & CRM Sync CallForge - 06 - Automate Sales Insights with Gong.io, Notion & AI CallForge - 07 - AI Marketing Data Processing with Gong & Notion CallForge - 08 - AI Product Insights from Sales Calls with Notion How to Customize This Workflow 💡 Modify Call Storage – Swap Notion for a different CRM or database (e.g., HubSpot, Airtable, Salesforce). 💡 Change AI Processing – Integrate a custom AI model for analyzing sales conversations. 💡 Customize Slack Notifications – Adjust Slack messages or send alerts via email instead. 💡 Expand with More Integrations – Connect with Salesforce, Pipedrive, or HubSpot for further enrichment. Why Use CallForge? 🚀 Automate Gong call tracking for seamless sales intelligence. 📊 Improve sales operations with structured, AI-powered insights. ⚡ Get real-time updates and keep your team informed instantly. Start optimizing your Gong call processing today!

Trigger on new Typeform form submission Write data to Google Sheet Check severity of problem If very severe post message to Slack If not so severe just send an email Assumptions Google Sheet Sheet in Spreadsheet called "Problems". Columns Names: Name Email Severity Problem Example Sheet: https://docs.google.com/spreadsheets/d/17fzSFl1BZ1njldTfp5lvh8HtS0-pNXH66b7qGZIiGRU Typeform Typeform formular with questions named exactly like the columns of the Google Sheet.

This workflow will trigger daily at 6am to retrieve your day's calendar events from Google Calendar and send them as a summary message to Slack. I've used a low-code method to filter the dates as I can't code much in JSON :) Contact me on https://twitter.com/sami_abid if you have any questions!