Workflow Templates

This workflow is designed to manage the assignment and validation of unique QR code coupons within a lead generation system with SuiteCRM. How it Works This workflow automates the process of assigning unique QR code coupons to leads generated through a form submission, ensuring no duplicates are created, and validating the usage of coupons. Here's how it operates: Webhook Trigger: The workflow starts with a Webhook node that listens for incoming requests containing QR code data. A Set coupon node extracts the QR code value from the request parameters. Validation of QR Code: An If node checks if the QR code exists in the incoming data. If it does, the process proceeds; otherwise, a "No coupon" response is sent back. Coupon Lookup: The Get Lead node queries a Google Sheets document to check if the QR code corresponds to an existing lead. A subsequent Not used? node verifies whether the coupon has already been used by checking the "USED COUPON?" field in the sheet. Lead Duplication Check: When a new lead submits the form (On form submission), the Duplicate Lead? node checks if the email already exists in the system to prevent duplicates. Coupon Assignment: If the lead is not a duplicate, the Get Coupon node retrieves an available unassigned coupon from the Google Sheets document. The Token SuiteCRM node generates an access token for SuiteCRM, and the Create Lead SuiteCRM node creates a new lead entry in SuiteCRM, associating it with the assigned coupon. QR Code Generation and Email Notification: The Get QR node generates a QR code image URL for the assigned coupon. The Send Email node sends an email to the lead with the QR code attached. Response Handling: Depending on the validation results, the workflow responds with appropriate messages: "Coupon OK" if the coupon is valid and unused. "Coupon KO" if the coupon has already been used. "Coupon not valid" if the QR code does not exist. Set Up Steps To replicate this workflow in your own n8n environment, follow these steps: Configuration: Set up an n8n instance either locally or via cloud services. Import the provided JSON configuration file into your workspace. Configure all required credentials, such as: Google Sheets OAuth2 API for accessing the spreadsheet. SuiteCRM API credentials (e.g., SUITECRMURL, CLIENTID, CLIENTSECRET). SMTP credentials for sending emails. Customization: Adjust the Webhook URL to match your deployment environment. Modify the Google Sheets document ID and sheet name in nodes like Duplicate Lead?, Get Coupon, Update Sheet, and Update coupon used. Update the SuiteCRM API endpoint and credentials in nodes like Token SuiteCRM and Create Lead SuiteCRM. Customize the email template in the Send Email node to match your branding and messaging requirements. Ensure the QR code generation URL in the Get QR node points to a valid QR code generator service. By following these steps, you can effectively implement and customize this workflow to manage lead generation and coupon assignments in your organization.

Shopify Order Fulfillment & Send Tracking Link via WhatsApp Using Rapiwa API Who is this for? This n8n workflow automatically sends WhatsApp notifications to customers when their Shopify orders are fulfilled. It extracts order details, validates customer phone numbers for WhatsApp compatibility using the Rapiwa API, sends tracking information via WhatsApp, and logs all interactions in Google Sheets with appropriate verification status. What this Workflow Does This n8n workflow listens for new order fulfillments on Shopify and automatically sends a WhatsApp message with tracking details to customers. It uses the Rapiwa API to verify if the customer's number is on WhatsApp, formats all the data, sends a message, and logs everything to Google Sheets for tracking and auditing purposes. Key Features Webhook-Triggered**: Activates on new Shopify fulfillment events Phone Number Validation**: Uses Rapiwa to check WhatsApp compatibility Tracking Message Automation**: Sends real-time tracking messages via WhatsApp Data Cleaning**: Formats phone numbers and customer data Smart Branching**: Separates verified and unverified WhatsApp users Google Sheets Logging**: Stores data with status labels for all messages Rate-Limit Protection**: Wait node helps space API calls Dual Sheet Logging**: Maintains separate records for verified and unverified numbers Requirements Tools & Services An n8n instance (self-hosted or cloud) A Shopify store with REST API access enabled A Rapiwa.com account with: Valid Bearer Token Connected and verified WhatsApp number A Google Sheet with the following columns: Like this Sample Sheet Google Sheets OAuth2 credentials** set up in n8n Shopify API credentials** added to n8n Rapiwa Bearer Token** added as httpBearerAuth credentials How to Use Step-by-step Setup Connect Shopify to n8n Use the Shopify Trigger node Set event to fulfillments/create to capture new fulfillment events Extract Webhook Data Use a Code Node to format the webhook response Capture order, customer, and tracking details Fetch Complete Order Information Add an HTTP Request Node using Shopify Admin API Include the order ID to retrieve customer phone, email, and product details Clean the Phone Number Use a Code Node to: Remove non-numeric characters Format number to international standard Combine customer first and last name Batch Process Orders Use the Split In Batches node to handle customers one-by-one Validate WhatsApp Number Use Rapiwa’s /verify-whatsapp endpoint with a Bearer Token Check if number exists on WhatsApp Conditional Branching Use an If Node: If data.exists === "true" → Verified path Else → Unverified path Send WhatsApp Message Send tracking info with a personalized message: Hi [Customer Name], Good news! Your order has just been fulfilled. Tracking Number: [Tracking Number] Track your package here: [Tracking URL] Thank you for shopping with us. -Team SpaGreen Creative Log Data to Google Sheets Log verified and unverified entries in separate sheets Include all relevant customer and tracking data Add Delay Between Messages Use the Wait Node to avoid rate limits on Rapiwa API Requirements A Shopify store with API access enabled A Google Sheet with required column like this ➤ Sample Rapiwa API account**: Connected WhatsApp number Valid Bearer Token n8n** with: Shopify API credentials Rapiwa Bearer Token Google Sheets OAuth2 credentials Google Sheet Column Reference A Google Sheet formatted like this ➤ Sample | customer_id | name | email | number | tracking_company | tracking_number | tracking_url | product_title | status | |---------------|-----------------|--------------------------------|---------------|------------------|-----------------|---------------------------------------------|----------------------------------------|------------| | 8986XXXX06 | Abdul Mannan | contact@spagreen.net | 8801322827799 | Amazon Logistics | SG-OT-02 | https://traxxxG-OT-02 | S25 Ultra 5G Smartphone | verified | | 883XXX7982 | Abdul Mannan | contact@spagreen.net | 8801322827799 | Amazon Logistics | SG-OT-N03 | https://traxxxGOT-N03| Samsung Galaxy S24 Ultra | verified | Workflow Logic Summary Shopify Webhook Trigger: On order fulfillment Extract Webhook Payload Fetch Order + Customer Details Clean and Format Phone Number Split into Single-Item Batch Check WhatsApp Validity via Rapiwa If Verified: Send WhatsApp Message Log to verified sheet If Not Verified: Skip message Log to unverified sheet Add Delay with Wait Node Repeat for Next Fulfillment Customization Ideas Modify WhatsApp message to include delivery date or store contact Send different messages for different product categories Use product_type or shipping_zone to trigger separate workflows Add admin alerts for unverified numbers Store message delivery status (e.g., success, failed) Notes & Warnings Rapiwa is an unofficial WhatsApp API — delivery reliability is not guaranteed The Google Sheet column name must include the space at the end Wait node** may need longer delay for high-volume stores Always format phone numbers in international format (e.g., 8801XXXXXXXXX) Shopify API version used is 2025-07 — update as newer versions release You must comply with WhatsApp terms and data privacy laws when messaging users Useful Links Dashboard:** https://app.rapiwa.com Official Website:** https://rapiwa.com Documentation:** https://docs.rapiwa.com Support WhatsApp Support: Chat Now Discord: Join SpaGreen Community Facebook Group: SpaGreen Support Website: https://spagreen.net Developer Portfolio: Codecanyon SpaGreen

AI Contact Enrichment 📋 Template Description Overview Automatically enhance and enrich contact data using AI to fill in missing information, generate insights, and create detailed buyer personas. Supports multiple AI providers (OpenAI, Anthropic, etc.) with automatic logging to Supabase. Description This workflow transforms incomplete contact records into rich, actionable profiles. By leveraging AI, it can infer job roles, company information, likely pain points, communication preferences, and buying motivations from minimal input data. Perfect for sales and marketing teams looking to improve data quality and personalize outreach. Key Benefits: Smart Data Completion**: Fill in missing contact fields using AI inference Buyer Persona Generation**: Create detailed profiles from basic information Universal AI Support**: Works with OpenAI, Anthropic Claude, or custom providers CRM Enhancement**: Automatically enrich contacts as they enter your system Lead Qualification**: Assess lead quality and fit based on enriched data Personalization Engine**: Generate insights for tailored outreach Data Quality**: Maintain clean, complete contact records Use Cases: Sales prospecting and lead enrichment Marketing persona development CRM data cleansing and completion Account-based marketing (ABM) research Lead scoring and qualification Personalized email campaign preparation Contact segmentation and targeting ⚙️ Setup Instructions Prerequisites n8n instance (cloud or self-hosted) AI Provider account (OpenAI, Anthropic, or custom) Supabase account with database access Step 1: Configure Environment Variables Add these to your n8n environment settings: AI_PROVIDER=openai # or 'anthropic', 'custom' AI_API_KEY=your_api_key_here AI_MODEL=gpt-3.5-turbo # or 'gpt-4', 'claude-3-sonnet-20240229' AI_ENDPOINT= # Only for custom providers Recommended Models: Cost-effective**: gpt-3.5-turbo (fast, affordable, good for basic enrichment) High-quality**: gpt-4 or claude-3-sonnet-20240229 (better inference, deeper insights) Premium**: claude-3-opus-20240229 (best for complex persona generation) How to set environment variables: n8n Cloud**: Go to Settings → Environment Variables Self-hosted**: Add to your .env file or docker-compose configuration Step 2: Set Up Supabase Database Create the logging table in your Supabase database: CREATE TABLE workflow_logs ( id BIGSERIAL PRIMARY KEY, workflow_name TEXT NOT NULL, data JSONB NOT NULL, ai_response JSONB NOT NULL, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); CREATE INDEX idx_workflow_logs_created_at ON workflow_logs(created_at); CREATE INDEX idx_workflow_logs_workflow_name ON workflow_logs(workflow_name); -- Optional: Create a view for enriched contacts CREATE VIEW enriched_contacts AS SELECT id, data->>'email' as email, data->>'name' as name, data->>'company' as company, ai_response as enrichment_data, created_at FROM workflow_logs WHERE workflow_name = 'AI Contact Enrichment' ORDER BY created_at DESC; To run this SQL: Open your Supabase project dashboard Go to the SQL Editor Paste the SQL above and click "Run" Step 3: Configure Supabase Credentials in n8n Go to Settings → Credentials Click Add Credential → Supabase API Enter your Supabase URL and API key (found in Project Settings → API) Name it Supabase API Click Save Step 4: Activate the Webhook Import this workflow into n8n Click the Activate toggle in the top-right corner Click on the "Webhook Trigger" node Copy the Production URL (this is your webhook endpoint) Save this URL for integration with your applications Step 5: Test the Workflow Send a test POST request to the webhook: curl -X POST https://your-n8n-instance.com/webhook/contact-enrichment \ -H "Content-Type: application/json" \ -d '{ "email": "john.doe@acmecorp.com", "name": "John Doe", "company": "Acme Corporation", "linkedin_url": "https://linkedin.com/in/johndoe" }' Successful Response: { "success": true, "workflow": "AI Contact Enrichment", "timestamp": "2025-01-14T12:00:00.000Z" } 📥 Expected Payload Format The webhook accepts JSON with basic contact information: Minimal Input { "email": "string (required or name required)", "name": "string (required or email required)" } Recommended Input { "email": "string", "name": "string", "company": "string", "job_title": "string", "linkedin_url": "string", "phone": "string", "location": "string", "website": "string" } Complete Input Example { "email": "sarah.chen@techstartup.io", "name": "Sarah Chen", "company": "TechStartup Inc.", "job_title": "VP of Marketing", "linkedin_url": "https://linkedin.com/in/sarahchen", "phone": "+1-555-0123", "location": "San Francisco, CA", "website": "https://techstartup.io", "industry": "B2B SaaS", "company_size": "50-200 employees", "notes": "Met at SaaS conference 2024" } Field Guidelines: At minimum, provide either email or name More input fields = better AI enrichment quality Include linkedin_url for best results company helps with firmographic enrichment Any additional context improves accuracy 🔄 Workflow Flow Webhook Trigger: Receives basic contact information from your application, form, or CRM Process Data: Adds unique ID and timestamp to the incoming data Prepare AI Request: Configures AI provider settings from environment variables Call AI API: Sends contact data to AI with enrichment prompt Save to Supabase: Archives original data and enrichment results Format Response: Returns success confirmation 🎯 Customization Tips Enhance AI Prompts for Better Enrichment Modify the "Prepare AI Request" node to customize enrichment: // Enhanced prompt for contact enrichment const systemPrompt = `You are an expert sales intelligence analyst. Analyze the provided contact information and generate a comprehensive enrichment including: INFERRED DETAILS: Fill in missing information based on available data Full job title and seniority level Department and reporting structure Years of experience (estimated) Professional background COMPANY INSIGHTS: If company name provided Industry and sub-industry Company size and revenue (estimated) Key products/services Recent news or developments BUYER PERSONA: Create a detailed profile Primary responsibilities Likely pain points and challenges Key priorities and goals Decision-making authority Budget influence level ENGAGEMENT STRATEGY: Provide outreach recommendations Best communication channels Optimal outreach timing Key talking points Personalization suggestions Content interests LEAD SCORE: Rate 1-10 based on: Fit for product/service (specify your ICP) Seniority and decision power Company size and maturity Engagement potential Return as structured JSON with clear sections.`; const userMessage = Contact Information:\n${JSON.stringify($json.data, null, 2)}; const aiConfig = { provider: $env.AI_PROVIDER || 'openai', apiKey: $env.AI_API_KEY, model: $env.AI_MODEL || 'gpt-3.5-turbo', endpoint: $env.AI_ENDPOINT, messages: [ { role: 'system', content: systemPrompt }, { role: 'user', content: userMessage } ] }; return { json: { aiConfig, data: $json } }; Add External Data Sources Enhance enrichment with third-party APIs: After "Process Data" node, add: Clearbit/Hunter.io Node: Get verified company data LinkedIn API: Pull professional information Company Database: Query internal customer data Web Scraping: Extract data from company websites Then merge all data before AI enrichment for best results Connect to Your CRM Auto-update contacts after enrichment: Salesforce Integration: // Add after "Call AI API" node // Update Salesforce contact with enriched data const enrichedData = JSON.parse($json.ai_response); return { json: { contactId: $json.data.salesforce_id, updates: { Description: enrichedData.buyer_persona, Custom_Score__c: enrichedData.lead_score, Pain_Points__c: enrichedData.pain_points } } }; HubSpot Integration: Add HubSpot node to update contact properties Map enriched fields to custom HubSpot properties Pipedrive Integration: Use Pipedrive node to update person records Add custom fields for AI insights Implement Lead Scoring Add scoring logic after enrichment: // Calculate lead score based on enrichment const enrichment = JSON.parse($json.ai_response); let score = 0; // Job title scoring if (enrichment.seniority === 'C-Level') score += 30; else if (enrichment.seniority === 'VP/Director') score += 20; else if (enrichment.seniority === 'Manager') score += 10; // Company size scoring if (enrichment.company_size === 'Enterprise') score += 25; else if (enrichment.company_size === 'Mid-Market') score += 15; // Decision authority scoring if (enrichment.decision_authority === 'High') score += 25; else if (enrichment.decision_authority === 'Medium') score += 15; // Budget influence if (enrichment.budget_influence === 'Direct') score += 20; return { json: { ...enrichment, lead_score: score } }; Add Compliance Checks Insert before AI processing: // Check for opt-out or compliance flags const email = $json.email.toLowerCase(); // Check against suppression list const suppressedDomains = ['competitor.com', 'spam.com']; const domain = email.split('@')[1]; if (suppressedDomains.includes(domain)) { throw new Error('Contact on suppression list'); } // Verify email format const emailRegex = /^+@+\.+$/; if (!emailRegex.test(email)) { throw new Error('Invalid email format'); } return { json: $json }; Batch Enrichment Process multiple contacts: Add Spreadsheet File trigger instead of webhook Add Split In Batches node (process 10-20 at a time) Run enrichment for each contact Combine results and export to CSV 🛠️ Troubleshooting Common Issues Issue: "Enrichment is too generic" Solution**: Provide more input data (company, job title, LinkedIn) Use GPT-4 or Claude models for better inference Enhance the system prompt with specific instructions Issue: "AI_API_KEY is undefined" Solution**: Ensure environment variables are set correctly Verify variable names match exactly (case-sensitive) Issue: "Enrichment contradicts actual data" Solution**: AI makes inferences - always validate critical information Add validation step to check enriched data against known facts Use external APIs for verification Issue: "Too slow for real-time use" Solution**: Implement queue system for async processing Use faster models (gpt-3.5-turbo) for speed Process in batches during off-peak hours Issue: "Supabase credentials not found" Solution**: Check credential name matches exactly: "Supabase API" Verify Supabase URL and API key are correct Debugging Tips Test with known contacts first to validate accuracy Compare AI enrichment against actual data Check execution logs for API errors Start with minimal prompt, then enhance gradually Use "Execute Node" to test individual steps 📊 Analyzing Enriched Data Query and analyze your enriched contacts: -- Get all enriched contacts SELECT * FROM enriched_contacts ORDER BY created_at DESC; -- Find high-value leads (assuming scoring implemented) SELECT email, name, company, ai_response->>'lead_score' as score FROM enriched_contacts WHERE (ai_response->>'lead_score')::int > 70 ORDER BY (ai_response->>'lead_score')::int DESC; -- Analyze enrichment by company SELECT data->>'company' as company, COUNT(*) as contact_count, AVG((ai_response->>'lead_score')::int) as avg_score FROM workflow_logs WHERE workflow_name = 'AI Contact Enrichment' AND ai_response->>'lead_score' IS NOT NULL GROUP BY data->>'company' ORDER BY contact_count DESC; -- Find contacts needing follow-up SELECT email, name, ai_response->>'engagement_strategy' as strategy, created_at FROM enriched_contacts WHERE created_at > NOW() - INTERVAL '7 days' ORDER BY created_at DESC; Export Enriched Data -- Export to CSV COPY ( SELECT data->>'email' as email, data->>'name' as name, data->>'company' as company, ai_response->>'job_title' as enriched_title, ai_response->>'seniority' as seniority, ai_response->>'lead_score' as score FROM workflow_logs WHERE workflow_name = 'AI Contact Enrichment' ) TO '/tmp/enriched_contacts.csv' WITH CSV HEADER; 📈 Integration Ideas Form Integration Automatically enrich new leads from forms: Typeform**: Trigger on form submission Google Forms**: Use Google Sheets trigger Calendly**: Enrich after meeting booking Webflow Forms**: Webhook trigger from form CRM Integration Real-time enrichment as contacts enter CRM: Salesforce**: Trigger on new lead/contact creation HubSpot**: Enrich on form submission or import Pipedrive**: Auto-enrich new persons Close**: Webhook on lead creation Email Tools Enhance cold outreach campaigns: Instantly.ai**: Enrich before campaign launch Lemlist**: Generate personalization variables Apollo.io**: Supplement with AI insights Mailshake**: Enrich prospect lists Marketing Automation Power ABM and segmentation: Marketo**: Enrich leads for scoring Pardot**: Enhance prospect profiles ActiveCampaign**: Personalization data Klaviyo**: E-commerce customer insights Slack Integration Team notifications and collaboration: Send enrichment summaries to sales channel Notify reps of high-value leads Share persona insights with marketing Alert on key account contacts 🔒 Security & Compliance Best Practices Data Protection Encrypt Sensitive Data: Use environment variables for all credentials Access Control: Limit webhook access with authentication Data Retention: Set automatic deletion policies in Supabase Audit Logging: Track all enrichment activities Privacy Compliance GDPR Compliance: Get consent before enriching personal data Allow contacts to request data deletion Document legal basis for processing CCPA Compliance: Honor do-not-sell requests Data Minimization: Only enrich necessary fields Right to Access: Allow contacts to view enriched data AI Ethics Bias Awareness: Review AI inferences for bias Accuracy Validation: Verify critical information Transparency: Disclose use of AI enrichment Human Oversight: Review before critical decisions 💡 Best Practices Input Data Quality Always include email or full name** as anchor point Add LinkedIn URLs** for 50% better accuracy Provide company name** for firmographic insights Include any known details** - more data = better results Prompt Engineering Be specific** about your ideal customer profile (ICP) Request structured output** (JSON format) Define scoring criteria** that match your business Ask for actionable insights** not just descriptions Post-Enrichment Workflow Always validate** critical information before use Review AI inferences** for accuracy and bias Update CRM promptly** to maintain data freshness Track enrichment ROI** (conversion rates, time saved) Performance Optimization Batch process** during off-peak hours Use appropriate models** (gpt-3.5 for speed, gpt-4 for quality) Cache common enrichments** to reduce API costs Set rate limits** to avoid API throttling 🏷️ Tags sales-automation, lead-enrichment, ai-automation, crm-integration, data-enrichment, contact-intelligence, buyer-personas, lead-scoring, webhook, supabase, openai, anthropic, b2b-sales 📝 License This workflow template is provided as-is for use with n8n. 🤝 Support For questions or issues: n8n Community Forum: https://community.n8n.io n8n Documentation: https://docs.n8n.io 🌟 Example Output Input: { "email": "mike.johnson@cloudtech.com", "name": "Mike Johnson", "company": "CloudTech Solutions", "job_title": "Director of IT" } AI-Generated Enrichment: { "full_title": "Director of Information Technology", "seniority": "Director", "department": "Technology/IT", "experience_years": "10-15", "company_insights": { "industry": "Cloud Computing", "size": "Mid-Market (100-500)", "revenue_estimate": "$10M-$50M" }, "buyer_persona": { "responsibilities": ["Infrastructure management", "Vendor selection", "Security oversight"], "pain_points": ["Legacy system migration", "Cost optimization", "Security compliance"], "priorities": ["Scalability", "Cost reduction", "Team efficiency"] }, "engagement_strategy": { "best_channels": ["Email", "LinkedIn"], "timing": "Tuesday-Thursday, 9-11 AM", "talking_points": ["ROI and cost savings", "Security features", "Ease of implementation"], "personalization": "Reference cloud migration challenges" }, "lead_score": 75 } 🔄 Version History v1.0.0** (2025-01-14): Initial release with universal AI provider support

Who we are We are Aprende n8n, the first n8n Spanish course for all n8n lovers. If you want to learn more, you can find out more at Aprende n8n. Task goal This task allows extracting data from any email body with a NoCode snippet. An small explanation You receive an email when a user submits a form from your website. All those emails usually have the same structure as the next one: Name: Miquel Email: miquel@aprenden8n.com Subject: Welcome aboard Message: Hi Miquel! Thank you for your signup! This task allows to parse any email body and assign all values to the defined labels, getting an output like this: { "Name": "Miquel", "Email": "miquel@aprenden8n.com", "Subject": "Welcome aboard", "Message" "Hi Miquel! Thank you for your signup!" } After importing it When you import the import, you get the next task in your n8n: We recommend importing this workflow into your current task and adapting it. You define a couple of variables in the "Set values" SET: body: the email body you want to parse. You can add this as an expression from previous variables. labels: the keywords you want to detect and parse. Labels are case insensitive. We define the next values: Body Name: Miquel Email: miquel@aprenden8n.com Subject: Welcome aboard Message: Hi Miquel! Thank you for your signup! Labels Name,Email,Subject,Message A screenshot of the Set output is the next one If we check the "Function item" Node, we get the next content after executing the task: Capabilities The task has the next features: You can detect as many labels as you want. Label detection is case insensitive. You can use the snippet as an independent workflow to call it generically, adding the Function item to the workflow and passing body and labels as paremeters. Limitations This task has limitations: The parser only accepts the multiline values at the end of the email. Help and comments If you have any doubt about this snippet, please, contact us at miquel@aprenden8n.com. You can contact us at Aprende n8n or in the Spanish n8n community

This workflow allows you to create, add an attachment, and send a draft using the Microsoft Outlook node. Microsoft Outlook node: This node creates a draft message with HTML content. You can either set the content as Text or HTML. You can also add the recipients to the draft in this node. HTTP Request node: This node fetches the logo of n8n from a URL and returns the binary data. You might want to fetch files from your machine or another email or a database. You can replace this node with the relevant node. Microsoft Outlook1 node: This node adds the attachment that we receive from the previous node to the draft message that we created. Microsoft Outlook2 node: This node sends the draft message to a recipient. Since we didn't mention the recipient in the Microsoft Outlook node, we add the recipient in this node. You can also enter multiple recipients.

Creates a new team for a project from webhook form data. When the project is created the current semester is added to it's relation attribute. More info can be found on using this workflow as part of a larger system here.

This workflow shows how to sum multiple items of data, like you would in Excel or Airtable when summing up the total of a column. It uses a Function node with some javascript to perform the aggregation of numeric data. The first node is simply mock data to avoid needing a credential to run the workflow. The second node actually performs the summation - the javascript has various comments in case you need to edit the JS. For example, to sum multiple items of data. Below is an example of the type of data this workflow can sum - so anything that is in a tabular form (Airtable, GSHeets, Postgres etc).

This workflow allows you to find and verify emails and enrich your contact from a Google Sheet with Dropacontact and add them to Lemlist. Start node: The start node exists by default when you create a new workflow. Google Sheet node: This node will list all the records from Google Sheet. Dropcontact node: This node will find the verified email of the contact and enrich it to be able to contact him lemlist node: This node will add a contact to an existing campaign

This workflow allows you to send a message on Mattermost when a workflow is updated. Workflow Trigger node: The Workflow Trigger node will trigger the workflow when the workflow gets updated. Mattermost node: This node will send a message on Mattermost, notifying you about the update.

Receives data from an incoming HTTP Request (set up to use respond to webhook node) Create dummy data Convert JSON to XML which gets returned Respond to Webhook which returns the data and the content type of the data

Send a simple JSON array via HTTP POST and get an Excel file. The default filename is Export.xlsx. By adding the (optional) request ?filename=xyz you can specify the filename. NOTE: do not forget to change the webhook path!

++How it Works:++ This RSS reader retrieves links from a Google Sheets file and goes through each link to retrieve the messages that are younger than 3 days, then saves them in a second Google Sheets file and then deletes all older entries in the second Google Sheets file! The retrieval can take a while due to the Google API block prevention, depending on the number of news feeds that were retrieved!==* Detailed Description is in the sticky Notes from the Workflow!*