Workflow Templates

Daily Stock Regulatory News Aggregator with Compliance Alerts and Google Sheets Tracking 🎯 Target Audience Compliance officers and regulatory teams Financial services firms monitoring regulatory updates Investment advisors tracking regulatory changes Risk management professionals Corporate legal departments Stock traders and analysts monitoring regulatory news 🚀 Problem Statement Manually monitoring regulatory updates from multiple agencies (SEC, FINRA, ESMA) is time-consuming and error-prone. This template automates daily regulatory news monitoring, aggregates updates from major regulatory bodies, filters for recent announcements, and instantly alerts compliance teams to critical regulatory changes, enabling timely responses and maintaining regulatory compliance. 🔧 How it Works This workflow automatically monitors regulatory news daily, scrapes the latest updates from major regulatory agencies using AI-powered web scraping, filters for updates from the last 24 hours, and sends Slack alerts while logging all updates to Google Sheets for historical tracking. Key Components Daily Schedule Trigger - Automatically runs the workflow every 24 hours to check for regulatory updates Regulatory Sources Configuration - Defines the list of regulatory agencies and their URLs to monitor (SEC, FINRA, ESMA) Batch Processing - Iterates through regulatory sources one at a time for reliable processing AI-Powered Scraping - Uses ScrapeGraphAI to intelligently extract regulatory updates including title, summary, date, agency, and source URL Data Flattening - Transforms scraped data structure into individual update records Time Filtering - Filters updates to keep only those from the last 24 hours Historical Tracking - Logs all filtered updates to Google Sheets for compliance records Compliance Alerts - Sends Slack notifications to compliance teams when new regulatory updates are detected 💰 Key Features Automated Regulatory Monitoring Daily Execution**: Runs automatically every 24 hours without manual intervention Multi-Agency Support**: Monitors SEC, FINRA, and ESMA simultaneously Error Handling**: Gracefully handles scraping errors and continues processing other sources Smart Filtering Time-Based Filtering**: Automatically filters updates to show only those from the last 24 hours Date Validation**: Discards updates with unreadable or invalid dates Recent Updates Focus**: Ensures compliance teams only receive actionable, timely information Alert System Compliance Alerts**: Instant Slack notifications for new regulatory updates Structured Data**: Alerts include title, summary, date, agency, and source URL Dedicated Channel**: Posts to designated compliance alerts channel for team visibility 📊 Output Specifications The workflow generates and stores structured data including: | Output Type | Format | Description | Example | |-------------|--------|-------------|---------| | Regulatory Updates | JSON Object | Extracted regulatory update information | {"title": "SEC Announces New Rule", "date": "2024-01-15", "agency": "SEC"} | | Update History | Google Sheets | Historical regulatory update records with timestamps | Columns: Title, Summary, Date, Agency, Source URL, Scraped At | | Slack Alerts | Messages | Compliance notifications for new updates | "📢 New SEC update: [Title] - [Summary]" | | Error Logs | System Logs | Scraping error notifications | "❌ Error scraping FINRA updates" | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets API access (OAuth2) Slack workspace with API access Google Sheets spreadsheet for regulatory update tracking Step-by-Step Configuration 1. Install Community Nodes Install ScrapeGraphAI community node npm install n8n-nodes-scrapegraphai 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure it's working 3. Set up Google Sheets Connection Add Google Sheets OAuth2 credentials Authorize access to your Google account Create or identify the spreadsheet for regulatory update tracking Note the spreadsheet ID and sheet name (default: "RegUpdates") 4. Configure Slack Integration Add Slack API credentials to your n8n instance Create or identify Slack channel: #compliance-alerts Test Slack connection with a sample message Ensure the bot has permission to post messages 5. Customize Regulatory Sources Open the "Regulatory Sources" Code node Update the urls array with additional regulatory sources if needed: const urls = [ 'https://www.sec.gov/news/pressreleases', 'https://www.finra.org/rules-guidance/notices', 'https://www.esma.europa.eu/press-news', // Add more URLs as needed ]; 6. Configure Google Sheets Update documentId in "Log to Google Sheets" node with your spreadsheet ID Update sheetName to match your sheet name (default: "RegUpdates") Ensure the sheet has columns: Title, Summary, Date, Agency, Source URL, Scraped At Create the sheet with proper column headers if starting fresh 7. Customize Slack Channel Open "Send Compliance Alert" Slack node Update the channel name (default: "#compliance-alerts") Customize the message format if needed Test with a sample message 8. Adjust Schedule Open "Daily Regulatory Poll" Schedule Trigger Modify hoursInterval to change frequency (default: 24 hours) Set specific times if needed for daily execution 9. Customize Scraping Prompt Open "Scrape Regulatory Updates" ScrapeGraphAI node Adjust the userPrompt to extract different or additional fields Modify the JSON schema in the prompt if needed Change the number of updates extracted (default: 5 most recent) 10. Test and Validate Run the workflow manually to verify all connections Check Google Sheets for data structure and format Verify Slack alerts are working correctly Test error handling with invalid URLs Validate date filtering is working properly 🔄 Workflow Customization Options Modify Monitoring Frequency Change hoursInterval in Schedule Trigger for different frequencies Switch to multiple times per day for critical monitoring Add multiple schedule triggers for different agency checks Extend Data Collection Modify ScrapeGraphAI prompt to extract additional fields (documents, categories, impact level) Add data enrichment nodes for risk assessment Integrate with regulatory databases for more comprehensive tracking Add sentiment analysis for regulatory updates Enhance Alert System Add email notifications alongside Slack alerts Create different alert channels for different agencies Add priority-based alerting based on update keywords Integrate with SMS or push notification services Add webhook integrations for other compliance tools Advanced Analytics Add data visualization nodes for regulatory trend analysis Create automated compliance reports with summaries Integrate with business intelligence tools Add machine learning for update categorization Track regulatory themes and topics over time Multi-Source Support Add support for additional regulatory agencies Implement agency-specific scraping strategies Add regional regulatory sources (FCA, BaFin, etc.) Include state-level regulatory updates 📈 Use Cases Compliance Monitoring**: Automatically track regulatory updates to ensure timely compliance responses Risk Management**: Monitor regulatory changes that may impact business operations or investments Regulatory Intelligence**: Build historical databases of regulatory announcements for trend analysis Client Communication**: Stay informed to provide timely updates to clients about regulatory changes Legal Research**: Track regulatory developments for legal research and case preparation Investment Strategy**: Monitor regulatory changes that may affect investment decisions 🚨 Important Notes Respect website terms of service and rate limits when scraping regulatory sites Monitor ScrapeGraphAI API usage to manage costs Ensure Google Sheets has proper column structure before first run Set up Slack channel before running the workflow Consider implementing rate limiting for multiple regulatory sources Keep credentials secure and rotate them regularly Test with one regulatory source first before adding multiple sources Verify date formats are consistent across different regulatory agencies Be aware that some regulatory sites may have anti-scraping measures 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Google Sheets logging failures: Check spreadsheet ID, sheet name, and column structure Slack notification failures: Verify channel name exists and bot has permissions Date filtering issues: Ensure dates from scraped content are in a parseable format Validation errors: Check that scraped data matches expected schema Empty results: Verify regulatory sites are accessible and haven't changed structure Optimization Tips: Start with one regulatory source to test the workflow Monitor API usage and costs regularly Use batch processing to avoid overwhelming scraping services Implement retry logic for failed scraping attempts Consider caching mechanisms for frequently checked sources Adjust the number of updates extracted based on typical volume Support Resources: ScrapeGraphAI documentation and API reference Google Sheets API documentation Slack API documentation for webhooks n8n community forums for workflow assistance n8n documentation for node configuration SEC, FINRA, and ESMA official websites for source verification

🌤️ Automated Indonesian Weather Monitoring with Smart Notifications Stay ahead of weather changes with this comprehensive monitoring system that fetches real-time data from Indonesia's official meteorological agency (BMKG) and delivers beautiful, actionable weather reports directly to your Telegram. ⚡ What This Workflow Does This intelligent weather monitoring system automatically: Fetches Official Data**: Connects to BMKG's public weather API for accurate Indonesian forecasts Smart Processing**: Analyzes temperature, humidity, precipitation, and wind conditions Risk Assessment**: Generates contextual warnings for extreme weather conditions Automated Alerts**: Sends formatted weather reports to Telegram every 6 hours Error Handling**: Includes robust error detection and notification system 🎯 Perfect For Local Communities**: Keep neighborhoods informed about weather changes Business Operations**: Plan outdoor activities and logistics based on weather Emergency Preparedness**: Receive early warnings for extreme weather conditions Personal Planning**: Never get caught unprepared by sudden weather changes Agricultural Monitoring**: Track conditions affecting farming and outdoor work 🛠️ Key Features 🔄 Automated Scheduling**: Runs every 6 hours with manual trigger option 📊 Comprehensive Reports**: Current conditions + 6-hour detailed forecasts ⚠️ Smart Warnings**: Contextual alerts for temperature extremes and rain probability 🎨 Beautiful Formatting**: Rich Telegram messages with emojis and structured data 🔧 Error Recovery**: Automatic error handling with notification system 📍 Location-Aware**: Supports any Indonesian location via BMKG regional codes 📋 What You'll Get Each weather report includes: Current temperature, humidity, and weather conditions 6-hour detailed forecast with timestamps Wind speed and direction information Rain probability and visibility data Personalized warnings and recommendations Average daily statistics and trends 🚀 Setup Requirements Telegram Bot Token**: Create a bot via @BotFather Chat ID**: Your personal or group chat identifier BMKG Location Code**: Regional administrative code for your area 💡 Pro Tips Customize the location by changing the adm4 parameter in the HTTP request Adjust scheduling interval based on your monitoring needs Modify warning thresholds in the processing code Add multiple chat IDs for broader distribution Integrate with other n8n workflows for advanced automation 🌟 Why Choose This Template Production Ready**: Includes comprehensive error handling and logging Highly Customizable**: Easy to modify for different locations and preferences Official Data Source**: Uses Indonesia's trusted meteorological service User-Friendly Output**: Clean, readable reports perfect for daily use Scalable Design**: Easily extend for multiple locations or notification channels Transform your weather awareness with this professional-grade monitoring system that brings Indonesia's official weather data right to your fingertips! Keywords: weather monitoring, BMKG API, Telegram notifications, Indonesian weather, automated alerts, meteorological data, weather forecasting, n8n automation, weather API integration

This automated n8n workflow checks daily travel itineraries, syncs upcoming trips to Google Calendar, and sends reminder notifications to travelers via email or SMS. Perfect for travel agencies, tour operators, and organizations managing group trips to keep travelers informed about their schedules and bookings. What This Workflow Does Automatically checks travel itineraries every day Identifies today's trips and upcoming departures Syncs trip information to Google Calendar Sends personalized reminders to assigned travelers Tracks reminder delivery status and logs activities Handles both email and SMS notification preferences Provides pre-travel checklists and booking confirmations Manages multi-day trip schedules and activities Main Components Daily Travel Check** - Triggers daily to check travel itineraries Read Travel Itinerary** - Retrieves today's trips and bookings from database/Excel Filter Today's Trips** - Identifies trips departing today and upcoming activities Has Trips Today?** - Checks if there are any trips scheduled Read Traveler Contacts** - Gets traveler contact information for assigned trips Sync to Google Calendar** - Creates/updates trip events in Google Calendar Create Traveler Reminders** - Generates personalized reminder messages with travel details Split Into Batches** - Processes reminders in manageable batches Email or SMS?** - Routes based on traveler communication preferences Prepare Email Reminders** - Creates detailed email reminder content with checklists Prepare SMS Reminders** - Creates SMS reminder content optimized for text Read Reminder Log** - Checks previous reminder history Update Reminder Log** - Records sent reminders with timestamps Save Reminder Log** - Saves updated log data for audit trail Essential Prerequisites Travel itinerary database/Excel file with trip assignments Traveler contact database with email and phone numbers Google Calendar API access and credentials SMTP server for email notifications SMS service provider (Twilio, Nexmo, etc.) for text reminders Reminder log file for tracking sent notifications Booking confirmation system (flight, hotel, transport) Required Data Files trip_itinerary.xlsx: Trip ID | Trip Name | Date | Departure Time | Duration Departure Location | Destination | Hotel | Flight Number Assigned Travelers | Status | Booking Reference | Cost traveler_contacts.xlsx: Traveler ID | First Name | Last Name | Email | Phone Preferred Contact | Assigned Trips | Passport Number | Emergency Contact reminder_log.xlsx: Log ID | Date | Traveler ID | Trip ID | Contact Method Status | Sent Time | Message Preview | Confirmation Key Features ⏰ Daily Automation: Runs automatically every day at scheduled times 📅 Calendar Sync: Syncs trips to Google Calendar for easy viewing 📧 Smart Reminders: Sends email or SMS based on traveler preference 👥 Batch Processing: Handles multiple travelers efficiently 📊 Activity Logging: Tracks all reminder activities and delivery status 🔄 Duplicate Prevention: Avoids sending multiple reminders 📱 Multi-Channel: Supports both email and SMS notifications ✈️ Travel-Specific: Includes flight numbers, locations, accommodation details 📋 Pre-Travel Checklist: Provides comprehensive packing and document reminders 🌍 Multi-Destination: Manages complex multi-stop itineraries Quick Setup Import workflow JSON into n8n Configure daily trigger schedule (recommended: 6 AM and 6 PM) Set up trip itinerary and traveler contact files Connect Google Calendar API credentials Configure SMTP server for emails Set up SMS service provider (Twilio, Nexmo, or similar) Map Excel sheet columns to workflow variables Test with sample trip data Activate workflow Parameters to Configure schedule_file_path: Path to trip itinerary file contacts_file_path: Path to traveler contacts file reminder_hours: Hours before departure to send reminder (default: 24) google_calendar_id: Google Calendar ID for syncing trips google_api_credentials: Google Calendar API credentials smtp_host: Email server settings smtp_user: Email username smtp_password: Email password sms_api_key: SMS service API key sms_phone_number: SMS sender phone number reminder_log_path: Path to reminder log file Sample Reminder Messages Email Subject: "✈️ Travel Reminder: [Trip Name] Today at [Time]" Email Body: Hello [Traveler Name], Your trip is happening today! Here are your travel details: Trip: [Trip Name] Departure: [Departure Time] From: [Departure Location] To: [Destination] Flight/Transport: [Flight Number] Hotel: [Hotel Name] Duration: [X] days Pre-Travel Checklist: ☑ Passport and travel documents ☑ Travel insurance documents ☑ Hotel confirmations ☑ Medications and toiletries ☑ Weather-appropriate clothing ☑ Phone charger and adapters ⚠️ Please arrive at the departure point 2 hours early! Have a wonderful trip! SMS: "✈️ Travel Reminder: '[Trip Name]' departs at [Time] today from [Location]. Arrive 2 hours early! Flight: [Number]" Tomorrow Evening Preview (SMS): "📅 Tomorrow: '[Trip Name]' departs at [Time] from [Location]. Pack tonight! ([X] days)" Use Cases Daily trip departure reminders for travelers Last-minute itinerary change notifications Flight cancellation and delay alerts Hotel check-in and checkout reminders Travel document expiration warnings Group tour activity scheduling Adventure/hiking trip departure alerts Business travel itinerary updates Family vacation coordination Study abroad program notifications Multi-city tour route confirmations Transport connection reminders Advanced Features Reminder Escalation 24-hour reminder: Full details with checklist 6-hour reminder: Quick confirmation with transport details 2-hour reminder: Urgent departure notification Conditional Logic Different messages for single-day vs. multi-day trips Domestic vs. international travel variations Group size-based messaging Weather-based travel advisories Integration Capabilities Connect to airline APIs for real-time flight status Link to hotel management systems for check-in info Integrate weather services for destination forecasts Sync with payment systems for booking confirmations Troubleshooting | Issue | Solution | |-------|----------| | Reminders not sending | Check email/SMS credentials and service quotas | | Calendar sync failing | Verify Google Calendar API permissions | | Duplicate reminders | Check for overlapping reminder time windows | | Missing traveler data | Verify contact file formatting and column mapping | | Batch processing slow | Reduce batch size in Split Into Batches node | Security Considerations Store API credentials in n8n environment variables Use OAuth2 for Google Calendar authentication Encrypt sensitive data in reminder logs Implement role-based access to trip data Audit log all reminder activities Comply with GDPR/privacy regulations for traveler data Performance Metrics Processing Time**: ~2-5 seconds per 50 travelers Success Rate**: >99% for delivery logging Calendar Sync**: Real-time updates Batch Limit**: 10 travelers per batch (configurable) Support & Maintenance Review reminder logs weekly for delivery issues Update traveler contacts as needed Monitor email/SMS service quotas Test workflow after system updates Archive old reminder logs monthly

📘 Description This workflow performs automated inventory reconciliation between Notion (physical counts) and Airtable (system counts), ensuring both databases stay synchronized. It fetches records from both systems, merges them into a unified comparison payload, validates the structure, and calculates discrepancies. If a mismatch is detected, the workflow automatically updates Airtable with the corrected count and notifies the operations team on Slack. If everything matches, a simple “No action needed” Slack message is sent. Any malformed or incomplete payloads are logged into Google Sheets for audit tracking. ⚙️ What This Workflow Does (Step-by-Step) 🟢 Manual Trigger – Execute Workflow Starts the reconciliation process on demand. 📥 Fetch Records from Notion Retrieves physical stock data (cycle count) stored in Notion. 📦 Fetch Records from Airtable Loads inventory data from Airtable’s system-of-record table. 🔀 Merge Notion + Airtable Inputs Combines both datasets into a single payload for unified processing. 🔍 Validate Payload Structure (IF Node) Ensures that key fields (like id) exist. Valid → continue Invalid → logged to Google Sheets. 🧾 Log Invalid Versioning Requests to Google Sheets Stores broken or incomplete payload entries for later review. 🧮 Build Combined Notion + Airtable Payload (Code Node) Constructs the structured comparison object: { notion: {...}, airtable: [...] } 📊 Compare Notion Record With Airtable Record (Code Node) Performs core reconciliation logic: Matches items by name Compares physical vs. system count Calculates difference Determines if a correction is needed If mismatch → flagged for update. 🔎 Check If Record Requires Update (IF Node) Branches logic into: Mismatch → Update Airtable + Alert Match → No action summary 🛠️ Update Airtable Record With Corrected Count Writes the accurate physical count from Notion into Airtable. 🧠 Configure GPT-4o – Slack Summary Models Two models: For “no action needed” summaries For “Airtable updated” discrepancy alerts 🤖 Generate Slack Summary / Generate Slack Summary1 AI produces short, precise, operations-friendly Slack messages based on whether a discrepancy existed. 💬 Slack – Send Summary Notification / Send Update Notification Sends final Slack message to the operations user, confirming: Stock match status Updates made Item details Difference values 🧩 Prerequisites Notion API integration Airtable API credentials Azure OpenAI GPT-4o Slack API connection Google Sheets OAuth 💡 Key Benefits ✔ Eliminates manual reconciliation errors ✔ Keeps Airtable continuously aligned with real physical counts ✔ Provides instant Slack visibility to operations teams ✔ Logs all invalid or malformed cases ✔ Centralizes Notion + Airtable consistency checks 👥 Perfect For Operations teams managing multi-system inventory Warehouse cycle count workflows Audit-driven companies needing accurate stock data Businesses using Notion + Airtable as parallel systems

Zoho CRM - Conversation Intelligence Analyzer This workflow automatically processes customer call recordings, transcribes them using OpenAI Whisper, extracts key topics, identifies commitments, analyzes sentiment, generates follow-up suggestions and updates the corresponding Zoho CRM Lead — all without manual efforts. It eliminates the need for listening to calls or writing summaries and equips your sales team with instant AI-generated insights. ⚡ Quick Start (Fast Setup) Import the workflow JSON into n8n. Add Zoho CRM OAuth2 & OpenAI API credentials. Copy the webhook URL and configure your telephony system to POST call recordings. Map Zoho custom fields. Upload a test recording → Confirm CRM updates → Activate workflow. 📘 What It Does This workflow turns every incoming call recording into structured insights which your sales & customer support team can immediately use. When a recording is received, the call is automatically transcribed using OpenAI’s Whisper model. That transcript is then processed by multiple AI nodes that detect topics, customer sentiment, commitments and possible follow-up actions. All extracted data — such as mood, sentiment score, subjects, action items and commitments is merged into a clean result object and pushed to the matching Lead in Zoho CRM. The sales team gets ready-to-use call intelligence instantly, improving decision-making, accuracy and speed. This automation works 24/7 and replaces hours of manual review work with reliable AI-generated summaries. 👤 Who’s It For Sales & Customer support teams using Zoho CRM. Support teams handling inbound/outbound calls. Businesses wanting call analytics without manual transcription. Zoho CRM admins who want automation with minimal maintenance. Organizations using telephony/VoIP systems that support call exports. 🧾 Requirements To use this workflow, you need: An n8n instance (self-hosted or cloud) Zoho CRM OAuth2 credentials OpenAI API key (Whisper + GPT models) A telephony system capable of POSTing audio files to a webhook Zoho fields to store: Topics Main subject Action items Sentiment Mood Follow-up text Commitments (optional) ⚙️ How It Works & How to Set Up 1. Webhook Trigger Your call system sends an audio file (.mp3, .wav, etc.) to the webhook. The workflow starts instantly—no polling required. 2. Workflow Configuration Static values like: sentimentThreshold = 0.7 minCommitmentConfidence = 0.8 ensure consistent logic across nodes. 3. Audio Transcription (OpenAI Whisper) The audio file is converted to text. This transcript becomes the base for all analysis nodes. 4. Key Topic Extraction AI identifies: Key topics Main subject Important action items 5. Sentiment & Mood Analysis AI analyzes: Customer mood Sales rep tone Overall sentiment Sentiment score 6. Commitment Extraction AI detects commitments using a structured JSON schema. 7. Follow-up Generation GPT generates 3–5 follow-up suggestions based on the transcript & commitments. 8. Combine All Insights A Set node merges transcription, topics, sentiment, commitments and follow-up text. 9. Update Zoho CRM Lead Updates Zoho custom fields so the sales team gets immediate insights. 🛠 How to Customize Nodes Transcription Node Switch to another Whisper/GPT model Add language options Topic Extraction Add more attributes (risks, objections, intent) Sentiment Analysis Tune thresholds Add more emotion labels Commitment Extraction Modify schema Add filtering logic CRM Update Map to different fields Append notes instead of overwriting ➕ Add-Ons (Optional Enhancements) Slack/Teams alerts for negative sentiment Email transcripts to teams Save files to Google Drive / S3 Create Zoho tasks from commitments Multi-language transcription Sales rep performance scoring 💼 Use Case Examples Sales Call Analysis** – Auto-summarize calls for follow-up. Support Hotline Monitoring** – Detect customer frustration. QA Audits** – Auto-generate evaluation notes. Voice-to-CRM Logging** – Store conversation data automatically. Compliance Tracking** – Capture legally relevant commitments. 🛠 Troubleshooting Guide | Issue | Possible Cause | Solution | |------|----------------|----------| | Workflow not triggered | Telephony not hitting webhook | Recheck webhook URL & logs | | Transcript empty | Unsupported/corrupted audio | Validate file before sending | | CRM not updating | Wrong Zoho field IDs | Verify field IDs in Zoho | | Commitments missing | Transcript unclear | Improve audio quality or edit schema | | Sentiment inaccurate | Model interpretation | Adjust sentimentThreshold | 🤝 Need Help? If you want to customize this workflow, integrate telephony systems or want to build advanced level CRM automation, then our n8n workflow development team at WeblineIndia team is happy to help. We’re here to support setup, scaling, and custom enhancements.

n8n Creators Leaderboard Workflow Why Use This Workflow? The n8n Creators Leaderboard Workflow is a powerful tool for analyzing and presenting detailed statistics about workflow creators and their contributions within the n8n community. It provides users with actionable insights into popular workflows, community trends, and top contributors, all while automating the process of data retrieval and report generation. Benefits Discover Popular Workflows**: Identify workflows with the most unique visitors and inserters (weekly and monthly). Understand Community Trends**: Gain insights into what workflows are resonating with the community. Recognize Top Contributors**: Highlight impactful creators to foster collaboration and inspiration. Save Time with Automation**: Automates data fetching, processing, and reporting for efficiency. Use Cases For Workflow Creators**: Track performance metrics of your workflows to optimize them for better engagement. For Community Managers**: Identify trends and recognize top contributors to improve community resources. For New Users**: Explore popular workflows as inspiration for building your own automations. How It Works This workflow aggregates data from GitHub repositories containing statistics about workflow creators and their templates. It processes this data, filters it based on user input, and generates a detailed Markdown report using an AI agent. Key Features Data Aggregation: Fetches creator and workflow statistics from GitHub JSON files. Custom Filtering: Focuses on specific creators based on a username provided via chat. AI-Powered Reports: Generates comprehensive Markdown reports with summaries, tables, and insights. Output Flexibility: Saves reports locally with timestamps for easy access. Data Retrieval & Processing Creators Data**: Retrieved via an HTTP Request node from a JSON file containing aggregated statistics about creators. Workflows Data**: Pulled from another JSON file with workflow metrics like visitor counts and inserter statistics. Data Merging**: Combines creator and workflow data by matching usernames to provide enriched statistics. Report Generation The AI agent generates a Markdown report that includes: A summary of the creator’s contributions. A table of workflows with key metrics (e.g., unique visitors, inserters). Insights into trends or community feedback. The report is saved locally as a file with a timestamp for tracking purposes. Quick Start Guide Prerequisites Ensure your n8n instance is running. Verify that the GitHub base URL and file variables are correctly set in the Global Variables node. Confirm that your OpenAI credentials are configured for the AI Agent node. How to Start Activate the Workflow: Make sure the workflow is active in your n8n environment. Trigger via Chat: Use the Chat Trigger node to initiate the workflow by sending a message like: show me stats for username [desired_username] Replace [desired_username] with the username you want to analyze. Processing & Report Generation: The workflow fetches data, processes it, and generates a Markdown report. View Output: The final report is saved locally as a file (with a timestamp), which you can review to explore leaderboard insights.

WABA Message Journey Flow Documentation This document outlines the automated workflow for sending WhatsApp messages to contacts, triggered hourly and managed through disposition and message count logic. The workflow is designed to ensure contacts receive messages based on their status and the frequency of previous interactions. Trigger and Data Retrieval The journey begins with a time-based trigger and data retrieval from the Supabase contacts table. Trigger: A "Schedule Trigger3" node initiates the workflow every hour. This ensures that the system regularly checks for contacts requiring messages. Get Contacts: The "Get many rows1" node (Supabase) then retrieves all relevant contact data from the contacts_ampere table in Supabase. This brings in contact details such as name, phone, Disposition, Count, and last_message_sent. Disposition-Based Segregation After retrieving the contacts, the workflow segregates them based on their Disposition status. Disposition Switch: The "Disposition Switch" node acts as the primary routing mechanism. It evaluates the Disposition field of each contact and directs them to different branches of the workflow based on predefined categories. Case 0: new_lead: Contacts with the disposition new_lead are routed to the "Count Switch" for further processing. Cases 1-4: The workflow also includes branches for test_ride, Booking, walk_in, and Sale dispositions, though the detailed logic for these branches is not fully laid out in the provided JSON beyond the switch nodes ("Switch2", "Switch3", "Switch4", "Switch5"). The documentation focuses on the new_lead disposition's detailed flow, which can be replicated for others. Message Count Logic (for new_lead Disposition) For contacts identified as new_lead, the workflow uses a "Count Switch" to determine which message in the sequence should be sent. Count Switch: This node evaluates the Count field for each new_lead contact. This Count likely represents the number of messages already sent to the contact within this specific journey. Count = 0: Directs to "Loop Over Items1" (first message in sequence). Count = 1: Directs to "Loop Over Items2" (second message in sequence). Count = 2: Directs to "Loop Over Items3" (third message in sequence). Count = 3: Directs to "Loop Over Items4" (fourth message in sequence). Looping and Interval Check Each "Loop Over Items" node processes contacts in batches and incorporates an "If Interval" check (except for Loop Over Items1). Loop Over Items (e.g., "Loop Over Items1", "Loop Over Items2", "Loop Over Items3", "Loop Over Items4"): These nodes iterate through the contacts received from the "Count Switch" output. Interval Logic: "If Interval" (for Count = 1 from "Loop Over Items2"): Checks if the interval is greater than or equal to 4. This interval value is handled by a separate Supabase cron job, which updates it every minute based on Current time - last api hit time in hours. "If Interval1" (for Count = 2 from "Loop Over Items3"): Checks if the interval is exactly 24 hours. "If2" (for Count = 3 from "Loop Over Items4"): Checks if the interval is exactly 24 hours. Sending WhatsApp Messages If a contact passes the interval check (or immediately for Count = 0), a WhatsApp message is sent using the Gallabox API. HTTP Request Nodes (e.g., "new_lead_0", "new_lead_", "new_lead_3", "new_lead_2"): These nodes are responsible for sending the actual WhatsApp messages via the Gallabox API. They are configured with: Method: POST URL: https://server.gallabox.com/devapi/messages/whatsapp Authentication: apiKey and apiSecret are used in the headers. Body: Contains channelId, channelType (whatsapp), and recipient (including name and phone). WhatsApp Message Content: Includes type: "template" and templateName (e.g., testing_rahi, wu_2, testing_rahi_1). The bodyValues dynamically insert the contact's name and other details. Some messages also include buttonValues for quick replies (e.g., "Show me Brochure"). Logging and Updating Contact Status After a message is sent (or attempted), the workflow logs the interaction and updates the contact's record. Create Logs (e.g., "Create Logs", "Create Logs1", "Create Logs2", "Create Logs3"): These Supabase nodes record details of the message send attempt into the logs_nurture_ampere table. This includes: message_id (from the Gallabox API response body) phone and name of the contact disposition and mes_count (which is Count + 1 from the contacts table) last_sent (timestamp from Gallabox API response headers) status_code and status_message (from Gallabox API response or error). These nodes are configured to "continueRegularOutput" on error, meaning the workflow will attempt to proceed even if logging fails. Status Code Check (e.g., "If StatusCode", "If StatusCode 202", "If StatusCode 203", "If StatusCode 204"): Immediately after attempting to create a log, an "If" node checks if the status_code from the message send attempt is "202" (indicating acceptance by the messaging service). Update Contact Row (e.g., "Update a row1", "Update a row2", "Update a row3", "Update a row4"): If the status code is 202, these Supabase nodes update the contacts_ampere table for the specific contact. The Count for the contact is incremented by 1 (Count + 1). The last_message_sent field is updated with the date from the Gallabox API response headers. These nodes are also configured to "continueRegularOutput" on error. This structured flow ensures that contacts are nurtured through a sequence of WhatsApp messages, with each interaction logged and the contact's status updated for future reference and continuation of the journey.

This automation template is an AI-powered booking agent that schedules property viewings and reserves restaurant tables for you, all coordinated through Telegram. It checks your calendar to avoid conflicts, places the calls on your behalf, negotiates times, confirms details, and delivers a crisp summary back to Telegram—hands-free. Note: This workflow uses a voice-calling provider for outbound calls, your calendar for availability, and Telegram for notifications. Usage costs depend on your telephony provider, call duration, and any API usage.* Who Is This For? Home Buyers & Renters:** Queue up and confirm viewings without calling around. Real Estate Agents & Property Managers:** Automate client viewing scheduling and confirmations. Relocation Specialists & Assistants:** Coordinate multi-property tours with calendar-aware logic. Busy Professionals:** Let AI handle restaurant bookings and post-viewing meals. Concierge & Ops Teams:** Standardize bookings with structured logs and Telegram updates. What Problem Does This Workflow Solve? Scheduling property viewings and restaurant tables often means endless calls, conflicts, and coordination. This workflow removes the friction by: AI Phone Calls on Your Behalf:** Natural voice calls to agents/venues to secure slots. Calendar-Aware Booking:** Checks your real-time availability and avoids overlaps. Preference Handling:** Location, budget, party size, time windows, language, and notes. Instant Telegram Summaries:** Clear outcomes (confirmed, waitlist, action needed) and quick next steps. Scalable Coordination:** Handles multiple properties and dining options with fallback logic. How It Works Intent Capture (Telegram): You send a simple message (e.g., “Viewings tomorrow 17:00–20:00, Eixample, 2-bed; table for 4 at 21:30 near there”). Calendar Check: Reads free/busy blocks and suggests viable windows or alternatives. AI Calling: Places outbound calls to listing agents/restaurants, negotiates slots, and confirms. Result Parsing: Extracts confirmed time, address, contact name, reservation name, and special instructions. Telegram Delivery: Sends a concise recap plus optional quick-reply buttons (confirm/cancel/map/nav). Optional Calendar Hold: Adds confirmed bookings to your calendar and blocks time. Logging (Optional): Writes outcomes to a sheet/database for tracking and analytics. Setup Telephony Provider: Connect your AI calling service (API key). Configure voice/language. Calendar Access: Link Google Calendar (or similar). Grant read (and optionally write) access. Telegram Bot: Create a bot with BotFather and add the bot token to credentials. Environment/Credentials: Store calling API token, calendar credentials, Telegram token in n8n. Preferences: Set defaults for viewings (areas, price range, time windows) and dining (party size, cuisine, budget). Test Run: Trial a single booking to verify calling, calendar sync, and Telegram delivery. Requirements Accounts:** n8n, telephony provider, calendar account, Telegram bot. API Keys:** Telephony API token, Calendar credentials, Telegram bot token. Permissions:** Calendar read (and write if auto-blocking); outbound calls enabled. Budget:** Telephony per-minute fees and minor API costs where applicable. Features Dual-Domain Booking:** Property viewings + restaurant tables in one flow. Calendar Intelligence:** Checks conflicts and proposes best-fit time slots. Telegram-Native UX:** Simple prompts, instant confirmations, and quick actions. Preference Profiles:** Time windows, neighborhoods, max budget, language, and notes. Fallbacks & Alternatives:** Suggests nearby times/venues if first choice is unavailable. Multilingual Voice:** Configure voice and language to match region/venue. Structured Logs:** Optional recording of outcomes for reporting and audits. Extensible:** Add CRM, maps, SMS, or payment links as needed. Automate your day from tours to tables: message your intent on Telegram, and let the AI book, confirm, and keep your calendar clean—so you just show up on time.

📘 Description: This workflow automates the Developer Q&A Classification and Documentation process using Slack, Azure OpenAI GPT-4o, Notion, Airtable, and Google Sheets. Whenever a new message is posted in a specific Slack channel, the workflow automatically: Captures and validates the message data Uses GPT-4o (Azure OpenAI) to check if the question matches any existing internal FAQs Logs answered questions into Notion as new FAQ entries Sends unanswered ones to Airtable for human follow-up Records any workflow or API errors into Google Sheets This ensures that every developer query is instantly categorized, documented, and tracked, turning daily Slack discussions into a continuously improving knowledge base. ⚙️ What This Workflow Does (Step-by-Step) 🟢 Slack Channel Trigger – Developer Q&A Triggers the workflow whenever a new message is posted in a specific Slack channel. Captures message text, user ID, timestamp, and channel info. 🧩 Validate Slack Message Payload (IF Node) Ensures the incoming message payload contains valid user and text data. ✅ True Path → Continues to extract and process the message ❌ False Path → Logs error to Google Sheets 💻 Extract Question Metadata (JavaScript) Cleans and structures the Slack message into a standardized JSON format — removing unnecessary characters and preparing a clean “question object” for AI processing. 🧠 Classify Developer Question (AI) (Powered by Azure OpenAI GPT-4o) Uses GPT-4o to semantically compare the question with an internal FAQ dataset. If a match is found → Marks as answered and generates a canonical response If not → Flags it as unanswered 🧾 Parse AI JSON Output (Code Node) Converts GPT-4o’s text output into structured JSON so that workflow logic can reference fields like status, answer_quality, and canonical_answer. ⚖️ Check If Question Was Answered (IF Node) If status == "answered", the question is routed to Notion for documentation; otherwise, it’s logged in Airtable for review. 📘 Save Answered Question to Notion FAQ Creates a new Notion page under the “FAQ” database containing the question, AI’s canonical answer, and answer quality rating — automatically building a self-updating internal FAQ. 📋 Log Unanswered Question to Airtable Adds unresolved or new questions into Airtable for manual review by the developer support team. These records later feed back into the FAQ training loop. 🚨 Log Workflow Errors to Google Sheets Any missing payloads, parsing errors, or failed integrations are logged in Google Sheets (error log sheet) for transparent tracking and debugging. 🧩 Prerequisites: Slack API credentials (for message trigger) Azure OpenAI GPT-4o API credentials Notion API connection (for FAQ database) Airtable API credentials (for unresolved questions) Google Sheets OAuth connection (for error logging) 💡 Key Benefits: ✅ Automates Slack Q&A classification ✅ Builds and updates internal FAQs with zero manual input ✅ Ensures all developer queries are tracked ✅ Reduces redundant questions in Slack ✅ Maintains transparency with error logs 👥 Perfect For: Engineering or support teams using Slack for developer communication Organizations maintaining internal FAQs in Notion Teams wanting to automatically capture and reuse knowledge from real developer interactions

🎯General Principles This workflow automates the import of leads into the Company table of a CRM built with Airtable. Its originality lies in leveraging the new "Data Table" node (an internal table within n8n) to generate an execution report. 📚 Why Data Tables: This approach eliminates the need for reading/writing operations on a Google Sheet file or an external database. 🧩 It is structured on 3 main key steps: Reading leads for which email address validity has been verified. Creating or updating company information. Generating of execution report. This workflow enables precise tracking of marketing actions while facilitating the historical record of interactions with prospects and clients. Prerequisites Leads file: A prior validation check on email address accuracy is required. Airtable: Must contain at least a Company table with the following fields: Company: company name Business Leader: name of the executive Activity: business sector (notary, accountant, plumber, electrician, etc.) Address: main company address Zip Code: postal code City: city Phone Number: phone number Email: email address of a manager URL Site: company website URL Opt-in: company’s consent for commercial prospecting Campaign: reserved for future marketing campaigns Valid Email: indicator confirming email verification ⚙️ Step-by-Step Description 1️⃣ Initialization and Lead Selection Data Table Initialization: An internal n8n table is created to build the execution report. Lead Selection: The workflow selects leads from the Google Sheet file (Sheet1 tab) where the condition "Valid Email" is equal to OK. 2️⃣ Iterative Loop Company Existence Check: The Search Company node is configured with Always Output Data enabled. A JavaScript code node distinguishes three possibilities: Company does not exist: create a new record and increment the created records counter. Company exists once: update the record and increment the updated records counter. Company appears multiple times: log the issue in the Leads file under the Logs tab, requiring a data quality procedure. 3️⃣ Execution Report Generation An execution report is generated and emailed, example format: Leads Import Report: Number of records read: 2392 Number of records created: 2345 Number of records updated: 42 If the sum of records created and updated differs from the total records read, it indicates the presence of duplicates. A counter for duplicated companies could be added. ✅ Benefits of this template Exception Management and Logging: Identification and traceability of inconsistencies during import with dedicated logs for issues. Data Quality and Structuring: Built-in checks for duplicate detection, validation, and mapping to ensure accurate analysis and compliance. Automated Reporting: Systematic production and delivery of a detailed execution report covering records read, created, and updated. 📬 Contact Need help customizing this (e.g., expanding Data Tables, connecting multiple surveys, or automating follow-ups)? 📧 smarthome.smartelec@gmail.com 🔗 guy.salvatore 🌐 smarthome-smartelec.fr

Description: Make your SDK documentation localization-ready before translation with this n8n automation template. The workflow pulls FAQ content from Notion, evaluates each entry using Azure OpenAI GPT-4o-mini, and scores its localization readiness based on jargon density, cultural context, and translation risk. It logs results into Google Sheets and notifies your team on Slack if an FAQ scores poorly (≤5). Perfect for developer documentation teams, localization managers, and globalization leads who want to identify high-risk content early and ensure smooth translation for multi-language SDKs. ✅ What This Template Does (Step-by-Step) ⚙️ Step 1: Fetch FAQs from Notion Retrieves all FAQ entries from your Notion database, including question, answer, and unique ID fields for tracking. 🤖 Step 2: AI Localization Review (GPT-4o-mini) Uses Azure OpenAI GPT-4o-mini to evaluate each FAQ for localization challenges such as: Heavy use of technical or cultural jargon Region-specific policy or legal references Non-inclusive or ambiguous phrasing Potential mistranslation risk Outputs a detailed report including: Score (1–10) – overall localization readiness Detected Issues – list of problematic elements Priority – high, medium, or low for translation sequencing Recommendations – actionable rewrite suggestions 🧩 Step 3: Parse AI Response Converts the raw AI output into structured JSON (score, issues, priority, recommendations) for clean logging and filtering. 📊 Step 4: Log Results to Google Sheets Appends one row per FAQ, storing fields like Question, Score, Priority, and Recommendations — creating a long-term localization quality tracker. 🚦 Step 5: Filter High-Risk Content (Score ≤5) Flags FAQs with low localization readiness for further review, ensuring that potential translation blockers are addressed first. 📢 Step 6: Send Slack Alerts Sends a Slack message with summary details for all high-risk FAQs — including their score and key issues — keeping localization teams informed in real time. 🧠 Key Features 🌍 AI-powered localization scoring for SDK FAQs 🤖 Azure OpenAI GPT-4o-mini integration 📊 Google Sheets-based performance logging 📢 Slack notifications for at-risk FAQs ⚙️ Automated Notion-to-AI-to-Sheets pipeline 💼 Use Cases 🧾 Audit SDK documentation before translation 🌐 Prioritize localization tasks based on content risk 🧠 Identify FAQs that need rewriting for non-native audiences 📢 Keep global documentation teams aligned on translation readiness 📦 Required Integrations Notion API – to fetch FAQ entries Azure OpenAI (GPT-4o-mini) – for AI evaluation Google Sheets API – for logging structured results Slack API – for sending alerts on high-risk FAQs 🎯 Why Use This Template? ✅ Detect localization blockers early in your SDK documentation ✅ Automate readiness scoring across hundreds of FAQs ✅ Reduce translation rework and cultural misinterpretation ✅ Ensure a globally inclusive developer experience

📘 Description: This workflow automates sales performance tracking and motivational updates by integrating HighLevel CRM, Notion, GPT-4o, and Slack. It pulls all deals from HighLevel, cleans and summarizes sales data per representative, creates performance dashboards in Notion, and uses GPT-powered AI to generate personalized motivational Slack messages. It eliminates manual leaderboard tracking and boosts sales engagement with real-time insights and daily motivation — ensuring every sales rep stays informed, recognized, and inspired. What This Workflow Does (Step-by-Step) 🟢 Manual Trigger – Starts the automation manually for data refresh or testing. 📦 Fetch All Deals from HighLevel CRM – Retrieves all opportunities from HighLevel CRM, including deal names, reps, values, and stages for full visibility. 🔍 Validate Deal Fetch Success (IF Node) – Verifies that fetched data contains valid deal IDs. ✅ True Path: Continues to data cleaning. ❌ False Path: Logs failed records to Google Sheets for debugging. 🧹 Clean & Structure Deal Data – Normalizes raw deal data into a consistent schema (deal ID, rep ID, client name, value, status). Ensures clean inputs for analytics. 📊 Summarize Sales by Representative – Aggregates deals per sales rep and computes: Total deals handled Total deal value Total deals won Average deal value 🧾 Generate Notion Performance Dashboard – Creates personalized Notion dashboards for each rep with daily updated performance summaries and motivation metrics. ⚙️ Transform Data for AI Input – Converts summarized data into AI-readable format for GPT-4o processing. 🧠 GPT-4o Model Configuration – Sets up Azure OpenAI GPT-4o model to generate motivational and contextual Slack messages. 🤖 AI-Generated Motivational Slack Messages – Uses LangChain + GPT-4o to create energetic, emoji-filled messages that celebrate rep achievements and encourage improvement. 💬 Notify Sales Team in Slack – Sends the AI-generated performance summaries and motivational blurbs directly to each rep or the team Slack channel for transparency and engagement. 🚨 Log Fetch or Validation Errors (Error Handling) – Records any fetch or validation failures in the Google Sheets “error log sheet” for easy review and error management. Prerequisites HighLevel CRM API credentials Google Sheets for “Error Log” tracking Notion API integration for dashboards Azure OpenAI (GPT-4o) credentials Slack API connection for notifications Key Benefits ✅ Fully automated daily performance tracking ✅ Personalized AI-powered motivation in Slack ✅ Transparent visibility for managers and reps ✅ Improved accountability and sales performance ✅ Seamless integration across CRM, Notion, and Slack 👥 Perfect For Sales teams seeking real-time motivation and transparency Managers who want automated performance dashboards Teams using HighLevel CRM and Slack for daily operations Companies aiming to gamify sales productivity