Workflow Templates

🔐 Human-in-the-Loop Approval Flow (n8n + Postgres + Telegram) 👥 Who’s it for Teams that need a manager approval step before a ticket or request can change status. Great for internal ops, IT requests, or any workflow where “a human must sign off.” ⚡ What it does 📨 Manager receives approval/reject link 🔑 Link is signed with HMAC + expiry (secure & tamper-proof) 🗄️ Postgres updates the ticket status 📝 Audit trail records every decision 📲 Telegram notifies both manager and requester ⏰ Expired or invalid links trigger alerts and logs 🛠 Requirements n8n instance (self-hosted) Postgres database (with tickets, ticket_audit, workflow_errors) Telegram bot token One environment variable set: SECRET_KEY ⚙️ How to set up Set SECRET_KEY in .env Create Postgres tables (SQL provided) Add Telegram + Postgres credentials in n8n Import the workflow JSON Test by opening an approval/reject link in your browser 🎨 How to customize Change who the “manager” is (currently hardcoded in the Code node). Swap Telegram for Slack or email notifications. Extend the audit schema to include more metadata (IP, username).

🌦️ Intelligent Aquaculture Automation for Indonesia Transform your fish farming operation with this cutting-edge n8n workflow that combines Indonesia's official BMKG weather data with IoT-powered feeding automation. This system intelligently reduces feed by 20% when rain probability exceeds 60%, preventing overfeeding during adverse weather conditions that could compromise water quality and fish health. 🚀 Key Features 🌦️ Real-time BMKG Integration: Fetches official Indonesian weather forecasts every 12 hours using BMKG's public API with precise ADM4 regional targeting 🤖 Smart Decision Engine: Advanced JavaScript algorithms analyze 6-hour and 12-hour rain probabilities to make optimal feeding decisions automatically 📱 ESP8266 IoT Control: Seamlessly sends HTTP webhook commands to your ESP8266/ESP32-based fish feeder hardware with JSON payloads 💬 Rich Telegram Notifications: Comprehensive reports including weather analysis, feeding decisions, hardware status, and next feeding schedule ⏰ Precision Scheduling: Automated execution at 05:30 and 16:30 WIB (Indonesian Western Time) with cron-based triggers 📊 Activity Logging: Complete audit trail with timestamps, weather data, and feeding decisions for operational monitoring 🛠️ Technical Architecture Core Node Components: Schedule Trigger:** Automated twice-daily execution HTTP Request:** BMKG API integration with timeout handling Code (JavaScript):** Weather parsing and feeding ratio calculations IF Condition:** Intelligent branching based on configurable rain thresholds Telegram:** Formatted notifications with markdown support Set Variables:** Secure credential management with placeholder tokens 📋 Prerequisites ✅ n8n Instance: Self-hosted or cloud deployment ✅ Telegram Bot: Create via @BotFather for notifications ✅ ESP8266/ESP32: Hardware with servo motor for automated feeding ✅ Arduino Skills: Basic programming knowledge for hardware setup ✅ Indonesian Location: Uses BMKG API with ADM4 regional codes ⚙️ Configuration Requirements 📍 Location Settings: Update latitude, longitude, and BMKG ADM4 code in the Config node 🤖 Telegram Bot: Configure bot token and chat ID in credentials 🔗 ESP8266 Webhook: Set your device's IP address for hardware communication 📊 Feeding Parameters: Customize rain threshold (default: 60%) and feed reduction (default: -20%) 🎯 Perfect For 🏭 Commercial Aquaculture: Large-scale fish farming operations requiring weather-aware feeding 🏠 Hobbyist Enthusiasts: Home aquarium and pond automation projects 🌱 Smart Agriculture: Integration with comprehensive farm management ecosystems 🔧 IoT Learning: Educational platform for weather-based automation development 🌍 Environmental Research: Combining meteorological data with livestock care protocols 📊 Rich Output Examples The workflow generates detailed Telegram reports featuring: Current Weather Analysis:** 6-hour and 12-hour rain probability breakdowns Feeding Decision Logic:** Clear rationale for feed adjustments with percentages Hardware Confirmation:** ESP8266 response status and command execution verification Schedule Preview:** Next automated feeding time with countdown Historical Logs:** Comprehensive activity tracking for pattern analysis 🔧 Hardware Integration Guide Designed for ESP8266-based feeders accepting HTTP POST commands. The workflow transmits structured JSON containing: { "command": "FEED_REDUCE_20", "feed_ratio": -20, "rain_prob": 75, "timestamp": "2024-09-18T10:30:00Z", "location": "Main Pond" } 🌍 Regional Adaptation Indonesia-Optimized: Built specifically for BMKG's official weather API with ADM4 regional precision Global Compatibility: Easily adaptable for international weather services by modifying HTTP requests and parsing logic Scalable Architecture: Supports multiple pond locations with separate ADM4 configurations 🔒 Security & Credentials All API keys use {{PLACEHOLDER}} format for secure credential management No hardcoded sensitive information in workflow nodes Telegram bot tokens managed through n8n's credential system ESP8266 webhooks support local network security 📈 Performance Benefits 20% Feed Optimization:** Automatic reduction during high rain probability periods Water Quality Protection:** Prevents overfeeding that degrades aquatic environment Cost Efficiency:** Reduces feed waste while maintaining fish health 24/7 Monitoring:** Continuous weather analysis without manual intervention Scalable Operations:** Supports multiple feeding locations from single workflow

Workflow Name: 👻 Exception Flow Template was created in n8n v1.90.2 Skill Level: Low Categories: n8n, Chatbot Stacks Error Trigger Slack node What this workflow does? This is a n8n Error Workflow. It will trigger when there is an error in another workflow. When this happens, it then tries to send an error notification to the preset Slack channel. How it works The Error Trigger node will trigger when there is an error in another workflow, as long as that workflow is set up to do so. A error notification will be sent to the Slack Channel. Set up instructions Create you Slack credentials, refer to n8n integration documentation for more information. Set up the Channel in 👻 Exception Alert node. For any n8n workflows to trigger this, switch to that workflow, select menu > settings, and set the Error Workflow to 👻 Exception Flow. How to adjust it to your needs Although this workflow template is part of the AI Chatbot Call Center Series, it could be used with any n8n workflows. Update the Channel in 👻 Exception Alert to your own channel https://chatpaylabs.com/blog/part-8-build-your-own-ai-chatbot-call-center-general-exception-flow-production-ready-n8n-workflow-free-download-

This workflow allows you to create, update and get a task in Microsoft To Do. Microsoft To Do node: This node will create a task with the importance High in the Tasks list. You can select a different list as well as the importance level. Microsoft To Do1 node: This node will update the status of the task that we created in the previous node. Microsoft To Do2 node: This node will get the task that we created earlier.

Who Is This For? This n8n automation workflow is designed for customer support teams, business owners, or service providers who want to automate customer interactions on WhatsApp. If you regularly receive customer queries about your products, services, or technical issues — and need a system that can instantly respond, fetch data from Google Sheets or Docs, log support tickets, and send human-like replies — this workflow is for you. It’s perfect for teams using Rapiwa, Google Sheets, and Google Docs who want to provide a smart, AI-driven, yet personal support experience. What This Workflow Does This workflow is structured around a single intelligent AI assistant called Rapiwa that interacts with customers in real time through WhatsApp. Key Features AI-Driven Support Assistant (Rapiwa) WhatsApp Integration via Rapiwa API Dynamic Data Access (Google Sheets + Docs) Knowledge Base Search Conversation Memory Automatic Logging Multi-Product Support Workflow Overview Rapiwa Trigger (Start Node) Starts the workflow automatically whenever a new WhatsApp message is received in your Rapiwa account. Example: When a customer sends a message like “What’s the price of SocialVibe?” or “I can’t access my dashboard”, this node triggers the workflow. If (Check Text) Detects if the incoming message contains text (not just images, videos, or audio). If it’s text, the workflow continues; otherwise, it stops or handles it differently. AI Agent – Customer Support Agent This is the brain of the system — your AI Assistant (Rapiwa). Interprets the user’s question, retrieves information, and replies in a clear, WhatsApp-friendly format. Reads product details and company info from Google Sheets/Docs. Fetches documentation links from the connected “Support Desk” and product-specific HTTP tools. Logs customer issues to the support sheet for tracking and analysis. Memory (Session Context) Stores chat history per user session so Rapiwa remembers context during a conversation. Research (AI Support Tool) Acts as Rapiwa’s research assistant — gathers and organizes information from multiple sources. Sources: Google Sheets, Google Docs, HTTP Tools, and Support Desk. Replay (Rapiwa Send Message) Sends the AI’s final message back to the customer on WhatsApp using the Rapiwa API. WhatsApp-optimized plain text messages only. Data & Integrations 🔹 Google Sheets (Database) Product Data Sheet:** Holds product names, descriptions, and pricing. Service Data Sheet:** Lists offered services with details. Support Log Sheet:** Records each issue (Issue, Category, Solution). 🔹 Google Docs Provides company information when a user asks about your organization. Example Use Case User Message: > “Hi, I’m having a problem with my Faculty login.” Rapiwa’s AI Response: > “I’m sorry you’re having trouble logging in to Faculty. Please try resetting your password here: https://faculty.spagreen.net/docs/#reset-password > If the issue continues, I can log this for support. Would you like me to do that?” Useful Links install process:** how to install rapiwa Dashboard:** https://app.rapiwa.com Official Website:** https://rapiwa.com Documentation:** https://docs.rapiwa.com Support & Help WhatsApp**: Chat on WhatsApp Discord**: SpaGreen Community Facebook Group**: SpaGreen Support Website**: https://spagreen.net Developer Portfolio**: Codecanyon SpaGreen

Summarize YouTube Videos with Gemini AI, Google Sheets & WhatsApp/Telegram Aim The aim of the YouTube Video Summarizer Workflow is to automate the process of summarizing or extracting transcripts from YouTube videos with the help of Gemini AI, while optionally storing results and distributing them to users via WhatsApp, Telegram, or Google Sheets. This enables fast, consistent generation and sharing of English summaries or transcripts from public YouTube content. Goal The goal is to: Allow users to submit a YouTube link through various channels (Form Webhook, WhatsApp, Telegram). Use Gemini AI to either summarize the content or transcribe the complete video, always outputting in English. Return the output to the user via their original channel and optionally log it to Google Sheets for record-keeping. Requirements The workflow relies on specific integrations and configurations: n8n Platform**: Self-hosted or cloud n8n instance to host and automate the workflow. Node Requirements**: Form/Webhook Trigger: Web form for pasting the YouTube link. WhatsApp Trigger: Starts workflow from incoming WhatsApp messages (YouTube link as input). Telegram Trigger: Initiates workflow from Telegram chat messages containing YouTube links. Gemini AI Node: Consumes the YouTube link and processes it for summarization or transcription (always in English). Google Sheets Node: Writes the result (summary/transcript) into a Google Sheet for logging and future reference. WhatsApp/Telgram Send Message Nodes: Delivers summarized results or transcripts back to the user on the same platform where they triggered the workflow. Credentials**: Gemini/Google AI Platform account for AI summarization and transcription. Google Sheets account for storing output. WhatsApp Business API for WhatsApp automation. Telegram Bot API for Telegram automation. Input Requirements**: Publicly-accessible YouTube video link (max ~30 min, as per summarized logic). Output**: English video summary or full transcript, delivered via user’s requested channel and/or stored in Google Sheets. API Usage The workflow integrates several APIs for optimal automation: Gemini AI API**: Used in the main summarization node. Receives the YouTube link and a prompt with detailed instructions. Returns either a clear, concise English summary or a full transcript translated into English, handling Hindi, English, or mixed-language videos. [Ref: Workflow JSON] Google Sheets API**: Used to log the output for each processed video, making it easy to reference histories or track requests. [Ref: Workflow JSON] WhatsApp Business API**: Sends back the summary or transcript to the user who initiated via WhatsApp. [Ref: Workflow JSON] Telegram Bot API**: Sends results back to Telegram users directly in chat. [Ref: Workflow JSON] Output Formatting/Conversion The AI output is always in English, tailored to the option chosen (summary vs transcript). Structured output: Bulleted, neutral, and easy to read, suitable for sharing with users or for business documentation. Google Sheets node maps and writes each video’s results to a dedicated row for easy history review. How to Use By default, the workflow uses a manual trigger via a web form, but you may add triggers for WhatsApp or Telegram to suit your needs. Users paste a YouTube link, then select whether they want a summary or transcript (based on your implementation logic). Results are returned in their channel and optionally logged to your Google Sheet. All processing is handled securely using your Gemini API credentials. You can expand this logic by adding more integrations (email, Slack, etc.). Customising this Workflow Custom prompts can be written for different styles or output formats (e.g., SEO key points, step-by-step guides). Add logic for batch processing multiple videos or bulk export to different cloud drives. Integrate into central dashboards, CRMs, or content pipelines using n8n’s hundreds of available integrations. Good to Know Gemini pricing:** At the time of writing, each YouTube video summarization costs $0.039 USD. See official Gemini Pricing for current rates. Geo-restriction:** The Gemini video model may be geo-restricted (error: “model not found” outside some regions). Video Limits:** Intended for videos up to ~30 minutes for best processing reliability. Scaling:** Can be easily adapted for high-volume operations using n8n’s queue and scheduling features. Workflow Summary The YouTube Video Summarizer Workflow automates summarizing and transcribing YouTube videos using AI and n8n. Users send video links via web forms, WhatsApp, or Telegram. Results are generated via Gemini, sent back in-app, and logged to Google Sheets, enabling effortless knowledge sharing and organizational automation at scale. Timestamp: 12:37 PM IST, Wednesday, September 17, 2025

Overview This n8n workflow automatically retrieves Brevo contact reports and inserts summarized engagement data into NocoDB. It groups campaign activity by email, creating a clean, unified record that includes sent, delivered, opened, clicked, and blacklisted events. This setup keeps your CRM or marketing database synchronized with the latest Brevo email performance data. ✅ Tasks ⏰ Runs automatically on schedule or manually 🌐 Fetches contact activity data from Brevo API 🧩 Groups all campaign activity per email 💾 Inserts summarized data into NocoDB ⚙️ Keeps engagement metrics synced between Brevo and NocoDB 🛠 How to Use 🧱 Prepare your NocoDB table Create a table with fields for: email, messagesSent, delivered, opened, clicked, done, and blacklisted. 🔑 Connect your Brevo credentials Add your Brevo API Key in the HTTP Request node to fetch contact data securely. 🧮 Review the Code Nodes These nodes group contact activity by email and prepare a single dataset for insertion. 🚀 Run or schedule the workflow Execute it manually or use a Schedule Trigger to automate the data sync process. 📌 Notes 🗂 Make sure the field names in NocoDB match those used in the workflow. 🔐 Keep your Brevo API Key secure and private. ⚙️ The workflow can be expanded to include additional fields or filters. 📊 Use the data for engagement analytics, segmentation, or campaign performance tracking.

Description: Ensure your customer SLAs never slip with this n8n automation template. The workflow runs on a schedule, fetching open tickets from Zendesk, calculating SLA time remaining, and sending proactive alerts to Slack when tickets approach breach thresholds (75% and 90%). It also updates ticket priority in Zendesk and logs compliance metrics to Google Sheets for reporting. Perfect for support operations, CX teams, and SaaS companies looking to maintain SLA compliance and reduce response delays automatically. ✅ What This Template Does (Step-by-Step) ⏰ Run Every Hour: Automatically triggers every hour to check for SLA-sensitive tickets. 📥 Fetch All Open Zendesk Tickets: Pulls all tickets via the Zendesk API, returning essential fields: ID, status, created_at, sla_due, and priority. 🔍 Filter Only “Open” Tickets: Excludes closed, on-hold, or pending tickets — monitoring focuses only on actionable cases. ⏱️ Calculate SLA Time Remaining: Computes total SLA duration, remaining minutes, and % of SLA consumed for each ticket. 🟡 Warn at 75% Threshold: When 75% of the SLA window has passed, automatically sends a Slack warning to the #general-information channel. 🔴 Escalate at 90% Threshold: For tickets nearing breach (≥90%), the workflow updates Zendesk ticket priority to “High,” adds escalation notes, and notifies the support team for immediate action. 📊 Log SLA Compliance in Google Sheets: Each ticket’s SLA metrics (ID, % elapsed, time remaining, timestamp) are appended to a Google Sheet for tracking and reporting. ✅ No-Ticket Confirmation: If no open tickets exist, the workflow posts a “✅ No open tickets” message to Slack — keeping teams informed of a clear queue. 🧠 Key Features ⏱️ Automated SLA tracking and escalation 📊 Real-time logging to Google Sheets ⚡ Hourly auto-trigger — no manual checks needed 📢 Slack alerts at warning and critical thresholds 🔄 Dynamic Zendesk ticket updates via API 💼 Use Cases 💬 Proactively manage customer support SLAs 🚨 Automatically escalate critical tickets before breach 📈 Maintain transparent SLA compliance reporting 📢 Keep your support team updated in real time 📦 Required Integrations Zendesk API – for ticket retrieval and updates Slack API – for alert notifications Google Sheets – for compliance and reporting logs 🎯 Why Use This Template? ✅ Prevent SLA breaches before they happen ✅ Automate escalation and communication ✅ Provide real-time visibility to support leads ✅ Build a historical SLA performance dataset

Description: Guarantee that only fully compliant stories and tasks make it into your release with this n8n automation template. The workflow monitors Jira for issue updates and link changes, validates whether each story meets the Definition of Done (DoD), and automatically flags non-compliant items. It also creates a tracking record in Monday.com for unresolved blockers and sends Slack alerts summarizing readiness status for every version. Perfect for release managers, QA leads, and engineering teams who need an automated guardrail for production readiness. ✅ What This Template Does (Step-by-Step) 🎯 Jira Webhook Trigger: Activates automatically when an issue is updated or linked in Jira — ideal for continuous readiness validation. 📋 Fetch Full Issue Details: Retrieves the complete issue payload, including custom fields, status, and Definition of Done flags. 🔄 Batch Processing (1-by-1): Ensures each issue is validated individually, allowing precise error handling and clean audit trails. ✅ Check Definition of Done (DoD): Evaluates whether the customfield_DoD field is marked as true — a key signal of readiness for release. ⚠️ Flag Non-Compliant Issues: If DoD isn’t met, marks the issue as “Non-Compliant” with the reason “Definition of Done not met.” 📊 Create Tracking Record in Monday.com: Logs non-compliant issues to a dedicated Release Issues board for visibility and coordination with cross-functional teams. 📢 Send Slack Notifications: Posts to the #release-updates channel summarizing compliant vs non-compliant items per version, helping the team take timely action. 🧠 Key Features 🚦 Real-time Jira readiness validation ✅ Automated DoD enforcement before release 📊 Monday.com tracker for all non-compliant issues 📢 Slack summary notifications for release teams ⚙️ Batch-wise validation for scalable QA 💼 Use Cases 🚀 Enforce Definition of Done across linked Jira stories 📦 Automate pre-release checks for every version increment 🧩 Provide visibility into blockers via Monday.com dashboard 📢 Keep engineering and QA teams aligned on release status 📦 Required Integrations Jira Software Cloud API – to monitor issue updates and retrieve details Monday.com API – to log and track non-compliant items Slack API – for real-time release alerts 🎯 Why Use This Template? ✅ Eliminates manual pre-release validation ✅ Reduces release delays due to missed criteria ✅ Keeps all stakeholders aligned on readiness status ✅ Creates a transparent audit trail of compliance

How It Works: The 5-Node Certificate Management Flow 🗓️ This workflow efficiently monitors your domains for certificate expiry. Scheduled Check (Cron Node): This is the workflow's trigger. It's configured to run on a regular schedule, such as every Monday morning, ensuring certificate checks are automated and consistent. List Domains to Monitor (Code Node): This node acts as a static database, storing a list of all the domains you need to track. Check Certificate Expiry (HTTP Request Node): For each domain in your list, this node makes a request to a certificate checking API. The API returns details about the certificate, including its expiry date. Is Certificate Expiring? (If Node): This is the core logic. It compares the expiry date from the API response with the current date. If the certificate is set to expire within a critical timeframe (e.g., less than 30 days), the workflow proceeds to the next step. Send Alert (Slack Node): If the If node determines a certificate is expiring, this node sends a high-priority alert to your team's Slack channel. The message includes the domain name and the exact expiry date, providing all the necessary information for a quick response. How to Set Up Here's a step-by-step guide to get this workflow running in your n8n instance. Prepare Your Credentials & API: Certificate Expiry API: You need an API to check certificate expiry. The workflow uses a sample API, so you may need to adjust the URL and parameters. For production use, you might use a service like Certspotter or a similar tool. Slack Credential: Set up a Slack credential in n8n and get the Channel ID of your security alert channel (e.g., #security-alerts). Import the Workflow JSON: Create a new workflow in n8n and choose "Import from JSON." Paste the JSON code for the "SSL/TLS Certificate Expiry Monitor" workflow. Configure the Nodes: Scheduled Check (Cron): Set the schedule according to your preference (e.g., every Monday at 8:00 AM). List Domains to Monitor (Code): Edit the domainsToMonitor array in the code and add all the domains you want to check. Check Certificate Expiry (HTTP Request): Update the URL to match the certificate checking API you are using. Is Certificate Expiring? (If): The logic is set to check for expiry within 30 days. You can adjust the 30 in the expression new Date(Date.now() + 30 * 24 * 60 * 60 * 1000) to change the warning period. Send Alert (Slack): Select your Slack credential and enter the correct Channel ID. Test and Activate: Manual Test: Run the workflow manually to confirm it fetches certificate data and processes it correctly. You can test with a domain that you know is expiring soon to ensure the alert is triggered. Verify Output: Check your Slack channel to confirm that alerts are formatted and sent correctly. Activate: Once you're confident everything works, activate the workflow. n8n will now automatically monitor your domain certificates on the schedule you set.

Smart IoT Device Health Monitor with AI-Powered Dashboard Analysis and Real-Time Alerting 🎯 Target Audience IT operations and infrastructure teams IoT system administrators and engineers Facility and building management teams Manufacturing and industrial operations managers Smart city and public infrastructure coordinators Healthcare technology administrators Energy and utilities monitoring teams Fleet and asset management professionals Security and surveillance system operators Property and facility maintenance teams 🚀 Problem Statement Monitoring hundreds of IoT devices across multiple dashboards is overwhelming and reactive, often leading to costly downtime, missed maintenance windows, and system failures. This template solves the challenge of proactive IoT device monitoring by automatically analyzing device health metrics, detecting issues before they become critical, and delivering intelligent alerts that help teams maintain optimal system performance. 🔧 How it Works This workflow automatically monitors your IoT dashboard every 30 minutes using AI-powered data extraction, analyzes device health patterns, calculates system-wide health scores, and sends intelligent alerts only when intervention is needed, preventing alert fatigue while ensuring critical issues are never missed. Key Components Schedule Trigger - Runs every 30 minutes for continuous device monitoring AI Dashboard Scraper - Uses ScrapeGraphAI to extract device data from any IoT dashboard without APIs Health Analyzer - Calculates system health scores and identifies problematic devices Smart Alert System - Sends notifications only when health drops below thresholds Telegram Notifications - Delivers formatted alerts with device details and recommendations Activity Logger - Maintains historical records for trend analysis and reporting 📊 Device Health Analysis Specifications The template monitors and analyzes the following device metrics: | Metric Category | Monitored Parameters | Analysis Method | Alert Triggers | Example Output | |-----------------|---------------------|-----------------|----------------|----------------| | Device Status | Online/Offline/Error | Real-time status check | Any offline devices | "Device-A01 is offline" | | Battery Health | Battery percentage | Low battery detection | Below 20% charge | "Sensor-B03 low battery: 15%" | | Temperature | Device temperature | Overheating detection | Above 70°C | "Gateway-C02 overheating: 75°C" | | System Health | Overall health score | Online device ratio | Below 80% health | "System health: 65%" | | Connectivity | Network status | Connection monitoring | Loss of communication | "3 devices offline" | | Performance | Response metrics | Trend analysis | Degraded performance | "Response time increasing" | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Telegram bot token and chat ID Access to your IoT dashboard URL Basic understanding of your device naming conventions Step-by-Step Configuration 1. Install Community Nodes Install required community nodes 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 Schedule Trigger Configure the monitoring frequency (default: every 30 minutes) Adjust timing based on your operational needs: Every 15 minutes: */15 * * * * Every hour: 0 * * * * Every 5 minutes: */5 * * * * 4. Configure Dashboard URL Update the "Get Data" node with your IoT dashboard URL Customize the AI prompt to match your dashboard structure Test data extraction to ensure proper JSON formatting Adjust device field mappings as needed 5. Set up Telegram Notifications Create a Telegram bot using @BotFather Get your chat ID from @userinfobot Configure Telegram credentials in n8n Test message delivery to ensure alerts work 6. Customize Health Thresholds Adjust health score threshold (default: 80%) Set battery alert level (default: 20%) Configure temperature warning (default: 70°C) Customize alert conditions based on your requirements 7. Test and Validate Run the workflow manually with your dashboard Verify device data extraction accuracy Test alert conditions and message formatting Confirm logging functionality works correctly 🔄 Workflow Customization Options Modify Monitoring Frequency Adjust schedule for different device criticality levels Add business hours vs. off-hours monitoring Implement variable frequency based on system health Add manual trigger for on-demand monitoring Extend Device Analysis Add more device metrics (memory, CPU, network bandwidth) Implement predictive maintenance algorithms Include environmental sensors (humidity, air quality) Add device lifecycle and warranty tracking Customize Alert Logic Implement escalation rules for critical alerts Add alert suppression during maintenance windows Create different alert channels for different severity levels Include automated ticket creation for persistent issues Output Customization Add integration with monitoring platforms (Grafana, Datadog) Implement email notifications for management reports Create executive dashboards with health trends Add integration with maintenance management systems 📈 Use Cases Industrial IoT Monitoring**: Track manufacturing equipment and sensors Smart Building Management**: Monitor HVAC, lighting, and security systems Fleet Management**: Track vehicle telematics and diagnostic systems Healthcare Device Monitoring**: Ensure medical device uptime and performance Smart City Infrastructure**: Monitor traffic lights, environmental sensors, and public systems Energy Grid Monitoring**: Track smart meters and distribution equipment 🚨 Important Notes Respect your dashboard's terms of service and rate limits Implement appropriate delays between requests to avoid overloading systems Regularly review and update device thresholds based on operational experience Monitor ScrapeGraphAI API usage to manage costs effectively Keep your credentials secure and rotate them regularly Ensure alert recipients are available to respond to critical notifications Consider implementing backup monitoring systems for critical infrastructure Maintain device inventories and update monitoring parameters as systems evolve 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Dashboard access issues: Check URL accessibility and authentication requirements Data extraction failures: Review AI prompt and dashboard structure changes Missing device data: Verify device naming conventions and field mappings Alert delivery failures: Check Telegram bot configuration and chat permissions False alerts: Adjust health thresholds and alert logic conditions Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Telegram Bot API documentation IoT platform-specific monitoring best practices Device manufacturer monitoring guidelines Industrial IoT monitoring standards and frameworks

Competitor Analysis & SEO Data Logging Workflow Using Competitor Analysis Semrush API Description This workflow automates SEO competitor analysis using the Competitor Analysis Semrush API and logs the data into Google Sheets for structured reporting. It captures domain overview, organic competitors, organic pages, and keyword-level insights from the Competitor Analysis Semrush API, then appends them to different sheets for easy tracking. Node-by-Node Explanation On form submission – Captures the website URL entered by the user. Competitor Analysis – Sends the website to the Competitor Analysis Semrush API via HTTP POST request. Re format output – Extracts and formats the domain overview data. Domain overview – Saves organic keywords and traffic into Google Sheets. Reformat – Extracts the organic competitors list. Organic Competitor – Logs competitor domains, relevance, and traffic into Google Sheets. Reformat 2 – Extracts organic pages data. Organic Pages – Stores page-level data such as traffic and keyword counts. Reformat2 – Extracts organic keywords details. organic keywords – Logs keyword data like CPC, volume, and difficulty into Google Sheets. Benefits ✅ Automated competitor tracking – No manual API calls, all logged in Google Sheets. ✅ Centralized SEO reporting – Data stored in structured sheets for quick access. ✅ Time-saving – Streamlines research by combining multiple reports in one workflow. ✅ Accurate insights – Direct data from the Competitor Analysis Semrush API ensures reliability. Use Cases 📊 SEO Research – Track domain performance and competitor strategies. 🔍 Competitor Monitoring – Identify competitor domains, keywords, and traffic. 📝 Content Strategy – Find top-performing organic pages and replicate content ideas. 💰 Keyword Planning – Use CPC and difficulty data to prioritize profitable keywords. 📈 Client Reporting – Generate ready-to-use SEO competitor analysis reports in Google Sheets.