by vinci-king-01
Smart Blockchain Monitor with ScrapeGraphAI Risk Detection and Instant Alerts 🎯 Target Audience Cryptocurrency traders and investors DeFi protocol managers and developers Blockchain security analysts Financial compliance officers Crypto fund managers and institutions Risk management teams Blockchain developers monitoring smart contracts Digital asset custodians 🚀 Problem Statement Manual blockchain monitoring is time-consuming and prone to missing critical events, often leading to delayed responses to high-value transactions, security threats, or unusual network activity. This template solves the challenge of real-time blockchain surveillance by automatically detecting, analyzing, and alerting on significant blockchain events using AI-powered intelligence and instant notifications. 🔧 How it Works This workflow automatically monitors blockchain activity in real-time, uses ScrapeGraphAI to intelligently extract transaction data from explorer pages, performs sophisticated risk analysis, and instantly alerts your team about significant events across multiple blockchains. Key Components Blockchain Webhook - Real-time trigger that activates when new blocks are detected Data Normalizer - Standardizes blockchain data across different networks ScrapeGraphAI Extractor - AI-powered transaction data extraction from blockchain explorers Risk Analyzer - Advanced risk scoring based on transaction patterns and values Smart Filter - Intelligently routes only significant events for alerts Slack Alert System - Instant formatted notifications to your team 📊 Risk Analysis Specifications The template performs comprehensive risk analysis with the following parameters: | Risk Factor | Threshold | Score Impact | Description | |-------------|-----------|--------------|-------------| | High-Value Transactions | >$10,000 USD | +15 per transaction | Individual transactions exceeding threshold | | Block Volume | >$1M USD | +20 points | Total block transaction volume | | Block Volume | >$100K USD | +10 points | Moderate block transaction volume | | Failure Rate | >10% | +15 points | Percentage of failed transactions in block | | Multiple High-Value | >3 transactions | Alert trigger | Multiple large transactions in single block | | Critical Failure Rate | >20% | Alert trigger | Extremely high failure rate indicator | Risk Levels: High Risk**: Score ≥ 50 (Immediate alerts) Medium Risk**: Score ≥ 25 (Standard alerts) Low Risk**: Score < 25 (No alerts) 🌐 Supported Blockchains | Blockchain | Explorer | Native Support | Transaction Detection | |------------|----------|----------------|----------------------| | Ethereum | Etherscan | ✅ Full | High-value, DeFi, NFT | | Bitcoin | Blockchair | ✅ Full | Large transfers, institutional | | Binance Smart Chain | BscScan | ✅ Full | DeFi, high-frequency trading | | Polygon | PolygonScan | ✅ Full | Layer 2 activity monitoring | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Slack workspace with webhook or bot token Blockchain data source (Moralis, Alchemy, or direct node access) Basic understanding of blockchain explorers 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 proper functionality 3. Set up Slack Integration Add Slack OAuth2 or webhook credentials Configure your target channel for blockchain alerts Test message delivery to ensure notifications work Customize alert formatting preferences 4. Configure Blockchain Webhook Set up the webhook endpoint for blockchain data Configure your blockchain data provider (Moralis, Alchemy, etc.) Ensure webhook payload includes block number and blockchain identifier Test webhook connectivity with sample data 5. Customize Risk Parameters Adjust high-value transaction threshold (default: $10,000) Modify risk scoring weights based on your needs Configure blockchain-specific risk factors Set failure rate thresholds for your use case 6. Test and Validate Send test blockchain data to trigger the workflow Verify ScrapeGraphAI extraction accuracy Check risk scoring calculations Confirm Slack alerts are properly formatted and delivered 🔄 Workflow Customization Options Modify Risk Analysis Adjust high-value transaction thresholds per blockchain Add custom risk factors (contract interactions, specific addresses) Implement whitelist/blacklist address filtering Configure time-based risk adjustments Extend Blockchain Support Add support for additional blockchains (Solana, Cardano, etc.) Customize explorer URL patterns Implement chain-specific transaction analysis Add specialized DeFi protocol monitoring Enhance Alert System Add email notifications alongside Slack Implement severity-based alert routing Create custom alert templates Add alert escalation rules Advanced Analytics Add transaction pattern recognition Implement anomaly detection algorithms Create blockchain activity dashboards Add historical trend analysis 📈 Use Cases Crypto Trading**: Monitor large market movements and whale activity DeFi Security**: Track protocol interactions and unusual contract activity Compliance Monitoring**: Detect suspicious transaction patterns Institutional Custody**: Alert on high-value transfers and security events Smart Contract Monitoring**: Track contract interactions and state changes Market Intelligence**: Analyze blockchain activity for trading insights 🚨 Important Notes Respect ScrapeGraphAI API rate limits and terms of service Implement appropriate delays to avoid overwhelming blockchain explorers Keep your API credentials secure and rotate them regularly Monitor API usage to manage costs effectively Consider blockchain explorer rate limits for high-frequency monitoring Ensure compliance with relevant financial regulations Regularly update risk parameters based on market conditions 🔧 Troubleshooting Common Issues: ScrapeGraphAI extraction errors: Check API key and account status Webhook trigger failures: Verify webhook URL and payload format Slack notification failures: Check bot permissions and channel access False positive alerts: Adjust risk scoring thresholds Missing transaction data: Verify blockchain explorer accessibility Rate limit errors: Implement delays and monitor API usage Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Blockchain explorer API documentation Slack API documentation for advanced configurations Cryptocurrency compliance and regulatory guidelines
by vinci-king-01
Copyright Infringement Detector with ScrapeGraphAI Analysis and Legal Action Automation 🎯 Target Audience Intellectual property lawyers and legal teams Brand protection specialists Content creators and publishers Marketing and brand managers Digital rights management teams Copyright enforcement agencies Media companies and publishers E-commerce businesses with proprietary content Software and technology companies Creative agencies protecting client work 🚀 Problem Statement Manual monitoring for copyright infringement is time-consuming, often reactive rather than proactive, and can miss critical violations that damage brand reputation and revenue. This template solves the challenge of automatically detecting copyright violations, analyzing infringement patterns, and providing immediate legal action recommendations using AI-powered web scraping and automated legal workflows. 🔧 How it Works This workflow automatically scans the web for potential copyright violations using ScrapeGraphAI, analyzes content similarity, determines legal action requirements, and provides automated alerts for immediate response to protect intellectual property rights. Key Components Schedule Trigger - Runs automatically every 24 hours to monitor for new infringements ScrapeGraphAI Web Search - Uses AI to search for potential copyright violations across the web Content Comparer - Analyzes potential infringements and calculates similarity scores Infringement Detector - Determines legal action required and creates case reports Legal Action Trigger - Routes cases based on severity and urgency Brand Protection Alert - Sends urgent alerts for high-priority violations Monitoring Alert - Tracks medium-risk cases for ongoing monitoring 📊 Detection and Analysis Specifications The template monitors and analyzes the following infringement types: | Infringement Type | Detection Method | Risk Level | Action Required | |-------------------|------------------|------------|-----------------| | Exact Text Match | High similarity score (>80%) | High | Immediate cease & desist | | Paraphrased Content | Moderate similarity (50-80%) | Medium | Monitoring & evidence collection | | Unauthorized Brand Usage | Brand name detection in content | High | Legal consultation | | Competitor Usage | Known competitor domain detection | High | DMCA takedown | | Image/Video Theft | Visual content analysis | High | Immediate action | | Domain Infringement | Suspicious domain patterns | Medium | Investigation | 🛠️ Setup Instructions Estimated setup time: 30-35 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Telegram or other notification service credentials Legal team contact information Copyrighted content database 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 24 hours) Adjust timing to match your business hours Set appropriate timezone for your legal team 4. Configure Copyrighted Content Database Update the Content Comparer node with your protected content Add brand names, slogans, and unique phrases Include competitor and suspicious domain lists Set similarity thresholds for different content types 5. Customize Legal Action Rules Update the Infringement Detector node with your legal thresholds Configure action plans for different infringement types Set up case priority levels and response timelines Define evidence collection requirements 6. Set up Alert System Configure Telegram bot or other notification service Set up different alert types for different severity levels Configure legal team contact information Test alert delivery and formatting 7. Test and Validate Run the workflow manually with test search terms Verify all detection steps complete successfully Test alert system with sample infringement data Validate legal action recommendations 🔄 Workflow Customization Options Modify Detection Parameters Adjust similarity thresholds for different content types Add more sophisticated text analysis algorithms Include image and video content detection Customize brand name detection patterns Extend Legal Action Framework Add more detailed legal action plans Implement automated cease and desist generation Include DMCA takedown automation Add court filing preparation workflows Customize Alert System Add integration with legal case management systems Implement tiered alert systems (urgent, high, medium, low) Add automated evidence collection and documentation Include reporting and analytics dashboards Output Customization Add integration with legal databases Implement automated case tracking Create compliance reporting systems Add trend analysis and pattern recognition 📈 Use Cases Brand Protection**: Monitor unauthorized use of brand names and logos Content Protection**: Detect plagiarism and content theft Legal Enforcement**: Automate initial legal action processes Competitive Intelligence**: Monitor competitor content usage Compliance Monitoring**: Ensure proper attribution and licensing Evidence Collection**: Automatically document violations for legal proceedings 🚨 Important Notes Respect website terms of service and robots.txt files Implement appropriate delays between requests to avoid rate limiting Regularly review and update copyrighted content database Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Ensure compliance with local copyright laws and regulations Consult with legal professionals before taking automated legal action Maintain proper documentation for all detected violations 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status False positive detections: Adjust similarity thresholds and detection parameters Alert delivery failures: Check notification service credentials Legal action errors: Verify legal team contact information Schedule trigger failures: Check timezone and interval settings Content analysis errors: Review the Code node's JavaScript logic Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Copyright law resources and best practices Legal automation and compliance guidelines Brand protection and intellectual property resources
by vinci-king-01
Carbon Footprint Tracker with ScrapeGraphAI Analysis and ESG Reporting Automation 🎯 Target Audience Sustainability managers and ESG officers Environmental compliance teams Corporate social responsibility (CSR) managers Energy and facilities managers Supply chain sustainability coordinators Environmental consultants Green building certification teams Climate action plan coordinators Regulatory compliance officers Corporate reporting and disclosure teams 🚀 Problem Statement Manual carbon footprint calculation and ESG reporting is complex, time-consuming, and often inaccurate due to fragmented data sources and outdated emission factors. This template solves the challenge of automatically collecting environmental data, calculating accurate carbon footprints, identifying reduction opportunities, and generating comprehensive ESG reports using AI-powered data collection and automated sustainability workflows. 🔧 How it Works This workflow automatically collects energy and transportation data using ScrapeGraphAI, calculates comprehensive carbon footprints across all three scopes, identifies reduction opportunities, and generates automated ESG reports for sustainability compliance and reporting. Key Components Schedule Trigger - Runs automatically every day at 8:00 AM to collect environmental data Energy Data Scraper - Uses ScrapeGraphAI to extract energy consumption data and emission factors Transport Data Scraper - Collects transportation emission factors and fuel efficiency data Footprint Calculator - Calculates comprehensive carbon footprint across Scope 1, 2, and 3 emissions Reduction Opportunity Finder - Identifies cost-effective carbon reduction opportunities Sustainability Dashboard - Creates comprehensive sustainability metrics and KPIs ESG Report Generator - Automatically generates ESG compliance reports Create Reports Folder - Organizes reports in Google Drive Save Report to Drive - Stores final reports for stakeholder access 📊 Carbon Footprint Analysis Specifications The template calculates and tracks the following emission categories: | Emission Scope | Category | Data Sources | Calculation Method | Example Output | |----------------|----------|--------------|-------------------|----------------| | Scope 1 (Direct) | Natural Gas | EPA emission factors | Consumption × 11.7 lbs CO2/therm | 23,400 lbs CO2 | | Scope 1 (Direct) | Fleet Fuel | EPA fuel economy data | Miles ÷ MPG × 19.6 lbs CO2/gallon | 11,574 lbs CO2 | | Scope 2 (Electricity) | Grid Electricity | EPA emission factors | kWh × 0.92 lbs CO2/kWh | 46,000 lbs CO2 | | Scope 3 (Indirect) | Employee Commute | EPA transportation data | Miles × 0.77 lbs CO2/mile | 19,250 lbs CO2 | | Scope 3 (Indirect) | Air Travel | EPA aviation factors | Miles × 0.53 lbs CO2/mile | 26,500 lbs CO2 | | Scope 3 (Indirect) | Supply Chain | Estimated factors | Electricity × 0.1 multiplier | 4,600 lbs CO2 | 🛠️ Setup Instructions Estimated setup time: 25-30 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Drive API access for report storage Organizational energy and transportation data ESG reporting requirements and standards 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 daily schedule (default: 8:00 AM UTC) Adjust timezone to match your business hours Set appropriate frequency for your reporting needs 4. Configure Data Sources Update the Energy Data Scraper with your energy consumption sources Configure the Transport Data Scraper with your transportation data Set up organizational data inputs (employees, consumption, etc.) Customize emission factors for your region and industry 5. Customize Carbon Calculations Update the Footprint Calculator with your organizational data Configure scope boundaries and calculation methodologies Set up industry-specific emission factors Adjust for renewable energy and offset programs 6. Configure Reduction Analysis Update the Reduction Opportunity Finder with your investment criteria Set up cost-benefit analysis parameters Configure priority scoring algorithms Define implementation timelines and effort levels 7. Set up Report Generation Configure Google Drive integration for report storage Set up ESG report templates and formats Define stakeholder access and permissions Test report generation and delivery 8. Test and Validate Run the workflow manually with test data Verify all calculation steps complete successfully Check data accuracy and emission factor validity Validate ESG report compliance and formatting 🔄 Workflow Customization Options Modify Data Collection Add more energy data sources (renewables, waste, etc.) Include additional transportation modes (rail, shipping, etc.) Integrate with building management systems Add real-time monitoring and IoT data sources Extend Calculation Framework Add more Scope 3 categories (waste, water, etc.) Implement industry-specific calculation methodologies Include carbon offset and credit tracking Add lifecycle assessment (LCA) capabilities Customize Reduction Analysis Add more sophisticated ROI calculations Implement scenario modeling and forecasting Include regulatory compliance tracking Add stakeholder engagement metrics Output Customization Add integration with sustainability reporting platforms Implement automated stakeholder notifications Create executive dashboards and visualizations Add compliance monitoring and alert systems 📈 Use Cases ESG Compliance Reporting**: Automate sustainability disclosure requirements Carbon Reduction Planning**: Identify and prioritize reduction opportunities Regulatory Compliance**: Meet environmental reporting mandates Stakeholder Communication**: Generate transparent sustainability reports Investment Due Diligence**: Provide ESG data for investors and lenders Supply Chain Sustainability**: Track and report on Scope 3 emissions 🚨 Important Notes Respect data source terms of service and rate limits Implement appropriate delays between requests to avoid rate limiting Regularly review and update emission factors for accuracy Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Ensure compliance with local environmental reporting regulations Validate calculations against industry standards and methodologies Maintain proper documentation for audit and verification purposes 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Data source access issues: Check website accessibility and rate limits Calculation errors: Verify emission factors and organizational data Report generation failures: Check Google Drive permissions and quotas Schedule trigger failures: Check timezone and cron expression Data accuracy issues: Validate against manual calculations and industry benchmarks Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance EPA emission factor databases and methodologies GHG Protocol standards and calculation guidelines ESG reporting frameworks and compliance requirements Sustainability reporting best practices and standards
by Harshil Agrawal
This workflow allows you to add a datapoint to Beeminder when a new activity is added to Strava. You can use this workflow to keep a track of your fitness activities and connect Strava with Beeminder. If you want to keep a track of different activities like the number of hours worked in a week or the number of tasks completed, you can use the relevant node. For example, you can use the Clockify Trigger node or the Toggl Trigger node.
by Yaron Been
Enhance Your Workflow with 2Ndmoises_Generator AI This n8n workflow integrates with Replicate’s moicarmonas/2ndmoises_generator model to generate custom outputs based on your prompt. It handles authentication, triggers the prediction, monitors progress, and processes the final result automatically. 📌 Section 1: Trigger & Authentication ⚡ On Clicking ‘Execute’ (Manual Trigger) Purpose: Start the workflow manually whenever you want to run it. Benefit: Great for testing or running on-demand without needing automation. 🔑 Set API Key (Set Node) Purpose: Stores your Replicate API key in the workflow. How it works: Adds your API key as a variable that other nodes can reference. Benefit: Keeps credentials secure and reusable. 📌 Section 2: Sending the AI Request 📤 Create Prediction (HTTP Request Node) Purpose: Sends a request to the Replicate API to start generating output with the model. Input Parameters: prompt (text you provide) seed (for reproducibility) width / height / lora\_scale (generation settings) Benefit: Allows full customization of the AI’s generation process. 🆔 Extract Prediction ID (Code Node) Purpose: Pulls out the Prediction ID and status from the API response. Why important: The ID is required to check the job’s progress later. Benefit: Automates polling without manual tracking. 📌 Section 3: Polling & Waiting ⏳ Wait (Wait Node) Purpose: Adds a short pause (2 seconds) between checks. Benefit: Prevents hitting the API too quickly and avoids errors. 🔄 Check Prediction Status (HTTP Request Node) Purpose: Calls Replicate again to ask if the prediction is finished. Benefit: Automates progress monitoring without manual refresh. ✅ Check If Complete (If Node) Purpose: Decides whether the model has finished generating. Paths: True → Move on to processing the result. False → Go back to Wait and recheck. Benefit: Ensures the workflow loops until a final output is ready. 📌 Section 4: Handling the Result 📦 Process Result (Code Node) Purpose: Cleans up the final API response and extracts: Status Output (generated result) Metrics Timestamps (created\_at / completed\_at) Model info Benefit: Provides a structured, ready-to-use output for other workflows or integrations. 📊 Workflow Overview Table | Section | Node Name | Purpose | | ----------------- | ----------------------- | ---------------------------- | | 1. Trigger & Auth | On Clicking ‘Execute’ | Starts the workflow manually | | | Set API Key | Stores API credentials | | 2. AI Request | Create Prediction | Sends request to Replicate | | | Extract Prediction ID | Gets prediction ID + status | | 3. Polling | Wait | Adds delay before recheck | | | Check Prediction Status | Monitors progress | | | Check If Complete | Routes based on completion | | 4. Result | Process Result | Extracts and cleans output | | Notes | Sticky Note | Explains setup + model info | 🎯 Key Benefits 🔐 Secure authentication using Set node for API key storage. 🤖 Hands-free generation — just provide a prompt, the workflow handles everything else. 🔄 Automated polling ensures you always get the final result without manual checking. 📦 Clean structured output ready for downstream use in apps, dashboards, or notifications.
by PDF Vector
Overview Organizations dealing with high-volume document processing face challenges in efficiently handling diverse document types while maintaining quality and tracking performance metrics. This enterprise-grade workflow provides a scalable solution for batch processing documents including PDFs, scanned documents, and images (JPG, PNG) with comprehensive analytics, error handling, and quality assurance. What You Can Do Process thousands of documents in parallel batches efficiently Monitor performance metrics and success rates in real-time Handle diverse document formats with automatic format detection Generate comprehensive analytics dashboards and reports Implement automated quality assurance and error handling Who It's For Large organizations, document processing centers, digital transformation teams, enterprise IT departments, and businesses that need to process thousands of documents reliably with detailed performance tracking and analytics. The Problem It Solves High-volume document processing without proper monitoring leads to bottlenecks, quality issues, and inefficient resource usage. Organizations struggle to track processing success rates, identify problematic document types, and optimize their workflows. This template provides enterprise-grade batch processing with comprehensive analytics and automated quality assurance. Setup Instructions: Configure Google Drive credentials for document folder access Install the PDF Vector community node from the n8n marketplace Configure PDF Vector API credentials with appropriate rate limits Set up batch processing parameters (batch size, retry logic) Configure quality thresholds and validation rules Set up analytics dashboard and reporting preferences Configure error handling and notification systems Key Features: Parallel batch processing for maximum throughput Support for mixed document formats (PDFs, Word docs, images) OCR processing for handwritten and scanned documents Comprehensive analytics dashboard with success rates and performance metrics Automatic document prioritization based on size and complexity Intelligent error handling with automatic retry logic Quality assurance checks and validation Real-time processing monitoring and alerts Customization Options: Configure custom document categories and processing rules Set up specific extraction templates for different document types Implement automated workflows for documents that fail quality checks Configure credit usage optimization to minimize costs Set up custom analytics and reporting dashboards Add integration with existing document management systems Configure automated notifications for processing completion or errors Implementation Details: The workflow uses intelligent batching to process documents efficiently while monitoring performance metrics in real-time. It automatically handles different document formats, applies OCR when needed, and provides detailed analytics to help organizations optimize their document processing operations. The system includes sophisticated error recovery and quality assurance mechanisms. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
by vinci-king-01
Creative Asset Manager with ScrapeGraphAI Analysis and Brand Compliance 🎯 Target Audience Creative directors and design managers Marketing teams managing brand assets Digital asset management (DAM) administrators Brand managers ensuring compliance Content creators and designers Marketing operations teams Creative agencies managing client assets Brand compliance officers 🚀 Problem Statement Managing creative assets manually is inefficient and error-prone, often leading to inconsistent branding, poor organization, and compliance issues. This template solves the challenge of automatically analyzing, organizing, and ensuring brand compliance for creative assets using AI-powered analysis and automated workflows. 🔧 How it Works This workflow automatically processes uploaded creative assets using ScrapeGraphAI for intelligent analysis, generates comprehensive tags, checks brand compliance, organizes files systematically, and maintains a centralized dashboard for creative teams. Key Components Asset Upload Trigger - Webhook endpoint that activates when new creative assets are uploaded ScrapeGraphAI Asset Analyzer - Uses AI to extract detailed information from visual assets Tag Generator - Creates comprehensive, searchable tags based on asset analysis Brand Compliance Checker - Evaluates assets against brand guidelines and standards Asset Organizer - Creates organized folder structures and standardized naming Creative Team Dashboard - Updates Google Sheets with organized asset information 📊 Google Sheets Column Specifications The template creates the following columns in your Google Sheets: | Column | Data Type | Description | Example | |--------|-----------|-------------|---------| | asset_id | String | Unique asset identifier | "asset_1703123456789_abc123def" | | name | String | Standardized filename | "image-social-media-2024-01-15T10-30-00.jpg" | | path | String | Storage location path | "/creative-assets/2024/01/image/social-media" | | asset_type | String | Type of creative asset | "image" | | dimensions | String | Asset dimensions | "1920x1080" | | file_format | String | File format | "jpg" | | primary_colors | Array | Extracted color palette | ["#FF6B35", "#004E89"] | | content_description | String | AI-generated content description | "Modern office workspace with laptop" | | text_content | String | Any text visible in asset | "Welcome to our workspace" | | style_elements | Array | Detected style characteristics | ["modern", "minimalist"] | | generated_tags | Array | Comprehensive tag list | ["high-resolution", "brand-logo", "social-media"] | | usage_context | String | Suggested usage context | "social-media" | | brand_elements | Array | Detected brand elements | ["logo", "typography"] | | compliance_score | Number | Brand compliance score (0-100) | 85 | | compliance_status | String | Approval status | "approved-with-warnings" | | compliance_issues | Array | List of compliance problems | ["Non-brand colors detected"] | | upload_date | DateTime | Asset upload timestamp | "2024-01-15T10:30:00Z" | | searchable_keywords | String | Search-optimized keywords | "image social-media modern brand-logo" | 🛠️ Setup Instructions Estimated setup time: 25-30 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets account with API access File upload system or DAM integration Brand guidelines document (for compliance configuration) 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 Google Sheets Connection Add Google Sheets OAuth2 credentials Grant necessary permissions for spreadsheet access Create a new spreadsheet for creative asset management Configure the sheet name (default: "Creative Assets Dashboard") 4. Configure Webhook Trigger Set up the webhook endpoint for asset uploads Configure the webhook URL in your file upload system Ensure asset_url parameter is passed in webhook payload Test webhook connectivity 5. Customize Brand Guidelines Update the Brand Compliance Checker node with your brand colors Configure approved file formats and size limits Set required brand elements and fonts Define resolution standards and quality requirements 6. Configure Asset Organization Customize folder structure preferences Set up naming conventions for different asset types Configure metadata extraction preferences Set up search optimization parameters 7. Test and Validate Upload a test asset to trigger the workflow Verify all analysis steps complete successfully Check Google Sheets for proper data formatting Validate brand compliance scoring 🔄 Workflow Customization Options Modify Analysis Parameters Adjust ScrapeGraphAI prompts for specific asset types Customize tag generation algorithms Modify color analysis sensitivity Add industry-specific analysis criteria Extend Brand Compliance Add more sophisticated brand guideline checks Implement automated correction suggestions Include legal compliance verification Add accessibility compliance checks Customize Organization Structure Modify folder hierarchy based on team preferences Implement custom naming conventions Add version control and asset history Configure backup and archiving rules Output Customization Add integration with DAM systems Implement asset approval workflows Create automated reporting and analytics Add team collaboration features 📈 Use Cases Brand Asset Management**: Automatically organize and tag brand assets Compliance Monitoring**: Ensure all assets meet brand guidelines Creative Team Collaboration**: Centralized asset management and sharing Marketing Campaign Management**: Organize assets by campaign and context Asset Discovery**: AI-powered search and recommendation system Quality Control**: Automated quality and compliance checks 🚨 Important Notes Respect ScrapeGraphAI API rate limits and terms of service Implement appropriate delays between requests to avoid rate limiting Regularly review and update brand guidelines in the compliance checker Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider data privacy and copyright compliance for creative assets Ensure proper backup and version control for important assets 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Webhook trigger failures: Check webhook URL and payload format Google Sheets permission errors: Check OAuth2 scope and permissions Asset analysis errors: Review the ScrapeGraphAI prompt configuration Brand compliance false positives: Adjust guideline parameters File organization issues: Check folder permissions and naming conventions Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Google Sheets API documentation for advanced configurations Digital asset management best practices Brand compliance and governance guidelines
by vinci-king-01
Influencer Content Monitor with ScrapeGraphAI Analysis and ROI Tracking 🎯 Target Audience Marketing managers and brand managers Influencer marketing agencies Social media managers Digital marketing teams Brand partnerships coordinators Marketing analysts and strategists Campaign managers ROI and performance analysts 🚀 Problem Statement Manual monitoring of influencer campaigns is time-consuming and often misses critical performance insights, brand mentions, and ROI calculations. This template solves the challenge of automatically tracking influencer content, analyzing engagement metrics, detecting brand mentions, and calculating campaign ROI using AI-powered analysis and automated workflows. 🔧 How it Works This workflow automatically monitors influencer profiles and content using ScrapeGraphAI for intelligent analysis, tracks brand mentions and sponsored content, calculates performance metrics, and provides comprehensive ROI analysis for marketing campaigns. Key Components Daily Schedule Trigger - Runs automatically every day at 9:00 AM to monitor influencer campaigns ScrapeGraphAI - Influencer Profiles - Uses AI to extract profile data and recent posts from Instagram Content Analyzer - Analyzes post content for engagement rates and quality scoring Brand Mention Detector - Identifies brand mentions and sponsored content indicators Campaign Performance Tracker - Tracks campaign metrics and KPIs Marketing ROI Calculator - Calculates return on investment for campaigns 📊 Data Analysis Specifications The template analyzes and tracks the following metrics: | Metric Category | Data Points | Description | Example | |----------------|-------------|-------------|---------| | Profile Data | Username, Followers, Following, Posts Count, Bio, Verification Status | Basic influencer profile information | "@influencer", "100K followers", "Verified" | | Post Analysis | Post URL, Caption, Likes, Comments, Date, Hashtags, Mentions | Individual post performance data | "5,000 likes", "150 comments" | | Engagement Metrics | Engagement Rate, Content Quality Score, Performance Tier | Calculated performance indicators | "3.2% engagement rate", "High performance" | | Brand Detection | Brand Mentions, Sponsored Content, Mention Count | Brand collaboration tracking | "Nike mentioned", "Sponsored post detected" | | Campaign Performance | Total Reach, Total Engagement, Average Engagement, Performance Score | Overall campaign effectiveness | "50K total reach", "85.5 performance score" | | ROI Analysis | Total Investment, Estimated Value, ROI Percentage, Cost per Engagement | Financial performance metrics | "$2,500 investment", "125% ROI" | 🛠️ Setup Instructions Estimated setup time: 20-25 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Instagram accounts to monitor (influencer usernames) Campaign budget and cost data for ROI calculations 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 daily schedule (default: 9:00 AM UTC) Adjust timezone to match your business hours Set appropriate frequency for your monitoring needs 4. Configure Influencer Monitoring Update the websiteUrl parameter with target influencer usernames Customize the user prompt to extract specific profile data Set up monitoring for multiple influencers if needed Configure brand keywords for mention detection 5. Customize Brand Detection Update brand keywords in the Brand Mention Detector node Add sponsored content indicators (#ad, #sponsored, etc.) Configure brand mention sensitivity levels Set up competitor brand monitoring 6. Configure ROI Calculations Update cost estimates in the Marketing ROI Calculator Set value per engagement and reach metrics Configure campaign management costs Adjust ROI calculation parameters 7. Test and Validate Run the workflow manually with test data Verify all analysis steps complete successfully Check data accuracy and calculation precision Validate ROI calculations with actual campaign data 🔄 Workflow Customization Options Modify Monitoring Parameters Adjust monitoring frequency (hourly, daily, weekly) Add more social media platforms (TikTok, YouTube, etc.) Customize engagement rate calculations Modify content quality scoring algorithms Extend Brand Detection Add more sophisticated brand mention detection Implement sentiment analysis for brand mentions Include competitor brand monitoring Add automated alert systems for brand mentions Customize Performance Tracking Modify performance tier thresholds Add more detailed engagement metrics Implement trend analysis and forecasting Include audience demographic analysis Output Customization Add integration with marketing dashboards Implement automated reporting systems Create alert systems for performance drops Add campaign comparison features 📈 Use Cases Influencer Campaign Monitoring**: Track performance of influencer partnerships Brand Mention Detection**: Monitor brand mentions across influencer content ROI Analysis**: Calculate return on investment for marketing campaigns Competitive Intelligence**: Monitor competitor brand mentions Performance Optimization**: Identify top-performing content and influencers Campaign Reporting**: Generate automated reports for stakeholders 🚨 Important Notes Respect Instagram's terms of service and rate limits Implement appropriate delays between requests to avoid rate limiting Regularly review and update brand keywords and detection parameters Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider data privacy and compliance requirements Ensure accurate cost data for ROI calculations 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Instagram access issues: Check account accessibility and rate limits Brand detection false positives: Adjust keyword sensitivity ROI calculation errors: Verify cost and value parameters Schedule trigger failures: Check timezone and cron expression Data parsing errors: Review the Code node's JavaScript logic Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Instagram API documentation and best practices Influencer marketing analytics best practices ROI calculation methodologies and standards
by Dariusz Koryto
Get automated weather updates delivered directly to your Telegram chat at scheduled intervals. This workflow fetches current weather data from OpenWeatherMap and sends formatted weather reports via a Telegram bot. Use Cases Daily morning weather briefings Regular weather monitoring for outdoor activities Automated weather alerts for specific locations Personal weather assistant for travel planning Prerequisites Before setting up this workflow, ensure you have: An OpenWeatherMap API account (free tier available) A Telegram bot token Your Telegram chat ID n8n instance (cloud or self-hosted) Setup Instructions Step 1: Create OpenWeatherMap Account Go to OpenWeatherMap and sign up for a free account Navigate to the API keys section in your account Copy your API key (you'll need this for the workflow configuration) Step 2: Create Telegram Bot Open Telegram and search for @BotFather Start a chat and use the /newbot command Follow the prompts to create your bot and get the bot token Save the bot token securely Step 3: Get Your Telegram Chat ID Start a conversation with your newly created bot Send any message to the bot Visit https://api.telegram.org/bot<YourBOTToken>/getUpdates in your browser Look for your chat ID in the response (it will be a number like 123456789) Step 4: Configure the Workflow Import this workflow into your n8n instance Configure each node with your credentials: Schedule Trigger Node Set your preferred schedule (default: daily at 8:00 AM) Use cron expression format (e.g., 0 8 * * * for 8 AM daily) Get Weather Node Add your OpenWeatherMap credentials Update the cityName parameter to your desired location Format: "CityName,CountryCode" (e.g., "London,UK") Send a text message Node Add your Telegram bot credentials (bot token) Replace XXXXXXX in the chatId field with your actual chat ID Customization Options Location Settings In the "Get Weather" node, modify the cityName parameter to change the location. You can specify: City name only: "Paris" City with country: "Paris,FR" City with state and country: "Miami,FL,US" Schedule Frequency In the "Schedule Trigger" node, adjust the cron expression: Every 6 hours: 0 */6 * * * Twice daily (8 AM & 6 PM): 0 8,18 * * * Weekly on Mondays at 9 AM: 0 9 * * 1 Message Format In the "Format Weather" node, you can customize the message template by modifying the message variable in the function code. Current format includes: Current temperature with "feels like" temperature Min/max temperatures for the day Weather description and precipitation Wind speed and direction Cloud coverage percentage Sunrise and sunset times Language Support In the "Get Weather" node, change the language parameter to get weather descriptions in different languages: English: "en" Spanish: "es" French: "fr" German: "de" Polish: "pl" Troubleshooting Common Issues Weather data not updating: Verify your OpenWeatherMap API key is valid and active Check if you've exceeded your API rate limits Ensure the city name format is correct Messages not being sent: Confirm your Telegram bot token is correct Verify the chat ID is accurate (should be a number, not username) Make sure you've started a conversation with your bot Workflow not triggering: Check if the workflow is activated (toggle switch should be ON) Verify the cron expression syntax is correct Ensure your n8n instance is running continuously Testing the Workflow Use the "Test workflow" button to run manually Check each node's output for errors Verify the final message format in Telegram Node Descriptions Schedule Trigger Automatically starts the workflow based on a cron schedule. Runs at specified intervals to fetch fresh weather data. Get Weather Connects to OpenWeatherMap API to retrieve current weather conditions for the specified location. Format Weather Processes the raw weather data and creates a user-friendly message with emojis and organized information. Send a text message Delivers the formatted weather report to your Telegram chat using the configured bot. Additional Features You can extend this workflow by: Adding weather alerts for specific conditions (temperature thresholds, rain warnings) Including weather forecasts for multiple days Sending reports to multiple chat recipients Adding location-based emoji selection Integrating with other notification channels (email, Slack, Discord) Security Notes Keep your API keys and bot tokens secure Don't share your chat ID publicly Consider using n8n's credential system for storing sensitive information Regularly rotate your API keys for better security Special thanks to Arkadiusz, the only person who supports me in n8n mission to make automation great again.
by Sparsh From Automation Jinn
Automated SEO Data Engine using DataForSEO & Airtable This workflow automatically pulls SERP rankings, competitor keywords, and related keyword ideas from DataForSEO and stores structured results in Airtable — making SEO tracking and keyword research streamlined and automated. 🏗️ What this automation does | Step | Component | Purpose | |------|-----------|---------| | 1 | Trigger (Manual: “Execute workflow”) | Starts the workflow on demand — optionally replaceable with a schedule or webhook. | | 2 | Read seed keywords from Airtable (SERP Keywords table) | Fetches the list of keywords for which to track SERP. | | 3 | Post SERP task to DataForSEO API | Requests Google organic SERP results (depth up to 10) for each keyword. | | 4 | Wait + Poll for results (after ~1 min) | Gives DataForSEO time to process, then retrieves the completed task results. | | 5 | Parse & store SERP results into Airtable (SERP rankings table) | Records rank, URL, domain, title, description, breadcrumb, etc. for each result. | | 6 | Read competitor list from Airtable (Competitor Research table) | Fetches competitors (domains/sites) marked for keyword research. | | 7 | Post competitor-site keywords task to DataForSEO | Fetches keywords used by competitor sites. | | 8 | Wait + Poll + Store competitor keywords into Airtable (Competitor Keywords Research) | Captures keyword, competition level, search volume, CPC, monthly volume trends. | | 9 | Aggregate seed keywords → request related keywords via DataForSEO | Retrieves related / similar keyword ideas for seed list (keyword expansion). | | 10 | Store related keywords into Airtable (Similar Keywords table) | Saves keyword data for long-tail / expansion analysis. | 📌 Key Integrations & Tools n8n** — Workflow automation and orchestration Airtable** — Storage for seed keywords, competitor list, and all result tables (SERP Rankings, Competitor Keywords, Similar Keywords) DataForSEO API** — For SERP data, competitor-site keywords, and related keyword suggestions Core n8n nodes: Trigger, HTTP Request, Wait, Split Out, Aggregate, Airtable (search & create) 📄 Data Output / Stored Fields SERP Rankings type, rank_group, rank_absolute, page, domain, title, description, url, breadcrumb Linked to original seed keyword via SERP Keywords reference Competitor Keywords & Similar Keywords Keyword Competition, Competition_Index Search_Volume, CPC, Low_Top_Of_Page_Bid, High_Top_Of_Page_Bid (if available) Monthly search-volume fields: Jan_2025, Feb_2025, …, Dec_2025 (mapped from API's monthly_searches) For competitor keywords: linked to competitor (company/domain) For similar keywords: linked to seed keyword 🔔 Important Notes Month-volume mapping:** Ensure the index mapping from API’s monthly_searches to months is correct — wrong indices will mislabel month data. Fixed wait time:** Current 1-minute wait may not always suffice — for large workloads or slow API responses, increase wait or implement polling/backoff logic. No deduplication:** Running repeatedly may produce duplicate Airtable records. Consider adding search-or-update logic to avoid duplicates. Rate limits / quotas:** Airtable and DataForSEO have limits — batch carefully, throttle requests or add spacing to avoid hitting limits. Credentials security:** Store Airtable and DataForSEO API credentials securely in n8n’s credentials manager — avoid embedding tokens directly in workflow JSON. 🚀 Why this Workflow is Useful Fully automates SERP tracking and competitor keyword research — no manual work needed after setup Maintains structured, historical data in Airtable — ideal for tracking rank changes, discovering competitor moves, and keyword expansion over time Great for SEO teams, agencies, content owners, or anyone needing systematic keyword intelligence and monitoring 🌟 Recommended Next Steps Replace manual trigger with a Schedule Trigger (daily/weekly) for automated runs Add deduplication (upsert) logic to prevent duplicate records and keep Airtable clean Improve robustness: add retry logic for API failures, rate-limit handling, and error notifications (Slack / email) Add logging of API response data (task IDs, raw responses) for debugging and audit trails (Optional) Build a reporting dashboard (Airtable Interface / BI tool) to visualise rank trends, keyword growth, and competitor comparisons 📝 Usage / Setup Checklist Configure Airtable base / tables: SERP Keywords, Competitor Research, SERP rankings, Competitor Keywords Research, Similar Keywords. Add credentials in n8n: Airtable API token; DataForSEO API credentials (HTTP Basic / Header auth). Import this workflow JSON into your n8n instance. Update any base/table/field IDs if different. (Optional) Replace Manual Trigger with Schedule Trigger, enable workflow. Run once with a small seed list — verify outputs, schema, and month-volume mapping. Enable periodic runs and monitor for rate limits or API errors.
by Milan Vasarhelyi - SmoothWork
Video Introduction Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin Overview This workflow automates sending personalized SMS messages directly from a Google Sheet using Twilio. Simply update a row's status to "To send" and the workflow automatically sends the text message, then updates the status to "Success" or "Error" based on delivery results. Perfect for event reminders, bulk notifications, appointment confirmations, or any scenario where you need to send customized messages to multiple recipients. Key Features Simple trigger mechanism**: Change the status column to "To send" to queue messages Personalization support**: Use [First Name] and [Last Name] placeholders in message templates Automatic status tracking**: The workflow updates your spreadsheet with delivery results Error handling**: Failed deliveries are clearly marked, making it easy to identify issues like invalid phone numbers Runs every minute**: The workflow polls your sheet continuously when active Setup Instructions Step 1: Copy the Template Spreadsheet Make a copy of the Google Sheets template by going to File → Make a copy. You must use your own copy so the workflow has permission to update status values. Step 2: Connect Your Accounts Google Sheets: Add your Google account credentials to the 'Monitor Google Sheet for SMS Queue' trigger node Twilio: Sign up for a free Twilio account (trial works for testing). From your Twilio dashboard, get your Account SID, Auth Token, and Twilio phone number, then add these credentials to the 'Send SMS via Twilio' node Step 3: Configure the Workflow In the Config node, update: sheet_url: Paste the URL of your copied Google Sheet from_number: Enter your Twilio phone number (include country code, e.g., +1234567890) Step 4: Activate and Test Activate the workflow using the toggle in the top right corner. Add a row to your sheet with the required information (ID, First Name, Phone Number, Message Template) and set the Status to "To send". Within one minute, the workflow will process the message and update the status accordingly.
by Pauline
This workflow allows you to have a Slack alert when one of your n8n workflows gets an issue. Error trigger**: This node launched the workflow when one of your active workflows gets an issue Slack node**: This node sends you a customized message to alert you and to check the error ⚠️ You don't have to activate this workflow for it to be effective