by vinci-king-01
Customer Support Analysis Dashboard with AI and Automated Insights 🎯 Target Audience Customer support managers and team leads Customer success teams monitoring satisfaction Product managers analyzing user feedback Business analysts measuring support metrics Operations managers optimizing support processes Quality assurance teams monitoring support quality Customer experience (CX) professionals 🚀 Problem Statement Manual analysis of customer support tickets and feedback is time-consuming and often misses critical patterns or emerging issues. This template solves the challenge of automatically collecting, analyzing, and visualizing customer support data to identify trends, improve response times, and enhance overall customer satisfaction. 🔧 How it Works This workflow automatically monitors customer support channels using AI-powered analysis, processes tickets and feedback, and provides actionable insights for improving customer support operations. Key Components Scheduled Trigger - Runs the workflow at specified intervals to maintain real-time monitoring AI-Powered Ticket Analysis - Uses advanced NLP to categorize, prioritize, and analyze support tickets Multi-Channel Integration - Monitors email, chat, help desk systems, and social media Automated Insights - Generates reports on trends, response times, and satisfaction scores Dashboard Integration - Stores all data in Google Sheets for comprehensive analysis and reporting 📊 Google Sheets Column Specifications The template creates the following columns in your Google Sheets: | Column | Data Type | Description | Example | |--------|-----------|-------------|---------| | timestamp | DateTime | When the ticket was processed | "2024-01-15T10:30:00Z" | | ticket_id | String | Unique ticket identifier | "SUP-2024-001234" | | customer_email | String | Customer contact information | "john@example.com" | | subject | String | Ticket subject line | "Login issues with new app" | | description | String | Full ticket description | "I can't log into the mobile app..." | | category | String | AI-categorized ticket type | "Technical Issue" | | priority | String | Calculated priority level | "High" | | sentiment_score | Number | Customer sentiment (-1 to 1) | -0.3 | | urgency_indicator | String | Urgency classification | "Immediate" | | response_time | Number | Time to first response (hours) | 2.5 | | resolution_time | Number | Time to resolution (hours) | 8.0 | | satisfaction_score | Number | Customer satisfaction rating | 4.2 | | agent_assigned | String | Support agent name | "Sarah Johnson" | | status | String | Current ticket status | "Resolved" | 🛠️ Setup Instructions Estimated setup time: 20-25 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets account with API access Help desk system API access (Zendesk, Freshdesk, etc.) Email service integration (optional) Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai npm install n8n-nodes-slack 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 customer support analysis Configure the sheet name (default: "Support Analysis") 4. Configure Support System Integration Update the websiteUrl parameters in ScrapeGraphAI nodes Add URLs for your help desk system or support portal Customize the user prompt to extract specific ticket data Set up categories and priority thresholds 5. Set up Notification Channels Configure Slack webhook or API credentials for alerts Set up email service credentials for critical issues Define alert thresholds for different priority levels Test notification delivery 6. Configure Schedule Trigger Set analysis frequency (hourly, daily, etc.) Choose appropriate time zones for your business hours Consider support system rate limits 7. Test and Validate Run the workflow manually to verify all connections Check Google Sheets for proper data formatting Test ticket analysis with sample data 🔄 Workflow Customization Options Modify Analysis Targets Add or remove support channels (email, chat, social media) Change ticket categories and priority criteria Adjust analysis frequency based on ticket volume Extend Analysis Capabilities Add more sophisticated sentiment analysis Implement customer churn prediction models Include agent performance analytics Add automated response suggestions Customize Alert System Set different thresholds for different ticket types Create tiered alert systems (info, warning, critical) Add SLA breach notifications Include trend analysis alerts Output Customization Add data visualization and reporting features Implement support trend charts and graphs Create executive dashboards with key metrics Add customer satisfaction trend analysis 📈 Use Cases Support Ticket Management**: Automatically categorize and prioritize tickets Response Time Optimization**: Identify bottlenecks in support processes Customer Satisfaction Monitoring**: Track and improve satisfaction scores Agent Performance Analysis**: Monitor and improve agent productivity Product Issue Detection**: Identify recurring problems and feature requests SLA Compliance**: Ensure support teams meet service level agreements 🚨 Important Notes Respect support system API rate limits and terms of service Implement appropriate delays between requests to avoid rate limiting Regularly review and update your analysis parameters Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider data privacy and GDPR compliance for customer data 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Google Sheets permission errors: Check OAuth2 scope and permissions Ticket parsing errors: Review the Code node's JavaScript logic Rate limiting: Adjust analysis frequency and implement delays Alert delivery failures: Check notification service credentials Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Google Sheets API documentation for advanced configurations Help desk system API documentation Customer support analytics best practices
by vinci-king-01
Competitor Price Monitoring Dashboard with AI and Real-time Alerts 🎯 Target Audience E-commerce managers and pricing analysts Retail business owners monitoring competitor pricing Marketing teams tracking market positioning Product managers analyzing competitive landscape Data analysts conducting pricing intelligence Business strategists making pricing decisions 🚀 Problem Statement Manual competitor price monitoring is inefficient and often leads to missed opportunities or delayed responses to market changes. This template solves the challenge of automatically tracking competitor prices, detecting significant changes, and providing actionable insights for strategic pricing decisions. 🔧 How it Works This workflow automatically monitors competitor product prices using AI-powered web scraping, analyzes price trends, and sends real-time alerts when significant changes are detected. Key Components Scheduled Trigger - Runs the workflow at specified intervals to maintain up-to-date price data AI-Powered Scraping - Uses ScrapeGraphAI to intelligently extract pricing information from competitor websites Price Analysis Engine - Processes historical data to detect trends and anomalies Alert System - Sends notifications via Slack and email when price changes exceed thresholds Dashboard Integration - Stores all data in Google Sheets for comprehensive analysis and reporting 📊 Google Sheets Column Specifications The template creates the following columns in your Google Sheets: | Column | Data Type | Description | Example | |--------|-----------|-------------|---------| | timestamp | DateTime | When the price was recorded | "2024-01-15T10:30:00Z" | | competitor_name | String | Name of the competitor | "Amazon" | | product_name | String | Product name and model | "iPhone 15 Pro 128GB" | | current_price | Number | Current price in USD | 999.00 | | previous_price | Number | Previous recorded price | 1099.00 | | price_change | Number | Absolute price difference | -100.00 | | price_change_percent | Number | Percentage change | -9.09 | | product_url | URL | Direct link to product page | "https://amazon.com/iphone15" | | alert_triggered | Boolean | Whether alert was sent | true | | trend_direction | String | Price trend analysis | "Decreasing" | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets account with API access Slack workspace for notifications (optional) Email service for alerts (optional) Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai npm install n8n-nodes-slack 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 price monitoring data Configure the sheet name (default: "Price Monitoring") 4. Configure Competitor URLs Update the websiteUrl parameters in ScrapeGraphAI nodes Add URLs for each competitor you want to monitor Customize the user prompt to extract specific pricing data Set appropriate price thresholds for alerts 5. Set up Notification Channels Configure Slack webhook or API credentials Set up email service credentials (SendGrid, SMTP, etc.) Define alert thresholds and notification preferences Test notification delivery 6. Configure Schedule Trigger Set monitoring frequency (hourly, daily, etc.) Choose appropriate time zones for your business hours Consider competitor website rate limits 7. Test and Validate Run the workflow manually to verify all connections Check Google Sheets for proper data formatting Test alert notifications with sample data 🔄 Workflow Customization Options Modify Monitoring Targets Add or remove competitor websites Change product categories or specific products Adjust monitoring frequency based on market volatility Extend Price Analysis Add more sophisticated trend analysis algorithms Implement price prediction models Include competitor inventory and availability tracking Customize Alert System Set different thresholds for different product categories Create tiered alert systems (info, warning, critical) Add SMS notifications for urgent price changes Output Customization Add data visualization and reporting features Implement price history charts and graphs Create executive dashboards with key metrics 📈 Use Cases Dynamic Pricing**: Adjust your prices based on competitor movements Market Intelligence**: Understand competitor pricing strategies Promotion Planning**: Time your promotions based on competitor actions Inventory Management**: Optimize stock levels based on market conditions Customer Communication**: Proactively inform customers about price changes 🚨 Important Notes Respect competitor websites' terms of service and robots.txt Implement appropriate delays between requests to avoid rate limiting Regularly review and update your monitoring parameters Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider legal implications of automated price monitoring 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Google Sheets permission errors: Check OAuth2 scope and permissions Price parsing errors: Review the Code node's JavaScript logic Rate limiting: Adjust monitoring frequency and implement delays Alert delivery failures: Check notification service credentials Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Google Sheets API documentation for advanced configurations Slack API documentation for notification setup
by WeblineIndia
IPA Size Tracker with Trend Alerts – Automated iOS Apps Size Monitoring This workflow runs on a daily schedule and monitors IPA file sizes from configured URLs. It stores historical size data in Google Sheets, compares current vs. previous builds and sends email alerts only when significant size changes occur (default: ±10%). A DRY_RUN toggle allows safe testing before real notifications go out. Who’s it for iOS developers tracking app binary size growth over time. DevOps teams monitoring build artifacts and deployment sizes. Product managers ensuring app size budgets remain acceptable. QA teams detecting unexpected size changes in release builds. Mobile app teams optimizing user experience by keeping apps lightweight. How it works Schedule Trigger (daily at 09:00 UTC) kicks off the workflow. Configuration: Define monitored apps with {name, version, build, ipa_url}. HTTP Request downloads the IPA file from its URL. Size Calculation: Compute file sizes in bytes, KB, MB and attach timestamp metadata. Google Sheets: Append size data to the IPA Size History sheet. Trend Analysis: Compare current vs. previous build sizes. Alert Logic: Evaluate thresholds (>10% increase or >10% decrease). Email Notification: Send formatted alerts with comparisons and trend indicators. Rate Limit: Space out notifications to avoid spamming recipients. How to set up 1. Spreadsheet Create a Google Sheet with a tab named IPA Size History containing: Date, Timestamp, App_Name, Version, Build_Number, Size_Bytes, Size_KB, Size_MB, IPA_URL 2. Credentials Google Sheets (OAuth)** → for reading/writing size history. Gmail** → for sending alert emails (use App Password if 2FA is enabled). 3. Open “Set: Configuration” node Define your workflow variables: APP_CONFIGS = array of monitored apps ({name, version, build, ipa_url}) SPREADSHEET_ID = Google Sheet ID SHEET_NAME = IPA Size History SMTP_FROM = sender email (e.g., devops@company.com) ALERT_RECIPIENTS = comma-separated emails SIZE_INCREASE_THRESHOLD = 0.10 (10%) SIZE_DECREASE_THRESHOLD = 0.10 (10%) LARGE_APP_WARNING = 300 (MB) SCHEDULE_TIME = 09:00 TIMEZONE = UTC DRY_RUN = false (set true to test without sending emails) 4. File Hosting Host IPA files on Google Drive, Dropbox or a web server. Ensure direct download URLs are used (not preview links). 5. Activate the workflow Once configured, it will run automatically at the scheduled time. Requirements Google Sheet with the IPA Size History tab. Accessible IPA file URLs. SMTP / gmail account (Gmail recommended). n8n (cloud or self-hosted) with Google Sheets + Email nodes. Sufficient local storage for IPA file downloads. How to customize the workflow Multiple apps**: Add more configs to APP_CONFIGS. Thresholds**: Adjust SIZE_INCREASE_THRESHOLD / SIZE_DECREASE_THRESHOLD. Notification templates**: Customize subject/body with variables: {{app_name}}, {{current_size}}, {{previous_size}}, {{change_percent}}, {{trend_status}}. Schedule**: Change Cron from daily to hourly, weekly, etc. Large app warnings**: Adjust LARGE_APP_WARNING. Trend analysis**: Extend beyond one build (7-day, 30-day averages). Storage backend**: Swap Google Sheets for CSV, DB or S3. Add-ons to level up Slack Notifications**: Add Slack webhook alerts with emojis & formatting. Size History Charts**: Generate trend graphs with Chart.js or Google Charts API. Environment separation**: Monitor dev/staging/prod builds separately. Regression detection**: Statistical anomaly checks. Build metadata**: Log bundle ID, SDK versions, architectures. Archive management**: Auto-clean old records to save space. Dashboards**: Connect to Grafana, DataDog or custom BI. CI/CD triggers**: Integrate with pipelines via webhook trigger. Common Troubleshooting No size data** → check URLs return binary IPA (not HTML error). Download failures** → confirm hosting permissions & direct links. Missing alerts** → ensure thresholds & prior history exist. Google Sheets errors** → check sheet/tab names & OAuth credentials. Email issues** → validate SMTP credentials, spam folder, sender reputation. Large file timeouts** → raise HTTP timeout for >100MB files. Trend errors** → make sure at least 2 builds exist. No runs** → confirm workflow is active and timezone is correct. Need Help? If you’d like this to customize this workflow to suit your app development process, then simply reach out to us here and we’ll help you customize the template to your exact use case.
by Growth AI
Who's it for Marketing teams, business intelligence professionals, competitive analysts, and executives who need consistent industry monitoring with AI-powered analysis and automated team distribution via Discord. What it does This intelligent workflow automatically monitors multiple industry topics, scrapes and analyzes relevant news articles using Claude AI, and delivers professionally formatted intelligence reports to your Discord channel. The system provides weekly automated monitoring cycles with personalized bot communication and comprehensive content analysis. How it works The workflow follows a sophisticated 7-phase automation process: Scheduled Activation: Triggers weekly monitoring cycles (default: Mondays at 9 AM) Query Management: Retrieves monitoring topics from centralized Google Sheets configuration News Discovery: Executes comprehensive Google News searches using SerpAPI for each configured topic Content Extraction: Scrapes full article content from top 3 sources per topic using Firecrawl AI Analysis: Processes scraped content using Claude 4 Sonnet for intelligent synthesis and formatting Discord Optimization: Automatically segments content to comply with Discord's 2000-character message limits Automated Delivery: Posts formatted intelligence reports to Discord channel with branded "Claptrap" bot personality Requirements Google Sheets account for query management SerpAPI account for Google News access Firecrawl account for article content extraction Anthropic API access for Claude 4 Sonnet Discord bot with proper channel permissions Scheduled execution capability (cron-based trigger) How to set up Step 1: Configure Google Sheets query management Create monitoring sheet: Set up Google Sheets document with "Query" sheet Add search topics: Include industry keywords, competitor names, and relevant search terms Sheet structure: Simple column format with "Query" header containing search terms Access permissions: Ensure n8n has read access to the Google Sheets document Step 2: Configure API credentials Set up the following credentials in n8n: Google Sheets OAuth2: For accessing query configuration sheet SerpAPI: For Google News search functionality with proper rate limits Firecrawl API: For reliable article content extraction across various websites Anthropic API: For Claude 4 Sonnet access with sufficient token limits Discord Bot API: With message posting permissions in target channel Step 3: Customize scheduling settings Cron expression: Default set to "0 9 * * 1" (Mondays at 9 AM) Frequency options: Adjust for daily, weekly, or custom monitoring cycles Timezone considerations: Configure according to team's working hours Execution timing: Ensure adequate processing time for multiple topics Step 4: Configure Discord integration Set up Discord delivery settings: Guild ID: Target Discord server (currently: 919951151888236595) Channel ID: Specific monitoring channel (currently: 1334455789284364309) Bot permissions: Message posting, embed suppression capabilities Brand personality: Customize "Claptrap" bot messaging style and tone Step 5: Customize content analysis Configure AI analysis parameters: Analysis depth: Currently processes top 3 articles per topic Content format: Structured markdown format with consistent styling Language settings: Currently configured for French output (easily customizable) Quality controls: Error handling for inaccessible articles and content How to customize the workflow Query management expansion Topic categories: Organize queries by industry, competitor, or strategic focus areas Keyword optimization: Refine search terms based on result quality and relevance Dynamic queries: Implement time-based or event-triggered query modifications Multi-language support: Add international keyword variations for global monitoring Advanced content processing Article quantity: Modify from 3 to more articles per topic based on analysis needs Content filtering: Add quality scoring and relevance filtering for article selection Source preferences: Implement preferred publisher lists or source quality weighting Content enrichment: Add sentiment analysis, trend identification, or competitive positioning Discord delivery enhancements Rich formatting: Implement Discord embeds, reactions, or interactive elements Multi-channel distribution: Route different topics to specialized Discord channels Alert levels: Add priority-based messaging for urgent industry developments Archive functionality: Create searchable message threads or database storage Integration expansions Slack compatibility: Replace or supplement Discord with Slack notifications Email reports: Add formatted email distribution for executive summaries Database storage: Implement persistent storage for historical analysis and trending API endpoints: Create webhook endpoints for third-party system integration AI analysis customization Analysis templates: Create topic-specific analysis frameworks and formatting Competitive focus: Enhance competitor mention detection and analysis depth Trend identification: Implement cross-topic trend analysis and strategic insights Summary levels: Create executive summaries alongside detailed technical analysis Advanced monitoring features Intelligent content curation The system provides sophisticated content management: Relevance scoring: Automatic ranking of articles by topic relevance and publication authority Duplicate detection: Prevents redundant coverage of the same story across different sources Content quality assessment: Filters low-quality or promotional content automatically Source diversity: Ensures coverage from multiple perspectives and publication types Error handling and reliability Graceful degradation: Continues processing even if individual articles fail to scrape Retry mechanisms: Automatic retry logic for temporary API failures or network issues Content fallbacks: Uses article snippets when full content extraction fails Notification continuity: Ensures Discord delivery even with partial content processing Results interpretation Intelligence report structure Each monitoring cycle delivers: Topic-specific summaries: Individual analysis for each configured search query Source attribution: Complete citation with publication date, source, and URL Structured formatting: Consistent presentation optimized for quick scanning Professional analysis: AI-generated insights maintaining factual accuracy and business context Performance analytics Monitor system effectiveness through: Processing metrics: Track successful article extraction and analysis rates Content quality: Assess relevance and usefulness of delivered intelligence Team engagement: Monitor Discord channel activity and report utilization System reliability: Track execution success rates and error patterns Use cases Competitive intelligence Market monitoring: Track competitor announcements, product launches, and strategic moves Industry trends: Identify emerging technologies, regulatory changes, and market shifts Partnership tracking: Monitor alliance formations, acquisitions, and strategic partnerships Leadership changes: Track executive movements and organizational restructuring Strategic planning support Market research: Continuous intelligence gathering for strategic decision-making Risk assessment: Early warning system for industry disruptions and regulatory changes Opportunity identification: Spot emerging markets, technologies, and business opportunities Brand monitoring: Track industry perception and competitive positioning Team collaboration enhancement Knowledge sharing: Centralized distribution of relevant industry intelligence Discussion facilitation: Provide common information baseline for strategic discussions Decision support: Deliver timely intelligence for business planning and strategy sessions Competitive awareness: Keep teams informed about competitive landscape changes Workflow limitations Language dependency: Currently optimized for French analysis output (easily customizable) Processing capacity: Limited to 3 articles per query (configurable based on API limits) Platform specificity: Configured for Discord delivery (adaptable to other platforms) Scheduling constraints: Fixed weekly schedule (customizable via cron expressions) Content access: Dependent on article accessibility and website compatibility with Firecrawl API dependencies: Requires active subscriptions and proper rate limit management for all integrated services
by Dustin
Short an simple: This Workflow will sync (add and delete) your Liked Songs to an custom playlist that can be shared. Setup: Create an app on the Spotify Developer Dashboard. Create Spotify Credentials - Just click on one of the Spotify Nodes in the Workflow an click on "create new credentials" and follow the guide. Create the Spotify Playlist that you want to sync to. Copy the exact name of you playlist, go into Node "Edit set Vars" and replace the value "CHANGE MEEEE" with your playlist name. Set your Spotify Credentiels on every Spotify Node. (Should be marekd with Yellow and Red Notes) Do you use Gotify? - No: Delete the Gotify Nodes (all the way to the right end of the Workflow) - Yes: Customize the Gotify Nodes to your needs.
by Anna Bui
Automatically monitor LinkedIn posts from your community members and create AI-powered content digests for efficient social media curation. This template is perfect for community managers, content creators, and social media teams who need to track LinkedIn activity from their network without spending hours manually checking profiles. It fetches recent posts, extracts key information, and creates digestible summaries using AI. Good to know API costs apply** - LinkedIn API calls ($0.01-0.05 per profile check) and OpenAI processing ($0.001-0.01 per post) Rate limiting included** - Built-in random delays prevent API throttling issues Flexible scheduling** - Easy to switch from daily schedule to webhook triggers for real-time processing Requires API setup** - Need RapidAPI access for LinkedIn data and OpenAI for content processing How it works Daily profile scanning** - Automatically checks each LinkedIn profile in your Airtable for posts from yesterday Smart data extraction** - Pulls post content, engagement metrics, author information, and timestamps AI-powered summarization** - Creates 30-character previews of posts for quick content scanning Duplicate prevention** - Checks existing records to avoid storing the same post multiple times Structured storage** - Saves all processed data to Airtable with clean formatting and metadata Batch processing** - Handles multiple profiles efficiently with proper error handling and delays How to use Set up Airtable base** - Create tables for LinkedIn profiles and processed posts using the provided structure Configure API credentials** - Add your RapidAPI LinkedIn access and OpenAI API key to n8n credentials Import LinkedIn profiles** - Add community members' LinkedIn URLs and URNs to your profiles table Test the workflow** - Run manually with a few profiles to ensure everything works correctly Activate schedule** - Enable daily automation or switch to webhook triggers for real-time processing Requirements Airtable account** - For storing profile lists and managing processed posts with proper field structure RapidAPI Professional Network Data API** - Access to LinkedIn post data (requires subscription) OpenAI API account** - For intelligent content summarization and preview generation LinkedIn profile URNs** - Properly formatted LinkedIn profile identifiers for API calls Customising this workflow Change monitoring frequency** - Switch from daily to hourly checks or use webhook triggers for real-time updates Expand data extraction** - Add company information, hashtag analysis, or engagement trending Integrate notification systems** - Add Slack, email, or Discord alerts for high-engagement posts Connect content tools** - Link to Buffer, Hootsuite, or other social media management platforms for direct publishing Add filtering logic** - Set up conditions to only process posts with minimum engagement thresholds Scale with multiple communities** - Duplicate workflow for different LinkedIn communities or industry segments
by Niranjan G
🛡️ Automated AWS Key Compromise Remediation Description This n8n workflow provides a secure, enterprise-grade response system for AWS IAM access key compromises with built-in form submission and human approval mechanisms. When an AWS access key is suspected to be compromised, this workflow enables rapid containment through a secure web form interface with basic authentication, human approval via Slack, and automated damage prevention through immediate key deactivation, credential invalidation, and comprehensive security reporting. How This Workflow is Useful Secure Form-Based Response Authenticated Form Submission**: Secure web form with basic authentication for capturing compromise details Human Approval Workflow**: Slack-based approval system for sensitive security operations Rapid Key Deactivation**: Instantly disables compromised access keys after approval Credential Invalidation**: Creates and applies security policies to invalidate existing temporary credentials Policy Analysis**: Automatically scans and analyzes both inline and attached IAM policies for the affected user AI-Powered Reporting**: Generates detailed security reports with intelligent analysis and team notifications Business Value Reduces Mean Time to Response (MTTR)**: Automates manual security procedures that typically take hours Minimizes Security Exposure**: Immediate containment prevents potential data breaches and unauthorized resource access Ensures Compliance**: Provides audit trails and documentation required for security compliance frameworks Cost Prevention**: Prevents potential financial damage from compromised credentials being used maliciously Rapid Response Capability**: Streamlines security response procedures when incidents are detected Technical Benefits AWS Best Practices**: Implements official AWS security recommendations for key compromise response Scalable Architecture**: Handles multiple access keys and complex IAM policy structures Error Handling**: Robust error handling ensures workflow continues even if individual steps fail Audit Trail**: Complete logging of all actions taken during the incident response Integration Ready**: Easily integrates with existing security tools and notification systems Use Cases 1. Incident Response Automation Automated response to security alerts from AWS CloudTrail Integration with SIEM systems for immediate key compromise response 24/7 security monitoring and automated containment 2. Compliance and Audit Meeting regulatory requirements for incident response documentation Providing audit trails for security compliance frameworks (SOC 2, ISO 27001, PCI DSS) Demonstrating due diligence in security incident handling 3. Multi-Account Management Centralized security response across multiple AWS accounts Consistent incident response procedures across different environments Standardized security automation for enterprise AWS deployments 4. Security Training and Testing Security team training on AWS incident response procedures Tabletop exercises and security drills Testing and validation of security response capabilities Key Features Core Functionality ✅ Secure Form Interface: Web form with basic authentication for secure data submission ✅ Human Approval Gate: Slack-based approval workflow for sensitive operations ✅ Authenticated Data Processing: Secure handling of form submissions with validation ✅ Immediate Key Deactivation: Instant disabling of compromised credentials after approval ✅ Security Policy Generation: Automatic creation and attachment of credential invalidation policies ✅ Policy Analysis: Deep analysis of user permissions and attached policies ✅ AI Security Analysis: Intelligent security report generation with risk assessment ✅ Team Notifications: Real-time Slack notifications to security teams ✅ Comprehensive Logging: Complete audit trail of all response actions Technical Specifications Secure Form Interface**: Web form with basic authentication for secure data capture Human Approval System**: Slack-based approval workflow for sensitive operations AWS API Integration**: Direct integration with AWS IAM APIs Authentication Layer**: Basic auth protection for form submissions Error Handling**: Robust error handling with continuation on non-critical failures Scalable Processing**: Handles multiple policies and complex IAM structures Security Best Practices**: No hardcoded credentials, uses AWS credential management Modular Design**: Easy to customize and extend for specific organizational needs Prerequisites Required Credentials AWS Credentials** with IAM permissions for: ListAccessKeys, UpdateAccessKey ListUserPolicies, ListAttachedUserPolicies CreatePolicy, AttachUserPolicy GetPolicy, GetPolicyVersion, GetUserPolicy Required Integrations Slack Workspace** for approval workflow and team notifications Basic Authentication Setup** for secure form access Optional Integrations AI Language Model** (Claude/OpenAI) for intelligent security analysis and report generation Installation and Setup Import the workflow into your n8n instance Configure AWS credentials in n8n credential manager Set up basic authentication for the secure form interface Configure Slack integration for approval notifications and team alerts Set up AI model (optional) for enhanced security analysis and reporting Configure approval workflow in Slack for human oversight Test in development environment before production use Workflow Inputs Secure Form Submission This workflow uses a secure web form with basic authentication to capture compromise details: Username**: The AWS IAM username of the compromised account Access Key ID**: The specific access key ID that has been compromised Authentication & Approval Process Form Authentication: Basic authentication protects the submission form Data Processing: Secure handling and validation of submitted credentials Human Approval: Slack notification sent to security team for approval Automated Execution: Upon approval, the workflow executes the security response This multi-layered approach ensures that sensitive security operations require both authentication and human oversight before execution. 🚀 Automate with Slack Integration Want to fully automate and simplify this workflow? Connect it with Slack for seamless team collaboration and instant response capabilities! Interactive Slack Automation Combine this AWS Key Compromise Response workflow with our Interactive Slack Approval & Data Submission System to create a fully automated incident response pipeline: Instant Slack Alerts**: Receive immediate notifications when key compromises are detected One-Click Response**: Trigger the AWS response workflow directly from Slack with interactive buttons Team Collaboration**: Enable security teams to respond collectively through Slack channels Approval Workflows**: Add human approval gates before executing critical security actions Real-time Updates**: Get live status updates and completion notifications in Slack How the Complete Solution Works Detection: External security monitoring tools (CloudTrail, SIEM, etc.) detect potential key compromise Secure Form Access: Security team accesses the authenticated web form to submit compromise details Form Submission: Credentials are securely submitted through the basic auth-protected form Human Approval: Slack notification sent to security team for review and approval Approved Execution: Upon approval, the AWS security response executes automatically Real-time Updates: Progress and completion notifications sent back to Slack Security Analysis: AI-powered analysis and comprehensive reporting delivered to the team Get Started with Full Automation To enable automatic notifications and complete the automation pipeline, use the Interactive Slack Approval & Data Submission System with Webhooks workflow: https://n8n.io/workflows/5049-interactive-slack-approval-and-data-submission-system-with-webhooks/ This integration transforms manual security responses into streamlined, team-collaborative automation that reduces response time from hours to minutes. Security Considerations Form Authentication**: Basic authentication protects the submission interface Human Approval Gate**: Slack-based approval prevents unauthorized execution AWS Credential Management**: Uses AWS credential management best practices No Sensitive Data Storage**: No sensitive data stored in workflow configuration Least-Privilege Access**: Implements least-privilege access principles Complete Audit Trails**: Provides complete audit trails for compliance Secure Data Processing**: Encrypted handling of form submissions and approvals Immediate Damage Prevention**: Designed for rapid containment after approval ⚠️ Important Disclaimer Use with Caution: Disabling access keys without proper understanding can significantly impact your personal or business operations. This workflow immediately deactivates AWS access keys, which may disrupt running applications, automated processes, or services that depend on these credentials. AWS Best Practices Recommendation: Use IAM Roles instead of Access Keys** whenever possible for enhanced security IAM roles provide temporary credentials and eliminate the need for long-term access keys Follow the principle of least privilege when assigning permissions Regularly rotate and audit your AWS credentials Implement proper monitoring and alerting for credential usage Before Using This Workflow: Ensure you understand which services and applications use the target access key Have a rollback plan in case of accidental disruption Test in a non-production environment first Coordinate with your team before executing in production For comprehensive AWS security best practices, refer to the AWS Security Best Practices Guide. For more workflows and automation solutions, visit: https://n8n.io/creators/niranjan/
by Vadym Nahornyi
How it works Automatically sends Telegram notifications when any n8n workflow fails. Includes workflow name, error message, and execution ID in the alert. Setup Complete setup instructions included in the workflow's sticky note in 5 languages: 🇬🇧 English 🇪🇸 Español 🇩🇪 Deutsch 🇫🇷 Français 🇷🇺 Русский Features Monitors all workflows 24/7 Instant Telegram notifications Zero configuration needed Just add your bot token and chat ID Important ⚠️ Keep this workflow active 24/7 to capture all errors.
by iamvaar
Quick overview Demo Link: https://www.linkedin.com/feed/update/urn:li:activity:7503832069614215168/ This workflow runs daily to scan property listings in Google Sheets, detect stale or low-performing leads using portfolio benchmarks, generate a diagnosis with Google Gemini, and post actionable alerts to Slack while logging alert history back to the sheet. How it works Runs every day at 8:00 AM on a schedule. Reads all listing rows from a Google Sheets “Listings” spreadsheet. Calculates days-on-market and conversion metrics, compares each listing to portfolio medians, and flags listings for review (or flags data-quality issues) while applying a 7-day alert cooldown and a 10-day grace period after price changes. Writes the latest enquiry/viewing/offer snapshot back to Google Sheets to track trends over time. For eligible flagged listings, sends the listing context to Google Gemini to return a JSON diagnosis (likely issue, suggested action, and severity). Posts an alert to a Slack channel (including escalation for repeat flags and optional agent/manager mentions) and updates Google Sheets with the alert date, issue/action, and incremented alert count. If the workflow errors, sends a failure notification to Slack with the workflow, node, and error details. Setup Connect Google Sheets using a Google Service Account credential and set the target spreadsheet and “Listings” sheet. Ensure the Google Sheet includes the required columns (for example: Listing ID, Property, Status, Listed Date or Days on Market, Enquiries, Viewings, Offers, Last Price Change Date, Prev Enquiries/Viewings/Offers, Prev Snapshot Date, Last Alert Date, Alert Count, Agent Name, and Agent Slack ID). Add a Google Gemini (PaLM) API credential for the Gemini HTTP request. Add a Slack credential and choose the channel to receive alerts and error notifications. Set the MANAGER_SLACK_ID environment variable if you want urgent alerts to mention a manager.
by WeblineIndia
Macro News: AI-Driven Market Impact Scoring Automation This workflow is a high-precision financial intelligence engine that monitors macroeconomic news to identify high-impact trading opportunities. It retrieves real-time data via SerpAPI, processes it through a dual-layered engine—combining a custom weighted Rule Engine with Groq-powered AI analysis—to calculate a final "Market Impact Score". By filtering out noise and focusing only on high-confidence signals, it stores critical events in Google Sheets and delivers formatted trade bias alerts (Forex, Crypto, Stocks) directly to your inbox. Quick Implementation Steps Import: Upload the JSON file into your n8n workspace. Authenticate**: Connect your Google Sheets, SerpAPI, Groq and Gmail credentials. Setup Rules**: Fill your "Macro Rules" Google Sheet with keywords (e.g., "Repo Rate") and their corresponding weights. Configure News: In the **Fetch Macro News node, enter your SerpAPI key and adjust the gl (country) or q (query) parameters to your target market. Deploy**: Run once manually to test, then activate the workflow for continuous monitoring. What It Does The workflow automates the end-to-end lifecycle of macro-fundamental analysis. It begins by fetching a library of "Macro Rules" from your Google Sheet, which acts as the DNA for your scoring logic. It then scouts for the latest news on topics like GDP, Inflation and Central Bank decisions. To ensure you aren't buried in repetitive data, a normalization script creates a unique fingerprint for every article, allowing a deduplication filter to ignore any story you have already processed. Once a fresh article is identified, it enters the dual-engine analysis phase. First, a JavaScript-based Rule Engine scans the text for your predefined keywords and tallies a weighted base score. Second, the Groq LLM Engine performs a deep qualitative analysis to determine market sentiment, affected asset classes and specific trade biases. In the final stage, the workflow synthesizes these two perspectives into a "Final Impact Score". It applies a mathematical boost to high-confidence AI signals and identifies articles that cross a specific "High Impact" threshold (defaulting at 90+). These elite signals are archived in your database and immediately dispatched as a professional HTML alert to your Gmail. Who It's For Macro Traders** who need to react instantly to Central Bank news and economic shifts. Portfolio Managers** looking to automate the tracking of fundamental "Black Swan" events. Financial Analysts** who want a structured, historical database of news impact scores. Crypto & Forex Investors** who require a disciplined "Buy/Sell/Neutral" bias based on fundamental data rather than just technical charts. Requirements to use this workflow n8n Instance: (Self-hosted or Cloud). SerpAPI Key**: To fetch live Google News results. Groq API Key**: To power the AI Market Impact Analysis. Google Account**: For Sheets (Rules/Database) and Gmail (Alerts). How It Works & Setup Guide 1. The Rules Engine (Precondition) Before starting, prepare a Google Sheet named Macro Rules. It must have the following headers: Keyword**: The term to look for (e.g., "Inflation"). Weight**: A number (1-50) representing the importance of this word. Asset/Sentiment/Impact**: Metadata for the AI to consider. 2. Geographic & Query Configuration The Fetch Macro News node is currently set to India (gl=in). You can change this to us (United States), gb (United Kingdom) or any other region code supported by SerpAPI to pivot your market focus. 3. Deduplication & Throttling The workflow uses a Normalize & Generate News ID node to hash article titles. The Rate Limit node adds a necessary 5-second pause before calling the AI to ensure you don't hit rate limits on your Groq account. 4. Final Scoring Logic The Calculate Final Score node combines the Rule Score + AI Confidence + Impact Boost. The Assign Final Impact Level node then classifies anything with a score of 90 or higher as "High Impact," which triggers the final alert. You can lower this number in the code if you want more frequent, lower-sensitivity alerts. How To Customize Nodes Adjust Sensitivity: Modify the score threshold (90) in the **Assign Final Impact Level node to make the workflow more or less "picky". Edit AI Prompt: Open the **AI Market Impact Analysis node to change the "Trade Bias" options or add new asset classes like "Commodities" or "Bonds". Custom News Sources: Modify the q parameter in the **Fetch Macro News node to include specific news outlets or different economic indicators. Add‑ons WhatsApp/Slack Alerts**: Connect a messaging node after the high-impact filter for mobile push notifications. Schedule Trigger: Replace the manual trigger with a **Schedule Trigger to run the scan every 15 minutes during market hours. Automated Trading**: Connect this to a broker API (like Alpaca or Interactive Brokers) to automatically execute "Trade Bias" signals. Use Case Examples Central Bank Monitoring**: Instantly scoring RBI, Fed or ECB interest rate decisions. Inflation Tracking**: Scoring CPI or WPI data releases and their immediate impact on Forex pairs. GDP Growth Alerts**: Identifying surprises in GDP data that could move the stock market. Crypto Macro Correlation**: Tracking how US Dollar strength or inflation news impacts Bitcoin volatility. Crisis Management**: Monitoring for keywords like "Sanctions," "Default," or "War" to signal an immediate shift to defensive assets. Troubleshooting Guide | Issue | Possible Cause | Solution | | :--- | :--- | :--- | | No news is fetched | SerpAPI key is empty or invalid | Enter your SerpAPI key in the Fetch Macro News query parameters. | | Duplicate alerts sent | ID column missing in sheet | Ensure your Fetch Existing News sheet has an "ID" column for comparison. | | AI node error | Groq rate limits | Increase the Rate Limit wait time or check your Groq API usage. | | Scores are too low | Weighted rules are too conservative | Increase the "Weight" values in your Macro Rules spreadsheet. | Need Help? Creating a custom market-scoring engine requires a perfect blend of financial logic and technical automation. If you need assistance fine-tuning your rules engine, modifying the AI trade bias or scaling this to multiple global markets, our n8n automation workflow experts are ready to support you. Contact WeblineIndia to help you build, customize or scale your professional financial automations today!
by WeblineIndia
Quick overview This workflow runs weekly to compare employee and supplier records in Google Sheets, score potential conflict-of-interest matches, and use Groq (LLM) to generate a neutral risk explanation. High-risk cases trigger a Slack alert, and medium/high-risk cases are logged to a Google Sheets audit tab. How it works Runs every week on a schedule trigger. Reads employee and supplier data from two Google Sheets tabs. Compares each employee against each supplier, scoring potential conflicts using matches on names, emails, phones, addresses, city/last name signals, and transaction activity. For each case above the scoring threshold, uses Groq to generate a short, neutral compliance explanation with recommended next steps. Flags cases with a risk score of 70+ as high risk and posts a formatted alert to a Slack channel. Appends high-risk and medium-risk cases (including score, reasons, and AI summary) to a Google Sheets “ConflictCases” tab for tracking. Setup Create and populate a Google Sheets file with tabs for Employees, Suppliers, and a ConflictCases log, and ensure the columns used in the workflow (for example EmployeeName/Email/Phone/Address and Supplier owner/contact/spend fields) match your sheet headers. Add Google Sheets credentials in n8n and update the spreadsheet ID and sheet/tab selections in the Google Sheets nodes if you use a different file. Add a Groq API credential and confirm the selected model in the Groq chat model node. Add Slack credentials, select the target channel (for example #conflict-of-interest), and adjust the alert message template if needed. Additional info How To Customize Nodes Code Node (Scoring Logic)** Adjust scoring weights (e.g., email match = 40 → change as needed) Modify risk thresholds (e.g., High Risk ≥ 80) IF Node** Change high-risk threshold from 70 to desired value AI Node** Customize prompt for more detailed or shorter explanations Slack Node** Modify message format or channel Google Sheets** Add/remove columns as per reporting needs Add-ons You can extend this workflow with: Email notifications for compliance teams Dashboard integration (Power BI / Tableau) Approval workflow for flagged cases Automatic case status updates (Resolved / Investigating) Historical trend analysis of conflicts Use Case Examples Detecting employees linked to supplier ownership Identifying suspicious vendor relationships in procurement Monitoring high-spend suppliers for conflicts Supporting internal audit investigations Ensuring compliance with corporate governance policies There can be many more use cases depending on business needs and data availability. Troubleshooting Guide | Issue | Possible Cause | Solution | | ---------------------------------- | ------------------------------------ | ------------------------------------------ | | No data fetched from Google Sheets | Wrong Sheet ID or permissions issue | Verify document ID and OAuth credentials | | Workflow not triggering | Schedule not configured properly | Check trigger timing and activate workflow | | No conflicts detected | Data mismatch or normalization issue | Verify input data format and fields | | AI node not responding | Missing or invalid API key | Reconfigure Groq credentials | | Slack message not sent | Wrong channel ID or permissions | Check Slack credentials and channel access | | Data not saved in Sheets | Column mismatch | Ensure column names match mapping | Need Help? If you need assistance setting up this workflow or want to customize it for your business needs, feel free to reach out. We at WeblineIndia specialize in building intelligent automation workflows using n8n, AI and cloud integrations. Whether you need enhancements, integrations or a completely custom solution, our team is here to help. 👉 Contact WeblineIndia to: Hire n8n developers Customize this workflow Add advanced AI capabilities Integrate with enterprise systems Build similar automation solutions
by Jitesh Dugar
Overview Advanced AI-powered stock analysis workflow that combines multi-timeframe technical analysis with real-time news sentiment to generate actionable BUY/SELL/HOLD recommendations. Uses sophisticated algorithms to process price data, news sentiment, and market context for informed trading decisions. Core Features Multi-Timeframe Technical Analysis 4-Hour Charts** - Intraday trend analysis and entry timing Daily Charts** - Primary trend identification and key levels Weekly Charts** - Long-term context and major trend direction Moving Average Analysis** - 5, 10, and 20-period trend indicators Support/Resistance Levels** - Dynamic price level identification Volume Analysis** - Trading activity and momentum confirmation AI-Powered News Sentiment Analysis Real-Time News Processing** - Latest market-moving headlines Sentiment Scoring** - Numerical sentiment rating (-1 to +1 scale) Impact Assessment** - News relevance to stock performance Multi-Source Analysis** - Comprehensive news coverage evaluation Context-Aware Processing** - Financial market-specific sentiment analysis Intelligent Recommendation Engine Professional Trading Logic** - Multi-timeframe alignment analysis Risk/Reward Calculations** - Minimum 1:2 ratio requirements Entry/Exit Price Targets** - Specific actionable price levels Stop-Loss Recommendations** - Risk management guidelines Confidence Scoring** - Recommendation strength assessment Technical Capabilities Data Sources & APIs TwelveData API** - Professional-grade price and volume data NewsAPI Integration** - Comprehensive news coverage Perplexity AI** - Additional sentiment context and analysis Chart-Img API** - Visual chart generation for analysis Real-Time Processing** - Live market data integration AI Models & Analysis GPT-4 Integration** - Advanced natural language processing Custom Sentiment Engine** - Financial market-tuned sentiment analysis Multi-Model Approach** - Cross-validation of recommendations Algorithmic Trading Logic** - Professional-grade decision frameworks Visual Analysis Tools Interactive Charts** - TradingView-style chart generation Technical Indicators** - Visual representation of analysis Dark Theme Support** - Professional trading interface Multiple Timeframes** - Comprehensive visual analysis Use Cases & Applications Individual Traders Day Trading Signals** - Short-term entry/exit recommendations Swing Trading Analysis** - Multi-day position guidance Risk Management** - Stop-loss and position sizing advice Market Timing** - Optimal entry point identification Investment Research Due Diligence** - Comprehensive stock analysis Sentiment Monitoring** - News impact assessment Technical Screening** - Multi-criteria stock evaluation Portfolio Optimization** - Individual stock recommendations Automated Trading Systems Signal Generation** - Systematic buy/sell/hold alerts Risk Controls** - Automated stop-loss calculations Multi-Asset Analysis** - Scalable across stock universe Backtesting Support** - Historical recommendation validation Financial Advisors & Analysts Client Reporting** - Professional analysis documentation Research Automation** - Streamlined analysis workflow Decision Support** - Data-driven recommendation framework Market Commentary** - AI-generated insights and rationale Key Benefits Professional-Grade Analysis Institutional Quality** - Bank-level analytical frameworks Multi-Dimensional** - Technical + fundamental + sentiment analysis Real-Time Processing** - Live market data integration Objective Decision Making** - Removes emotional bias from analysis Time Efficiency Instant Analysis** - Seconds vs hours of manual research Automated Processing** - Continuous market monitoring Scalable Operations** - Analyze multiple stocks simultaneously 24/7 Availability** - Round-the-clock market analysis Risk Management Built-in Stop Losses** - Automatic risk level calculation Position Sizing** - Risk-appropriate recommendation sizing Multi-Timeframe Validation** - Reduces false signals Conservative Approach** - Defaults to HOLD when uncertain Setup Requirements API Keys Needed TwelveData API - Free tier available at twelvedata.com NewsAPI Key - Free tier available at newsapi.org OpenAI API - For GPT-4 analysis capabilities Perplexity API - Additional sentiment analysis Chart-Img API - Optional chart visualization (chart-img.com) Configuration Steps API Integration - Add your API keys to respective nodes Symbol Format - Supports company names or stock symbols Risk Parameters - Customize stop-loss and target calculations Notification Setup - Configure alert delivery methods Testing & Validation - Verify API connections and data flow Advanced Features Natural Language Processing Company Name Recognition** - Automatic symbol conversion Context Understanding** - Market-aware news interpretation Multi-Language Support** - Global news source analysis Entity Extraction** - Key information identification Error Handling & Reliability API Failure Recovery** - Graceful degradation strategies Data Validation** - Input/output quality checks Rate Limit Management** - Automatic throttling controls Backup Data Sources** - Redundant information feeds Customization Options Timeframe Selection** - Adjustable analysis periods Risk Tolerance** - Configurable risk/reward ratios Sentiment Weighting** - Balance technical vs fundamental analysis Alert Thresholds** - Custom trigger conditions Important Disclaimers This tool provides educational and informational analysis only. All trading decisions should: Consider your personal risk tolerance and financial situation Be validated with additional research and professional advice Account for market volatility and potential losses Follow proper risk management principles Performance Optimization Speed Enhancements Parallel Processing** - Simultaneous data retrieval Caching Strategies** - Reduced API call frequency Efficient Algorithms** - Optimized calculation methods Memory Management** - Scalable resource usage Accuracy Improvements Multi-Source Validation** - Cross-reference data points Historical Backtesting** - Performance validation Continuous Learning** - Algorithm refinement Market Adaptation** - Evolving analysis criteria Transform your investment research with AI-powered analysis that combines the speed of automation with the depth of professional-grade financial analysis.