Workflow Templates

Error Alert Aggregator – Email and Jira This workflow aggregates error logs arriving from multiple sources, deduplicates identical events within a configurable time-window, and sends a single consolidated notification via Email and Jira. It prevents alert fatigue by batching similar errors and guarantees that responsible teams are informed through both channels. Pre-conditions/Requirements Prerequisites n8n instance (self-hosted ≥ v1.0 or n8n.cloud account) Basic understanding of your log source’s payload structure SMTP server or n8n Email credentials configured Jira Cloud or Jira Server account with API access Required Credentials Email (SMTP/IMAP or n8n Email node credential)** — to dispatch alert emails Jira** — Create issues automatically in the chosen project HTTP Request Auth (optional)** — If your log endpoint requires authentication Specific Setup Requirements | Setting | Recommended Value | Notes | |-----------------------------|----------------------------------------|-----------------------------------------------------------| | Batch window (Wait node) | 10 minutes | Time allowed to collect & deduplicate errors | | Deduplication key (Code) | error_id or message field | Choose a unique attribute representing the same incident | | Email recipients | Security & DevOps distribution list | Use semicolons for multiple addresses | | Jira project key | SEC | Project where alert tickets should be filed | How it works This workflow aggregates error logs arriving from multiple sources, deduplicates identical events within a configurable time-window, and sends a single consolidated notification via Email and Jira. It prevents alert fatigue by batching similar errors and guarantees that responsible teams are informed through both channels. Key Steps: Schedule Trigger**: Runs every X minutes to poll/collect new log items. HTTP Request**: Pulls error events from your monitoring or log system. IF Node**: Quickly filters out non-error or resolved events. Code Node (Deduplicator)**: Hashes & stores unique error signatures, skipping already-seen items. Wait Node**: Holds processing for the batching period (e.g., 10 min). Merge Node**: Combines all unique errors gathered during the window. Set Node**: Formats the consolidated message for Email & Jira. Email Send**: Dispatches the summary email. Jira Node**: Creates (or updates) an issue with the same summary. Sticky Notes**: Provide inline documentation right inside the workflow for easier maintenance. Set up steps Setup Time: 15-20 minutes Import template: Download the JSON template and drag & drop it into your n8n editor. Configure Schedule Trigger: Set polling interval (e.g., every 5 minutes). HTTP Request Node: Enter the URL of your log endpoint. Add authentication if required. Adjust IF filter: Modify the condition to match your log’s error severity field (status === "error"). Customize Code Node: Replace error_id with the field that uniquely identifies an error. Optionally tweak deduplication TTL. Wait Node: Set the batch time (e.g., 600 seconds). Set Node: Edit the email subject/body and Jira issue summary/description placeholders. Credentials: Add or select your Email credential in Email Send. Add or select your Jira credential in Jira node. Test run the workflow to verify that: Duplicate events are collapsed. Email and Jira tickets show combined information. Activate the workflow to start production monitoring. Node Descriptions Core Workflow Nodes: Schedule Trigger** – Initiates workflow on a fixed interval. HTTP Request** – Retrieves fresh error logs from an external API. IF** – Only lets true error events proceed. Code (Deduplicator)** – Uses JavaScript to remove already-known errors via n8n static data. Wait** – Creates a batching window for aggregation. Merge (Queue mode)** – Joins events accumulated during the wait. Set** – Crafts a human-readable report for Email & Jira. Email Send** – Dispatches the consolidated message to stakeholders. Jira** – Opens/updates an issue containing the same error digest. Sticky Note** – Provides inline explanations for future maintainers. Data Flow: Schedule Trigger → HTTP Request → IF → Code Code → Wait → Merge → Set Set → Email Send & Jira Customization Examples Change Deduplication Strategy // Code Node snippet // Use error 'stacktrace' + 'service' for uniqueness const signature = ${item.json.stacktrace}_${item.json.service}; if ($workflow.staticData.signatureCache?.includes(signature)) { // duplicate, skip return []; } $workflow.staticData.signatureCache = [ ...( $workflow.staticData.signatureCache || [] ), signature ]; return item; Update Existing Jira Issue Instead of Creating New // Jira Node settings // Search for an open ticket with the same summary // If found, add a comment instead of creating { "operation": "comment", "issueKey": "={{$node['Set'].json['jiraIssueKey']}}", "comment": "New occurrences: {{$json.errorCount}}" } Data Output Format The workflow outputs structured JSON data: { "errors": [ { "id": "ERR123", "message": "Database timeout", "count": 5, "firstSeen": "2024-03-14T10:12:00Z", "lastSeen": "2024-03-14T10:22:00Z" } ], "emailStatus": "success", "jiraStatus": "issue_created" } Troubleshooting Common Issues No data returned from HTTP Request – Verify endpoint URL, authentication headers, and that your monitoring tool actually has recent error events. Duplicate alerts still coming through – Increase the Wait node’s batching window or refine the deduplication key in the Code node. Performance Tips Cache HTTP responses if the log API supports it to reduce bandwidth. Use selective fields in the HTTP Request’s query parameters to limit payload size. Pro Tips: Store a rolling hash list in external Redis or DB for large-scale deduplication. Add a second IF branch to auto-resolve Jira tickets when an error disappears for X hours. Use Slack or Microsoft Teams nodes in parallel to broaden alert coverage. This is a community-contributed n8n workflow template provided “as-is.” Thoroughly test in a non-production environment before deploying to production.

Decodo Amazon Product Recommender delivers instant, AI-powered shopping recommendations directly through Telegram. Send any product name and receive Amazon product analysis featuring price comparisons, ratings, sales data, and categorized recommendations (budget, premium, best value) in under 40 seconds—eliminating hours of manual research. Why Use This Workflow? Time Savings: Reduce product research from 45+ minutes to under 30 seconds Decision Quality: Compare 20+ products automatically with AI-curated recommendations Zero Manual Work: Complete automation from message input to formatted recommendations Ideal For E-commerce Entrepreneurs:** Quickly research competitor products, pricing strategies, and market trends for inventory decisions Smart Shoppers & Deal Hunters:** Get instant product comparisons with sales volume data and discount tracking before purchasing Product Managers & Researchers:** Analyze Amazon marketplace positioning, customer sentiment, and pricing ranges for competitive intelligence How It Works Trigger: User sends product name via Telegram (e.g., "iPhone 15 Pro Max case") AI Validation: Gemini 2.5 Flash extracts core product keywords and validates input authenticity Data Collection: Decodo API scrapes Amazon search results, extracting prices, ratings, reviews, sales volume, and product URLs Processing: JavaScript node cleans data, removes duplicates, calculates value scores, and categorizes products (top picks, budget, premium, best value, most popular) Intelligence Layer: AI generates personalized recommendations with Telegram-optimized markdown formatting, shortened product names, and clean Amazon URLs Output & Delivery: Formatted recommendations sent to user with categorized options and direct purchase links Error Handling: Admin notifications via separate Telegram channel for workflow monitoring Setup Guide Prerequisites | Requirement | Type | Purpose | |-------------|------|---------| | n8n instance | Essential | Workflow execution platform | | Decodo Account | Essential | Amazon product data scraping | | Telegram Bot Token | Essential | Chat interface for user interactions | | Google Gemini API | Essential | AI-powered product validation and recommendations | | Telegram Account | Optional | Admin error notifications | Installation Steps Import the JSON file to your n8n instance Configure credentials: Decodo API: Sign up at decodo.com → Dashboard → Scraping APIs → Web Advanced → Copy BASIC AUTH TOKEN Telegram Bot: Message @BotFather on Telegram → /newbot → Copy HTTP API token (format: 123456789:ABCdefGHI...) Google Gemini: Obtain API key from Google AI Studio for Gemini 2.5 Flash model Update environment-specific values: Replace YOUR-CHAT-ID in "Notify Admin" node with your Telegram chat ID for error notifications Verify Telegram webhook IDs are properly configured Customize settings: Adjust AI prompt in "Generate Recommendations" node for different output formats Set character limits (default: 2500) for Telegram message length Test execution: Send test message to your Telegram bot: "iPhone 15 Pro" Verify processing status messages appear Confirm recommendations arrive with properly formatted links Customization Options Basic Adjustments: Character Limit**: Modify 2500 in AI prompt to adjust response length (Telegram max: 4096) Advanced Enhancements: Multi-language Support**: Add language detection and translation nodes for international users Price Tracking**: Integrate Google Sheets to log historical prices and trigger alerts on drops Image Support**: Enable Telegram photo messages with product images from scraping results Troubleshooting Common Issues: | Problem | Cause | Solution | |---------|-------|----------| | "No product detected" for valid inputs | AI validation too strict or ambiguous query | Add specific product details (model number, brand) in user input | | Empty recommendations returned | Decodo API rate limit or Amazon blocking | Wait 60 seconds between requests; verify Decodo account status | | Telegram message formatting broken | Special characters in product names | Ensure Telegram markdown mode is set to "Markdown" (legacy) not "MarkdownV2" | Use Case Examples Scenario 1: E-commerce Store Owner Challenge: Needs to quickly assess competitor pricing and product positioning for new inventory decisions without spending hours browsing Amazon Solution: Sends "wireless earbuds" to bot, receives categorized analysis of 20+ products with price ranges ($15-$250), top sellers, and discount opportunities Result: Identifies $35-$50 price gap in market, sources comparable product, achieves 40% profit margin Scenario 2: Smart Shopping Enthusiast Challenge: Wants to buy a laptop backpack but overwhelmed by 200+ Amazon options with varying prices and unclear value propositions Solution: Messages "laptop backpack" to bot, gets AI recommendations sorted by budget ($30), premium ($50+), best value (highest discount + good ratings), and most popular (by sales volume) Result: Purchases "Best Value" recommendation with 35% discount, saves $18 and 45 minutes of research time Created by: Khaisa Studio Category: AI | Productivity | E-commerce | Tags: amazon, telegram, ai, product-research, shopping, automation, gemini Need custom workflows? 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Description This workflow automatically scans companies for signs of financial distress across filings, insolvency registers, and financial news. It helps procurement, credit, and risk teams detect early warning signals before a supplier or partner defaults. Overview This workflow uses Bright Data to scrape financial filings, insolvency registers, and news sources for distress signals like bankruptcy, restructuring, or payment defaults. AI classifies the type and severity of distress, applies probability weighting and confidence guardrails, then generates structured business decisions — including: Supplier Monitoring risk status Onboarding Approval recommendations Portfolio Exposure classifications All outputs are logged into Google Sheets for tracking and auditability. Tools Used n8n**: Automation platform orchestrating the workflow Bright Data**: Scrapes filings, insolvency registers, and financial news without getting blocked OpenRouter**: AI-powered distress classification, risk scoring, and business decision generation Google Sheets**: Logs supplier risk status, onboarding decisions, portfolio exposure, and errors How to Install 1. Import the Workflow Download the .json file and import it into your n8n instance. 2. Configure Bright Data Add your Bright Data API credentials to all Bright Data nodes. 3. Configure OpenRouter Add your OpenRouter API key for AI distress classification and decision generation. 4. Set Up Google Sheets Create a spreadsheet following the "Google Sheets Setup" sticky note inside the workflow. Connect each Google Sheets node to your document. 5. Customize Edit the configuration node to define: Target company Country Risk indicators Monitoring scope Use Cases Procurement Teams Monitor supplier financial health and get alerts before disruptions hit your supply chain. Credit Risk Analysts Screen new vendors or partners for bankruptcy signals and insolvency red flags. Onboarding Workflows Automate go/no-go decisions for new supplier or partner approvals. Portfolio Managers Track financial exposure across your vendor or investment portfolio. Finance Teams Detect early signs of distress in key business relationships before they become critical. Connect with Me Website: https://www.nofluff.online YouTube: https://www.youtube.com/@YaronBeen/videos LinkedIn: https://www.linkedin.com/in/yaronbeen/ Get Bright Data: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) Tags #n8n #automation #brightdata #webscraping #creditrisk #financialdistress #riskmanagement #suppliermonitoring #supplychainrisk #insolvency #bankruptcy #duediligence #vendorscreening #portfoliorisk #financialanalysis #n8nworkflow #workflow #nocode #businessintelligence #riskassessment #creditanalysis #procurementautomation #supplierrisk #financialmonitoring #earlywarning

This n8n workflow template automatically processes phone interview transcripts using AI to evaluate candidates against specific criteria and saves the results to Google Sheets. Perfect for HR departments, recruitment agencies, or any business conducting phone screenings. What This Workflow Does This automated workflow: Receives phone interview transcripts via webhook Uses OpenAI GPT models to analyze candidate responses against predefined qualification criteria Extracts key information (name, phone, location, qualification status) Automatically saves structured results to a Google Sheet for easy review and follow-up The workflow is specifically designed for driving job interviews but can be easily adapted for any position with custom evaluation criteria. Tools & Services Used N8N** - Workflow automation platform OpenAI API** - AI-powered transcript analysis (GPT-4o-mini) Google Sheets** - Data storage and management Webhook** - Receiving transcript data Prerequisites Before implementing this workflow, you'll need: N8N Instance - Self-hosted or cloud version OpenAI API Account - For AI transcript processing Google Account - For Google Sheets integration Phone Interview System - That can send webhooks (like Vapi.ai) Step-by-Step Setup Instructions Step 1: Set Up OpenAI API Access Visit OpenAI's API platform Create an account or log in Navigate to API Keys section Generate a new API key Copy and securely store your API key Step 2: Create Your Google Sheet Option 1: Use Our Pre-Made Template (Recommended) Copy our template: Driver Interview Results Template Click "File" → "Make a copy" to create your own version Rename it as desired Copy your new sheet's URL - you'll need this for the workflow Option 2: Create From Scratch Go to Google Sheets Create a new spreadsheet Name it "Driver Interview Results" (or your preferred name) Set up the following column headers in row 1: A1: name B1: phone C1: cityState D1: qualifies E1: reasoning Copy the Google Sheet URL - you'll need this for the workflow Step 3: Import and Configure the N8N Workflow Import the Workflow Copy the workflow JSON from the template In your N8N instance, go to Workflows → Import from JSON Paste the JSON and import Configure OpenAI Credentials Click on either "OpenAI Chat Model" node Set up credentials using your OpenAI API key Test the connection to ensure it works Configure Google Sheets Integration Click on the "Save to Google Sheets" node Set up Google Sheets OAuth2 credentials Select your spreadsheet from the dropdown Choose the correct sheet (usually "Sheet1") Update the Webhook Click on the "Webhook" node Note the webhook URL that n8n generates This URL will receive your transcript data Step 4: Customize Evaluation Criteria The workflow includes predefined criteria for a Massachusetts driving job. To customize for your needs: Click on the "Evaluate Candidate" node Modify the system message to include your specific requirements Update the evaluation criteria checklist Adjust the JSON output format if needed Current Evaluation Criteria: Valid Massachusetts driver's license No felony convictions Clean driving record (no recent tickets/accidents) Willing to complete background check Can pass drug test (including marijuana) Available full-time Monday-Friday Lives in Massachusetts Step 5: Connect to Vapi.ai (Phone Interview System) This workflow is specifically designed to work with Vapi.ai's phone interview system. Here's how to connect it: Setting Up the Vapi Integration Copy Your N8N Webhook URL In your n8n workflow, click on the "Webhook" node Copy the webhook URL (it should look like: https://your-n8n-instance.com/webhook-test/351ffe7c-69f2-4657-b593-c848d59205c0) Configure Your Vapi Assistant Log into your Vapi.ai dashboard Create or edit your phone interview assistant In the assistant settings, find the "Server" section Set the Server URL to your n8n webhook URL Set timeout to 20 seconds (as configured in the workflow) Configure Server Messages In your Vapi assistant settings, enable these server messages: end-of-call-report transcript[transcriptType="final"] Set Up the Interview Script Use the provided interview script in your Vapi assistant (found in the workflow's system message) This ensures consistent data collection for the AI evaluation Expected Data Format from Vapi The workflow expects Vapi to send data in this specific format: { "body": { "message": { "artifact": { "transcript": "AI: Hi. Are you interested in driving for Bank of Transport?\nUser: Yes.\nAI: Great. Before we go further..." } } } } Vapi Configuration Checklist ✅ Webhook URL set in Vapi assistant server settings ✅ Server messages enabled: end-of-call-report, transcript[transcriptType="final"] ✅ Interview script configured in assistant ✅ Assistant set to send webhooks on call completion Alternative Phone Systems If you're not using Vapi.ai, you can adapt this workflow for other phone systems by: Modifying the "Edit Fields2" node to extract transcripts from your system's data format Updating the webhook data structure expectations Ensuring your phone system sends the complete interview transcript Step 6: Test the Workflow Test with Sample Data Use the "Execute Workflow" button with test data Verify that data appears correctly in your Google Sheet Check that the AI evaluation logic works as expected End-to-End Testing Send a test webhook with a real transcript Monitor each step of the workflow Confirm the final result is saved to Google Sheets Workflow Node Breakdown Webhook - Receives transcript data from your phone system Edit Fields2 - Extracts the transcript from the incoming data Evaluate Candidate - AI analysis using GPT-4o-mini to assess qualification Convert to JSON - Ensures proper JSON formatting with structured output parser Save to Google Sheets - Automatically logs results to your spreadsheet Customization Options Modify Evaluation Criteria Edit the system prompt in the "Evaluate Candidate" node Add or remove qualification requirements Adjust the scoring logic Change Output Format Modify the JSON schema in the "Structured Output Parser" node Update Google Sheets column mapping accordingly Add Additional Processing Insert nodes for email notifications Add Slack/Discord alerts for qualified candidates Integrate with your CRM or ATS system Troubleshooting Common Issues: OpenAI API Errors**: Check API key validity and billing status Google Sheets Not Updating**: Verify OAuth permissions and sheet access Webhook Not Receiving Data**: Confirm URL and POST format from your phone system AI Evaluation Inconsistencies**: Refine the system prompt with more specific criteria Usage Tips Monitor Token Usage**: OpenAI charges per token, so monitor your usage Regular Review**: Periodically review AI evaluations for accuracy Backup Data**: Export Google Sheets data regularly for backup Privacy Compliance**: Ensure transcript handling complies with local privacy laws Need Help with Implementation? For professional setup, customization, or troubleshooting of this workflow, contact: Robert - Ynteractive Solutions Email**: rbreen@ynteractive.com Website**: www.ynteractive.com LinkedIn**: linkedin.com/in/robert-interactive Specializing in AI-powered workflow automation, business process optimization, and custom integration solutions.

Automated AWS IAM Compliance Workflow for MFA Enforcement and Access Key Deactivation > This workflow leverages AWS IAM APIs and n8n automation to ensure strict security compliance by continuously monitoring IAM users for MFA (Multi-Factor Authentication) enforcement. .jpg) Who’s it for This workflow is designed for DevOps, Security, or Cloud Engineers responsible for maintaining IAM security compliance in AWS accounts. It's ideal for teams who want to enforce MFA usage and automatically disable access for non-compliant IAM users. How it works / What it does This automated workflow performs a daily check to detect IAM users without an MFA device and deactivate their access keys. Step-by-step: Daily scheduler: Triggers the workflow once a day. Get many users: Retrieves a list of all IAM users in the account. Get IAM User MFA Devices: Calls AWS API to get MFA device info for each user. Filter out IAM users with MFA: Keeps only users without any MFA device. Send warning message(s): Sends Slack alerts for users who do not have MFA enabled. Get User Access Key(s): Fetches access keys for each non-MFA user. Parse the list of user access key(s): Extracts and flattens key information like AccessKeyId, Status, and UserName. Filter out inactive keys: Keeps only active access keys for further action. Deactivate Access Key(s): Calls AWS API to deactivate each active key for non-MFA users. How to set up Configure AWS credentials in your environment (IAM role or AWS access key with required permissions). Connect Slack via the Slack node for alerting (set channel and credentials). Set the scheduler to your preferred frequency (e.g., daily at 9AM). Adjust any Slack message template or filtering conditions as needed. Requirements IAM user or role credentials with the following AWS IAM permissions: iam:ListUsers iam:ListMFADevices iam:ListAccessKeys iam:UpdateAccessKey Slack credentials (Bot token with chat:write permission). n8n environment with: Slack integration AWS credentials (set via environment or credentials manager) How to customize the workflow Alert threshold**: Instead of immediate deactivation, you can delay action (e.g., alert first, wait 24h, then disable). Change notification channel**: Modify the Slack node to send alerts to a different channel or add email integration. Whitelist exceptions**: Add a Set or IF node to exclude specific usernames (e.g., service accounts). Add audit logging**: Use Google Sheets, Airtable, or a database to log which users were flagged or had access disabled. Extend access checks**: Include console password check (GetLoginProfile) if needed.

Overview This comprehensive n8n workflow automates the generation and distribution of detailed monthly technical support reports from GLPI (IT Service Management platform). The workflow intelligently calculates SLA compliance, analyzes technician performance, and delivers professionally formatted HTML reports via email. ✨ Key Features Intelligent SLA Calculation Business Hours Tracking: Automatically calculates resolution time considering only working hours (excludes weekends and lunch breaks) Configurable Schedule: Customizable work hours (default: 8 AM - 12 PM, 1 PM - 6 PM) Dynamic SLA Monitoring: Real-time compliance tracking with configurable thresholds (default: 24 hours) Visual Indicators: Color-coded alerts for critical SLA breaches and high-volume warnings Comprehensive Reporting General Summary: Total cases, open, in-progress, resolved, and closed tickets Performance Metrics: Total and average resolution hours in both decimal and formatted (hours/minutes) display Technician Breakdown: Individual performance analysis per technician including case distribution and SLA compliance Smart Alerts: Automatic warnings for high case volumes (>100 in-progress) and critical SLA levels (<50%) Professional Email Delivery Responsive HTML Design: Mobile-optimized email templates with elegant styling Dynamic Content: Conditional formatting based on performance metrics Automatic Scheduling: Monthly execution on the 6th day to ensure accurate SLA measurement 💼 Business Benefits Time Savings Eliminates Manual Work: Saves 2-4 hours per month previously spent compiling reports manually Automated Data Collection: No more exporting CSVs or copying data between systems One-Click Setup: Configure once and receive reports automatically every month Improved Decision Making Real-Time Insights: Identify bottlenecks and performance issues immediately Technician Accountability: Clear visibility into individual and team performance SLA Compliance Tracking: Proactively manage service level agreements before they become critical Enhanced Communication Stakeholder Ready: Professional reports suitable for management presentations Consistent Format: Standardized metrics ensure month-over-month comparability Instant Distribution: Automatic email delivery to relevant stakeholders 🔧 Technical Specifications Requirements n8n instance (self-hosted or cloud) GLPI server with API access enabled Gmail account (or any SMTP-compatible email service) GLPI API credentials (App-Token and User credentials) Configuration Points Variables Node: Server URL, API tokens, entity name, work hours, SLA limits Schedule Trigger: Monthly execution timing (default: 6th of each month) Email Recipient: Target email address for report delivery Date Range Logic: Automatic previous month calculation Data Processing Retrieves up to 999 tickets per execution (configurable) Filters by entity and date range Excludes weekends and non-business hours from calculations Groups data by technician for detailed analysis 📋 Setup Instructions Prerequisites GLPI Configuration: Enable API and configure the Tickets panel with required fields (ID, -Title, Status, Opening Date, Closing Date, Resolution Date, Priority, Requester, Assigned To) API Credentials: Create Basic Auth credentials in n8n for GLPI API access Email Authentication: Set up Gmail OAuth2 or SMTP credentials in n8n Implementation Steps Import the workflow JSON into your n8n instance Configure the Variables node with your GLPI server details and business hours Set up GLPI API credentials in the HTTP Request nodes Configure email credentials in the Gmail node Update the recipient email address Test the workflow manually before enabling the schedule Activate the workflow for automatic monthly execution 🎯 Use Cases IT Support Teams: Track helpdesk performance and SLA compliance Service Managers: Monitor team productivity and identify training needs Executive Reporting: Provide high-level summaries to stakeholders Resource Planning: Identify workload distribution and capacity issues Compliance Auditing: Maintain historical records of SLA performance 📈 ROI Impact Time Savings: 24-48 hours annually in manual reporting eliminated Error Reduction: Eliminates human calculation errors in SLA tracking Faster Response: Early alerts enable proactive issue resolution Better Visibility: Data-driven insights improve team management

How it works This workflow automatically detects at-risk customers by listening for inactivity signals from Mixpanel, scoring their churn risk, syncing everything to HubSpot, creating a prioritized ClickUp follow-up task, and alerting the customer success team on Slack — all without any manual effort. Step-by-step Trigger & parse** Mixpanel Webhook – Receives POST events from Mixpanel when a customer inactivity event is fired (e.g. user_inactive_30_days). Supports both single events and batch arrays. Parse Mixpanel Payload – Normalizes the raw Mixpanel payload, extracting email, name, user ID, company, plan, MRR, inactive days, last active date, last feature used, total sessions, location, and CSM assignment. Skips records with no identifying information. Event validation** Is Churn Event – Confirms the event name matches user_inactive_30_days, has no parse errors, and includes a valid email before proceeding. Risk scoring** Enrich User Data – Calculates churn score (Medium / High / Critical) and task priority based on inactive days, sets a due date of 24 hours and a follow-up date of 3 days, and builds the alert subject line with risk level. CRM lookup** HubSpot Find Contact – Searches HubSpot for an existing contact by email, retrieving name, company, lifecycle stage, owner, lead status, and phone. Contact Found – Routes to the full retention flow if a contact exists, or to a Slack fallback alert if no contact is found. Slack Contact Not Found – Posts a Slack warning to the team for manual action when no HubSpot contact matches the churn event email. Deal management** HubSpot Get Deals – Fetches all deals associated with the found HubSpot contact via the CRM Associations API. Deal Exists – Routes to update the existing deal or create a new one based on whether an associated deal is found. HubSpot Update Deal – Updates the existing deal stage to appointmentscheduled and sets the close date to the follow-up date. HubSpot Create Deal – Creates a new churn risk deal linked to the contact, with annual value (MRR × 12), deal name, and follow-up close date. Set Deal ID – Normalizes the deal ID from either the update or create branch for consistent downstream use. CRM logging & task creation** HubSpot Log Note – Posts a detailed churn risk note on the HubSpot contact record linked to both the contact and the deal, including all churn signals and MRR data. ClickUp Create Task – Creates a prioritized CS follow-up task with a full action checklist covering outreach, Mixpanel review, discovery call scheduling, and escalation criteria. Due within 24 hours. Slack CS Alert – Posts a comprehensive churn risk alert to the CS Slack channel with customer details, revenue impact, churn signals, risk score, HubSpot links, and a full action checklist. Why use this? Automatically catches every at-risk customer the moment Mixpanel detects inactivity — no manual monitoring needed Three-tier risk scoring (Medium / High / Critical) ensures the CS team prioritizes the highest-value accounts first Keeps HubSpot fully in sync by creating or updating deals and logging churn risk notes directly on the contact record Auto-creates ClickUp tasks with a structured action checklist so every churn risk has an owner and a deadline Alerts the team instantly on Slack with full context — revenue, signals, CRM links, and next steps — so they can act within hours

Stop letting email attachments pile up in your inbox. Let automation file every document the moment it arrives. Manually downloading and organising email attachments is repetitive, inconsistent, and easy to forget. This workflow checks your Outlook inbox every hour, extracts every valid attachment, uploads it to OneDrive in a structured folder hierarchy, logs the archive record to SharePoint, and marks the email as read, with zero manual intervention. How it works The workflow runs in five sequential stages: 1. Ingestion Checks Outlook every hour for unread emails. If the inbox is empty, the workflow exits cleanly with no further execution. If emails are found, they are queued for processing. 2. Per-Email Processing Emails are split and processed one at a time using a batch loop. For each email, all attachments are fetched from Outlook, 0-byte items are dropped, binary data is decoded, and a structured OneDrive folder path is computed automatically from the sender's domain and the email's received date: /Archives/{year}/{month}/{sender-domain}/ 3. Upload & Aggregation Emails with valid file attachments are uploaded to OneDrive into the computed folder path. Emails with no real attachments skip the upload step entirely and go directly to aggregation. Results are collected into a summary item containing the sender, file names, upload count, and folder path. 4. Audit Logging The upload summary is appended to a SharePoint list as a compliance audit record. Every archived email produces one row with full traceability, sender address, file names, destination folder, and timestamp. 5. Completion The email is marked as read in Outlook and tagged with the Archived category. The batch loop then moves to the next email until all emails in the current run are processed. Key benefits Automatic folder structure:** No manual folder creation needed, the archive path is derived from the sender domain and received date on every run 0-byte guard:** Inline images and empty attachment objects are filtered out before upload, so only real files reach OneDrive Audit-ready:** Every processed email produces a SharePoint list record for compliance tracking, regardless of whether attachments were found Batch loop:** Emails are processed one at a time to avoid API throttling and keep execution predictable Global error handling:** A dedicated Error Trigger catches any execution failure, sends an alert email with the execution ID and failed node name, and logs upload failures separately to the SharePoint audit list Setup Microsoft Outlook: Connect your Outlook OAuth2 credential to the Get Unread Emails, Get Email Attachments, Mark Email as Read, and Send Error Email nodes Microsoft OneDrive: Connect your OneDrive OAuth2 credential to the Upload to OneDrive node and set your target drive ID and folder path expression to {{ $json.folderPath }} Microsoft SharePoint: Connect your SharePoint OAuth2 credential to the Log to SharePoint Audit List and Log Failed Upload to SharePoint nodes, and set your Site ID and List ID Error email: Update the recipient address in the Send Error Email node (it-admin@company.fi) Activate: Turn the workflow on and run it manually once to verify the folder structure is created correctly in OneDrive Who this is for Operations and admin teams** receiving high volumes of emailed documents, invoices, contracts, and reports. that need consistent filing Finance departments** archiving supplier invoices and statements automatically without manual download and upload Compliance teams** that need a timestamped audit trail of every document received and where it was stored IT teams at SMEs** replacing manual file management with a lightweight, credential-based Microsoft 365 automation Required credentials Microsoft Outlook OAuth2 Microsoft OneDrive OAuth2 Microsoft SharePoint OAuth2 Folder structure Attachments are organized automatically: /Archives/{year}/{month}/{sender-domain}/filename.ext How to customize it Change the folder structure:** Modify the path logic in the Build Folder Path + Decode Attachment node to organise by department, document type, or any other field available on the email Filter by sender:** Add an IF node after Get Unread Emails to only process emails from specific domains or addresses Add more destinations:** Fan out from Has File Attachments? to also copy files to SharePoint document libraries or Azure Blob Storage Adjust the schedule:** Change the trigger interval from hourly to every 15 minutes or once per day depending on your email volume Extend the audit log:** Add columns to the SharePoint list for file size, content type, or a generated document ID for downstream reference

Description Transform your investment strategy with a fully automated, AI-driven trading bot. This workflow bridges the gap between AI-powered market insights and real-world trading by executing buy and sell orders directly through the Alpaca paper trading API. Designed to work in tandem with the Automated Stock Sentiment Analysis workflow, this solution takes the top-performing stocks based on daily news sentiment and automatically rebalances your portfolio. It's perfect for algorithmic traders, data-driven investors, and n8n enthusiasts who want to see their AI analysis translate into tangible actions, all while maintaining a comprehensive log of every transaction in Google Sheets. Key Features & Benefits Automated Trading Execution:** Automatically places buy and sell orders on the Alpaca paper trading platform without manual intervention. Sentiment-Driven Decisions:** Leverages the output from the sentiment analysis workflow to make informed decisions, selling positions with waning sentiment and buying into those with high positive sentiment. Dynamic Portfolio Rebalancing:** Intelligently calculates which positions to close and how to allocate the resulting funds into new, high-potential stocks. Paper Trading Ready:** Safely test and refine your trading strategies in a risk-free environment using Alpaca's paper trading API. Daily Performance Tracking:** Automatically logs your account equity and daily percentage change to a Google Sheet, giving you a clear view of your portfolio's performance. Detailed Trade Logging:** Every buy and sell order is meticulously recorded in a Google Sheet for easy review and historical analysis. Scheduled and Autonomous:** The entire process runs on a daily schedule, making it a "set and forget" solution for systematic trading. How It Works This workflow executes a sophisticated, automated trading strategy in a few key stages: Daily Kick-off & Snapshot: The workflow triggers on a daily schedule, first fetching your current Alpaca account balance and logging it to a Google Sheet to track daily performance. Strategy Formulation: It then reads the daily sentiment scores produced by the accompanying "Stock Sentiment Analysis" workflow. A Code node filters these results to identify the top four stocks with the highest positive sentiment. The Decision Engine: The core of the workflow is a custom Code node that acts as the trading brain. It: Retrieves your currently open positions from Alpaca. Compares your holdings against the day's top four sentiment stocks. Generates a "sell list" of positions you hold that are no longer in the top four. Generates a "buy list" of top-sentiment stocks that you don't yet own. Calculates the total cash value from the "sell list" and determines the exact notional value to invest in each stock on the "buy list." Trade Execution: The workflow first iterates through the "sell list" and executes a DELETE request to Alpaca for each, closing the positions. A Wait node pauses the workflow for two minutes to ensure the sell orders are filled and the account balance is updated. It then iterates through the "buy list," executing POST requests to Alpaca to purchase the new assets with the calculated funds. Record Keeping: All executed orders (both buys and sells) are merged and logged in a dedicated Google Sheet, giving you a permanent and detailed transaction history. Nodes Used Schedule Trigger HttpRequest (Alpaca API) Google Sheets Code (JavaScript) SplitOut Wait Merge This workflow is the perfect next step for anyone looking to take their AI analysis to the next level. Take the emotion out of your trading and let this bot systematically execute your data-driven strategy.

Facebook Group Auto-Moderation This workflow automatically monitors Facebook Group posts, analyzes them using AI, detects policy violations, logs incidents, notifies moderators and automatically hides high-severity posts to keep the community clean and safe. This workflow listens to new Facebook Group posts in real time, processes each post individually and sends the post content to AI for moderation.If a post violates group rules (spam, scam, hate, adult content or aggressive promotion), the workflow alerts moderators, stores the violation in Airtable and automatically hides the post if the severity is high. You receive: Real-time AI moderation of Facebook Group posts** Automatic hiding of high-risk content** Slack alerts for moderation actions** Airtable logging for audit and tracking** Ideal for Facebook Group admins who want fast, consistent and automated moderation without manual review of every post. Quick Start – Implementation Steps Connect the Facebook Group Webhook to your n8n instance. Add your Facebook Page Access Token as an environment variable. Connect OpenAI credentials for content moderation. Configure Slack for alerts and Airtable for logging. Test using sample Facebook post data. Activate the workflow. What It Does This workflow automates Facebook Group moderation: Receives new group posts via webhook. Splits and processes posts one by one. Normalizes post data (ID, message, user, time). Sends post content to AI for moderation analysis. Determines: Violation or not Category (spam, scam, hate, adult, etc.) Severity (low / medium / high) Logs violations into Airtable. Sends alerts to Slack. Automatically hides posts marked as high severity. Notifies the team whether auto-hide succeeded or failed. Who’s It For This workflow is ideal for: Facebook Group admins & moderators Community management teams Social media operations teams Platforms handling large group volumes Anyone needing automated moderation at scale Requirements to Use This Workflow To run this workflow, you need: n8n instance** (cloud or self-hosted) Facebook Group Webhook subscription** Facebook Page Access Token** OpenAI API key** Slack workspace** with API access Airtable base** + Personal Access Token How It Works Receive Facebook Post – Webhook captures new group posts. Process Posts – Posts are handled one at a time. Normalize Data – Extracts clean post and user details. AI Moderation – AI analyzes the post for rule violations. Violation Check – Determines whether action is needed. Severity Check – Only high-risk posts are auto-hidden. Hide Post – Facebook API hides the post automatically. Log & Notify – Slack alerts + Airtable records are created. Setup Steps Import the workflow JSON into n8n. Configure the Webhook node and subscribe it to your Facebook Group. Add FB\_PAGE\_ACCESS\_TOKEN in n8n environment variables. Connect OpenAI, Slack and Airtable credentials. Verify Airtable field names match the workflow mapping. Test using pinned sample data. Activate the workflow. How To Customize Nodes Customize Moderation Rules Edit the AI Content Moderation node to: Adjust strictness Add or remove categories Change severity logic Customize Slack Alerts You can add: Emojis Mentions (@channel / @here) Direct links to the Facebook post Customize Auto-Hide Logic Change the Severity High? IF node to: Auto-hide medium severity Disable auto-hide completely Add manual approval steps Add-Ons (Optional Enhancements) You can extend this workflow to: Add moderator approval before hiding posts Auto-ban repeat offenders Track user violation history Generate daily moderation summaries Add sentiment analysis Create dashboards using Airtable Interfaces Support multiple Facebook Groups Use Case Examples 1\. Spam Control Automatically hide promotional or scam posts. 2\. Community Safety Detect hate or adult content instantly. 3\. Moderator Efficiency Reduce manual review workload. 4\. Audit & Compliance Maintain a clear violation history in Airtable. 5\. Large Group Management Scale moderation without adding moderators. Troubleshooting Guide | Issue | Possible Cause | Solution | |----------------------|----------------------------|-------------------------------------------------------| | No posts received | Webhook not subscribed | Verify Facebook webhook setup | | AI result missing | OpenAI error | Check API key & rate limits | | Post not hidden | Token permission issue | Verify Page Access Token permissions | | Slack alert not sent | Invalid Slack credentials | Reconnect Slack API | | Airtable error | Field mismatch | Match Airtable column names exactly | Need Help? If you need help extending this workflow with multi-group moderation, advanced AI rules, dashboards or production-scale automation, our n8n automation experts at WeblineIndia can assist with custom workflow design and deployment.

This workflow automatically monitors and reports data quality for any SQL table using configurable checks and thresholds. It evaluates key metrics—including null values, duplicate records, row count anomalies, and outliers—and assigns a clear PASS, WARN, or FAIL status. Designed for efficiency, the workflow dynamically injects table and column names from a central Config node, so you don’t need to edit SQL queries manually. All checks run in parallel, and results are consolidated into a structured HTML report with clear status indicators. The report is automatically sent via email and logged into Google Sheets for historical tracking, auditing, and trend analysis. ⚙️ Setup Update the Config node with your table name, column names, thresholds, and email recipient. Connect your database credentials (Postgres/MySQL) in all query nodes. Set up Gmail or SMTP credentials in the email node. Connect your Google Sheets account and ensure required columns exist. Activate the workflow (runs daily by default, can be customized). This workflow is ideal for data analysts and analytics engineers who want a lightweight, automated solution to proactively monitor data quality without exporting large datasets or building complex pipelines.

Zoho CRM Sales Cycle Performance Analyzer & Improver This workflow automatically analyzes your Zoho CRM deal cycles with AI-powered intelligence, compares them against historical performance data from Google Sheets, and delivers actionable insights to Slack. It identifies bottlenecks, predicts outcomes, analyzes sentiment, generates smart recommendations, creates data visualizations, and builds a historical dataset for future intelligence—all without manual reporting. Quick Implementation Steps Connect Accounts: Set up credentials for Zoho CRM, Google Sheets, Slack, and OpenAI in n8n. Prepare Sheet: Create a Google Sheet with headers: Deal_Name, Stage, Created_Time, Closed_Time (or Modified_Time). Configure Nodes: Zoho Trigger: Ensure it pulls your deals. Google Sheets: Link your "Historical Data" sheet to both the "Fetch" and "Log" nodes. OpenAI Nodes: Configure your OpenAI API key for AI analysis. Slack: Select your #sales-insights channel. Activate: Turn on the workflow to start receiving AI-enhanced real-time insights on deal closure. What It Does This n8n workflow serves as an AI-powered automated data analyst for your sales team. Whenever a deal is fetched from Zoho CRM, the workflow first filters for relevance (e.g., recently closed or modified deals). It then cross-references this specific deal against your historical sales data stored in Google Sheets to calculate key performance metrics like "Days to Close" and "Stage Dwell Time." 🤖 AI-Enhanced Features: Sentiment Analysis**: Analyzes deal descriptions and communications for emotional tone and risk indicators Predictive Analytics**: Uses historical patterns to predict win probability and expected close dates Smart Recommendations**: Generates AI-powered, data-driven process improvement suggestions Data Visualization**: Creates charts and trend analysis for performance metrics Performance Scoring**: Calculates comprehensive performance scores and risk levels Beyond simple calculations, the workflow applies AI intelligence to generate human-readable insights. It determines if a deal was faster or slower than average, identifies which stage caused delays, analyzes sentiment for risk assessment, predicts outcomes, and suggests specific process improvements based on the data. Finally, it closes the loop by broadcasting these AI-enhanced focused insights to a Slack channel for immediate team visibility and logging the new deal's performance back into Google Sheets. This ensures your historical dataset grows richer and more accurate with every closed deal, continuously improving the quality of future AI predictions. Who’s It For Sales Managers**: To monitor team performance and identify coaching opportunities without digging into CRM reports. RevOps Professionals**: To automate the collection of cycle-time data and spot process bottlenecks. Small Business Owners**: To get enterprise-grade sales analytics without hiring a data analyst. Sales Teams**: To get immediate feedback on their wins and losses, fostering a culture of continuous improvement. Prerequisites n8n Instance**: A self-hosted or cloud version of n8n. Zoho CRM Account**: With permission to read Deals. Google Account**: Access to Google Sheets. Slack Workspace**: Permission to post messages to channels. OpenAI Account**: API access for GPT-4 model integration. Google Sheet**: A formatted sheet to store and retrieve historical deal data. How to Use & Setup 1. Google Sheet Setup Create a new Google Sheet. In the first row, add the following headers (the workflow tries to match various case formats, but these are recommended): Deal_Name Stage Created_Time Closed_Time Stage_History (Optional, for advanced dwell time analysis) 2. Configure Credentials In your n8n dashboard, ensure you have authenticated: Zoho CRM Google Sheets Slack OpenAI** (for AI-powered analysis) 3. Node Configuration Zoho CRM - Deal Trigger**: This node is set to "Get All" deals. You might want to adjust this to a Trigger node that listens for "Deal Updated" or "Deal Created" events for real-time automation, or keep it as a scheduled poll. Filter Recent Deals (Code Node)**: Currently configured to process deals closed in the last 7 days and limit to 10 items. No changes needed unless you want to process larger batches. Fetch Historical Averages (Google Sheets)**: Select your Credential. Resource: Document -> Select your prepared Sheet. Operation: Get Many ("GetAll" or "Read"). Return All: True. AI Sentiment Analysis (OpenAI)**: Select your OpenAI Credential. Model: GPT-4 (recommended for best results). Automatically analyzes deal sentiment and emotional tone. AI Predictive Analytics (OpenAI)**: Uses historical data to predict outcomes and win probabilities. Provides risk assessment and expected close dates. AI Smart Recommendations (OpenAI)**: Generates intelligent, context-aware recommendations. Prioritizes suggestions based on impact and feasibility. Advanced Data Visualization**: Creates charts for cycle trends, stage distribution, and performance metrics. Generates data for visual analysis and reporting. Slack Notification**: Select your Credential. Channel: Enter the name of your channel (e.g., #sales-insights). Now includes AI-enhanced insights in the message format. Log to Historical Sheet (Google Sheets)**: Select your Credential. Resource: Document -> Select the same sheet as above. Operation: Append. 4. Running the Workflow Test**: Click "Execute Workflow" manually to test with the "Zoho CRM - Deal Trigger" (conceptually acting as a manual fetch here). Production*: Switch the trigger to a legitimate *Schedule Trigger (e.g., run every morning) or a Zoho CRM Trigger (Real-time) to automate the process. How To Customize Nodes Adjusting the Risk/Insight Logic The core intelligence lives in the Analyze Cycle code node. You can modify the JavaScript here to change thresholds. Change "Slow" Threshold**: Look for if (totalDays > avgDays * 1.25). Change 1.25 to 1.5 to only flag deals that are 50% slower than average. custom Suggestions**: Add new if statements in the // Process improvement suggestions section to add your own coaching advice based on specific stages or owners. Customizing AI Prompts The AI nodes use specific prompts that can be customized: AI Sentiment Analysis**: Modify the prompt in the OpenAI node to focus on specific aspects (e.g., competitor mentions, pricing concerns). AI Predictive Analytics**: Adjust the prediction criteria or add custom factors relevant to your business. AI Smart Recommendations**: Customize the recommendation style or focus on specific business objectives. Changing the Output Format The Slack Notification node uses a template. You can customize the message layout by editing the Text field. You can use standard Slack markdown (e.g., bold, italics) and add variables from specific fields in your CRM (like "Lead Source" or "Competitor"). AI Model Configuration Model Selection**: Change from GPT-4 to GPT-3.5-turbo for faster processing (slightly less accurate). Temperature Adjustment**: Modify creativity level in AI responses (0.0 = deterministic, 1.0 = highly creative). Token Limits**: Adjust response length for more detailed or concise AI outputs. Add‑ons To extend the functionality of this workflow, consider adding: Weekly Report Email**: Add an "Email" node at the end to send a summary digest to the CEO every Friday. Manager Alert**: Add an IF node before Slack to tag the Sales Manager (@user) only if the totalDays exceeds 60 days or if AI risk level is "High". CRM Update: Write the calculated "Days to Close" and **AI predictions back into custom fields in Zoho CRM so you can report on it directly inside Zoho. Dashboard Integration**: Send visualization data to tools like Grafana or Power BI for real-time dashboards. Competitor Analysis**: Add AI node to analyze deal descriptions for competitor mentions and market trends. Use Case Examples 1. Post-Mortem on Lost Deals When a deal is marked "Closed Lost," the workflow calculates how long it sat in each stage. AI sentiment analysis detects negative communication patterns, and the Slack alert highlights this bottleneck, prompting a review of the negotiation strategy. 2. Celebrating Efficiency A deal closes in 15 days when the average is 45. The workflow identifies this anomaly, calculates it is "66% faster than average," AI predicts high success factors, and posts a celebratory message, asking the rep to share what worked. 3. Reviewing Stalled Deals By changing the trigger to look for open deals, you can use this logic to flag active deals that have already exceeded the average winning cycle time, signaling they are "at risk." AI predictive analytics provides win probability for each stalled deal. 4. Onboarding Usage New sales reps can see immediate feedback on their deals compared to the company historical average, helping them calibrate their pace without constant manager intervention. AI recommendations provide personalized coaching tips. 5. Product/Service Specific Analysis Duplicate the workflow and filter by "Product Type" in the Code node. Maintain separate Google Sheets for "Enterprise" vs "SMB" deal cycles to get more accurate baselines for different business lines. AI sentiment analysis can identify product-specific communication patterns. 6. AI-Enhanced Deal Scoring NEW: The workflow now provides AI-powered deal scoring, sentiment-based risk assessment, and predictive win probabilities, enabling sales teams to prioritize high-potential deals and focus resources effectively. Troubleshooting Guide | Issue | Possible Cause | Solution | | :--- | :--- | :--- | | No insights generated | Google Sheet is empty or headers don't match. | Ensure your Google Sheet has at least one row of valid historical data with matching headers (Created_Time, Closed_Time). | | "Invalid Date" errors | Date formats in Zoho or Sheets are inconsistent. | Check that your system regional settings match. The Code node expects standard date strings. | | Slack message is empty | Deal_Name or sensitive data is missing. | The "Check Valid Data" node filters out incomplete records. Ensure your test deals have a Name and timestamps. | | Workflow times out | Too many deals being processed. | The "Filter Recent Deals" node limits to 10 items. If you remove this limit, n8n may timeout on large datasets. Keep the batch size small. | | Google Sheets Error | Authentication or Sheet ID missing. | Re-authenticate your Google account and re-select the Document and Sheet from the list in the node settings. | | AI nodes not working | OpenAI API key missing or invalid. | Configure your OpenAI credentials in n8n settings and ensure the API key has sufficient credits. | | AI responses too slow | Using GPT-4 with large datasets. | Switch to GPT-3.5-turbo for faster processing, or reduce the amount of data sent to AI nodes. | | Sentiment analysis inaccurate | Limited deal description data. | Ensure your Zoho deals have meaningful descriptions and communication logs for better sentiment analysis. | | Predictions seem wrong | Insufficient historical data. | AI predictions improve with more historical data. Ensure at least 50+ historical deals for accurate predictions. | Need Help? Setting up custom analytics or complex logic in Code nodes can be tricky. If you need help tailoring this workflow to your specific business rules, creating advanced Add-ons or integrating with other CRMs: Contact WeblineIndia We specialize in building robust business process automation solutions. Whether you need a simple tweak or a fully custom enterprise automation suite, our experts are ready to assist. Reach out to us today to unlock the full potential of your sales data!