Workflow Templates

Quick overview This workflow manually runs to review Salesforce opportunities listed in Google Sheets, evaluates each deal against sales playbook rules using Google Gemini, updates compliance fields in Salesforce, alerts in Slack for low scores, and logs results back to Google Sheets. How it works Starts manually and loads a list of opportunities from Google Sheets. Processes each row in batches and fetches the latest matching Opportunity record from Salesforce. Adds predefined playbook rules (including a passing score) and combines them with the opportunity data. Checks whether at least two meetings are completed, and if not, auto-generates a non-compliant evaluation without using AI. For opportunities that pass the meeting minimum, sends the opportunity details and rules to Google Gemini to produce a compliance decision, missing steps, score, reason, and risk level, then normalizes the response into structured fields. Updates the Opportunity in Salesforce with compliance status, risk, score, and timestamp, and sends a Slack alert when the compliance score is below 70. For compliant opportunities, writes the final status and compliance metrics back to a Google Sheets results tab (append or update by opportunity_id). Setup Connect credentials for Google Sheets OAuth2, Salesforce OAuth2, Slack OAuth2, and the Google Gemini (PaLM) API. Ensure your input Google Sheet includes the required columns (opportunity_id, stage, meetings_done, demo_done, approval_status, rep_name) and update the spreadsheet and sheet IDs if you use a different file. Verify the Salesforce Opportunity custom fields exist and match the API names used (Compliance_Status__c, Compliance_Risk__c, Compliance_Checked_At__c, Compliance_Score__c). Set the target Slack channel in the Slack message step and adjust the alert threshold or message text if needed. Review and edit the playbook rules and passing_score values to match your sales process before activating the workflow. Additional info How To Customize Nodes Modify Playbook Rules Edit the “Define Playbook Rules” node Adjust rules and passing score based on your process Change AI Evaluation Logic Update the prompt inside “Evaluate Compliance” Customize scoring or decision criteria Adjust Slack Alerts Modify message format in “Send Slack Alert” Change channel ID Update Salesforce Fields Edit field mappings in: “Update Salesforce Compliance Fields” Change Risk Threshold Modify condition in: “Check High Risk Deals” node Add-ons (Extend This Workflow) You can enhance this workflow with: Email notifications for managers Dashboard integration (e.g., BI tools) Auto-task creation in Salesforce for non-compliant deals Weekly compliance summary reports Integration with CRM notes or activity logs Use Case Examples Here are some practical ways to use this workflow: Sales Playbook Enforcement Ensure reps follow required steps before advancing deals Deal Risk Identification Automatically flag risky opportunities early Sales Coaching Identify gaps like missing demos or meetings Pipeline Quality Monitoring Maintain high-quality opportunities in pipeline Compliance Reporting Track historical compliance trends in Google Sheets There can be many more use cases depending on your sales process and business needs. Troubleshooting Guide | Issue | Possible Cause | Solution | |------|--------------|---------| | No data fetched from Google Sheets | Incorrect Sheet ID or permissions | Reconnect Google Sheets and verify access | | Salesforce data not updating | Invalid credentials or field mapping | Check Salesforce API credentials and field IDs | | AI response is empty | API issue or incorrect parsing | Verify Gemini API key and response handling | | Slack alert not sent | Wrong channel ID or permissions | Confirm Slack bot access and channel ID | | Incorrect compliance score | Prompt logic mismatch | Review and update AI prompt logic | | Workflow stops mid-way | Node execution error | Check execution logs in n8n | Need Help? If you need help setting up, customizing or extending this workflow, we’re here to assist. Whether you want to: Tailor this workflow to your sales process Add advanced automations Integrate with more tools Build custom AI-powered workflows Reach out to WeblineIndia to hire n8n workflow developers for expert support and development services. We can help you turn your business processes into fully automated, intelligent workflows.

Facebook Page Comment Moderation Scoreboard → Team Report This workflow automatically monitors Facebook Page comments, analyzes them using AI for intent, toxicity & spam, stores moderation results in a database and sends a clear summary report to Slack and Telegram. This workflow runs every few hours to fetch Facebook Page comments and analyze them using OpenAI. Each comment is classified as positive, neutral or negative, checked for toxicity, spam & abusive language and then stored in Supabase. A simple moderation summary is sent to Slack and Telegram. You receive: Automated Facebook comment moderation AI-based intent, toxicity, and spam detection Database logging of all moderated comments Clean Slack & Telegram summary reports Ideal for teams that want visibility into comment quality without manually reviewing every message. Quick Start – Implementation Steps Import the workflow JSON into n8n. Add your Facebook Page access token to the HTTP Request node. Connect your OpenAI API key for comment analysis. Configure your Supabase table for storing moderation data. Connect Slack and Telegram credentials and choose target channels. Activate the workflow — moderation runs automatically. What It Does This workflow automates Facebook comment moderation by: Running on a scheduled interval (every 6 hours). Fetching recent comments from a Facebook Page. Preparing each comment for AI processing. Sending comments to OpenAI for moderation analysis. Extracting structured moderation data: Comment intent Toxicity score Spam detection Abusive language detection Flagging risky comments based on defined rules. Storing moderation results in Supabase. Generating a summary report. Sending the report to Slack and Telegram. This ensures consistent, repeatable moderation with no manual effort. Who’s It For This workflow is ideal for: Social media teams Community managers Marketing teams Customer support teams Moderation and trust & safety teams Businesses managing high-volume Facebook Pages Anyone wanting AI-assisted comment moderation Requirements to Use This Workflow To run this workflow, you need: n8n instance** (cloud or self-hosted) Facebook Page access token** OpenAI API key** Supabase project and table** Slack workspace** with API access Telegram bot** and chat ID Basic understanding of APIs and JSON (helpful but not required) How It Works Scheduled Trigger – Workflow starts automatically every 6 hours. Fetch Comments – Facebook Page comments are retrieved. Prepare Data – Comments are formatted for processing. AI Moderation – OpenAI analyzes each comment. Normalize Results – AI output is cleaned and standardized. Store Data – Moderation results are saved in Supabase. Aggregate Stats – Summary statistics are calculated. Send Alerts – Reports are sent to Slack and Telegram. Setup Steps Import the workflow JSON into n8n. Open the Fetch Facebook Page Comments node and add: Page ID Access token Connect your OpenAI account in the AI moderation node. Create a Supabase table and map fields correctly. Connect Slack and select a reporting channel. Connect Telegram and set the chat ID. Activate the workflow. How To Customize Nodes Customize Flagging Rules Update the normalization logic to: Change toxicity thresholds Flag only spam or abusive comments Add custom moderation rules Customize Storage You can extend Supabase fields to include: Language AI confidence score Reviewer notes Resolution status Customize Notifications Slack and Telegram messages can include: Emojis Mentions (@channel) Links to Facebook comments Severity labels Add-Ons (Optional Enhancements) You can extend this workflow to: Auto-hide or delete toxic comments Reply automatically to positive comments Detect language and region Generate daily or weekly moderation reports Build dashboards using Supabase or BI tools Add escalation alerts for high-risk comments Track trends over time Use Case Examples 1. Community Moderation Automatically identify harmful or spam comments. 2. Brand Reputation Monitoring Spot negative sentiment early and respond faster. 3. Support Oversight Detect complaints or frustration in comments. 4. Marketing Insights Measure positive vs negative engagement. 5. Compliance & Auditing Keep historical moderation logs in a database. Troubleshooting Guide | Issue | Possible Cause | Solution | |-----|---------------|----------| | No comments fetched | Invalid Facebook token | Refresh token & permissions | | AI output invalid | Prompt formatting issue | Use strict JSON prompt | | Data not saved | Supabase mapping mismatch | Verify table fields | | Slack message missing | Channel or credential error | Recheck Slack config | | Telegram alert fails | Wrong chat ID | Confirm bot permissions | | Workflow not running | Trigger disabled | Enable Cron node | Need Help? If you need help customizing, scaling or extending this workflow — such as advanced moderation logic, dashboards, auto-actions or production hardening, then our n8n workflow development team at WeblineIndia can assist with expert automation solutions.

Overview This workflow implements an AI-powered incident investigation and root cause analysis system that automatically analyzes operational signals when a system incident occurs. When an incident is triggered via webhook, the workflow gathers operational context including application logs, system metrics, recent deployments, and feature flag changes. These signals are processed to detect error patterns, cluster similar failures, and correlate them with recent system changes. The workflow uses vector embeddings to group similar log messages, allowing it to detect dominant failure patterns across services. It then aligns these failures with contextual events such as deployments, configuration changes, or traffic spikes to identify potential causal relationships. An AI agent analyzes all available evidence and generates structured root cause hypotheses, including confidence scores, supporting evidence, and recommended remediation actions. Finally, the workflow posts a detailed incident report directly to Slack, enabling engineering teams to quickly understand the issue and respond faster. This architecture helps teams reduce mean time to resolution (MTTR) by automating the early stages of incident investigation. How It Works 1. Incident Trigger The workflow begins when an incident alert is received through a webhook endpoint. The webhook payload may include information such as: incident ID severity level timestamp affected service This event starts the automated investigation process. 2. Workflow Configuration A configuration node defines the operational parameters used throughout the workflow, including: Logs API endpoint Metrics API endpoint Deployments API endpoint Feature flags API endpoint Time window for analysis Slack channel for incident notifications This allows the workflow to be easily adapted to different observability stacks. 3. Incident Context Collection The workflow collects system context from multiple sources: application logs infrastructure or service metrics recent deployments active feature flags Gathering this information provides the signals required to understand what happened before and during the incident. 4. Log Normalization and Denoising Raw logs are processed to remove low-value entries such as debug or informational messages. The workflow extracts structured error information including: timestamps log severity services involved request or session IDs error messages and stack traces This step ensures that only relevant failure signals are analyzed. 5. Failure Pattern Clustering Error messages are converted into embeddings using OpenAI. The workflow stores these embeddings in an in-memory vector store to group similar log messages together. This clustering step identifies dominant failure patterns that may appear across multiple sessions or services. 6. Failure Pattern Analysis Clustered log data is analyzed to detect recurring error types and dominant failure clusters. The workflow calculates statistics such as: total error volume most common error types error distribution across clusters dominant failure patterns These insights help highlight the primary issues affecting the system. 7. Event Correlation Analysis Failure patterns are then aligned with contextual events such as: deployments configuration changes traffic spikes The workflow calculates correlation scores based on temporal proximity and assigns likelihood scores to potential causes. This allows the system to identify events that may have triggered the incident. 8. AI Root Cause Analysis An AI agent analyzes the collected signals and generates structured root cause hypotheses. The agent considers: error clusters deployment timing configuration changes traffic patterns system metrics The output includes: multiple root cause hypotheses confidence scores supporting evidence recommended remediation actions 9. Incident Ticket Creation The final analysis is formatted into a structured incident report and posted to Slack. The Slack message contains: incident metadata root cause hypotheses confidence scores evidence recommended actions affected services This enables engineers to quickly review the investigation results and take action. Setup Instructions 1. Configure Observability APIs Update the Workflow Configuration node with API endpoints for: Logs API Metrics API Deployments API Feature Flags API These APIs should return JSON responses containing recent operational data. 2. Configure OpenAI Credentials Add OpenAI credentials for: OpenAI Embeddings OpenAI Chat Model These are used for log clustering and root cause analysis. 3. Configure Slack Integration Add Slack credentials and specify the Slack channel ID in the configuration node. Incident reports will be posted automatically to this channel. 4. Configure the Incident Trigger Deploy the webhook endpoint generated by the Incident Trigger node. Your monitoring or alerting system (PagerDuty, Grafana, Datadog, etc.) can call this webhook when incidents occur. 5. Activate the Workflow Once configured, activate the workflow in n8n. When incidents are triggered, the workflow will automatically run the investigation pipeline and generate a Slack incident report. Use Cases Automated Incident Investigation Automatically analyze operational signals when alerts are triggered to identify possible causes. AI-Assisted Site Reliability Engineering Provide engineers with AI-generated root cause hypotheses and investigation insights. Deployment Impact Detection Detect whether a recent deployment or configuration change caused a system failure. Observability Signal Correlation Combine logs, metrics, and system events to produce a unified incident analysis. Faster Incident Response Reduce mean time to resolution (MTTR) by automating the early stages of incident debugging. Requirements n8n with LangChain nodes enabled OpenAI API credentials Slack credentials APIs for retrieving: system logs service metrics deployment history feature flag status

WhatsApp Number Verify & Confirmation System with Rapiwa API and Google Sheets Who is this for? This n8n workflow makes it easy to verify WhatsApp numbers submitted through a form. When someone fills out the form, the automation kicks in—capturing the data via a webhook, checking the WhatsApp number using the Rapiwa API, and sending a confirmation message if the number is valid. All submissions, whether verified or not, are logged into a Google Sheet with a clear status. It’s a great solution for businesses, marketers, or developers who need a reliable way to verify leads, manage event signups, or onboard customers using WhatsApp. How it works? This n8n automation listens for form submissions via a webhook, validates the provided WhatsApp number using the Rapiwa API, sends a confirmation message if the number is verified, and then appends the submission data to a Google Sheet, marking each entry as verified or unverified. Features Webhook Trigger**: Captures form submissions via HTTP POST Data Cleaning**: Formats and sanitizes the WhatsApp number Rapiwa API Integration**: Checks if the number is registered on WhatsApp Conditional Messaging**: Sends confirmation messages only to verified WhatsApp users Google Sheets Integration**: Appends all submissions with a validity status Auto Timestamping**: Adds the submission date in YYYY-MM-DD format Throttling Support**: Built-in delay to avoid hitting API or sheet rate limits Separation of Verified/Unverified**: Distinct handling for both types of entries Nodes Used in the Workflow Webhook** Format Webhook Response Data** (Code) Loop Over Items** (Split In Batches) Cleane Number** (Code) check valid whatsapp number** (HTTP Request) If** (Conditional) Send Message Using Rapiwa** verified append row in sheet** (Google Sheets) unverified append row in sheet** (Google Sheets) Wait1** How to set up? Webhook Add a Webhook node to the canvas. Set HTTP Method to POST. Copy the Webhook URL path (/a9b6a936-e5f2-4xxxxxxxxxe0a970d5). In your frontend form or app, make a POST request to: The request body should include: { "business_name": "ABC Corp", "location": "New York", "whatsapp": "+1 234-567-8901", "email": "user@example.com", "name": "John Doe" } Format Webhook Response Data Add a Code node after the Webhook node. Use this JavaScript code: const result = $input.all().map(item => { const body = item.json.body || {}; const submitted_date = new Date().toISOString().split('T')[0]; return { business_name: body.business_name, location: body.location, whatsapp: body.whatsapp, email: body.email, name: body.name, submitted_date: submitted_date }; }); return result; Loop Over Items Insert a SplitInBatches node after the data formatting. Set the Batch Size to a reasonable number (e.g. 1 or 10). This is useful for processing multiple submissions at once, especially if your webhook receives arrays of entries. Note: If you expect only one submission at a time, it still helps future-proof your workflow. Cleane Number Add a Code node named Cleane Number. Paste the following JavaScript: const items = $input.all(); const updatedItems = items.map((item) => { const waNo = item?.json["whatsapp"]; const waNoStr = typeof waNo === 'string' ? waNo : (waNo !== undefined && waNo !== null ? String(waNo) : ""); const cleanedNumber = waNoStr.replace(/\D/g, ""); item.json["whatsapp"] = cleanedNumber; return item; }); return updatedItems; Check WhatsApp Number using Rapiwa Add an HTTP Request node. Set: Method: POST URL: https://app.rapiwa.com/api/verify-whatsapp Add authentication: Type: HTTP Bearer Credentials: Select or create Rapiwa token In Body Parameters, add: number: ={{ $json.whatsapp }} This API call checks if the WhatsApp number exists and is valid. Expected Output: { "success": true, "data": { "number": "+88017XXXXXXXX", "exists": true, "jid": "88017XXXXXXXXXXXXX", "message": "✅ Number is on WhatsApp" } } Conditional If Check Add an If node after the Rapiwa validation. Configure the condition: Left Value: ={{ $json.data.exists }} Operation: true If true → valid number → go to messaging and append as "verified". If false → go to unverified sheet directly. Note: This step branches the flow based on the WhatsApp verification result. Send WhatsApp Message (Rapiwa) Add an HTTP Request node under the TRUE branch of the If node. Set: Method: POST URL: https://app.rapiwa.com/api/send-message Authentication: Type: HTTP Bearer Use same Rapiwa token Body Parameters: number: ={{ $json.data.phone }} message_type: text message: Hi {{ $('Cleane Number').item.json.name }}, Thanks! Your form has been submitted successfully. This sends a confirmation message via WhatsApp to the verified number. Google Sheets – Verified Data Add a Google Sheets node under the TRUE branch (after the message is sent). Set: Operation: Append Document ID: Choose your connected Google Sheet Sheet Name: Set to your active sheet (e.g., Sheet1) Column Mapping: Business Name: ={{ $('Cleane Number').item.json.business_name }} Location: ={{ $('Cleane Number').item.json.location }} WhatsApp Number: ={{ $('Cleane Number').item.json.whatsapp }} Email : ={{ $('Cleane Number').item.json.email }} Name: ={{ $('Cleane Number').item.json.name }} Date: ={{ $('Cleane Number').item.json.submitted_date }} validity: verified Use OAuth2 Google Sheets credentials for access. Note: Make sure the sheet has matching column headers. Google Sheets – Unverified Data Add a Google Sheets node under the FALSE branch of the If node. Use the same settings as the verified node, but set: validity: unverified This stores entries with unverified WhatsApp numbers in the same Google Sheet. Wait Node Add a Wait node after both Google Sheets nodes. Set Wait Time: Value: 2 seconds This delay prevents API throttling and adds buffer time before processing the next item in the batch. Google Sheet Column Reference A Google Sheet formatted like this ➤ Sample Sheet | Business Name | Location | WhatsApp Number | Email | Name | validity | Date | |---------------------|--------------------|------------------|----------------------|------------------|------------|------------| | SpaGreen Creative | Dhaka, Bangladesh | 8801322827799| contact@spagreen.net | Abdul Mannan | unverified | 2025-09-14 | | SpaGreen Creative | Bagladesh | 8801322827799| contact@spagreen.net| Abdul Mannan | verified | 2025-09-14 | > Note: The Email column includes a trailing space. Ensure your column headers match exactly to prevent data misalignment. How to customize the workflow Modify confirmation message with your brand tone Add input validation for missing or malformed fields Route unverified submissions to a separate spreadsheet or alert channel Add Slack or email notifications on new verified entries Notes & Warnings Ensure your Google Sheets credential has access to the target sheet Rapiwa requires an active subscription for API access Monitor Rapiwa API limits and adjust wait time as needed Keep your webhook URL protected to avoid misuse Support & Community WhatsApp Support: Chat Now Discord: Join SpaGreen Community Facebook Group: SpaGreen Support Website: spagreen.net Developer Portfolio: Codecanyon SpaGreen

Quick overview This workflow runs daily to compare department budgets and actual spend from Google Sheets, calculates variance, uses Google Gemini to generate root-cause insights, sends Slack alerts when variance breaches a threshold, logs results back to Google Sheets, and emails a daily report via Gmail. How it works Runs every day at 9:00 AM on a schedule. Reads budget and actual spend data from two Google Sheets tabs and matches records by Department. Calculates spend variance and variance percentage, then assigns a status label (Healthy/Warning/Critical) and run context like date and summary. Sends the variance details to Google Gemini to generate a JSON response with root cause, risk level, and recommendations. Checks whether the variance percentage is greater than 10% and sends a Slack alert with the AI insights when the threshold is exceeded. Appends the final analysis (including whether an alert was sent) to a Google Sheets log and sends the same report via Gmail. Setup Connect Google Sheets OAuth credentials and set the correct spreadsheet and sheet tabs for Budget Data, Actual spend, and the Final report log. Add a Google Gemini (PaLM) API credential for the Gemini model used in the analysis step. Add Slack OAuth2 credentials and choose the channel where budget variance alerts should be posted. Add Gmail OAuth2 credentials and set the recipient(s) for the daily budget monitoring email. Update the variance alert threshold (currently >10% variance) and status thresholds (10%/20%) to match your policy. Requirements n8n instance (self-hosted or cloud) Google Sheets OAuth2 credentials Google Gemini (PaLM) API credentials Slack OAuth2 credentials Gmail OAuth2 credentials Properly structured Google Sheets: Budget Sheet → Department, Budget Spend Sheet → Department, Spend Report Sheet → Predefined columns for logging Customization Variance Threshold**: Modify IF node condition (> 10) to any value Update logic in Add Status & Context Details node: Critical: > 20% or Warning: > 10% Gemini Prompt*: Customize prompt inside *AI-Based Budget Analysis node to get different insights Slack Message Format**: Modify message body to include/exclude fields Email Template**: Customize HTML in Gmail node for branding or formatting Schedule Time**: Change trigger time as per business needs Additional info Who’s It For Finance and accounting teams Procurement departments Operations managers Business analysts Startups and enterprises managing departmental budgets Add-ons (Enhancements) Add monthly or weekly summary reports Integrate Power BI / dashboards for visualization Add multi-level approvals for high-risk alerts Store data in a database (MySQL/PostgreSQL) instead of Sheets Add SMS or WhatsApp alerts for critical cases Include historical trend analysis using AI Use Case Examples Corporate Budget Monitoring Track departmental spending daily and prevent overspending Procurement Cost Control Identify supplier-related cost deviations early Startup Financial Discipline Maintain strict control over limited budgets Project-Based Budget Tracking Monitor cost overruns across multiple projects Audit & Compliance Reporting Maintain a clear log of financial decisions and anomalies These are just a few examples—this workflow can be adapted for many other financial monitoring scenarios. Troubleshooting Guide | Issue | Possible Cause | Solution | |------|--------------|---------| | No data fetched | Incorrect Google Sheets configuration | Verify sheet IDs and column names | | Merge not working | Department names mismatch | Ensure consistent naming across sheets | | Wrong variance values | Non-numeric data | Ensure Budget and Spend fields are numbers | | No Slack alerts | Condition not met or Slack not configured | Check IF condition and Slack credentials | | AI output missing | Gemini API issue | Verify API credentials and prompt | | Email not sent | Gmail credentials issue | Reconnect Gmail OAuth | | Data not logged | Sheet mapping issue | Check column mapping in final node | | Workflow not running | Trigger inactive | Activate workflow | Need Help? If you need help setting up this workflow, customizing it for your business, or adding advanced features like dashboards, AI enhancements or integrations, our n8n workflow development team at WeblineIndia is here to assist you. We specialize in building scalable automation workflows tailored to your business needs. Reach out to us for: Workflow setup & deployment Custom automation solutions AI-powered business workflows Integration with enterprise systems Let us help you turn automation into a competitive advantage.

How It Works This workflow automates student progress monitoring and academic intervention orchestration through intelligent AI-driven analysis. Designed for educational institutions, learning management systems, and academic advisors, it solves the critical challenge of identifying at-risk students while coordinating timely interventions across faculty and support services. The system receives student data via webhook, fetches historical learning records, and merges these sources for comprehensive progress analysis. It employs a dual-agent AI framework for student progress validation and academic orchestration, detecting performance gaps, engagement issues, and intervention opportunities. The workflow intelligently routes findings based on validation status, triggering orchestration actions for students requiring support while logging compliant progress for successful learners. By executing multi-channel interventions through HTTP APIs and email notifications, it ensures educators and students receive timely guidance while maintaining complete audit trails for academic accountability and accreditation compliance. Setup Steps Configure Student Data Webhook trigger endpoint Connect Workflow Configuration node with academic performance parameters Set up Fetch Student Learning History node with LMS API credentials Configure Merge Student Data node for data consolidation Connect Student Progress Validation Agent with Claude/OpenAI API credentials Set up AI processing nodes Configure Route by Validation Status node with performance thresholds Connect Academic Orchestration Agent with AI API credentials for intervention planning Set up orchestration processing Prerequisites Claude/OpenAI API credentials for AI agents, learning management system API access Use Cases Universities identifying students requiring academic support, online learning platforms detecting engagement drops Customization Adjust validation thresholds for institutional academic standards Benefits Reduces student identification lag by 75%, eliminates manual progress tracking

Who's it for This workflow is ideal for: Content creators who want to replicate successful LinkedIn strategies Social media managers monitoring competitor content performance Marketing teams analyzing trending topics in their industry Personal brands looking to create data-driven content Agencies managing multiple LinkedIn accounts What it does This comprehensive workflow automates the entire LinkedIn content lifecycle: it scrapes viral posts from target accounts, analyzes engagement patterns, identifies trending topics, generates original AI-powered content based on those trends, creates accompanying images, and automatically publishes to your LinkedIn profile or company page. How it works Phase 1: Data Collection (Runs every 12 hours) Scheduler triggers the workflow twice daily Fetches LinkedIn profile URLs from Google Sheets Processes profiles in batches of 3 to respect API limits Uses Apify API to scrape recent posts from each profile Adds 3-second delays between requests to avoid rate limiting Filters for high-engagement posts (20+ likes, comments, or reposts) Saves viral posts to Google Sheets with full metadata Phase 2: Content Generation (Triggered by new data) Monitors Google Sheets for new viral posts every minute Filters posts published within the last 3 days that haven't been analyzed Aggregates trending content into a single dataset Analyzes patterns using Google Gemini AI to identify: Common themes and topics Engagement triggers and hooks Successful content structures Trending hashtags and formats Generates original LinkedIn post with proper formatting Creates AI image prompt optimized for minimal text Generates professional image using Google Imagen Publishes complete post to your LinkedIn account Marks analyzed posts as complete to prevent duplication Setup steps 1. Configure Google Sheets Create a new Google Sheet with two tabs: Tab 1: "usernames & links" - Add LinkedIn profile URLs you want to monitor Tab 2: "scrape data" - Leave empty (auto-populated by workflow) Connect your Google Sheets credentials in both nodes Replace all instances of YOUR_GOOGLE_SHEET_ID with your actual sheet ID Replace SHEET_GID values with your actual sheet GIDs 2. Set up Apify API Sign up for Apify account and get API token Replace YOUR_APIFY_API_TOKEN in "Scrape LinkedIn Posts API" node Note: Apify has free tier with limited requests 3. Configure Google Gemini credentials Obtain Google PaLM API credentials Add credentials to both "Google Gemini Chat Model" and "Generate an image" nodes 4. Set up LinkedIn publishing Connect your LinkedIn credentials in "Publish to LinkedIn" node If posting as organization, replace YOUR_LINKEDIN_ORGANIZATION_ID with your company page ID If posting as individual, change "postAs" parameter to "person" 5. Configure scheduling Default schedule: every 12 hours Adjust "LinkedIn Content Automation Scheduler" trigger if needed Consider your API rate limits when changing frequency 6. Test the workflow Manually trigger Phase 1 to scrape posts Verify data appears in Google Sheets "scrape data" tab Wait for Phase 2 trigger or manually activate it Check that content is generated and published correctly Verify posts are marked as analyzed in Google Sheets Requirements Google Sheets API access (free) Google Sheets Trigger OAuth2 (free) Apify API token (free tier available, $49/month for more) Google PaLM/Gemini API key (pay-per-use pricing) LinkedIn OAuth credentials (free) How to customize Adjust scraping targets: Add more LinkedIn profile URLs to your Google Sheets Change batch size in "Process Profiles in Batches" (default: 3) Modify post limit per profile in Apify API call (default: 1 post) Modify engagement filters: Edit "Filter High-Engagement Posts" node thresholds Default: 20+ likes OR 20+ comments OR 20+ reposts Adjust based on your niche's typical engagement rates Add additional criteria like views or impressions Customize content analysis window: Change "Filter Recent Posts (3 Days)" to analyze different timeframes Options: 24 hours for fast-moving trends, 7 days for broader patterns Balance between recency and data volume Refine AI content generation: Edit system prompt in "LinkedIn Content Strategy AI" node Adjust content length, tone, or style preferences Add industry-specific guidelines Include brand voice requirements Modify hashtag strategy Customize image generation: Edit image prompt structure in AI prompt Change visual style, colors, or composition Adjust for brand guidelines Modify dimensions or aspect ratios Change posting schedule: Adjust "LinkedIn Content Automation Scheduler" frequency Consider optimal posting times for your audience Balance between content quality and posting frequency Coordinate with other marketing activities Enhance data collection: Increase posts per profile in Apify settings Add more profile URLs to monitor Implement competitor tracking Track additional metrics like impressions or click-through rates Add notifications: Connect Slack/Email nodes after successful posts Set up alerts for high-performing content Create reports of analyzed trends Monitor API usage and errors

Automate your post-event networking with this intelligent n8n workflow. Triggered instantly after an event, it collects attendee and interaction data, enriches profiles with LinkedIn insights, and uses GPT-4 to analyze engagement and generate tailored follow-up messages. High-value leads are prioritized, messages are sent via email, LinkedIn, or Slack, and all activity is logged in your CRM and database. Save hours of manual follow-up while boosting relationship-building and ROI. 🤝✨ Advanced Features Webhook automation** – Starts instantly on event completion Multi-Source Enrichment** – Combines event data, interactions, and LinkedIn profiles AI-Powered Insights** – GPT-4 analyzes behavior and suggests personalized talking points Smart Priority Filtering** – Routes leads into High, Medium, and Low priority paths Personalized Content Generation** – AI crafts custom emails and LinkedIn messages Multi-Channel Outreach** – Sends via Email, LinkedIn DM, and Slack CRM Integration** – Automatically updates HubSpot with contact notes and engagement PostgreSQL Logging** – Stores full interaction history and analytics ROI Dashboard** – Tracks response rates, meetings booked, and pipeline impact What It Does Collects attendee data from your event platform Enriches with LinkedIn profiles & real-time interaction logs Scores networking potential using engagement algorithms Uses AI to analyze conversations, roles, and mutual interests Generates hyper-personalized follow-up emails and LinkedIn messages Sends messages through preferred channels (email, LinkedIn, Slack) Updates HubSpot CRM with follow-up status and next steps Logs all actions and tracks analytics for performance reporting Workflow Process The Webhook Trigger initiates the workflow via POST request with event and attendee data. Get Attendees** fetches participant list from the event platform. Get Interactions** pulls Q&A, chat, poll, and networking activity logs. Enrich LinkedIn Data** retrieves professional profiles, job titles, and company details via LinkedIn API. Merge & Enrich Data** combines all sources into a unified lead profile. AI Analyze Profile** uses GPT-4 to evaluate interaction depth, role relevance, and conversation context. Filter High Priority** routes top-tier leads (e.g., decision-makers with strong engagement). Filter Medium Priority** handles warm prospects for lighter follow-up. AI Agent1** generates personalized email content using chat model and memory. Generate Email** creates a professional, context-aware follow-up email. Send Email** delivers the message to the lead’s inbox. AI Agent2** crafts a concise, friendly LinkedIn connection message. Generate LinkedIn Msg** produces a tailored outreach note. Send LinkedIn** posts the message via LinkedIn API. Slack Notification** alerts your team in real-time about high-priority outreach. Update CRM (HubSpot)** adds contact, tags, and follow-up tasks automatically. Save to Database (Insert)** logs full lead journey and message content in PostgreSQL. Generate Analytics** compiles engagement metrics and success rates. Send Response** confirms completion back to the event system. Setup Instructions Import the workflow JSON into n8n Configure credentials: Event Platform API (for attendees & interactions) LinkedIn API (OAuth2) OpenAI (GPT-4) SMTP (for email) or Email Service (SendGrid, etc.) HubSpot API Key PostgreSQL Database Slack Webhook URL Trigger with a webhook POST containing event ID and settings Watch personalized outreach happen automatically! Prerequisites Event platform with webhook + attendee/interaction API LinkedIn Developer App with API access OpenAI API key with GPT-4 access HubSpot account with API enabled PostgreSQL database (table for leads & logs) Slack workspace (optional, for team alerts) Example Webhook Payload { "eventId": "evt_spring2025", "eventName": "Annual Growth Summit", "triggerFollowUp": true, "priorityThreshold": { "high": 75, "medium": 50 } } Modification Options Adjust scoring logic in AI Analyze Profile (e.g., weight Q&A participation higher) Add custom email templates in Generate Email with your brand voice Include meeting booking links (Calendly) in high-priority messages Route VIP leads to Send SMS via Twilio Export analytics to Google Sheets or BI tools (Looker, Tableau) Add approval step before sending LinkedIn messages Ready to 10x your event ROI? Get in touch with us for custom n8n automation!

How It Works Scheduled runs collect data from oil markets, global shipping movements, news sources, and official reports. The system performs statistical checks to detect anomalies and volatility shifts. An AI-driven geopolitical model evaluates emerging risks and assigns a crisis score. Based on severity thresholds, results are routed to the appropriate alert channels for rapid response. Setup Steps Data Sources: Connect the oil price API, OPEC report feeds, shipping databases, and news sources. AI Model: Configure the OpenRouter ChatGPT model for geopolitical and risk analysis. Alerts: Define severity rules and route alerts to Email, Slack, or Dashboard APIs. Storage: Configure a database for historical records, audit logging, and trend tracking. Prerequisites Oil market API credentials; news feed access; OPEC data source; OpenRouter API key; Slack/email/dashboard integrations Use Cases Supply chain risk monitoring; energy market crisis detection; geopolitical threat assessment; trader decision support; operational risk management Customization Adjust risk thresholds; add market data sources; modify alert routing rules Benefits Reduces crisis detection lag 90%; consolidates fragmented data; enables proactive response

Description Turn incoming Gmail messages into structured Zendesk tickets, enriched by Azure OpenAI, and log key details to Google Sheets for tracking. Ideal for IT Support teams needing fast, consistent intake and documentation. ⚡ What This Template Does Fetches new emails via Gmail Trigger. ✉️ Normalizes Gmail data and formats it for downstream steps. Enriches and structures content with Azure OpenAI Chat Model and Output Parsers. Creates Zendesk tickets from the processed data. 🎫 Appends or updates logs in Google Sheets for auditing and reporting. 📊 Key Benefits Saves time by automating ticket creation and logging. ⏱️ Improves ticket quality with AI-driven normalization and structure. Ensures consistent records in Google Sheets for easy reporting. Reduces manual errors in IT Support intake. ✅ Features Gmail-triggered intake flow for new messages. AI enrichment using Azure OpenAI Chat Model with parsing and memory tooling. Zendesk ticket creation (create: ticket) with structured fields. Google Sheets logging (appendOrUpdate: sheet). Modular design with Execute Workflow nodes for reuse and scaling. Requirements n8n instance (Cloud or self-hosted). Gmail credentials configured in n8n for the Gmail Trigger. Zendesk credentials with permission to create tickets. Google Sheets credentials with access to the target spreadsheet (append/update enabled). Azure OpenAI credentials configured for the Azure OpenAI Chat Model and associated parsing. Target Audience IT Support and Helpdesk teams handling email-based requests. 🛠️ Operations teams standardizing inbound email workflows. Agencies and MSPs offering managed support intake. Internal automation teams centralizing ticket capture and logging. Step-by-Step Setup Instructions Connect Gmail credentials in n8n and select the inbox/label for the Gmail Trigger. Add Zendesk credentials and confirm ticket creation permissions. Configure Google Sheets credentials and select the target sheet for logs. Add Azure OpenAI credentials to the Azure OpenAI Chat Model node and verify parsing steps. Import the workflow, assign credentials to each node, update any placeholders, and run a test. Rename the final email/logging nodes descriptively (e.g., “Log to Support Sheet”) and schedule if needed.

NGO TPM Request Management System Benefits For Beneficiaries: 24/7 Accessibility** - Submit requests anytime via familiar Telegram interface Language Flexibility** - Communicate in Arabic through text or voice messages Instant Acknowledgment** - Receive immediate confirmation that requests are logged No Technical Barriers** - Works on basic smartphones without special apps For TPM Teams: Centralized Tracking** - All requests automatically logged with timestamps and user details Smart Prioritization** - AI categorizes issues by urgency and type for efficient response Action Guidance** - Specific recommended actions generated for each request type Performance Analytics** - Track response patterns and common issues over time For NGO Operations: Cost Reduction** - Automated intake reduces manual processing overhead Data Quality** - Standardized categorization ensures consistent reporting Audit Trail** - Complete record of all beneficiary interactions for compliance Scalability** - Handle high volumes without proportional staff increases How it Works Multi-Input Reception - Accepts both text messages and voice recordings via Telegram Voice Transcription - Uses OpenAI Whisper to convert Arabic voice messages to text AI Categorization - GPT-4 analyzes requests and categorizes issues (aid distribution, logistics, etc.) Action Planning - AI generates specific recommended actions for TPM team in Arabic Data Logging - Records all requests, categories, and actions in Google Sheets with user details Confirmation Feedback - Sends acknowledgment message back to users via Telegram Set up Steps Setup Time: ~20 minutes Create Telegram Bot - Get bot token from @BotFather and configure webhook Configure APIs - Set up OpenAI (transcription + chat) and Google Sheets credentials Customize AI Prompts - Adjust system messages for your NGO's specific operations Set Up Spreadsheet - Link Google Sheets for request tracking and reporting Test Workflows - Verify both text and voice message processing paths Detailed Arabic language configuration and TPM-specific categorization examples are included as sticky notes within the workflow. What You'll Need: Telegram Bot Token (free from @BotFather) OpenAI API key (Whisper + GPT-4) Google Sheets API credentials Google Spreadsheet for logging requests Sample Arabic text/voice messages for testing Key Features: Dual input support (text + voice messages) Arabic language processing and responses Structured data extraction (category + recommended action) Complete audit trail with user information Real-time confirmation messaging TPM team-specific workflow optimization

How It Works – Data Deduplication in n8n This tutorial demonstrates how to remove duplicate records from a dataset using JavaScript logic inside n8n's Code nodes. It simulates real-world data cleaning by generating sample user data with intentional duplicates (based on email addresses) and walks you through the process of deduplication step-by-step. The process includes: Creating Sample Data with duplicates. Filtering Out Duplicates using filter() and findIndex() based on email. Displaying Cleaned Results with simple statistics for before-and-after comparison. This is ideal for scenarios like CRM imports, ETL processes, and general data hygiene. ⚙️ Set-Up Steps 🔹 Step 1: Manual Trigger Node: When clicking 'Test workflow' Purpose: Initiates the workflow manually for testing. 🔹 Step 2: Generate Sample Data Node: Create Sample Data (Code node) What it does: Creates 6 users, including 2 intentional duplicates (by email). Outputs data as usersJson with metadata (totalCount, message). Mimics real-world messy datasets. 🔹 Step 3: Deduplicate the Data Node: Deduplicate Users (Code node) What it does: Parses usersJson. Uses .filter() + .findIndex() to keep only the first instance of each email. Logs total, unique, and removed counts. Outputs clean user list as separate items. 🔹 Step 4: Display Results Node: Display Results (Code node) What it does: Outputs structured summary: Unique users Status Timestamp Prepares results for review or downstream use. 📈 Sample Output Original count: 6 users Deduplicated count: 4 users Duplicates removed: 2 users 🎯 Learning Objectives You'll learn how to: Use .filter() and .findIndex() in n8n Code nodes Clean JSON data within workflows Create simple, effective deduplication pipelines Output structured summaries for reporting or integration 🧠Best Practices Validate input format (e.g., JSON schema) Handle null or missing fields gracefully Use logging for visibility Add error handling for production use Use pagination/chunking for large datasets