by Trung Tran
AI-Powered AWS S3 Manager with Audit Logging in n8n (Slack/ChatOps Workflow) > This n8n workflow empowers users to manage AWS S3 buckets and files using natural language via Slack or chat platforms. Equipped with an OpenAI-powered Agent and integrated audit logging to Google Sheets, it supports operations like listing buckets, copying/deleting files, managing folders, and automatically records every action for compliance and traceability. 👥 Who’s it for This workflow is built for: DevOps engineers who want to manage AWS S3 using natural chat commands. Technical support teams interacting with AWS via Slack, Telegram, etc. Automation engineers building ChatOps tools. Organizations that require audit logs for every cloud operation. Users don’t need AWS Console or CLI access — just send a message like “Copy file from dev to prod”. ⚙️ How it works / What it does This workflow turns natural chat input into automated AWS S3 actions using an OpenAI-powered AI Agent in n8n. 🔁 Workflow Overview: Trigger: A user sends a message in Slack, Telegram, etc. AI Agent: Interprets the message Calls one of 6 S3 tools: ListBuckets ListObjects CopyObject DeleteObject ListFolders CreateFolder S3 Action: Performs the requested AWS S3 operation. Audit Log: Logs the tool call to Google Sheets using AddAuditLog: Includes timestamp, tool used, parameters, prompt, reasoning, and user info. 🛠️ How to set up Step-by-step Setup: Webhook Trigger Slack, Telegram, or custom chat platform → connects to n8n. OpenAI Agent Model: gpt-4 or gpt-3.5-turbo Memory: Simple Memory Node Prompt: Instructs agent to always follow tool calls with an AddAuditLog call. AWS S3 Nodes Configure each tool with AWS credentials. Tools: getAll: bucket getAll: file copy: file delete: file getAll: folder create: folder Google Sheets Node Sheet: AWS S3 Audit Logs Operation: Append or Update Row Columns (must match input keys): timestamp, tool, status, chat_prompt, parameters, user_name, tool_call_reasoning Agent Tool Definitions Include AddAuditLog as a 7th tool. Agent calls it immediately after every S3 action (except when logging itself). ✅ Requirements [ ] n8n instance with AI Agent feature [ ] OpenAI API Key [ ] AWS IAM user with S3 access [ ] Google Sheet with required columns [ ] Chat integration (Slack, Telegram, etc.) 🧩 How to customize the workflow | Feature | Customization Tip | |----------------------|--------------------------------------------------------------| | 🌎 Multi-region S3 | Let users include region in the message or agent memory | | 🛡️ Restricted actions| Use memory/user ID to limit delete/copy actions | | 📁 Folder filtering | Extend ListObjects with prefix/suffix filters | | 📤 Upload file | Add PutObject with pre-signed URL support | | 🧾 Extra logging | Add IP, latency, error trace to audit logs | | 📊 Reporting | Link Google Sheet to Looker Studio for audit dashboards | | 🚨 Security alerts | Notify via Slack/Email when DeleteObject is triggered |
by Vigh Sandor
PKI Certificate & CRL Monitor - Auto Expiration Alert System Overview This n8n workflow provides automated monitoring of Public Key Infrastructure (PKI) components including CA certificates, Certificate Revocation Lists (CRLs), and associated web services. It extracts certificate information from the TSL (Trusted Service List) -- the Hungarian is the example list as default in the workflow -- , monitors expiration dates, and sends alerts via Telegram and SMS when critical thresholds are reached. Features Automated extraction of certificate URLs from TSL XML CA certificate expiration monitoring CRL expiration tracking Website availability monitoring with retry mechanism Multi-channel alerting (Telegram and SMS) Scheduled execution every 12 hours 17-hour warning threshold for expirations Setup Instructions Prerequisites n8n Instance: Running n8n installation with Linux environment Telegram Bot: Created via @BotFather Textbelt API Key: For SMS notifications (optional) Network Access: To reach TSL source and certificate URLs Linux Tools: OpenSSL, curl, libxml2-utils, jq (auto-installed) Configuration Steps 1. Telegram Setup Create Telegram Bot: Open Telegram and search for @BotFather Send /newbot and follow prompts Save the bot token (format: 1234567890:ABCdefGHIjklMNOpqrsTUVwxyz) Create Alert Channel: Create a new Telegram channel for alerts Add your bot as administrator Get channel ID: Send a test message to the channel Visit: https://api.telegram.org/bot<YOUR_BOT_TOKEN>/getUpdates Find "chat":{"id":-100XXXXXXXXXX} - this is your channel ID 2. SMS Setup (Optional) Textbelt Configuration: Register at https://textbelt.com Purchase credits and obtain API key Note: Free tier allows 1 SMS/day for testing 3. Configure Alert Nodes Update these nodes with your credentials: CRL Alert Node: Open CRL Alert --- Telegram & SMS node Replace YOUR-TELEGRAM-BOT-TOKEN with your bot token Replace YOUR-TELEGRAM-CHANNEL-ID with your channel ID Replace +36301234567 with target phone number(s) Replace YOUR-TEXTBELT-API-KEY with your Textbelt key CA Alert Node: Open CA Alert --- Telegram & SMS node Apply same replacements as above Website Down Alert Node: Open Send Website Down - Telegram & SMS node Apply same replacements as above 4. TSL Source Configuration The workflow defaults to Hungarian TSL: URL: http://www.nmhh.hu/tl/pub/HU_TL.xml To change, edit the Collect Checking URL list node Trust list references: https://ec.europa.eu/tools/lotl/eu-lotl.xml (to find more TSL list to change the default), and https://www.etsi.org/deliver/etsi_ts/119600_119699/119615/01.02.01_60/ts_119615v010201p.pdf (to Technical Specification of the Trust Lists) 5. Threshold Configuration Default warning threshold: 17 hours before expiration To modify CRL threshold: Edit nextUpdate - TimeFilter node To modify CA threshold: Edit nextUpdate - TimeFilter1 node Change value in condition: if (diffHours < 17) Activation Save all configuration changes Test with Execute With Manual Start trigger Verify alerts are received Toggle workflow to Active status for scheduled operation How to Use Automatic Operation Once activated, the workflow runs automatically: Frequency**: Every 12 hours Process**: Downloads TSL XML Extracts all certificate URLs Checks each URL type (CRL, CA, or other) Validates expiration dates Sends alerts for critical items Manual Execution For immediate checks: Open the workflow Click Execute With Manual Start node Click "Execute Node" Monitor execution progress Understanding Alerts CRL Expiration Alert Message Format: ALERT! with [Issuer CN] !!!CRL EXPIRATION!!! Will be under 17 hour ([Next Update Time])! Last updated: [Last Update Time] Trigger Conditions: CRL expires in less than 17 hours CRL download successful but expiration imminent CA Certificate Alert Message Format: ALERT!/EXPIRED! with [Subject CN] !!!CA EXPIRATION PROBLEM!!! The expiration time: ([Not After Date]) Last updated: ([Not Before Date]) Trigger Conditions: Certificate expires in less than 17 hours (ALERT!) Certificate already expired (EXPIRED!) Website Down Alert Message Format: ALERT! The [URL] !!!NOT AVAILABLE!!! Service outage probable! Intervention required! Trigger Conditions: Initial HTTP request fails Retry after wait period also fails HTTP status code not 200 Monitoring Dashboard Execution History Navigate to n8n Executions tab Filter by workflow name Review successful/failed runs Alert History Check Telegram channel for: Alert timestamps Affected certificates/services Expiration details Troubleshooting No Alerts Received Check Telegram Bot: Verify bot is admin in channel Test with manual message via API Confirm channel ID is correct Check Workflow Execution: Review execution logs in n8n Look for error nodes (red indicators) Verify TSL URL is accessible False Positives Verify system time is correct Check timezone settings Review threshold values Missing Certificates Some certificates may not have URLs TSL may be temporarily unavailable Check XML parsing in logs Performance Issues Slow Execution: Large TSL files take time to parse Network latency affects URL checks Consider increasing timeout values Memory Issues: Workflow processes many URLs sequentially Monitor n8n server resources Consider increasing batch intervals Advanced Configuration Modify Check Frequency Edit Execute With Scheduled Start node: Change interval type (hours/days/weeks) Adjust interval value Consider peak/off-peak scheduling Add Custom TSL Sources In Collect Checking URL list node: URL="https://your-tsl-source.com/tsl.xml" Customize Alert Messages Edit alert nodes to modify message templates: Add organization name Include escalation contacts Add remediation instructions Filter Certificate Types Modify URL detection patterns: Is this CRL?** node: Adjust CRL detection Is this CA?** node: Adjust CA detection Add new patterns as needed Adjust Retry Logic Wait B4 Retry node: Default: Immediate retry Can add delay (seconds/minutes) Useful for transient network issues Maintenance Regular Tasks Weekly**: Review alert frequency Monthly**: Validate phone numbers/channels Quarterly**: Update TSL source URLs Annually**: Review threshold values Log Management Clear old execution logs periodically Archive alert history from Telegram Document false positives for tuning Updates Keep n8n updated for security patches Monitor OpenSSL versions for compatibility Update notification service APIs as needed Security Considerations Store API keys in n8n credentials manager Use environment variables for sensitive data Restrict workflow edit access Monitor for unauthorized changes Regularly rotate API keys Use HTTPS for TSL sources when available Compliance Notes Ensure monitoring aligns with PKI policies Document alert response procedures Maintain audit trail of certificate issues Consider regulatory requirements for uptime Integration Options Connect to ticketing systems for alert tracking Add database logging for compliance Integrate with monitoring dashboards Create escalation workflows for critical alerts Best Practices Test alerts monthly to ensure delivery Maintain multiple notification channels Document response procedures for each alert type Set up redundant monitoring if critical Review and tune thresholds based on operational needs Keep contact lists updated Consider time zones for global operations
by Kamran habib
| N8N Workflow | AI Reddit Problem Detection & Auto-Solution Commenter 🤖 This n8n workflow automates Reddit community engagement by detecting posts that discuss problems and automatically replying with AI-generated solutions — powered by Google Gemini. It’s designed for developers, automation creators, and brands who want to provide helpful, automated responses to Reddit users discussing issues in their niche. How It Works The workflow starts with a Manual Trigger (When clicking ‘Execute workflow’). Search for a Post: It scans the r/n8n subreddit (or any subreddit you set) for recent posts containing the keyword “Why I stopped using”. Filter Posts (If Node): Filters posts that have 2 or more upvotes and non-empty text, ensuring only quality discussions are analyzed. Edit Fields: Extracts post details such as title, body text, upvotes, creation time, and subreddit ID for AI processing. AI Agent + Google Gemini Chat Model: The first AI node analyzes the post and decides whether it’s describing a problem or frustration related to AI automation. Gemini responds with “Yes” or “No.” Conditional Branch (If1 Node): If “Yes,” the post is confirmed as discussing a problem. The workflow then triggers the second AI Agent. AI Agent 2 + Gemini: The second AI node uses Gemini to generate a helpful and concise solution addressing the issue mentioned in the Reddit post (for example, offering a fix, suggestion, or new idea). Merge & Log Data: The AI’s findings (post details + solution) are merged and saved into a connected Google Sheet for tracking community insights. Comment on Reddit: The workflow automatically posts the AI-generated solution as a comment reply on the original Reddit thread, engaging users directly. How To Use Import the provided JSON workflow into your n8n dashboard. Set up the required credentials: Reddit OAuth2 API – for searching and posting comments. Google Gemini (PaLM) API – for AI text analysis and solution generation. Google Sheets API – for logging post data and AI results. Adjust the subreddit name, search keyword, or prompts to fit your niche. Click Execute Workflow to run the automation. Requirements Reddit Developer Account (OAuth2 credentials). Google Gemini (PaLM) API account for AI processing. Google Sheets account for saving analysis results. How To Customize Change the search keyword (e.g., “help with automation,” “issue with API,” etc.). Modify the AI prompts to tailor the solution style (technical, friendly, educational, etc.). Edit the Google Sheet fields to capture more or fewer details. Enable/disable the comment node if you want to manually approve replies before posting. Adjust the Gemini model name (e.g., models/gemini-2.0-flash) or parameters for faster or more creative responses.
by RamK
Monitor GitHub Repositories for Unauthorized Actions How it works: This workflow monitors GitHub for high-risk activities to ensure that only authorized users can modify the repository. It periodically polls GitHub for events such as PushEvent, MemberEvent, and PublicEvent. For each event, the workflow extracts the username of actor and looks it up in the it_whitelist data table to determine the user’s role. A Switch node then routes the event to the appropriate validation logic. • Member & Public events: Only users with the admin role are allowed. Any non-admin action such as adding a repository member or changing a private repository to public triggers an alert. • Push events: The user must exist in the whitelist. If no role is found, the user is treated as unknown and flagged. All unauthorized actions are reported to Slack, including the event type, actor name, and repository details. Setup steps: Credentials: Connect your GitHub Personal Access Token and Slack Bot Token. Create a Data Table named it_whitelist with columns github_username and role. Add your GitHub username with the role admin to prevent self-alerts. Accordingly, add other developers and members in your organization with appropriate roles to white list them. Switch Configuration: Use the expression {{ $item.json.type }} in 'Rules' mode to route events. Logic: Configure the PushEvent IF node to flag users whose role is empty or missing. Configure the Member and Public IF nodes to flag users whose role is not admin.
by Mohammad
🔐 Human-in-the-Loop Approval Flow (n8n + Postgres + Telegram) 👥 Who’s it for Teams that need a manager approval step before a ticket or request can change status. Great for internal ops, IT requests, or any workflow where “a human must sign off.” ⚡ What it does 📨 Manager receives approval/reject link 🔑 Link is signed with HMAC + expiry (secure & tamper-proof) 🗄️ Postgres updates the ticket status 📝 Audit trail records every decision 📲 Telegram notifies both manager and requester ⏰ Expired or invalid links trigger alerts and logs 🛠 Requirements n8n instance (self-hosted) Postgres database (with tickets, ticket_audit, workflow_errors) Telegram bot token One environment variable set: SECRET_KEY ⚙️ How to set up Set SECRET_KEY in .env Create Postgres tables (SQL provided) Add Telegram + Postgres credentials in n8n Import the workflow JSON Test by opening an approval/reject link in your browser 🎨 How to customize Change who the “manager” is (currently hardcoded in the Code node). Swap Telegram for Slack or email notifications. Extend the audit schema to include more metadata (IP, username).
by n8n Automation Expert | Template Creator | 2+ Years Experience
🌦️ Intelligent Aquaculture Automation for Indonesia Transform your fish farming operation with this cutting-edge n8n workflow that combines Indonesia's official BMKG weather data with IoT-powered feeding automation. This system intelligently reduces feed by 20% when rain probability exceeds 60%, preventing overfeeding during adverse weather conditions that could compromise water quality and fish health. 🚀 Key Features 🌦️ Real-time BMKG Integration: Fetches official Indonesian weather forecasts every 12 hours using BMKG's public API with precise ADM4 regional targeting 🤖 Smart Decision Engine: Advanced JavaScript algorithms analyze 6-hour and 12-hour rain probabilities to make optimal feeding decisions automatically 📱 ESP8266 IoT Control: Seamlessly sends HTTP webhook commands to your ESP8266/ESP32-based fish feeder hardware with JSON payloads 💬 Rich Telegram Notifications: Comprehensive reports including weather analysis, feeding decisions, hardware status, and next feeding schedule ⏰ Precision Scheduling: Automated execution at 05:30 and 16:30 WIB (Indonesian Western Time) with cron-based triggers 📊 Activity Logging: Complete audit trail with timestamps, weather data, and feeding decisions for operational monitoring 🛠️ Technical Architecture Core Node Components: Schedule Trigger:** Automated twice-daily execution HTTP Request:** BMKG API integration with timeout handling Code (JavaScript):** Weather parsing and feeding ratio calculations IF Condition:** Intelligent branching based on configurable rain thresholds Telegram:** Formatted notifications with markdown support Set Variables:** Secure credential management with placeholder tokens 📋 Prerequisites ✅ n8n Instance: Self-hosted or cloud deployment ✅ Telegram Bot: Create via @BotFather for notifications ✅ ESP8266/ESP32: Hardware with servo motor for automated feeding ✅ Arduino Skills: Basic programming knowledge for hardware setup ✅ Indonesian Location: Uses BMKG API with ADM4 regional codes ⚙️ Configuration Requirements 📍 Location Settings: Update latitude, longitude, and BMKG ADM4 code in the Config node 🤖 Telegram Bot: Configure bot token and chat ID in credentials 🔗 ESP8266 Webhook: Set your device's IP address for hardware communication 📊 Feeding Parameters: Customize rain threshold (default: 60%) and feed reduction (default: -20%) 🎯 Perfect For 🏭 Commercial Aquaculture: Large-scale fish farming operations requiring weather-aware feeding 🏠 Hobbyist Enthusiasts: Home aquarium and pond automation projects 🌱 Smart Agriculture: Integration with comprehensive farm management ecosystems 🔧 IoT Learning: Educational platform for weather-based automation development 🌍 Environmental Research: Combining meteorological data with livestock care protocols 📊 Rich Output Examples The workflow generates detailed Telegram reports featuring: Current Weather Analysis:** 6-hour and 12-hour rain probability breakdowns Feeding Decision Logic:** Clear rationale for feed adjustments with percentages Hardware Confirmation:** ESP8266 response status and command execution verification Schedule Preview:** Next automated feeding time with countdown Historical Logs:** Comprehensive activity tracking for pattern analysis 🔧 Hardware Integration Guide Designed for ESP8266-based feeders accepting HTTP POST commands. The workflow transmits structured JSON containing: { "command": "FEED_REDUCE_20", "feed_ratio": -20, "rain_prob": 75, "timestamp": "2024-09-18T10:30:00Z", "location": "Main Pond" } 🌍 Regional Adaptation Indonesia-Optimized: Built specifically for BMKG's official weather API with ADM4 regional precision Global Compatibility: Easily adaptable for international weather services by modifying HTTP requests and parsing logic Scalable Architecture: Supports multiple pond locations with separate ADM4 configurations 🔒 Security & Credentials All API keys use {{PLACEHOLDER}} format for secure credential management No hardcoded sensitive information in workflow nodes Telegram bot tokens managed through n8n's credential system ESP8266 webhooks support local network security 📈 Performance Benefits 20% Feed Optimization:** Automatic reduction during high rain probability periods Water Quality Protection:** Prevents overfeeding that degrades aquatic environment Cost Efficiency:** Reduces feed waste while maintaining fish health 24/7 Monitoring:** Continuous weather analysis without manual intervention Scalable Operations:** Supports multiple feeding locations from single workflow
by ChatPayLabs
Workflow Name: 👻 Exception Flow Template was created in n8n v1.90.2 Skill Level: Low Categories: n8n, Chatbot Stacks Error Trigger Slack node What this workflow does? This is a n8n Error Workflow. It will trigger when there is an error in another workflow. When this happens, it then tries to send an error notification to the preset Slack channel. How it works The Error Trigger node will trigger when there is an error in another workflow, as long as that workflow is set up to do so. A error notification will be sent to the Slack Channel. Set up instructions Create you Slack credentials, refer to n8n integration documentation for more information. Set up the Channel in 👻 Exception Alert node. For any n8n workflows to trigger this, switch to that workflow, select menu > settings, and set the Error Workflow to 👻 Exception Flow. How to adjust it to your needs Although this workflow template is part of the AI Chatbot Call Center Series, it could be used with any n8n workflows. Update the Channel in 👻 Exception Alert to your own channel https://chatpaylabs.com/blog/part-8-build-your-own-ai-chatbot-call-center-general-exception-flow-production-ready-n8n-workflow-free-download-
by Harshil Agrawal
This workflow allows you to create, update and get a task in Microsoft To Do. Microsoft To Do node: This node will create a task with the importance High in the Tasks list. You can select a different list as well as the importance level. Microsoft To Do1 node: This node will update the status of the task that we created in the previous node. Microsoft To Do2 node: This node will get the task that we created earlier.
by Rapiwa
Who Is This For? This n8n automation workflow is designed for customer support teams, business owners, or service providers who want to automate customer interactions on WhatsApp. If you regularly receive customer queries about your products, services, or technical issues — and need a system that can instantly respond, fetch data from Google Sheets or Docs, log support tickets, and send human-like replies — this workflow is for you. It’s perfect for teams using Rapiwa, Google Sheets, and Google Docs who want to provide a smart, AI-driven, yet personal support experience. What This Workflow Does This workflow is structured around a single intelligent AI assistant called Rapiwa that interacts with customers in real time through WhatsApp. Key Features AI-Driven Support Assistant (Rapiwa) WhatsApp Integration via Rapiwa API Dynamic Data Access (Google Sheets + Docs) Knowledge Base Search Conversation Memory Automatic Logging Multi-Product Support Workflow Overview Rapiwa Trigger (Start Node) Starts the workflow automatically whenever a new WhatsApp message is received in your Rapiwa account. Example: When a customer sends a message like “What’s the price of SocialVibe?” or “I can’t access my dashboard”, this node triggers the workflow. If (Check Text) Detects if the incoming message contains text (not just images, videos, or audio). If it’s text, the workflow continues; otherwise, it stops or handles it differently. AI Agent – Customer Support Agent This is the brain of the system — your AI Assistant (Rapiwa). Interprets the user’s question, retrieves information, and replies in a clear, WhatsApp-friendly format. Reads product details and company info from Google Sheets/Docs. Fetches documentation links from the connected “Support Desk” and product-specific HTTP tools. Logs customer issues to the support sheet for tracking and analysis. Memory (Session Context) Stores chat history per user session so Rapiwa remembers context during a conversation. Research (AI Support Tool) Acts as Rapiwa’s research assistant — gathers and organizes information from multiple sources. Sources: Google Sheets, Google Docs, HTTP Tools, and Support Desk. Replay (Rapiwa Send Message) Sends the AI’s final message back to the customer on WhatsApp using the Rapiwa API. WhatsApp-optimized plain text messages only. Data & Integrations 🔹 Google Sheets (Database) Product Data Sheet:** Holds product names, descriptions, and pricing. Service Data Sheet:** Lists offered services with details. Support Log Sheet:** Records each issue (Issue, Category, Solution). 🔹 Google Docs Provides company information when a user asks about your organization. Example Use Case User Message: > “Hi, I’m having a problem with my Faculty login.” Rapiwa’s AI Response: > “I’m sorry you’re having trouble logging in to Faculty. Please try resetting your password here: https://faculty.spagreen.net/docs/#reset-password > If the issue continues, I can log this for support. Would you like me to do that?” Useful Links install process:** how to install rapiwa Dashboard:** https://app.rapiwa.com Official Website:** https://rapiwa.com Documentation:** https://docs.rapiwa.com Support & Help WhatsApp**: Chat on WhatsApp Discord**: SpaGreen Community Facebook Group**: SpaGreen Support Website**: https://spagreen.net Developer Portfolio**: Codecanyon SpaGreen
by Yashraj singh sisodiya
Summarize YouTube Videos with Gemini AI, Google Sheets & WhatsApp/Telegram Aim The aim of the YouTube Video Summarizer Workflow is to automate the process of summarizing or extracting transcripts from YouTube videos with the help of Gemini AI, while optionally storing results and distributing them to users via WhatsApp, Telegram, or Google Sheets. This enables fast, consistent generation and sharing of English summaries or transcripts from public YouTube content. Goal The goal is to: Allow users to submit a YouTube link through various channels (Form Webhook, WhatsApp, Telegram). Use Gemini AI to either summarize the content or transcribe the complete video, always outputting in English. Return the output to the user via their original channel and optionally log it to Google Sheets for record-keeping. Requirements The workflow relies on specific integrations and configurations: n8n Platform**: Self-hosted or cloud n8n instance to host and automate the workflow. Node Requirements**: Form/Webhook Trigger: Web form for pasting the YouTube link. WhatsApp Trigger: Starts workflow from incoming WhatsApp messages (YouTube link as input). Telegram Trigger: Initiates workflow from Telegram chat messages containing YouTube links. Gemini AI Node: Consumes the YouTube link and processes it for summarization or transcription (always in English). Google Sheets Node: Writes the result (summary/transcript) into a Google Sheet for logging and future reference. WhatsApp/Telgram Send Message Nodes: Delivers summarized results or transcripts back to the user on the same platform where they triggered the workflow. Credentials**: Gemini/Google AI Platform account for AI summarization and transcription. Google Sheets account for storing output. WhatsApp Business API for WhatsApp automation. Telegram Bot API for Telegram automation. Input Requirements**: Publicly-accessible YouTube video link (max ~30 min, as per summarized logic). Output**: English video summary or full transcript, delivered via user’s requested channel and/or stored in Google Sheets. API Usage The workflow integrates several APIs for optimal automation: Gemini AI API**: Used in the main summarization node. Receives the YouTube link and a prompt with detailed instructions. Returns either a clear, concise English summary or a full transcript translated into English, handling Hindi, English, or mixed-language videos. [Ref: Workflow JSON] Google Sheets API**: Used to log the output for each processed video, making it easy to reference histories or track requests. [Ref: Workflow JSON] WhatsApp Business API**: Sends back the summary or transcript to the user who initiated via WhatsApp. [Ref: Workflow JSON] Telegram Bot API**: Sends results back to Telegram users directly in chat. [Ref: Workflow JSON] Output Formatting/Conversion The AI output is always in English, tailored to the option chosen (summary vs transcript). Structured output: Bulleted, neutral, and easy to read, suitable for sharing with users or for business documentation. Google Sheets node maps and writes each video’s results to a dedicated row for easy history review. How to Use By default, the workflow uses a manual trigger via a web form, but you may add triggers for WhatsApp or Telegram to suit your needs. Users paste a YouTube link, then select whether they want a summary or transcript (based on your implementation logic). Results are returned in their channel and optionally logged to your Google Sheet. All processing is handled securely using your Gemini API credentials. You can expand this logic by adding more integrations (email, Slack, etc.). Customising this Workflow Custom prompts can be written for different styles or output formats (e.g., SEO key points, step-by-step guides). Add logic for batch processing multiple videos or bulk export to different cloud drives. Integrate into central dashboards, CRMs, or content pipelines using n8n’s hundreds of available integrations. Good to Know Gemini pricing:** At the time of writing, each YouTube video summarization costs $0.039 USD. See official Gemini Pricing for current rates. Geo-restriction:** The Gemini video model may be geo-restricted (error: “model not found” outside some regions). Video Limits:** Intended for videos up to ~30 minutes for best processing reliability. Scaling:** Can be easily adapted for high-volume operations using n8n’s queue and scheduling features. Workflow Summary The YouTube Video Summarizer Workflow automates summarizing and transcribing YouTube videos using AI and n8n. Users send video links via web forms, WhatsApp, or Telegram. Results are generated via Gemini, sent back in-app, and logged to Google Sheets, enabling effortless knowledge sharing and organizational automation at scale. Timestamp: 12:37 PM IST, Wednesday, September 17, 2025
by Nima Salimi
Overview This n8n workflow automatically retrieves Brevo contact reports and inserts summarized engagement data into NocoDB. It groups campaign activity by email, creating a clean, unified record that includes sent, delivered, opened, clicked, and blacklisted events. This setup keeps your CRM or marketing database synchronized with the latest Brevo email performance data. ✅ Tasks ⏰ Runs automatically on schedule or manually 🌐 Fetches contact activity data from Brevo API 🧩 Groups all campaign activity per email 💾 Inserts summarized data into NocoDB ⚙️ Keeps engagement metrics synced between Brevo and NocoDB 🛠 How to Use 🧱 Prepare your NocoDB table Create a table with fields for: email, messagesSent, delivered, opened, clicked, done, and blacklisted. 🔑 Connect your Brevo credentials Add your Brevo API Key in the HTTP Request node to fetch contact data securely. 🧮 Review the Code Nodes These nodes group contact activity by email and prepare a single dataset for insertion. 🚀 Run or schedule the workflow Execute it manually or use a Schedule Trigger to automate the data sync process. 📌 Notes 🗂 Make sure the field names in NocoDB match those used in the workflow. 🔐 Keep your Brevo API Key secure and private. ⚙️ The workflow can be expanded to include additional fields or filters. 📊 Use the data for engagement analytics, segmentation, or campaign performance tracking.
by Rahul Joshi
Description: Ensure your customer SLAs never slip with this n8n automation template. The workflow runs on a schedule, fetching open tickets from Zendesk, calculating SLA time remaining, and sending proactive alerts to Slack when tickets approach breach thresholds (75% and 90%). It also updates ticket priority in Zendesk and logs compliance metrics to Google Sheets for reporting. Perfect for support operations, CX teams, and SaaS companies looking to maintain SLA compliance and reduce response delays automatically. ✅ What This Template Does (Step-by-Step) ⏰ Run Every Hour: Automatically triggers every hour to check for SLA-sensitive tickets. 📥 Fetch All Open Zendesk Tickets: Pulls all tickets via the Zendesk API, returning essential fields: ID, status, created_at, sla_due, and priority. 🔍 Filter Only “Open” Tickets: Excludes closed, on-hold, or pending tickets — monitoring focuses only on actionable cases. ⏱️ Calculate SLA Time Remaining: Computes total SLA duration, remaining minutes, and % of SLA consumed for each ticket. 🟡 Warn at 75% Threshold: When 75% of the SLA window has passed, automatically sends a Slack warning to the #general-information channel. 🔴 Escalate at 90% Threshold: For tickets nearing breach (≥90%), the workflow updates Zendesk ticket priority to “High,” adds escalation notes, and notifies the support team for immediate action. 📊 Log SLA Compliance in Google Sheets: Each ticket’s SLA metrics (ID, % elapsed, time remaining, timestamp) are appended to a Google Sheet for tracking and reporting. ✅ No-Ticket Confirmation: If no open tickets exist, the workflow posts a “✅ No open tickets” message to Slack — keeping teams informed of a clear queue. 🧠 Key Features ⏱️ Automated SLA tracking and escalation 📊 Real-time logging to Google Sheets ⚡ Hourly auto-trigger — no manual checks needed 📢 Slack alerts at warning and critical thresholds 🔄 Dynamic Zendesk ticket updates via API 💼 Use Cases 💬 Proactively manage customer support SLAs 🚨 Automatically escalate critical tickets before breach 📈 Maintain transparent SLA compliance reporting 📢 Keep your support team updated in real time 📦 Required Integrations Zendesk API – for ticket retrieval and updates Slack API – for alert notifications Google Sheets – for compliance and reporting logs 🎯 Why Use This Template? ✅ Prevent SLA breaches before they happen ✅ Automate escalation and communication ✅ Provide real-time visibility to support leads ✅ Build a historical SLA performance dataset