Workflow Templates

How It Works This workflow automates predictive maintenance for vehicle fleets by combining real-time telemetry analysis with historical pattern recognition to identify potential failures before they occur. Designed for fleet managers, maintenance supervisors, and transportation operations teams, it solves the critical challenge of preventing unexpected vehicle breakdowns while optimizing maintenance scheduling and resource allocation. The system triggers on schedule, fetches current vehicle telemetry data alongside historical maintenance records, merges datasets for comprehensive analysis, then deploys specialized AI agents using Anthropic's Claude to detect anomalies and prioritize maintenance interventions. The workflow calculates urgency levels using machine learning models and business rules, formats findings into standardized maintenance records and urgent alerts, generates audit logs for compliance tracking, and routes notifications to appropriate maintenance teams based on severity. Setup Steps Configure Schedule Trigger with desired monitoring frequency for fleet checks Set up API credentials for Fetch Real-Time Vehicle Telemetry node with fleet management system Configure Fetch Historical Vehicle Data node with maintenance database API access Connect Anthropic API credentials for both Anomaly Detection and Maintenance Prioritization agents Update Anomaly Detection Model with your fleet's baseline performance parameters Customize UL Calculation Tool and Maintenance Prioritization Output Parser Prerequisites Active Anthropic API account, fleet telemetry system with API access, historical maintenance database Use Cases Commercial fleet preventive maintenance, vehicle health monitoring, breakdown prediction Customization Modify anomaly detection thresholds for vehicle types, adjust prioritization algorithms for operational priorities Benefits Reduces unexpected breakdowns by 80%, decreases maintenance costs through predictive scheduling

Automatically detect, classify, and document GitHub API errors using AI. This workflow connects GitHub, OpenAI (GPT-4o), Airtable, Notion, and Slack to build a real-time, searchable API error knowledge base — helping engineering and support teams respond faster, stay aligned, and maintain clean documentation. ⚙️📘💬 🚀 What This Template Does 1️⃣ Triggers on new or updated GitHub issues (API-related). 🪝 2️⃣ Extracts key fields (title, body, repo, and link). 📄 3️⃣ Classifies issues using OpenAI GPT-4o, identifying error type, category, root cause, and severity. 🤖 4️⃣ Validates & parses AI output into structured JSON format. ✅ 5️⃣ Creates or updates organized FAQ-style entries in Airtable for quick lookup. 🗂️ 6️⃣ Logs detailed entries into Notion, maintaining an ongoing issue knowledge base. 📘 7️⃣ Notifies the right Slack team channel (DevOps, Backend, API, Support) with concise summaries. 💬 8️⃣ Tracks & prevents duplicates, keeping your error catalog clean and auditable. 🔄 💡 Key Benefits ✅ Converts unstructured GitHub issues into AI-analyzed documentation ✅ Centralizes API error intelligence across teams ✅ Reduces time-to-resolution for recurring issues ✅ Maintains synchronized records in Airtable & Notion ✅ Keeps DevOps and Support instantly informed through Slack alerts ✅ Fully automated, scalable, and low-cost using GPT-4o ⚙️ Features Real-time GitHub trigger for API or backend issues GPT-4o-based AI classification (error type, cause, severity, confidence) Smart duplicate prevention logic Bi-directional sync to Airtable + Notion Slack alerts with contextual AI insights Modular design — easy to extend with Jira, Teams, or email integrations 🧰 Requirements GitHub OAuth2 credentials OpenAI API key (GPT-4o recommended) Airtable Base & Table IDs (with fields like Error Code, Category, Severity, Root Cause) Notion integration with database access Slack Bot token with chat:write scope 👥 Target Audience Engineering & DevOps teams managing APIs Customer support & SRE teams maintaining FAQs Product managers tracking recurring API issues SaaS orgs automating documentation & error visibility 🪜 Step-by-Step Setup Instructions 1️⃣ Connect your GitHub account and enable the “issues” webhook event. 2️⃣ Add OpenAI credentials (GPT-4o model for classification). 3️⃣ Create an Airtable base with fields: Error Code, Category, Root Cause, Severity, Confidence. 4️⃣ Configure your Notion database with matching schema and access. 5️⃣ Set up Slack credentials and choose your alert channels. 6️⃣ Test with a sample GitHub issue to validate AI classification. 7️⃣ Enable the workflow — enjoy continuous AI-powered issue documentation!

Quick Overview This workflow runs daily to fetch invoice data from an HTTP endpoint, calculate early-payment savings and annualized ROI, and use Groq (Llama 3.3) to prioritize eligible discounts. It logs eligible and expired invoices to Google Sheets and emails a digest of HIGH-priority recommendations via Gmail. How it works Runs every day at 9:00 to start an invoice scan. Fetches invoice records from an external HTTP API endpoint. Calculates potential savings, days saved by paying early, annualized ROI, and whether each invoice is still eligible before the discount deadline. Routes eligible invoices to Groq (Llama 3.3) to return a JSON decision with a YES/NO recommendation, HIGH/MEDIUM/LOW priority, and a short reason, then normalizes the AI response. Appends eligible invoice evaluations (including ROI, savings, priority, recommendation, and reason) to a Google Sheets log. Filters for invoices marked YES and HIGH priority, aggregates them into a daily batch, and emails a plain-text digest to the finance team via Gmail. Marks ineligible invoices as expired with a skip reason and appends them to a separate Google Sheets audit log. Setup Add a Groq API credential and ensure the selected Groq model is available in your account. Add Google Sheets OAuth2 credentials and update the spreadsheet ID, sheet tabs, and column headers to match your logging sheets. Add Gmail OAuth2 credentials and set the target recipient(s) for the finance summary email. Update the invoice API URL to your real invoice source and ensure each record includes amount, discount_percent, discount_deadline, payment_due, vendor, and vendor_email fields. Adjust the available cash amount and the ROI/cash threshold rules in the prompt and/or data preparation step to match your finance policy.

How it works This workflow runs daily to review all active deals and evaluate their likelihood of closing successfully. It enriches deal data with recent engagement activity and applies AI-based behavioral scoring to predict conversion probability. High-risk or stalled deals are flagged automatically. Actionable alerts are sent to the sales team, and all analysis is logged for forecasting and tracking. Step-by-step Trigger and fetch deals** Schedule Trigger – Runs the workflow automatically at a fixed time each day. Get Active Deals from HubSpot – Retrieves all open, non-closed deals with key properties. Formatting Data – Normalizes deal fields such as value, stage, age, contacts, and activity dates. Enrich deals with engagement data** If – Filters only active deals for further processing. Loop Over Items – Processes each deal individually. HTTP Request – Fetches engagement associations for the current deal. Get an engagement – Retrieves detailed engagement records from HubSpot. Extracts Data – Structures engagement content, timestamps, and metadata for analysis. Analyze risk, alert, and store results** OpenAI Chat Model – Provides the language model used for analysis. AI Agent – Evaluates behavioral signals, predicts conversion probability, and recommends actions. Format Data – Parses AI output into structured, machine-readable fields. Filter Alerts Needed – Identifies deals that need immediate attention. Send Slack Alert – Sends detailed alerts for high-risk or stalled deals. Append or update row in sheet – Logs analysis results into Google Sheets for reporting. Why use this? Automatically identify high-risk deals before they stall or fail Give sales teams clear, data-driven next actions instead of raw CRM data Improve forecasting accuracy with AI-powered probability scoring Maintain a historical deal health log for audits and performance reviews Reduce manual pipeline reviews while increasing response speed

Data Quality Checker This workflow automatically monitors pricing data in Google Sheets, detects anomalies such as missing values or sudden spikes/drops, generates AI-based short explanations for flagged rows, updates the sheet with status and reason and sends Slack alerts for critical issues. Quick Implementation Steps: Prepare your Google Sheet with columns: price, previous_price, status, reason, row_number. Connect n8n with your Google Sheets, Slack and Groq AI credentials. Trigger the workflow manually or via webhook. Each row is processed to detect anomalies and generate a reason using AI. Flagged rows are updated in Google Sheets and a Slack alert is sent automatically. What It Does The Data Quality Checker workflow is designed to help you maintain accurate pricing data effortlessly. It fetches data from Google Sheets, evaluates each row for anomalies and ensures that unusual changes in prices do not go unnoticed. It automatically flags rows with missing values, sudden spikes or sudden drops in price. The workflow uses a Groq AI model to generate concise explanations for any anomalies found, providing actionable insights directly in your sheet. For rows without issues, it marks the status as OK with a standard reason. A Slack alert is sent for flagged data to keep teams informed in real-time, preventing unnoticed errors and enabling faster corrective actions. Who It's For Data analysts monitoring pricing or inventory data. E-commerce managers tracking product price fluctuations. Finance teams validating large datasets for inconsistencies. Operations teams needing automated anomaly detection with alerts. This workflow suits anyone who relies on timely, accurate data and wants automated anomaly detection and reporting. Requirements to Use This Workflow n8n account (cloud or self-hosted). Google Sheets account** with read/write access to the target sheet. Slack workspace** with a channel for alerts. Groq AI account** for anomaly explanation generation. Sheet structure: columns price, previous_price, status, reason, row_number. How It Works & Setup Guide Prepare Google Sheet Ensure your sheet has price, previous_price, status, reason, row_number. Populate initial pricing data. Connect Credentials in n8n Add Google Sheets OAuth2 for reading and updating the sheet. Add Slack API credentials to send alerts. Add Groq AI credentials for AI reasoning. Trigger the Workflow Use a manual trigger or configure a webhook to run automatically. Process Data The workflow fetches data from Google Sheets. SplitInBatches node ensures each row is processed individually. Check Price Issues node checks for missing values or sudden changes. Anomaly Handling If node determines whether an anomaly exists. For flagged rows, AI generates a short reason (Generate Issue Reason). Prepare Flag Data formats data for updating the sheet. Update & Alert Update Flagged Row writes flagged status and reason back to Google Sheets. Update Normal Row writes OK status for rows with no issues. Send Slack Alert sends real-time notifications for flagged anomalies. How To Customize Nodes Google Sheets nodes:** update documentId and sheetName to match your own sheets. Slack node:** change channelId and alert text to match your workspace and notification style. AI node:** adjust the prompt in Generate Issue Reason to change explanation style or length. Code node:** modify the logic in Check Price Issues to define what constitutes a spike or drop. Add-ons Connect to email notifications instead of Slack. Extend AI logic to suggest corrective actions. Include historical trend analysis in Google Sheets for better insights. Integrate with other data sources like CSV or databases. Use Case Examples E-commerce pricing validation: Automatically detect and explain unusual price changes for hundreds of products. Inventory data verification: Ensure stock values and price adjustments are accurate daily. Finance anomaly detection: Detect sudden cost or rate fluctuations in financial datasets. Market monitoring: Track competitor pricing changes with automated alerts. General data quality assurance: Any dataset requiring automated checks for missing or inconsistent values. This workflow is flexible and can be adapted to other types of tabular data beyond pricing. Troubleshooting Guide | Issue | Possible Cause | Solution | | ------------------------ | --------------------------------------- | --------------------------------------------------------- | | Workflow not triggering | Trigger node not active | Enable the manual or webhook trigger | | Data not fetched | Wrong documentId or sheetName | Verify Google Sheets node settings and credentials | | Slack alert not sent | Invalid channel ID or credentials | Update Slack credentials and ensure channel ID is correct | | AI reasoning fails | Groq API issues or prompt misconfigured | Check Groq AI credentials and review the prompt text | | Wrong anomaly detection | Code logic in Check Price Issues | Adjust thresholds or conditions in the code node | | Sheet updates not saving | Google Sheets permission issue | Ensure OAuth2 account has write access to the sheet | Need Help? If you face issues setting up this workflow, customizing nodes or integrating add-ons, our n8n automation team can help. We specialize in building and optimizing n8n workflows for automation, data quality and alerting. Contact us to implement this workflow, enhance it or create custom automation solutions tailored to your business needs.

Analyze error logs with AI and auto-create GitHub issues with fix suggestions Who is this for DevOps engineers, SREs, and development teams who want to automate error monitoring and reduce mean time to resolution (MTTR). Ideal for teams using GitHub for issue tracking and Slack for incident response. What this workflow does This workflow automates the entire error management lifecycle - from log ingestion to GitHub issue creation and Slack notification - using GPT-4o-powered root cause analysis. When an application error log is received, it parses the payload, checks for duplicates against existing GitHub issues, generates a structured root cause analysis with fix suggestions, creates a formatted GitHub Issue, and routes Slack notifications by severity. A 30-minute Wait node prevents notification flooding. How to set up Add your GitHub Personal Access Token (repo scope) credential Add your OpenAI API credential to the AI analysis node Add your Slack OAuth2 credential (chat:write scope) to all Slack nodes Configure n8n Variables: GITHUB_OWNER and GITHUB_REPO Update Slack channel names (#incident / #dev-alerts) to match your workspace Activate the workflow and copy the webhook URL for your application logger Requirements GitHub repository with Personal Access Token (repo scope) OpenAI API account with GPT-4o access Slack workspace with OAuth2 app installed Two Slack channels: one for critical incidents, one for general dev alerts How to customize Adjust the duplicate detection score threshold (default 60) in the Code node. Modify the GPT-4o prompt to focus on specific error categories. The Wait node duration (30 minutes) can be tuned to match your alerting policy. Key features Scoring-based duplicate detection (no extra API calls required) GPT-4o structured JSON output with graceful fallback parser Severity-based Slack routing (#incident vs #dev-alerts) Dynamic GitHub labels: bug, auto-generated, environment, and critical n8n Variables used for GitHub owner and repo - no hardcoded values Node List | # | Node Name | Type | Purpose | |---|-----------|------|---------| | 1 | Webhook for Error Logs | Webhook | Accepts error log payload via HTTP POST | | 2 | Parse and Enrich Log | Code | Normalizes level, extracts error type, builds duplicate search keyword | | 3 | Search GitHub Issues | GitHub | Fetches open issues labeled bug,auto-generated from the target repo | | 4 | Score GitHub Issue Duplicates | Code | Scores each issue for similarity; flags duplicates at threshold ≥ 60 | | 5 | Check for Duplicates | If | Routes to skip path (true) or analysis path (false) | | 6 | Notify Duplicate to Slack | Slack | Posts link to existing issue when duplicate detected | | 7 | Respond with Duplicate Status | Respond to Webhook | Returns 200 OK JSON acknowledgment for duplicate path | | 8 | OpenAI Error Analysis | OpenAI | GPT-4o analyzes root cause and returns structured JSON fix suggestions | | 9 | Build GitHub Issue Body | Code | Parses AI JSON and builds Markdown issue body with tables and code blocks | | 10 | Create GitHub Issue | GitHub | Creates GitHub issue with title, body, and dynamic labels | | 11 | If Critical Error | If | Checks isCritical flag to route Slack notification channel | | 12 | Post Critical Alert to Slack | Slack | Posts @here alert to #incident with full error details | | 13 | Post Error Summary to Slack | Slack | Posts summary to #dev-alerts with GitHub issue link | | 14 | Wait 30 Minutes | Wait | Enforces 30-minute cooldown to prevent notification flooding | Total: 14 nodes (+ 6 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Receive and parse log | White | Covers webhook reception and log parsing | | 3 | Check for duplicate GitHub issues | White | Covers GitHub search and duplicate scoring | | 4 | Handle duplicates and notify | White | Covers duplicate branch (notification + webhook response) | | 5 | Analyze error and suggest fixes | White | Covers AI analysis and issue creation | | 6 | Send alerts and summarize | White | Covers severity check, Slack notifications, and wait | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Webhook payload schema { "service": "payment-api", "level": "CRITICAL", "message": "NullPointerException at PaymentProcessor.java:142", "stack_trace": "java.lang.NullPointerException...", "environment": "production", "timestamp": "2025-01-15T09:23:45Z", "trace_id": "abc-123-xyz", "endpoint": "/api/v2/payments", "http_method": "POST", "status_code": 500, "user_id": "user_98765" } Required fields: service, level, message Optional fields: All others - missing values are handled gracefully with fallbacks. How duplicate detection works | Match condition | Points | |-----------------|--------| | Service name found in issue title | +40 | | Error type found in issue title | +40 | | Keyword overlap (words > 4 chars) | +5 per word (max +20) | Threshold: Score ≥ 60 → duplicate detected → skip issue creation, notify Slack. Tags ai gpt-4 openai github slack error-monitoring devops automation

Quick overview This workflow runs every 6 hours to monitor competitor product pages stored in Airtable, scrape current price and stock with Firecrawl, extract structured pricing with Anthropic, send priority-based alerts to Slack, and log changes to Google Sheets while syncing the latest values back to Airtable. How it works A Schedule trigger fires every 6 hours and fetches your full competitor list from Airtable. Each record is processed one at a time through a SplitInBatches loop. Firecrawl scrapes the live product page and returns the content as Markdown. The Anthropic API extracts structured pricing data (price, currency, stock status) using tool use. A built-in deduplication check skips items where nothing has changed since the last run. Each item is assigned an alert priority — Critical, High, Medium, Info, or Skip — based on the price delta and stock change thresholds. Critical and High events trigger an AI-generated countermeasure strategy followed by an immediate Slack alert. Medium events get a lighter Slack notification. Info items pass through silently. Skipped items return directly to the loop. All actionable events update the Airtable record with the latest price and stock status, and append a timestamped row to Google Sheets for historical trend tracking. Setup Connect credentials for Airtable, Firecrawl, Anthropic, Slack, and Google Sheets. Replace the Airtable Base ID and Table ID with your own and ensure records include fields for Competitor Name, Product Name, Product URL, Last Price, and In Stock. Set the Slack channel ID in both Slack message steps. Set the Google Sheets document ID and sheet tab name used for the historical append log. Requirements Firecrawl API account (for product page scraping) Anthropic API key with access to claude-3-5-sonnet-20241022 Airtable base with the required competitor table and fields Slack workspace with a channel configured for alerts Google Sheets document with a pre-created tab for historical logging Customization Adjust the schedule interval in the Every 6 Hours node to scan more or less frequently. Modify the priority thresholds in Extract Price & Build Delta — currently Critical is ≤ -15%, High is ≤ -5%. Update the Slack message templates in both Slack nodes to match your team's alert format. Extend the Google Sheets columns to capture additional fields such as currency or stock count. Additional info The deduplication logic compares both price and stock status against the previous run. If neither has changed, the item is skipped entirely and no write operations occur. If Firecrawl fails to return content or the Anthropic API returns no valid price, the item is marked _skip and passed back to the loop without crashing the workflow. The workflow does not include an error trigger node in this version. For production use, consider adding one to catch and log node-level failures.

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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🚀 Transform Your Job Hunt with AI-Powered Telegram Bot Turn job searching into a conversational experience! This intelligent Telegram bot automatically scrapes job postings from LinkedIn, Indeed, and Monster, filters for sales & marketing positions, and delivers personalized results directly to your chat. ✨ Key Features Interactive Telegram Commands**: Simple /jobs [keyword] [location] searches Multi-Platform Scraping**: Simultaneous data collection from 3 major job boards AI-Powered Filtering**: Smart relevance detection and experience level classification Real-Time Notifications**: Instant job alerts delivered to Telegram Automated Data Storage**: Saves results to Google Sheets and Airtable Duplicate Removal**: Advanced deduplication across platforms Mobile-First Experience**: Full job search functionality through Telegram 🎯 Perfect For Sales Professionals**: Account managers, sales representatives, business development Marketing Experts**: Digital marketers, marketing managers, growth specialists Recruiters**: Streamlined candidate sourcing and job market analysis Job Seekers**: Hands-free job discovery with instant notifications 🛠️ Setup Requirements Required Credentials: Telegram Bot Token**: Create bot via @BotFather Bright Data API**: Professional web scraping service (LinkedIn/Indeed datasets) Google Sheets OAuth2**: For spreadsheet integration Airtable Token**: Database storage and management Prerequisites: n8n instance with HTTPS enabled (required for Telegram webhooks) Valid domain name with SSL certificate Basic understanding of Telegram bot commands 🔧 How It Works User Experience: Send /start to activate the bot and see available commands Use /jobs sales manager New York to search for specific positions Receive formatted job results instantly in Telegram Click "Apply Now" links to go directly to job postings All jobs automatically saved to your connected spreadsheets Behind the Scenes: Command Processing: Bot parses user input for keywords and location Parallel Scraping: Simultaneous API calls to LinkedIn, Indeed, and Monster AI Processing: Intelligent filtering, experience level detection, remote work identification Data Enhancement: Salary extraction, duplicate removal, relevance scoring Multi-Format Storage: Automatic saving to Google Sheets, Airtable, and JSON export Real-Time Response: Formatted results delivered back to Telegram chat 🎨 Telegram Bot Commands /start - Welcome message and command overview /jobs [keyword] [location] - Search for jobs (e.g., /jobs marketing manager remote) /help - Show detailed help information /status - Check bot status and recent activity 📊 Sample Output The bot delivers beautifully formatted job results: 🎯 Job Search Results 🎯 Found 7 relevant opportunities Platforms: linkedin, indeed, monster Remote jobs: 3 ─────────────────── 💼 Senior Sales Manager 🏢 TechCorp Industries 📍 New York, NY 💰 $80,000 - $120,000 🌐 Remote Available 📊 senior level 🔗 Apply Now 🔒 Security & Best Practices Rate Limiting**: Built-in Telegram API compliance (30 requests/second) Error Handling**: Graceful failure recovery with user-friendly messages Input Validation**: Sanitized user input to prevent injection attacks Credential Management**: Secure API key storage using n8n credentials system HTTPS Enforcement**: Required for production Telegram webhook integration 📈 Benefits & ROI 95% Time Reduction**: Automated job discovery vs manual searching Multi-Source Coverage**: Access 3 major job platforms simultaneously Mobile Accessibility**: Search jobs anywhere using Telegram mobile app Real-Time Alerts**: Never miss new opportunities with instant notifications Data Organization**: Automatic spreadsheet management for job tracking Market Intelligence**: Comprehensive job market analysis and trends 🚀 Advanced Customization Custom Keywords**: Modify filtering logic for specific industries Location Targeting**: Adjust geographic search parameters Experience Levels**: Fine-tune senior/mid/entry level detection Additional Platforms**: Easily add more job boards via HTTP requests Notification Scheduling**: Set up periodic automated job alerts Team Integration**: Deploy for multiple users or team channels 💡 Use Cases Individual Job Seekers**: Personal job hunting assistant Recruitment Agencies**: Streamlined candidate sourcing Sales Teams**: Territory-specific opportunity monitoring Marketing Departments**: Industry trend analysis and competitor tracking Career Coaches**: Client job market research and opportunity identification Ready to revolutionize your job search? Deploy this workflow and start receiving personalized job opportunities directly in Telegram!

Jira MCP Server Integration with n8n Overview Transform your Jira project management with the power of AI and automation! This n8n workflow template demonstrates how to create a seamless integration between chat interfaces, AI processing, and Jira Software using MCP (Model Context Protocol) server architecture. What This Workflow Does Chat-Driven Automation**: Trigger Jira operations through simple chat messages AI-Powered Issue Creation**: Automatically generate detailed Jira issues with descriptions and acceptance criteria Complete Jira Management**: Get issue status, changelogs, comments, and perform full CRUD operations Memory Integration**: Maintain context across conversations for smarter automations Zero Manual Entry**: Eliminate repetitive data entry and human errors Key Features ✅ Natural Language Processing: Use Google Gemini to understand and process chat requests ✅ MCP Server Integration: Secure, efficient communication with Jira APIs ✅ Comprehensive Jira Operations: Create, read, update, delete issues and comments ✅ Smart Memory: Context-aware conversations for better automation ✅ Multi-Action Workflow: Handle multiple Jira operations from a single trigger Demo Video 🎥 Watch the Complete Demo: Automate Jira Issue Creation with n8n & AI | MCP Server Integration Prerequisites Before setting up this workflow, ensure you have: n8n instance** (cloud or self-hosted) Jira Software** account with appropriate permissions Google Gemini API** credentials MCP Server** configured and accessible Basic understanding of n8n workflows Setup Guide Step 1: Import the Workflow Copy the workflow JSON from this template In your n8n instance, click Import > From Text Paste the JSON and click Import Step 2: Configure Google Gemini Open the Google Gemini Chat Model node Add your Google Gemini API credentials Configure the model parameters: Model: gemini-pro (recommended) Temperature: 0.7 for balanced creativity Max tokens: As per your requirements Step 3: Set Up MCP Server Connection Configure the MCP Client node: Server URL: Your MCP server endpoint Authentication: Add required credentials Timeout: Set appropriate timeout values Ensure your MCP server supports Jira operations: Issue creation and retrieval Comment management Status updates Changelog access Step 4: Configure Jira Integration Set up Jira credentials in n8n: Go to Credentials > Add Credential Select Jira Software API Add your Jira instance URL, email, and API token Configure each Jira node: Get Issue Status: Set project key and filters Create Issue: Define issue type and required fields Manage Comments: Set permissions and content rules Step 5: Memory Configuration Configure the Simple Memory node: Set memory key for conversation context Define memory retention duration Configure memory scope (user/session level) Step 6: Chat Trigger Setup Configure the When Chat Message Received trigger: Set up webhook URL or chat platform integration Define message filters if needed Test the trigger with sample messages Usage Examples Creating a Jira Issue Chat Input: Can you create an issue in Jira for Login Page with detailed description and acceptance criteria? Expected Output: New Jira issue created with structured description Automatically generated acceptance criteria Proper labeling and categorization Getting Issue Status Chat Input: What's the status of issue PROJ-123? Expected Output: Current issue status Last updated information Assigned user details Managing Comments Chat Input: Add a comment to issue PROJ-123: "Ready for testing in staging environment" Expected Output: Comment added to specified issue Notification sent to relevant team members Customization Options Extending Jira Operations Add more Jira operations (transitions, watchers, attachments) Implement custom field handling Create multi-project workflows AI Enhancement Fine-tune Gemini prompts for better issue descriptions Add custom validation rules Implement approval workflows Integration Expansion Connect to Slack, Discord, or Teams Add email notifications Integrate with time tracking tools Troubleshooting Common Issues MCP Server Connection Failed Verify server URL and credentials Check network connectivity Ensure MCP server is running and accessible Jira API Errors Validate Jira credentials and permissions Check project access rights Verify issue type and field configurations AI Response Issues Review Gemini API quotas and limits Adjust prompt engineering for better results Check model parameters and settings Performance Tips Optimize memory usage for long conversations Implement rate limiting for API calls Use error handling and retry mechanisms Monitor workflow execution times Best Practices Security: Store all credentials securely using n8n's credential system Testing: Test each node individually before running the complete workflow Monitoring: Set up alerts for workflow failures and API limits Documentation: Keep track of custom configurations and modifications Backup: Regular backup of workflow configurations and credentials Happy Automating! 🚀 This workflow template is designed to boost productivity and eliminate manual Jira management tasks. Customize it according to your team's specific needs and processes.

Real-Time WooCommerce Return Surge Detection with Slack Alerts & Airtable Logging This n8n workflow monitors WooCommerce refund activity to detect unusual spikes in product returns at the SKU level. It compares return volumes across rolling 24-hour windows, alerts teams in Slack when defined thresholds are exceeded and logs all detected events into Airtable for tracking and analysis. 🚀 Quick Start – Get This Running Fast Import the workflow into n8n. Connect your WooCommerce API credentials. Configure Slack and Airtable credentials. Set your preferred schedule interval. Activate the workflow and start monitoring returns automatically. What It Does This workflow is designed to automatically detect abnormal return behavior in a WooCommerce store. On every scheduled run, it fetches recent orders and refunds directly from the WooCommerce REST API. Refund records are mapped back to their original orders to accurately identify affected SKUs. Using a rolling time-window comparison, the workflow calculates current versus previous return counts per SKU. It identifies significant increases—either large percentage spikes or unusually high absolute return volumes. This ensures early detection of potential product quality, packaging or fulfillment issues. When a return surge is detected, the workflow sends a structured alert to a Slack channel and stores the alert data in Airtable. This creates a searchable, historical log that supports investigations, trend analysis and operational decision-making. Who’s It For This workflow is ideal for: eCommerce operations teams. Quality assurance and product managers. Customer support leads. Supply chain and fulfillment teams. Store owners running WooCommerce at scale. Requirements to Use This Workflow To use this workflow, you will need: An active WooCommerce store with REST API access. WooCommerce API credentials** (Consumer Key & Secret). An active Slack workspace with permission to post messages. An Airtable base and table for logging alerts. An n8n instance (self-hosted or cloud). How It Works & How To Set Up Workflow Execution Flow Schedule Trigger runs the workflow at a fixed interval. Time Window node defines current and previous 24-hour comparison windows. HTTP Orders fetches recent WooCommerce orders. HTTP Refunds fetches refund records. Orders_Fetch (Code) maps refunds to parent orders and extracts SKU-level data. Refund_details (Code) aggregates returns, compares windows, and calculates increases. IF Node checks surge conditions: ≥100% increase OR ≥25 current returns Set Fields enriches data with status, run date, and cooldown key. Slack Node sends a formatted alert message. Code Node normalizes Slack output into structured fields. Airtable Node stores alert records for future reference. Setup Instructions Replace {your_woocommerce_domain} with your actual store domain. Verify WooCommerce API permissions allow order and refund access. Select the correct Slack channel in the Slack node. Ensure Airtable column names match the workflow mappings. How To Customize Nodes You can easily adapt this workflow by: Changing the schedule frequency in the Schedule Trigger. Adjusting WINDOW_HOURS in the Code nodes. Modifying alert thresholds in the IF node. Customizing the Slack message format. Adding or removing Airtable fields for reporting needs. Add-ons (Optional Enhancements) This workflow can be extended with: Email or Microsoft Teams notifications. Jira or Linear ticket creation. Product auto-pause for extreme return spikes. Dashboard reporting using BI tools. Cooldown logic to prevent repeated alerts per SKU. Use Case Examples Common use cases include: Detecting defective product batches early. Identifying packaging or shipping damage trends. Monitoring supplier quality issues. Supporting refund root-cause analysis. Improving customer satisfaction metrics. There can be many more operational and analytical use cases based on your business needs. Troubleshooting Guide | Issue | Possible Cause | Solution | |------|---------------|----------| | No Slack alerts | Threshold not met | Lower IF condition limits | | Empty SKU values | Missing SKU in WooCommerce | Use product name or ID fallback | | No data in Airtable | Column mismatch | Verify field names and types | | API errors | Invalid credentials | Re-authorize WooCommerce API | | Duplicate alerts | Frequent schedule | Add cooldown or deduplication logic | Need Help? Need assistance setting this up or customizing it for your business? WeblineIndia can help you implement, extend or build similar automation workflows tailored to your operational needs. Whether you want advanced alerting, deeper analytics or cross-system integrations, our team is ready to help you get the most out of n8n automation.

Overview Transform your accounts payable department with this enterprise-grade invoice processing solution. This workflow automates the entire invoice lifecycle - from document ingestion through payment processing. It handles invoices from multiple sources (Google Drive, email attachments, API submissions), extracts data using AI, validates against purchase orders, routes for appropriate approvals based on amount thresholds, and integrates seamlessly with your ERP system. The solution includes vendor master data management, duplicate invoice detection, real-time spend analytics, and complete audit trails for compliance. What You Can Do This comprehensive workflow creates an intelligent invoice processing pipeline that monitors multiple input channels (Google Drive, email, webhooks) for new invoices and automatically extracts data from PDFs, images, and scanned documents using AI. It validates vendor information against your master database, matches invoices to purchase orders, and detects discrepancies. The workflow implements multi-level approval routing based on invoice amount and department, prevents duplicate payments through intelligent matching algorithms, and integrates with QuickBooks, SAP, or other ERP systems. Additionally, it generates real-time dashboards showing processing metrics and cash flow insights while sending automated reminders for pending approvals. Who It's For Perfect for medium to large businesses, accounting departments, and financial service providers processing more than 100 invoices monthly across multiple vendors. Ideal for organizations that need to enforce approval hierarchies and spending limits, require integration with existing ERP/accounting systems, want to reduce processing time from days to minutes, need audit trails and compliance reporting, and seek to eliminate manual data entry errors and duplicate payments. The Problem It Solves Manual invoice processing creates significant operational challenges including data entry errors (3-5% error rate), processing delays (8-10 days per invoice), duplicate payments (0.1-0.5% of invoices), approval bottlenecks causing late fees, lack of visibility into pending invoices and cash commitments, and compliance issues from missing audit trails. This workflow reduces processing time by 80%, eliminates data entry errors, prevents duplicate payments, and provides complete visibility into your payables process. Setup Instructions Google Drive Setup: Create dedicated folders for invoice intake and configure access permissions PDF Vector Configuration: Set up API credentials with appropriate rate limits for your volume Database Setup: Deploy the provided schema for vendor master and invoice tracking tables Email Integration: Configure IMAP credentials for invoice email monitoring (optional) ERP Connection: Set up API access to your accounting system (QuickBooks, SAP, etc.) Approval Rules: Define approval thresholds and routing rules in the configuration node Notification Setup: Configure Slack/email for approval notifications and alerts Key Features Multi-Channel Invoice Ingestion**: Automatically collect invoices from Google Drive, email attachments, and API uploads Advanced OCR and AI Extraction**: Process any invoice format including handwritten notes and poor quality scans Vendor Master Integration**: Validate and enrich vendor data, maintaining a clean vendor database 3-Way Matching**: Automatically match invoices to purchase orders and goods receipts Dynamic Approval Routing**: Route based on amount, department, vendor, or custom rules Duplicate Detection**: Prevent duplicate payments using fuzzy matching algorithms Real-Time Analytics**: Track KPIs like processing time, approval delays, and early payment discounts Exception Handling**: Intelligent routing of problematic invoices for manual review Audit Trail**: Complete tracking of all actions, approvals, and system modifications Payment Scheduling**: Optimize payment timing to capture discounts and manage cash flow Customization Options This workflow can be customized to add industry-specific extraction fields, implement GL coding rules based on vendor or amount, create department-specific approval workflows, add currency conversion for international invoices, integrate with additional systems (banks, expense management), configure custom dashboards and reporting, set up vendor portals for invoice status inquiries, and implement machine learning for automatic GL coding suggestions. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.