Workflow Templates

Quick Overview This error-handling workflow captures failures from other n8n workflows, uses Google Gemini to diagnose the cause in plain language, logs each incident to Google Sheets, and sends a detailed alert to a Slack channel with a link to the failed execution. How it works Triggers whenever a connected n8n workflow execution fails via the workflow error trigger. Extracts key failure context such as workflow name/ID, last executed node, execution URL, and the error message/description. Sends the error context to Google Gemini and asks for a structured diagnosis including category, severity, plain-language explanation, likely cause, and an n8n-specific suggested fix. Normalizes the AI output and falls back to the raw error details if the AI response is missing or incomplete. Appends the failure and diagnosis details to a Google Sheets “Errors” tab for ongoing tracking. Posts a formatted Slack message with the diagnosis and execution link to your selected channel. Setup Add credentials for Google Gemini (PaLM) API, Google Sheets OAuth2, and Slack OAuth2. Select your target Google Sheets document and ensure it has an “Errors” sheet/tab for appending new rows. Choose the Slack channel to receive alerts and confirm the message permissions for your Slack connection. In each workflow you want monitored, set Settings → Error Workflow to point to this workflow.

Automated SEO Watchlist: Continuous Audits Powered by Decodo, Gemini and Google Sheets Automate continuous SEO audits with Decodo and Gemini AI — live data, smart insights, and Google Sheets tracking with team alerts. Who’s it for This workflow is designed for SEO specialists, marketing teams, agencies, and website owners who want an effortless, automated way to monitor SEO health. It’s perfect for ongoing audits, content monitoring, and proactive SEO management — without the manual workload. How it works / What it does Every five days, the workflow: Reads a list of URLs from Google Sheets. Uses Decodo to fetch live on-page data — titles, meta descriptions, headings, schema, links, and Core Web Vitals. Passes that data to Gemini AI for an advanced SEO analysis and scoring based on key factors (content, metadata, links, speed, and structure). Parses results via a Structured Output Parser for clean JSON output. Stores findings in Google Sheets and sends a Telegram alert when the audit completes. Why Decodo matters Decodo is the backbone of this workflow. It powers the real-time page inspection, ensuring Gemini AI has complete, accurate data to analyze. Decodo transforms static audits into live, intelligent monitoring — making your SEO insights far more actionable and reliable. How to set up Connect your Decodo API credentials. Add your Google Sheets URL list. Configure your Telegram bot credentials. Enable the workflow — it runs automatically every 5 days. Requirements Decodo API credentials Google Sheets OAuth connection Telegram Bot token n8n instance (Cloud or Self-hosted) How to customize the workflow Change the trigger interval in the Schedule Trigger node. Modify the SEO Analyzer (LLM Chain) weights for different scoring. Extend the Store Result node to integrate with dashboards or databases. Adjust the AI prompt for additional SEO checks (e.g., backlinks, readability, image optimization). ✅ Highlights Automated SEO auditing Real-time data from Decodo Smart analysis powered by Gemini AI Structured reporting in Google Sheets Team notifications via Telegram

Quick overview Youtube Video: https://youtu.be/zJlncb1T074 This workflow runs monthly, checks Supabase for up-to-date real estate market records for a configured ZIP code, and either reports existing month-over-month rent and sale trends or fetches fresh market stats from the Rentcast API, upserts them into Supabase, and posts to Slack. How it works Runs on a monthly schedule and sets the target ZIP code to analyze. Queries Supabase for rental and sales market records for that ZIP code that are older than the start of the current month. If prior data exists, calculates the last 3 months of average rent and average sale price changes from Supabase history fields and posts a status summary to Slack (or alerts if comparisons are not possible). If no up-to-date data exists, requests the latest market statistics from the Rentcast markets API for the same ZIP code. Upserts the returned rental and sales metrics (including history) into Supabase tables for market_rentals and market_sales. Computes month-over-month changes from the newly fetched history and posts the formatted market update to Slack (or sends an error alert if insufficient data is returned). Setup Create/connect Supabase credentials and ensure the market_rentals and market_sales tables exist with columns that match the fields being written (for example zip_code, last_updated_date, and history_rentals/history_sales). Add your Rentcast API key and update the HTTP request header (X-Api-Key) and any query parameters (ZIP code, historyRange) as needed. Connect Slack OAuth credentials and set the target channel for all Slack messages. Update the ZIP code value and adjust the monthly schedule timing to match your reporting cadence.

Social Media Sentiment Analysis Dashboard with AI and Real-time Monitoring 🎯 Target Audience Social media managers and community managers Marketing teams monitoring brand reputation PR professionals tracking public sentiment Customer service teams identifying trending issues Business analysts measuring social media ROI Brand managers protecting brand reputation Product managers gathering user feedback 🚀 Problem Statement Manual social media monitoring is overwhelming and often misses critical sentiment shifts or trending topics. This template solves the challenge of automatically collecting, analyzing, and visualizing social media sentiment data across multiple platforms to provide actionable insights for brand management and customer engagement. 🔧 How it Works This workflow automatically monitors social media platforms using AI-powered sentiment analysis, processes mentions and conversations, and provides real-time insights through a comprehensive dashboard. Key Components Scheduled Trigger - Runs the workflow at specified intervals to maintain real-time monitoring AI-Powered Sentiment Analysis - Uses advanced NLP to analyze sentiment, emotions, and topics Multi-Platform Integration - Monitors Twitter, Reddit, and other social platforms Real-time Alerting - Sends notifications for critical sentiment changes or viral content Dashboard Integration - Stores all data in Google Sheets for comprehensive analysis and reporting 📊 Google Sheets Column Specifications The template creates the following columns in your Google Sheets: | Column | Data Type | Description | Example | |--------|-----------|-------------|---------| | timestamp | DateTime | When the mention was recorded | "2024-01-15T10:30:00Z" | | platform | String | Social media platform | "Twitter" | | username | String | User who posted the content | "@john_doe" | | content | String | Full text of the post/comment | "Love the new product features!" | | sentiment_score | Number | Sentiment score (-1 to 1) | 0.85 | | sentiment_label | String | Sentiment classification | "Positive" | | emotion | String | Primary emotion detected | "Joy" | | topics | Array | Key topics identified | ["product", "features"] | | engagement | Number | Likes, shares, comments | 1250 | | reach_estimate | Number | Estimated reach | 50000 | | influence_score | Number | User influence metric | 0.75 | | alert_priority | String | Alert priority level | "High" | 🛠️ Setup Instructions Estimated setup time: 20-25 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets account with API access Slack workspace for notifications (optional) Social media API access (Twitter, Reddit, etc.) Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai npm install n8n-nodes-slack 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure it's working 3. Set up Google Sheets Connection Add Google Sheets OAuth2 credentials Grant necessary permissions for spreadsheet access Create a new spreadsheet for sentiment analysis data Configure the sheet name (default: "Sentiment Analysis") 4. Configure Social Media Monitoring Update the websiteUrl parameters in ScrapeGraphAI nodes Add URLs for social media platforms you want to monitor Customize the user prompt to extract specific sentiment data Set up keywords, hashtags, and brand mentions to track 5. Set up Notification Channels Configure Slack webhook or API credentials Set up email service credentials for alerts Define sentiment thresholds for different alert levels Test notification delivery 6. Configure Schedule Trigger Set monitoring frequency (every 15 minutes, hourly, etc.) Choose appropriate time zones for your business hours Consider social media platform rate limits 7. Test and Validate Run the workflow manually to verify all connections Check Google Sheets for proper data formatting Test sentiment analysis with sample content 🔄 Workflow Customization Options Modify Monitoring Targets Add or remove social media platforms Change keywords, hashtags, or brand mentions Adjust monitoring frequency based on platform activity Extend Sentiment Analysis Add more sophisticated emotion detection Implement topic clustering and trend analysis Include influencer identification and scoring Customize Alert System Set different thresholds for different sentiment levels Create tiered alert systems (info, warning, critical) Add sentiment trend analysis and predictions Output Customization Add data visualization and reporting features Implement sentiment trend charts and graphs Create executive dashboards with key metrics Add competitor sentiment comparison 📈 Use Cases Brand Reputation Management**: Monitor and respond to brand mentions Crisis Management**: Detect and respond to negative sentiment quickly Customer Feedback Analysis**: Understand customer satisfaction and pain points Product Launch Monitoring**: Track sentiment around new product releases Competitor Analysis**: Monitor competitor sentiment and engagement Influencer Identification**: Find and engage with influential users 🚨 Important Notes Respect social media platforms' terms of service and rate limits Implement appropriate delays between requests to avoid rate limiting Regularly review and update your monitoring keywords and parameters Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider privacy implications and data protection regulations 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Google Sheets permission errors: Check OAuth2 scope and permissions Sentiment analysis errors: Review the Code node's JavaScript logic Rate limiting: Adjust monitoring frequency and implement delays Alert delivery failures: Check notification service credentials Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Google Sheets API documentation for advanced configurations Social media platform API documentation Sentiment analysis best practices and guidelines

Autonomous Traffic Spike Reporting: Databox, AI & Slack This workflow gives PPC agencies and performance teams a daily traffic and performance check across multiple clients using Databox data. It pulls yesterday’s and day-before-yesterday’s data for key metrics, compares changes, analyzes the results with AI, and sends both client-level and cross-client reports to Slack. Who’s it for PPC agencies managing multiple client accounts Performance marketing teams tracking daily traffic changes Account managers who need fast visibility into client performance Operations leads who want a daily cross-client summary How it works A daily trigger starts the workflow. The first Code node stores the client list and Databox metric setup. The loop processes each client one by one. Databox pulls yesterday and day-before-yesterday data for: Sessions New users Ad cost Clicks The workflow merges and formats the metric data. AI checks for traffic spikes, drops, and unusual performance changes. A client-level Slack report is sent for each client. After all clients are processed, a second AI agent creates a leadership summary across all clients. The final cross-client report is sent to Slack. How to set up Connect the Databox MCP Client Tool with your endpoint and authentication headers. Add your clients in the first Code node, including: Client name Databox data source IDs Metric keys for sessions, new users, ad cost, and clicks Connect your OpenAI credentials. Connect the Slack nodes to the right channels. Adjust the daily schedule if needed. You can ask Databox Genie to provide the correct data source IDs and metric keys for each client, then format them in the same structure as the example Code node. Requirements n8n 1.0+ Databox MCP access Metrics available in Databox for each client OpenAI credentials Slack workspace Output The workflow sends two reports: Client-level report:** highlights daily traffic spikes, drops, and metric changes for each client. Leadership report:** summarizes performance changes and priority risks across all processed clients.

How it works This workflow runs on a daily schedule to analyze all active HubSpot deals and their latest engagement activity. It applies AI-driven behavioral scoring to predict conversion probability and deal health. High-risk or stalled deals automatically trigger Slack alerts. All insights are logged in Google Sheets for forecasting and performance tracking. Step-by-step Step 1 – Trigger and collect active deals** Schedule Trigger – Runs the workflow automatically at a fixed time each day. Get Active Deals from HubSpot – Retrieves all non-closed deals with key properties like value, stage, and activity dates. Formatting Data – Cleans and normalizes deal data while calculating metrics such as deal age and inactivity duration. Step 2 – Enrich deals with engagement data** If – Filters only active deals to ensure closed deals are excluded. Loop Over Items – Processes each deal individually to handle enrichment safely. HTTP Request – Fetches engagement associations linked to each deal. Get an engagement – Retrieves detailed engagement records from HubSpot. Extracts Data – Structures engagement content, timestamps, and internal notes for AI analysis. Step 3 – Analyze risk and notify the team** AI Agent – Analyzes behavioral signals and predicts conversion probability, risk level, and next actions. Format Data – Parses the AI output into structured fields and risk indicators. Filter Alerts Needed – Identifies deals that require immediate attention. Send Slack Alert – Sends a detailed alert with risks, signals, and recommended actions. Append or update row in sheet – Stores analysis results in Google Sheets for tracking and forecasting. Why use this? Detect deal risk early using consistent, AI-based analysis Reduce manual pipeline reviews for sales managers Provide clear, actionable next steps to sales reps Keep a historical log of deal health and forecasts Improve close rates through timely, data-driven intervention

How It Works This workflow automates engineering governance by deploying a multi-agent AI system that validates designs, checks compliance, optimises safety, and predicts maintenance needs. Designed for engineering teams, quality assurance officers, and operations managers in regulated industries, it eliminates manual review bottlenecks and ensures systemic risk issues are escalated promptly. A schedule trigger fetches design specifications and operational data, merges them, then routes to three parallel agent tracks: Design Validation (with Compliance Verification, Resource Coordination, and Testing Validation sub-agents), Safety Optimisation, and Predictive Maintenance. All outputs consolidate into a risk score calculator, which routes by risk level—critical and high issues trigger Slack alerts immediately, while medium and low issues are logged for review. Setup Steps Set schedule trigger interval to match governance review frequency. Add Anthropic API credentials to all Anthropic Model nodes. Connect design specification and operational data sources to fetch nodes. Configure Slack credentials for critical and high-priority alert channels. Define risk scoring thresholds in the Calculate Risk Scores node. Prerequisites Slack workspace with bot token Design and operational data sources (API or database) Use Cases Automated design compliance auditing for aerospace or manufacturing Real-time safety risk detection in industrial operations Customization Add sub-agents for environmental, cost, or regulatory compliance Benefits Automates multi-dimensional engineering governance on a schedule

📊 Description Monitor daily brand visibility and reputation with an automated AI-powered mention tracker. 🔍🤖 This workflow checks Hacker News every morning for new stories matching your brand keyword, classifies each mention’s sentiment and urgency using GPT-4o-mini, and delivers a clean daily summary to Slack. If no mentions are found, the workflow sends a simple “no mentions today” update instead—ensuring your team is always informed without manual monitoring. Perfect for reputation tracking, competitive intelligence, and early warning alerts. 📈💬 🔁 What This Template Does 1️⃣ Triggers every morning at 09:00 to begin the analysis. ⏰ 2️⃣ Loads brand name + keyword filters from configuration. 🏷️ 3️⃣ Fetches relevant mentions from Hacker News using the Algolia API. 🌐 4️⃣ Normalizes raw API data into clean fields (title, URL, snippet, author, points). 📄 5️⃣ Classifies each mention’s sentiment, stance, topic, and urgency using GPT-4o-mini. 🤖 6️⃣ Builds a ranked daily summary including top 10 mentions and sentiment totals. 📊 7️⃣ Sends the report to Slack, formatted cleanly and ready for team consumption. 💬 8️⃣ If no mentions exist, sends a simple “no new mentions today” message. 🚫 9️⃣ Includes an error handler that notifies Slack of any workflow failures. ⚠️ ⭐ Key Benefits ✅ Automatically tracks brand presence without manual searching ✅ AI-powered sentiment & urgency analysis for deeper insights ✅ Clean Slack summaries keep teams aligned and aware ✅ Early detection of negative or high-urgency mentions ✅ Zero manual monitoring — runs fully on schedule ✅ Suitable for brand monitoring, PR, marketing, and leadership teams 🧩 Features Daily schedule trigger Hacker News API (Algolia) integration Structured data normalization GPT-4o-mini classification (sentiment, stance, topic, urgency) Slack notifications (detailed report or no-mention message) Error-handling pipeline with Slack alerts Fully configurable brand keywords 🔐 Requirements Slack API credentials OpenAI API key (GPT-4o-mini) No authentication required for Hacker News API n8n with LangChain nodes enabled 🎯 Target Audience Brand monitoring & PR teams AI companies tracking public sentiment Founders monitoring mentions of their product Marketing teams watching trends & community feedback

📊 Description Simplify your social media publishing process by automating post scheduling from Google Sheets directly to Meta (Facebook Pages). 📅💬 This workflow detects pending posts, uploads images with captions to your Facebook Page, updates the sheet status, and sends real-time notifications via Slack and email — keeping your marketing team always in sync. 🚀 What This Template Does 1️⃣ Trigger – Monitors a Google Sheet for new or pending posts every minute. ⏰ 2️⃣ Filter – Identifies the latest “pending” entry for publishing. 🔍 3️⃣ Extract – Captures post details like caption, image URL, and ID. 🧾 4️⃣ Publish – Uploads the post to your Meta (Facebook) Page using the Graph API. 📤 5️⃣ Validate – Confirms success or failure of the post operation. ✅ 6️⃣ Notify – Sends instant Slack and email updates on publishing status. 💌 7️⃣ Update – Marks the published post as “Completed” in Google Sheets. 📊 Key Benefits ✅ Hands-free publishing from Google Sheets to Meta ✅ Instant Slack and email alerts for post outcomes ✅ Prevents duplicate or failed post uploads ✅ Centralized content tracking and status updates ✅ Improves consistency and speed in social media operations Features Google Sheets trigger for post scheduling Facebook Graph API integration for auto-posting Slack and Outlook notifications for success/error alerts Automatic sheet updates post-publication Error handling and reporting for failed posts Requirements Google Sheets OAuth2 credentials Facebook Page Access Token via Graph API Slack Bot token for notifications Outlook or SMTP credentials for email updates Target Audience Marketing teams managing Facebook content calendars 📆 Social media managers seeking automated posting 📣 Agencies coordinating client content delivery 📋 Teams tracking campaign publishing performance 📊

Create Airtable records from new ClickUp Doc pages This workflow automates the process of turning content from ClickUp Docs into structured data in Airtable. When a new task is created in ClickUp with a link to a ClickUp Doc in its name, this workflow triggers, fetches the entire content of that Doc, parses it into individual records, and then creates a new record for each item in a specified Airtable base and table. Who's it for This template is perfect for content creators, project managers, and operations teams who use ClickUp Docs for drafting or knowledge management and Airtable for tracking and organizing data. It helps bridge the gap between unstructured text and a structured database. How it works Trigger: The workflow starts when a new task is created in a specific ClickUp Team. Fetch & Parse URL: It gets the new task's details and extracts the ClickUp Doc URL from the task name. Get Doc Content: It uses the URL to fetch the main Doc and all its sub-pages from the ClickUp API. Process Content: A Code node parses the text from each page. It's designed to split content by * * * and separate notes by looking for the "notes:" keyword. Find Airtable Destination: The workflow finds the correct Airtable Base and Table IDs by matching the names you provide. Create Records: It loops through each parsed content piece and creates a new record in your specified Airtable table. How to set up Configure the Set Node: Open the "Configure Variables" node and set the following values: clickupTeamId: Your ClickUp Team ID. Find it in your ClickUp URL (e.g., app.clickup.com/9014329600/...). airtableBaseName: The exact name of your target Airtable Base. airtableTableName: The exact name of your target Airtable Table. airtableVerticalsTableName: The name of the table in your base that holds "Vertical" records, which are linked in the main table. Set Up Credentials: Add your ClickUp (OAuth2) and Airtable (Personal Access Token) credentials to the respective nodes. Airtable Fields: Ensure your Airtable table has fields corresponding to the ones in the Create New Record in Airtable node (e.g., Text, Status, Vertical, Notes). You can customize the mapping in this node. Activate Workflow: Save and activate the workflow. Test: Create a new task in your designated ClickUp team. In the task name, include the full URL of the ClickUp Doc you want to process. How to customize the workflow Parsing Logic:* You can change how the content is parsed by modifying the JavaScript in the Parse Content from Doc Pages Code node. For example, you could change the delimiter from * * to something else. Field Mapping:** Adjust the Create New Record in Airtable node to map data to different fields or add more fields from the source data. Trigger Events:** Modify the Trigger on New ClickUp Task node to respond to different events, such as taskUpdated or taskCommentPosted.

This automated n8n workflow monitors real-time cryptocurrency prices using CoinGecko API and sends smart alerts when price conditions are met. It supports multi-coin tracking, dynamic conditions, and instant notifications via Email, Telegram, and Discord. Good to Know Reads crypto watchlist data from Google Sheets. Monitors prices at defined intervals (24/7 monitoring). Handles upper and lower price limits with direction-based alerts (above, below, both). Implements cooldown logic to avoid duplicate alerts. Updates last alert price and timestamp in Google Sheets. Supports multiple alert channels: Email, Telegram, Discord. Uses CoinGecko API for price data (Free tier supported). How It Works 24/7 Crypto Trigger – Runs every minute (or custom interval) to check latest prices. Read Crypto Watchlist – Fetches symbols and conditions from Google Sheets. Parse Crypto Data – Converts Google Sheet data into structured JSON. Fetch Live Crypto Price – Uses CoinGecko API to get latest market price for each coin. Smart Crypto Alert Logic – Compares live price with upper/lower limits and evaluates conditions: Above – Trigger alert if price > upper\_limit. Below – Trigger alert if price < lower\_limit. Both – Trigger alert if either condition is met. Implements cooldown\_minutes to prevent repeated alerts. Check Crypto Alert Conditions – Validates alerts before sending notifications. Send Crypto Email Alert – Sends email alert if condition is true. Send Telegram Crypto Alert – Sends Telegram alert. Send Discord Crypto Alert – Sends Discord alert. Update Crypto Alert History – Updates last_alert_price and last_alert_time in Google Sheet. Crypto Alert Status Check – Ensures alert process completed successfully. Success Notification – Sends confirmation message on success. Error Notification – Sends an error alert if something fails. Google Sheet Columns (A-G) | Column | Description | | ------ | ---------------------------------- | | A | symbol (BTC, ETH, SOL, etc.) | | B | upper_limit (e.g., 45000) | | C | lower_limit (e.g., 40000) | | D | direction (both / above / below) | | E | cooldown_minutes (e.g., 10) | | F | last_alert_price (auto-updated) | | G | last_alert_time (auto-updated) | How to Use Import the workflow into n8n. Configure Google Sheets credentials and link your watchlist sheet. Add your CoinGecko API endpoint in the Fetch Price node (Free tier). Set up Email, Telegram, and Discord credentials for notifications. Test with sample data: Example: BTC, upper\_limit=45000, lower\_limit=40000, direction=both. Execute the workflow and monitor alerts. Requirements n8n environment with execution permissions. Google Sheets integration (with API credentials). CoinGecko API (Free tier supported). Notification channels: Email (SMTP settings in n8n). Telegram Bot Token. Discord Webhook URL. Customizing This Workflow Add more coins in Google Sheet. Modify alert conditions (e.g., percentage change, moving averages). Add SMS or WhatsApp notifications. Integrate with Slack or Microsoft Teams. Use AI-based price predictions for smarter alerts.

🎯 Smart Job Hunter Pro - AI-Powered Multi-Platform Job Automation Transform your job search with this comprehensive n8n workflow that automatically searches, analyzes, and applies to relevant positions across multiple job platforms. Perfect for developers, engineers, and tech professionals looking to streamline their job hunting process. ✨ Key Features 🔄 Multi-Platform Job Search**: Simultaneously searches Jooble, JobStreet, Indeed, and WhatJobs APIs 🤖 AI-Powered Job Analysis**: Uses Google Gemini AI to analyze job compatibility and generate tailored cover letters 📊 Smart Scoring System**: Automatically scores job matches based on your skills and requirements 📝 Auto-Apply Threshold**: Only applies to jobs above your specified compatibility score 📋 Notion Integration**: Automatically tracks applications in organized Notion database 💬 Telegram Notifications**: Real-time alerts for high-match job opportunities ☁️ Google Drive Storage**: Saves personalized cover letters for each application ⚠️ Error Handling**: Comprehensive error tracking with Telegram notifications ⏰ Automated Scheduling**: Runs every 8 hours to find fresh opportunities 🛠 What This Workflow Does Scheduled Search: Automatically searches multiple job platforms every 8 hours Data Normalization: Standardizes job data from different API sources AI Analysis: Gemini AI evaluates each job posting against your skills profile Smart Filtering: Only processes jobs above your compatibility threshold (default: 75%) Application Tracking: Creates detailed records in Notion with match scores and status Instant Alerts: Sends Telegram notifications for promising opportunities Cover Letter Generation: AI creates personalized cover letters for each position Document Management: Automatically saves all cover letters to Google Drive 🔧 Required Integrations Job APIs**: Jooble API, WhatJobs API (JobStreet & Indeed use web scraping) AI Service**: Google Gemini API for job analysis Productivity**: Notion database for application tracking Communication**: Telegram bot for notifications Storage**: Google Drive for cover letter management 💡 Perfect For Software Developers** seeking JavaScript, React, Node.js positions Full-Stack Engineers** wanting automated job discovery Tech Professionals** needing organized application tracking Remote Workers** searching across multiple platforms Career Changers** looking for systematic job hunting 🎛 Customizable Variables Job Keywords**: Define your target roles and skills Location & Radius**: Set geographic search parameters Auto-Apply Threshold**: Control compatibility score requirements Results Limit**: Adjust number of jobs per platform Schedule Frequency**: Modify search intervals 📈 Benefits Save 10+ hours weekly** on manual job searching Never miss opportunities** with automated monitoring Professional application tracking** with detailed analytics Personalized cover letters** for every application Instant notifications** for high-match positions Complete audit trail** of all job search activities 🚀 Getting Started Import the workflow to your n8n instance Configure API credentials for all job platforms Set up Notion database with provided template structure Create Telegram bot and Google Drive folder Customize job search parameters for your profile Activate workflow and start receiving opportunities! 📝 Additional Notes Uses placeholder credentials for security ({{PLACEHOLDER_API_KEY}}) Comprehensive error handling prevents workflow failures Includes detailed setup instructions via sticky notes Optimized for Indonesian job market (JobStreet.co.id) Easily adaptable for other regions and job types Perfect for developers, engineers, and automation enthusiasts who want to leverage AI and n8n's power to dominate their job search process! 🚀