Workflow Templates

AI-Powered SEO Keyword Research Workflow with n8n > automates comprehensive keyword research for content creation Table of Contents Introduction Workflow Architecture NocoDB Integration Data Flow Core Components Setup Requirements Possible Improvements Introduction This n8n workflow automates SEO keyword research using AI and data-driven analytics. It combines OpenAI's language models with DataForSEO's analytics to generate comprehensive keyword strategies for content creation. The workflow is triggered by a webhook from NocoDB, processes the input data through multiple stages, and returns a detailed content brief with optimized keywords. Workflow Architecture The workflow follows a structured process: Input Collection: Receives data via webhook from NocoDB Topic Expansion: Generates keywords using AI Keyword Metrics Analysis: Gathers search volume, CPC, and difficulty metrics Competitor Analysis: Analyzes competitor content for ranking keywords Final Strategy Creation: Combines all data to generate a comprehensive keyword strategy Output Storage: Saves results back to NocoDB and sends notifications NocoDB Integration Database Structure The workflow integrates with two tables in NocoDB: Input Table Schema This table collects the input parameters for the keyword research: | Field Name | Type | Description | | --------------- | ------------- | --------------------------------------------------------------------------- | | ID | Auto Number | Unique identifier | | Primary Topic | Text | The main keyword/topic to research | | Competitor URLs | Text | Comma-separated list of competitor websites | | Target Audience | Single Select | Description of the target audience (Solopreneurs, Marketing Managers, etc.) | | Content Type | Single Select | Type of content (Blog, Product page, etc.) | | Location | Single Select | Target geographic location | | Language | Single Select | Target language for keywords | | Status | Single Select | Workflow status (Pending, Started, Done) | | Start Research | Checkbox | Active Workflow when you set this to true | Output Table Schema This table stores the generated keyword strategy: | Field Name | Type | Description | | ------------------ | ----------- | ------------------------------------------------ | | ID | Auto Number | Unique identifier | | primary_topic_used | Text | The topic that was researched | | report_content | Long Text | The complete keyword strategy in Markdown format | | generatedAt | Datetime | Automatically generated by NocoDb | Webhook Settings NocoDB Webhook Settings Data Flow The workflow handles data in the following sequence: Webhook Trigger: Receives input from NocoDB when a new keyword research request is created Field Extraction: Extracts primary topic, competitor URLs, audience, and other parameters AI Topic Expansion: Uses OpenAI to generate related keywords, categorized by type and intent Keyword Analysis: Sends primary keywords to DataForSEO to get search volume, CPC, and difficulty Competitor Research: Analyzes competitor pages to identify their keyword rankings Strategy Generation: Combines all data to create a comprehensive keyword strategy Storage & Notification: Saves the strategy to NocoDB and sends a notification to Slack Core Components 1. Topic Expansion This component uses OpenAI and a structured output parser to generate: 20 primary keywords 30 long-tail keywords with search intent 15 question-based keywords 10 related topics 2. DataForSEO Integration Two API endpoints are used: Search Volume & CPC**: Gets monthly search volume and cost-per-click data Keyword Difficulty**: Evaluates how difficult it would be to rank for each keyword 3. Competitor Analysis This component: Analyzes competitor URLs to identify which keywords they rank for Identifies content gaps or opportunities Determines the search intent their content targets 4. Final Keyword Strategy The AI-generated strategy includes: Top 10 primary keywords with metrics 15 long-tail opportunities with low competition 5 question-based keywords to address in content Content structure recommendations 3 potential content titles optimized for SEO Setup Requirements To use this workflow, you'll need: n8n Instance: Either cloud or self-hosted NocoDB Account: For data input and storage API Keys: OpenAI API key DataForSEO API credentials Slack API token (for notifications) Database Setup: Create the required tables in NocoDB as described above Possible Improvements The workflow could be enhanced with the following improvements: Enhanced Keyword Strategy Add topic clustering to group related keywords Enhance the final output with more specific content structure suggestions Include word count recommendations for each content section Additional Data Sources Integrate Google Search Console data for existing content optimization Add Google Trends data to identify rising topics Include sentiment analysis for different keyword groups Improved Competitor Analysis Analyze content length and structure from top-ranking pages Identify common backlink sources for competitor content Extract content headings to better understand content organization Automation Enhancements Add scheduling capabilities to run updates on existing content Implement content performance tracking over time Create alert thresholds for changes in keyword difficulty or search volume Example Output Here is an example Output the Workflow generated based on the following inputs. Inputs: Primary Topic: AI Automation Competitor URLs: n8n.io, zapier.com, make.com Target Audience: Small Business Owners Content Type: Landing Page Location: United States Language: English Output: Final Keyword Strategy The workflow provides a powerful automation for content marketers and SEO specialists to develop data-driven keyword strategies with minimal manual effort. > Original Workflow: AI-Powered SEO Keyword Research Automation - The vibe Marketer

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works? This workflow is an intelligent SEO analysis pipeline that ethically scrapes blog content and performs comprehensive SEO evaluation using AI. It receives blog URLs via webhook, validates permissions through robots.txt compliance, extracts content, and generates detailed SEO insights across four strategic dimensions: Content Optimization, Keyword Strategy, Technical SEO, and Backlink Building potential. The system prioritizes ethical web scraping by checking robots.txt permissions before proceeding, ensuring compliance with website policies. Upon successful analysis, it returns a structured JSON report with actionable SEO recommendations, performance scores, and optimization strategies. Technical Specifications Trigger: HTTP POST webhook Processing Time: 30-60 seconds depending on content size AI Model: GPT-4.1 minimum with specialized SEO analysis prompt. Output Format: Structured JSON Error Handling: Graceful failure with informative messages Compliance: Respects website robots.txt policies

🚀 Discover trending and viral YouTube videos easily with this powerful n8n automation! This workflow helps you perform bulk research on YouTube videos related to any search term, analyzing engagement data like views, likes, comments, and channel statistics — all in one streamlined process. ✨ Perfect for: Content creators wanting to find viral video ideas Marketers analyzing competitor content YouTubers optimizing their content strategy How It Works 🎯 1️⃣ Input Your Search Term — Simply enter any keyword or topic you want to research. 2️⃣ Select Video Format — Choose between short, medium, or long videos. 3️⃣ Choose Number of Videos — Define how many videos to analyze in bulk. 4️⃣ Automatic Data Fetch — The workflow grabs video IDs, then fetches detailed video data and channel statistics from the YouTube API. 5️⃣ Performance Scoring — Videos are scored based on engagement rates with easy-to-understand labels like 🚀 HOLY HELL (viral) or 💀 Dead. 6️⃣ Export to Google Sheets — All data, including thumbnails and video URLs, is appended to your Google Sheet for comprehensive review and easy sharing. Setup Instructions 🛠️ Google API Key Get your YouTube Data API key from Google Developers Console. Add it securely in the n8n credentials manager (do not hardcode). Google Sheets Setup Create a Google Sheet to store your results (template link is provided). Share the sheet with your Google account used in n8n. Update the workflow with your sheet's Document ID and Sheet Name if needed. Run the Workflow Trigger the form webhook via browser or POST call. Enter search term, format, and number of videos. Let it process and check your Google Sheet for insights! Features ✨ Bulk fetches the latest and top-viewed YouTube videos. Intelligent video performance scoring with emojis for quick insights 🔥🎬. Organizes data into Google Sheets with thumbnail previews 🖼️. Easy to customize search parameters via an intuitive form. Fully automated, no manual API calls needed. Get Started Today! 🌟 Boost your YouTube content strategy and stay ahead with this powerful viral video research automation! Try it now on your n8n instance and tap into the world of viral content like a pro 🎥💡

Who this is for? The Structured Data Extract & Data Mining workflow is crafted for researchers, content analysts, SEO strategists, and AI developers who need to transform semi-structured web data (like markdown content or scraped HTML) into actionable structured datasets. It is ideal for: Content Analysts** - Organizing and mining large volumes of markdown or HTML content. SEO & Trend Researchers** - Exploring topics by location and category. AI Engineers & NLP Developers** - Looking to automate insight extraction from unstructured inputs. Growth Marketers** - Tracking topic-level trends for strategic campaigns. Automation Specialists** - Streamlining workflows from scrape to storage. What problem is this workflow solving? Extracting insights from markdown or HTML documents typically requires manual review, formatting, and parsing. This becomes unscalable when dealing with large datasets or when real-time response is needed. Additionally, trend and topic extraction usually involves external tools, custom scripts, and inconsistent formatting. This workflow solves: Automatic text extraction from markdown or structured content. Location and category-based trend mining with semantic grouping. AI-driven topic extraction and summarization Real-time notification via webhook with rich structured payloads. Persistent storage of mined data to disk for audits or further processing. What this workflow does Receives input: Sets the URL for the data extraction and analysis. Uses Bright Data's Web Unlocker to extract content from relevant sites. A Markdown/Text Extractor node parses the content into clean plaintext The cleaned data is passed to Google Gemini to: Identify trends by location and category Extract key topics and themes Format the response into structured JSON The structured insights are sent via Webhook Notification to external systems (e.g., Slack, Web apps, Zapier) The final output is saved to disk Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. A Google Gemini API key (or access through Vertex AI or proxy). Update the Set URL and Bright Data Zone for setting the brand content URL and the Bright Data Zone name. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs Update Source** : Update the workflow input to read from Google Sheet or Airbase for dynamically tracking multiple brands or topics. Gemini Prompt Customization** : Extract trends within a custom category (e.g., E-commerce design patterns in the US) Output topics with popularity metrics Structure the output as per your database schema (e.g., [{ topic, trend_score, location }]) Webhook Output** : Send notifications to - Slack – with AI summaries in rich blocks Internal APIs – for use in dashboards Zapier/Make – for multi-step automation Persistence** Save output to: Remote FTP or SFTP storage Amazon S3, Google Cloud Storage etc.

🧩 Jamf Policies Export to Slack Quickly export and review your entire Jamf policy configuration—including triggers, frequencies, and scope—directly in Slack. This enables IT and security teams to audit policy setups without logging into Jamf or generating reports manually. ❗The Problem Jamf Pro lacks a straightforward way to quickly review or share a list of all configured policies, including key attributes like frequency, scope, or triggers. Security teams often need this for audit or compliance reviews, but navigating Jamf’s UI or exporting via the API is time-consuming. 🔧 This Fixes It This workflow fetches all policies, extracts the most relevant fields, compiles them into a csv file, and posts that readble file into a designated Slack channel—automatically or on demand. ✅ Prerequisites • A Jamf Pro API key (OAuth2) with read access to policies • A Slack app with permission to post files into your chosen channel 🔍 How it works • Manually trigger or use the webhook to initiate the flow • Retrieve all policies from Jamf via the XML API • Convert the XML response into JSON • Split and loop through each policy ID • Retrieve detailed data for each policy • Format relevant fields (ID, name, trigger, scope, etc.) • Convert the final data set into an .csv file • Upload the file to your Slack channel ⚙️ Set up steps • Takes ~10 minutes to configure • Set the Jamf BaseURL in the “Jamf Server” node • Configure Jamf OAuth2 credentials in the HTTP Request nodes • Adjust the fields for export in the “Set-fields” node • Set your Slack credentials and target channel in the “Post to Slack” node • Optional: Customize the exported fields or filename 🔄 Automation Ready Schedule this flow daily/weekly, or tie it to change events to keep your team informed.

Who is this for? Security professionals Developers Individuals interested in data breach awareness Use Case Automated monitoring for new breaches Proactive identity protection Demonstration of simple cache mechanism What this workflow does Checks the Have I Been Pwned API every 15 minutes for the latest breaches. Compares new breach data against previously notified breaches. Demonstrates a simple cache mechanism to track previously seen breaches. How the Cache Functionality Works Read from Cache**: Retrieves the last known breach from cache.json to avoid redundant alerts for the same breach. Compare Against Current Breach**: The workflow checks if the latest fetched breach differs from the cached one. Update the Cache**: If a new breach is detected, it updates cache.json with the latest breach data. Setup instructions The endpoint used in this workflow does not require an API key. Add your desired alert mechanism in the red box attached to the New breach node. How to customize this workflow to your needs Modify Notification Settings**: Tailor where alerts are sent (email, Slack, etc.). Add the desired node after the New breach node. This node contains all the data from the breach so it is eaisily available. You can choose from a variety of n8n nodes to send alerts when a new breach is detected. Below are a few common options you might consider adding after the New breach node: Email Node What it does: Sends an email notification to one or more recipients. Use case: Great for simple alerts to your inbox or a team distribution list. Customization: You can include breach details in the subject or body of the email, using data from the New breach node. Slack Node What it does: Sends a message to a Slack channel or user. Use case: Perfect for real-time alerts to your team in Slack. Customization: You can post breach details directly in a channel or DM. You can also format the message (bold, code blocks, etc.). Microsoft Teams Node What it does: Sends a message to a Teams channel. Use case: For organizations that use Microsoft Teams for communication. Customization: Similar to Slack, you can customize the message content and include all relevant breach information. Discord Node What it does: Sends an alert message to a Discord channel. Use case: Useful for teams or communities that coordinate via Discord. Customization: Add formatted messages with breach details for easy viewing. Telegram Node What it does: Sends messages to a Telegram chat or group. Use case: Good for mobile notifications and fast alerts. Customization: You can include breach summaries or detailed information, and even use bots to automate this. Webhook Node (as a sender) What it does: Sends breach data to another service via a webhook. Use case: If you have an external system or app that handles alerts, you can push the data directly to it. Customization: Send JSON payloads with detailed breach information to trigger actions in other systems. SMS Nodes (like Twilio) What it does: Sends an SMS notification to one or more phone numbers. Use case: For urgent alerts that need to be seen immediately. Customization: Keep messages concise, including key breach details like the time, type of breach, and affected system. Adjust Check Frequency**: Change the interval in the Schedule Trigger node (e.g., hourly or daily).

Turn plain-language chat like “Tomorrow 9 AM: write blog post” into neatly organised Todoist tasks with GPT-4o and n8n—zero code. 🪄 Ultimate Personal Todoist Agent Turn natural-language requests into perfectly-organized Todoist tasks—all on autopilot inside n8n. > “Add Finish quarterly report by Friday afternoon” → the agent creates the task, sets the due date & priority, and even drops it into the right project. ✨ 🌟 Why this workflow rocks All-in-one Todoist super‑powers** – create, update, complete, move, archive… every major Todoist endpoint is wired up (tasks, projects, sections, labels, comments). LLM‑powered intent detection** – an OpenAI model interprets plain-English (or emoji‑filled!) messages so you don’t have to remember slash‑commands. Minimal setup** – just two credentials and you’re live. Battle‑tested building block** – use it as‑is, or plug the Todoist Agent node into your own agents & chatbots. 🛠️ What you’ll need | Credential | Where it’s used | How to set it up | | ------------------ | -------------------------------------- | --------------------------------------------------------------------------------------------- | | OpenAI API | Orchestrator & LLM nodes | Paste your OpenAI secret key into an OpenAI credential in n8n. | | Todoist OAuth2 | Todoist node and HTTP Request node | Log in Todoist from your browser to set up credential in n8n. | > That’s it—no webhooks, no extra secrets. > Tested with *gpt‑4o‑latest* – the fastest & most accurate model in our trials. ⚡ Quick‑start (5 minutes) Import the JSON template (hit ▶️ Try it out on the n8n template page or drag‑drop the file into your canvas). Select your credentials in the two credential dropdowns. Click Test workflow. In the sample Function node, tweak the message field (e.g. “Tomorrow at 9 am: write blog post”). Run → watch your new Todoist task appear. (Optional) Swap the Function node for your favourite chat trigger (Telegram, Slack, WhatsApp, Discord, you name it). Boom—your personal Todoist genie is alive! 🧞‍♂️ 🧩 How it works (under the hood) [Trigger / Chat message] │ ▼ [🗂️ Orchestrator Agent] ← OpenAI Chat Model + Short‑term Memory │ ↳ Parses intent & entities │ ▼ [🤖 Todoist Agent] ← 15+ Todoist endpoints │ ↳ Executes the right call (create, update, complete, etc.) ▼ [Done ✅ ] The Orchestrator is an example. In production you can drop it and simply expose the Todoist Agent as a tool for any other agent workflow. 🎛️ Customising & extending | Idea | How to do it | | ------------------------- | ---------------------------------------------------------------------------------------- | | Notion / Sheets sync | After the Todoist Agent node, add a Notion or Google Sheets node to log completed items. | | Voice commands | Swap the chat trigger for a Speech‑to‑Text node (e.g. Whisper). | 🤝 Need custom automations? Want me to build or tweak something for you? → Email maxemelyanenko@gmail.com and let’s make it happen! ⚠️ What’s not included (yet) Shared projects & other Todoist Pro/Business endpoints. File attachments in the comments. Editing comments. Pull requests welcome! 🙌

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for The Brave Search Structured Data Extractor workflow is designed for professionals and teams that need high-quality, structured insights from Brave search results in real time. Whether you're performing market research, tracking competitors, training AI models, or powering content engines, this workflow offers a robust and automated solution. This workflow is tailored for: Market Researchers - Who analyze trends across multimedia channels AI Developers - Who require clean, structured datasets for model fine-tuning SEO & Content - Analysts looking to monitor visibility across news, images, and videos Media Researchers - Curating timely and relevant information across formats Automation Engineers - Integrating search insights into downstream workflows What problem is this workflow solving? Traditional web scraping and search result parsing is fragmented, inconsistent, and prone to errors, especially when dealing with multimedia (images, videos, news) data from search engines. This workflow provides: Centralized Brave search data extraction across all content types. Switches the search execution based upon the type of search that is being set. ex: news, images, videos, all Automated structured data transformation using Google Gemini Unified output persistence and notification across disk, webhook, and Google Sheets What this workflow does Input Configuration Define your Brave search query Set the search type: videos, images, news, or all Configure your Bright Data MCP zone Bright Data MCP Search Execution Initiates a Brave search via Bright Data MCP using the correct URL pattern for each search type Returns raw HTML of search results Google Gemini LLM Structured Data Extraction Transforms raw results into structured data (e.g., title, URL, source, snippet) Output Handling Save to disk (e.g., JSON or CSV file) Send Webhook notification with structured data (e.g., Slack, internal dashboards) Store in Google Sheets for team-wide access or dashboarding Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token> How to customize this workflow to your needs Enhance Output Analysis Add additional LLM prompts for topic classification, sentiment scoring, or trend forecasting. Output Format Options Choose to output CSV, Markdown, or HTML reports based on your integration target. Schedule Automation Trigger the workflow on a schedule (daily/weekly) to keep monitoring topical content.

Daily Magento 2 stock check Automation It identifies SKUs with low inventory per source and sends daily alerts via: 📬 Gmail (HTML email) 💬 Slack (formatted text message) This automation empowers store owners and operations teams to stay ahead of inventory issues by proactively monitoring stock levels across all Magento 2 sources. By receiving early alerts for low-stock products, businesses can restock before items sell out—ensuring continuous product availability, reducing missed sales opportunities, and maintaining customer trust. Avoiding stockouts not only protects your brand reputation but also keeps your store competitive by preventing customers from turning to competitors due to unavailable items. Timely restocking leads to higher fulfillment rates, improved customer satisfaction, and ultimately, stronger revenue and long-term loyalty. ✅ Features: Filters out configurable, virtual, and downloadable products Uses Magento 2 MSI stock per source Customizable thresholds (default: ≤10 overall or ≤5 per source) HTML-formatted email report Slack notification with a code-formatted Runs daily via Cron (08:50 AM) No need of any 3rd part Modules One time Setup 🔑 Credentials Used HTTP Request (Magento 2 REST API using Bearer Token) Gmail (OAuth2) Slack (OAuth2 or Webhook) 📊 Tags Magento, Inventory, MSI, Stock Alert, Ecommerce, Slack, Gmail, Automation 📂 Category E-commerce → Magento 2 (Adobe Commerce) 👤 Author Kanaka Kishore Kandregula Certified Magento 2 Developer https://gravatar.com/kmyprojects https://www.linkedin.com/in/kanakakishore

This n8n template introduces the Dynamic Prompts Ai workflow pattern which are incredible for certain types of data extraction tasks where attributes are unknown or need to remain flexible. The general idea behind this pattern is that the prompts for requested attributes to be extracted live outside the template and so can be changed at any time - without needing to edit the template. This seriously cuts down on maintainance requirements and is reusable for any number of tables at little cost. Check out the video demo I did for n8n Studio here: https://www.youtube.com/watch?v=_fNAD1u8BZw Check out the example Airtable here: https://airtable.com/appAyH3GCBJ56cfXl/shrXzR1Tj99kuQbyL Looking for the Baserow Version? https://n8n.io/workflows/2780-ai-data-extraction-with-dynamic-prompts-and-baserow/ How it works Given we have an "input" field for context and a number of fields for the data we want to extract, this template will run in the background to react to any changes to either the "input" or fields and automatically update the rows accordingly. The key is that Airtable fields have a special property called the "field description". In this pattern, we use this property to allow the user to store a simple prompt describing the data that should exist in the column. Our n8n template reads these column descriptions aka "prompts" to use as instructions to perform tasks on the "input". In this template, the "input" is a PDF of a resume/CV and the columns are attributes a HR person would want to extract from it - such as full name, address, last position, years of experience etc. How to use First publish this template and ensure it's accessible via webhook URL. You then have to run the "create airtable webhooks" mini-flow to configure your Airtable to send change events to the n8n template. This mini-flow exists in the template but you'll have to update the IDs. Check the template for more instructions. Requirements Airtable for Tables/Database OpenAI for LLM and extraction. Feel free to choose another LLM if preferred. Customising this workflow If you're not using files, you can replace the "input" field with anything you like. For example, the "input" could be single line text.

🚀 Google Search Console MCP Server 📋 Description This n8n workflow serves as a Model Context Protocol (MCP) server, connecting MCP-compatible AI tools (like Claude) directly to the Google Search Console APIs. With this workflow, users can automate critical SEO tasks and manage Google Search Console data effortlessly via MCP endpoints. Included Functionalities: 📌 List Verified Sites 📌 Retrieve Detailed Site Information 📌 Access Search Analytics Data 📌 Submit and Manage Sitemaps 📌 Request URL Indexing OAuth2 is fully supported for secure and seamless API interactions. 🛠️ Setup Instructions 🔑 Prerequisites n8n instance** (cloud or self-hosted) Google Cloud project with enabled APIs: Google Search Console API Web Search Indexing API OAuth2 Credentials from Google Cloud ⚙️ Workflow Setup Step 1: Import Workflow Open n8n, select "Import from JSON", and paste this workflow JSON. Step 2: Configure OAuth2 Credentials Navigate to Settings → Credentials. Add new credentials (Google OAuth2 API): Client ID and Client Secret from Google Cloud Scopes: https://www.googleapis.com/auth/webmasters.readonly https://www.googleapis.com/auth/webmasters https://www.googleapis.com/auth/indexing Step 3: Configure Webhooks Webhook URLs auto-generate in MCP Server Trigger node. Ensure webhooks are publicly accessible via HTTPS. Step 4: Testing Test your endpoints with sample HTTP requests to confirm everything is working correctly. 🎯 Usage Examples List Sites**: Fetch all verified Search Console sites. Get Site Info**: Get detailed information about a particular site. Search Analytics**: Pull metrics such as clicks, impressions, and rankings. Submit Sitemap**: Automatically submit sitemaps. Request URL Indexing**: Trigger Google's indexing for specific URLs instantly. 🚩 Use Cases & Applications SEO automation workflows AI-driven SEO analytics Real-time website performance monitoring Automated sitemap management

Apply to jobs automatically from Google Sheets with status tracking Who's it for Job seekers who want to streamline their application process, save time on repetitive tasks, and never miss following up on applications. Perfect for anyone managing multiple job applications across different platforms. What it does This workflow automatically applies to jobs from a Google Sheet, tracks application status, and keeps you updated with notifications. It handles the entire application lifecycle from submission to status monitoring. Key features: Reads job listings from Google Sheets with filtering by priority and status Automatically applies to jobs on LinkedIn, Indeed, and other platforms Updates application status in real-time Checks application status every 2 days and notifies you of changes Sends email notifications for successful applications and status updates Prevents duplicate applications and manages rate limiting How it works The workflow runs on two main schedules: Daily Application Process (9 AM, weekdays): Reads your job list from Google Sheets Filters for jobs marked as "Not Applied" with Medium/High priority Processes each job individually to prevent rate limiting Applies to jobs using platform-specific APIs (LinkedIn, Indeed, etc.) Updates the sheet with application status and reference ID Sends confirmation email for each application Status Monitoring (Every 2 days at 10 AM): Checks all jobs with "Applied" status Queries job platforms for application status updates Updates the sheet if status has changed Sends notification emails for status changes (interviews, rejections, etc.) Requirements Google account with Google Sheets access Gmail account for notifications Resume stored online (Google Drive, Dropbox, etc.) API access to job platforms (LinkedIn, Indeed) - optional for basic version n8n instance (self-hosted or cloud) How to set up Step 1: Create Your Job Tracking Sheet Create a Google Sheet with these exact column headers: | Job_ID | Company | Position | Status | Applied_Date | Last_Checked | Application_ID | Notes | Job_URL | Priority | |--------|---------|----------|--------|--------------|--------------|----------------|-------|---------|----------| | JOB001 | Google | Software Engineer | Not Applied | | | | | https://careers.google.com/jobs/123 | High | | JOB002 | Microsoft | Product Manager | Not Applied | | | | | https://careers.microsoft.com/jobs/456 | Medium | Column explanations: Job_ID**: Unique identifier (JOB001, JOB002, etc.) Company**: Company name Position**: Job title Status**: Not Applied, Applied, Under Review, Interview Scheduled, Rejected, Offer Applied_Date**: Auto-filled when application is submitted Last_Checked**: Auto-updated during status checks Application_ID**: Platform reference ID (auto-generated) Notes**: Additional information or application notes Job_URL**: Direct link to job posting Priority**: High, Medium, Low (Low priority jobs are skipped) Step 2: Configure Google Sheets Access In n8n, go to Credentials → Add Credential Select Google Sheets OAuth2 API Follow the OAuth setup process to authorize n8n Test the connection with your job tracking sheet Step 3: Set Up Gmail Notifications Add another credential for Gmail OAuth2 API Authorize n8n to send emails from your Gmail account Test by sending a sample email Step 4: Update Workflow Configuration In the "Set Configuration" node, update these values: spreadsheetId**: Your Google Sheet ID (found in the URL) resumeUrl**: Direct link to your resume (make sure it's publicly accessible) yourEmail**: Your email address for notifications coverLetterTemplate**: Customize your cover letter template Step 5: Customize Application Logic For basic version (no API access): The workflow includes placeholder HTTP requests that you can replace with actual job platform integrations. For advanced version (with API access): Replace LinkedIn/Indeed HTTP nodes with actual API calls Add your API credentials to n8n's credential store Update the platform detection logic for additional job boards Step 6: Test and Activate Add 1-2 test jobs to your sheet with "Not Applied" status Run the workflow manually to test Check that the sheet gets updated and you receive notifications Activate the workflow to run automatically How to customize the workflow Adding New Job Platforms Update Platform Detection: Modify the "Check Platform Type" node to recognize new job board URLs Add New Application Node: Create HTTP request nodes for new platforms Update Status Checking: Add status check logic for the new platform Customizing Application Strategy Rate Limiting**: Add "Wait" nodes between applications (recommended: 5-10 minutes) Application Timing**: Modify the cron schedule to apply during optimal hours Priority Filtering**: Adjust the filter conditions to match your criteria Multiple Resumes**: Use conditional logic to select different resumes based on job type Enhanced Notifications Slack Integration**: Replace Gmail nodes with Slack for team notifications Discord Webhooks**: Send updates to Discord channels SMS Notifications**: Use Twilio for urgent status updates Dashboard Updates**: Connect to Notion, Airtable, or other productivity tools Advanced Features AI-Powered Personalization**: Use OpenAI to generate custom cover letters Job Scoring**: Implement scoring logic based on job requirements vs. your skills Interview Scheduling**: Auto-schedule interviews when status changes Follow-up Automation**: Send follow-up emails after specific time periods Important Notes Platform Compliance Always respect rate limits to avoid being blocked Follow each platform's Terms of Service Use official APIs when available instead of web scraping Don't spam job boards with excessive applications Data Privacy Store credentials securely using n8n's credential store Don't hardcode API keys or personal information in nodes Regularly review and clean up old application data Ensure your resume link is secure but accessible Quality Control Start with a small number of jobs to test the workflow Review application success rates and adjust strategy Monitor for errors and set up proper error handling Keep your job list updated and remove expired postings This workflow transforms job searching from a manual, time-consuming process into an automated system that maximizes your application efficiency while maintaining quality and compliance.