Workflow Templates

How it works creates a new page in Confluence based on a page template also defined in Confluence replaces any number of placeholders with data from your workflow generic implementation for maximum flexibility Set up steps All parameters you need to change are defined in the Set node Set your Atlassian-domain Set the template id you want to use as the basis for new pages Set the target space and parent page for new pages added based on that template. 🎥 Explainer video has all the details. =) Feedback Any feedback is welcome. If you have ideas for improvements, let me know.

Who is this template for? This workflow template is designed for content creators, researchers, educators, and professionals who need quick, accurate summaries of YouTube videos. It’s ideal for those looking to save time, extract key insights, or repurpose video content into concise formats for reports, studies, or social media. What does it do? The workflow automates the process of summarizing YouTube videos by extracting the transcript, analyzing the content, and generating a concise summary. It leverages AI tools to ensure accuracy and relevance, making it easier to digest lengthy videos in seconds. Why is it useful? This template saves hours of manual effort by automating video summarization, enabling users to focus on analyzing or sharing insights rather than watching entire videos. It’s particularly useful for staying updated with trends, conducting research, or creating content efficiently. How does it work? The workflow integrates with YouTube’s Transcript API powered by Apify Actor to fetch video transcripts, process the text using AI-powered summarization tools, and deliver a clear, concise summary. Setup Instructions You need an Apify account and an API key to connect with the Actor. Follow the steps below: Create a Free Account. Choose the appropriate Actor from the Apify search. Under the Integration tab, click on “Use API endpoints.” Select the API that best suits your needs.

Who is this for? NVD (National Vulnerability Database) data is essential for security analysts, vulnerability managers, and DevSecOps professionals who need to perform both CVE lookups and monitor historical change logs. This workflow helps streamline those efforts by providing structured outputs for audit, triage, or compliance tracking purposes. 📝 Note: While this example uses Google Sheets as the destination, you can easily modify the final destination node (e.g., send to Slack, email, database, etc.) based on your specific automation needs.? What problem is this solving? Security teams often manually look up CVE data and track changes across multiple tools. This process is inefficient and error-prone. This workflow automates the CVE lookup and historical change tracking by logging enriched vulnerability data into Google Sheets in real-time. What this workflow does This workflow is designed for CVE API lookup and change history tracking. In many vulnerability automation pipelines, it is essential to determine not only the metadata of a CVE but also how it has evolved over time. Based on the operational need—whether it's enrichment, risk scoring, or remediation validation—this workflow becomes particularly handy in surfacing both current and historical CVE data. This template performs the following actions: Accepts incoming webhook requests containing a CVE ID Queries the NVD CVE Lookup API to fetch vulnerability metadata Queries the NVD CVE History API to retrieve all historical changes Flattens both datasets into a sheet-compatible structure Appends vulnerability metadata to one sheet and change history to another within the same Google Spreadsheet Setup 🔑 Request an NVD API Key To request an NVD API Key, please provide your organization name, a valid email address, and indicate your organization type at NVD API Key Request. You must scroll to the end of the Terms of Use Agreement and check "I agree to the Terms of Use" to obtain an API Key. After submission, you will receive a single-use hyperlink via email to activate and view your API Key. If not activated within seven days, a new request must be submitted. 📊 API Rate Limits Without an API key, you're limited to 5 requests per 30-second window. With an API key, you’re allowed up to 50 requests in the same period. To prevent request throttling, it's recommended to introduce slight delays between consecutive API calls in production setups. Clone or import this workflow into your n8n instance. Set up the following credentials: Google Sheets OAuth2 NVD API Key (via HTTP Header Auth) The workflow logs data to a Google Sheet titled NVD Database, with Sheet 1 named CVE Lookup and Sheet 2 named CVE History. Trigger each workflow using the respective webhook URL, appending ?cveId=CVE-XXXX-XXXX as a query parameter. 🔍 Example Webhook Request (CVE Change History) You can test this workflow with the following example: GET https://your-domain.com/webhook/cve-history?cveId=CVE-2023-34362 How to customize this workflow Use the Edit Fields node (optional) to centralize configuration like sheet name or query input Extend the CVE flattening logic to include more nested metadata if needed Integrate notification systems (e.g., Slack or email) by branching from the processing nodes Modify webhook paths for better endpoint organization 🔐 Production Security Tips Use HTTP Header Auth on the webhook for secure access > ⚠️ This template uses webhooks and NVD API access with authentication headers. This template uses two flows: Webhook 1:** NVD CVE Lookup — Lookup CVE vulnerability metadata from NVD and sync to Google Sheet Webhook 2:** NVD CVE Change History — Track change history for CVEs via NVD and log each update Each flow: Hits NVD’s respective endpoint Uses custom JS Code node to flatten the nested JSON Syncs data to dedicated Google Sheet tabs 🧩 4 nodes: Webhook → API Call → Parse → Sheet Sync Make sure both flows are activated and webhooks exposed for external access. Based on your needs, ensure you have a secure setup—whether hosted internally or in a cloud environment—when running n8n in production.

🔍 Glassdoor Job Finder: Bright Data Scraping + Keyword-Based Automation A comprehensive n8n automation that scrapes Glassdoor job listings using Bright Data's web scraping service based on user-defined keywords, location, and country parameters, then automatically stores the results in Google Sheets. 📋 Overview This workflow provides an automated job search solution that extracts job listings from Glassdoor using form-based inputs and stores organized results in Google Sheets. Perfect for recruiters, job seekers, market research, and competitive analysis. Workflow Description: Automates Glassdoor job searches using Bright Data's web scraping capabilities. Users submit keywords, location, and country via form trigger. The workflow scrapes job listings, extracts company details, ratings, and locations, then automatically stores organized results in Google Sheets for easy analysis and tracking. ✨ Key Features 🎯 Form-Based Input: Simple web form for job type, location, and country 🔍 Glassdoor Integration: Uses Bright Data's Glassdoor dataset for accurate job data 📊 Smart Data Processing: Automatically extracts key job information 📈 Google Sheets Storage: Organized data storage with automatic updates 🔄 Status Monitoring: Built-in progress tracking and retry logic ⚡ Fast & Reliable: Professional scraping with error handling 🎯 Keyword Flexibility: Search any job type with location filters 📝 Structured Output: Clean, organized job listing data 🎯 What This Workflow Does Input Job Keywords:** Job title or role (e.g., "Software Engineer", "Marketing Manager") Location:** City or region for job search Country:** Target country for job listings Processing Form Submission Data Scraping via Bright Data Status Monitoring Data Extraction Data Processing Sheet Update Output Data Points | Field | Description | Example | |-------|-------------|---------| | Job Title | Position title from listing | Senior Software Engineer | | Company Name | Employer name | Google Inc. | | Location | Job location | San Francisco, CA | | Rating | Company rating score | 4.5 | | Job Link | Direct URL to listing | https://glassdoor.com/job/... | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Google account with Sheets access Bright Data account with Glassdoor scraping dataset access 5–10 minutes for setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON and click Import Step 2: Configure Bright Data Set up Bright Data credentials in n8n Ensure access to dataset: gd_lpfbbndm1xnopbrcr0 Update API tokens in: "Scrape Job Data" node "Check Delivery Status of Snap ID" node "Getting Job Lists" node Step 3: Configure Google Sheets Integration Create a new Google Sheet (e.g., "Glassdoor Job Tracker") Set up Google Sheets OAuth2 credentials in n8n Prepare columns: Column A: Job Title Column B: Company Name Column C: Location Column D: Rating Column E: Job Link Step 4: Update Workflow Settings Update "Update Job List" node with your Sheet ID and credentials Test the form trigger and webhook URL Step 5: Test & Activate Submit test data (e.g., "Software Engineer" in "New York") Activate the workflow Verify Google Sheet updates and field extraction 📖 Usage Guide Submitting Job Searches Navigate to your workflow's webhook URL Fill in: Search Job Type Location Country Submit the form Reading the Results Real-time job listing data Company ratings and reviews Direct job posting links Location-specific results Processing timestamps 🔧 Customization Options More Data Points:** Add job descriptions, salary, company size, etc. Search Parameters:** Add filters for salary, experience, remote work Data Processing:** Add validation, deduplication, formatting 🚨 Troubleshooting Bright Data connection failed:** Check API credentials and dataset access No job data extracted:** Validate search terms and location format Google Sheets permission denied:** Re-authenticate and check sharing Form submission failed:** Check webhook URL and form config Workflow execution failed:** Check logs, add retry logic Advanced Troubleshooting Check execution logs in n8n Test individual nodes Verify data formats Monitor rate limits Add error handling 📊 Use Cases & Examples Recruitment Pipeline:** Track job postings, build talent database Market Research:** Analyze job trends, hiring patterns Career Development:** Monitor opportunities, salary trends Competitive Intelligence:** Track competitor hiring activity ⚙️ Advanced Configuration Batch Processing:** Accept multiple keywords, loop logic, delays Search History:** Track trends, compare results over time External Tools:** Integrate with CRM, Slack, databases, BI tools 📈 Performance & Limits Single search:** 2–5 minutes Data accuracy:** 95%+ Success rate:** 90%+ Concurrent searches:** 1–3 (depends on plan) Daily capacity:** 50–200 searches Memory:** ~50MB per execution API calls:** 3 Bright Data + 1 Google Sheets per search 🤝 Support & Community n8n Community Forum:** community.n8n.io Documentation:** docs.n8n.io Bright Data Support:** Via your dashboard GitHub Issues:** Report bugs and features Contributing: Share improvements, report issues, create variations, document best practices. Need Help? Check the full documentation or visit the n8n Community for support and workflow examples.

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 1) Description This workflow automates video creation using Luma AI Dream Machine and n8n. It generates dynamic videos based on custom prompts, random camera motion, and predefined settings, then stores the video and thumbnail URLs in Airtable for easy access and tracking. This automation makes it easy to create high-quality videos at scale with minimal effort. 👉 Airtable Base Template 🎥 Tutorial Video Setup 1. Luma AI Setup Create an account with Luma AI. Generate an API key from Luma AI for authentication. Ensure the API key has permission to create and manage video requests. 2. Airtable Setup Create an Airtable base with the following fields: Generation ID** – To match incoming webhook data. Status** – Workflow status (e.g., "Done"). Video URL** – Stores the generated video URL. Thumbnail URL** – Stores the thumbnail URL. Prompt** – The video prompt used in the request. Aspect Ratio** – Defines the video format (e.g., 9:16). Duration** – Length of the video. 👉 Use the Airtable template linked above to simplify setup. 3. n8n Setup Install n8n (local or cloud). Set up Luma AI and Airtable credentials in n8n. Import the workflow and customize the settings based on your needs. How It Works 1. Global Settings Configuration The Set node defines key settings such as: Prompt** – Example: "A crocheted parrot in a crocheted pirate outfit swinging on a crocheted perch." Aspect Ratio** – Example: "9:16" Loop** – Example: "true" Duration** – Example: "5 seconds" Cluster ID** – Used to group related videos for easy tracking. Callback URL** - Used for the Webhook workflow in Part 2 2. Random Camera Motion The Code node randomly selects a camera motion (e.g., Zoom In, Pan Left, Crane Up) to create dynamic and visually engaging videos. 3. API Request to Luma AI The HTTP Request node sends a POST request to Luma AI’s API with the following parameters: Prompt – Uses the defined global settings. Aspect Ratio – Matches the target platform (e.g., TikTok or YouTube). Duration – Length of the video. Loop – Determines if the video should loop. Callback URL – Sends a POST response when the video is complete. 4. Capture API Response Luma AI sends a POST response to the callback URL once video generation is complete. The response includes: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Generation ID – Used to match the record in Airtable. 5. Store in Airtable The Airtable node updates the record with the video and thumbnail URLs. Generation ID** is crucial for matching future webhook responses to the correct video record. Why This Workflow is Useful ✅ Automates high-quality video creation ✅ Reduces manual effort by handling prompt generation and API calls ✅ Random camera motion makes videos more dynamic ✅ Ensures organized tracking with Airtable ✅ Scalable – Ideal for automating large-scale content creation Next Steps Part 2** – Handling webhook responses and updating Airtable automatically. Future Enhancements** – Adding more camera motions, multi-platform support, and automated video editing.

What this workflow does Monitors Google Drive: The workflow triggers whenever a new CSV file is uploaded. Uses AI to Identify PII Columns: The OpenAI node analyzes the data and identifies PII-containing columns (e.g., name, email, phone). Removes PII: The workflow filters out these columns from the dataset. Uploads Cleaned File: The sanitized file is renamed and re-uploaded to Google Drive, ensuring the original data remains intact. How to customize this workflow to your needs Adjust PII Identification: Modify the prompt in the OpenAI node to align with your specific data compliance requirements. Include/Exclude File Types: Adjust the Google Drive Trigger settings to monitor specific file types (e.g., CSV only). Output Destination: Change the folder in Google Drive where the sanitized file is uploaded. Setup Prerequisites: A Google Drive account. An OpenAI API key. Workflow Configuration: Configure the Google Drive Trigger to monitor a folder for new files. Configure the OpenAI Node to connect with your API Set the Google Drive Upload folder to a different location than the Trigger folder to prevent workflow loops.

Who is this for? Marketers, lead generation agencies, freelancers, consultants, and sales teams who need to collect business leads from Google Maps. Small business owners looking to build targeted local business lists. Anyone interested in automating web scraping without coding skills. What problem is this workflow solving? Manually scraping business data from Google Maps is time-consuming and repetitive. This automation simplifies the process by: Collecting business details based on search terms and location. Filtering out irrelevant results. Delivering qualified leads directly to your inbox. What this workflow does This workflow automates Google Maps lead scraping using APIFY and sends the gathered leads via email. The steps include: Collecting user input through a simple form (business type, location, recipient email). Sending an HTTP request to APIFY to run a Google Maps scraper (actor). Filtering results to include only businesses with email addresses. Converting results to CSV format. Sending an automated email with the leads as a CSV attachment via Gmail. Setup Create an APIFY Account: Sign up at APIFY.COM (https://apify.com/). You get $5 in free credits (~1,000 leads). Get Your API Key: Copy your API key from APIFY Prepare n8n: Create a new workflow. Add an HTTP Request node to interact with APIFY. Configure authentication with your API key. Customize the Form: Build a simple form inside n8n to collect user inputs: Business Type, City, Country, Recipient Email. Filter Results: Use IF and Filter nodes to remove entries without email addresses. Convert to CSV: Use a "Spreadsheet File" node to generate a CSV from the filtered leads. Send Email: Use the Gmail node (or any email node) to send the CSV file to the provided recipient. How to customize this workflow to your needs Change search parameters to target different business niches or locations. Add filters to only include businesses with websites. Customize the email subject and body. Integrate with CRM or marketing platforms for direct lead injection. Expand filtering logic for more refined targeting. Notes This template uses APIFY (paid service after free credits). You will need an APIFY API key and a Gmail account (or SMTP credentials) to run this automation. For self-hosted n8n users: ensure you have internet access and proper credentials set up for external HTTP requests. Want A Video Tutorial on How To Setup This Automation : https://www.youtube.com/watch?v=Kz_Gfx7OH6o

What this workflow does This workflow in n8n demonstrates how to send a message in Lark using a Lark bot. It begins with a manual trigger and then retrieves the necessary Lark token via a POST request. The token is used to authenticate and send a message to a specific chat using the Lark API. The input node provides the required app_id, app_secret, chat_id, and message content. After obtaining the token, the message is sent with the Lark API's message/v4/send/ endpoint. Who This Is For This n8n workflow is ideal for organizations, teams, and developers who need to automate message sending within Lark, especially those managing notifications, alerts, or team reminders. It can help users reduce manual messaging tasks by leveraging a Lark bot to deliver messages at specific intervals or based on particular conditions, enhancing team communication and responsiveness. Setup Fill the Input node with your values Exchange the bearer token in the Send Message node with your token Author: Hiroshi

LinkedIn Profile Enrichment Workflow Who is this for? This workflow is ideal for recruiters, sales professionals, and marketing teams who need to enrich LinkedIn profiles with additional data for lead generation, talent sourcing, or market research. What problem is this workflow solving? Manually gathering detailed LinkedIn profile information can be time-consuming and prone to errors. This workflow automates the process of enriching profile data from LinkedIn, saving time and ensuring accuracy. What this workflow does Input: Reads LinkedIn profile URLs from a Google Sheet. Validation: Filters out already enriched profiles to avoid redundant processing. Data Enrichment: Uses RapidAPI's Fresh LinkedIn Profile Data API to retrieve detailed profile information. Output: Updates the Google Sheet with enriched profile data, appending new information efficiently. Setup Google Sheet: Create a sheet with a column named linkedin_url and populate it with the profile URLs to enrich. RapidAPI Account: Sign up at RapidAPI and subscribe to the Fresh LinkedIn Profile Data API. API Integration: Replace the x-rapidapi-key and x-rapidapi-host values with your credentials from RapidAPI. Run the Workflow: Trigger the workflow and monitor the updates to your Google Sheet. How to customize this workflow Filter Criteria**: Modify the filter step to include additional conditions for processing profiles. API Configuration**: Adjust API parameters to retrieve specific fields or extend usage. Output Format**: Customize how the enriched data is appended to the Google Sheet (e.g., format, column mappings). Error Handling**: Add steps to handle API rate limits or missing data for smoother automation. This workflow streamlines LinkedIn profile enrichment, making it faster and more effective for data-driven decision-making.

This workflow is perfect for technical writers, content creators, marketers, and developers who write in Markdown but need to collaborate or publish using Google Docs format. Ideal for teams that want to streamline their content creation and review process. What problem does this workflow solve? Manual conversion from Markdown to Google Docs is time-consuming and often loses formatting. This workflow eliminates the tedious copy-paste process, automatically preserves formatting, and creates organized, timestamped documents in your Google Drive. Perfect for content teams who write in Markdown but need Google Docs for collaboration and review. What this workflow does Converts Markdown to HTML** with proper formatting preservation (headers, lists, links, tables) Creates timestamped Google Docs** documents with automatic naming Adds Drive location metadata** for better organization and reference Maintains document structure** including emojis, tables, and text formatting Automates file creation** in specified Google Drive folders Setup Google Drive OAuth2 credentials configured in n8n Target Google Drive folder URL Input your content title and Markdown text in the "Set Input Data" node How to customize this workflow to your needs Modify HTML formatting options** in the Markdown conversion node Change file naming patterns** to match your organization system Adjust Drive folder structure** and metadata inclusion Update MIME type handling** for different output requirements Add additional processing steps** like notifications or integrations Perfect for technical documentation workflows, content publishing pipelines, blog preparation, and automated report generation. Setup Instructions - Markdown to Google Docs Converter Prerequisites n8n instance** (local or cloud) Google account** with Google Drive access Basic understanding** of n8n workflow configuration Step 1: Import the Workflow Open n8n and navigate to Workflows Click "Add workflow" → "Import from JSON" Upload the Export_Markdown_Content_do_Google_Docs_Document.json file Save the workflow with a descriptive name Step 2: Configure Google Drive Credentials Create Google Drive OAuth2 Credentials In n8n, go to Settings → Credentials Click "Add credential" → "Google Drive OAuth2 API" Follow the OAuth setup to authorize n8n access to Google Drive: Visit Google Cloud Console Create or select a project Enable Google Drive API Create OAuth2 credentials Add authorized redirect URI for your n8n instance Name the credential (e.g., "Google Drive - Markdown Converter") Configure Google Drive Nodes Update these nodes with your Google Drive credentials: Create Empty File Update Document with Correct HTML Formatting In each node: Select your Google Drive credential from the dropdown Test the connection to ensure it works properly Step 3: Prepare Your Google Drive Create Target Folder Go to Google Drive (drive.google.com) Create a new folder for your converted documents Copy the folder URL (will look like: https://drive.google.com/drive/folders/FOLDER_ID) Ensure the folder has proper permissions for your Google account Step 4: Configure Input Data Set Your Default Values Open the "Set Input Data" node Update the assignments with your preferences: Google Drive URL: Replace the example URL with your target folder URL Format: https://drive.google.com/drive/folders/YOUR_FOLDER_ID Content Title: Set a default title or leave placeholder text This will be used in the document filename Content in Markdown: Add your Markdown content or keep example for testing Supports standard Markdown syntax (headers, lists, links, tables) Step 5: Test the Workflow Initial Test Run Ensure all credentials are configured Click the "Test workflow" button on the Manual Trigger node Monitor the execution - check for any errors in node outputs Verify the result: Check your Google Drive folder Look for a new document with timestamp in the name Open the document to verify formatting Troubleshooting Common Issues Google Drive Permission Errors: Verify OAuth2 credentials are properly configured Check that the target folder exists and is accessible Ensure Google Drive API is enabled in Google Cloud Console Markdown Conversion Issues: Check that your Markdown syntax is valid Test with simple content first (headers, paragraphs, lists) Verify the "Change Markdown To HTML" node settings File Creation Problems: Confirm the Google Drive folder URL format is correct Check that the folder ID in the URL is valid Ensure your Google account has write permissions to the folder Step 6: Customize for Your Needs Modify HTML Formatting Options In the "Change Markdown To HTML" node: Enable/disable emoji support** (currently enabled) Adjust table formatting** (currently enabled) Modify header ID generation** (currently disabled) Configure space requirements** for headers Update File Naming Pattern In the "Create Empty File" node: Change the naming convention**: Currently uses _PUB {Content Title} {timestamp} Modify timestamp format**: Currently yyyy-MM-dd HH:mm:ss Add prefixes or suffixes** as needed for your organization Step 7: Production Usage Regular Workflow Execution Update the "Set Input Data" node with new content Execute the workflow manually or set up triggers Monitor execution logs for any issues Check Google Drive for generated documents Integration Options Webhook Integration: Add a Webhook trigger to accept external Markdown content Useful for automated content publishing workflows Email Integration: Add email notifications when documents are created Include links to generated Google Docs Advanced Configuration Error Handling Add error handling nodes after critical operations Implement retry logic for API failures Set up notifications for failed executions Performance Optimization Adjust the "Wait for Document Creation" timing if needed Consider file size limits for Google Docs Support and Troubleshooting Common Solutions Timeout errors**: Increase wait time in "Wait for Document Creation" Authentication failures**: Refresh Google OAuth2 credentials Formatting issues**: Test with simpler Markdown first Getting Help Check n8n community forums for Google Drive integration issues Review Google Drive API documentation for rate limits Test with minimal Markdown content to isolate problems Total setup time: ~15-20 minutes Difficulty level: Intermediate Requirements: Google account, n8n instance, basic OAuth2 setup knowledge

This workflow randomly select recipes from a Mealie instance (can use a specific category) and then creates a meal plan in Mealie with those recipes. How it works: Workflow has a scheduled trigger (set to run weekly on a Friday) Config node sets a few properties to configure the workflow A call to the Mealie API to get the list of recipes The code node holds most of the logic, this will loop through the number of recipes defined in the config node and randomly select a recipe from the list (making sure not to double up any recipes) Once all the recipes are selected it will call the Mealie API to set up the meal plan on the days Setup Add your Mealie API token as a credential and set it on the Http Request nodes Set the relevant schedule trigger to run when you like Update the Config node with the config you want numberOfRecipes - Number of recipes to populate for the meal plan offsetPlanDays - Number of days in the future to start the plan (0 will start it today, 1 tomorrow, etc.) mealieCategoryId - A category id of the category you want to pull in recipes from (default to select from all recipes) mealieBaseUrl - The base url of your Mealie instance

Retrieve 20 Latest TechCrunch Articles Who is this for? This workflow is designed for developers, content creators, and data analysts who need to scrape recent articles from TechCrunch. It’s perfect for anyone looking to aggregate news articles or create custom feeds for analysis, reporting, or integration into other systems. What problem is this workflow solving? This workflow automates the process of scraping recent articles from TechCrunch. Manually collecting article data can be time-consuming and inefficient, but with this workflow, you can quickly gather up-to-date news articles with relevant metadata, saving time and effort. What this workflow does This workflow retrieves the latest 20 news articles from TechCrunch’s “Recent” page. It extracts the article URLs, metadata (such as titles and publication dates), and main content for each article, allowing you to access the information you need without any manual effort. Setup Clone or download the workflow template. Ensure you have a working n8n environment. Configure the HTTP Request nodes with your desired parameters to connect to the TechCrunch API. (Optional) Customize the workflow to target specific sections or topics of interest. Run the workflow to retrieve the latest 20 articles. How to customize this workflow to your needs Modify the HTTP request to pull articles from different pages or sections of TechCrunch. Adjust the number of articles to retrieve by changing the selection criteria. Add additional processing steps to further filter or analyze the article data. Workflow Steps Send an HTTP request to the TechCrunch "Recent" page. Parse a posts box that holds the list of articles. Parse all posts to extract all articles. spilt out posts for each article. Extract the URL and metadata from each article. Send an HTTP request for each article using its URL. Locate and parse the main content of each article. Note: Be sure to update the HTTP Request nodes with any necessary headers or authentication to work with TechCrunch’s website.