Workflow Templates

This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n. This workflow demonstrates how to build and expose a sophisticated n8n AI Agent as a single, callable tool using the Multi-Agent Collaboration Protocol (MCP). It allows external clients or other AI systems to easily query software library documentation via Context7, without needing to manage the underlying tool orchestration or complex conversational logic. Core Idea: Instead of building complex agentic loops on the client-side (e.g., in Python, a VS Code extension, or another AI development environment), this workflow offloads the entire agent's reasoning and tool-use process to n8n. The client simply sends a natural language query (like "How do I use Flexbox in Tailwind CSS?") to an SSE endpoint, and the n8n agent handles the rest. Key Features & How It Works: Public MCP Endpoint: The main workflow uses the Context7 MCP Server Trigger node to create an SSE endpoint. This makes the agent accessible to any MCP-compatible client. The path for the endpoint is kept long and random for basic 'security by obscurity'. Tool Workflow as an Interface: A Tool Workflow node (named call_context7_ai_agent in this example) is connected to the MCP Server Trigger. This node defines the single "tool" that external clients will see and call. Dedicated AI Agent Sub-Workflow: The call_context7_ai_agent tool invokes a separate sub-workflow which contains the actual AI logic. This sub-workflow starts with a Context7 Workflow Start node to receive the user's query. A Context7 AI Agent node (using Google Gemini in this example) is the brain, equipped with: A system prompt to guide its behavior. Simple Memory to retain context for each execution (using {{ $execution.id }} as the session key). Two specialized Context7 MCP client tools: context7-resolve-library-id: To convert library names (e.g., 'Next.js') into Context7-specific IDs. context7-get-library-docs: To fetch documentation using the resolved ID, with options for specific topics and token limits. Seamless Tool Use: The AI Agent autonomously decides when and how to use the resolve-library-id and get-library-docs tools based on the user's query, handling the multi-step process internally. Benefits of This Approach: Simplified Client Integration:** Clients interact with a single, powerful tool, sending a simple query. Reduced Client-Side Token Consumption:** The detailed prompts, tool descriptions, and conversational turns are managed server-side by n8n, saving tokens on the client (especially useful if the client is another LLM). Centralized Agent Management:** Update your agent's capabilities, tools, or LLM model within n8n without any changes needed on the client side. Modularity for Agentic Systems:** Perfect for building complex, multi-agent systems where this n8n workflow can act as a specialized "expert" agent callable by others (e.g., from environments like Smithery). Cost-Effective:** By using a potentially less expensive model (like Gemini Flash) for the agent's orchestration and leveraging the free tier or efficient pricing of services like Context7, you can build powerful solutions economically. Use Cases: Providing an intelligent documentation lookup service for coding assistants or IDE extensions. Creating specialized AI "micro-agents" that can be consumed by larger AI applications. Building internal knowledge base query systems accessible via a simple API-like interface. Setup: Ensure you have the necessary n8n credentials for Google Gemini (or your chosen LLM) and the Context7 MCP client tools. The Path in the Context7 MCP Server Trigger node should be unique and secure. Clients connect to the "Production URL" (SSE endpoint) provided by the trigger node. This workflow is a great example of how n8n can serve as a powerful backend for building and deploying modular AI agents. I've made a video to try and explain this a bit too https://www.youtube.com/watch?v=dudvmyp7Pyg

Use Case Automate chat interactions in Bitrix24 with a customizable bot that can handle various events and respond to user messages. What This Workflow Does Processes incoming webhook requests from Bitrix24 Handles authentication and token validation Routes different event types (messages, joins, installations) Provides automated responses and bot registration Manages secure communication between Bitrix24 and external services Setup Instructions Configure Bitrix24 webhook endpoints Set up authentication credentials Customize bot responses and behavior Deploy and test the workflow

> Note: This template requires a self-hosted community edition of n8n. Does not work on cloud. Try It Out This n8n template shows how to validate API requests with Auth0 Authorization tokens. Auth0 doesn't work with the standard JWT auth option because: 1) Auth0 tokens use the RS256 algorithm. 2) RS256 JWT credentials in n8n require the user to use private and public keys and not secret phrase. 3) Auth0 does not give you access to your Auth0 instance private keys. The solution is to handle JWT validation after the webhook is received using the code node. How it works There are 2 approaches to validate Auth0 tokens: using your application's JWKS file or using your signing cert. Both solutions uses the code node to access nodeJS libraries to verify the token. JWKS**: the JWK-RSA library is used to validate the application's JWKS URI hosted on Auth0 Signing Cert**: the application's signing cert is imported into the workflow and used to verify token. In both cases, when the token is found to be invalid, an error is thrown. However, as we can use error outputs for the code node, the error does not stop the workflow and instead is redirected to a 401 unauthorized webhook response. When token is validated, the webhook response is forwarded on the success branch and the token decoded payload is attached. How to use Follow the instructions as stated in each scenario's sticky notes. Modify the Auth0 details with that of your application and Auth0 instance. Requirements Self-hosted community edition of n8n Ability to install npm packages Auth0 application and some way to get either the JWK url or signing cert.

Who is this for? This workflow is perfect for HR teams, recruiters, and hiring platforms that need to automate the extraction of key candidate details—like name, email, skills, and education—from resume files submitted in various formats. What problem does this solve? Manually reviewing and extracting structured data from resumes is time-consuming and error-prone. This automation eliminates that bottleneck, standardizing candidate data for seamless integration into CRMs, applicant tracking systems, or Google Sheets. What this workflow does This n8n template listens for uploaded resume files, detects their format (PDF, DOC, TXT, CSV, etc.), and automatically extracts the raw text using n8n’s built-in file extraction tools. The extracted text is then parsed using an OpenAI-powered agent that returns structured fields such as: Full Name Email Address Skill Keywords Education Details Optionally, you can push the structured output to Google Sheets (node included, currently disabled). Setup Clone this workflow into your n8n instance. Enable the When chat message received trigger if using n8n chat. Provide your OpenAI credentials and enable the LangChain Agent node. (Optional) Connect Google Sheets by authenticating with your Google account and filling in your target document and sheet. Watch the setup and demo video here: 🎥 https://youtu.be/2SUPiNmLWdA How to customize Modify the OpenAI system message to extract different fields (e.g., phone number, LinkedIn). Replace the Google Sheets node with a webhook to push results to your ATS. Add filters to limit accepted file types or max file size. > ⚠️ This template is designed to be secure. It uses credentials stored in the n8n credential manager—no hardcoded secrets required.

This template is for Self-Hosted N8N Instances only. This n8n demonstrates how to build a simple SQLite MCP server to perform local database operations as well as use it for Business Intelligence. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite How it works A MCP server trigger is used and connected to 5 tools: 2 Code Node and 3 Custom Workflow. The 2 Code Node tools use the SQLLite3 library and are simple read-only queries and as such, the Code Node tool can be simply used. The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion. Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves. All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler. Finally, we use our Code nodes to handle select, insert and update operations. The responses are then sent back to the the MCP client. How to use This SQLite MCP server allows any compatible MCP client to manage a SQLite database by supporting select, create and update operations. You will need to have a SQLite database available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please create a table to store business insights and add the following..." "what business insights do we have on current retail trends?" "Who has contributed the most business insights in the past week?" Requirements SQLite for database. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This n8n template demonstrates how to use AI to compose or "stitch" separate images together to generate a new image which retains the source assets and consistent style. Use cases are many: Try producing storyboard scenes with consistent characters, marketing material with existing product assets or trying on different articles on fashion! Good to know At time of writing, each image generated will cost $0.039 USD. See Gemini Pricing for updated info. The model used in this workflow is geo-restricted! If it says model not found, it may not be available in your country or region. How it works We'll import our required assets via our Cloud storage using the HTTP node. The images are then converted to base64 strings and aggregated so we can use it for our AI model. Gemini's image generation model is used which takes all 3 images and a prompt that we define. Our prompt instructs the model on how to compose the final image. Gemini generates a new image but uses the original 3 assets to do so. The consistency to the source images is very high and shows little signs of hallucinations! Gemini's output is base64 so we use a "Convert to file" node to convert the data to binary. The final binary image is then uploaded to Google Drive to complete the demonstration. How to use The manual trigger node is used as an example but feel free to replace this with other triggers such as webhook or even a form. Technically, you should be able to compose even more images but of course, the generation will take longer and cost more. Requirements Gemini account for LLM and Image generation Google drive for upload Customising this workflow AI Image editing can be used for many use-cases. Try a popular use-case such as virtual try-on for fashion or applying branding on editing image assets.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. What is DIGIPIN? DIGIPIN (Digital Pincode) is a 10-character alphanumeric code introduced by India Post. It maps any 3x3 meter square in India to a unique digital address. This helps precisely locate homes, shops, or landmarks, especially in areas where physical addresses are inconsistent or missing. What this workflow does This workflow creates a fully offline DIGIPIN microservice using only JavaScript - no external APIs are used. You get two HTTP endpoints: GET /generate-digipin?lat={latitude}&lon={longitude} → returns a DIGIPIN GET /decode-digipin?digipin={code} → returns the latitude and longitude You can plug this into any system to: Convert GPS coordinates to a DIGIPIN Convert a DIGIPIN back to coordinates How it works An HTTP Webhook node receives the request A JS Function node either encodes or decodes based on input The result is returned as a JSON response All the logic is handled inside the workflow - no API keys, no external calls. Why use this Fast and lightweight Easily extendable: you can connect this to forms, CRMs, apps, or spreadsheets Ideal for field agents, address validation, logistics, or rural operations

Who’s it for: Sales and business development professionals who want to monitor company news, hiring trends, and business signals for their leads. How it works / What it does: Add a company to the form, and the workflow will automatically search for the latest news, recent hires, company stage, and LinkedIn activity. The results are sent straight to your Google Sheet, helping you stay up to date with your leads and prospects. How to set up: Register for Extruct at www.extruct.ai/. Open the Extruct table template, copy the table ID from the browser’s address bar. Make a copy of the Google Sheets template to your Drive. Enter the table ID into the variables node in your n8n flow. Set up Bearer authentication in all HTTP Request nodes using your Extruct API token. In the Google Sheets node, paste your template link and connect your Google account. Run the flow once to load the mapping fields, then match each output to the correct column. Activate the flow and start adding companies through the form. Requirements: Extruct account and API token Extruct table template Google account with Google Sheets How to customize the workflow: To track more business development signals, add new columns in both the Extruct table and your Google Sheet, then map them in the Google Sheets node.

This template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs Add custom instructions to your Agent by changing the prompts in it. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. Describe your data properly in the Q&A tool Exchange the Simple Vector Store nodes with your own vector store tools ready for production. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.

Workflow Setup Guide This workflow collects the most-viewed videos from specified YouTube channels and saves the data to a Google Sheet. Follow these steps to set it up: 1. Credentials Setup Google Sheets:** You need to have a Google Sheets credential configured in your n8n instance. If you don't have one, go to the 'Credentials' section in n8n and add a new credential for Google Sheets. YouTube API Key:** You need a YouTube Data API v3 key. Go to the Google Cloud Console. Create a new project or select an existing one. Go to 'APIs & Services' > 'Library' and enable the YouTube Data API v3. Go to 'APIs & Services' > 'Credentials', click 'Create Credentials', and choose 'API key'. Copy the generated API key. 2. Google Sheet Setup You will need one Google Sheet with two separate sheets (tabs) inside it. Input Sheet Use Template This sheet provides the list of YouTube channels to process. Required Columns:** Create a sheet with the following two columns: ChannelID: The ID of the YouTube channel (e.g., T7M3PpjBZzw). video_num_to_get: The number of top videos to retrieve for that channel (e.g., 5). Output Sheet This sheet is where the results will be saved. Required Columns:** The workflow will automatically append data to the following columns. You can create them beforehand or let the workflow do it. channelName title videoId videoLink 3. Node Configuration Read Channel Info from Sheet:** Select your Google Sheets credential. Enter your Spreadsheet ID. Enter the name of your Input Sheet. Fetch Most-Viewed Videos via YouTube API:** Replace YOUR_YOUTUBE_API_KEY with the API key you generated in Step 1. Append Video Details to Sheet:** Select your Google Sheets credential. Enter your Spreadsheet ID (the same one as before). Enter the name of your Output Sheet.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Prerequisites A n8n account (free trial available) A Scrapeless account and API key A Google account to access Google Sheets 🛠️ Step-by-Step Setup 1. Create a New Workflow in n8n Start by creating a new workflow in n8n. Add a Manual Trigger node to begin. 2. Add the Scrapeless Node Add the Scrapeless node and choose the Scrape operation Paste in your API key Set your target website URL Execute the node to fetch data and verify results 3. Clean Up the Data Add a Code node to clean and format the scraped data. Focus on extracting key fields like: Title Description URL 4. Set Up Google Sheets Create a new spreadsheet in Google Sheets Name the sheet (e.g., Business Leads) Add columns like Title, Description, and URL 5. Connect Google Sheets in n8n Add the Google Sheets node Choose the operation Append or update row Select the spreadsheet and worksheet Manually map each column to the cleaned data fields 6. Run and Test the Workflow Click "Execute Workflow" in n8n Check your Google Sheet to confirm the data is properly inserted Results With this automated workflow, you can continuously extract business lead data, clean it, and push it directly into a spreadsheet — perfect for outbound sales, lead lists, or internal analytics. How to Use ⚙️ Open the Variables node and plug in your Scrapeless credentials. 📄 Confirm the Google Sheets node points to your desired spreadsheet. ▶️ Run the workflow manually from the Start node. Perfect For: Sales teams doing outbound prospecting Marketers building lead lists Agencies running data aggregation tasks

🐦 Twitter Profile Scraper via Bright Data API with Google Sheets Output A comprehensive n8n automation that scrapes Twitter profile data using Bright Data's Twitter dataset and stores comprehensive tweet analytics, user metrics, and engagement data directly into Google Sheets. 📋 Overview This workflow provides an automated Twitter data collection solution that extracts profile information and tweet data from specified Twitter accounts within custom date ranges. Perfect for social media analytics, competitor research, brand monitoring, and content strategy analysis. ✨ Key Features 🔗 Form-Based Input: Easy-to-use form for Twitter URL and date range selection 🐦 Twitter Integration: Uses Bright Data's Twitter dataset for accurate data extraction 📊 Comprehensive Data: Captures tweets, engagement metrics, and profile information 📈 Google Sheets Storage: Automatically stores all data in organized spreadsheet format 🔄 Progress Monitoring: Real-time status tracking with automatic retry mechanisms ⚡ Fast & Reliable: Professional scraping with built-in error handling 📅 Date Range Control: Flexible time period selection for targeted data collection 🎯 Customizable Fields: Advanced data field selection and mapping 🎯 What This Workflow Does Input Twitter Profile URL**: Target Twitter account for data scraping Date Range**: Start and end dates for tweet collection period Custom Fields**: Configurable data points to extract Processing Form Trigger: Collects Twitter URL and date range from user input API Request: Sends scraping request to Bright Data with specified parameters Progress Monitoring: Continuously checks scraping job status until completion Data Retrieval: Downloads complete dataset when scraping is finished Data Processing: Formats and structures extracted information Sheet Integration: Automatically populates Google Sheets with organized data Output Data Points | Field | Description | Example | |-------|-------------|---------| | user_posted | Username who posted the tweet | @elonmusk | | name | Display name of the user | Elon Musk | | description | Tweet content/text | "Exciting updates coming soon..." | | date_posted | When the tweet was posted | 2025-01-15T10:30:00Z | | likes | Number of likes on the tweet | 1,234 | | reposts | Number of retweets | 567 | | replies | Number of replies | 89 | | views | Total view count | 12,345 | | followers | User's follower count | 50M | | following | Users they follow | 123 | | is_verified | Verification status | true/false | | hashtags | Hashtags used in tweet | #AI #Technology | | photos | Image URLs in tweet | image1.jpg, image2.jpg | | videos | Video content URLs | video1.mp4 | | user_id | Unique user identifier | 12345678 | | timestamp | Data extraction timestamp | 2025-01-15T11:00:00Z | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Bright Data account with Twitter dataset access Google account with Sheets access Valid Twitter profile URLs to scrape 10-15 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: Credentials → + Add credential → HTTP Header Auth Enter your Bright Data API credentials Test the connection Configure dataset: Ensure you have access to Twitter dataset (gd_lwxkxvnf1cynvib9co) Verify dataset permissions in Bright Data dashboard Step 3: Configure Google Sheets Integration Create a Google Sheet: Go to Google Sheets Create a new spreadsheet named "Twitter Data" or similar Copy the Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit Set up Google Sheets credentials: In n8n: Credentials → + Add credential → Google Sheets OAuth2 API Complete OAuth setup and test connection Prepare your data sheet with columns: Use the column headers from the data points table above The workflow will automatically populate these fields Step 4: Update Workflow Settings Update Bright Data nodes: Open "🚀 Trigger Twitter Scraping" node Replace BRIGHT_DATA_API_KEY with your actual API token Verify dataset ID is correct Update Google Sheets node: Open "📊 Store Twitter Data in Google Sheet" node Replace YOUR_GOOGLE_SHEET_ID with your Sheet ID Select your Google Sheets credential Choose the correct sheet/tab name Step 5: Test & Activate Add test data: Use the form trigger to input a Twitter profile URL Set a small date range for testing (e.g., last 7 days) Test the workflow: Submit the form to trigger the workflow Monitor progress in n8n execution logs Verify data appears in Google Sheet Check all expected columns are populated 📖 Usage Guide Running the Workflow Access the workflow form trigger URL (available when workflow is active) Enter the Twitter profile URL you want to scrape Set the start and end dates for tweet collection Submit the form to initiate scraping Monitor progress - the workflow will automatically check status every minute Once complete, data will appear in your Google Sheet Understanding the Data Your Google Sheet will show: Real-time tweet data** for the specified date range User engagement metrics** (likes, replies, retweets, views) Profile information** (followers, following, verification status) Content details** (hashtags, media URLs, quoted tweets) Timestamps** for each tweet and data extraction Customizing Date Ranges Recent data**: Use last 7-30 days for current activity analysis Historical analysis**: Select specific months or quarters for trend analysis Event tracking**: Focus on specific date ranges around events or campaigns Comparative studies**: Use consistent time periods across different profiles 🔧 Customization Options Modifying Data Fields Edit the custom_output_fields array in the "🚀 Trigger Twitter Scraping" node to add or remove data points: "custom_output_fields": [ "id", "user_posted", "name", "description", "date_posted", "likes", "reposts", "replies", "views", "hashtags", "followers", "is_verified" ] Changing Google Sheet Structure Modify the column mapping in the "📊 Store Twitter Data in Google Sheet" node to match your preferred sheet layout and add custom formulas or calculations. Adding Multiple Recipients To process multiple Twitter profiles: Modify the form to accept multiple URLs Add a loop node to process each URL separately Implement delays between requests to respect rate limits 🚨 Troubleshooting Common Issues & Solutions "Bright Data connection failed" Cause: Invalid API credentials or dataset access Solution: Verify credentials in Bright Data dashboard, check dataset permissions "No data extracted" Cause: Invalid Twitter URLs or private/protected accounts Solution: Verify URLs are valid public Twitter profiles, test with different accounts "Google Sheets permission denied" Cause: Incorrect credentials or sheet permissions Solution: Re-authenticate Google Sheets, check sheet sharing settings "Workflow timeout" Cause: Large date ranges or high-volume accounts Solution: Use smaller date ranges, implement pagination for high-volume accounts "Progress monitoring stuck" Cause: Scraping job failed or API issues Solution: Check Bright Data dashboard for job status, restart workflow if needed Advanced Troubleshooting Check execution logs in n8n for detailed error messages Test individual nodes by running them separately Verify data formats and ensure consistent field mapping Monitor rate limits if scraping multiple profiles consecutively Add error handling and implement retry logic for robust operation 📊 Use Cases & Examples 1. Social Media Analytics Goal: Track engagement metrics and content performance Monitor tweet engagement rates over time Analyze hashtag effectiveness and reach Track follower growth and audience interaction Generate weekly/monthly performance reports 2. Competitor Research Goal: Monitor competitor social media activity Track competitor posting frequency and timing Analyze competitor content themes and strategies Monitor competitor engagement and audience response Identify trending topics and hashtags in your industry 3. Brand Monitoring Goal: Track brand mentions and sentiment analysis Monitor specific Twitter accounts for brand mentions Track hashtag campaigns and user-generated content Analyze sentiment trends and audience feedback Identify influencers and brand advocates 4. Content Strategy Development Goal: Analyze successful content patterns Identify high-performing tweet formats and topics Track optimal posting times and frequencies Analyze hashtag performance and reach Study audience engagement patterns 5. Market Research Goal: Collect social media data for market analysis Gather consumer opinions and feedback Track industry trends and discussions Monitor product launches and market reactions Support product development with social insights ⚙ Advanced Configuration Batch Processing Multiple Profiles To monitor multiple Twitter accounts efficiently: Create a master sheet with profile URLs and date ranges Add a loop node to process each profile separately Implement delays between requests to respect rate limits Use separate sheets or tabs for different profiles Adding Data Analysis Enhance the workflow with analytical capabilities: Create additional sheets for processed data and insights Add formulas to calculate engagement rates and trends Implement data visualization with charts and graphs Generate automated reports and summaries Integration with Business Tools Connect the workflow to your existing systems: CRM Integration**: Update customer records with social media data Slack Notifications**: Send alerts when data collection is complete Database Storage**: Store data in PostgreSQL/MySQL for advanced analysis BI Tools**: Connect to Tableau/Power BI for comprehensive visualization 📈 Performance & Limits Expected Performance Single profile**: 30 seconds to 5 minutes (depending on date range) Data accuracy**: 95%+ for public Twitter profiles Success rate**: 90%+ for accessible accounts Daily capacity**: 10-50 profiles (depends on rate limits and data volume) Resource Usage Memory**: ~200MB per execution Storage**: Minimal (data stored in Google Sheets) API calls**: 1 Bright Data call + multiple Google Sheets calls per profile Bandwidth**: ~5-10MB per profile scraped Execution time**: 2-10 minutes for typical date ranges Scaling Considerations Rate limiting**: Add delays for high-volume scraping Error handling**: Implement retry logic for failed requests Data validation**: Add checks for malformed or missing data Monitoring**: Track success/failure rates over time Cost optimization**: Monitor API usage to control costs 🤝 Support & Community Getting Help n8n Community Forum**: community.n8n.io Documentation**: docs.n8n.io Bright Data Support**: Contact through your dashboard GitHub Issues**: Report bugs and feature requests Contributing Share improvements with the community Report issues and suggest enhancements Create variations for specific use cases Document best practices and lessons learned 📋 Quick Setup Checklist Before You Start ☐ n8n instance running (self-hosted or cloud) ☐ Bright Data account with Twitter dataset access ☐ Google account with Sheets access ☐ Valid Twitter profile URLs ready for scraping ☐ 10-15 minutes available for setup Setup Steps ☐ Import Workflow - Copy JSON and import to n8n ☐ Configure Bright Data - Set up API credentials and test ☐ Create Google Sheet - New sheet with proper column structure ☐ Set up Google Sheets credentials - OAuth setup and test ☐ Update workflow settings - Replace API keys and sheet IDs ☐ Test with sample data - Add 1 Twitter URL and small date range ☐ Verify data flow - Check data appears in Google Sheet correctly ☐ Activate workflow - Enable form trigger for production use Ready to Use! 🎉 Your workflow URL: Access form trigger when workflow is active 🎯 Happy Twitter Scraping! This workflow provides a solid foundation for automated Twitter data collection. Customize it to fit your specific social media analytics and research needs. For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/