Agents & Automations

Adobe provides an API to perform all sorts of manipulation on PDF files: - Split PDF - Combine PDF - OCR - Insert page, delete page, replace page, reorder page - Content extraction (text content, tables, pictures) The free tier allows up to 500 PDF operations/month. As it comes directly from Adobe, it often works better than other alternatives. ### What does this workflow do? The API can be a bit painful to use. To perform a transformation on a PDF, it requires: 1. Authenticate and get a temporal token 2. Register a new asset (file) 3. Upload your PDF to the registered asset 4. Perform a query according to the transformation requested 5. Wait for the query to be processed by Adobe backend 6. Download the result This workflow is a generic wrapper to perform all these steps for any transformation endpoint. I usually use it from other workflows with an Execute Workflow node. Examples are given in the workflow. ### Example use case This service is useful for cleaning PDF data for an AI/RAG system. My favorite use case is to extract tables as images and forward the images to an AI for image recognition/description, which is often more accurate than feeding raw tabular data to a LLM.

The Ultimate Scraper for n8n uses Selenium and AI to retrieve any information displayed on a webpage. You can also use session cookies to log in to the targeted webpage for more advanced scraping needs. ⚠️ Important: This project requires specific setup instructions. Please follow the guidelines provided in the GitHub repository: n8n Ultimate Scraper Setup. The workflow version on n8n and the GitHub project may differ; however, the most up-to-date version will always be the one available on the GitHub repository. How to use: Deploy the project with all the requirements and request your webhook. Example of request:curl -X POST <http://localhost:5678/webhook-test/yourwebhookid> \\ -H "Content-Type: application/json" \\ -d '{ "subject": "Hugging Face", "Url": "github.com", "Target data": [ { "DataName": "Followers", "description": "The number of followers of the GitHub page" }, { "DataName": "Total Stars", "description": "The total numbers of stars on the different repos" } ], "cookie": [] }' Or to just scrap a URL: curl -X POST <http://localhost:5678/webhook-test/67d77918-2d5b-48c1-ae73-2004b32125f0> \\ -H "Content-Type: application/json" \\ -d '{ "Target Url": "<https://github.com>", "Target data": [ { "DataName": "Followers", "description": "The number of followers of the GitHub page" }, { "DataName": "Total Stars", "description": "The total numbers of stars on the different repo" } ], "cookies": [] }'

Detailed Title "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n" This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp. Workflow DetailsTrigger: Strava Activity UpdatesNode: Strava TriggerPurpose: Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more.Integration: Uses Strava API for real-time synchronization. Step 1: Data PreprocessingNode: CodePurpose: Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes.Logic: A recursive function flattens JSON input to create a clean and readable structure. Step 2: AI Analysis with Google GeminiNode: Google Gemini Chat ModelPurpose: Leverages Google Gemini's advanced language model to analyze the activity data.Functionality: - Identifies key performance metrics. - Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling). - Offers tailored recommendations and motivational advice. Step 3: Generate Structured OutputNode: Structure OutputPurpose: Processes the AI-generated response to create a structured format, such as headings, paragraphs, and bullet lists.Output: Formats the response for clear communication. Step 4: Convert to HTMLNode: Convert to HTMLPurpose: Converts the structured output into an HTML format suitable for email or other presentation methods.Output: Ensures the response is visually appealing and easy to understand. Step 5: Send Email with Training InsightsNode: Send EmailPurpose: Sends a detailed email to the athlete with performance insights, training recommendations, and motivational messages.Integration: Utilizes Gmail or SMTP for secure and efficient email delivery. Optional Step: WhatsApp NotificationsNode: WhatsApp Business CloudPurpose: Sends a summary of the activity analysis and key recommendations via WhatsApp for instant access.Integration: Connects to WhatsApp Business Cloud for automated messaging. Additional NotesCustomization: You can modify the AI prompt to adapt the recommendations to the athlete's specific goals or fitness levels. The workflow is flexible and can accommodate additional nodes for more advanced analysis or output formats. Scalability: Ideal for individual athletes or coaches managing multiple athletes. Can be expanded to include additional metrics or insights based on user preferences. Performance Metrics Handled: - Swimming: SWOLF, stroke count, pace. - Cycling: Cadence, power zones, elevation. - Running: Pacing, stride length, heart rate zones. Implementation Steps 1. Set Up Strava API Key: Log in to Strava Developers to generate your API key. Integrate the API key into the Strava Trigger node. 2. Configure Google Gemini Integration: Use your Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node. 3. Customize Email and WhatsApp Messaging: Update the Send Email and WhatsApp Business Cloud nodes with the recipient’s details. 4. Automate Execution: Deploy the workflow and use n8n's scheduling features or cron jobs for periodic execution. By using this workflow, triathletes and coaches can elevate training to the next level with AI-powered insights and actionable recommendations.

🤖 AI-Powered Gmail MCP Server for n8n This n8n workflow template leverages an external AI Model Control Plane (MCP) Server to automate various Gmail tasks, such as composing emails, replying to threads, and handling follow-ups using dynamically generated content. It uses the native n8n Gmail nodes available from v1.88.0 onwards. Who is this template for? Ideal for developers, automation engineers, and power users using self-hosted n8n (v1.88.0+) who want to integrate artificial intelligence directly into their email workflows via a dedicated MCP Server for enhanced control and customization over AI interactions. What problem does this workflow solve? ⚙️ Reduces Manual Effort: Decreases the work involved in writing, sending, and following up on emails in Gmail. ✅ Consistency and Quality: Ensures standardized, professional responses free from typos by leveraging controlled AI prompts. 🔄 Complete Automation: Automates the entire email cycle: from the initial send, through waiting for a reply, to sending automated follow-ups based on AI logic. Workflow Overview This template provides a structured approach to integrating Gmail with an MCP Server: 📡 MCP Trigger (“MCP_GMAIL”): An n8n Webhook node that receives HTTP calls from your MCP Server. It standardizes the inputs (like recipient, subject, AI prompt) for all subsequent Gmail nodes. (You will need to configure your MCP Server to call this webhook URL). 📤 SEND_EMAIL (Gmail Node v2.1): Sends new messages. The email body (message field) is typically populated by content generated from an AI prompt processed by your MCP server and passed via the trigger. 🔄 REPLY_EMAIL (Gmail Node v2.1): Automatically replies to existing conversations (threads). It uses AI-generated content (via MCP) to formulate the reply based on the thread context. Requires Message ID and/or Thread ID. 📥 GET_EMAIL (Gmail Node v2.1): Fetches data for a specific message (using Message ID) for analysis, processing, or archiving. Useful for retrieving context before replying. ⏳ SEND_AND_WAIT (Gmail Node v2.1): Sends an email and pauses the workflow execution until a reply is received in that specific conversation (thread). This is crucial for building automated follow-up sequences. It then outputs data from the reply message. Prerequisites Ensure you have the following before importing: 🚀 A self-hosted n8n instance running version 1.88.0 or higher. ☁️ A Google Cloud project with the Gmail API correctly enabled. 🔑 Gmail OAuth2 credentials configured in your n8n instance. Navigate to Settings > Credentials > New > Google > Gmail (OAuth2 API) to set this up if you haven't already. 🧠 Access to your MCP Server and an API Key (or other authentication method) required to interact with it via HTTP requests. How to Import and Configure Follow these steps to get the template running: 1. In your n8n interface, navigate to Templates → Import from URL. 2. Paste the JSON link provided for this workflow template. 3. Configure the necessary credentials within n8n under Credentials: - Gmail OAuth2: Select the Google OAuth2 credential you previously configured that has access to the desired Gmail account. - MCP API Key: You'll likely need to configure credentials for interacting with your MCP Server. This might involve setting up a Header Auth credential in n8n with your MCP API Key, or configuring the HTTP Request node within the workflow directly, depending on your MCP's authentication scheme. Link this credential where needed (e.g., in the Trigger node if MCP calls n8n with auth, or in HTTP Request nodes if n8n calls MCP). 4. Activate the Workflow: Ensure the workflow toggle is set to "Active" in the top right corner. 5. Webhook URL: Copy the Webhook URL from the "MCP_GMAIL" Trigger node (Test or Production URL as needed) and configure your MCP Server to send requests to this URL. Recommendation Rename the nodes with clear, descriptive names relevant to your specific use case (e.g., ✨ Generate Sales Email Body via MCP, 📥 Fetch Customer Replies). Utilize the workflow notes (sticky notes on the canvas) to document specific logic, prompt details, or configuration choices for future reference. Customization & Technical Guidance Tailor the workflow to your specific needs: 🔍 Search Filters: In nodes that fetch emails (like GET_EMAIL or if you add a Gmail - Get Many node), refine the search using the Search field (standard Gmail search operators) or filter by Label Names to process specific emails (e.g., unread from a specific sender, emails with a certain label). ✍️ AI Fine-Tuning (Prompt Engineering): The core of the AI integration happens in the prompts sent to your MCP Server. Modify these prompts (often constructed within function nodes or directly in the trigger input expected from MCP) in the message or body fields passed to the send/reply nodes. Adjust prompts to control: - Tone & Style: Formal, informal, empathetic, technical, etc. - Content & Format: Request bullet points, summaries, specific data extraction. - Dynamic Variables: Inject data from previous n8n nodes (e.g., customer name, order details, previous email content) into the prompt for context-aware generation. Example: "Reply to the following email thread [{{ $json.thread_content }}] addressing the customer {{ $json.customer_name }} about their query..." 🔗 Post-Response Actions: Extend the workflow after key actions, especially the SEND_AND_WAIT node. Add nodes to: - Log results to a database (MySQL, PostgreSQL, Airtable). - Update CRM records (HubSpot, Salesforce). - Send notifications (Slack, Discord, Telegram). - Trigger other n8n workflows. 🛡️ Error Handling: Implement robust error handling. Connect the red output pins (error output) of critical nodes (like Gmail nodes or HTTP Requests to MCP) to an Error Trigger node. From there, you can: - Log detailed error information to a monitoring tool or spreadsheet. - Send failure notifications. - Implement retry logic (using loops or specific retry settings on nodes). - Route to alternative paths or fallback workflows.

If you want to build a system that teaches you how AI automation really works, this template is made for you. You’ll learn how to: • Create an app in Meta Business to connect your social accounts, • Organize your ideas in a centralized database, • Intelligently link tools together using Make, • Automate transcription, text generation, visual creation, video production, and even content publishing. This template isn’t just a plug-and-play solution. It’s a gateway to mastering strategic AI automation systems. You'll understand every step, every connection, and the logic behind the entire workflow. Result: you’ll be able to confidently pivot toward more powerful and optimized systems that are tailored to your content flow - while already having a fully functional system that saves you time and money.

Every time you add a new media file in a specified folder in Google Drive, Make will automatically upload that file to Cloudinary, then it will be transformed using your settings and uploaded back to Google Drive.

This is a demo workflow to showcase how to use Supabase to embed a document, retrieve information from the vector store via chat, and update the database. Setup steps: - Set your credentials for Supabase. - Set your credentials for an AI model of your choice. - Set credentials for any service you want to use to upload documents. Please follow the guidelines in the workflow itself (Sticky Notes). Feedback & Questions If you have any questions or feedback about this workflow, feel free to get in touch at [email protected].

Automatically monitor Google My Business reviews, log responses in Google Sheets, and generate replies using ChatGPT for enhanced customer engagement.

Overview This workflow leverages the LangChain code node to implement a fully customizable conversational agent. It is ideal for users who need granular control over their agent's prompts while reducing unnecessary token consumption from reserved tool-calling functionality (compared to n8n's built-in Conversation Agent). Setup InstructionsConfigure Gemini Credentials: Set up your Google Gemini API key (Get API key here if needed). Alternatively, you may use other AI provider nodes. Interaction Methods: - Test directly in the workflow editor using the "Chat" button. - Activate the workflow and access the chat interface via the URL provided by the When Chat Message Received node. Customization OptionsInterface Settings: Configure chat UI elements (e.g., title) in the When Chat Message Received node. Prompt Engineering: Define agent personality and conversation structure in the Construct & Execute LLM Prompt node's template variable. ⚠️ Template must preserve {chat_history} and {input} placeholders for proper LangChain operation. Model Selection: Swap language models through the language model input field in Construct & Execute LLM Prompt. Memory Control: Adjust conversation history length in the Store Conversation History node. Requirements: ⚠️ This workflow uses the LangChain Code node, which only works on self-hosted n8n. (Refer to LangChain Code node docs)

Who this is for? This workflow enables automated, scalable collection of high-quality, AI-ready data from websites using Bright Data’s Web Unlocker, with a focus on preparing that data for LLM training. Leveraging LLM Chains and AI agents, the system formats and extracts key information, then stores the structured embeddings in a Pinecone vector database. This workflow is tailored for: - ML Engineers & Researchers building or fine-tuning domain-specific LLMs. - AI Startups needing clean, structured content for product training. - Data Teams preparing knowledge bases for enterprise-grade AI apps. - LLM-as-a-Service Providers sourcing dynamic web content across niches. What problem is this workflow solving? Training a large language model (LLM) requires vast amounts of clean, relevant, and structured data. Manual collection is slow, error-prone, and lacks scalability. This workflow: - Automatically extracts web data from specified URLs. - Bypasses anti-bot measures using Bright Data’s Web Unlocker. - Formats, cleans, and transforms raw content using LLM agents. - Stores semantically searchable vectors in Pinecone. - Makes datasets AI-ready for fine-tuning, RAG, or domain-specific training. What this workflow does This workflow automates the process of collecting, cleaning, and vectorizing web content to create structured, high-quality datasets that are ready to be used for LLM (Large Language Model) training or retrieval-augmented generation (RAG). - Web Crawling with Bright Data Web Unlocker. - AI Information Extraction and Data Formatting. - AI Data Formatting to produce structured data. - Persistence in Pinecone Vector DB. - Handle Webhook notification of structured data. How to customize this workflow to your needs - Set Your Target URLs. Target sites that are high-quality, domain-specific, and relevant to your LLM's purpose. - Adjust Bright Data Web Unlocker Settings. Geo-location, Headers / User-Agent strings, Retry rules, and proxies. - Modify the Information Extraction Logic. Change prompts to extract specific attributes. Use structured templates or few-shot examples in prompts. - Swap the Embedding Model. Use OpenAI, Hugging Face, or your own hosted embedding model API. - Customize Pinecone Metadata Fields. Store extra fields in Pinecone for better filtering & semantic querying. - Add Data Validation or Deduplication. Skip duplicates or low-quality content.

WordPress Post Auto-Categorization Workflow 💡 Click here to read detailed case study 📺 Click here to watch YouTube tutorialPurpose Automatically categorize WordPress blog posts using AI, saving hours of manual work. This workflow analyzes your post titles and assigns them to predefined categories using artificial intelligence. What This Workflow Does • Connects to your WordPress site • Retrieves all uncategorized posts • Uses AI to analyze post titles • Automatically assigns appropriate category IDs • Updates posts with new categories • Processes dozens of posts in minutesSetup Requirements - WordPress site with admin access - Predefined categories in WordPress - OpenAI API credentials (or your preferred AI provider) - n8n with WordPress credentialsConfiguration Steps 1. Add your WordPress categories (manually in WordPress) 2. Note down category IDs 3. Update the AI prompt with your category IDs 4. Configure WordPress credentials in n8n 5. Set up AI API connectionCustomization Options • Modify AI prompts for different categorization criteria • Adjust for multiple category assignments • Add tag generation functionality • Customize for different content types • Add additional metadata updatesImportant Notes • Backup your WordPress database before running • Test with a few posts first • Review AI categorization results initially • Categories must be created manually firstBonus Features • Can be modified for tag generation • Works with scheduled posts • Handles bulk processing • Maintains categorization consistency Perfect for content managers, bloggers, and website administrators looking to organize their WordPress content efficiently.

Automated YouTube Video Promotion Workflow Automate the promotion of new YouTube videos on X (formerly Twitter) with minimal effort. This workflow is perfect for content creators, marketers, and social media managers who want to keep their audience updated with fresh content consistently. How it works This workflow triggers every 30 minutes to check for new YouTube videos from a specified channel. If a new video is found, it utilizes OpenAI's ChatGPT to craft an engaging, promotional message for X. Finally, the workflow posts the generated message to Twitter, ensuring your latest content is shared with your audience promptly. Set up steps 1. Schedule the workflow to run at your desired frequency. 2. Connect to your YouTube account and set up the node to fetch new videos based on your Channel ID. 3. Integrate with OpenAI to generate promotional messages using GPT-3.5 turbo. 4. Link to your X account and set up the node to post the generated content. Please note, you'll need API keys and credentials for YouTube, OpenAI, and X. Check out this quick video tutorial to make the setup process a breeze. Additional Tips - Customize the workflow to match your branding and messaging tone. - Test each step to ensure your workflow runs smoothly before going live.

Who is this for? This workflow is designed for Customer Success Managers (CSM), sales, support, or marketing teams using HubSpot CRM who want to automate customer engagement tracking when new emails arrive. It’s ideal for businesses looking to streamline CRM updates without manual data entry. Problem Solved / Use Case Manually logging email interactions in HubSpot is time-consuming. This workflow automatically parses incoming emails, checks if the sender exists in HubSpot, and either: - Creates a new contact and logs the email as an engagement (if the sender is new). - Logs the email as an engagement for an existing contact. What This Workflow Does Triggers when a new email arrives in a connected IMAP inbox. Parses the email using AI (OpenAI) to extract structured data. Searches HubSpot for the sender’s email address. Updates HubSpot: - Creates a contact (if missing) and logs the email as an engagement. - Or logs the engagement for an existing contact. Setup - Configure Email Account: Replace the default IMAP node with your email provider. - HubSpot Credentials: Add your HubSpot API key in the HubSpot nodes. - OpenAI Integration: Ensure your OpenAI API key is set for email parsing. Customization Tips - Improve AI Prompt: Modify the OpenAI prompt to extract specific email data (e.g., customer intent). - Add Filters: Exclude auto-replies or spam by adding a filter node. - Extend Functionality: Use the parsed data to trigger follow-up tasks (e.g., Slack alerts, tickets).Need Help? Contact [email protected] for workflow modifications or help.

LinkedIn ICP Research What it does: Paste a LinkedIn URL → Get a short, research-backed summary of the person/lead → Also includes 3 personalized invite message options you can copy/paste into LinkedIn. Who it’s for: Founders doing cold outreach, SDRs who want to skip the research time, Agencies doing lead gen at scale. Powered by LeadAI using Relevance AI. This automation uses the platform, RelevanceAI, so you’ll need to import the JSON into RelevanceAI directly.

Eliminate redundant data in your Airtable database using ChatGPT, a powerful AI-driven solution for maintaining clean, accurate information and improved database management.

This intelligent n8n automation streamlines the process of collecting, extracting, and scoring resumes sent to a Gmail inbox—making it an ideal solution for recruiters who regularly receive hundreds of applications. The workflow scans incoming emails with attachments, extracts relevant candidate information from resumes using AI, evaluates each candidate based on customizable criteria, and logs their scores alongside contact details in a connected Google Sheet. Who Is This For? - Recruiters & Hiring Managers: Automate the resume screening process and save hours of manual work. - HR Teams at Startups & SMBs: Quickly evaluate talent without needing large HR ops infrastructure. - Agencies & Talent Acquisition Firms: Screen large volumes of resumes efficiently and with consistent criteria. - Solo Founders Hiring for Roles: Use AI to help score and shortlist top candidates from email applications. What Problem Does This Workflow Solve? Manually reviewing resumes is time-consuming, error-prone, and inconsistent. This workflow solves these challenges by: - Automatically detecting and extracting resumes from Gmail attachments. - Using OpenAI to intelligently extract candidate info from unstructured PDFs. - Scoring resumes using customizable evaluation criteria (e.g., relevant experience, skills, education). - Logging all candidate data (Name, Email, LinkedIn, Score) in a centralized, filterable Google Sheet. - Enabling faster, fairer, and more efficient candidate screening. How It Works 1. Gmail Trigger: Runs on a scheduled interval (e.g., every 6 or 24 hours). Scans a connected Gmail inbox (using OAuth credentials) for unread emails that contain PDF attachments. 2. Extract Attachments: Downloads the attached resumes from matching emails. 3. Parse Resume Text: Sends the PDF file to OpenAI's API (via GPT-4 or GPT-3.5 with file support or via base64 + PDF-to-text tool). Prompts GPT with a structured format to extract fields like Name, Email, LinkedIn, Skills, and Education. 4. Score Resume: Evaluates the resume on predefined scoring logic using AI or logic inside the workflow (e.g., "Has X skill = +10 points"). 5. Log to Google Sheets: Appends a new row in a connected Google Sheet, including: - Candidate Name - Email Address - LinkedIn URL - Resume Score Requirements - Accounts: - n8n - Google (Gmail, Sheets, Drive, Cloud Console) - OpenAI - API Keys & Credentials: - OpenAI API Key - Google Cloud OAuth Credentials - Gmail Access Scopes (for reading attachments) - Configured Google Sheet - OpenAI usage (after free tier) - Google Cloud API usage (if exceeding free quota)

When an image is added to a monitored album in Google Photos, Picsart automatically removes the background and re-uploads the processed image back to the Google Photos album. This template was created by Picsart and not tested by Make. For any inquiries about the template, please reach out to the template creator: [email protected].

⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part, there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: - url – an address of the page to fetch - method – full / simplified - maxlimit – maximum length for the final page. For longer pages, an error message is returned back to the agent. Page content fetching is a multistep process: 1. An HTTP Request mode tries to get the page content. 2. If the page content was successfully retrieved, a series of post-processing begins: - Extract HTML BODY; content - Remove all unnecessary tags to reduce the page size - Further eliminate external URLs and IMG src values (based on the method query parameter) The remaining HTML is converted to Markdown, thus reducing the page length even more while preserving the basic page structure. The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description; it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard if you need further assistance with your n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.

YouTube Video This n8n workflow is designed to assist YouTube content creators in identifying trending topics within a specific niche. By leveraging YouTube's search and data APIs, it gathers and analyzes video performance metrics from the past two days to provide insights into what content is gaining traction. Here's how the workflow operates: - Trigger Setup: The workflow begins when a user sends a query through the chat_message_received node. If no niche is provided, the AI prompts the user to select or input one. - AI Agent (Language Model): The central node utilizes a GPT-based AI agent to: - Understand the user's niche or content preferences. - Generate tailored search terms related to the niche. - Process YouTube API responses and summarize trends using insights such as common themes, tags, and audience engagement metrics (views, likes, and comments). - YouTube Search: The youtube_search node runs a secondary workflow to query YouTube for relevant videos published within the last two days. It retrieves basic video data such as video IDs, relevance scores, and publication dates. - Video Details Retrieval: The workflow fetches additional details for each video: - Video Snippet: Metadata like title, description, and tags. - Video Statistics: Metrics such as views, likes, and comments. - Content Details: Video duration, ensuring only content longer than 3 minutes and 30 seconds is analyzed. - Data Processing: - Video metadata is cleaned, sanitized, and stored in memory. - Tags, titles, and descriptions are analyzed to identify patterns and trends across multiple videos. - Output: The workflow compiles insights and presents them to the user, highlighting: - The most common themes or patterns within the niche. - URLs to trending videos and their respective channels. - Engagement statistics, helping the user understand the popularity of the content. Key Notes for Setup: - API Keys: Ensure valid YouTube API credentials are configured in the get_videos, find_video_snippet, find_video_statistics, and find_video_data nodes. - Memory Buffer: The window_buffer_memory node ensures the AI agent retains context during analysis, enhancing the quality of the generated insights. - Search Term Customization: The AI agent dynamically creates search terms based on the user’s niche to improve search precision. Use Case: This workflow is ideal for YouTubers or marketers seeking data-driven inspiration for creating content that aligns with current trends, maximizing the potential to engage their audience. Example Output: For the niche "digital marketing": - Trending Topic: Videos about "mental triggers" and "psychological marketing." - Tags: "SEO," "Conversion Rates," "Social Proof." - Engagement: Videos with over 200K views and high likes/comment ratios are leading trends. - Video links: - https://www.youtube.com/watch?v=video_id1 - https://www.youtube.com/watch?v=video_id2

Effortlessly create a comprehensive brand strategy using ChatGPT’s AI insights, organized and documented through Google Docs. This template helps you maintain consistency and thoroughness in your strategic planning. Estimated number of ChatGPT tokens used by this template: 4,000 tokens for prompts and 1,000 tokens for a completion.

Google Maps Data Extraction Workflow for Lead Generation This workflow is ideal for sales teams, marketers, entrepreneurs, and researchers looking to efficiently gather detailed business information from Google Maps for: - Lead generation - Market analysis - Competitive researchWho Is This Workflow For? - Sales professionals aiming to build targeted contact lists - Marketers looking for localized business data - Researchers needing organized, comprehensive business information Problem This Workflow Solves Manually gathering business contact details from Google Maps is: - Tedious - Error-prone - Time-consuming This workflow automates data extraction to increase efficiency, accuracy, and productivity. What This Workflow Does - Automates extraction of business data (name, address, phone, email, website) from Google Maps - Crawls and extracts additional website content - Integrates OpenAI to enhance data processing - Stores structured results in Google Sheets for easy access and analysis - Uses Google Search API to fill in missing information Setup 1. Import the provided n8n workflow JSON into your n8n instance. 2. Set your OpenAI and Google Sheets API credentials. 3. Provide your Google Maps Scraper and Website Content Crawler API keys. 4. Ensure SerpAPI is configured to enhance data completeness. Customizing This Workflow to Your Needs - Adjust scraping parameters: - Location - Business category - Country code - Customize Google Sheets output format to fit your current data structure. - Integrate additional AI processing steps or APIs for richer data enrichment. Final Notes This structured approach ensures: - Accurate and compliant data extraction from Google Maps - Streamlined lead generation - Actionable and well-organized data ready for business use