Workflow Templates

Overview Automates your complete social media content pipeline: sources articles from Wallabag RSS, generates platform-specific posts with AI, creates contextual images, and publishes via GetLate API. Built with 63 nodes across two workflows to handle LinkedIn, Instagram, and Bluesky—with easy expansion to more platforms. Ideal for: Content marketers, solo creators, agencies, and community managers maintaining a consistent multi-platform presence with minimal manual effort. How It Works Two-Workflow Architecture: Content Aggregation Workflow Monitors Wallabag RSS feeds for tagged articles (#to-share-linkedin, #to-share-instagram, etc.) Extracts and converts content from HTML to Markdown Stores structured data in Airtable with platform assignment AI Generation & Publishing Workflow Scheduled trigger queries Airtable for unpublished content Routes to platform-specific sub-workflows (LinkedIn, Instagram, Bluesky) LLM generates optimized post text and image prompts based on custom brand parameters Optionally generates AI images and hosts them on Imgbb CDN Publishes via GetLate API (immediate or draft mode) Updates Airtable with publication status and metadata Key Features: Tag-based content routing using Wallabag's native system Swappable AI providers (Groq, OpenAI, Anthropic) Platform-specific optimization (tone, length, hashtags, CTAs) Modular design—duplicate sub-workflows to add new platforms in \~30 minutes Centralized Airtable tracking with 17 data points per post Set Up Steps Setup time: \~45-60 minutes for initial configuration Create accounts and get API keys (\~15 min) Wallabag (with RSS feeds enabled) GetLate (social media publishing) Airtable (create base with provided schema—see sticky notes) LLM provider (Groq, OpenAI, or Anthropic) Image service (Hugging Face, Fal.ai, or Stability AI) Imgbb (image hosting) Configure n8n credentials (\~10 min) Add all API keys in n8n's credential manager Detailed credential setup instructions in workflow sticky notes Set up Airtable database (\~10 min) Create "RSS Feed - Content Store" base Add 19 required fields (schema provided in workflow sticky notes) Get Airtable base ID and API key Customize brand prompts (\~15 min) Edit "Set Custom SMCG Prompt" node for each platform Define brand voice, tone, goals, audience, and image preferences Platform-specific examples provided in sticky notes Configure platform settings (\~10 min) Set GetLate account IDs for each platform Enable/disable image generation per platform Choose immediate publish vs. draft mode Adjust schedule trigger frequency Test and deploy Tag test articles in Wallabag Monitor the first few executions in draft mode Activate workflows when satisfied with the output Important: This is a proof-of-concept template. Test thoroughly with draft mode before production use. Detailed setup instructions, troubleshooting tips, and customization guidance are in the workflow's sticky notes. Technical Details 63 nodes**: 9 Airtable operations, 8 HTTP requests, 7 code nodes, 3 LangChain LLM chains, 3 RSS triggers, 3 GetLate publishers Supports**: Multiple LLM providers, multiple image generation services, unlimited platforms via modular architecture Tracking**: 17 metadata fields per post, including publish status, applied parameters, character counts, hashtags, image URLs Prerequisites n8n instance (self-hosted or cloud) Accounts: Wallabag, GetLate, Airtable, LLM provider, image generation service, Imgbb Basic understanding of n8n workflows and credential configuration Time to customize prompts for your brand voice Detailed documentation, Airtable schema, prompt examples, and troubleshooting guides are in the workflow's sticky notes. Category Tags #social-media-automation, #ai-content-generation, #rss-to-social, #multi-platform-posting, #getlate-api, #airtable-database, #langchain, #workflow-automation, #content-marketing

Video Guide I prepared a comprehensive guide detailing how to create a Smart Agent that automates meeting task management by analyzing transcripts, generating tasks in Airtable, and scheduling follow-ups when necessary. Youtube Link Who is this for? This workflow is ideal for project managers, team leaders, and business owners looking to enhance productivity during meetings. It is particularly helpful for those who need to convert discussions into actionable items swiftly and effectively. What problem does this workflow solve? Managing action items from meetings can often lead to missed tasks and poor follow-up. This automation alleviates that issue by automatically generating tasks from meeting transcripts, keeping everyone informed about their responsibilities and streamlining communication. What this workflow does The workflow leverages n8n to create a Smart Agent that listens for completed meeting transcripts, processes them using AI, and generates tasks in Airtable. Key functionalities include: Capturing completed meeting events through webhooks. Extracting relevant meeting details such as transcripts and participants using API calls. Generating structured tasks from meeting discussions and sending notifications to clients. Webhook Integration: Listens for meeting completion events to trigger subsequent actions. API Requests for Data: Pulls necessary details like transcripts and participant information from Fireflies. Task and Notification Generation: Automatically creates tasks in Airtable and notifies clients of their responsibilities. Setup N8N Workflow Configure the Webhook: Set up a webhook to capture meeting completion events and integrate it with Fireflies. Retrieve Meeting Content: Use GraphQL API requests to extract meeting details and transcripts, ensuring appropriate authentication through Bearer tokens. AI Processing Setup: Define system messages for AI tasks and configure connections to the AI chat model (e.g., OpenAI's GPT) to process transcripts. Task Creation Logic: Create structured tasks based on AI output, ensuring necessary details are captured and records are created in Airtable. Client Notifications: Use an email node to notify clients about their tasks, ensuring communications are client-specific. Scheduling Follow-Up Calls: Set up Google Calendar events if follow-up meetings are required, populating details from the original meeting context.

This n8n workflow template is designed to integrate a DeepSeek AI agent with Telegram, incorporating long-term memory capabilities for personalized and context-aware responses. Here's a detailed breakdown: Core Features Telegram Integration Uses a webhook to receive messages from Telegram users. Validates user identity and message content before processing. AI-Powered Responses Employs DeepSeek's AI models for conversational interactions. Includes memory capabilities to personalize responses based on past interactions. Error Handling Sends an error message if the input cannot be processed. Model Options 🧠 DeepSeek-V3 Chat**: Handles general conversational tasks. DeepSeek-R1 Reasoning**: Provides advanced reasoning capabilities for complex queries. Memory Buffer Window**: Maintains session context for ongoing conversations. Quick Setup 🛠️ Telegram Webhook Configuration Set up a webhook using the Telegram Bot API: https://api.telegram.org/bot{my_bot_token}/setWebhook?url={url_to_send_updates_to} Replace {my_bot_token} with your bot's token and {url_to_send_updates_to} with your n8n webhook URL. Verify the webhook setup using: https://api.telegram.org/bot{my_bot_token}/getWebhookInfo DeepSeek API Configuration Base URL: https://api.deepseek.com Obtain your API key from the DeepSeek platform. Implementation Details 🔧 User Validation The workflow validates the user's first name, last name, and ID using data from incoming Telegram messages. Only authorized users proceed to the next steps. Message Routing Routes messages based on their type (text, audio, or image) using a switch node. Ensures appropriate handling for each message format. AI Agent Interaction Processes text input using DeepSeek-V3 or DeepSeek-R1 models. Customizable system prompts define the AI's behavior and rules, ensuring user-centric and context-aware responses. Memory Management Retrieves long-term memories stored in Google Docs to enhance personalization. Saves new memories based on user interactions, ensuring continuity across sessions.

LinkedIn Enrichment & Ice Breaker Generator For SDRs, growth marketers, and founders looking to scale personalized outreach. This workflow enriches LinkedIn profile data using Bright Data and generates AI-powered ice breakers using Claude (Anthropic). It automates research and messaging to help you connect smarter and faster — without manual effort. 🧩 How It Works This workflow combines Google Sheets, Brigt Data, and Claude (Anthropic) to fully automate your outreach research: Trigger Manually trigger the workflow or run it on a schedule (via Manual Trigger or Schedule Trigger). Read Input Sheet Fetches rows from a Google Sheet. Each row must contain at least a Linkedin_URL_Person and row_number. Prepare Input Formats each row for Bright Data’s API using Set and SplitInBatches nodes. Enrich Profile (Bright Data API) Sends LinkedIn URLs to Bright Data’s Dataset API via HTTP Request. Waits for snapshot to be ready using polling logic with Wait, If, and Snapshot Progress nodes. Once ready, retrieves the enriched profile data including: Name City Current company About section Recent posts Update Sheet with Profile Data Writes the retrieved enrichment data into the corresponding row in Google Sheets (via row_number). Generate Ice Breaker (Claude AI) Sends enriched profile content to Claude (Anthropic) using a custom prompt. Focuses on recent posts for crafting relevant, respectful, 1–4-line ice breakers. Update Sheet with Ice Breaker Writes the generated ice breaker to the Ice Breaker 1 column in the original row. ✅ Requirements To use this workflow, you must have the following: Google Sheets A Google account A Google Sheet with at least one sheet/tab containing: Column: Linkedin_URL_Person Column: row_number (used for mapping input and output rows) Bright Data A Bright Data account with access to the Dataset API An active dataset that accepts LinkedIn URLs API key with Dataset API access Anthropic Claude An Anthropic API key (for Claude 3.5 Haiku or other Claude models) n8n Environment Access to HTTP Request, Set, Wait, SplitInBatches, If, and Google Sheets nodes Access to Claude integration (via LangChain nodes: @n8n/n8n-nodes-langchain) Credential manager properly configured with: Google Sheets OAuth2 credentials Bright Data API key Anthropic API key ⚙️ Setup Instructions Step 1: Copy the Google Sheets Template > 📄 Click here to make a copy Fill the Linkedin_URL_Person column with LinkedIn profile URLs you want to enrich Do not modify headers or add filters to the sheet Leave other columns (name, city, about, posts, ice breaker) blank — the workflow fills them Step 2: Connect Your Accounts in n8n Google Sheets: Create a credential under Google Sheets OAuth2 API Bright Data: Add your API key as a credential under HTTP Request (Authorization header) Anthropic: Create a credential for Anthropic API with your Claude key Step 3: Import and Configure the Workflow Import the workflow into your n8n instance. In each Google Sheets node: Select the copied Google Sheet Select the correct tab (usually input or Sheet1) In the HTTP Request node to Bright Data: Paste your Bright Data dataset ID In the Claude prompt node: Optionally adjust the tone and length of the ice breaker prompt Step 4: Run the Workflow Test it using the Manual Trigger node For daily automation, enable the Schedule Trigger and configure interval settings Watch your Google Sheet populate with enriched data and tailored ice breakers 🧠 Tips & Best Practices Bright Data Delay**: Snapshots may take time. The workflow polls the status until complete. Retry Protection**: If and Wait nodes avoid infinite loops by checking snapshot status. Mapping via row_number**: Critical to ensure data is updated in the right row. Prompt Engineering**: You can fine-tune Claude's behavior by editing the text prompt. 🧾 Output Example Once complete, each row in your Google Sheet will contain: | Linkedin_URL_Person | Name | City | Company | Recent Post | Ice Breaker | |---------------------|------|------|---------|-------------|--------------| | linkedin.com/... | Jane Doe | NYC | ACME Corp | “Why AI should replace meetings” | "Loved your post about AI and meetings — finally someone said it!" | 💬 Support & Feedback Questions? Want to tweak the prompt or expand the enrichment? 📧 Email: Yaron@nofluff.online 📺 YouTube: @YaronBeen 🔗 LinkedIn: linkedin.com/in/yaronbeen

This workflow is designed to analyze YouTube videos by extracting their transcripts, summarizing the content using AI models, and sending the analysis via email. This workflow is ideal for content creators, marketers, or anyone who needs to quickly analyze and summarize YouTube videos for research, content planning, or educational purposes. How It Works: Trigger: The workflow starts with a manual trigger, allowing you to test it by clicking "Test workflow." You can also set a YouTube video URL manually or dynamically. YouTube Video ID Extraction: The workflow extracts the YouTube video ID from the provided URL using a custom JavaScript function. This ID is necessary for fetching the transcript. Transcript Generation: The video ID is sent via an HTTP request to generate the transcript. You need to replace APIKEY with a free API key from the service. Transcript Validation: The workflow checks if a transcript exists for the video. If a transcript is available, it proceeds; otherwise, it stops. Full Text Extraction: If a transcript exists, the workflow combines all transcript segments into a single text variable for further analysis. AI-Powered Analysis: The full transcript is passed to an AI model (DeepSeek, OpenAI, or OpenRouter) for analysis. The AI generates a structured summary, including a title and key points, formatted in markdown. Email Notification: The analysis results (title and summary) are sent via email using SMTP credentials. The email contains the structured summary of the video. Set Up Steps: YouTube Transcript API: Obtain a free API key from youtube-transcript.io and replace APIKEY in the "Generate transcript" node with your key. AI Model Configuration: Configure the AI model nodes (DeepSeek, OpenAI, or OpenRouter) with the appropriate API credentials. You can choose one or multiple models depending on your preference. Email Setup: Configure the "Send Email" node with your SMTP credentials (e.g., Gmail, Outlook, or any SMTP service). Ensure the email settings are correct to send the analysis results. Key Features: Free Tools: Uses **youtube-transcript.io for free transcript generation. AI Models**: Supports multiple AI models (DeepSeek, OpenAI, OpenRouter) for flexible analysis. Email Notifications**: Sends the analysis results directly to your inbox. Customizable**: Easily adapt the workflow to analyze different videos or use different AI models.

This n8n template uses a Telegram chatbot to conduct a Product Satisfaction Survey and fetches questions and stores answers in a Google sheet. It augments an AI Agent to ask follow-up questions to engage the user and uncover more insights in their responses. This template is intended to demonstrate how you'd realistically approach a workflow where there is structured conversation (static questions) but you still want to include an free-form element (follow-up questions) which can only be accomplished via AI. Check out an example Survey results: https://docs.google.com/spreadsheets/d/e/2PACX-1vQWcREg75CzbZd8loVI12s-DzSTj3NE_02cOCpAh7umj0urazzYCfzPpYvvh7jqICWZteDTALzBO46i/pubhtml?gid=0&single=true How it works A chat session is started with the user who needs to enter the bot command "/next" to start the survey. Once started, the template pulls in questions from a google sheet to ask the user. Questions are asked in sequence from left column to right column. When the user answers the question, a text classifier node is used to determine if a follow-up question could be asked. If so, a mini conversation is initiated by the AI agent to get more details. If not, the survey proceeds to the next question. All answers and mini-conversations are recorded in the Google Sheet under the respective question. When all questions are answered, the template will stop the survey and give the user a chance to restart. How to use You'll need to setup a Telegram bot (see docs) Create a google sheet with an ID column. Populate the rest of the columns with your survey questions (see sample) Ensure you have a Redis instance to capture state. Either self-host or sign-up to Upstash for a free account. Update the "Set Variable" node with your google sheet ID and survey title. Share your bot to allow others to participate in your survey. Requirements Telegram for Chatbot Google Sheets for Survey questions and answers Redis for State Management and Chat Memory Community+ license and above for Execution data node - you can remove this node if you don't have this licence. Customising this workflow Not using Telegram? This template technically works with other chat apps such as Whatsapp, wechat and even n8n's hosted chat! This state management pattern can also be applied to other use-cases and scenarios. Try it for other types of surveys!

This n8n template demonstrates an approach to perform bot-to-human handoff using Human-in-the-loop functionality as a switch. In this experiment, we play with the idea of states we want our agent to be in which controls it's interacton with the user. First state** - the agent is onboarding the user by collecting their details for a sales inquiry. After which, they are handed-off / transferred to a human to continue the call. Second state** - the agent is essentially "deactivated" as further messages to the bot will not reach it. Instead, a canned response is given to the user. The human agent must "reactivate" the bot by completing the human-in-the-loop form and give a summary of their conversation with the user. Third state** - the agent is "reactivated" with context of the human-to-user conversation and is set to provide after sales assistance. An tool is made available to the agent to again delegate back to the human agent when requested. How it works This template uses telegram to handle the interaction between the user and the agent. Each user message is checked for a session state to ensure it is guided to the right stage of the conversation. For this, we can use Redis as a simple key-value store. When no state is set, the user is directed through an onboarding step to attain their details. Once complete, the agent will "transfer" the user to a human agent - technically, all this involves is an update to the session state and a message to another chat forwarding the user's details. During this "human" state, the agent cannot reply to the user and must wait until the human "transfers" the conversation back. The human can do this by replying to "human-in-the-loop" message with a summary of their conversation with the user. This session state now changes to "bot" and the context is implanted in the agent's memory so that the agent can respond to future questions. At this stage of the conversation, the agent is now expected to handle and help the user with after-sales questions. The user can at anytime request transfer back to the human agent, repeating the previous steps as necessary. How to use Plan your user journey! Here is a very basic example of a sales inquiry with at most 3 states. More thought should be developed when many more states are involved. You may want to better log and manage session states so no user is left in limbo. Try connecting the user and sessions to your CRM. Note, the Onboarding agent and After-Sales agent have separate chat memories. When adding more agents, it is recommend to continue having separate chat memories to help focus between states. Requirements Telegram for chatbot & interface Redis for session store and chat memory OpenAI for AI agent Customising this workflow Not using Telegram? This template works with Whatsapp and other services with equivalent functionality.

This n8n template uses existing emails from customers as context to customise and "finetune" outreach emails to them using AI. By now, it should be common knowledge that we can leverage AI to generate unique emails but in a way, they can remain generic as the AI lacks the customer context to be truly personalised. One way to solve this is by pulling in a source of customer data - and what better way then by using existing email correspondence. How it works Customers to target are pulled from Hubspot and each customer is then run in a loop. We're using a loop as the retrieved emails for each customer become separate items and a loop helps with item reference. We connect to our Gmail account to pull all emails recieved from the customer. The contents of the email will be suitable to build a short persona of the customer. We use the Information Extractor to get our AI model to pull out the key attributes of this persona such as decision making style and communication preferences. With this persona, we can now pass this to our AI model to generate a personalised outreach email specifically for our customer. Finally, a draft email is created for human review before sending. If you would rather send the email straight away, this is also possible. How to use Define the topic of the outreach email in the "variables" node. This directs the AI on what outreach email to generate. Ensure the emails are pulled from the right account. If emails may contain sensitive data, adjust the filters and text parsing to ensure these are not leaked to the AI (which might then leak into the generated email). Requirements Hubspot for Contacts List OpenAI for LLM Gmail for Existing Emails and Sending Emails Customising this workflow Not using Hubspot? Any CRM would work just as well or even a simple text csv! If you have customer past deals or engagements in your CRM, consider using this as additional context for the AI to use.

This workflow optimizes the management of inquiries received through a contact form (Contact Form 7 - CF7 Plugin) on a WordPress site, automating the process of classification, response drafting, and data storage. This workflow is particularly useful for businesses that receive multiple daily inquiries and want to improve their efficiency in managing customer communications. Benefits: ✅ Automation & Speed – Reduces the time needed to handle inquiries manually. ✅ Better Email Management – Ensures every message receives a timely and accurate response. ✅ Customization – The generated draft can be edited before sending, maintaining a personal touch. ✅ Inquiry History – Storing data in Google Sheets allows for easy tracking of customer interactions. ✅ Easy Integration – Works seamlessly with Contact Form 7 without complex configurations. How It Works Form Submission Handling: The workflow starts with a WordPress form submission captured via a webhook. The form data (first name, last name, email, phone, and message) is extracted and structured using the "Set Fields" node. Message Classification: The submitted message is classified into predefined categories (e.g., "Product Info," "Order Info," or "Other") using the "Message Classifier" node, powered by Google Gemini. Automated Email Drafting: Based on the classification, the workflow generates a professional email draft using one of three "Email Writer" nodes (for Product, Order, or Other requests). Each node uses Google Gemini to craft a personalized response with a structured format (subject and body). Email Draft Creation: The drafted email is sent as a Gmail draft to the appropriate department, including the original form data for context. Data Logging: All submissions, along with their classifications and email drafts, are logged in a Google Sheets spreadsheet for record-keeping and further action. Set Up Steps Install WordPress Plugin: Install the "CF7 to Webhook" plugin on WordPress and configure it to send form submissions to the n8n webhook URL. Configure Webhook in n8n: Set up the "From Wordpress" webhook node in n8n to receive POST requests from the WordPress form. Google Gemini Integration: Ensure the Google Gemini nodes are properly authenticated with the correct API credentials. Gmail and Google Sheets Setup: Authenticate the Gmail and Google Sheets nodes with the appropriate OAuth2 credentials and specify the target spreadsheet and sheet name. Customize Classification Categories: Adjust the categories in the "Message Classifier" node to match your business needs. Test the Workflow: Trigger a test form submission to verify the workflow processes data correctly, classifies the message, generates an email draft, and logs the data. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Daily Economic News Brief for Israel (Hebrew, RTL, GPT-4o) Overview Stay ahead of the curve with this AI-powered workflow that delivers a daily economic summary tailored for professionals tracking the Israeli economy. At 8:00 PM Israel Time, this workflow: Retrieves the latest articles from Calcalist and Mako via RSS Filters duplicates and irrelevant stories Uses OpenAI’s GPT-4o to identify the 5 most important stories of the day Summarizes each article in concise, readable Hebrew Generates a fully styled, responsive HTML email (with proper RTL layout) Sends it to your inbox using your preferred SMTP email provider Perfect for economists, analysts, investors, or policymakers who want an actionable and personalized news digest -- no distractions, no fluff. Setup Instructions Estimated setup time: 10 minutes Required credentials: OpenAI API Key SMTP credentials (for email delivery) Steps: Import this template into your n8n instance. Add your OpenAI API Key under credentials. Configure the SMTP Email node with: Host (e.g. smtp.gmail.com) Port (465 or 587) Username (your email) Password (app-specific password or login) Set your target email address in the last node. (Optional) Customize the GPT prompt to adjust tone or audience (e.g. general public, policy makers). Activate the workflow and receive daily updates straight to your inbox. Customization Tips Change the RSS sources to pull from other Hebrew or international news websites Modify the summarization prompt to fit different sectors (e.g. tech, health, politics) Add integrations like Notion, Airtable, or Telegram for logging or distribution Apply your branding to the HTML output (logos, footer, colors) Why Use This? This is more than a news digest. It’s an intelligent economic assistant that filters noise, highlights what matters, and keeps you informed-automatically. You can set it up in 10 minutes and benefit every single day.

Automatically deploy n8n workflows by simply dropping JSON files into a Google Drive folder—this template watches for new exports, cleans and imports them into your n8n instance, applies a tag, and then archives the processed files. Who is this template for? This workflow template is designed for n8n power users, and automation specialists who need a simple, reliable way to bulk‑deploy or version‑control n8n workflows via Google Drive. It’s perfect if you: Manage multiple n8n instances (staging, production, etc.) Want an easy “drop‑in” approach to publish new or updated workflows Prefer storing/exporting JSON in Drive rather than editing in the UI Use case Manually importing .json exports into n8n is slow and error‑prone. With this template you can: Keep your workflows in a shared Drive folder (version control friendly) Automatically sanitize each file so only supported settings go through Tag deployed workflows consistently for easy filtering Move processed files to a “Deployed” folder for clear change tracking How it works Watch “ToDeploy” folder in Google Drive for new .json files Download & parse each file into a JSON object Clean payload: strip out everything except the allowed executionOrder (and timezone if you choose) POST the cleaned workflow to your n8n instance via /api/v1/workflows PUT a predefined tag onto the newly created workflow Move file to your “Deployed” folder when import succeeds, or capture the workflow name & error if it fails Setup instructions 1. In Google Drive create a ToDeploy folder and a Deployed folder Update "Google Drive Trigger -ToDeploy folder" to your ToDeploy folder Update "Move JSON file to Deployed folder" to you Deployed folder 2. Create a n8n API key: +Go to Settings > n8n API +Select Create an API key +Copy API Key 3. In "Get Existing Workflow Tags" node: Create n8n API Authentication Authentication: Predefined Credential Type Credential Type: n8n API Create new credential: +Paste in API key +Baseurl: https://SUBDOMAIN.YOURDOMAINNAME.com/api/v1/ 4. Add n8n API authentication to: "Create n8n Workflow" node "Set Workflow Tag" node 5. Add your N8N instance URL to the N8N_Instance_URL variable in "Set n8n URL variable" node. 6. Run "1. Get Workflow Tags" flow and copy the ID of your chosen tag. 7. In "Set n8n API URL & Tag ID variables" node: Add the Workflow Tag ID to the N8N_Instance_Tag variable Add your N8N instance URL to the N8N_Instance_URL variable 8. Set workflow to Active How to adjust it to your needs Use different tags: run Get Existing Workflow Tags on start‑up to refresh available tags, or hard‑code multiple tags in the Set Workflow Tag node. Add notifications**: connect the error branch to Slack or Email nodes so you get alerted if an import fails. Swap Drive for another storage**: replace Google Drive nodes with Dropbox, S3, or GitHub triggers if you prefer a different source for your JSON files.

🔍 Competitor Review Scraper & Ad Copy Generator (Trustpilot + Bright Data + GPT-4o-mini) 📌 Who It's For Marketers, business owners, and agencies looking to: Analyze competitor pain points Generate high-impact Facebook ad copy Automate manual data processing 🧩 How It Works This n8n-based workflow combines Bright Data, Google Sheets, and OpenAI to scrape, process, and transform Trustpilot reviews into ready-to-use ad copy. 🔹 Step-by-Step Breakdown Trigger (Manual Form Submission) Input required: Competitor’s Trustpilot URL Review timeframe (30d, 3m, 6m, 12m) Fetch Reviews Calls Bright Data’s Dataset API with URL & timeframe Polls until snapshot is ready Retrieve & Store Extracts all reviews Saves them into a structured Google Sheet Filter & Aggregate Filters to only 1–2 star reviews Summarizes common negative feedback Generate Ad Copy Sends the summary to OpenAI GPT-4o-mini Produces 3 variations of ad copy targeting pain points Distribute Insights Sends ad copy + summary via email to the marketing team ✅ Requirements -LLM Account -Google Sheets - Copy this sheet: https://docs.google.com/spreadsheets/d/1Zi758ds2_aWzvbDYqwuGiQNaurLgs-leS9wjLWWlbUU/edit?gid=0#gid=0 -Bright Data account ⚙️ Setup Instructions **Step 1: Google Sheets ** Copy this Google Sheets template Do not change column headers **Step 2: n8n Credential Setup ** Google Sheets: OAuth2 Bright Data: Authorization Header OpenAI: API Key for GPT-4o-mini **Step 3: Import Workflow ** Import the .json file into n8n Configure your sheet + dataset ID Adjust GPT prompts as needed **Step 4: Run the Workflow ** Trigger via form Receive ad copy + review insights via email 🧠 Tips & Best Practices Bright Data snapshots may take time — polling is handled Focusing on 1–2 star reviews yields the most actionable pain points You can customize GPT-4o-mini prompts for tone or vertical 💬 Support & Feedback Need help or customization? 📧 Email: Yaron@nofluff.online 📺 YouTube: @YaronBeen 🔗 LinkedIn: linkedin.com/in/yaronbeen 📚 Bright Data Docs: docs.brightdata.com/introduction