Workflow Templates

This template can be used to find the content gaps in your competitors' discourse: identifying the topics they are not yet connecting and giving you an opportunity to fill in this gap with your content and product ideas. It will also generate research questions that will help bridge the gaps and generate new ideas. The template showcases the use of multiple n8n nodes and processes: enriching Google sheets file with the new data data extraction content enhancement using GraphRAG approach content gap / research question generation This approach can be very useful for research, marketing, and SEO applications as you can quickly get an overview of the main topics that are available online for a certain niche and understand what is missing. What are Content Gaps in Marketing and SEO? In the context of SEO, content gaps are usually understood as the topics that your competitors rank for but you do not. However, it's hard to rank for these topics because there's very high competition. So a much more effective way is to identify the gaps between the topics your competitors are talking about that are not yet bridged in their discourse. If you address these gaps in your content, you will increase the informational gain that your content offers and also offer a novel perspective while touching upon the topics that are relevant in your field. For example, if we analyze the top websites for "body and physical practices, fitness, etc." we will see that most of them are talking about the health and fitness aspects and another big topic is the community aspect. However, there is a gap between the two topics: which means that most of the websites (companies) that talk about this topic don't mention the two in the same context. This might be an opportunity: bridging the gap between health, fitness but also emphasizing the community aspect that comes with a collective practice. How it works This template consists of the two stages: 1) Data enrichment of a Google sheet file with a list of your competitors using InfraNodus' GraphRAG to generate topical summaries and graph summaries for every URL you're analyzing. 2) Insight generation (using InfraNodus to identify the main topical clusters and gaps in those summaries, these insights are then added to the Google sheet file. Additionally, it contains a sub workflow that you can activate and launch to ask Perplexity model to conduct a market research and find the companies that operate in your field and populate the original Google sheet file. Here's a description step by step: Step 0: Populate the Google sheets file with the company data (either manually or using the sub-workflow provided or Manus AI / Deep Research) Steps 1-2: Triggering and Launching the workflow, extracting the company URL from the Google sheet row Step 3: Scraping the url content from the companies' websites and cleaning the data Steps 5-7: Use InfraNodus GraphRAG Content Enhancer to get a topical summary and graph summary. This is what you're going to get: Steps 8-10: Use InfraNodus AI to generate insight advice and research questions based on the content gaps How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Create a separate knowledge graph for each expert (using PDF / content import options) in InfraNodus For each graph, go to the workflow, paste the name of the graph into the body name field. Keep other settings intact or learn more about them at the InfraNodus access points page. Once you add one or more graphs as experts to your flow, add the LLM key to the OpenAI node and launch the workflow Requirements An InfraNodus account and API key A Google Sheet account and an authorization key Note: OpenAI key is not required. But you might want to get a Perplexity AI key if you'd like to use the sub-workflow that populates the Google sheet with your competitors' website addresses (if you don't have this list yet). Customizing this workflow You can use this same workflow with a Telegram bot or Slack (to be notified of the summaries and ideas). You can also hook up automated social media content creation workflows in the end of this template, so you can generate posts that are relevant (covering the important topics in your niche) but also novel (because they connect them in a new way). Check out our n8n templates for ideas at https://n8n.io/creators/infranodus/ Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20234254556828-Find-Content-Gaps-in-Websites-Market-Research-and-SEO-n8n-Workflow Also check the full tutorial with a conceptual explanation at https://support.noduslabs.com/hc/en-us/articles/20454382597916-Beat-Your-Competition-Target-Their-Content-Gaps-with-this-n8n-Automation-Workflow Also check out the video tutorial with a demo: For support and help with this workflow, please, contact us at https://support.noduslabs.com

Who is this for? This workflow is ideal for: Finance teams** that need to process incoming invoices faster with minimal errors Small to mid-sized businesses** that want to automate invoice intake, review, and storage Operations managers** who require approval workflows and centralized record-keeping What problem is this workflow solving? Manually processing invoices is time-consuming, error-prone, and often lacks structure. This workflow solves those challenges by: Automating the intake of invoices** from multiple sources (email, Google Drive, web form) Extracting invoice data using AI**, eliminating manual data entry Implementing an email-based approval system** to add human oversight Automatically storing approved invoice data** in Google Sheets for easy access and reporting Notifying stakeholders** when invoices are approved or rejected What this workflow does This end-to-end invoice processing workflow includes: Three invoice input methods: Google Drive folder monitor, Gmail attachments, and web form uploads PDF to text extraction for each input method using native PDF parsing AI-powered invoice analysis with GPT-4 to extract structured fields such as vendor, total, and due date Dynamic categorization of invoice type (e.g., Travel, Software, Utilities) via AI Email-based approval workflow with embedded forms to collect decisions and notes Automated Google Sheets logging of all invoice data, approval status, and reviewer feedback Rejection notifications sent automatically to your finance team for transparency and follow-up Setup Copy the Google Sheet template here: 👉 PDF Invoice Parser with Approval Workflow – Google Sheet Template Connect your Google Drive account and specify the invoice folder ID Set up Gmail to monitor incoming invoices with PDF attachments Enable your form trigger to accept direct uploads from your internal or external users Enter your OpenAI API key in the AI processing node for data extraction Configure Google Sheets with a target spreadsheet to store invoice data Set recipient email addresses for invoice approvals and rejection notifications Test with a sample invoice to ensure end-to-end flow is working How to customize this workflow to your needs Change input sources**: Replace Gmail with Outlook or use Slack uploads instead Add validation steps**: Include regex or keyword checks before AI analysis Customize the AI schema**: Modify the expected JSON structure based on your internal finance system Integrate with accounting tools**: Add Xero, QuickBooks, or custom API nodes to push data Route based on category**: Add conditional logic to handle invoices differently based on vendor or category Multi-level approvals**: Add additional email steps if higher-level signoff is needed Audit logging**: Use database or Google Sheets to maintain a historical log of approvals and rejections

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

For Who? Content Creators Youtube Automation Marketing Team How it works? 1 - Enter your content idea in the Edit Fields node in a "raw" format. Ex : Boil Eggs Perfectly 2 - LLM create 3 keywords request based on the idea and Apify scrape the YTB Search 3 - Wait until the dataset is completed in Apify 4 - Retrieve Dataset from Apify, calculate approximation of CTR and filter top performing videos 5 - LLM analyze patterns of best performing titles and create a prompt based on it. Another LLM create 5 titles based on these criteria 6 - LLM analyze patterns of best performing thumbnails and create a prompt based on it. Another LLM create 1 thumbnail based on these criteria 7 - Return titles and thumbnail in a HTML Page 📺 YouTube Video Tutorial: SETUP Setup Input Content Idea : Enter Keyword Related to the niche you want. Trigger can be replaced with anything as long as you retrieve a content idea. For example : Form submission, Database entry, etc ... If you want to change the number of keywords, update the data accordingly in the "Create Keywords" LLM Chain node ➡️ Structured Output Parser AND in the "YTB Search Scrape" HTTP Request Node in Body ➡️ JSON ➡️ searchQueries. If you want to change the number of scraped videos for each keyword, update the data accordingly in the "Create Videos Dataset" HTTP Request Node in Body ➡️ JSON ➡️ maxResults. If you want to adjust the CTR Calculation feel free to update it in the Code Node ➡️ Follow the Comments (after "//") to find what you're looking for. If you want to adjust the level of virality of the videos kept for analaysis go to Filter Node ➡️ Value. Setup Output HTML Page : You can also replace this part with any type of storage. For example : Airtable Database, Google Drive/Google Sheet, Send to an email, etc ... APIs : For the following third-party integrations, replace ==[YOUR_API_TOKEN]== with your API Token or connect your account via Client ID / Secret to your n8n instance : Apify : https://docs.apify.com/api/v2/getting-started OpenAI : https://platform.openai.com/docs/overview (base URL : https://api.openai.com/v1) OR OpenRouter : https://openrouter.ai/docs/quickstart (base URL : https://openrouter.ai/api/v1) HuggingFace (FLUX.1) : https://huggingface.co/docs 👨‍💻 More Workflows : https://n8n.io/creators/nasser/

Tags: Scrapping, Events, European Union, Networking Context Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting. We use AI, automation, and data to support sustainable and data-driven operations across all types of organizations. This workflow is part of our networking strategy (as a business) to track official EU events that may relate to topics we cover. > Want to stay ahead of critical EU meetings and events without checking the website every day? This n8n workflow automatically scrapes the EU’s official event portal and logs the latest entries with clean metadata including date, location, category, and link. 📬 For collaborations, feel free to connect with me on LinkedIn Who is this template for? This workflow is useful for: Policy & public affairs teams** following institutional activities Sustainability teams** watching for relevant climate-related summits NGOs and researchers** interested in event calendars Data teams** building dashboards on public event trends What does it do? This n8n workflow: 🌐 Scrapes the EU events portal for new meetings and conferences 📅 Extracts event metadata (title, date, location, type, and link) 🔁 Handles pagination across multiple pages 🚫 Checks for duplicates already stored 📊 Saves new records into a connected Google Sheet How it works Triggered daily via cron HTTP node loads the event listing HTML Extract HTML blocks for each event article Parse event name, link, type, location, and full date Concatenate and clean dates for easy tracking Store non-duplicate entries in Google Sheets The workflow uses static data to track pagination and ensure only new events are stored, making it ideal for building up a clean dataset over time. What do I need to get started? You’ll need: A Google Sheet connected to your n8n instance No code or AI tools needed — just n8n and this template Follow the Guide! Sticky notes are included directly inside the workflow to guide you step-by-step through setup and customisation. 🎥 Watch My Tutorial Notes This is ideal for analysts and consultants who want clean, structured data from the EU portal You can add filtering, email alerts, or AI classifiers later This workflow was built using n8n version 1.93.0 Submitted: June 1, 2025

Who this is for? The LinkedIn Profile Extract and JSON Resume Builder is a powerful workflow that scrapes professional profile data from LinkedIn using Bright Data's infrastructure, then transforms that data into a clean, structured JSON resume using Google Gemini. The workflow is ideal for automating resume parsing, candidate profiling, or integrating into recruiting platforms. This workflow is tailored for: HR professionals & recruiters automating resume screening Talent acquisition platforms enriching candidate profiles Developers & AI builders creating resume-parsing AI pipelines Data scientists working on labor market analytics Growth hackers profiling prospects via public data What problem is this workflow solving? Parsing resumes or LinkedIn profiles into machine-readable formats is often a manual, error-prone process. Most scraping tools either fail due to anti-bot protections or return unstructured HTML that's hard to work with. This workflow solves that by: Using Bright Data's Web Unlocker for reliable, CAPTCHA-free LinkedIn scraping Extracting clean text and structured profile data via Google Gemini LLM Automatically generating a standards-compliant JSON Resume and Skills Sending the resume to webhooks or storing it for downstream usage What this workflow does Accepts LinkedIn Profile URL and required metadata (Bright Data zone, webhook) Scrapes LinkedIn profile using Bright Data Web Unlocker Extracts clean content and skills using Google Gemini LLM Builds a JSON-formatted resume following the JSON resume schema Sends the JSON resume via Webhook Notification Persists the output by saving the file to disk Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Set URL and Bright Data Zone node with the LinkedIn profile, Bright Data Zone and the Webhook notification URL. For testing purposes, you can obtain a webhook url using https://webhook.site/ How to customize this workflow to your needs Add Language Translation Insert a translation LLM node to support multilingual profiles. Generate PDF Resumes Convert JSON to formatted PDF resumes using an HTML-to-PDF module. Push to ATS or CRM Add integration nodes to pipe data into applicant tracking systems (ATS), CRMs, or databases. Use Alternative LLMs Swap Gemini with OpenAI or Anthropic Claude if preferred.

📝 DESCRIPTION OF THE WORKFLOW This workflow connects Salesforce and Geotab to streamline fleet tracking for field service jobs (Work Orders). When a new Work Order is created in Salesforce (with a 'New' status and valid coordinates), it creates a circular geofence zone in Geotab and updates the Work Order with the zone ID. If geolocation is missing, an alert email is sent to dedicated email. The workflow uses a Salesforce Outbound Message to trigger an n8n webhook. It includes robust credential handling and optional logic to skip or notify on bad data. Use Cases: Automating vehicle geofence setup for service visits Enhancing CRM-to-fleet system synchronisation Enforcing work orders data quality via alerts Integrations Used: Salesforce Geotab API Microsoft Outlook (or any SMTP-compatible service) Tags: geotab, salesforce, fleet management, gps tracking, field service, crm, automation, webhook, integration ADDITIONAL RESOURCES 🔗 Salesforce Salesforce Login \[Salesforce Setup (Admin Console)]\(https://login.salesforce.com/ → click “Setup” gear icon) Outbound Messages Documentation Salesforce Developer Documentation Salesforce Workbench (API Testing Tool) 🔗 Geotab Geotab Login (MyGeotab) Geotab Developer Portal Geotab API Explorer Geotab SDK (JavaScript Samples) Geotab Support Centre

AI Comprehensive Research on User's Query with Gemini and Web Search What is this? Perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search (by Gemini) , reflecting on the results to identify knowledge gaps, and iteratively refining its search until it can provide a well-supported answer with citations. (like Perplexity) This workflow is a reproduction of gemini-fullstack-langgraph-quickstart in N8N. The gemini‑fullstack‑langgraph‑quickstart is a demo by the Google‑Gemini team that showcases how to build a powerful full‑stack AI agent using Gemini and LangGraph How It Works Generate Query 💬 generates one or more search queries tasks based on the User's question. uses Gemini 2.0 Flash Web Research 🌐 execute web search tasks using the native Google Search API tool in combination with Gemini 2.0 Flash. Reflection 📚 Identifies knowledge gaps and generates potential follow-up queries. Setup Configure API Credentials: Create Google Gemini(PaLM) Api Credential using you own Gemini key Connect the credential with three nodes: Google Gemini Chat Model and GeminiSearch and reflection Configure Redis Source: prepare a Redis service that can be accessed by n8n Create Redis Crediential and connect it with all Redis node Customize Try using different Gemini models. Try modifying the parameters number_of_initial_queries and max_research_loops. Why use Redis? Use Redis as an external storage to maintain global variables (counter, search results, etc.) This workflow contains a loop process, which need global variables (as State in LangGraph). It is difficult to achieve global variables management without external storage in n8n.

🎯 Automated TikTok Influencer Discovery & Analysis A complete n8n automation that discovers TikTok influencers using Bright Data, evaluates their fit using Claude AI, and sends personalized outreach emails. Designed for marketing teams and brands that need a scalable, intelligent way to find and connect with relevant creators. 📋 Overview This workflow provides a full-service influencer discovery pipeline: it finds TikTok profiles using search keywords, uses AI to assess alignment with your brand, and initiates contact with qualified influencers. Ideal for influencer marketing, brand partnerships, and campaign planning. ✨ Key Features 🔍 Keyword-Based Discovery** Locate TikTok influencers by specific niche-related keywords. 📊 Bright Data Integration** Access accurate TikTok profile data from Bright Data’s datasets. 🤖 AI-Powered Analysis** Claude AI evaluates each profile's fit with your brand based on bio, content, and more. 📧 Smart Email Notifications** Sends tailored outreach emails to creators deemed highly relevant. 📈 Data Storage** Google Sheets stores profile details, AI evaluation results, and outreach status. 🎯 Intelligent Filtering** Processes only influencers who meet your criteria (e.g., 5000+ followers, industry match). ⚡ Fast & Reliable** Uses professional scraping with robust error handling. 🔄 Batch Processing** Supports bulk influencer processing through a single automated flow. 🎯 What This Workflow Does Input Search Keywords** – TikTok terms for finding niche creators Business Info** – Brand description and industry Collaboration Criteria** – Follower count minimum, niche alignment Processing Steps Form Submission TikTok Discovery via Bright Data Data Extraction and Normalization Save to Google Sheets Relevance Scoring via Claude AI Filtering Based on AI Score + Follower Count Personalized Email Outreach Output Data Points | Field | Description | Example | |---------------|------------------------------------|----------------------------------| | Profile ID | TikTok profile identifier | tiktoker123456 | | Username | TikTok handle | @creativecreator | | URL | Profile link | https://tiktok.com/@creativecreator | | Description | Creator bio | "Fashion & lifestyle content..." | | Followers | Total follower count | 50,000 | | Collaboration | AI assessment of brand fit | "Highly Relevant" | | Analysis | 50-word Claude AI relevance summary| "Strong alignment with fashion..."| 🚀 Setup Instructions Prerequisites n8n (cloud or self-hosted) Bright Data account with TikTok access Google Sheets + Gmail Anthropic Claude API key 10–15 minutes setup time Step-by-Step Setup Import Workflow via JSON in n8n Configure Bright Data – Add API credentials and dataset ID Google Sheets – Setup credentials and map columns Claude AI – Insert API key and select desired model Gmail – Authenticate Gmail and update mail node settings Update Variables – Replace placeholders with business info Test & Launch – Submit a sample form and verify all outputs 📖 Usage Guide Adding Search Keywords Submit the form with search terms, business description, and industry category to trigger the workflow. Understanding AI Analysis Emails are sent only if: Collaboration status = Highly Relevant Follower count ≥ 5000 Industry alignment confirmed Claude AI returns a 50-word analysis justifying the match Customizing Filters Edit the "Find the Collaborator" prompt to adjust: Follower thresholds Industry relevance Additional metrics (e.g., engagement rate) Viewing Results Google Sheets log includes: Influencer metadata AI scores and rationale Collaboration status Email delivery timestamp 🔧 Customization Options Add More Fields:** Engagement rate, contact email, content themes Email Personalization:** Customize message templates or integrate other mail services Enhanced Filtering:** Use engagement rates, region, content frequency 🚨 Troubleshooting | Issue | Fix | |-------|-----| | Bright Data fails | Recheck API and dataset ID | | No influencer data | Adjust keywords or dataset scope | | Sheets permission error | Re-authenticate and check sharing | | Claude fails | Validate API key and prompt | | Emails not sent | Re-auth Gmail or update recipient field | | Form not triggering | Reconfirm webhook URL and permissions | Advanced Debugging Check n8n execution logs Run individual nodes for pinpointing failures Confirm all data formats Handle API rate limits Add error-catch nodes for retries 📊 Use Cases & Examples Brand Discovery:** Fashion, tech, fitness creators Competitor Insights:** Find influencers used by rival brands Campaign Planning:** Build targeted influencer lists Market Research:** Identify creator trends across regions ⚙️ Advanced Configuration Batch Execution:** Process multiple keywords with delay logic Engagement Metrics:** Scrape and calculate likes-to-follower ratios CRM Integration:** Sync qualified profiles to HubSpot, Salesforce, or Slack 📈 Performance & Limits Processing Time:** 3–5 minutes per keyword Concurrency:** 3–5 simultaneous fetches (depends on plan) Accuracy:** >95% influencer data reliability Success Rate:** 90%+ for outreach and processing

How it works • Extract AI Overviews from Google Search - Receives data from browser extension via webhook • Convert HTML to Markdown - Automatically processes and cleans AI Overview content • Store in Google Sheets - Archives all extracted AI Overviews with metadata and sources • Generate SEO Guidelines - AI analyzes page content vs AI Overview to suggest improvements • Automate Analysis - Batch process multiple URLs and schedule regular checks Set up steps • Import workflow - Load the JSON template into your n8n instance (2 minutes) • Configure Google Sheets - Set up OAuth connection and create spreadsheet with required columns (5 minutes) • Set up AI provider - Add OpenRouter API credentials for Gemini 2.5 Pro (3 minutes) • Install browser extension - Deploy the companion Chrome/Firefox extension for data extraction (5 minutes) • Test webhook endpoint - Verify the connection between extension and n8n workflow (2 minutes) Total setup time: ~15 minutes What you'll need: Google account for Sheets integration Google Sheet template with required columns OpenRouter API key for Gemini 2.5 Pro model access Browser extension: Chrome Extension or Firefox Add-on n8n instance (local or cloud) Use cases: SEO agencies** - Monitor AI Overview presence for client keywords Content marketers** - Analyze what content gets featured in AI Overviews E-commerce** - Track AI Overview coverage for product-related searches Research** - Build datasets of AI Overview content across different topics The workflow comes with a free browser extension (Chrome | Firefox) that automatically extracts AI Overview content from Google Search and sends it via webhook to your n8n workflow for processing and analysis. GitHub Repository: https://github.com/romek-rozen/ai-overview-extractor/ Detailed Setup Instructions - AI Overview Extractor Prerequisites n8n instance** (local or cloud) - version 1.95.3+ Google account** for Sheets integration OpenRouter API account** for Gemini 2.5 Pro access Browser** (Chrome/Firefox) for the extension Step 1: Import the Workflow Open n8n and navigate to Workflows Click "Add workflow" → "Import from JSON" Upload the AI_OVERVIES_EXTRACTOR_TEMPLATE.json file Save the workflow Step 2: Configure Google Sheets Create Google Sheets Document Create new Google Sheet with these columns: extractedAt | searchQuery | sources | markdown | myURL | task | guidelines | key Here is public google sheet template: https://docs.google.com/spreadsheets/d/15xqZ2dTiLMoyICYnnnRV-HPvXfdgVeXowr8a7kU4uHk/edit?gid=0#gid=0 Copy the Google Sheets URL (you'll need it for the workflow) Set up Google Sheets Credentials In n8n, go to Settings → Credentials Click "Add credential" → "Google Sheets OAuth2 API" Follow the OAuth setup to authorize n8n access to Google Sheets Name the credential (e.g., "Google Sheets AI Overview") Configure Google Sheets Nodes Update these nodes with your Google Sheets URL: Get URLs to Analyze Save AI Overview to Sheets Save SEO Guidelines to Sheets In each node: Set documentId to your Google Sheets URL Set sheetName to your Google Sheets URL Select your Google Sheets credential Step 3: Configure AI Provider (OpenRouter) Get OpenRouter API Key Sign up at https://openrouter.ai/ Generate API key in your account settings Add credits to your account Set up OpenRouter Credentials In n8n, go to Settings → Credentials Click "Add credential" → "OpenRouter API" Enter your API key Name the credential (e.g., "OpenRouter AI Overview") Configure OpenRouter Node Select the Gemini 2.5 Pro Model node Choose your credential from the dropdown Verify the model (default: google/gemini-2.5-pro-preview) Step 4: Install Browser Extension Install in Chrome Official Extension (Recommended) Visit: https://chromewebstore.google.com/detail/ai-overview-extractor/cbkdfibgmhicgnmmdanlhnebbgonhjje Click "Add to Chrome" Install in Firefox Official Add-on Visit: https://addons.mozilla.org/en-US/firefox/addon/ai-overview-extractor/ Click "Add to Firefox" Step 5: Configure Webhook Connection Get Webhook URL In n8n workflow, click on the Webhook node Copy the webhook URL (should be like: http://localhost:5678/webhook/ai-overview-extractor-template-123456789) Configure Extension Go to Google Search and perform any search with AI Overview Click the browser extension button (AI Overview Extractor) In webhook configuration section, paste your webhook URL Click "Test" - should show ✅ Test successful Click "Save" to store the configuration Step 6: Activate and Test Activate Workflow In n8n, toggle the workflow to "Active" (top right switch) Verify all nodes are properly configured Test End-to-End Go to Google Search Search for something that shows AI Overview Use the extension to extract AI Overview Send via webhook - check your Google Sheets for the data Verify the markdown conversion worked correctly Optional: Batch Analysis Setup For SEO Analysis Features In your Google Sheets, add URLs in the myURL column Set task column to "create guidelines" Run the workflow manually or wait for the 15-minute scheduler Check guidelines column for AI-generated SEO recommendations Troubleshooting Webhook Issues Ensure n8n is running on port 5678 Check if workflow is activated Verify webhook URL format Google Sheets Errors Confirm OAuth credentials are working Check sheet URL format Verify column names match exactly Ensure nodes Get URLs to Analyze, Save AI Overview to Sheets, and Save SEO Guidelines to Sheets are properly configured OpenRouter Issues Check API key validity Ensure sufficient account credits Try different models if Gemini 2.5 Pro fails Verify the Gemini 2.5 Pro Model node is properly connected Extension Problems Check browser console for errors Verify extension is properly installed Ensure you're on google.com/search pages Confirm webhook URL is correctly configured in extension Next Steps Customize AI prompts** in the Generate SEO Recommendations node for your specific needs Adjust scheduler frequency** (default: 15 minutes) Add more URL analysis** by populating Google Sheets Monitor usage** and API costs Support GitHub Issues**: https://github.com/romek-rozen/ai-overview-extractor/issues n8n Community**: https://community.n8n.io/ Template Documentation**: Check the included README files

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for This workflow automates the real-time extraction of Job Descriptions and Salary Information from job listing pages using Bright Data MCP and analyzes content using OpenAI GPT-4o mini. This workflow is ideal for: Recruiters & HR Tech Startups**: Automate job data collection from public listings Market Intelligence Teams**: Analyze compensation trends across companies or geographies Job Boards & Aggregators**: Power search results with structured, enriched listings AI Workflow Builders**: Extend to other career platforms or automate resume-job match analysis Analysts & Researchers**: Track hiring signals and salary benchmarks in real time What problem is this workflow solving? Traditional scraping of job portals can be challenging due to cluttered content, anti-scraping measures, and inconsistent formatting. Manually analyzing salary ranges and job descriptions is tedious and error-prone. This workflow solves the problem by: Simulating user behavior using Bright Data MCP Client to bypass anti-scraping systems Extracting structured, clean job data in Markdown format Using OpenAI GPT-4o mini to analyze and extract precise salary details and refined job descriptions Merging and formatting the result for easy consumption Delivering final output via webhook, Google Sheets, or file system What this workflow does Components & Flow Input Nodes job_search_url: The job listing or search result URL job_role: The title or role being searched for (used in logging/formatting) MCP Client Operations MCP Salary Data Extractor Simulates browser behavior and scrapes salary-related content (if available) MCP Job Description Extractor Extracts full job description as structured Markdown content OpenAI GPT-4o mini Nodes Salary Information Extractor Uses GPT-4o mini to detect, clean, and standardize salary range data (if any) Job Description Refiner Extracts role responsibilities, qualifications, and benefits from unstructured text Company Information Extractor Uses Bright Data MCP and GPT-4o mini to extract the company information Merge Node Combines the refined job description and extracted salary information into a unified JSON response object Aggregate node Aggregates the job description and salary information into a single JSON response object Final Output Handling The output is handled in three different formats depending on your downstream needs: Save to Disk** Output stored with filename including timestamp and job role Google Sheet Update** Adds a new row with job role, salary, summary, and link Webhook Notification** Pushes merged response to an external system Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the OpenAi account credentials. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token> How to customize this workflow to your needs Modify Input Source Change the job_search_url to point to any job board or aggregator Customize job_role to reflect the type of jobs being analyzed Tweak LLM Prompts (Optional) Refine GPT-4o mini prompts to extract additional fields like benefits, tech stacks, remote eligibility Change Output Format Customize the merged object to output JSON, CSV, or Markdown based on downstream needs Add additional destinations (e.g., Slack, Airtable, Notion) via n8n nodes

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for The DNB Company Search & Extract workflow is designed for professionals who need to gather structured business intelligence from Dun & Bradstreet (DNB). It is ideal for: Market Researchers B2B Sales & Lead Generation Experts Business Analysts Investment Analysts AI Developers Building Financial Knowledge Graphs What problem is this workflow solving? Gathering business information from the DNB website usually involves manual browsing, copying company details, and organizing them in spreadsheets. This workflow automates the entire data collection pipeline — from searching DNB via Google, scraping relevant pages, to structuring the data and saving it in usable formats. What this workflow does This workflow performs automated search, scraping, and structured extraction of DNB company profiles using Bright Data’s MCP search agents and OpenAI’s 4o mini model. Here's what it includes: Set Input Fields: Provide search_query and webhook_notification_url. Bright Data MCP Client (Search): Performs Google search for the DNB company URL. Markdown Scrape from DNB: Scrapes the company page using Bright Data and returns it as markdown. OpenAI LLM Extraction: Transforms markdown into clean structured data. Extracts business information (company name, size, address, industry, etc.) Webhook Notification: Sends structured response to your provided webhook. Save to Disk: Persists the structured data locally for logging or auditing. Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the OpenAi account credentials. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>. Update the Set input fields for search_query and webhook_notification_url. Update the file name and path to persist on disk. How to customize this workflow to your needs Search Engine**: Default is Google, but you can change the MCP client engine to Bing, or Yandex if needed. Company Scope**: Modify search query logic for niche filtering, e.g., "biotech startups site:dnb.com". Structured Fields**: Customize the LLM prompt to extract additional fields like CEO name, revenue, or ratings. Integrations**: Push output to Notion, Airtable, or CRMs like HubSpot using additional n8n nodes. Formatting**: Convert output to PDF or CSV using built-in File and Spreadsheet nodes.