Workflow Templates

Purpose This workflow creates a versioned backup of an entire Clockify workspace split up into monthly reports. How it works This backup routine runs daily by default The Clockify reports API endpoint is used to get all data from the workspace based on time entries A report file is being retrieved for every month starting with the current one, going back 3 month in total by default If changes happened during a day to any report, it is being updated in Github Prerequisites Create a private Github repository Create credentials for both Clockify and Github (make sure to give permissions for read and write operations) Setup Clone the workflow and select the belonging credentials Follow the instructions given in the yellow sticky notes Activate the workflow

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.

⚙️ How It Works: LocalRAG.AI ⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups. LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to answer questions intelligently. It combines LangChain, Ollama, Qdrant, and Postgres into a powerful AI pipeline — all running locally for maximum data privacy. 🔄 What It Does Monitors Your Google Drive Folders for new or updated files. Downloads the file, extracts the text, and prepares it. Generates Embeddings using your local Ollama model (e.g., LLaMA 3). Stores them in Qdrant, your local vector database. During a chat, it: Uses vector search to retrieve relevant chunks. Combines them with chat history stored in Postgres. Responds via a LangChain AI agent using your local model. 🛠️ Setup Steps (Self-hosted Only) Install and Self-host n8n (e.g., via Docker). Set up your Ollama instance locally and load your desired LLM (e.g., llama3). Deploy Qdrant locally for vector storage. Connect a Postgres DB to store chat history. Create and import the workflow in n8n. Authenticate Google Drive to monitor folders. Connect credentials for Ollama, Qdrant, Postgres in the n8n workflow. Start chatting through the Webhook Trigger or custom UI. 🧠 Perfect For: Research teams handling confidential data Internal documentation Q&A AI chatbots that don’t rely on OpenAI or cloud

The YogiAI workflow automates sending daily yoga pose reminders and related information via Line Push Messages . This automation leverages data from a Google Sheets database containing yoga pose details such as names, image URLs, and links to ensure users receive personalized and engaging content every day. Purpose Provide users with daily yoga pose suggestions tailored to their practice. Deliver visually appealing and informative content through Line's Flex Messages, including images and clickable links. Log user interactions and preferences back into Google Sheets to refine future recommendations. Key Features Automated Daily Reminders : Sends a curated list of yoga poses at a scheduled time (21:30 Bangkok time). Dynamic Content Generation : Uses AI to rewrite and format messages in a user-friendly manner, complete with emojis and clear instructions. Integration with Google Sheets : Pulls data from a predefined Google Sheet and logs interactions for continuous improvement. Customizable Messaging : Ensures JSON outputs are properly formatted for Line’s Flex Message API, allowing for interactive and visually rich content. Data Source Google Sheets Structure The workflow relies on a Google Sheet structured as follows: PoseName : The name of the yoga pose. uri : The image URL representing the pose. url : A clickable link directing users to more information about the pose. Sample Data Layout Supine Angle https://example.com/SupineAngle-tn146.png https://example.com/pose/SupineAngle Warrior II https://example.com/WarriorII-tn146.png https://example.com/pose/WarriorII *Note : Ensure that you update the Google Sheet with your own data. Refer to this sample sheet for reference. * Scheduled Trigger The workflow is triggered daily at 21:30 (9:30 PM) Bangkok Time (Asia/Bangkok) . This ensures timely delivery of reminders to users, keeping them engaged with their yoga practice. Workflow Process Data Retrieval Node: Get PoseName Fetches yoga pose details from the specified range in the Google Sheet. Content Generation Node: WritePosesToday Utilizes Azure OpenAI to craft user-friendly text, complete with emojis and clear instructions. Node: RewritePosesToday Formats the AI-generated text specifically for Line messaging, ensuring compatibility and visual appeal. JSON Formatting Node: WriteJSONflex Generates JSON structures required for Line’s Flex Messages, enabling carousel displays of yoga pose images and links. Node: Fix JSON Ensures all JSON outputs are correctly formatted before being sent via Line. Message Delivery Node: Line Push with Flex Bubble Sends the final message, including both text and Flex Message carousels, directly to users via Line Push Messages. Logging Interactions Nodes: YogaLog & YogaLog2 Logs each interaction back into Google Sheets to track which poses were sent and how often they appear, refining future recommendations. Setup Prerequisites Google Sheets Account : Set up a Google Sheet with the required structure and populate it with your yoga pose data. Line Developer Account : Create a Line channel to obtain necessary credentials for sending push messages. Azure OpenAI Account : Configure access to Azure OpenAI services for generating and formatting content. Intended Audience This workflow is ideal for: Yoga Instructors : Seeking to engage students with daily pose suggestions. Fitness Enthusiasts : Looking to maintain consistency in their yoga practice. Content Creators : Interested in automating personalized and visually appealing content distribution.

Summary This n8n workflow implements an AI-powered "Local Event Finder" agent. It takes user criteria (like event type, city, date, and interests), uses a suite of search tools (Brave Web Search, Brave Local Search, Google Gemini Search) and a web scraper (Jina AI) to find relevant events, and returns formatted details. The entire agent is exposed as a single, easy-to-use MCP (Multi-Capability Peer) tool, making it simple to integrate into other workflows or applications. This template cleverly combines the MCP server endpoint and the AI agent logic into a single n8n workflow file for ease of import and management. Key Features Intelligent Multi-Tool Search:** Dynamically utilizes web search, precise local search, and advanced Gemini semantic search to find events. Detailed Information via Web Scraping:** Employs Jina AI to extract comprehensive details directly from event web pages. Simplified MCP Tool Exposure:** Makes the complex event-finding logic available as a single, callable tool for other MCP-compatible clients (e.g., Roo Code, Cline, other n8n workflows). Customizable AI Behavior:** The core AI agent's behavior, tool usage strategy, and output formatting can be tailored by modifying its System Prompt. Modular Design:** Uses distinct nodes for LLM, memory, and each external tool, allowing for easier modification or extension. Benefits Simplifies Client-Side Integration:** Offloads the complexity of event searching and data extraction from client applications. Provides Richer Event Data:** Goes beyond simple search links to extract and format key event details. Flexible & Adaptable:** Can be adjusted to various event search needs and can incorporate new tools or data sources. Efficient Processing:** Leverages specialized tools for different aspects of the search process. Nodes Used MCP Trigger Tool Workflow Execute Workflow Trigger AI Agent Google Gemini Chat Model (ChatGoogleGenerativeAI) Simple Memory (Window Buffer Memory) MCP Client (for Brave Search tools via Smithery) Google Gemini Search Tool Jina AI Tool Prerequisites An active n8n instance. Google AI API Key:** For the Gemini LLM (Google Gemini Chat Model node) and the Google Gemini Search Tool. Ensure your key is enabled for these services. Jina AI API Key:** For the jina_ai_web_page_scraper node. A free tier is often available. Access to a Brave Search MCP Provider (Optional but Recommended):** This template uses MCP Client nodes configured for Brave Search via a provider like Smithery. You'll need an account/API key for your chosen Brave Search MCP provider to configure the smithery brave search credential. Alternatively, you could adapt these to call Brave Search API directly if you manage your own access, or replace them with other search tools. Setup Instructions Import Workflow: Download the JSON file for this template and import it into your n8n instance. Configure Credentials: Google Gemini LLM: Locate the Google Gemini Chat Model node. Select or create a "Google Gemini API" credential (named Google Gemini Context7 in the template) using your Google AI API Key. Google Gemini Search Tool: Locate the google_gemini_event_search node. Select or create a "Gemini API" credential (named Gemini Credentials account in the template) using your Google AI API Key (ensure it's enabled for Search/Vertex AI). Jina AI Web Scraper: Locate the jina_ai_web_page_scraper node. Select or create a "Jina AI API" credential (named Jina AI account in the template) using your Jina AI API Key. Brave Search (via MCP): You'll need an MCP Client HTTP API credential to connect to your Brave Search MCP provider (e.g., Smithery). Create a new "MCP Client HTTP API" credential in n8n. Name it, for example, smithery brave search. Configure it with the Base URL and any required authentication (e.g., API key in headers) for your Brave Search MCP provider. Locate the brave_web_search and brave_local_search MCP Client nodes in the workflow. Assign the smithery brave search (or your named credential) to both of these nodes. Activate Workflow: Ensure the workflow is active. Note MCP Trigger Path: Locate the local_event_finder (MCP Trigger) node. The Path field (e.g., 0ca88864-ec0a-4c27-a7ec-e28c5a900697) combined with your n8n webhook base URL forms the endpoint for client calls. Example Endpoint: YOUR_N8N_INSTANCE_URL/webhooks/PATH-TO-MCP-SERVER Customization AI Behavior:** Modify the "System Message" parameter within the event_finder_agent node to change the AI's persona, its strategy for using tools, or the desired output format. LLM Model:** Swap the Google Gemini Chat Model node with another compatible LLM node (e.g., OpenAI Chat Model) if desired. You'll need to adjust credentials and potentially the system prompt. Tools:** Add, remove, or replace tool nodes (e.g., use a different search provider, add a weather API tool) and update the event_finder_agent's system prompt and tool configuration accordingly. Scraping Depth:** Be mindful of the jina_ai_web_page_scraper's usage due to potential timeouts. The system prompt already guides the LLM on this, but you can adjust its usage instructions.

Google Maps Business Phone No Scraper with Bright Data & Sheets Overview This n8n workflow automates the process of scraping business phone numbers and information from Google Maps using the Bright Data API and saves the results to Google Sheets. Workflow Components 1. Form Trigger - Submit Location and Keywords Type: Form Trigger Purpose: Start the workflow when a form is submitted Fields: Location (required) Keywords (required) Configuration: Form Title: "GMB" Webhook ID: 8b72dcdf-25a1-4b63-bb44-f918f7095d5d 2. Bright Data API - Request Business Data Type: HTTP Request Purpose: Sends scraping request to Bright Data API Method: POST URL: https://api.brightdata.com/datasets/v3/trigger Query Parameters: dataset_id: gd_m8ebnr0q2qlklc02fz include_errors: true type: discover_new discover_by: location limit_per_input: 2 Headers: Authorization: Bearer BRIGHT_DATA_API_KEY Request Body: { "input": [ { "country": "{{ $json.Location }}", "keyword": "{{ $json.keywords }}", "lat": "" } ], "custom_output_fields": [ "url", "country", "name", "address", "description", "open_hours", "reviews_count", "rating", "reviews", "services_provided", "open_website", "phone_number", "permanently_closed", "photos_and_videos", "people_also_search" ] } 3. Check Scraping Status Type: HTTP Request Purpose: Check if data scraping is completed Method: GET URL: https://api.brightdata.com/datasets/v3/progress/{{ $json.snapshot_id }} Query Parameters: format: json Headers: Authorization: Bearer BRIGHT_DATA_API_KEY 4. Check If Status Ready Type: Conditional (IF) Purpose: Determine if scraping is ready or needs to wait Condition: {{ $json.status }} equals "ready" 5. Wait Before Retry Type: Wait Purpose: Pause 1 minute before checking status again Duration: 1 minute Webhook ID: 7047efad-de41-4608-b95c-d3e0203ef620 6. Check Records Exist Type: Conditional (IF) Purpose: Proceed only if business records are found Condition: {{ $json.records }} not equals 0 7. Fetch Business Data Type: HTTP Request Purpose: Get business information including phone numbers Method: GET URL: https://api.brightdata.com/datasets/v3/snapshot/{{ $json.snapshot_id }} Query Parameters: format: json Headers: Authorization: Bearer BRIGHT_DATA_API_KEY 8. Save to Google Sheets Type: Google Sheets Purpose: Store business data in Google Sheets Operation: Append Document ID: YOUR_GOOGLE_SHEET_ID Sheet Name: GMB Column Mapping: Name:** {{ $json.name }} Address:** {{ $json.address }} Rating:** {{ $json.rating }} Phone Number:** {{ $json.phone_number }} URL:** {{ $json.url }} Workflow Flow Start: User submits form with location and keywords Request: Send scraping request to Bright Data API Monitor: Check scraping status periodically Wait Loop: If not ready, wait 1 minute and check again Validate: Ensure records exist before proceeding Fetch: Retrieve the scraped business data Save: Store results in Google Sheets Setup Requirements API Keys & Credentials Bright Data API Key:** Replace BRIGHT_DATA_API_KEY with your actual API key Google Sheets OAuth2:** Configure with your Google Sheets credential ID Google Sheet ID:** Replace YOUR_GOOGLE_SHEET_ID with your actual sheet ID Google Sheets Setup Create a Google Sheet with a tab named "GMB" Ensure the following columns exist: Name Address Rating Phone Number URL Workflow Status Active:** No (currently inactive) Execution Order:** v1 Version ID:** 0bed9bf1-00a3-4eb6-bf7c-cf07bee006a2 Workflow ID:** Hm7iTSgpu2of6gz4 Notes The workflow includes a retry mechanism with 1-minute waits Data validation ensures only successful scrapes are processed All business information is automatically saved to Google Sheets The form trigger allows easy initiation of scraping jobs For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/

Complete MCP server exposing all ProfitWell Tool operations to AI agents. Zero configuration needed - all 2 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every ProfitWell Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n ProfitWell Tool tool with full error handling 📋 Available Operations (2 total) Every possible ProfitWell Tool operation is included: 🔧 Company (1 operations) • Get settings for your company 🔧 Metric (1 operations) • Get a metric 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native ProfitWell Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every ProfitWell Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

🎯 CV Keyword Optimizer An AI-powered n8n workflow that automatically tailors your resume to any job description by injecting relevant keywords — without touching your formatting, layout, or design. How It Works Architecture ┌─────────────────────────────────────────────────────────────────┐ │ User Input (Form) │ │ CV Google Docs Link + Job URL or Pasted JD │ └──────────────────────────┬──────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 1. Read CV from Google Docs API │ │ 2. Extract full CV text (handles tables, paragraphs, etc.) │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 3. Get Job Description │ │ ├── URL provided? → Scrape job page, strip HTML to text │ │ │ └── Scrape failed? → Fall back to manual JD │ │ └── No URL? → Use manually pasted JD directly │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 4. 🏠 Local Ollama (llama3.1:8b) │ │ Analyzes JD + CV → Extracts & ranks 10-20 ATS keywords │ │ Output: keyword, priority, target bullet, reason │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 5. ☁️ Groq API (Llama 3.3 70B) │ │ Takes ranked keywords + CV → Produces find/replace pairs │ │ Naturally weaves keywords into experience bullet points │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 6. Copy original CV via Google Drive API │ │ (preserves ALL formatting, tables, styles) │ │ │ │ 7. Apply replacements via Google Docs batchUpdate API │ │ (replaceAllText — formatting stays intact) │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 8. Output │ │ ✅ New Google Doc link │ │ 📋 Changelog: original text → updated text + keywords added │ └──────────────────────────────────────────────────────────────────┘ Why Two AI Models? | Step | Model | Why | |------|-------|-----| | Keyword Extraction | Ollama llama3.1:8b (local) | Free, private, no API costs. Reasoning about which keywords actually matter for ATS | | Text Rewriting | Groq llama-3.3-70b-versatile (cloud) | Larger model = better at natural language. Produces find/replace pairs that read naturally | What Gets Modified ✅ Experience/work bullet points ✅ Skills/technical skills lines ❌ Name, contact info, education, dates, company names, job titles — never touched Formatting Preservation The workflow copies your original Google Doc (not recreates it), then uses replaceAllText to swap text in-place. This means: ✅ Tables, columns, fonts, colors — all preserved ✅ Bold, italic, underline — all preserved ✅ Custom spacing, margins — all preserved ✅ Original doc is untouched (changes go to the copy) Setup Steps Prerequisites Docker installed Ollama installed locally A Groq API key (free tier works) Google account with Docs & Drive access 1. Install & Start Ollama macOS brew install ollama Start the Ollama server ollama serve Pull the model (in another terminal) ollama pull llama3.1:8b Verify it's running: curl http://localhost:11434/api/tags 2. Get a Groq API Key Go to console.groq.com Sign up / log in Navigate to API Keys → Create a new key Copy the key (starts with gsk_...) 3. Configure Environment cd cv-generator Create .env from template cp .env.example .env Edit .env and add your Groq key GROQ_API_KEY=gsk_your_key_here 4. Start n8n docker compose up -d n8n will be available at http://localhost:5678 Default credentials: Username: admin Password: changeme > ⚠️ Change these in docker-compose.yml for production use. 5. Import the Workflow Open n8n at http://localhost:5678 Go to Workflows → Import from File Select cv-keyword-optimizer.json You'll see credential warnings on some nodes — that's expected 6. Set Up Google Credentials In n8n, go to Settings → Credentials Create a Google Docs OAuth2 credential Follow n8n's OAuth2 setup guide for Google Required scopes: https://www.googleapis.com/auth/documents Create a Google Drive OAuth2 credential Required scopes: https://www.googleapis.com/auth/drive Click each node with a ⚠️ warning → select your credential from the dropdown 7. Activate & Use Toggle the workflow Active Open the form URL shown in the trigger node (or go to http://localhost:5678/form/cv-keyword-optimizer-form) Fill in: Google Docs CV Link (required) Job Posting URL or Job Description (at least one) Submit and wait ~30-60 seconds Get your optimized CV link + detailed changelog Project Structure cv-generator/ ├── cv-keyword-optimizer.json # n8n workflow definition ├── docker-compose.yml # n8n container config ├── .env # Environment variables (not committed) ├── .env.example # Template for .env ├── .gitignore # Ignores .env └── README.md # This file Troubleshooting | Issue | Solution | |-------|----------| | Ollama connection refused | Make sure ollama serve is running. n8n reaches it via host.docker.internal:11434 | | Groq 429 rate limit | Free tier has limits. Wait a minute and retry | | Scraping fails on LinkedIn | LinkedIn blocks scrapers. Paste the JD manually instead | | Google Docs auth error | Re-check OAuth2 credentials in n8n. Ensure correct scopes | | Replacements don't apply | The AI's "find" text must exactly match the CV. Check the Changes Summary for what was attempted | | Empty response from Ollama | Model may still be loading. First run takes longer. Timeout is set to 5 min |

Pinterest Keyword-Based Content Scraper with AI Agent & BrightData Automation Overview This n8n workflow automates Pinterest content scraping based on user-provided keywords using BrightData's API and Claude Sonnet 4 AI agent. The system intelligently processes keywords, initiates scraping jobs, monitors progress, and formats the extracted data into structured outputs. Architecture Components 🧠 AI-Powered Controller Claude Sonnet 4 Model**: Processes and understands keywords before initiating scrape AI Agent**: Acts as the intelligent controller coordinating all scraping steps 📥 Data Input Form Trigger**: User-friendly keyword input interface Keywords Field**: Required input field for Pinterest search terms 🚀 Scraping Pipeline Launch Scraping Job: Sends keywords to BrightData API Status Monitoring: Continuously checks scraping progress Data Retrieval: Downloads completed scraped content Data Processing: Formats and structures the raw data Storage: Saves results to Google Sheets Workflow Nodes 1. Pinterest Keyword Input Type**: Form Trigger Purpose**: Entry point for user keyword submission Configuration**: Form title: "Pinterest" Required field: "Keywords" 2. Anthropic Chat Model Type**: Language Model (Claude Sonnet 4) Model**: claude-sonnet-4-20250514 Purpose**: AI-powered keyword processing and workflow orchestration 3. Keyword-based Scraping Agent Type**: AI Agent Purpose**: Orchestrates the entire scraping process Instructions**: Initiates Pinterest scraping with provided keywords Monitors scraping status until completion Downloads final scraped data Presents raw scraped data as output 4. BrightData Pinterest Scraping Type**: HTTP Request Tool Method**: POST Endpoint**: https://api.brightdata.com/datasets/v3/trigger Parameters**: dataset_id: gd_lk0sjs4d21kdr7cnlv include_errors: true type: discover_new discover_by: keyword limit_per_input: 2 Purpose**: Creates new scraping snapshot based on keywords 5. Check Scraping Status Type**: HTTP Request Tool Method**: GET Endpoint**: https://api.brightdata.com/datasets/v3/progress/{snapshot_id} Purpose**: Monitors scraping job progress Returns**: Status values like "running" or "ready" 6. Fetch Pinterest Snapshot Data Type**: HTTP Request Tool Method**: GET Endpoint**: https://api.brightdata.com/datasets/v3/snapshot/{snapshot_id} Purpose**: Downloads completed scraped data Trigger**: Executes when status is "ready" 7. Format & Extract Pinterest Content Type**: Code Node (JavaScript) Purpose**: Parses and structures raw scraped data Extracted Fields**: URL Post ID Title Content Date Posted User Likes & Comments Media Image URL Categories Hashtags 8. Save Pinterest Data to Google Sheets Type**: Google Sheets Node Operation**: Append Mapped Columns**: Post URL Title Content Image URL 9. Wait for 1 Minute (Disabled) Type**: Code Tool Purpose**: Adds delay between status checks (currently disabled) Duration**: 60 seconds Setup Requirements Required Credentials Anthropic API Credential ID: ANTHROPIC_CREDENTIAL_ID Required for Claude Sonnet 4 access BrightData API API Key: BRIGHT_DATA_API_KEY Required for Pinterest scraping service Google Sheets OAuth2 Credential ID: GOOGLE_SHEETS_CREDENTIAL_ID Required for data storage Configuration Placeholders Replace the following placeholders with actual values: WEBHOOK_ID_PLACEHOLDER: Form trigger webhook ID GOOGLE_SHEET_ID_PLACEHOLDER: Target Google Sheets document ID WORKFLOW_VERSION_ID: n8n workflow version INSTANCE_ID_PLACEHOLDER: n8n instance identifier WORKFLOW_ID_PLACEHOLDER: Unique workflow identifier Data Flow User Input (Keywords) ↓ AI Agent Processing (Claude) ↓ BrightData Scraping Job Creation ↓ Status Monitoring Loop ↓ Data Retrieval (when ready) ↓ Content Formatting & Extraction ↓ Google Sheets Storage Output Data Structure Each scraped Pinterest pin contains: URL**: Direct link to Pinterest pin Post ID**: Unique Pinterest identifier Title**: Pin title/heading Content**: Pin description text Date Posted**: Publication timestamp User**: Pinterest username Engagement**: Likes and comments count Media**: Media type information Image URL**: Direct image link Categories**: Pin categorization tags Hashtags**: Associated hashtags Comments**: User comments text Usage Instructions Initial Setup: Configure all required API credentials Replace placeholder values with actual IDs Create target Google Sheets document Running the Workflow: Access the form trigger URL Enter desired Pinterest keywords Submit the form to initiate scraping Monitoring Progress: The AI agent will automatically handle status monitoring No manual intervention required during scraping Accessing Results: Structured data will be automatically saved to Google Sheets Each run appends new data to existing sheet Technical Notes Rate Limiting**: BrightData API has built-in rate limiting Data Limits**: Current configuration limits 2 pins per keyword Status Polling**: Automatic status checking until completion Error Handling**: Includes error capture in scraping requests Async Processing**: Supports long-running scraping jobs Customization Options Adjust Data Limits**: Modify limit_per_input parameter Enable Wait Timer**: Activate the disabled wait node for longer jobs Custom Data Fields**: Modify the formatting code for additional fields Alternative Storage**: Replace Google Sheets with other storage options Sample Google Sheets Template Create a copy of the sample sheet structure: https://docs.google.com/spreadsheets/d/SAMPLE_SHEET_ID/edit Required columns: Post URL Title Content Image URL Troubleshooting Authentication Errors**: Verify all API credentials are correctly configured Scraping Failures**: Check BrightData API status and rate limits Data Formatting Issues**: Review the JavaScript formatting code for parsing errors Google Sheets Errors**: Ensure proper OAuth2 permissions and sheet access For any questions or support, please contact: Email or fill out this form

Introduction The "High-Level Service Page SEO Blueprint Report" workflow is a powerful, AI-driven solution designed to generate comprehensive SEO content strategies for service-based businesses. By analyzing competitor websites and user intent, this workflow creates a detailed blueprint that outlines the optimal structure, content, and conversion elements for a service page. The workflow leverages the JINA Reader API to extract content from competitor websites and uses Google Gemini AI to perform deep analysis across multiple dimensions: competitor content structure, user intent, strategic opportunities, and conversion optimization. The final output is a professionally formatted Markdown document that provides actionable guidance for creating a high-performing service page that satisfies both user needs and search engine requirements. This workflow eliminates the time-consuming process of manually analyzing competitors and developing content strategies, providing a data-driven foundation for service page creation that would typically require hours of expert analysis. Who is this for? This workflow is designed for digital marketers, SEO specialists, content strategists, and web developers who need to create or optimize service pages for businesses. It's particularly valuable for marketing agencies and freelancers who regularly develop content strategies for clients across various industries. Users should have a basic understanding of SEO concepts, content marketing, and website structure. While technical SEO knowledge is beneficial, the workflow is designed to provide comprehensive guidance even for those with intermediate-level expertise. The ideal user is someone who wants to streamline their content planning process and ensure their service pages are built on data-driven insights rather than guesswork. What problem is this workflow solving? Creating effective service pages that rank well in search engines while converting visitors is a complex challenge that typically requires extensive competitive research, content planning, and conversion optimization expertise. This workflow addresses several key pain points: Time-consuming competitor analysis: Manually analyzing multiple competitor websites to identify content patterns, heading structures, and meta tag strategies can take hours. Difficulty identifying content gaps: Determining what topics competitors are missing that could provide a competitive advantage requires deep analysis and industry knowledge. Balancing SEO and conversion elements: Creating content that satisfies both search engines and user needs while driving conversions is a delicate balance that many struggle to achieve. Lack of structured approach: Many content creators work without a comprehensive blueprint, leading to inconsistent results and missed opportunities. Difficulty translating analysis into actionable recommendations: Even when analysis is performed, turning those insights into a concrete content plan can be challenging. This workflow automates these processes, providing a structured, data-driven approach to service page creation that saves hours of research and planning time. What this workflow does Overview The workflow takes a list of competitor URLs and a target keyword as input, then performs a multi-stage analysis to generate a comprehensive service page blueprint. It extracts and analyzes competitor content, evaluates user intent, identifies strategic opportunities, and creates detailed recommendations for page structure, content, and conversion elements. The final output is a professionally formatted Markdown document that serves as a complete roadmap for creating an effective service page. Process Data Collection: The workflow begins with a form that collects essential information: competitor URLs, target keyword, services offered, brand name, and whether the page is a homepage. Competitor Content Extraction: The workflow processes each competitor URL, using the JINA Reader API to extract the HTML content from each site. Content Structure Analysis: For each competitor site, the workflow extracts and analyzes heading structures, meta tags, schema markup, and recurring phrases (n-grams). Competitor Analysis Report: The AI synthesizes the competitive data to identify patterns in meta titles/descriptions, common outline sections, key heading concepts, and structural elements. User Intent Analysis: The workflow analyzes the target keyword to determine primary and secondary user intents, user personas, and their position in the buyer's journey. Gap Analysis: The AI identifies content overlaps ("table stakes"), content gaps (opportunities), SEO keyword priorities, and potential UX/conversion advantages. Page Outline Generation: Based on the previous analyses, the workflow creates an optimal page structure with H1, H2s, H3s, and potentially H4s, with justifications for each section. UX & Conversion Recommendations: The workflow adds detailed recommendations for calls-to-action, trust signals, copywriting tone, visual elements, and risk reversal strategies. Final Blueprint Creation: All analyses and recommendations are compiled into a comprehensive, well-structured Markdown document that serves as a complete service page blueprint. Setup Download or import the "High-Level Service Page SEO Blueprint Report" workflow JSON file into your n8n instance. Create a JINA Reader API key by visiting https://jina.ai/api-dashboard/key-manager. You can claim a free API key that allows up to 1 million tokens. Set up Google Gemini (PaLM) credentials by following the guide at https://docs.n8n.io/integrations/builtin/credentials/googleai/#using-geminipalm-api-key. Update the "Edit Fields" node with: Your JINA Reader API Key Adjust the "Waiting Time" to 20 seconds if using the free Google Gemini API tier (which limits to 5 requests per minute) Optionally change the Gemini model if needed Activate the workflow and start the form trigger. Complete the form with: Competitors (up to 5 direct competitor URLs) Target Keyword (the query related to your service) Services Offered (details of your complete service offerings) Brand Name (your company name) Whether the page is a homepage After processing, download the generated .txt file, which contains the blueprint in Markdown format. How to customize this workflow to your needs Adjust AI parameters: Modify the temperature settings in the Google Gemini Chat Model nodes to control creativity vs. precision in the AI outputs. Customize extraction logic: Edit the "Extract HTML Elements" code node to focus on specific HTML elements that are most relevant to your industry or content type. Modify analysis prompts: Customize the prompts in the various analysis nodes to focus on specific aspects of SEO or content strategy that are most important for your use case. Add industry-specific guidance: Enhance the prompts with industry-specific instructions or examples to make the output more relevant to particular sectors. Integrate with content management systems: Extend the workflow to automatically send the blueprint to content management systems, project management tools, or document storage platforms. Add competitor scoring: Implement a scoring system to evaluate and rank competitors based on specific criteria relevant to your strategy. Expand the analysis: Add additional analysis nodes to evaluate other aspects of competitor websites, such as page speed, mobile-friendliness, or backlink profiles.

Who Is This For? This workflow is designed for AI engineers, automation specialists, and content creators who need a scalable system to dynamically manage prompts stored in GitHub. It eliminates manual updates, enforces required variable checks, and ensures that AI interactions always receive fully processed prompts. 🚀 What Problem Does This Solve? Manually managing AI prompts can be inefficient and error-prone. This workflow: ✅ Fetches dynamic prompts from GitHub ✅ Auto-populates placeholders with values from the setVars node ✅ Ensures all required variables are present before execution ✅ Processes the formatted prompt through an AI agent 🛠 How This Workflow Works This workflow consists of three key branches, ensuring smooth prompt retrieval, variable validation, and AI processing. 1️⃣ Retrieve the Prompt from GitHub (HTTP Request → Extract from File → SetPrompt) The workflow starts manually or via an external trigger. It fetches a text-based prompt stored in a GitHub repository. The Extract from File Node retrieves the content from the GitHub file. The SetPrompt Node stores the prompt, making it accessible for processing. 📌 Note: The prompt must contain n8n expression format variables (e.g., {{ $json.company }}) so they can be dynamically replaced. 2️⃣ Extract & Auto-Populate Variables (Check All Prompt Vars → Replace Variables) A Code Node scans the prompt for placeholders in the n8n expression format ({{ $json.variableName }}). The workflow compares required variables against the setVars node: ✅ If all variables are present, it proceeds to variable replacement. ❌ If any variables are missing, the workflow stops and returns an error listing them. The Replace Variables Node replaces all placeholders with values from setVars. 📌 Example of a properly formatted GitHub prompt: Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. This ensures seamless replacement when processed in n8n. 3️⃣ AI Processing & Output (AI Agent → Prompt Output) The Set Completed Prompt Node stores the final, processed prompt. The AI Agent Node (Ollama Chat Model) processes the prompt. The Prompt Output Node returns the fully formatted response. 📌 Optional: Modify this to use OpenAI, Claude, or other AI models. ⚠️ Error Handling: Missing Variables If a required variable is missing, the workflow stops execution and provides an error message: ⚠️ Missing Required Variables: ["launch_date"] This ensures no incomplete prompts are sent to AI agents. ✅ Example Use Case 📜 GitHub Prompt File (Using n8n Expressions) Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. 🔹 Variables in setVars Node { "company": "PropTechPro", "features": "AI-powered Property Management", "launch_date": "March 15, 2025" } ✅ Successful Output Hello PropTechPro, your product AI-powered Property Management launches on March 15, 2025. 🚨 Error Output (If Missing launch_date) ⚠️ Missing Required Variables: ["launch_date"] 🔧 Setup Instructions 1️⃣ Connect Your GitHub Repository Store your prompt in a public or private GitHub repo. The workflow will fetch the raw file using the GitHub API. 2️⃣ Configure the SetVars Node Define the required variables in the SetVars Node. Make sure the variable names match those used in the prompt. 3️⃣ Test & Run Click Test Workflow to execute. If variables are missing, it will show an error. If everything is correct, it will output the fully formatted prompt. ⚡ How to Customize This Workflow 💡 Need CRM or Database Integration? Connect the setVars node to an Airtable, Google Sheets, or HubSpot API to pull variables dynamically. 💡 Want to Modify the AI Model? Replace the Ollama Chat Model with OpenAI, Claude, or a custom LLM endpoint. 📌 Why Use This Workflow? ✅ No Manual Updates Required – Fetches prompts dynamically from GitHub. ✅ Prevents Broken Prompts – Ensures required variables exist before execution. ✅ Works for Any Use Case – Handles AI chat prompts, marketing messages, and chatbot scripts. ✅ Compatible with All n8n Deployments – Works on Cloud, Self-Hosted, and Desktop versions.

Who this is for? This workflow is designed for professionals and teams who need real-time, structured insights from Perplexity Search results without manual effort. What problem is this workflow solving? This n8n workflow solves the problem of automating Perplexity Search result extraction, cleanup, summarization, and AI-enhanced formatting for downstream use like sending the results to a webhook or another system. What this workflow does Automates Perplexity Search via Bright Data Uses Bright Data’s proxy-based SERP API to run a Google Search query programmatically. Makes the process repeatable and scriptable with different search terms and regions/zones. Cleans and Extracts Useful Content The Readable Data Extractor uses LLM-based cleaning to remove HTML/CSS/JS from the response and extract pure text data. Converts messy, unstructured web content into structured, machine-readable format. Summarizes Search Results Through the Gemini Flash + Summarization Chain, it generates a concise summary of the search results. Ideal for users who don’t have time to read full pages of search results. Formats Data Using AI Agent The AI Agent acts like a virtual assistant that: - Understands search results Formats them in a readable, JSON-compatible form Prepares them for webhook delivery Delivers Results to Webhook Sends the final summary + structured search result to a webhook (could be your app, a Slack bot, Google Sheets, or CRM). 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 Perplexity Search Request node with the prompt you wish to perform the search. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs 1. Change the Perplexity Search Input Default: It searches a fixed query or dataset. Customize: Accept input from a Google Sheet, Airtable, or a form. Auto-trigger searches based on keywords or schedules. 2. Customize Summarization Style (LLM Output) Default: General summary using Google Gemini or OpenAI. Customize: Add tone: formal, casual, technical, executive-summary, etc. Focus on specific sections: pricing, competitors, FAQs, etc. Translate the summaries into multiple languages. Add bullet points, pros/cons, or insight tags. 3.Choose Where the Results Go Options: Email, Slack, Notion, Airtable, Google Docs, or a dashboard. Auto-create content drafts for WordPress or newsletters. Feed into CRM notes or attach to Salesforce leads.