Workflow Templates

Overview This workflow implements a complete Retrieval-Augmented Generation (RAG) system for document ingestion and intelligent querying. It allows users to upload documents, convert them into vector embeddings, and query them using natural language. The system retrieves relevant document context and generates accurate AI responses while using caching to improve performance and reduce costs. This workflow is ideal for building AI knowledge bases, document assistants, and internal search systems. How It Works 1. Input & Configuration Receives requests via webhook (rag-system) Supports two actions: upload → process documents query → answer questions Defines: Chunk size & overlap TopK retrieval count Database table names Document Upload Flow Text Extraction Extracts text from uploaded PDF documents Text Chunking Splits text into overlapping chunks for better retrieval accuracy Document Structuring Converts chunks into structured documents Embedding Generation Generates vector embeddings using OpenAI Vector Storage Stores embeddings in PGVector (Postgres) Upload Logging Logs document metadata (user, filename, timestamp) Response Returns success message via webhook Query Flow Cache Check Checks if query result exists in cache (last 1 hour) Cache Routing If cached → return cached response If not → proceed to retrieval Cache Hit Flow Format Cached Response Standardizes cached output format Respond to User Returns cached answer with cached: true Cache Miss Flow Vector Retrieval Retrieves top relevant document chunks from PGVector AI Answer Generation Uses LLM with retrieved context Generates accurate, context-based answer Cache Storage Saves query + response in database for reuse Response Returns generated answer with cached: false Setup Instructions Webhook Setup Configure endpoint (rag-system) Send payload with: action: upload / query user_id document or query OpenAI Setup Add API credentials for: Embeddings Chat model Postgres + PGVector Enable PGVector extension Create tables: documents query_cache upload_log Configure Parameters Adjust: Chunk size (e.g., 1000) Overlap (e.g., 200) TopK (e.g., 5) Optional Enhancements Add authentication layer Add multi-tenant filtering (user_id) Use Cases AI document search systems Internal knowledge base assistants Customer support knowledge retrieval Legal or compliance document analysis SaaS AI chat with custom data Requirements OpenAI API key Postgres database with PGVector n8n instance (cloud or self-hosted) Key Features Full RAG architecture (upload + query) PDF document ingestion pipeline Semantic search with vector embeddings Context-aware AI responses Query caching for performance optimization Multi-user support via metadata filtering Scalable and modular design Summary A complete RAG-based AI system that enables document ingestion, semantic search, and intelligent query answering. It combines vector databases, LLMs, and caching to deliver fast, accurate, and scalable AI-powered knowledge retrieval.

Automatically discover trending developer and security topics, generate SEO-optimized blog posts, and publish them to WordPress as drafts — complete with AI-generated featured images. How it works Discover trends — Perplexity AI identifies the hottest topic from the last 24-48 hours Queue topics — Topics are saved to Google Sheets for tracking and management Generate content — Perplexity creates complete blog posts with titles, sections, keywords, and meta descriptions Create draft — Content is published as a WordPress draft for your review Generate image — HuggingFace FLUX creates a featured image based on the content Attach media — The image is uploaded to WordPress and assigned to the post Setup steps Add credentials for Perplexity AI, Google Sheets, WordPress, and HuggingFace Create a Google Sheet with columns: Topic, is_generated, title, content, keywords, meta_description Replace YOUR_GOOGLE_SHEET_ID in the Google Sheets nodes with your sheet ID Replace your-site.com with your WordPress site URL Replace YOUR_TOKEN_HERE with your HuggingFace API token Update the authorId in the WordPress node to match your author Tools used Perplexity AI** — Trend discovery and content generation Google Sheets** — Topic queue and workflow tracking WordPress REST API** — Post creation and media uploads HuggingFace FLUX** — AI image generation Ideal for developers, content marketers, and agencies who want automated content pipelines with editorial control.

Who is this for? This workflow is designed for Content Marketing Teams, Agencies, and Professional Editors who prefer writing in Google Docs but need a seamless way to publish to WordPress. Unlike generic "AI Writers" that generate content from scratch (which often fails AI detection), this workflow focuses on "Document Ops"—automating the tedious task of moving, cleaning, and optimizing existing human-written content. Why use this workflow? (The SEO Advantage) Most automation templates leave your SEO score at 0/100 because they fail to map RankMath metadata. This workflow hits the ground running with an immediate 65-70/100 RankMath Score. By using a Gemini AI Agent to analyze your content and mapping it to hidden RankMath API fields, it automatically passes these critical checks: ✅ Focus Keyword in SEO Title: AI automatically inserts the target keyword at the beginning. ✅ Focus Keyword in Meta Description: AI crafts a compelling description containing the keyword. ✅ Focus Keyword in URL: AI generates a clean, short, keyword-rich slug. ✅ Focus Keyword at the Start: The workflow intelligently injects a "hook" sentence containing the keyword at the very top of your post. ✅ Content Length: Preserves your original long-form content. How it works Monitors Google Drive: Watches for new HTML/Doc files in a specific "Drafts" folder. Cleans Content: Sanitizes raw HTML from Google Docs (removing messy styles and tags). Smart Duplicate Check: Checks if the post already exists on WordPress (via slug) to decide whether to Create a new draft or Update an existing one. AI Analysis (Gemini): Extracts the best Focus Keyword, SEO Title, and Meta Description from your content. RankMath Integration: Pushes these SEO values directly into RankMath's custom meta keys. Archiving: Moves processed files to a "Published" folder to keep your Drive organized. Critical Prerequisites (Must Read) To allow n8n to update RankMath SEO data and prevent 401 Unauthorized errors, you MUST add a helper snippet to your WordPress site. Access your WordPress files via FTP/File Manager. Navigate to wp-content/mu-plugins/ (Create the folder mu-plugins if it doesn't exist). Create a file named n8n-rankmath-helper.php and paste the following code: <?php /* Plugin Name: n8n RankMath & Auth Helper Description: Fixes Basic Auth Header for n8n and exposes RankMath meta keys to REST API. */ // 1. Fix Authorization Header (Solves 401 Errors on Apache/LiteSpeed) add_filter('wp_is_application_passwords_available', '__return_true'); if ( !function_exists('aiops_enable_basic_auth') ) { function aiops_enable_basic_auth() { if ( isset( $_SERVER['HTTP_AUTHORIZATION'] ) ) { $auth = $_SERVER['HTTP_AUTHORIZATION']; if ( strpos( $auth, 'Basic ' ) === 0 ) { list( $username, $password ) = explode( ':', base64_decode( substr( $auth, 6 ) ) ); $_SERVER['PHP_AUTH_USER'] = $username; $_SERVER['PHP_AUTH_PW'] = $password; } } } add_action('init', 'aiops_enable_basic_auth'); } // 2. Expose RankMath Meta Keys to REST API add_action( 'rest_api_init', function () { $meta_keys = [ 'rank_math_title', 'rank_math_description', 'rank_math_focus_keyword', 'rank_math_robots', 'rank_math_canonical_url' ]; foreach ( $meta_keys as $meta_key ) { register_meta( 'post', $meta_key, [ 'show_in_rest' => true, 'single' => true, 'type' => 'string', 'auth_callback' => function() { return current_user_can( 'edit_posts' ); } ] ); } }); ?>; How to set up 1. Configure Credentials: Google Drive OAuth2** (Drive scopes). Google Gemini (PaLM)** API Key. WordPress: Connect using **Application Passwords (Users > Profile > Application Passwords). 2. Global Configuration (First Node): Open the node named CONFIG - Edit Settings Here. wp_base_url**: Enter your site URL (e.g., https://your-site.com - no trailing slash). drive_published_folder_id**: Enter the ID of the Google Drive folder where you want to move published files. 3. Trigger Setup: Open the Google Drive Trigger node. Select your specific "Drafts" folder in the Folder to Watch field. Future Roadmap We are actively improving this template. Upcoming V2 will feature: AI Featured Image Generation: Auto-create branded thumbnails. Content Illustrations: Auto-insert relevant images into the body content. Need Help or Want to Customize This? Contact me for consulting and support: Email: cuongnguyen@aiops.vn

Quick overview A Slack bot that reviews any n8n workflow on demand. Mention it or DM it a workflow URL and it replies in-thread with a graded review, plain-English overview, and findings tiered as Must Fix, Should Fix, and Nice to Have. How it works Triggers when the bot is @mentioned in Slack or receives a direct message. Adds an :eyes: reaction, extracts a /workflow/ from the message text, and validates that a workflow ID is present. Uses the n8n API to fetch the top-level workflow JSON and pulls the workflow review checklist and skills file index from the public n8n-io/skills GitHub repository. Detects any Execute Workflow references in the workflow, fetches those direct subworkflow JSONs via the n8n API, and aggregates them into a single payload. Sends the workflow JSON, subworkflows, checklist, and available skill file paths to an OpenRouter-hosted model that can fetch additional skill markdown files as needed. Removes the :eyes: reaction and posts the graded review back to the originating Slack thread as a Block Kit message, or posts a threaded error message if the URL, API fetch, GitHub fetch, or agent run fails. Setup Connect a Slack API credential with permission to receive app mentions/DMs, add reactions, and post threaded replies, and configure the trigger to watch the intended workspace. Add an n8n API credential that can read workflows, and ensure the n8n host in your workflow URLs matches the instance the credential points to. Add an OpenRouter API credential and choose the model you want to use for the reviewer agent and (optionally) the structured-output fixer. Install the Slack app, enable event subscriptions for app_mention and direct messages, and invite the app to any channels where you want to request reviews. If your environment restricts outbound HTTP, allow access to raw.githubusercontent.com and api.github.com so the workflow can load the n8n-io/skills checklist and file index.

How it works You have several resumes you need to review manually? well this workflows allows you to upload upto 20 bunches pdf at once. AI does the heavy lifting, saving time, reducing repetive tasks and achieving high accuracy. The job description and qualificattion goes under the agent System message. Setup steps. It will take you roughly 20minutes to finish setting up this workflow. n8n Form Allow multiple file submission JavaScript Code allow mapping of each file individually System message adjust the system message to fit the job description and qualification. Google Sheet make a copy

Overview This n8n workflow automatically generates professionally formatted Word documents (DOCX) with consistent company branding using AI. It leverages Json2Doc and the Json2Doc MCP server to transform simple text prompts into complete, multi-page documents. Get your free API key at: app.json2doc.com Use Cases Generate first drafts of: Contracts and legal agreements Internal forms and templates Company announcements and notices Internal documentation and policies Business reports and presentations Guidelines and procedures and much more ... Key Features Consistent Company Branding Custom fonts, colors, and typography Company logo in headers Page numbers in footers Controlled spacing and layout Professional heading styles Multi-Page Document Support Page-based sections (new page) Flow sections (continuous across pages) Automatic pagination** Consistent headers and footers throughout Rich Content Types Multiple heading levels Formatted text and paragraphs Tables with custom styling Ordered and unordered lists Images and logos Auto Generated QR Codes AI-Driven Generation Uses Claude Sonnet 4.5 to: Generate appropriate document structure Apply correct formatting Create professional, coherent content How It Works 1. Input Form Users provide: Prompt** - Description of the desired document (e.g., "Generate an employment contract template") Logo URL** - Web-accessible URL to company logo 2. Company Styling Pre-configured branding is applied (See workflow for Description on how to Update): Font, font Styles (for H1,H2, ...) Header: Company name + logo Footer: Page numbers ("Page X of Y") Spacing rules for all content types Table Styles 3. AI Document Generation The AI agent: Retrieves the Json2Doc section schema Generates JSON configuration for the document Validates the configuration Creates a document generation job Returns the Job ID 4. Processing & Download Waits for document completion (3 seconds initially) Polls job status via API Retries if not complete Downloads the final DOCX file when ready Setup Requirements Authentication You need a Json2Doc API key from app.json2doc.com (Permanently free version available). Processing Times Configuration Generation (Model-dependent) The AI model generates the JSON configuration: Simple documents (1-2 pages): 10-30 seconds Medium documents (3-5 pages): 30-60 seconds Complex documents (10-20 pages): 60-120 seconds Time varies based on the selected AI model and document complexity. Json2Doc Processing Once the configuration is created, Json2Doc generates the DOCX file in 2-6 seconds regardless of document size. Extensions This workflow can be integrated with: Cloud storage (Google Drive, Dropbox) Email services for automated delivery Approval workflows Document management systems Important Limitation This workflow is only suitable for documents up to 20 pages, as larger documents will exceed the AI model's context window. For longer documents, use the Builder Mode instead: DocumentBuilder Docs

Nano Banana Pro AI Product Advertisement Generator via Telegram Who's It For E-commerce businesses needing quick product ads Social media marketers without design resources Small business owners creating promotional content Product photographers seeking automated enhancements What It Does Transforms basic product photos into professional advertisements using AI. Users send a product image with caption text via Telegram, and receive commercial-grade ads with studio lighting, premium backgrounds, and typography overlays. How It Works User sends product photo with caption to Telegram bot Image converted to base64 for AI processing Google Gemini analyzes image and extracts marketing text from caption AI generates detailed design enhancement instructions (400+ words) Nano Banana Pro creates 1-2 professional advertisement versions Enhanced images automatically sent back to user Requirements Telegram Bot API credentials (via BotFather) Google Gemini API key with nano-banana-pro-preview access n8n instance (self-hosted or cloud) Setup Instructions Create Telegram Bot Message BotFather on Telegram Send /newbot command and follow prompts Copy the API token Configure n8n Credentials Add Telegram Bot API token Add Google Gemini API key Import workflow JSON Update credential references Activate workflow Test the Workflow Send image with caption format: "Product Name | Tagline | Call to Action" Example: "Premium Sneakers | Mountain Edition | Shop Now" Key Features Original product remains 100% unchanged Text extracted only from user's caption (no AI-generated taglines) Professional design enhancements applied Studio-quality lighting and color grading Luxury background selection based on product category Typography overlays using caption text 30-60 second processing time Returns 1-2 advertisement variants Node Breakdown Telegram Trigger - Listens for messages with images Download Image File - Retrieves image from Telegram servers Image to Base64 - Converts image for AI processing AI Design Analysis - Gemini extracts caption text and generates design blueprint covering composition, lighting, backgrounds, color grading, effects, and typography Combine Image & Analysis - Merges image data with design instructions Prepare API Payload - Structures data for Nano Banana Pro API Generate Enhanced Image - Creates professional ad using AI Convert Base64 to Image - Converts first generated ad to file Convert Base64 to Image1 - Converts second ad variant (if available) Send Image - Returns enhanced ads to user via Telegram Customization Options Adjust Design Style Modify AI Design Analysis prompt to change lighting intensity, background preferences, color grading, or typography styles Change Caption Parsing Update extraction rules for different text elements or multi-language support Add Output Formats Request different aspect ratios (16:9 social media, 4:5 Instagram, 9:16 Stories) Error Handling Add fallback nodes to handle image generation failures Usage Analytics Insert database node to track requests and caption data Caption Examples "NIKE AIR MAX | Run Beyond Limits | Shop Now" "Himalayan Coffee Beans - Fresh from the Mountains - Order Today" "Luxury Smartwatch | Track Your Success | Available Now" Important Notes Product never altered, only enhanced visually Empty captions result in ads without text overlays Best with clear photos on simple backgrounds Monitor API quotas to avoid rate limits Processing time varies by API response speed

About This Template This workflow turns complex data or topics sent via LINE into beautiful, easy-to-understand Infographics. It combines Gemini (to analyze data and structure the visual layout) and Nano Banana Pro (accessed via Kie.ai API) to generate high-quality, data-rich graphics (Charts, timelines, processes). How It Works Input: User sends a topic or data points via LINE (e.g., "Japan's Energy Mix: 20% Solar, 10% Wind..."). Data Visualization Logic: Gemini acts as an Information Designer, deciding the best chart type (Pie, Bar, Flow) and layout for the data. Render: Nano Banana generates a professional 3:4 Vertical Infographic. Smart Polling: The workflow uses a loop to check the API status every 5 seconds, ensuring it waits exactly as long as needed. Delivery: Uploads to S3 and sends the visual report back to LINE. Who It’s For Social Media Managers needing quick visual content. Educators and presenters summarizing data. Consultants creating quick visual reports on the go. Requirements n8n** (Cloud or Self-hosted). Kie.ai API Key** (Nano Banana Pro). Google Gemini API Key**. AWS S3 Bucket** (Public access). LINE Official Account**. Setup Steps Credentials: Configure Header Auth for Kie.ai and your other service credentials. Webhook: Add the production URL to LINE Developers console.

Turn Internet Into Database — n8n Workflow Description This n8n template automates the entire process of turning any website into a structured database — no manual scraping required. It uses MrScraper's AI-powered agents to crawl a domain, extract listing pages, scrape detail pages, and export everything into Google Sheets with an email notification via Gmail. Whether you're building a real estate database, product catalog, job board aggregator, or competitor price tracker, this workflow handles the full pipeline end-to-end. How It Works Phase 1 – Discover URLs (Crawling):** The Map Agent crawls your target domain and discovers all relevant URLs based on your include/exclude patterns. It returns a clean list of listing/search page URLs. Phase 2 – Scrape Listing Pages:** The workflow loops through each discovered listing URL and runs the Listing Agent to extract all detail page URLs. Duplicates are automatically removed. Phase 3 – Scrape Detail Pages:** Each detail URL is looped through the General Agent, which extracts structured fields (title, price, location, description, etc.). Nested JSON is automatically flattened into clean, spreadsheet-ready rows. Phase 4 – Export & Notify:** Scraped records are appended or upserted into Google Sheets using a unique key. Once complete, a Gmail notification is sent with a run summary. How to Set Up Create 3 scrapers in your MrScraper account: Map Agent Scraper (for crawling/URL discovery) Listing Agent Scraper (for extracting detail URLs from listing pages) General Agent Scraper (for extracting structured data from detail pages) Copy the scraperId for each — you'll need these in n8n. Enable AI Scraper API access in your MrScraper account settings. Add your credentials in n8n: MrScraper API token Google Sheets OAuth2 Gmail OAuth2 Configure the Map Agent node: Set your target domain URL (e.g. https://example.com) Set includePatterns to match listing pages (e.g. /category/) Adjust maxDepth, maxPages, and limit as needed Configure the Listing Agent node: Enter the Listing scraperId Set maxPages based on how many pages per listing URL to scrape Configure the General Agent node: Enter the General scraperId Connect Google Sheets: Enter your spreadsheet and sheet tab URL Choose append or upsert strategy (recommended: upsert by url) Configure Gmail: Set recipient email, subject line, and message body Requirements MrScraper** account with API access enabled Google Sheets** (OAuth2 connected) Gmail** (OAuth2 connected) Good to Know The workflow uses batch looping, so large sites with hundreds of pages are handled gracefully without overloading. The Flatten Object node automatically normalizes nested JSON — no manual field mapping needed for most sites. Set a unique match key (e.g. url) in the Google Sheets upsert step to avoid duplicate rows on re-runs. Scraping speed and cost will depend on MrScraper's pricing plan and the number of pages processed. Customising This Workflow Different site types:** Works for real estate listings, job boards, e-commerce catalogs, directory sites, and more — just adjust your URL patterns. Add filtering:** Insert a Code or Filter node after Phase 3 to drop incomplete records before saving. Schedule it:** Replace the manual trigger with a Schedule Trigger to run daily or weekly and keep your database fresh automatically. Multi-site:** Duplicate Phase 1–3 branches to scrape multiple domains in a single workflow run.

AI Agent that uses MCP Server to execute actions requested via Evolution API. This workflow receives messages and media from WhatsApp via the Evolution API, converts the content into structured inputs, and forwards them to an AI Agent capable of triggering MCP tools to execute external actions. 🔧 How it works A Webhook receives messages sent to WhatsApp via the Evolution API. The "Message Type" node detects and forwards the received media. It handles the types Text, Image, Audio, and Document. If it is another media type, the fallback forwards a "media not supported" message to the user. The message goes to the system where it retrieves the Base64 of the media. The media is converted into Binary File(s) and a Gemini node will generate a text input for the agent. The AI Agent receives the structured input and calls the appropriate MCP Tool. In this example, only one MCP Server was configured. The AI ​​Agent generates the output and sends it to the user. 🗒️ Requirements Evolution API Account, with the instance configured. Gemini API. Google Calendar API. MCP Server (Internal or external, whichever you prefer) configured and with a URL to link to the MCP Tool. ✔️ How to set up Configure the Evolution API webhook** Copy the webhook URL generated in the first node. In the Evolution API panel, go to the instance > webhook > paste the URL into the corresponding field. Configure Google Calendar credentials** In n8n, go to Credentials → Create New and select Google Calendar OAuth2. Select this credential in all Google Calendar MCP nodes (Get, Create, Update, Delete). Enable MCP Server nodes** Copy the MCP Server URL and paste it into the “Endpoint field of the MCP Tool. Configure Evolution API nodes** In all Evolution API nodes, you need to fill in the “instance field with the name of your Evolution API instance. 🦾 how to adapt it? Customize or extend the MCP Tools** You can add new MCP tools (e.g., Google Sheets, Notion, ClickUp). Only the agent prompt needs to be updated; the workflow structure remains the same. I opted to use simple memory, but if you want the agent to remember the entire conversation, I recommend changing the memory type; as it is, it will only remember the last 8 messages. If you're going to use a tool like Chatwoot or TypeBot, simply change the webhook URL and pay attention to the objects that the switch (Message Type) uses.

Short description Automates collection, technical extraction, and automatic generation of Nuclei templates from public CVE PoCs. Converts verified PoCs into reproducible detection templates ready for testing and distribution. Purpose Provide a reliable pipeline that turns public proof-of-concept data into usable detection artifacts. Reduce manual work involved in finding PoCs, extracting exploit details, validating sources, and building Nuclei templates. How it works (technical summary) Runs a scheduled SSH job that executes vulnx with filters for recent, high-severity PoCs. Parses the raw vulnx output and splits it into individual CVE entries. Extracts structured fields: CVE ID, severity, title, summary, risk, remediation, affected products, POCs, and references. Extracts URLs from PoC sections using regex. Validates each URL with HTTP requests. Invalid or unreachable links are logged and skipped. Uses an AI agent (OpenAI via LangChain) to extract technical artifacts: exploit steps, payloads, endpoints, raw HTTP requests/responses, parameters, and reproduction notes. The prompt forces technical-only output. Sends the extracted technical content to ProjectDiscovery Cloud API to generate Nuclei templates. Validates AI and API responses. Accepted templates are saved to a configured Google Drive folder. Produces JSON records and logs for each processed CVE and URL. Output Nuclei templates in ProjectDiscovery format (YAML) stored in Google Drive. Structured JSON per CVE with metadata and extracted technical details. Validation logs for URL checks, AI extraction, and template generation. Intended audience Bug bounty hunters. Security researchers and threat intel teams. Automation engineers who need reproducible detection templates. Setup & requirements n8n instance with workflow imported. SSH access to a host with vulnx installed. OpenAI API key for technical extraction. ProjectDiscovery API key for template generation. Google Drive OAuth2 credentials for storing templates. Configure schedule trigger and target Google Drive folder ID. Security and usage notes Performs static extraction and validation only. No active exploitation. Processes only PoCs that meet configured filters (e.g., CVSS > 6). Use responsibly. Do not target systems you do not own or have explicit permission to test.

Transform any website into a custom logo in seconds with AI-powered analysis—no design skills required! 📋 What This Template Does This workflow receives a website URL via webhook, captures a screenshot and fetches the page content, then leverages OpenAI to craft an optimized prompt based on the site's visuals and text. Finally, Google Gemini generates a professional logo image, which is returned as a binary response for immediate use. Automates screenshot capture and content scraping for comprehensive site analysis Intelligently generates tailored logo prompts using multimodal AI Produces high-quality, context-aware logos with Gemini's image generation Delivers the logo directly via webhook response 🔧 Prerequisites n8n self-hosted or cloud instance with webhook support ScreenshotOne account for website screenshots OpenAI account with API access Google AI Studio account for Gemini API 🔑 Required Credentials ScreenshotOne API Setup Sign up at screenshotone.com and navigate to Dashboard → API Keys Generate a new access key with screenshot permissions In the workflow, replace "[Your ScreenshotOne Access Key]" in the "Capture Website Screenshot" node with your key (no n8n credential needed—it's an HTTP query param) OpenAI API Setup Log in to platform.openai.com → API Keys Create a new secret key with chat completions access Add to n8n as "OpenAI API" credential type and assign to "OpenAI Prompt Generator" node Google Gemini API Setup Go to aistudio.google.com/app/apikey Create a new API key (free tier available) Add to n8n as "Google PaLM API" credential type and assign to "Generate Logo Image" node ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign the required credentials to the OpenAI and Google Gemini nodes Replace the placeholder API key in the "Capture Website Screenshot" node's query parameters Activate the workflow to enable the webhook Test by sending a POST request to the webhook URL with JSON body: {"websiteUrl": "https://example.com"} 🎯 Use Cases Marketing teams prototyping brand assets**: Quickly generate logo variations for client websites during pitches, saving hours on manual design Web developers building portfolios**: Auto-create matching logos for new sites to enhance visual consistency in demos Freelance designers iterating ideas**: Analyze competitor sites to inspire custom logos without starting from scratch Educational projects on AI design**: Teach students how multimodal AI combines text and images for creative outputs ⚠️ Troubleshooting Screenshot fails (timeout/error)**: Increase "timeout" param to 120s or check URL accessibility; verify API key and quotas at screenshotone.com Prompt generation empty**: Ensure OpenAI credential has sufficient quota; test node isolation with a simple query Logo image blank or low-quality**: Refine the prompt in "Generate Logo Prompt" for more specifics (e.g., add style keywords); check Gemini API limits Webhook not triggering**: Confirm POST method and JSON body format; view execution logs for payload details