Workflow Templates

AI Lead Capture System - Complete Setup Guide Prerequisites n8n instance (cloud or self-hosted) Google AI Studio account (free tier available) Google account for Sheets integration Website with chat widget capability Phase 1: Core Infrastructure Setup Step 1: Set Up Google AI Studio Go to Google AI Studio Create account or sign in with Google Navigate to "Get API Key" Create new API key for your project Copy and securely store the API key Free tier limits: 15 requests/minute, 1 million tokens/month Step 2: Configure Google Sheets Create new Google Sheet for lead storage Add column headers (exact names): Full Name Company Name Email Address Phone Number Project Intent/Needs Project Timeline Budget Range Preferred Communication Channel How they heard about DAEX AI Copy the Google Sheet ID from URL (between /d/ and /edit) Ensure sheet is accessible to your Google account Step 3: Import n8n Workflow Open your n8n instance Create new workflow Click "..." menu → Import from JSON Paste the provided workflow JSON Workflow will appear with all nodes connected Phase 2: Credential Configuration Step 4: Set Up Google Gemini API In n8n, go to Credentials → Add Credential Search for "Google PaLM API" Enter your API key from Step 1 Test connection Link to the "Google Gemini Chat Model" node Step 5: Configure Google Sheets Access Go to Credentials → Add Credential Select "Google Sheets OAuth2 API" Follow OAuth flow to authorize your Google account Test connection with your sheet Link to the "Google Sheets" node Phase 3: Workflow Customization Step 6: Update Company Information Open the AI Agent node In the system message, replace all mentions of: Company name and description Service offerings and specializations FAQ knowledge base Typical project timelines and pricing ranges Adjust conversation tone to match your brand voice Step 7: Configure Lead Qualification Fields In the AI Agent system message, modify the required information list: Add/remove qualification questions Adjust budget ranges for your services Customize timeline options Update communication channel preferences In Google Sheets node, update column mappings if you changed fields Step 8: Set Up Sheet Integration Open Google Sheets node Click on Document ID dropdown Select your lead capture sheet Verify all column mappings match your sheet headers Test with sample data Phase 4: Website Integration Step 9: Get Webhook URL Open Webhook node in n8n Copy the webhook URL (starts with your n8n domain) Note: URL format is https://your-n8n-domain.com/webhook/[unique-id] Step 10: Connect Your Chat Widget Choose your integration method: Option A: Direct JavaScript Integration javascript// Add to your website function sendMessage(message, sessionId) { fetch('YOUR_WEBHOOK_URL', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ message: message, sessionId: sessionId || 'visitor-' + Date.now() }) }) .then(response => response.json()) .then(data => { // Display AI response in your chat widget displayMessage(data.message); }); } Option B: Chat Platform Webhook Open your chat platform settings (Intercom, Crisp, etc.) Find webhook/integration section Add webhook URL pointing to your n8n endpoint Configure to send message and session data Option C: Zapier/Make.com Integration Create new Zap/Scenario Trigger: New chat message from your platform Action: HTTP POST to your n8n webhook Map message content and session ID Phase 5: Testing & Optimization Step 11: Test Complete Flow Send test message through your chat widget Verify AI responds appropriately Check conversation context is maintained Confirm lead data appears in Google Sheets Test with various conversation scenarios Step 12: Monitor Performance Check n8n execution logs for errors Monitor Google Sheets for data quality Review conversation logs for improvement opportunities Track response times and conversion rates Step 13: Fine-Tune Conversations Analyze real conversation logs Update system prompts based on common questions Add new FAQ knowledge to the AI agent Adjust qualification questions based on lead quality Optimize for your specific customer patterns Phase 6: Advanced Features (Optional) Step 14: Add Lead Scoring Create new column in Google Sheets for "Lead Score" Update AI agent to calculate scores based on: Budget range (higher budget = higher score) Timeline urgency (sooner = higher score) Project complexity (complex = higher score) Add conditional formatting in Google Sheets to highlight high-value leads Step 15: Set Up Notifications Add email notification node after Google Sheets Configure to send alerts for high-priority leads Include lead details and conversation summary Set up different notification rules for different lead scores Step 16: Analytics Dashboard Connect Google Sheets to Google Data Studio or similar Create dashboard showing: Daily lead volume Conversion rates by source Average qualification time Lead quality scores Revenue pipeline from captured leads Troubleshooting Common Issues AI Not Responding Check Google Gemini API key validity Verify API quota not exceeded Review n8n execution logs for errors Data Not Saving to Sheets Confirm Google Sheets permissions Check column name matching Verify sheet ID is correct Chat Widget Not Connecting Test webhook URL directly with curl/Postman Verify JSON format matches expected structure Check CORS settings if browser-based integration Conversation Context Lost Ensure sessionId is unique per visitor Check memory node configuration Verify sessionId is passed consistently

Automate Customer Feedback Analysis with Google Sheets, WhatsApp, and Email Introduction: Drowning in Data, Starving for Insight? Imagine this: Your team launches a new feature. Feedback starts pouring in emails, support tickets, social media mentions, and survey responses. You know gold is buried in there, but manually reading, tagging, and summarising hundreds, maybe thousands, of comments? It takes days, maybe weeks. By the time you have a clear picture, the moment might have passed. Sounds exhausting, right? What if you could have an AI assistant tirelessly working 24/7, instantly analysing every piece of feedback the moment it arrives? This isn't science fiction anymore. AI-powered automation can transform this slow, manual chore into a real-time insight engine, giving you the pulse of your customer base almost instantly. Let's explore how. What's the Goal? Understanding the Workflow Objective The core challenge is transforming raw, unstructured customer feedback into actionable intelligence quickly and efficiently. The Problem: Manual Overload: Sifting through vast amounts of feedback manually is incredibly time-consuming and prone to human error or bias. Delayed Insights: The lag between receiving feedback and understanding it means missed opportunities and slow responses to critical issues. Inconsistent Analysis: Different team members might interpret or categorize feedback differently, leading to unreliable trend spotting. The AI Solution: Automated Data Collection: Connects directly to feedback sources (surveys, social media, review sites, helpdesks). AI-Powered Analysis: Uses Large Language Models (LLMs) like GPT-4 or Claude to analyze sentiment, extract key topics, and summarize comments. Intelligent Categorization: Automatically tags feedback based on predefined or dynamically identified themes (e.g., "bug report," "feature request," "pricing issue"). Real-time Reporting: Pushes structured insights into dashboards, databases, or triggers notifications for immediate awareness. Outcome: You move from reactive problem-solving based on stale data to proactive, strategic decisions driven by a near real-time understanding of customer sentiment and needs. Why Does It Matter? Achieving 100X Productivity and Efficiency Look, automating feedback isn't just about saving time; it's about scaling your ability to listen and respond smarter, not harder. When you leverage AI, the gains aren't incremental - they're exponential. Here’s why this is a game changer: Blazing Speed: Analyse feedback 100x Faster (or more!) than manual methods. Insights appear in minutes or hours, not days or weeks. Unhuman Scalability: Process virtually unlimited volumes of feedback without needing to scale your human team proportionally. AI doesn't get tired or bored. Consistent Accuracy: AI applies analysis rules consistently, reducing human bias and ensuring reliable categorisation and sentiment scoring over time. Proactive Trend Spotting: Identify emerging issues or popular requests much earlier by analysing aggregated data automatically. Spot patterns humans might miss. Free Up Your Team: Let your talented team focus on acting on insights – improving products, fixing issues, engaging customers – instead of drowning in data entry. How It Works: AI Automation Step by Step Getting this set up is more straightforward than you might think, especially with tools like n8n acting as the central hub. Automated Feedback Triggering CRM/Website Event Node Trigger feedback requests after: Purchases (eCommerce) Support ticket resolution Feature usage (SaaS) Time-Based Node Schedule recurring NPS surveys Customer health check-ups Chat App Node (WhatsApp/Telegram/Messenger) Send conversational feedback prompts: "How was your recent experience with [specific interaction]?" Multi-Channel Feedback Collection Email Node (SendGrid/Mailchimp) Send personalized feedback requests Embed 1-5 rating widgets SMS Node (Twilio) Short mobile surveys: "Reply 1-5: How satisfied with your purchase?" Webhook Node Capture in-app feedback Process chatbot responses Social Media Node Monitor Twitter/X, Instagram mentions Analyze comments for unsolicited feedback AI-Powered Real-Time Analysis OpenAI/ChatGPT Node (Sentiment Analysis) Prompt: "Analyze sentiment (positive/neutral/negative) and key themes from: [customer feedback]" Output fields: Sentiment score (1-5) Urgency flag (high/medium/low) Key topics (billing, support, product, etc.) Translation Node (Optional) Convert multilingual feedback into a consistent language Instant AI Response System Conditional Node (Routing Logic) Positive feedback → Send thank-you + referral ask Neutral feedback → Follow-up question for details Negative feedback → Escalate to the human team AI Response Generator Node Prompt: "Create a personalized response to [feedback type] about [topic] with sentiment [score]" Adjust tone (professional/friendly/empathetic) Escalation Node Route critical issues to the support team with full context Automated Insights & Alerts Dashboard Node Real-time sentiment tracking Emerging issue detection Alert Node (Slack/Teams/Email) Notify teams of negative trends: "3+ complaints about checkout flow in the past hour!" Report Node Auto-generate weekly/monthly summaries: "Top 5 customer pain points this week" Product Board Integration Auto-create feature requests Prioritize based on feedback volume Tools of the Trade: AI & Automation Tech Stack You don't need a massive, complex tech stack. Focus on a few core, powerful tools: n8n: The workflow automation platform. This is the 'glue' that connects everything and orchestrates the process without needing deep coding knowledge. Honestly, it's incredibly versatile. OpenAI (GPT-4/GPT-4o): State-of-the-art LLM for high-quality text analysis, summarization, and classification. Great for complex understanding. Anthropic (Claude 3 Sonnet/Opus): Another top-tier LLM, known for strong performance in analysis and handling large contexts. Often, a great alternative or complement to GPT models. Feedback Sources APIs: Connectors for where your feedback lives (e.g., Typeform, SurveyMonkey, Twitter API, Zendesk API, Google Play/App Store review APIs). Data Storage/Destination: Where the processed insights go (e.g., Google Sheets, Airtable, Notion, PostgreSQL database, BigQuery). (Optional) Visualization Tool: Tools like Metabase, Grafana, Looker Studio, or Power BI to create dashboards from your structured feedback data. What's the Cost? Estimated Budget Let's talk investment. You're mainly looking at: Setup Costs: Primarily your time (or a consultant's) to design and build the initial workflow in n8n. Depending on complexity, this could range from a few hours to a few days. No major software licenses are usually needed upfront if using self-hosted n8n or starting with free/low-tier cloud plans. AI API Calls: You pay per usage to OpenAI/Anthropic. Costs depend heavily on volume but can start from $20-$50/month for moderate usage and scale up. Newer models are getting more cost-effective. n8n Hosting: Free if self-hosted (requires a server), or tiered cloud pricing starting around $20/month. Feedback Source APIs: Some platforms might have API access costs or rate limits on free tiers. Total Estimated Monthly Cost: For many businesses, ongoing costs can range from $50 - $500+ per month, highly dependent on feedback volume and AI model choice. The Return on Investment (ROI) is typically rapid. Consider the hours saved from manual analysis, the value of faster issue resolution, preventing churn, and the benefits of making product decisions based on real-time data. It often pays for itself very quickly. Who Benefits? Target Users and Industries This automated feedback loop isn't niche; it's valuable across many sectors and roles: Top Industries: SaaS (Software as a Service): Understanding user friction, feature requests, bug reports. E-commerce & Retail: Analyzing product reviews, post-purchase surveys, and support chats. Hospitality & Travel: Processing guest reviews, survey feedback. Mobile Apps: Monitoring app store reviews, in-app feedback. Financial Services: Gauging customer satisfaction with services, identifying pain points. Key Roles: Product Managers: Prioritizing features, understanding user needs, tracking launch reception. Customer Experience (CX) / Success Managers: Monitoring customer health, identifying churn risks, and improving support processes. Marketing Teams: Understanding brand perception, campaign feedback, and voice of the customer. Support Leads: Identifying recurring issues, measuring support quality, spotting training needs. This approach works for businesses of all sizes, from startups wanting to stay lean and agile to large enterprises needing to manage massive feedback volumes. How to use workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built or shared workflows to save time. Below is a step-by-step guide to import a workflow in n8n, based on the official documentation and community resources. Steps to Import a Workflow in n8n 1. Obtain the Workflow JSON Source the Workflow:** Workflows are typically shared as JSON files or code snippets. You might receive them from: The n8n community (e.g., n8n.io workflows page). A colleague or tutorial (e.g., a .json file or copied JSON code). Exported from another n8n instance (see export instructions below if needed). Format:** Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or as text copied to your clipboard. 2. Access the n8n Workflow Editor Log in to n8n:** Open your n8n instance (via n8n Cloud or your - self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Open a New Workflow:** Click Add Workflow to create a blank workflow, or open an existing workflow if you want to merge the imported workflow. 3. Import the Workflow Option 1: Import via JSON Code (Clipboard): In the n8n editor, click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code of the workflow into the provided text box. Click Import to load the workflow into the editor. Option 2: Import via JSON File: In the n8n editor, click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import the workflow. Note: If the workflow includes nodes for apps requiring credentials (e.g., Google Sheets), you’ll need to configure those credentials separately after importing.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automates summarizing YouTube videos by accepting a YouTube URL via a form, fetching the video transcript using Apify, and then generating a concise summary with OpenAI GPT. Setup Instructions Prerequisites: Apify account with access to the YouTube Transcript actor. OpenAI API key (for GPT-4o-mini model). n8n instance with the Apify and OpenAI credentials configured. Configuration Steps Apify Setup: Configure Apify API credentials in the Apify node. Ensure the YouTube Transcript actor ID (1s7eXiaukVuOr4Ueg) is correct. OpenAI Setup: Add your OpenAI API key in the OpenAI Chat Model node. Confirm model selection is set to gpt-4o-mini. Customization Modify form field to accept additional inputs if needed. Adjust Apify actor input JSON in the Payload node for extra metadata extraction. Customize the summarization options to tweak summary length or style. Change OpenAI prompt or model parameters in the OpenAI Chat Model node for different output quality or tone. Steps 1. On Form Submission Node:** Form Trigger Purpose:** Collect the YouTube video URL from the user via a web form. 2. Prepare Payload Node:** Set Purpose:** Format the YouTube URL and options into the JSON payload for Apify input. 3. Fetch Transcript Node:** Apify Purpose:** Run the YouTube Transcript actor to retrieve video captions and metadata. 4. Extract Captions Purpose:** Isolate the captions field from the Apify response for processing. 5. Summarize Transcript Purpose:** Generate a concise summary of the video captions.

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

This workflow is a experiment to build HTML pages from a user input using the new Structured Output from OpenAI. How it works: Users add what they want to build as a query parameter The OpenAI node generate an interface following a structured output defined in the body The JSON output is then converted to HTML along with a title The HTML is encapsulated in an HTML node (where the Tailwind css script is added) The HTML is rendered to the user via the Webhook response. Set up steps Create an OpenAI API Key Create the OpenAI credentials Use the credentials for both nodes HTTP Request (as Predefined Credential type) and OpenAI Activate your workflow Once active, go to the production URL and add what you'd like to build as the parameter "query" Example: https://production_url.com?query=a%20signup%20form Example of generated page

How it works This workflow changes the file name, and therefore the extension and MIME type, of any binary file passed to it. This is perfect for converting file formats on the fly, like turning a Telegram voice message (.oga) into an MP3 for an AI transcription service. Set New File Name: The SET OUTPUT FILE NAME node is where you define the desired output file name and extension (e.g., audio.mp3). It also dynamically captures the property name of the incoming binary (e.g., data). Extract Binary Data: The workflow temporarily converts the binary file into a Base64 text string to make it accessible in the next step. Rebuild Binary with New Name: A Code node takes the Base64 data and reconstructs it as a binary file, but this time, it assigns the new file name you specified. n8n automatically sets the MIME type based on the new file extension. Set up steps Setup time: < 1 minute This workflow is designed to be used as a sub-workflow. In your main workflow, add an Execute Sub-Workflow node where you need to change a file's type. In the Workflow parameter, select this "Change Binary MimeType/Extension" workflow. Open this workflow and go to the SET OUTPUT FILE NAME node. Modify the output_file_name value to your desired file name (e.g., voice_message.mp3 or document.pdf). Save this workflow. Now, any binary file you send to it from your main workflow will be returned with the new fileName and mimeType.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. What this workflow does Monitors Google Drive for new driver license image uploads Downloads and processes images using VLM Run AI OCR Extracts key information including license number, name, DOB, and dates Saves structured data to Google Sheets for instant access Setup Prerequisites: Google Drive account, VLM Run API credentials, Google Sheets access, self-hosted n8n. You need to install VLM Run community node Quick Setup: Configure Google Drive OAuth2 and create license upload folder Add VLM Run API credentials Set up Google Sheets integration for data storage Update folder/sheet IDs in workflow nodes Test with sample license images and activate Perfect for Customer onboarding and identity verification KYC compliance and document processing HR employee verification and record keeping Insurance claim processing and validation Any business requiring license data extraction Key Benefits Asynchronous processing** handles high-resolution images without timeouts Multi-format support** for JPG, PNG, PDF, HEIC, WebP formats Structured data output** ready for databases and integrations Eliminates manual entry** saving hours of data input time High accuracy OCR** with multi-state license support How to customize Extend by adding: Address and additional field extraction Data validation and error checking Integration with CRM or customer databases Email notifications for processing completion Audit trails and compliance reporting Duplicate detection and data deduplication This workflow transforms manual license data entry into an automated, accurate, and compliant process, making identity verification seamless and reliable for your business operations.

Guidebook: How the Website ChatBot Template Works Chapter 1: Introduction & Objectives This guidebook provides a comprehensive walkthrough of the Website ChatBot developed using n8n and OpenAI. The chatbot is designed to qualify real estate leads and encourage site visits for the Alcove New Kolkata Sangam project through personalized, intelligent conversations. Chapter 2: Tools Required 1. n8n Workflow Automation Tool An open-source workflow builder to automate data flows between services. 2. OpenAI Account with GPT-4o-mini Access For generating AI-based chatbot responses. 3. Web Chat Widget Frontend integration that sends messages via webhook to the chatbot. Chapter 3: Workflow Breakdown Step 1: Webhook Receives POST requests from the chat widget. Endpoint: /webhook/chatbot-webhook Step 2: Set User Message Extracts message from the JSON body. Stores it as user_message. Step 3: Memory Setup Uses session ID to track conversation across messages. Step 4: OpenAI Chat Model GPT-4o-mini processes queries using the defined agent prompt. Step 5: AI Agent (Khusboo) Persona of a pre-sales agent. Uses AIDA + BANT + SPIN + PAS frameworks. Shares videos, responds in Hinglish, schedules site visits. Step 6: Respond to Webhook Formats the chatbot's reply into a JSON response. Chapter 4: Strategy & Psychology Behind Responses | Framework | Purpose | | --------- | ---------------------------------------------------- | | AIDA | Capture attention, interest, desire, action | | BANT | Qualify Budget, Authority, Need, Timing | | SPIN | Understand user's Situation, Problems, Implications | | PAS | Tackle objections using Problem, Agitation, Solution | The chatbot aims to qualify leads and gently move them toward booking a site visit without pushing or over-informing. Chapter 5: Setup Instructions A. n8n Workflow Setup Import the JSON workflow. Ensure OpenAI credentials are set up. Enable webhook at /webhook/chatbot-webhook. B. Frontend Widget Integration Send message as POST to the webhook with structure: { "message": "Looking for 2 BHK", "session_id": "user123" } Chapter 6: Testing & Troubleshooting Test via Postman Send sample request to verify AI response. Common Issues | Issue | Fix | | ---------------- | ----------------------------------- | | No response | Check webhook URL or credentials | | Repeated replies | Ensure memory node is active | | Wrong language | Check system message language rules | Chapter 7: Sample Conversations User: Hi, I’m looking for a home near the Ganga. Bot: Namaste! Main Khusboo hoon, Alcove New Kolkata Sangam se. Aapka naam kya hai? User: Rajat. Bot: Great Rajat! Kya aap apne family ke saath shift hone ka plan kar rahe ho? ... (continues using frameworks) Chapter 8: FAQs & Maintenance Tips Q: Can I update the AI agent persona? A: Yes, by modifying the system message inside the AI Agent node. Q: How do I share new videos or links? A: Add them in the sharingVideos or UserRequests section in the system message. Q: How to scale this for multiple projects? A: Duplicate the workflow and update the aboutProject and links accordingly. End of Guidebook.

Use Case When tracking your contacts and leads in Hubspot CRM, every new contact might be a potential customer. To guarantee that you're keeping the overview you'd normally need to look at every new lead that is coming in manually to identify high-quality leads to prioritize their engagement and optimize the sales process. This workflow saves the work and does it for you. What this workflow does The workflow runs every 5 minutes. On every run, it checks the Hubspot CRM for contacts that were added since the last check. It then checks if they meet certain criteria (in this case if they are making +5m annual revenue) and alerts you in Slack for every match. Setup Add Hubspot, and Slack credentials. Click on Test workflow. How to adjust this workflow to your needs Change the schedule interval Adjust the criteria to send alerts

This n8n template demonstrates how to get started with Gemini 2.0's new Bounding Box detection capabilities in your workflows. The key difference being this enables prompt-based object detection for images which is pretty powerful for things like contextual search over an image. eg. "Put a bounding box around all adults with children in this image" or "Put a bounding box around cars parked out of bounds of a parking space". How it works An image is downloaded via the HTTP node and an "Edit Image" node is used to extract the file's width and height. The image is then given to the Gemini 2.0 API to parse and return coordinates of the bounding box of the requested subjects. In this demo, we've asked for the AI to identify all bunnies. The coordinates are then rescaled with the original image's width and height to correctl align them. Finally to measure the accuracy of the object detection, we use the "Edit Image" node to draw the bounding boxes onto the original image. How to use Really up to the imagination! Perhaps a form of grounding for evidence based workflows or a higher form of image search can be built. Requirements Google Gemini for LLM Customising the workflow This template is just a demonstration of an experimental version of Gemini 2.0. It is recommended to wait for Gemini 2.0 to come out of this stage before using in production.

Who's this for? If you own a website and need to analyze your keyword rankings If you need to create a keyword report on your rankings If you want to grow your keyword positions SerpBear is an opensourced SEO tool specifically for keyword analytics. Click here to read details of how I use it Example output of A.I. Key Observations about Ranking Performance: The top-performing keyword is “Openrouter N8N” with a current position of 7 and an improving trend. Two keywords, “Best Docker Synology” and “Bitwarden Synology”, are not ranking in the top 100 and have a stable trend. Three keywords, “Obsidian Second Brain”, “AI Generated Reference Letter”, and “Actual Budget Synology”, and “N8N Workflow Generator” are not ranking well and have a declining trend. Keywords showing the most improvement: “Openrouter N8N” has an improving trend and a relatively high ranking of 7. Keywords needing attention: “Obsidian Second Brain” has a declining trend and a low ranking of 69. “AI Generated Reference Letter” has a declining trend and a low ranking of 84. “Actual Budget Synology”, “N8N Workflow Generator”, “Best Docker Synology”, and “Bitwarden Synology” are not ranking in the top 100. Use case Instead of hiring an SEO expert, I run this report weekly. It checks the keyword rankings of the past week and gives me recommendations on what to improve. How it works The workflow gathers SerpBear analytics for the past 7 days. It passes the data to openrouter.ai for A.I. analysis. Finally it saves to baserow. How to use this Input your SerpBearcredentials Enter your domain name Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Date, Note, Blog Created by Rumjahn

This workflow randomly select recipes from a Mealie instance (can use a specific category) and then creates a meal plan in Mealie with those recipes. How it works: Workflow has a scheduled trigger (set to run weekly on a Friday) Config node sets a few properties to configure the workflow A call to the Mealie API to get the list of recipes The code node holds most of the logic, this will loop through the number of recipes defined in the config node and randomly select a recipe from the list (making sure not to double up any recipes) Once all the recipes are selected it will call the Mealie API to set up the meal plan on the days Setup Add your Mealie API token as a credential and set it on the Http Request nodes Set the relevant schedule trigger to run when you like Update the Config node with the config you want numberOfRecipes - Number of recipes to populate for the meal plan offsetPlanDays - Number of days in the future to start the plan (0 will start it today, 1 tomorrow, etc.) mealieCategoryId - A category id of the category you want to pull in recipes from (default to select from all recipes) mealieBaseUrl - The base url of your Mealie instance