Workflow Templates

AI Proposal Generator System Categories* Sales Automation Document Generation AI Business Tools This workflow creates a complete AI-powered proposal generation system that transforms simple form inputs into professional, personalized proposals in under 30 seconds and can be deployed during live sales calls, allowing you to send polished proposals before the call even ends. Benefits* Instant Proposal Generation - Convert 30-second form inputs into professional proposals automatically High-Value Business Tool - Generates $1,500-$5,000 per client implementation Live Sales Integration - Generate and send proposals during active sales calls Complete Automation Pipeline - From form submission to email delivery with zero manual work Professional Presentation - Produces proposals indistinguishable from manually crafted documents Dual Platform Support - Works with both Google Slides (free) and PandaDoc (premium) integration How It Works* Smart Form Interface: Simple N8N form captures essential deal information Collects prospect details, problems, solutions, scope, timeline, and budget Designed for rapid completion during live sales conversations Advanced AI Processing: Uses sophisticated GPT-4 prompting with example-based training Converts basic form inputs into professionally written proposal sections Applies consistent tone, formatting, and business language automatically Dynamic Document Generation: Creates duplicate proposal templates for each new prospect Replaces template variables with AI-generated personalized content Maintains professional formatting and visual consistency Automated Email Delivery: Sends personalized email with proposal link immediately after generation Includes professional messaging and clear next steps Optionally includes invoice for immediate payment processing Premium PandaDoc Integration: Advanced version includes built-in payment processing Combines proposal, agreement, and invoice in single document Enables immediate signature and payment collection Business Use Cases* Service-Based Businesses - Generate proposals for consulting, agencies, and professional services Automation Agencies - Offer proposal generation as a high-value service to clients Sales Teams - Accelerate proposal creation and improve close rates Freelancers - Professionalize client interactions with instant custom proposals Consultants - Streamline business development with automated proposal workflows B2B Companies - Scale personalized proposal generation across entire sales organization Difficulty Level: Intermediate Estimated Build Time: 2-3 hours Monthly Operating Cost: $20-150 (depending on Google Slides vs PandaDoc) Watch My Complete Live Build* Want to see me build this entire $2,485 proposal system from scratch? I walk through every component live - including the AI prompting strategies, form design, Google Slides integration, and the advanced PandaDoc setup that enables payment collection. 🎥 See My Live Build Process: "I Built A $2,485 AI Proposal Generator In N8N (Copy This)" This comprehensive tutorial shows the real development process - including advanced AI prompting, template design, API integrations, and the exact pricing strategy that generates $1,500-$5,000 per client. Required Template Setup* Google Slides Template: Create a professional proposal template with these variable placeholders (wrapped in double curly braces): {{proposalTitle}} - Main proposal heading {{descriptionName}} - Project subtitle/description {{oneParagraphProblemSummary}} - Problem analysis section {{solutionHeadingOne}}, {{solutionHeadingTwo}}, {{solutionHeadingThree}} - Solution titles {{shortScopeTitleOne}} through {{shortScopeTitleThree}} - Scope sections {{milestoneOneDay}} through {{milestoneFourDay}} - Timeline milestones {{cost}} - Project pricing Form Field Requirements: The N8N form must include these exact field labels: First Name, Last Name, Company Name, Email, Website Problem (textarea) - Client's current challenges Solution (textarea) - Your proposed approach Scope (textarea) - Specific deliverables Cost - Project pricing How soon? - Timeline expectations PandaDoc Setup (Premium): Configure PandaDoc template with token placeholders matching the AI-generated content structure. Template must include pricing tables and signature fields for complete proposal-to-payment automation. Set Up Steps* Form Design & Integration: Create N8N form with optimized fields for proposal generation Design form flow for rapid completion during sales calls Configure form triggers and data validation AI Content Generation Setup: Configure OpenAI API for sophisticated proposal writing Implement example-based training with input/output pairs Set up JSON formatting for structured content generation Google Slides Integration (Free Version): Create professional proposal templates with variable placeholders Set up Google Cloud Console API access and credentials Configure template duplication and text replacement workflows Email Automation Setup: Configure Gmail integration for automated proposal delivery Design professional email templates with proposal links Set up dynamic content insertion and personalization PandaDoc Integration (Premium Version): Set up PandaDoc API for advanced document generation Configure payment processing and signature collection Implement proposal-to-payment automation workflows Testing & Quality Control: Test complete workflow with various proposal scenarios Validate AI output quality and professional presentation Optimize form fields and content generation based on results Advanced Features* Premium system includes: Payment Processing Integration: Collect payments immediately after proposal acceptance Digital Signature Collection: Streamline agreement execution with electronic signatures Custom Branding: Apply company branding and visual identity automatically Multi-Template Support: Generate different proposal types based on service offerings CRM Integration: Automatically sync proposal data with existing sales systems Why This System Works* The competitive advantage lies in speed and professionalism: 30-second generation time vs. hours of manual proposal writing Professional presentation that matches or exceeds manual proposals Live sales integration - send proposals during active conversations Consistent quality - eliminates human error and formatting inconsistencies Immediate follow-up - maintain sales momentum with instant delivery System Architecture* The workflow follows a simple but powerful 6-step process: Form Trigger - Captures essential deal information AI Processing - Converts inputs to professional content Template Duplication - Creates unique document for each prospect Content Replacement - Populates template with AI-generated content Email Delivery - Sends proposal with professional messaging Payment Collection (PandaDoc) - Enables immediate signature and payment Check Out My Channel* For more high-value automation systems and proven business-building strategies, explore my YouTube channel where I share the exact systems used to build successful automation businesses and scale to $72K+ monthly revenue.

This workflow auto-fetches top financial headlines, cleans the content, and uses AI to summarize it into a short investor-friendly email. Good to know The workflow runs daily and relies on stable webpage access; check the URL (e.g., https://www.ft.com/) for availability. AI costs may apply depending on the LLM model used (e.g., GPT-4 or Gemini); refer to provider pricing. How it works Trigger the workflow daily with the Schedule Daily Trigger node. Fetch financial news from a webpage using the Fetch Webpage News node. Add a Delay to Ensure Page Load node to ensure content is fully loaded. Extract and clean headlines with the Extract News Headlines & Clean Extracted Data node. Process the data with the LLM Chat Model node to generate a summary. Send the summarized report via email using the Email Daily Financial Summary node. How to use Import the workflow into n8n and configure the nodes with your webpage URL and email credentials. Test the workflow to verify content fetching and email delivery. Requirements Webpage access (e.g., financial news site API or RSS) Email service (e.g., SMTP or API) LLM model credentials (e.g., GPT-4 or Gemini) Customising this workflow Adjust the Fetch Webpage News node to target different news sources or modify the LLM Chat Model prompt for a different summary style.

How it works This workflow is a hands-on tutorial for the Code node in n8n, covering both basic and advanced concepts through a simple data processing task. Provides Sample Data: The workflow begins with a sample list of users. Processes Each Item (Run Once for Each Item): The first Code node iterates through each user to calculate their fullName and age. This demonstrates basic item-by-item data manipulation using $input.item.json. Fetches External Data (Advanced): The second Code node showcases a more advanced feature. For each user, it uses the built-in this.helpers.httpRequest function to call an external API (genderize.io) to enrich the data with a predicted gender. Processes All Items at Once (Run Once for All Items): The third Code node receives the fully enriched list of users and runs only once. It uses $items() to access the entire list and calculate the averageAge, returning a single summary item. Create a Binary File: The final Code node gets the fully enriched list of users once again and creates a binary CSV file to show how to use binary data Buffer in JavaScript. Set up steps Setup time: < 1 minute This workflow is a self-contained tutorial and requires no setup. Explore the Nodes: Click on each of the Code nodes to read the code and the comments explaining each step, from basic to advanced. Run the Workflow: Click "Execute Workflow" to see it in action. Check the Output: Click on each node after the execution to see how the data is transformed at each stage. Notice how the data is progressively enriched. Experiment! Try changing the data in the 1. Sample Data node, or modify the code in the Code nodes to see what happens.

🔥 AI Lead Scoring Agent: Smart Contact Form Triager Automatically score every contact form lead as Hot/Warm/Cold and alert your sales team instantly. This intelligent workflow captures contact form submissions, uses GPT-4 to analyze message content and score lead quality, then sends formatted alerts to Slack - ensuring your sales team always focuses on the hottest prospects first. 🚀 What It Does Instant Lead Capture: Automatically receives contact form submissions via webhook endpoint AI-Powered Scoring: GPT-4 analyzes message content and classifies leads as Hot 🔥, Warm 🌤, or Cold ❄️ Smart Data Extraction: Cleanly extracts name, email, and message from form submissions Real-Time Slack Alerts: Sends formatted notifications to your sales team with lead details and AI scoring 🎯 Key Benefits ✅ Never Miss Hot Prospects: AI identifies urgent leads automatically ✅ Save Sales Time: Focus effort on highest-probability leads first ✅ Instant Team Alerts: Real-time notifications in Slack channels ✅ Smart Prioritization: AI scoring eliminates guesswork in lead quality ✅ Zero Manual Work: Complete automation from form to sales alert ✅ Universal Integration: Works with any contact form or landing page 🏢 Perfect For Sales & Marketing Teams SaaS companies managing inbound leads Service businesses qualifying prospects E-commerce stores identifying serious buyers Agencies prioritizing client inquiries Business Applications Lead Qualification**: Identify purchase-ready prospects instantly Sales Efficiency**: Focus team effort on highest-value opportunities Response Prioritization**: Handle urgent inquiries first Team Coordination**: Keep entire sales team informed of new leads ⚙️ What's Included Complete Workflow: Ready-to-deploy lead scoring automation Webhook Endpoint: Receives submissions from any contact form AI Classification: GPT-4 powered lead interest analysis Slack Integration: Professional team notifications with emojis and formatting Data Processing: Clean extraction and formatting of lead information 🔧 Quick Setup Requirements n8n Platform**: Cloud or self-hosted instance OpenAI API**: GPT-4 access for lead scoring Slack Workspace**: Team channel for lead notifications Contact Form**: Any form that can POST to webhook endpoint 📱 Sample Slack Alert 🔥 New Lead: Sarah Johnson (sarah@techstartup.com) Message: "We're looking for a project management solution for our 50-person team. Need to implement ASAP as we're scaling fast. Can we schedule a demo this week?" Triage: 🔥 Hot ❄️ New Lead: John Smith (john@email.com) Message: "Just browsing your website. Might be interested in learning more someday." Triage: ❄️ Cold 🎨 Customization Options Scoring Criteria: Adjust AI prompts for industry-specific lead qualification Team Channels: Route different lead types to specific Slack channels Additional Fields: Capture company size, budget, timeline data CRM Integration: Connect to Salesforce, HubSpot, or Pipedrive Follow-up Automation: Trigger email sequences based on lead temperature Analytics Tracking: Monitor lead quality trends and conversion rates 🏷️ Tags & Categories #lead-scoring #sales-automation #contact-form-processing #ai-qualification #slack-integration #prospect-management #inbound-marketing #sales-productivity #lead-generation #openai-integration #webhook-automation #crm-automation #sales-alerts #lead-triage #ai-agent 💡 Use Case Examples SaaS Company: Score demo requests based on company size and urgency mentions Consulting Firm: Identify clients ready to start projects vs those still researching E-commerce Store: Spot bulk buyers and wholesale inquiries vs casual browsers Marketing Agency: Prioritize clients with specific budgets and timelines mentioned 📈 Expected Results 70% faster** lead response times through smart prioritization 3x higher** conversion rates focusing on Hot leads first 50% time savings** on manual lead qualification 100% lead coverage** - never miss or ignore a prospect again 🛠️ Setup & Support 5-Minute Setup: Simple webhook configuration with any contact form Universal Integration: Works with WordPress, Webflow, custom forms, landing pages Team Training: Clear Slack notification format anyone can understand Scalable: Handles unlimited form submissions automatically 📞 Get Help & Resources YouTube: https://www.youtube.com/@YaronBeen/videos 💼 Sales Automation Support LinkedIn: https://www.linkedin.com/in/yaronbeen/ 📧 Direct Help Email: Yaron@nofluff.online - Response within 24 hours Ready to never miss another hot lead? Get this AI Lead Scoring Agent and transform your contact forms into intelligent lead qualification systems. Your sales team will always know which prospects to call first, and you'll never waste time on cold leads again. Stop treating all leads equally. Start prioritizing the ones ready to buy.

This plug-and-play n8n workflow automates medical record digitization using Mistral’s OCR API and stores clean, structured data in Google Sheets. Whether you run a clinic or healthtech product, this no-code solution simplifies data entry from scanned or uploaded medical documents. 📌 Works seamlessly on both self-hosted and cloud-based n8n environments. 👥 Who is this for? Hospitals and private clinics Healthtech platforms & startups Medical admin and document processing teams Clinical researchers and labs 😓 What problem does it solve? ❌ Manual entry from printed forms ❌ Unstructured, scattered records ❌ Errors in data transcription ❌ Inconsistent document storage ✅ This automation brings consistency, structure, and speed to the way you handle medical documents. ✅ What this workflow does Captures uploaded documents through a public form Uploads file to Mistral for OCR processing Extracts clean text from each page (PDF or image) Parses patient fields (Name, DOB, Diagnosis, Medications, etc.) Saves records into a structured Google Sheet 🛠️ Setup Instructions Step 1: Google Sheet Prep Create a Google Sheet with these columns (case-sensitive): Name, Date of Birth, Patient ID, Date of Visit, Referring Physician, Department, Symptoms, Blood Pressure, Heart Rate, Temperature, Lab Results, Diagnosis, Medications, Next Appointment, Notes Step 2: Mistral API Access Sign up at Mistral AI Get your API key Ensure your plan supports file upload & OCR endpoints Step 3: Google OAuth Credentials (Self-hosted or Cloud) Go to n8n → Settings → Credentials, and add: Google Sheets OAuth2 Scopes needed: https://www.googleapis.com/auth/spreadsheets Step 4: Import Workflow Go to Workflows > Import from File Upload your JSON file Replace: Google Sheet document ID in the "Google Sheets" node Your Mistral API key in HTTP Header Auth Step 5: (Optional) Make Form Public In Cloud-based n8n: You can expose the form as a public page Otherwise, connect it to your website form via webhook 🧩 Customization Tips Extract More Fields Update the "Data cleaning" node and extend the list of fields: const fields = ["Name", "Diagnosis", "Medications", "Symptoms", ...]; Add EHR or Database Integration After Google Sheets, chain your custom system: PostgreSQL Airtable Supabase MongoDB Change Output Format Want JSON or Markdown output for internal tools? Use the Set or Code node before the final output step. 🧪 Troubleshooting Issue Fix File upload fails Check Mistral API key and file type Google Sheets not updating Verify credentials and document ID No data parsed Check OCR quality; verify field labels in document Workflow not triggering Ensure webhook or form is configured correctly 🌐 Self-Hosted vs Cloud Comparison Feature Self-Hosted n8n Cloud Public Form Access Manual setup Built-in OAuth App Config Required Pre-configured Storage Limits Depends on server Included with plan Scalability Fully customizable Scales automatically 📣 Getting Support n8n Docs Mistral API Docs n8n Community Or reach out to: David Olusola (dimejicole21@gmail.com) 🌟 Like this template? Give it a star in the template library and help other no-code builders discover it. "Turn scanned documents into structured data with zero code."

This n8n template enables you to instantly retrieve detailed geolocation information for any given IP address by simply sending a webhook request. Leverage the power of IP-API.com to gain insights into user locations, personalize experiences, or enhance security protocols within your automated workflows. 🔧 How it works Receive IP Webhook: This node acts as the entry point, listening for incoming POST requests. It expects a JSON body containing an ip property with the IP address you wish to look up. Get IP Geolocation: This node makes an HTTP GET request to the IP-API.com service, passing the IP address from your webhook. The API responds with a comprehensive JSON object detailing the IP's location (country, city, region), ISP, organization, and more. Respond with Geolocation Data: This node sends the full geolocation data received from IP-API.com back to the service that initiated the webhook. 👤 Who is it for? This workflow is ideal for: Marketing & Sales Teams: Personalize website content, offers, or ads based on a visitor's geographic location. Tailor email campaigns by region. Customer Support: Quickly identify a customer's location to provide more localized or relevant assistance. Security & Fraud Detection: Analyze incoming connection IPs to identify suspicious activity, block known malicious regions, or flag potential fraud. Analytics & Reporting: Augment your analytics data with geographical insights about your users or traffic. Developers & Integrators: Easily add IP lookup functionality to custom applications, internal tools, or monitoring systems. Content Delivery Networks (CDNs): Route users to the closest servers for faster content delivery (though advanced CDNs usually handle this automatically). 📑 Data Structure When you trigger the webhook, send a POST request with a JSON body structured as follows: { "ip": "8.8.8.8" // Replace with the IP address you want to look up } The workflow will return a JSON response similar to this (data will vary based on IP): { "status": "success", "country": "United States", "countryCode": "US", "region": "VA", "regionName": "Virginia", "city": "Ashburn", "zip": "20149", "lat": 39.0437, "lon": -77.4875, "timezone": "America/New_York", "isp": "Google LLC", "org": "Google Public DNS", "as": "AS15169 Google LLC", "query": "8.8.8.8" } ⚙️ Setup Instructions Import Workflow: In your n8n editor, click "Import from JSON" and paste the provided workflow JSON. Configure Webhook Path: Double-click the Receive IP Webhook node. In the 'Path' field, set a unique and descriptive path (e.g., /ip-lookup). Activate Workflow: Save and activate the workflow. 📝 Tips This workflow, while simple, is a powerful building block. Here's how you can make it even more useful: Conditional Logic: Add IF nodes after "Get IP Geolocation" to create conditional branches. For example: If countryCode is 'CN' or 'RU', send an alert to your security team. If city is 'New York', route the request to a specific sales representative. Data Enrichment: Integrate this workflow into larger automation. For instance: When a new sign-up occurs, pass their IP address to this workflow, then save the returned geolocation data (country, city, ISP) alongside their user profile in your CRM or database. For e-commerce, use the location data to pre-fill shipping fields or suggest local currency/language. Logging & Analytics: Store the lookup results in a spreadsheet (Google Sheets), database (PostgreSQL, Airtable), or logging service. This can help you track where your users are coming from over time. Rate Limiting: IP-API.com has rate limits for its free tier. If you anticipate high usage, consider adding a Delay node or implementing a caching mechanism with a Cache node to avoid hitting limits. For heavy use, you might need to upgrade to a paid plan. Dynamic Response: Instead of returning the full JSON, you could use a Function node to extract only specific pieces of information (e.g., just the country and city) and return a more concise response. Input Validation: For robust production use, add a Function node after the webhook to validate that the incoming ip value is indeed a valid IP address. If it's not, you can return an error message to the caller.

AI Social Media Content Generator & Scheduler Transform your social media strategy with AI-powered content generation that creates platform-specific posts in seconds! 🚀 What It Does This workflow uses AI to generate optimized content for multiple social media platforms from a single topic input. Perfect for marketers, content creators, and businesses looking to maintain consistent social media presence. ✨ Key Features Multi-Platform Support**: LinkedIn, Twitter/X, Instagram, Facebook, TikTok AI-Powered Generation**: Uses GPT-4 for creative, engaging content Platform Optimization**: Respects character limits and best practices Hashtag Generation**: Platform-specific hashtag strategies Posting Time Suggestions**: Optimal times for each platform Tone Customization**: Professional, casual, friendly, or custom Multi-Language Support**: Generate content in any language Engagement Predictions**: Estimate reach and engagement Daily Automation**: Schedule automatic content generation Bulk Processing**: Generate content for multiple topics at once 📊 Use Cases Marketing Teams: Streamline content creation across channels Small Businesses: Maintain consistent social presence Content Agencies: Scale content production efficiently Personal Brands: Build thought leadership E-commerce: Product launches and promotions 🛠️ Setup Instructions Add OpenAI Credentials Get API key from OpenAI Add to n8n credentials Configure Webhook (Optional) Set custom path if needed Enable for external integrations Customize Settings Adjust tone and style Set platform preferences Configure posting schedule Test Generation Use example prompts Verify output quality 💡 Example Inputs "New product launch - eco-friendly water bottle" "Company milestone - 10 years in business" "Industry insights - Future of AI in healthcare" "Team spotlight - Meet our new developer" "Seasonal campaign - Summer sale 50% off" 📈 Benefits 10x Faster**: Create content in seconds vs hours Consistency**: Maintain brand voice across platforms Engagement**: Platform-optimized for maximum reach Scalability**: Generate unlimited content Cost-Effective**: Reduce content creation costs by 80% 🔧 Customization Options Custom brand voice training Industry-specific content rules Competitor analysis integration A/B testing capabilities Analytics webhook integration Auto-posting to platforms Image generation add-on Translation services 🎯 Pro Tips Train the AI with your best-performing posts Use platform analytics to refine strategies Test different tones for audience engagement Schedule content during peak hours Monitor and iterate based on performance Start creating engaging social media content today! Categories: Marketing & Growth Content Creation Social Media AI & Automation Productivity Difficulty: Beginner Required Services: OpenAI API (or compatible LLM) n8n instance Optional: Social media APIs for auto-posting

Reusable and Independently Testable Sub-workflow This n8n workflow provides a standardized structure for building and testing sub-workflows in isolation. Its purpose is to help you create robust, reusable, and maintainable automations by enabling you to test the sub-workflow's logic without needing a separate parent workflow. Setup Instructions: Define Sub-workflow Inputs: Double-click the Execute Sub-workflow Trigger node to define the parameters (e.g., color) that your sub-workflow will expect from a parent workflow. Configure Test Data: Use the Test Input node (an Edit Fields (Set) node connected to the Manual Trigger) to provide sample data for isolated testing. Connect Inputs: The Combine Input node (an Edit Fields (Set) node) is the entry point for your sub-workflow's core logic. It should have two inputs: one from the Execute Sub-workflow Trigger and one from the Test Input node. Merge Inputs: Ensure the Combine Input node has the 'Include Other Input Fields' option enabled to merge data from both the live and test paths seamlessly. You can read the full blog post that explains this workflow setup in detail here.

This n8n workflow template checks for new major releases (tagged with .0) of the n8n project using its official GitHub releases feed. It runs multiple times a day and sends notifications via email and Telegram if a new release is found. > ⚠️ Note: You must *activate the workflow* to start receiving release notifications. 🚀 What It Does Monitors the n8n GitHub releases feed Detects major versions (e.g., 1.0.0, 2.0.0) Sends alert messages via Telegram and email (SES) when a release is published ⏰ Scheduling Details The Cron node checks for new releases three times per day: 10:00, 14:00, and 18:00 server time. 🛠️ Step-by-Step Setup Configure Telegram Bot Connect your Telegram bot and specify the chat ID where you want to receive notifications. Set up AWS SES Credentials Use a verified sender email and set up AWS SES credentials in your n8n instance. Activate the Workflow Enable the workflow in your instance to start receiving notifications. Customize Notification Messages (Optional) You can modify the email subject, Telegram format, or filter logic. 🧠 How It Works: Workflow Overview Cron Trigger Runs the workflow at 10:00, 14:00, and 18:00 daily. Read RSS Feed Pulls data from https://github.com/n8n-io/n8n/releases.atom. Filter by Current Day Filters the feed to match: Releases published in the last 4 hours Titles starting with n8n@ and ending with .0 Condition Check Uses a regex to check if the filter result contains any release data. Notifications If a new major release is found, sends: Telegram message to a specified chat Email via AWS SES with release info 📨 Final Output You'll receive a Telegram message and email when a new major n8n version is released. 🔐 Credentials Used Telegram API** – For sending chat notifications AWS SES** – To send email alerts ✨ Customization Tips Change Notification Channels**: Add Slack, Discord, or other preferred channels. Adjust Cron Schedule**: Modify the Cron node to fit your check frequency. Modify Filters**: Detect patch or beta versions by changing the .0 condition. Send Release Notes**: Extend the feed parsing to include release content. ❓Questions? Template created by Miquel Colomer and n8nhackers.com. Need help customizing or deploying? Contact us for consulting and support.

Description Turn your Telegram bot into a powerful OCR (Optical Character Recognition) tool. This workflow allows you to send any image (like a screenshot, a photo of a document, or a picture of a sign) to your bot, and it will instantly extract and send back the text from that image. Powered by Google's advanced Gemini AI, this automation is perfect for quickly digitizing notes, saving important snippets, or avoiding manual typing. How it works This workflow performs a few high-level steps: It triggers when a new image is sent to your Telegram bot. It sends the image to the Google Gemini Vision API to be analyzed. It extracts the text found in the image. It sends the extracted text back to you as a message in Telegram. Set up steps Estimated set up time: Less than 5 minutes. The setup is straightforward. You only need to configure two credentials: Telegram Bot Credentials: To connect your bot. Google Gemini API Credentials: To use the OCR feature. You can get a free API key from Google AI Studio.

Ok google download "movie name" I develop this automation to improve my quality of life in handling torrents in my media-center. Goal Automate the search operations of a movie based on its name and trigger a download using your transmission-daemon. Setup Prerequisite Transmission daemon up and running and its authentication method N8N configured self-hosted or with the possibility to add npm package better with docker-compose.yaml Telegram bot credential [optional] Configuration Create a folder where your docker-compose.yaml belongs n8n_dir and proceed in installing the node package. cd ~/n8n_dir npm i torrent-search-api Configuring your docker-compose.yaml file this way. You must include all the dependencies of torrent-search-api. This will let you run the new torrent search node presented in this workflow. version: '3.3' services: n8n: container_name: n8n ports: '5678:5678' restart: always volumes: '~/n8n_dir/.n8n:/home/node/.n8n' '~/n8n_dir/node_modules/@tootallnate:/usr/local/lib/node_modules/@tootallnate' '~/n8n_dir/node_modules/accepts:/usr/local/lib/node_modules/accepts' '~/n8n_dir/node_modules/agent-base:/usr/local/lib/node_modules/agent-base' '~/n8n_dir/node_modules/ajv:/usr/local/lib/node_modules/ajv' '~/n8n_dir/node_modules/ansi-styles:/usr/local/lib/node_modules/ansi-styles' '~/n8n_dir/node_modules/asn1:/usr/local/lib/node_modules/asn1' '~/n8n_dir/node_modules/assert:/usr/local/lib/node_modules/assert' '~/n8n_dir/node_modules/assert-plus:/usr/local/lib/node_modules/assert-plus' '~/n8n_dir/node_modules/ast-types:/usr/local/lib/node_modules/ast-types' '~/n8n_dir/node_modules/asynckit:/usr/local/lib/node_modules/asynckit' '~/n8n_dir/node_modules/aws-sign2:/usr/local/lib/node_modules/aws-sign2' '~/n8n_dir/node_modules/aws4:/usr/local/lib/node_modules/aws4' '~/n8n_dir/node_modules/base64-js:/usr/local/lib/node_modules/base64-js' '~/n8n_dir/node_modules/batch:/usr/local/lib/node_modules/batch' '~/n8n_dir/node_modules/bcrypt-pbkdf:/usr/local/lib/node_modules/bcrypt-pbkdf' '~/n8n_dir/node_modules/bluebird:/usr/local/lib/node_modules/bluebird' '~/n8n_dir/node_modules/boolbase:/usr/local/lib/node_modules/boolbase' '~/n8n_dir/node_modules/brotli:/usr/local/lib/node_modules/brotli' '~/n8n_dir/node_modules/bytes:/usr/local/lib/node_modules/bytes' '~/n8n_dir/node_modules/caseless:/usr/local/lib/node_modules/caseless' '~/n8n_dir/node_modules/chalk:/usr/local/lib/node_modules/chalk' '~/n8n_dir/node_modules/cheerio:/usr/local/lib/node_modules/cheerio' '~/n8n_dir/node_modules/cloudscraper:/usr/local/lib/node_modules/cloudscraper' '~/n8n_dir/node_modules/co:/usr/local/lib/node_modules/co' '~/n8n_dir/node_modules/color-convert:/usr/local/lib/node_modules/color-convert' '~/n8n_dir/node_modules/color-name:/usr/local/lib/node_modules/color-name' '~/n8n_dir/node_modules/combined-stream:/usr/local/lib/node_modules/combined-stream' '~/n8n_dir/node_modules/component-emitter:/usr/local/lib/node_modules/component-emitter' '~/n8n_dir/node_modules/content-disposition:/usr/local/lib/node_modules/content-disposition' '~/n8n_dir/node_modules/content-type:/usr/local/lib/node_modules/content-type' '~/n8n_dir/node_modules/cookiejar:/usr/local/lib/node_modules/cookiejar' '~/n8n_dir/node_modules/core-util-is:/usr/local/lib/node_modules/core-util-is' '~/n8n_dir/node_modules/css-select:/usr/local/lib/node_modules/css-select' '~/n8n_dir/node_modules/css-what:/usr/local/lib/node_modules/css-what' '~/n8n_dir/node_modules/dashdash:/usr/local/lib/node_modules/dashdash' '~/n8n_dir/node_modules/data-uri-to-buffer:/usr/local/lib/node_modules/data-uri-to-buffer' '~/n8n_dir/node_modules/debug:/usr/local/lib/node_modules/debug' '~/n8n_dir/node_modules/deep-is:/usr/local/lib/node_modules/deep-is' '~/n8n_dir/node_modules/degenerator:/usr/local/lib/node_modules/degenerator' '~/n8n_dir/node_modules/delayed-stream:/usr/local/lib/node_modules/delayed-stream' '~/n8n_dir/node_modules/delegates:/usr/local/lib/node_modules/delegates' '~/n8n_dir/node_modules/depd:/usr/local/lib/node_modules/depd' '~/n8n_dir/node_modules/destroy:/usr/local/lib/node_modules/destroy' '~/n8n_dir/node_modules/dom-serializer:/usr/local/lib/node_modules/dom-serializer' '~/n8n_dir/node_modules/domelementtype:/usr/local/lib/node_modules/domelementtype' '~/n8n_dir/node_modules/domhandler:/usr/local/lib/node_modules/domhandler' '~/n8n_dir/node_modules/domutils:/usr/local/lib/node_modules/domutils' '~/n8n_dir/node_modules/ecc-jsbn:/usr/local/lib/node_modules/ecc-jsbn' '~/n8n_dir/node_modules/ee-first:/usr/local/lib/node_modules/ee-first' '~/n8n_dir/node_modules/emitter-component:/usr/local/lib/node_modules/emitter-component' '~/n8n_dir/node_modules/enqueue:/usr/local/lib/node_modules/enqueue' '~/n8n_dir/node_modules/enstore:/usr/local/lib/node_modules/enstore' '~/n8n_dir/node_modules/entities:/usr/local/lib/node_modules/entities' '~/n8n_dir/node_modules/error-inject:/usr/local/lib/node_modules/error-inject' '~/n8n_dir/node_modules/escape-html:/usr/local/lib/node_modules/escape-html' '~/n8n_dir/node_modules/escape-string-regexp:/usr/local/lib/node_modules/escape-string-regexp' '~/n8n_dir/node_modules/escodegen:/usr/local/lib/node_modules/escodegen' '~/n8n_dir/node_modules/esprima:/usr/local/lib/node_modules/esprima' '~/n8n_dir/node_modules/estraverse:/usr/local/lib/node_modules/estraverse' '~/n8n_dir/node_modules/esutils:/usr/local/lib/node_modules/esutils' '~/n8n_dir/node_modules/extend:/usr/local/lib/node_modules/extend' '~/n8n_dir/node_modules/extsprintf:/usr/local/lib/node_modules/extsprintf' '~/n8n_dir/node_modules/fast-deep-equal:/usr/local/lib/node_modules/fast-deep-equal' '~/n8n_dir/node_modules/fast-json-stable-stringify:/usr/local/lib/node_modules/fast-json-stable-stringify' '~/n8n_dir/node_modules/fast-levenshtein:/usr/local/lib/node_modules/fast-levenshtein' '~/n8n_dir/node_modules/file-uri-to-path:/usr/local/lib/node_modules/file-uri-to-path' '~/n8n_dir/node_modules/forever-agent:/usr/local/lib/node_modules/forever-agent' '~/n8n_dir/node_modules/form-data:/usr/local/lib/node_modules/form-data' '~/n8n_dir/node_modules/format-parser:/usr/local/lib/node_modules/format-parser' '~/n8n_dir/node_modules/formidable:/usr/local/lib/node_modules/formidable' '~/n8n_dir/node_modules/fs-extra:/usr/local/lib/node_modules/fs-extra' '~/n8n_dir/node_modules/ftp:/usr/local/lib/node_modules/ftp' '~/n8n_dir/node_modules/get-uri:/usr/local/lib/node_modules/get-uri' '~/n8n_dir/node_modules/getpass:/usr/local/lib/node_modules/getpass' '~/n8n_dir/node_modules/graceful-fs:/usr/local/lib/node_modules/graceful-fs' '~/n8n_dir/node_modules/har-schema:/usr/local/lib/node_modules/har-schema' '~/n8n_dir/node_modules/har-validator:/usr/local/lib/node_modules/har-validator' '~/n8n_dir/node_modules/has-flag:/usr/local/lib/node_modules/has-flag' '~/n8n_dir/node_modules/htmlparser2:/usr/local/lib/node_modules/htmlparser2' '~/n8n_dir/node_modules/http-context:/usr/local/lib/node_modules/http-context' '~/n8n_dir/node_modules/http-errors:/usr/local/lib/node_modules/http-errors' '~/n8n_dir/node_modules/http-incoming:/usr/local/lib/node_modules/http-incoming' '~/n8n_dir/node_modules/http-outgoing:/usr/local/lib/node_modules/http-outgoing' '~/n8n_dir/node_modules/http-proxy-agent:/usr/local/lib/node_modules/http-proxy-agent' '~/n8n_dir/node_modules/http-signature:/usr/local/lib/node_modules/http-signature' '~/n8n_dir/node_modules/https-proxy-agent:/usr/local/lib/node_modules/https-proxy-agent' '~/n8n_dir/node_modules/iconv-lite:/usr/local/lib/node_modules/iconv-lite' '~/n8n_dir/node_modules/inherits:/usr/local/lib/node_modules/inherits' '~/n8n_dir/node_modules/ip:/usr/local/lib/node_modules/ip' '~/n8n_dir/node_modules/is-browser:/usr/local/lib/node_modules/is-browser' '~/n8n_dir/node_modules/is-typedarray:/usr/local/lib/node_modules/is-typedarray' '~/n8n_dir/node_modules/is-url:/usr/local/lib/node_modules/is-url' '~/n8n_dir/node_modules/isarray:/usr/local/lib/node_modules/isarray' '~/n8n_dir/node_modules/isobject:/usr/local/lib/node_modules/isobject' '~/n8n_dir/node_modules/isstream:/usr/local/lib/node_modules/isstream' '~/n8n_dir/node_modules/jsbn:/usr/local/lib/node_modules/jsbn' '~/n8n_dir/node_modules/json-schema:/usr/local/lib/node_modules/json-schema' '~/n8n_dir/node_modules/json-schema-traverse:/usr/local/lib/node_modules/json-schema-traverse' '~/n8n_dir/node_modules/json-stringify-safe:/usr/local/lib/node_modules/json-stringify-safe' '~/n8n_dir/node_modules/jsonfile:/usr/local/lib/node_modules/jsonfile' '~/n8n_dir/node_modules/jsprim:/usr/local/lib/node_modules/jsprim' '~/n8n_dir/node_modules/koa-is-json:/usr/local/lib/node_modules/koa-is-json' '~/n8n_dir/node_modules/levn:/usr/local/lib/node_modules/levn' '~/n8n_dir/node_modules/lodash:/usr/local/lib/node_modules/lodash' '~/n8n_dir/node_modules/lodash.assignin:/usr/local/lib/node_modules/lodash.assignin' '~/n8n_dir/node_modules/lodash.bind:/usr/local/lib/node_modules/lodash.bind' '~/n8n_dir/node_modules/lodash.defaults:/usr/local/lib/node_modules/lodash.defaults' '~/n8n_dir/node_modules/lodash.filter:/usr/local/lib/node_modules/lodash.filter' '~/n8n_dir/node_modules/lodash.flatten:/usr/local/lib/node_modules/lodash.flatten' '~/n8n_dir/node_modules/lodash.foreach:/usr/local/lib/node_modules/lodash.foreach' '~/n8n_dir/node_modules/lodash.map:/usr/local/lib/node_modules/lodash.map' '~/n8n_dir/node_modules/lodash.merge:/usr/local/lib/node_modules/lodash.merge' '~/n8n_dir/node_modules/lodash.pick:/usr/local/lib/node_modules/lodash.pick' '~/n8n_dir/node_modules/lodash.reduce:/usr/local/lib/node_modules/lodash.reduce' '~/n8n_dir/node_modules/lodash.reject:/usr/local/lib/node_modules/lodash.reject' '~/n8n_dir/node_modules/lodash.some:/usr/local/lib/node_modules/lodash.some' '~/n8n_dir/node_modules/lru-cache:/usr/local/lib/node_modules/lru-cache' '~/n8n_dir/node_modules/media-typer:/usr/local/lib/node_modules/media-typer' '~/n8n_dir/node_modules/methods:/usr/local/lib/node_modules/methods' '~/n8n_dir/node_modules/mime:/usr/local/lib/node_modules/mime' '~/n8n_dir/node_modules/mime-db:/usr/local/lib/node_modules/mime-db' '~/n8n_dir/node_modules/mime-types:/usr/local/lib/node_modules/mime-types' '~/n8n_dir/node_modules/monotonic-timestamp:/usr/local/lib/node_modules/monotonic-timestamp' '~/n8n_dir/node_modules/ms:/usr/local/lib/node_modules/ms' '~/n8n_dir/node_modules/negotiator:/usr/local/lib/node_modules/negotiator' '~/n8n_dir/node_modules/netmask:/usr/local/lib/node_modules/netmask' '~/n8n_dir/node_modules/nth-check:/usr/local/lib/node_modules/nth-check' '~/n8n_dir/node_modules/oauth-sign:/usr/local/lib/node_modules/oauth-sign' '~/n8n_dir/node_modules/object-assign:/usr/local/lib/node_modules/object-assign' '~/n8n_dir/node_modules/on-finished:/usr/local/lib/node_modules/on-finished' '~/n8n_dir/node_modules/optionator:/usr/local/lib/node_modules/optionator' '~/n8n_dir/node_modules/pac-proxy-agent:/usr/local/lib/node_modules/pac-proxy-agent' '~/n8n_dir/node_modules/pac-resolver:/usr/local/lib/node_modules/pac-resolver' '~/n8n_dir/node_modules/parseurl:/usr/local/lib/node_modules/parseurl' '~/n8n_dir/node_modules/performance-now:/usr/local/lib/node_modules/performance-now' '~/n8n_dir/node_modules/prelude-ls:/usr/local/lib/node_modules/prelude-ls' '~/n8n_dir/node_modules/process-nextick-args:/usr/local/lib/node_modules/process-nextick-args' '~/n8n_dir/node_modules/promise-polyfill:/usr/local/lib/node_modules/promise-polyfill' '~/n8n_dir/node_modules/proxy-agent:/usr/local/lib/node_modules/proxy-agent' '~/n8n_dir/node_modules/proxy-from-env:/usr/local/lib/node_modules/proxy-from-env' '~/n8n_dir/node_modules/psl:/usr/local/lib/node_modules/psl' '~/n8n_dir/node_modules/punycode:/usr/local/lib/node_modules/punycode' '~/n8n_dir/node_modules/qs:/usr/local/lib/node_modules/qs' '~/n8n_dir/node_modules/querystring:/usr/local/lib/node_modules/querystring' '~/n8n_dir/node_modules/raw-body:/usr/local/lib/node_modules/raw-body' '~/n8n_dir/node_modules/readable-stream:/usr/local/lib/node_modules/readable-stream' '~/n8n_dir/node_modules/request:/usr/local/lib/node_modules/request' '~/n8n_dir/node_modules/request-promise:/usr/local/lib/node_modules/request-promise' '~/n8n_dir/node_modules/request-promise-core:/usr/local/lib/node_modules/request-promise-core' '~/n8n_dir/node_modules/request-x-ray:/usr/local/lib/node_modules/request-x-ray' '~/n8n_dir/node_modules/safe-buffer:/usr/local/lib/node_modules/safe-buffer' '~/n8n_dir/node_modules/safer-buffer:/usr/local/lib/node_modules/safer-buffer' '~/n8n_dir/node_modules/selectn:/usr/local/lib/node_modules/selectn' '~/n8n_dir/node_modules/setprototypeof:/usr/local/lib/node_modules/setprototypeof' '~/n8n_dir/node_modules/sliced:/usr/local/lib/node_modules/sliced' '~/n8n_dir/node_modules/smart-buffer:/usr/local/lib/node_modules/smart-buffer' '~/n8n_dir/node_modules/socks:/usr/local/lib/node_modules/socks' '~/n8n_dir/node_modules/socks-proxy-agent:/usr/local/lib/node_modules/socks-proxy-agent' '~/n8n_dir/node_modules/source-map:/usr/local/lib/node_modules/source-map' '~/n8n_dir/node_modules/sshpk:/usr/local/lib/node_modules/sshpk' '~/n8n_dir/node_modules/statuses:/usr/local/lib/node_modules/statuses' '~/n8n_dir/node_modules/stealthy-require:/usr/local/lib/node_modules/stealthy-require' '~/n8n_dir/node_modules/stream-to-string:/usr/local/lib/node_modules/stream-to-string' '~/n8n_dir/node_modules/string-format:/usr/local/lib/node_modules/string-format' '~/n8n_dir/node_modules/string_decoder:/usr/local/lib/node_modules/string_decoder' '~/n8n_dir/node_modules/superagent:/usr/local/lib/node_modules/superagent' '~/n8n_dir/node_modules/superagent-proxy:/usr/local/lib/node_modules/superagent-proxy' '~/n8n_dir/node_modules/supports-color:/usr/local/lib/node_modules/supports-color' '~/n8n_dir/node_modules/toidentifier:/usr/local/lib/node_modules/toidentifier' '~/n8n_dir/node_modules/torrent-search-api:/usr/local/lib/node_modules/torrent-search-api' '~/n8n_dir/node_modules/tough-cookie:/usr/local/lib/node_modules/tough-cookie' '~/n8n_dir/node_modules/tslib:/usr/local/lib/node_modules/tslib' '~/n8n_dir/node_modules/tunnel-agent:/usr/local/lib/node_modules/tunnel-agent' '~/n8n_dir/node_modules/tweetnacl:/usr/local/lib/node_modules/tweetnacl' '~/n8n_dir/node_modules/type-check:/usr/local/lib/node_modules/type-check' '~/n8n_dir/node_modules/type-is:/usr/local/lib/node_modules/type-is' '~/n8n_dir/node_modules/universalify:/usr/local/lib/node_modules/universalify' '~/n8n_dir/node_modules/unpipe:/usr/local/lib/node_modules/unpipe' '~/n8n_dir/node_modules/uri-js:/usr/local/lib/node_modules/uri-js' '~/n8n_dir/node_modules/util:/usr/local/lib/node_modules/util' '~/n8n_dir/node_modules/util-deprecate:/usr/local/lib/node_modules/util-deprecate' '~/n8n_dir/node_modules/uuid:/usr/local/lib/node_modules/uuid' '~/n8n_dir/node_modules/vary:/usr/local/lib/node_modules/vary' '~/n8n_dir/node_modules/verror:/usr/local/lib/node_modules/verror' '~/n8n_dir/node_modules/word-wrap:/usr/local/lib/node_modules/word-wrap' '~/n8n_dir/node_modules/wrap-fn:/usr/local/lib/node_modules/wrap-fn' '~/n8n_dir/node_modules/x-ray:/usr/local/lib/node_modules/x-ray' '~/n8n_dir/node_modules/x-ray-crawler:/usr/local/lib/node_modules/x-ray-crawler' '~/n8n_dir/node_modules/x-ray-parse:/usr/local/lib/node_modules/x-ray-parse' '~/n8n_dir/node_modules/x-ray-scraper:/usr/local/lib/node_modules/x-ray-scraper' '~/n8n_dir/node_modules/xregexp:/usr/local/lib/node_modules/xregexp' '~/n8n_dir/node_modules/yallist:/usr/local/lib/node_modules/yallist' '~/n8n_dir/node_modules/yieldly:/usr/local/lib/node_modules/yieldly' image: 'n8nio/n8n:latest-rpi' environment: N8N_BASIC_AUTH_ACTIVE=true N8N_BASIC_AUTH_USER=username N8N_BASIC_AUTH_PASSWORD=your_secret_n8n_password EXECUTIONS_DATA_PRUNE=true EXECUTIONS_DATA_MAX_AGE=120 EXECUTIONS_TIMEOUT=300 EXECUTIONS_TIMEOUT_MAX=500 GENERIC_TIMEZONE=Europe/Berlin NODE_FUNCTION_ALLOW_EXTERNAL=torrent-search-api Once configured this way run n8n and create a new workflow coping the one proposed. Configure workflow Transmission In order to send command to transmission you must validate the Basic Auth. To do so: open the Start download node and edit the Credentials. Perform the same operation choosing the new credentials also in node Start download new token. In this automation we call transmission twice due to a security protocol in transmission system that prevents single click commands to be triggered, performing the request twice bypasses this security mechanism. https://en.wikipedia.org/wiki/Cross-site_request_forgery We use the X-Transmission-Session-Id provided by the first request to authenticate the second request. Telegram In order to make the workflow work as expected you must create a telegram bot and configure the nodes (Torrent not found and Telegram1) to send your message once the workflow is complete. Here's an easy guide to follow https://docs.n8n.io/nodes/n8n-nodes-base.telegram/ In those nodes you also should configure the Chat ID, you may use your telegram username or use a bot to retrieve your id. You may chat with useridinfobot that sends you your id. Ok google automation Since right now we do not have a n8n client for mobile that can trigger automation using google assistant I decided to use an IFTTT automation to trigger the webhook. I connect my IFTTT account with google assistant and pick the trigger. Say a phrase with a text ingredient as in the picture below. And configure the trigger this way. scarica $ -> download $ or metti in download $ -> put in download $ or some other trigger you may want. Then configure your server to trigger the webhook of n8n. Conclusion In conclusion we provide a fully working automation that integrates in n8n a node library and provides an easy trigger to perform a complex operation. Security concern Giving the ability to trigger a download may be problematic for potential unwanted torrent malware download, so you may decide to authenticate the webhook request passing in the body another field with a shared token between the two endpoints. Moreover the torrent-search-api library and its dependencies have some vulnerability that you may want to avoid on your own media-center, this will hopefully be patched soon in a further release of the library. This is just an interesting proof of concept. Quality of the download You may want to introduce another block between torrent search and webhook trigger to search for a movie based on the words detected by google assistant, sometimes it misinterprets something and you may end up downloading potential copyrighted material. Please use this automation only for free and open source movies and music.

Workflow Documentation Description: This workflow is designed to optimize prompts by enhancing user inputs for clarity and specificity using AI. The workflow takes a user-provided prompt as input and uses a Natural Language Processing (NLP) model to refine and improve the prompt. The optimized prompt is then sent back to the user, ready for use in further workflows or processes. Setup: This workflow is suitable for users who want to improve their prompts for better communication and understanding in their workflows. The workflow utilizes an AI Agent powered by an OpenAI Chat Model to enhance user prompts. Expected Outcomes: Users can provide vague or imprecise prompts as input to the workflow. The AI Agent will refine and optimize the prompt, adding clarity and specific details. The optimized prompt will be delivered back to the user via Telegram or can be input for the next nodes. Extra Information: A. A Telegram node is used to deliver the optimized prompt back to the user. B. Ensure you have the necessary credentials set up for Telegram and OpenAI accounts. C. Customize the workflow's settings, such as the AI model used for prompt optimization, to suit your requirements. D. Activate the workflow once all configurations are set to start optimizing prompts efficiently.