Workflow Templates

This workflow collects images from web search on a specific query, detects labels in them, and stores this information in a Google Sheet.

This workflow allows you to send an SMS to a number whenever you go out. Pushcut is an app for iOS that lets you create smart notifications to kick off shortcuts, URLs, and online automation. You can have multiple actions for a notification. You can use the IF node to check which action was selected and build the workflow accordingly. Pushcut Trigger node: This node triggers the workflow when an action is selected by the user. Twilio node: The Twilio node sends an SMS with the input given by the user. Based on your use-case, you might want to do something else, for example, send a Tweet, dim or turn off your Philips Hue lights, add activity to Strava or play music on Spotify. Replace the Twilio node with these nodes to customize the workflow as per your needs.

Triggers worfklow all 15 minutes Reads the data from Google Sheet Converts data to XLS Uploads the file to Dropbox

Tags*: AI Agent, MCP Server, n8n API, Monitoring, Debugging, Workflow Analytics, Automation Context Hi! I’m Samir — a Supply Chain Engineer and Data Scientist based in Paris, and founder of LogiGreen Consulting. This workflow is part of my latest project: an AI assistant that automatically analyses n8n workflow executions, detects failures, and identifies root causes through natural conversation with Claude Desktop. > Turn your automation logs into intelligent conversations with an AI that understands your workflows. The idea is to use Claude Desktop to help monitor and debug your workflows deployed in production. The workflow shared here is part of the setup. 📬 For business inquiries, you can find me on LinkedIn Who is this template for? This template is designed for automation engineers, data professionals, and AI enthusiasts who manage multiple workflows in n8n and want a smarter way to track errors or performance without manually browsing execution logs. If you’ve ever discovered a failed workflow hours after it happened — this is for you. What does this workflow do? This workflow acts as the bridge between your n8n instance and the Claude MCP Server. It exposes three main routes that can be triggered via a webhook: get_active_workflows → Fetches all currently active workflows get_workflow_executions → Retrieves the latest executions and calculates health KPIs get_execution_details → Extracts detailed information about failed executions for debugging Each request is automatically routed and processed, providing Claude with structured execution data for real-time analysis. How does it fit in the overall setup? Here’s the complete architecture: Claude Desktop ←→ MCP Server ←→ n8n Monitor Webhook ←→ n8n API The MCP Server (Python-based) communicates with your n8n instance through this workflow. The Claude Desktop app can then query workflow health, execution logs, and error patterns using natural language. The n8n workflow aggregates, cleans, and returns the relevant metrics (failures, success rates, timing, alerts). 📘 The full concept and architecture are explained in my article published on my blog: 👉 Deploy your AI Assistant to Monitor and Debug n8n Workflows using Claude and MCP 🎥 Tutorial The full setup tutorial (with source code and demo) is available on YouTube: How does it work? 🌐 Webhook Trigger receives the MCP server requests 🔀 Switch node routes actions based on "action" parameter ⚙️ HTTP Request nodes fetch execution and workflow data via the n8n API 🧮 A Code node calculates KPIs (success/failure rates, timing, alerts) 📤 The processed results are returned as JSON for Claude to interpret Example use cases Once connected, you can ask Claude questions like: “Show me all workflows that failed in the last 25 executions.” “Why is my Bangkok Meetup Scraper workflow failing?” “Give me a health report of my n8n instance.” Claude will reply with structured insights, including failure patterns, node diagnostics, and health status indicators (🟢🟡🔴). What do I need to get started? You’ll need: A self-hosted n8n instance Claude Desktop** app installed The MCP server source code (shared in the tutorial description) The webhook URL from this workflow is configured in your .env file Follow the tutorial for more details, don't hesitate to leave your questions in the comment section. Next Steps 🗒️ Use the sticky notes inside the workflow to: Replace <YOUR_N8N_INSTANCE> with your own URL Test the webhook routes individually using the “Execute Workflow” button Connect the MCP server and Claude Desktop to start monitoring This template was built using n8n v.116.2 Submitted: November 2025

Description Automated workflow that creates Jira issues directly from Streamlit form submissions. Receives webhook data, validates and transforms it to Jira's API schema, creates the issue, and returns the ticket details to the frontend application. Context Bridges the gap between lightweight Streamlit prototypes and enterprise Jira workflows. Enables rapid ticket creation while maintaining Jira as the authoritative source of truth. Includes safety mechanisms to prevent duplicate submissions and malformed requests. Target Users Product Managers building internal request portals. Engineering Managers creating demo applications. Teams requiring instant Jira integration without complex UI development. Project Manager using Jira pour mangement and reporting. Organizations wanting controlled ticket creation without exposing Jira directly. Technical Requirements n8n instance (cloud or self-hosted) with webhook capabilities Jira Cloud project with API token and issue creation permissions Streamlit application configured to POST to n8n webhook endpoint Optional: Custom field IDs for Story Points (typically customfield_10016) Workflow Steps Webhook Trigger - Receives POST from Streamlit with ticket payload. Deduplication Guard - Filters out ping requests and rapid duplicate submissions. Data Validation - Ensures required fields are present and properly formatted. Schema Transformation - Maps Streamlit fields to Jira API structure. Jira API Call - Creates issue via REST API with error handling. Response Formation - Returns success status with issue key and URL. Key Features Duplicate submission prevention. Rich text description formatting for Jira. Configurable priority and issue type mapping. Story points integration for agile workflows. Comprehensive error handling and logging. Clean JSON response for frontend feedback. Validation Testing Ping/test requests are ignored without creating issues. First submission creates Jira ticket with proper formatting. Rapid resubmission is blocked to prevent duplicates. All field types (priority, labels, due dates, story points) map correctly. Error responses are handled gracefully. Expected Output Valid Jira issue created in specified project JSON response: {ok: true, jiraKey: "PROJ-123", url: "https://domain.atlassian.net/browse/PROJ-123"} No orphaned or duplicate tickets. Audit trail in n8n execution logs. Implementation Notes Jira Cloud requires accountId for assignee (not username). Date format must be YYYY-MM-DD for due dates. Story Points field ID varies by Jira configuration. Enable response output in HTTP node for debugging. Consider rate limiting for high-volume scenarios. Tutorial video: Watch the Youtube Tutorial video How it works ⏰ Trigger: Webhook fires when the app submits. 🧹 Guard: Ignore pings/invalid, deduplicate rapid repeats. 🧱 Prepare: Normalize to Jira’s field model (incl. Atlassian doc description). 🧾 Create: POST to /rest/api/3/issue and capture the key. 🔁 Respond: Send { ok, jiraKey, url } back to Streamlit for instant UI feedback. About me : I'm Yassin, IT Project Manager, Agile & Data specialist. Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

Dental Clinic Automation: Scheduling, Availability & Patient Lookup This workflow automates dental appointment management through a phone-based assistant. It listens for requests like booking, rescheduling, canceling, checking insurance, looking up appointments, and finding available time slots. Each request is processed through a Switch node and then routed to your Supabase database for action. How it works Once a request is received, the workflow uses the patient’s phone number to identify them. Then, it: Booking: Checks for available time, creates or retrieves the patient record, and stores the appointment. Rescheduling: Confirms the new date, avoids double-booking, and updates the record. Canceling: Removes the appointment and sends a confirmation. Insurance: Looks up the member ID and provides a status (accepted or not). Availability: Finds the doctor’s existing appointments and generates available 60-minute slots. Appointment & doctor lists: Retrieves and presents clean, structured information for the assistant. Each action ends with a webhook response that the phone system reads back to the patient. Setup steps Add your Supabase credentials to the Supabase nodes. Connect your phone/voice system to the webhook URL. Ensure Supabase table and column names match the workflow. Test all actions (booking, rescheduling, canceling, etc.) before going live. Customization tips (optional) You can update working hours, appointment durations, or add new services by modifying the availability logic or Switch node routing.

Workflow: Auto-Translate WordPress Posts Using AI Translate Pro This n8n workflow automates the translation of WordPress blog content into any language using the AI Translate Pro API, and inserts the translated text into a Google Doc. 📌 Workflow Steps Manual Trigger Initiates the workflow manually (can be replaced with a webhook or schedule trigger). WordPress Node Retrieves a specific blog post (by ID) from your WordPress site using the REST API. HTTP Request Node Sends the blog content to AI Translate Pro via multipart/form-data. Google Docs Node Appends the translated text into a specified Google Document using Google Docs API. 🌍 API Usage: AI Translate Pro Endpoint: POST https://ai-translate-pro.p.rapidapi.com/translate.php Content-Type: multipart/form-data Required Parameters: | Field | Type | Description | |-----------|--------|-----------------------------------------| | text | string | The text or HTML content to translate | | language| string | Target language (e.g., Hindi, French) | Headers: | Header Name | Value | |---------------------|---------------------------------------| | x-rapidapi-host | ai-translate-pro.p.rapidapi.com | | x-rapidapi-key | Your RapidAPI key | ✅ Benefits of Using AI Translate Pro ⚡ Fast AI-Powered Translation – Instantly translate content with no need for manual input. 🌎 Supports Multiple Languages – Easily switch languages to serve global audiences. 🧠 Context-Aware – More accurate than basic dictionary translation tools. 🛠 Easy Integration with n8n – No-code/low-code implementation. 🗃 Content Reuse – Save translations directly into Google Docs for future use or edits. 💼 Cost-Effective – Efficient alternative to expensive manual translation services. 🛠️ Problems Solved ❌ Manual copy-pasting into Google Translate ❌ Limited or slow in-house translation ❌ Difficulty managing multilingual content ❌ Inconsistent formatting or storage ✅ With AI Translate Pro, translations are fast, automated, and saved where your team can access them instantly. ✅ Example Use Case Translate WordPress blog posts from English to Hindi. Store translated content in Google Docs for editing or reuse. Expand to multilingual sites with a simple language switch. Use AI Translate Pro in any low-code or no-code platform like n8n. 📎 Requirements WordPress REST API credentials RapidAPI access to AI Translate Pro Google Docs API service account 🔗 More Info Explore full documentation and pricing on the AI Translate Pro RapidAPI listing page. Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

Filters articles based on keywords, checks against MongoDB for unique links, then sends results to different webhooks

Use case Error workflows are an important part of running workflows in production. How to setup Add Slack creds Add this error workflow to other workflows (docs)

Overview This workflow utilizes the Defapi API with Sora 2 AI model to generate stunning viral videos with creative AI-generated motion, effects, and storytelling. Simply provide a creative prompt describing your desired video scene, and optionally upload an image as a reference. The AI generates professional-quality video content perfect for tiktok, youtube, marketing campaigns, and creative projects. Input: Creative prompt (required) + optional image Output: AI-generated viral video ready for social media and content marketing Users can interact through a simple form, providing a text prompt describing the desired video scene and optionally uploading an image for context. The system automatically submits the request to the Defapi Sora 2 API, monitors the generation status in real time, and retrieves the final video output. This solution is ideal for content creators, social media marketers, video producers, and businesses who want to quickly generate engaging video content with minimal setup. Prerequisites A Defapi account and API key: Sign up at Defapi.org to obtain your API key for Sora 2 access. An active n8n instance (cloud or self-hosted) with HTTP Request and form submission capabilities. Basic knowledge of AI prompts for video generation to achieve optimal results. Example prompt: A pack of dogs driving tiny cars in a high-speed chase through a city, wearing sunglasses and honking their horns, with dramatic action music and slow-motion jumps over fire hydrants. For 15-second HD videos, prefix your prompt with (15s,hd). (Optional) An image to use as a reference or starting point for video generation. Image Restrictions: Avoid uploading images with real people or highly realistic human faces, as they will be rejected during content review. Important Notes**: The API requires proper authentication via Bearer token for all requests. Content undergoes multi-stage moderation. Avoid violence, adult content, copyrighted material, and living celebrities in both prompts and images. Setup Instructions Obtain API Key: Register at Defapi.org and generate your API key with Sora 2 access. Store it securely—do not share it publicly. Configure Credentials: In n8n, create HTTP Bearer Auth credentials named "Defapi account" with your API key. Configure the Form: In the "Upload Image" form trigger node, ensure the following fields are set up: Prompt (text field, required) - Describe the video scene you want to generate Image (file upload, optional) - Optionally upload .jpg, .png, or .webp image files as reference Test the Workflow: Click "Execute Workflow" in n8n to activate the form trigger. Access the generated form URL and enter your creative video prompt. Optionally upload an image for additional context. The workflow will process any uploaded image through the "Convert to JSON" node, converting it to base64 format. The request is sent to the Sora 2 API endpoint at Defapi.org. The system will wait 10 seconds and then poll the API status until video generation is complete. Handle Outputs: The final "Format and Display Results" node formats and displays the generated video URL for download or embedding. Workflow Structure The workflow consists of the following nodes: Upload Image (Form Trigger) - Collects user input: creative prompt (required) and optional image file Convert to JSON (Code Node) - Converts any uploaded image to base64 data URI and formats prompt Send Sora 2 Generation Request to Defapi.org API (HTTP Request) - Submits video generation request to Sora 2 API Wait for Processing Completion (Wait Node) - Waits 10 seconds before checking status Obtain the generated status (HTTP Request) - Polls API task query endpoint for completion status Check if Image Generation is Complete (IF Node) - Checks if status equals 'success' Format and Display Results (Set Node) - Extracts and formats final video URL output Technical Details API Endpoint**: https://api.defapi.org/api/sora2/gen (POST request) Model Used**: Sora 2 AI video generation model Video Capabilities**: Supports 15-second videos and high-definition (HD) output Status Check Endpoint**: https://api.defapi.org/api/task/query (GET request) Wait Time**: 10 seconds between status checks Image Processing**: If an image is uploaded, it is converted to base64 data URI format (data:image/[type];base64,[data]) for API submission Authentication**: Bearer token authentication using the configured Defapi account credentials Request Body Format**: { "prompt": "Your video description here", "images": ["data:image/jpeg;base64,..."] } Note: The images array can contain an image or be empty if no image is provided Response Format**: The API returns a task_id which is used to poll for completion status. Final result contains data.result.video with the video URL. Accepted Image Formats**: .jpg, .png, .webp Specialized For**: Viral video content, social media videos, creative video marketing Customization Tips Enhance Prompts**: Include specifics like: Scene description and action sequences Character behaviors and emotions Camera movements and angles (e.g., slow-motion, dramatic zoom) Audio/music style (e.g., dramatic, upbeat, cinematic) Visual effects and atmosphere Timing and pacing details Enable 15s and HD Output**: To generate 15-second high-definition videos, start your prompt with (15s,hd). For example: (15s,hd) A pack of dogs driving tiny cars in a high-speed chase through a city... Content Moderation The API implements a three-stage content review process: Image Review: Rejects images with real people or highly realistic human faces Prompt Filtering: Checks for violence, adult content, copyrighted material, and living celebrities Output Review: Final check after generation (often causes failures at 90%+ completion) Best Practices: Avoid real human photos; use illustrations or cartoons instead Keep prompts generic; avoid brand names and celebrity names You can reference verified Sora accounts (e.g., "let @sama dance") If generation fails at 90%+, simplify your prompt and try again Example Prompts "A pack of dogs driving tiny cars in a high-speed chase through a city, wearing sunglasses and honking their horns, with dramatic action music and slow-motion jumps over fire hydrants." "(15s,hd) Animated fantasy landscape with floating islands, waterfalls cascading into clouds, magical creatures flying, golden sunset lighting, epic orchestral music." "(15s,hd) Product showcase with 360-degree rotation, dramatic lighting changes, particle effects, modern electronic background music." Use Cases Social Media Content**: Generate eye-catching videos for Instagram Reels, TikTok, and YouTube Shorts Marketing Campaigns**: Create unique promotional videos from product images Creative Projects**: Transform static images into dynamic storytelling videos Content Marketing**: Produce engaging video content without expensive production costs Viral Content Creation**: Generate shareable, attention-grabbing videos for maximum engagement

This workflow allows you to get the price of BTC in EUR and send an SMS when the price is larger than EUR 9000

This workflow demonstrates how to create a new deployment when new content gets added to the database. This example workflow can be used when building a JAMstack site. Webhook node: This node triggers the workflow when new content gets added. For this example, we have configured the webhook in GraphCMS. Netlify node: This node will start the build process and deploy the website. You will have to select your site from the Site ID dropdown list. To identify the deployment, we are passing a title.