Workflow Templates

Description This workflow automatically collects weather data from multiple sources and compiles it into comprehensive reports. It helps you make informed decisions based on accurate weather forecasts without manually checking multiple weather services. Overview This workflow automatically scrapes weather data from multiple sources and compiles it into a comprehensive report. It uses Bright Data to access weather websites and can be configured to send you regular weather updates for your locations of interest. Tools Used n8n:** The automation platform that orchestrates the workflow. Bright Data:** For scraping weather websites and forecast data without getting blocked. Notification Services:** Email, messaging apps, or other platforms. How to Install Import the Workflow: Download the .json file and import it into your n8n instance. Configure Bright Data: Add your Bright Data credentials to the Bright Data node. Set Up Notifications: Configure how you want to receive weather reports. Customize: Add your locations of interest and reporting frequency. Use Cases Event Planners:** Get weather forecasts for upcoming outdoor events. Farmers:** Monitor weather conditions for agricultural planning. Travelers:** Check weather forecasts for destinations before trips. Connect with Me Website:** https://www.nofluff.online YouTube:** https://www.youtube.com/@YaronBeen/videos LinkedIn:** https://www.linkedin.com/in/yaronbeen/ Get Bright Data:** https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #weather #weatherforecasts #brightdata #webscraping #weatherreports #weatheralerts #weatherdata #weathermonitoring #n8nworkflow #workflow #nocode #weatherautomation #weatherscraping #weathertracking #weathernotifications #weatherupdates #forecastdata #weatherplanning #weatherservice #outdoorevents #weatherapi #weatherinformation #climatedata #weathertech

3 nodes357 views

Generate 3D Mesh Models from Images with Part Crafter AI on Replicate

by Yaron Been

Fire Part Crafter Image Generator Description PartCrafter is a structured 3D mesh generation model that creates multiple parts and objects from a single RGB image. Overview This n8n workflow integrates with the Replicate API to use the fire/part-crafter model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters image** (string): Input image for 3D mesh generation Optional Parameters seed** (integer, default: 0): Random seed for reproducibility. Use 0 for random seed num_parts** (integer, default: 16): Number of parts to generate num_tokens** (string, default: 2048): Number of tokens for generation guidance_scale** (number, default: 7): Guidance scale for generation remove_background** (boolean, default: False): Remove background from input image use_flash_decoder** (boolean, default: False): Use flash decoder for faster inference (Tempermental?) num_inference_steps** (integer, default: 50): Number of inference steps How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: fire/part-crafter API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Split In Batches node noItemsLeft example

by Harshil Agrawal

This workflow demonstrates how to use noItemsLeft to check if there are items left to be processed by the SplitInBatches node. Function node: This node generates mock data for the workflow. Replace it with the node whose data you want to split into batches. SplitInBatches node: This node splits the data with the batch size equal to 1. Based on your use-case, set the value of the Batch Size. IF node: This node check if all the data by the SplitInBatches are not processed or not. It uses the expression {{$node["SplitInBatches"].context["noItemsLeft"]}} which returns a boolean value. If there is data yet to be processed, the expression will return false, otherwise true. Set node: This node prints a message No Items Left. Based on your use-case, connect the false output of the IF node to the input of the node you want to execute, after the data is processed by the SplitInBatches node.

🤖AI & Machine Learning

0 nodes5814 views

Evaluate Animal Advocacy Text with Hugging Face Open Paws AI Models

by Open Paws

This sub-workflow uses two custom Hugging Face regression models from Open Paws to evaluate and predict the real-world performance and advocacy alignment of text content. It’s designed to support animal advocacy organizations in optimizing their messaging across platforms like social media, email campaigns, and more. 🛠️ What It Does Sends input text to two deployed Hugging Face endpoints: Predicted Performance Model – Estimates real-world content success (e.g., engagement, shares, opens) based on patterns from real online data. Advocate Preference Model – Predicts how well the content will resonate with animal advocates (emotional impact, relevance, rationality, etc.) Outputs structured scores for both models Can be integrated into larger workflows for automated content review, filtering, or revision 📊 About the Models Text Performance Prediction Model** Trained on real-world data from 30+ animal advocacy organizations, this model predicts actual online performance of content—including social media, email marketing, and other outreach channels. Advocate Preference Prediction Model** Trained on ratings from animal advocates to evaluate how well a piece of text aligns with advocacy goals and values. Model Repositories: open-paws/text_performance_prediction_longform open-paws/animal_advocate_preference_prediction_longform > 📌 You must deploy each model as an inference endpoint on Hugging Face. Click "Deploy" on each model’s repo, then add the endpoint URL and your Hugging Face access token using n8n credentials. 📦 Use Cases Advocacy content review before publishing Automated scoring of outreach messages Filtering or flagging content with low predicted impact A/B testing support for message optimization

1 nodes18 views

Generate Text-to-Video Content with Google Veo 3 via Replicate API

by Yaron Been

Google Veo 3 Video Generator Description Sound on: Google’s flagship Veo 3 text to video model, with audio Overview This n8n workflow integrates with the Replicate API to use the google/veo-3 model. This powerful AI model can generate high-quality video content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Text prompt for video generation Optional Parameters seed** (integer, default: None): Random seed. Omit for random generations resolution** (string, default: 720p): Resolution of the generated video negative_prompt** (string, default: None): Description of what to discourage in the generated video How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate video content Access the generated output from the final node API Reference Model: google/veo-3 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of video generation parameters

Post New Articles from Feeds to Slack Channel

by Paul Taylor

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 📄 Post New Articles from Feeds to Slack Channel 🧠 What This Workflow Does This workflow automates the discovery and sharing of fresh articles from a curated list of RSS feeds. It performs the following steps: Reads a list of RSS feed URLs from a Google Sheet (Feeds tab). Fetches the latest articles from each feed. Checks for duplicates against previously published links stored in another sheet (Posted Articles tab). Filters out already shared articles. Posts the new articles to a designated Slack channel with formatted titles and links. Logs the newly shared articles back into the Google Sheet to prevent duplicates. 🛠️ Prerequisites To use this workflow, you must have: ✅ Google Sheets OAuth2 credentials set up in n8n (Used to access and update the RSS feed and post history sheets) ✅ Slack OAuth2 credentials (Used to post messages to a specific Slack channel) ✅ A Google Spreadsheet with: Feeds tab – Columns: title, link Posted Articles tab – Columns: title, link, pubDate 🔧 Environment Variables or Custom Values You will need to set the following n8n variable or replace with direct input: {{$vars.Daily_Industry_News_Automation_Google_Sheet}}: Reference to the Google Sheet Document ID (you can use a static ID if preferred) Also update: Slack channelId: Replace with your actual Slack channel ID if not dynamically referenced ⏰ Trigger & Scheduling Trigger type**: Cron node Default schedule: Every day at **7:00 AM You can modify this in the “Trigger Workflow” node to suit your own schedule. 🎯 Intended Use Case This workflow is ideal for: Marketing teams curating daily or weekly news digests Founders or industry professionals monitoring sector updates Automating internal Slack news updates Avoiding duplicate content when sourcing from multiple feeds

⚙️Business Automation

3 nodes180 views

Notify on Telegram When a New Order is Created in WooCommerce 📦

by AmirHossein MnasouriZade

📦 Send Telegram Notifications for New WooCommerce Orders This workflow automatically sends a Telegram notification when an order status in WooCommerce changes to "Processing." Perfect for online store owners who want instant updates on order fulfillment. ⚙️ Set Up Telegram Alerts for WooCommerce Orders Configure WooCommerce Webhook to trigger on order updates. Create a Telegram Bot and obtain the API token. Set Up Telegram Credentials in n8n. Configure the Telegram Node with your chat ID. Activate and Test the workflow by placing a new order. ##💡 Notes You can customize the message format in the 🖋️ Design Message Template node to include additional order details. Contact me on [Telegram]: https://t.me/amir676080 Message structure includes the following details 🆔 Order Number: 11234 👦🏻 Customer Name: John Doe 💵 Amount: 299.99 USD 📅 Order Date: ➖ 25th November 2024 at 14:42 🏙 City: New York 📞 Phone: +1 555-1234 ✍🏻 Order Note: Fast delivery requested 📦 Ordered Products: 🔹 Wireless Earbuds (2 items) 📝 Type: Premium Sound Edition Contact me on [Telegram]: https://t.me/amir676080

2 nodes3017 views

Automated URL Phishing & Threat Analysis with NixGuard AI

by Jonathan | NEX

Stop manually checking suspicious links. This free n8n workflow provides the foundation for a powerful, automated URL analysis pipeline. Using the NixGuard AI engine, you can instantly analyze suspicious URLs from emails, logs, or tickets to uncover phishing attempts, malware hosting sites, and malicious redirects. What You Will Automate: 🤖 Instant Threat Triage: Get an immediate AI-powered summary of why a URL is malicious, saving you critical investigation time. 🎯 Actionable IOC Extraction: Automatically extract the final redirected URL, malicious domains, and IPs to fuel your threat hunting and blocking rules. 🚀 SOAR-Ready Foundation: This workflow is the perfect starting point for your security playbooks. Use the output to: Alert: Send instant notifications to Slack or Teams. Respond: Create tickets in Jira or TheHive. Block: Add malicious domains to your firewall or DNS filter. Download this free template and automate your first line of defense against web-based threats in minutes! Don't have the main workflow yet? Get it HERE! 🔗 Learn more about NixGuard: thenex.world 🔗 Get started with a free security subscription: thenex.world/security/subscribe For search: URL Scanning, Phishing, Threat Intelligence, SOAR, SOC Automation, NixGuard, Free, AI, Incident Response, Cybersecurity, Automation, Link Analysis, MTTR, Malware, VirusTotal

1 nodes4 views

WhatsApp Group Onboarding with Automated Welcome Messages & Airtable Point System

by David w/ SimpleGrow

Receive Webhook Notification The workflow starts when a webhook receives a POST request from Whapi, notifying that a new participant has joined a WhatsApp group. Filter the Event The workflow checks two conditions: The event is for the correct WhatsApp group (matching the specific group ID). The action type is "add" (meaning a user was added to the group). Send Welcome Message If both conditions are met, the workflow sends a personalized welcome message to the new participant via Whapi. The message explains the group rules and how the user can earn points and participate in weekly raffles. Create Airtable Record After sending the welcome message, the workflow creates a new record in the Airtable database for the new participant. The record includes: The participant’s WhatsApp ID An initial engagement count of 100 points The date of the last interaction (set to today) Result Every new group member is automatically welcomed and registered in your engagement database with starter points, ready to participate in your group’s activities and rewards. This workflow ensures new users are greeted, informed, and instantly included in your engagement tracking system.

Automated AI image tagging and writing keywords into image (via community node)

by Friedemann Schuetz

Welcome to my Automated Image Metadata Tagging Workflow! DISCLAIMER: This workflow only works with self-hosted n8n instances! You have to install the n8n-nodes-exif-data Community Node! This workflow automatically analyzes the image content with the help of AI and writes it directly back into the image file as keywords. (https://n8n.io/workflows/2995).** This workflow has the following steps: Google Drive trigger (scan for new files added in a specific folder) Download the added image file Analyse the content of the image Merge Metadata and image file Write the Keywords into the Metadata (dc:subject/keywords) and create new image file Update the original file in the Google Drive folder The following accesses are required for the workflow: You have to install the n8n-nodes-exif-data Community Node** Google Drive: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) You can contact me via LinkedIn, if you have any questions: https://www.linkedin.com/in/friedemann-schuetz

🤖AI & Machine Learning

2 nodes1034 views

Generate High-Quality Images with Replicate's Fire/Flux AI Model

by Yaron Been