Agents & Automations

Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Extract, Transform LinkedIn Data with Bright Data MCP Server & Google Gemini workflow is an automated solution that scrapes LinkedIn content via Bright Data MCP Server then transforms the response using a Gemini LLM. The final output is sent via webhook notification and also persisted on disk. This workflow is tailored for: - Data Analysts: Who require structured LinkedIn datasets for analytics and reporting. - Marketing and Sales Teams: Looking to enrich lead databases, track company updates, and identify market trends. - Recruiters and Talent Acquisition Specialists: Who want to automate candidate sourcing and company research. - AI Developers: Integrating real-time professional data into intelligent applications. - Business Intelligence Teams: Needing current and comprehensive LinkedIn data to drive strategic decisions. What problem is this workflow solving? Gathering structured and meaningful information from the web is traditionally slow, manual, and error-prone. This workflow solves: - Reliable web scraping using Bright Data MCP Server LinkedIn tools. - LinkedIn person and company web scrapping with AI Agents setup with the Bright Data MCP Server tools. - Data extraction and transformation with Google Gemini LLM. - Persists the LinkedIn person and company info to disk. - Performs a Webhook notification with the LinkedIn person and company info. What this workflow does? This n8n workflow performs the following steps: 1. Trigger: Start manually. 2. Input URL(s): Specify the LinkedIn person and company URL. 3. Web Scraping (Bright Data): Use Bright Data's MCP Server, LinkedIn tools for the person and company data extract. 4. Data Transformation & Aggregation: Uses the Google LLM for handling the data transformation. 5. Store / Output: Save results into disk and also performs a Webhook notification. Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol. You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio. You need to install the Bright Data MCP Server @brightdata/mcp. You need to install the n8n-nodes-mcp. Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp. Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above. Update the LinkedIn URL person and company workflow. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. Update the file name and path to persist on disk. How to customize this workflow to your needs - Different Inputs: Instead of static URLs, accept URLs dynamically via webhook or form submissions. - Data Extraction: Modify the LinkedIn Data Extractor node with the suitable prompt to format the data as you wish. - Outputs: Update the Webhook endpoints to send the response to Slack channels, Airtable, Notion, CRM systems, etc.

Workflow updated on 17/06/2024: Added 'Summarize' node to avoid creating a row for each Notion content block in the Supabase table. Store Notion's Pages as Vector Documents into Supabase This workflow automates the process of storing Notion pages as vector documents in a Supabase database with a vector column. The steps are as follows: Notion Page Added Trigger: Monitors a specified Notion database for newly added pages. You can create a specific Notion database where you copy the pages you want to store in Supabase. Retrieve Page Content: Fetches all block content from the newly added Notion page. Filter Non-Text Content: Excludes blocks of type "image" and "video" to focus on textual content. Summarize Content: Concatenates the Notion blocks content to create a single text for embedding. Store in Supabase: Stores the processed documents and their embeddings into a Supabase table with a vector column. Generate Embeddings: Utilizes OpenAI's API to generate embeddings for the textual content. Create Metadata and Load Content: Loads the block content and creates associated metadata, such as page ID and block ID. Split Content into Chunks: Divides the text into smaller chunks for easier processing and embedding generation.

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini Effortlessly transform BBC news articles into engaging podcasts with this automated n8n workflow. Who is this for? This template is perfect for: - Content creators who want to quickly produce podcasts from current events. - Students looking for an efficient way to create audio content for projects or assignments. - Individuals interested in generating their own podcasts without technical expertise. Setup Information 1. Install n8n: If you haven't already, download and install n8n from n8n.io. 2. Import the Workflow: Copy the JSON code for this workflow and import it into your n8n instance. 3. Configure Credentials: - Gemini API: Set up your Gemini API credentials in the workflow's LLM nodes. - Hugging Face Token: Obtain an access token from Hugging Face and add it to the HTTP Request node for the text-to-speech model. 4. Customize (Optional): - Filtering Criteria: Adjust the News Classifier node to fine-tune the selection of news articles based on your preferences. - Output Options: Modify the workflow to save the generated audio file to a cloud storage service or publish it to a podcast hosting platform. Prerequisites - An active n8n instance. - Basic understanding of n8n workflows (no coding required). - API credentials for Gemini and a Hugging Face account with an access token. What problem does it solve? This workflow eliminates the manual effort involved in creating podcasts from news articles. It automates the entire process, from fetching and filtering news to generating the final audio file. What are the benefits? - Time-saving: Create podcasts in minutes, not hours. - Easy to use: No coding or technical skills required. - Customizable: Adapt the workflow to your specific needs and preferences. - Cost-effective: Leverage free or low-cost services like Gemini and Hugging Face. How does it work? The workflow fetches news articles from the BBC website. It filters articles based on their suitability for a podcast. It extracts the full content of the selected articles. It uses Gemini LLM to create a podcast script. It converts the script to speech using Hugging Face's text-to-speech model. The final podcast audio is ready for use. Nodes in the Workflow - Fetch BBC News Page: Retrieves the main BBC News page. - News Classifier: Categorizes news articles using Gemini LLM. - Fetch BBC News Detail: Extracts detailed content from suitable articles. - Basic Podcast LLM Chain: Generates a podcast script using Gemini LLM. - HTTP Request: Converts the script to speech using Hugging Face. I'm excited to share this workflow with the n8n community and help content creators and students easily produce engaging podcasts! Additional Tips Explore the n8n documentation and community resources for more advanced customization options. Experiment with different filtering criteria and LLM prompts to achieve your desired podcast style.

This automation generates Google Docs content using Perplexity AI and ChatGPT based on new Google Sheets data entries, streamlining document creation and content generation processes.

This HARPA AI + MAKE.COM scenario enriches your name-email database and creates personalized outreach emails to promote your offerings. You only need a name-email database that works with Google Sheets. Don't have one? You can export contacts from services like Hunter.io, Tomba.io, Dropcontact.io, findymail.com, or Instantly. If Google Sheets isn't your preferred tool, feel free to use Airtable or another CRM solution. Here's how it works: - HARPA scans company websites after receiving an email to gather targeting data. - Updates information in your Google Sheets. - Creates a personalized email message. - Sends through your email service of choice (works with Mailgun, Gmail, and most providers). Required AI Commands to import into your HARPA: 1. Cold Outreach Email Generator: https://harpa.ai/library/cold-outreach-email-generator/67a22cb61a57f0776dfdfc6d 2. Database Enrichment: https://harpa.ai/library/database-enrichment/6728a09322b006c16f7f43e3

Use case Manually monitoring Reddit for viable business ideas is time-consuming and inconsistent. This workflow automatically analyzes trending Reddit discussions using AI to surface high-potential opportunities, filter irrelevant content, and generate actionable insights - saving entrepreneurs 10+ hours weekly in market research. What this workflow does This AI-powered workflow automatically collects trending Reddit discussions, analyzes posts for viable business opportunities using GPT-4, applies smart filters to exclude low-value content, and generates scored opportunity reports with market insights. It identifies unmet customer needs through sentiment analysis, prioritizes high-potential ideas using custom criteria, and outputs structured data to Google Sheets for actionable decision-making. Setup - Add Reddit, Google, and OpenAI credentials - Configure target subreddits in Subreddit node - Test workflow by testing workflow - Review generated opportunity report in Google Sheets How to adjust this template - Change data sources: Replace Reddit trigger with Twitter/X or Hacker News API - Modify criteria: Adjust scoring thresholds in Opportunity Calculator node - Add integrations: - Create automatic Slack alerts for urgent opportunities - Generate draft business plans using AI Document Writer

This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs, and the Pinecone vector database, it provides accurate and context-aware responses to your questions about how to use the GitHub API. You could adapt this to any OpenAPI specification for any public or private API, thus creating a documentation chatbot that anyone in your company can use. How it works:Data Ingestion: The workflow fetches the complete GitHub API OpenAPI 3 specification directly from the GitHub repository. Chunking and Embeddings: It splits the large API spec into smaller, manageable chunks. OpenAI's embedding models then generate vector embeddings for each chunk, capturing their semantic meaning. Vector Database Storage: These embeddings, along with the corresponding text chunks, are stored in a Pinecone vector database. Chat Interface and Query Processing: The workflow provides a simple chat interface. When you ask a question, it generates an embedding for your query using the same OpenAI model. Semantic Search and Retrieval: Pinecone is queried to find the most relevant text chunks from the API spec based on the query embedding. Response Generation: The retrieved chunks and your original question are fed to OpenAI's gpt-4o-mini LLM, which generates a concise, informative, and contextually relevant answer, including code snippets when applicable. Set up steps: 1. Create accounts: You'll need accounts with OpenAI and Pinecone. 2. API keys: Obtain API keys for both services. 3. Configure credentials: In your n8n environment, configure credentials for OpenAI and Pinecone using your API keys. 4. Import the workflow: Import this workflow into your n8n instance. 5. Pinecone Index: Ensure you have a Pinecone index named "n8n-demo" or adjust the workflow accordingly. The workflow is set up to work with this index out of the box. Setup Time: Approximately 15-20 minutes. Why use this workflow? - Learn RAG in Action: This is a practical, hands-on example of how to build a RAG-powered chatbot. - Adaptable Template: Easily modify this workflow to create chatbots for other APIs or knowledge bases. - n8n Made Easy: See how n8n simplifies complex integrations between data sources, vector databases, and LLMs.

How it Works This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the key operational steps: Email Reception and Summarization: The workflow starts with an Email Trigger (IMAP) node that listens for new emails in a specified inbox. Once an email is received, its HTML content is processed by a Markdown node to convert it into plain text if necessary, followed by an Email Summarization Chain node which uses AI to create a concise summary of the email's content using prompts tailored for this purpose. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, utilizing predefined templates and guidelines such as keeping responses under 100 words and ensuring they're formatted correctly in HTML. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through a Gmail node configured with double-approval settings. If approved (Approve?), the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. Set Up Steps To replicate this workflow within your own n8n environment, follow these essential configuration steps: Configuration: Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site. Import the provided JSON configuration file into your workspace, making sure all required credentials like IMAP, SMTP, OpenAI API keys, etc., are properly set up under the Credentials section since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals. Customization: Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?. Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems like Google Sheets or similar platforms where records might need tracking post-interaction completion.

This n8n workflow takes in a document such as a research paper, marketing or sales deck, or company filings, and breaks them down into three templates: study guide, briefing doc, and timeline. These templates are designed to help a student, associate, or clerk quickly summarize, learn, and understand the contents to be more productive. - Study guide: A short quiz of questions and answers generated by the AI Agent using the contents of the document. - Briefing Doc: Key information and insights are extracted by the AI into a digestible form. - Timeline: Key events, durations, and people are identified and listed into a simple to understand timeline by the AI. How it works A local file trigger watches a local network directory for new documents. New documents are imported into the workflow, its contents extracted and vectorized into a Qdrant vector store to build a mini-knowledge base. The document then passes through a series of template generating prompts where the AI will perform "research" on the knowledge base to generate the template contents. Generated study guide, briefing, and timeline documents are exported to a designated folder for the user. Requirements - Self-hosted version of n8n. - Qdrant instance for knowledge base. - Mistral.ai account for embeddings and AI model. Customizing your workflow Try adding your own templates or adjusting the existing templates to suit your unique use case. Anything is quite possible and limited only by your imagination! Want to go fully local? A version of this workflow is available which uses Ollama instead.

This workflow is for anyone looking to automatically fetch, validate, and parse complex language-based queries into a structured format. Its unique capability lies in not only processing language but also fixing invalid outputs before structuring them. Note that to use this template, you need to be on n8n version 1.19.4 or later.

Automatically transcribe Google Drive files into Google Docs using OpenAI GPT-3. Streamline document creation by converting audio files into text effortlessly.

Who is the template for? This workflow is specifically designed for content creators and social media professionals, enabling Instagram and X (Twitter) influencers to produce highly artistic visual posts. It empowers marketing teams to quickly generate event promotional graphics, assists blog authors in creating featured images and illustrations, and helps knowledge-based creators transform key insights into easily shareable card visuals. Set up Instructions - Fill in your API key from PiAPI. - Fill in Basic Params Node following the sticky note guidelines. - Set up a design template in Canvas Switchboard. - Make a simple template in Switchboard. - Click Crul and get the API code to fill in JSON of Design in Canvas. - Click Test Workflow and get a URL result. Use Case Here we will provide some setting examples to help users find a proper way to use this workflow. Users could change these settings based on specific purposes. Basic Params Setting: - theme: Hope - scenario: Don't know about the future, confused and feel lost with tech-development. - style: Cinematic Grandeur, Sci-Tech Aesthetic, 3D style - example: 1. March. Because of your faith, it will happen. 2. Something in me will save me. 3. To everyone carrying a heavy heart in silence. You are going to be okay. 4. Tomorrow will be better. - image prompt: A cinematic sci-fi metropolis where Deep Neural Nets control a hyper-connected society. Holographic interfaces glow in the air as robotic agents move among humans, symbolizing Industry 4.0. The scene contrasts organic human emotion with cold machine precision, rendered in a hyper-realistic 3D style with futuristic lighting. Epic wide shots showcase the grandeur of this civilization’s industrial evolution.

Supabase AI Agent with RAG & Multi-Tenant CRUDVersion: 1.0.0n8n Version: 1.88.0+Author: KoresolucoesLicense: MIT A stateful AI agent workflow powered by Supabase and Retrieval-Augmented Generation (RAG). Enables persistent memory, dynamic CRUD operations, and multi-tenant data isolation for AI-driven applications like customer support, task orchestration, and knowledge management. Key Features: 🧠 RAG Integration: Leverages OpenAI embeddings and Supabase vector search for context-aware responses. 🗃️ Full CRUD: Manage agent_messages, agent_tasks, agent_status, and agent_knowledge in real time. 📤 Multi-Tenant Ready: Supports per-user/organization data isolation via dynamic table names and webhooks. 🔒 Secure: Role-based access control via Supabase Row Level Security (RLS). Use Cases: - Customer Support Chatbots: Persist conversation history and resolve queries using institutional knowledge. - Automated Task Management: Track and update task statuses dynamically. - Knowledge Repositories: Store and retrieve domain-specific information for AI agents.

This n8n workflow shows how to convert PDF files into PNG format with the PDF Toolkit from www.customjs.space. What this workflow does: - Generate a PDF file from the requested HTML. - Convert the PDF to PNG images. - Use a Code node to handle URLs that point to PDF files. - Convert the PDF to PNG format. Requirements: - Self-hosted n8n instance. - CustomJS API key for converting PDF to PNG. - HTML Data to convert PDF files. - Code node for handling URLs that indicate PDF files. Workflow Steps: 1. Manual Trigger: - Runs with user interaction. 2. HTML to PDF: - Request HTML Data. - Convert HTML to PDF. - Request PDF from Code. 3. Convert PDF to PNG: - Convert the generated PNG from PDF. Usage: - Get API key from CustomJS. - Sign up for the CustomJS platform. - Navigate to your profile page. - Press the "Show" button to get the API key. - Set Credentials for CustomJS API on n8n. - Copy and paste your API key generated from CustomJS here. Design workflow: - A Manual Trigger for starting the workflow. - HTTP Request Nodes for downloading PDF files. - Code node for handling URLs that indicate PDF files. - Convert PDF to PNG. You can replace logic for triggering and returning results. For example, you can trigger this workflow by calling a webhook and get a result as a response from the webhook. Simply replace Manual Trigger and Write to Disk nodes.

Who Is This For This workflow is ideal for content creators, bloggers, marketers, and professionals seeking to automate the creation and publication of SEO-optimized articles. It's particularly beneficial for those utilizing Notion for content management and WordPress for publishing. What Problem Does This Workflow Solve Manually creating SEO-friendly articles is time-consuming and requires consistent effort. This workflow streamlines the entire process—from detecting updates in Notion to publishing on WordPress—by leveraging AI for content generation, thereby reducing the time and effort involved. What This Workflow Does - Monitor Notion Updates: Detects changes in a specified Notion database. - AI Content Generation: Utilizes an AI model to produce an SEO-optimized article based on Notion data. - Publish to WordPress: Automatically posts the generated article to a WordPress site. - Email Notification: Sends an email containing the article's title and URL. - Update Notion Database: Updates the corresponding entry in the Notion database with the article details. Setup GuidePrerequisites - WordPress account with API access. - API key for the AI model used. - Notion integration with the relevant database ID. - Credentials for the email service used (e.g., Gmail). Community Node Requirement: This workflow utilizes the n8n-nodes-mcp community node, which is only compatible with self-hosted instances of n8n. For more information on installing and managing community nodes, refer to the n8n documentation. Steps 1. Import the workflow into your self-hosted n8n instance. 2. Install the required community node (n8n-nodes-mcp). 3. Configure API credentials for WordPress, the AI service, Notion, and the email service. 4. Define necessary variables, such as the notification email address and Notion database IDs. 5. Activate the workflow to automate the process. How to Customize This Workflow - AI Prompt: Adjust the prompt used for content generation to align with your preferred tone and style. - Article Structure: Modify the structure of the generated article by tweaking settings in the content generation node. - Notifications: Customize the content and recipients of the emails sent post-publication. - Notion Updates: Tailor the fields updated in Notion to suit your specific requirements.

Template for Kids' Story in Arabic The n8n template for creating kids' stories in Arabic offers a versatile platform for storytellers to captivate young audiences with educational and interactive tales. It allows for customization to suit various use cases and can be set up effortlessly. Use CasesEducational Platforms: Educational platforms can automate the creation and distribution of educational stories in Arabic for children using this template. By incorporating visual and auditory elements into the storytelling process, educational platforms can enhance learning experiences and engage young learners effectively. Children's Libraries: Children's libraries can utilize this template to curate and share a diverse collection of Arabic stories with young readers. The automated generation of visual content and audio files enhances the storytelling experience, encouraging children to immerse themselves in new worlds and characters through captivating narratives. Language Learning Apps: Language learning apps focused on Arabic can integrate this template to offer culturally rich storytelling experiences for children learning the language. By translating stories into Arabic and supplementing them with visual and auditory components, these apps can facilitate language acquisition in an enjoyable and interactive manner. By following these node descriptions, users can effectively configure the n8n template for kids' stories in Arabic, tailoring it to specific use cases for a seamless and engaging storytelling experience for young audiences.

This automation allows you to generate AI images from text prompts and deliver them directly to Telegram users, all without manual intervention. Using Google Sheets as the command center, users can input prompts, while the system automatically generates images using AI tools like OpenAI or Stability, stores them in Google Drive, and sends them via a Telegram bot. Ideal for creators, marketers, or service providers who want to streamline content creation and delivery to their audience or clients.

🤖 This n8n workflow creates an intelligent Telegram bot that processes multiple types of messages and provides automated responses using AI capabilities. The bot serves as a personal assistant that can handle text, voice messages, and images through a sophisticated processing pipeline. Core ComponentsMessage Reception and Validation 📥 🔄 Implements webhook-based message reception for real-time processing. 🔐 Features a robust user validation system that verifies sender credentials. 🔀 Supports both testing and production webhook endpoints for development flexibility. Message Processing Pipeline ⚡ 🔄 Uses a smart router to detect and categorize incoming message types. 📝 Processes three main message formats: 💬 Text messages 🎤 Voice recordings 📸 Images with captions. AI Integration 🧠 🤖 Leverages OpenAI's GPT-4 for message classification and processing. 🗣️ Incorporates voice transcription capabilities for audio messages. 👁️ Features image analysis using GPT-4 Vision API for processing visual content. Technical ArchitectureWebhook Management 🔌 🌐 Maintains separate endpoints for testing and production environments. 📊 Implements automatic webhook status monitoring. ⚡ Provides real-time webhook configuration updates. Error Handling ⚠️ 🔍 Features comprehensive error detection and reporting. 🔄 Implements fallback mechanisms for unprocessable messages. 💬 Provides user feedback for failed operations. Message Classification System 📋 🏷️ Categorizes incoming messages into tasks and general conversation. 🔀 Implements separate processing paths for different message types. 🧩 Maintains context awareness across message processing. Security FeaturesUser Authentication 🔒 ✅ Validates user credentials against predefined parameters. 👤 Implements first name, last name, and user ID verification. 🚫 Restricts access to authorized users only. Response SystemIntelligent Responses 💡 🤖 Generates contextual responses based on message classification.

About this AI Agent This workflow is designed to automate web interactions by simulating a human user, using a combination of the Agent node and AI tools powered by Airtop. How does this workflow work?Form Submission Trigger: The workflow starts with a form submission trigger node named "On form submission." This node collects user instructions for the web AI agent, including a prompt and an optional Airtop profile name for sites requiring authentication. AI Agent: The core of the workflow is the "AI Agent" node, which uses a smart web agent to manage a remote web browser. It is designed to fulfill user requests by interacting with the browser through various tools. Browser Session ManagementStart Browser: The "Start browser" node initiates a new browser session and window. It is essential for obtaining the sessionId and windowId required for subsequent operations. Session and Window Management: The workflow includes nodes for creating and managing browser sessions and windows, such as "Session" and "Window." Web Interaction Tools: - Load URL: This node loads a specified URL into the browser window. - Query: The "Query" node allows the agent to ask questions and extract information from the current web page. - Click: This node simulates clicking on elements within the web page. - Type: The "Type" node types text into specified elements on the page. Session Termination: The "End session" node is used to terminate the browser session once the tasks are completed. Output HandlingStructured Output Parser: This node processes the agent's results into a structured format. Output: The final results are set and prepared for output. Slack Integration: Although currently disabled, there is a "Slack" node intended to send messages to a Slack channel, potentially for notifications or live view URLs. Setting up your agentAirtop API Credentials: Users must have valid Airtop API credentials to interact with the web browser tools. This includes nodes like "Click," "Query," "Load URL," "End session," "Type," "Session," and "Window." Slack API Credentials (Optional): If users want to enable Slack notifications, they need to configure Slack OAuth2 credentials. The Slack node is currently disabled but can be used to send messages to a Slack channel. Anthropic API Credentials: The "Claude 3.5 Haiku" node requires Anthropic API credentials. Users need to have access to this API to utilize the language model features.

Free template for voice & text messages with short-term memory. This n8n workflow template is a blueprint for an AI Telegram bot that processes both voice and text messages. It is ready to use with minimal setup. The bot remembers the last several messages (10 by default), understands commands, and provides responses in HTML. You can easily swap GPT-4 and Whisper for other language and speech-to-text models to suit your needs. Core Features: - Text: send or forward messages - Voice: transcription via Whisper Extend this template by adding LangChain tools. Requirements: - Telegram Bot API - OpenAI API (for GPT-4 and Whisper) 💡 New to Telegram bots? Check our step-by-step guide on creating your first bot and setting up OpenAI access. Use Cases: - Personal AI assistant - Customer support automation - Knowledge base interface - Integration hub for services that you use: - Connect to any API via HTTP Request Tool - Trigger other n8n workflows with Workflow Tool

This scenario automates content posting to your WordPress website. It requires minimal setup and runs using an existing HARPA AI Command. To use it, simply add useful article links to a Google Sheet. The script will run at set intervals, pick an unprocessed article, rewrite it, and post it to your WordPress blog or website. This lets you automate daily content publishing without constant monitoring. For more information, visit: www.sabrina.dev