Workflow Templates

This n8n workflow builds an appointment scheduling AI agent which can Take enquiries from prospective customers and help them book an appointment by checking appointment availability Where no appointment is booked, the Agent is able to send follow-up messages to re-engage leads. After an appointment is booked, the agent is able reschedule or even cancel the booking for the user without human intervention. For small outfits, this workflow could contribute the necessary "man-power" required to increase business sales. The sample Airtable can be found here: https://airtable.com/appO2nHiT9XPuGrjN/shroSFT2yjf87XAox 2024-10-22 Updated to Cal.com API v2. How it works The customer sends an enquiry via SMS to trigger our workflow. For this trigger, we'll use a Twilio webhook. The prospective or existing customer's number is logged in an Airtable Base which we'll be using to track all our enquries. Next, the message is sent to our AI Agent who can reply to the user and decide if an appointment booking can be made. The reply is made via SMS using Twilio. A scheduled trigger which runs every day, checks our chat logs for a list of prospective customers who have yet to book an appointment but still show interest. This list is sent to our AI Agent to formulate a personalised follow-up message to each lead and ask them if they want to continue with the booking. The follow-up interaction is logged so as to not to send too many messages to the customer. Requirements A Twilio account to receive customer messages. An Airtable account and Base to use as our datastore for enquiries. Cal.com account to use as our scheduling service. OpenAI account for our AI model. Customising this workflow Not using Airtable? Swap this out for your CRM of choice such as hubspot or your own service. Not using Cal.com? Swap this out for API-enabled services such as Acuity Scheduling or your own service.

This workflow automates the process of gathering LinkedIn advice articles, extracting their content, and generating unique contributions for each article using an AI model. The contributions are then posted to a Slack channel and a NocoDB database for record-keeping. The workflow is triggered weekly to ensure new articles are continuously collected and responded to. Who is this for? This workflow is designed for professionals, marketers, and content creators looking to boost their LinkedIn presence by regularly engaging with LinkedIn advice articles. It’s especially useful for those who want to be seen as a "thought leader" or "top voice" in their niche by contributing relevant and unique advice to trending topics. What problem is this workflow solving? Manually searching for relevant LinkedIn articles, reading through them, and crafting thoughtful contributions can be time-consuming. This workflow solves that by automating the process of finding new articles, extracting key content, and generating AI-powered contributions. It helps users stay consistently active on LinkedIn, contributing value to trending discussions. What this workflow does Triggers Weekly: The workflow is set to run every Monday at 8:00 AM. Search Google for LinkedIn Advice Articles: Uses a predefined Google search URL to find the latest LinkedIn advice articles based on the user's area of expertise. Extract LinkedIn Article Links: A code node extracts all LinkedIn advice article links from the search results. Retrieve Article Content: For each article link, the workflow retrieves the HTML content and extracts the article title, topics, and existing contributions. Generate AI-Powered Contributions: The workflow sends the extracted article content to an AI model, which generates unique, helpful advice for each topic within the article. Post to Slack & NocoDB: The AI-generated contributions, along with the article links, are posted to a designated Slack channel and stored in a NocoDB database for future reference. Setup Google Search URL: Update the Google search URL with the relevant LinkedIn advice query for your field (e.g., "site:linkedin.com/advice 'marketing automation'"). Slack Integration: Connect your Slack account and specify the Slack channel where you want the contributions to be posted. NocoDB Integration: Set up your NocoDB project to store the generated contributions along with the article titles and links. How to customize this workflow Change Search Terms**: Modify the Google search URL to focus on a different LinkedIn topic or expertise area. Adjust Trigger Frequency**: The workflow is set to run weekly, but you can adjust the frequency by changing the schedule trigger. Enhance Contribution Quality**: Customize the AI model's prompt to generate contributions that align with your brand voice or content strategy. Workflow Summary This workflow helps users maintain a consistent presence on LinkedIn by automating the discovery of new advice articles and generating unique contributions using AI. It is ideal for professionals who want to engage with LinkedIn content regularly without spending too much time manually searching and drafting responses.

🤖 Universal E-Commerce AI Assistant (Shopify, WooCommerce & RAG) This powerful n8n workflow deploys a sophisticated, multi-talented AI chatbot designed to streamline your e-commerce and customer support operations. The AI assistant can intelligently understand user queries and route them to the correct specialized agent, whether it's for Shopify, WooCommerce, or general knowledge questions answered by a Retrieval-Augmented Generation (RAG) system. This template automates responses to a wide range of inquiries, from checking Shopify order statuses with GraphQL to fetching product lists from WooCommerce, and even answering general questions by looking up information in a Pinecone vector database. How It Works ⚙️ The workflow operates in a series of logical steps, starting from the moment a user sends a message. 💬 Chat Trigger: The workflow activates when a user sends a message in the n8n chat interface. It captures the user's input and a unique session ID to track the conversation. 🧠 Intelligent Routing: The user's query is first sent to a Router Agent powered by GPT-4o-mini. This agent's sole purpose is to classify the intent of the message and output one of three keywords: SHOPIFY, WOOCOMMERCE, or None of them. 🔀 Conditional Branching: Based on the Router's output, a series of IF nodes direct the conversation down one of three paths: General Queries Path Shopify Path WooCommerce Path 📚 General Queries (RAG): If the query is not about e-commerce, it's handled by a RAG agent. Embedding: The user's question is converted into a vector embedding using AWS Bedrock. Retrieval: The workflow searches a Pinecone Vector Store to find the most relevant information from your knowledge base. Generation: A GPT-4o-mini agent receives the context from Pinecone and generates a comprehensive, helpful answer. 🛍️ E-Commerce Specialists: If the query is about Shopify or WooCommerce, it's passed to a dedicated agent. Shopify Agent: This agent uses Google Gemini and has a suite of tools to manage Shopify tasks. It can Get Order info, Fetch All Products, or run complex queries using the powerful GraphQL tool. WooCommerce Agent: This agent also uses Google Gemini and is equipped with tools to Fetch Order Details and Fetch All Products from a WooCommerce store. 🗣️ Conversation Memory: Each agent (Router, General, Shopify, WooCommerce) is connected to its own Memory node. This allows the chatbot to remember previous parts of the conversation for a more natural and context-aware interaction. 🏁 Merge & Respond: All three paths converge at a final Merge node. This ensures that no matter which agent handled the request, the final answer is streamlined into a single output and sent back to the user in the chat. Nodes Used 🔗 Triggers: Chat Trigger: Starts the workflow when a chat message is received. AI & Agents: AI Agent: Four separate agents for Routing, Shopify, WooCommerce, and General Queries. OpenAI Chat Model: Uses GPT-4o-mini for the Router and General Queries agent. Google Gemini Chat Model: Uses Google Gemini for the Shopify and WooCommerce agents. Tools & Data: Shopify Tool: To get products and order information from Shopify. WooCommerce Tool: To get products and order information from WooCommerce. GraphQL Tool: For advanced, custom queries to the Shopify API. Pinecone Vector Store: To retrieve context for the RAG agent. AWS Bedrock Embeddings: To create vector embeddings for Pinecone. Logic & Memory: IF Node: To conditionally route the workflow. Merge Node: To consolidate the different branches before ending. Window Buffer Memory: Four nodes to provide conversational memory to each agent. Setup Guide 🛠️ To use this workflow, you'll need to configure several nodes with your own credentials and settings. 1\. AI Model Credentials OpenAI: Create an API key in your OpenAI Platform dashboard. Add this credential to the Router Model and GPT-4o-mini nodes. Google Gemini: Create an API key in your Google AI Studio dashboard. Add this credential to the Shopify Chat Model and WooCommerce Chat Model nodes. 2\. E-Commerce Platform Credentials Shopify: You will need a Shopify Access Token. Follow the n8n documentation to generate one. Add the credential to the Fetch All Products and Get Order info nodes. WooCommerce: Create API credentials from your WordPress dashboard. Add the credential to the Fetch All Products2 and Fetch Order Details nodes. 3\. RAG System Credentials (Pinecone & AWS) Pinecone: Sign up for a Pinecone account and create an API key. Add your Pinecone credentials in n8n. In the Pinecone Vector Store node, set the pineconeIndex to the name of your index. You must have a pre-existing index with data for the RAG to work. AWS: Create an AWS account and an IAM user with programmatic access to Amazon Bedrock. Add your AWS credentials in n8n. Select your AWS credentials in the AWS Bedrock Embeddings node. 4\. GraphQL Node Configuration In the GraphQL node, you must update the endpoint URL. Replace the placeholder https://{subdomain}.myshopify.com/admin/api/2025-04/graphql.json with your own Shopify store's GraphQL API endpoint.

Overview Transform your Bitrix24 Open Line channels with an intelligent chatbot that leverages Retrieval-Augmented Generation (RAG) technology to provide accurate, document-based responses to customer inquiries in real-time. Use Case This workflow is designed for organizations that want to enhance their customer support capabilities in Bitrix24 by providing automated, knowledge-based responses to customer inquiries. It's particularly useful for: Customer service teams handling repetitive questions Support departments with extensive documentation Sales teams needing quick access to product information Organizations looking to provide 24/7 customer support What This Workflow Does Smart Document Processing Automatically processes uploaded PDF documents Splits documents into manageable chunks Generates vector embeddings for semantic understanding Indexes content for efficient retrieval AI-Powered Responses Utilizes Google Gemini AI to generate natural language responses Constructs answers based on relevant document content Maintains conversation context for coherent interactions Provides fallback responses when information is not available Vector Database Integration Stores document embeddings in Qdrant vector database Enables semantic search beyond simple keyword matching Retrieves the most relevant information for each query Maintains a persistent knowledge base that grows over time Webhook Handler Processes incoming messages from Bitrix24 Open Line channels Handles authentication and security validation Routes different types of events to appropriate handlers Manages session and conversation state Event Routing Intelligently routes different event types: ONIMBOTMESSAGEADD: Processes new user messages ONIMBOTJOINCHAT: Handles bot joining a conversation ONAPPINSTALL: Manages application installation ONIMBOTDELETE: Handles bot deletion Document Management Organizes processed documents in designated folders Tracks document processing status Moves indexed documents to appropriate locations Maintains document metadata for reference Interactive Menu Provides menu-based options for common user requests Customizable menu items and responses Easy navigation for users seeking specific information Fallback to operator option when needed Technical Architecture Components Webhook Handler: Receives and validates incoming requests from Bitrix24 Credential Manager: Securely manages authentication tokens and API keys Event Router: Directs events to appropriate processing functions Document Processor: Handles document loading, chunking, and embedding Vector Store: Qdrant database for storing and retrieving document embeddings Retrieval System: Searches for relevant document chunks based on user queries LLM Integration: Google Gemini model for generating natural language responses Response Manager: Formats and sends responses back to Bitrix24 Integration Points Bitrix24 API**: For bot registration, message handling, and user interaction Ollama API**: For generating document embeddings Qdrant API**: For vector storage and retrieval Google Gemini API**: For AI-powered response generation Setup Instructions Prerequisites Active Bitrix24 account with Open Line channels enabled Access to n8n workflow system Ollama API credentials Qdrant vector database access Google Gemini API key Configuration Steps Initial Setup Import the workflow into your n8n instance Configure credentials for all services Set up webhook endpoints Bitrix24 Configuration Create a new Bitrix24 application Configure webhook URLs Set appropriate permissions Install the application to your Bitrix24 account Document Storage Create a designated folder in Bitrix24 for knowledge base documents Configure folder paths in the workflow settings Upload initial documents to be processed Bot Configuration Customize bot name, avatar, and description Configure welcome messages and menu options Set up fallback responses Testing Verify successful installation Test document processing pipeline Send test queries to evaluate response qu

Printify Automation - Update Title and Description Workflow This n8n workflow automates the process of retrieving products from Printify, generating optimized product titles and descriptions, and updating them back to the platform. It leverages OpenAI for content generation and integrates with Google Sheets for tracking and managing updates. Features Integration with Printify**: Fetch shops and products through Printify's API. AI-Powered Optimization**: Generate engaging product titles and descriptions using OpenAI's GPT model. Google Sheets Tracking**: Log and manage updates in Google Sheets. Custom Brand Guidelines**: Ensure consistent tone by incorporating brand-specific instructions. Loop Processing**: Iteratively process each product in batches. Workflow Structure Nodes Overview Manual Trigger: Manually start the workflow for testing purposes. Printify - Get Shops: Retrieves the list of shops from Printify. Printify - Get Products: Fetches product details for each shop. Split Out: Breaks down the product list into individual items for processing. Loop Over Items: Iteratively processes products in manageable batches. Generate Title and Desc: Uses OpenAI GPT to create optimized product titles and descriptions. Google Sheets Integration: Trigger: Monitors Google Sheets for changes. Log Updates: Records product updates, including old and new titles/descriptions. Conditional Logic: If Nodes: Ensure products are ready for updates and stop processing once completed. Printify - Update Product: Sends updated titles and descriptions back to Printify. Brand Guidelines + Custom Instructions: Sets brand tone and seasonal instructions. Setup Instructions Prerequisites n8n Instance: Ensure n8n is installed and configured. Printify API Key: Obtain an API key from your Printify account. Add it to n8n under HTTP Header Auth. OpenAI API Key: Obtain an API key from OpenAI. Add it to n8n under OpenAI API. Google Sheets Integration: Share your Google Sheets with the Google API service account. Configure Google Sheets credentials in n8n. Workflow Configuration Set Brand Guidelines: Update the Brand Guidelines + Custom Instructions node with your brand name, tone, and seasonal instructions. Batch Size: Configure the Loop Over Items node for optimal batch sizes. Google Sheets Configuration: Set the correct Google Sheets document and sheet names in the integration nodes. Run the Workflow: Start manually or configure the workflow to trigger automatically. Key Notes Customization**: Modify API calls to support other platforms like Printful or Vistaprint. Scalability**: Use batch processing for efficient handling of large product catalogs. Error Handling**: Configure retries or logging for any failed nodes. Output Examples Optimized Content Example Input Title**: "Classic White T-Shirt" Generated Title**: "Stylish Classic White Tee for Everyday Wear" Input Description**: "Plain white T-shirt made of cotton." Generated Description**: "Discover comfort and style with our classic white tee, crafted from premium cotton for all-day wear. Perfect for casual outings or layering." Next Steps Monitor Updates: Use Google Sheets to review logs of updated products. Expand Integration: Add support for more Printify shops or integrate with other platforms. Enhance AI Prompts: Customize prompts for different product categories or seasonal needs. Feel free to reach out for additional guidance or troubleshooting!

YouTube Content Repurposing Automation Who's it for This workflow is for content creators, marketers, agencies, coaches, and businesses who want to maximize their YouTube content ROI by automatically generating multiple content assets from single videos. It's especially useful for professionals who want to: Repurpose YouTube videos into blogs, social posts, newsletters, and tutorials without manual effort Scale their content production across multiple channels and platforms Create consistent, high-quality content derivatives while saving time and resources Build automated content systems that generate multiple revenue streams Maintain active presence across social media, email, and blog platforms simultaneously What problem is this workflow solving Content creators face significant challenges when trying to maximize their video content: Time-intensive manual repurposing: Converting one YouTube video into multiple content formats traditionally requires hours of manual writing, editing, and formatting across different platforms. Inconsistent content quality: Manual repurposing often leads to varying quality levels and missed opportunities to optimize content for specific platforms. High costs for content services: Hiring ghostwriters or content agencies to repurpose videos can cost thousands of dollars monthly. Scaling bottlenecks: Manual processes prevent creators from efficiently scaling their content across multiple channels and formats. This workflow solves these problems by automatically extracting YouTube video transcripts, using AI to generate multiple high-quality content formats (tutorials, blog posts, social media content, newsletters), and organizing everything in Airtable for easy management and distribution. How it works Automated Video Processing Starts with a manual trigger and retrieves YouTube URLs from your Airtable configuration, processing only videos marked as "selected" while filtering out those marked for deletion. Intelligent Transcript Extraction Uses Scrape Creator API to extract video transcripts, automatically cleaning and formatting the text for optimal AI processing and content generation. Multi-Format Content Generation Leverages OpenRouter models, o you can easily test different AI models and choose the one that delivers the best results for your needs: Step-by-step tutorials with code snippets and technical details YouTube scripts with hooks, titles, and conclusions Blog posts optimized for lead generation Structured summaries with key takeaways LinkedIn posts with engagement triggers Newsletter content for email marketing Twitter/X posts for social media Smart Content Filtering Processes only the content types you've selected in Airtable, ensuring efficient resource usage and faster execution times. Automated Content Organization Matches and combines all generated content pieces by URL, then updates your Airtable with complete, ready-to-use content assets organized by type and source video. How to set up Required credentials OpenRouter API key** Airtable Personal Access Token** Scrape Creators API Key** - For YouTube transcript extraction and processing Airtable base setup Create an Airtable base with one main table: Videos Table: title** (Single line text): Video title for reference url** (URL): YouTube video URL to process Status** (Single select): Options: "selected", "delete", "processed" output** (Multiple select): Content types to generate summary tutorial blog-post linkedin newsletter tweeter youtube summary** (Long text): Generated video summary tutorial** (Long text): Generated step-by-step tutorial key_take_aways** (Long text): Extracted key insights blog_post** (Long text): Generated blog post content linkedin** (Long text): LinkedIn post content newsletter** (Long text): Email newsletter content tweeter** (Long text): Twitter/X post content youtube_titles** (Long text): YouTube video title suggestions youtube_hook** (Long text): Video opening hooks youtube_steps** (Long text): Video step breakdowns youtube_conclusion** (Long text): Video ending/CTAs API Configuration Scrape Creator Setup: Sign up for Scrape Creator API Obtain your API key from the dashboard Configure the HTTP Request node with your credentials Set the endpoint to: https://api.scrapecreators.com/v1/youtube/video/transcript OpenAI Setup: Create an OpenRouter account and generate an API key Workflow Configuration Import the workflow JSON into your n8n instance Update all credential references with your API keys Configure the Airtable nodes with your base and table IDs Test the workflow with a single video URL first Requirements n8n instance** (self-hosted or cloud) Active API subscriptions** for OpenRouter (or the LLM or your choice), Airtable, and Scrape Creator YouTube video URLs** - Must be publicly accessible videos with available transcripts Airtable account** - Free tier sufficient for most use cases How to customize the workflow Modify content generation prompts Edit the LLM Chain nodes to customize content style and format: Tutorial node**: Adjust technical depth and formatting preferences Blog post node**: Modify tone, length, and CTA strategies LinkedIn node**: Customize engagement hooks and professional tone Newsletter node**: Tailor subject lines and email marketing approach Adjust AI model selection Update the OpenRouter Chat Model to use different models Add new content formats Create additional LLM Chain nodes for new content types: Instagram captions TikTok scripts Podcast descriptions Course outlines

Stop reading job listings that aren't a fit. This workflow automatically crawls five major job boards (LinkedIn, Indeed, WeAreDevelopers, Stepstone, and Xing) and scores every listing against your personal applicant profile, and emails you the jobs worth your time. An AI agent rates each job from 0 to 100 based on role, tech stack, salary, location, and deal-breakers you define. Anything below 60 gets dropped. The rest lands in your inbox, formatted and ready to review. What problem is it solving? Job searching is time-consuming and noisy. Most listings are irrelevant, but you still have to read each one to know that. Manually filtering across five platforms for a specific stack, salary band, and working arrangement takes hours a week. This workflow removes that friction entirely. You define your profile once and the crawler does the rest on a schedule. You get a clean digest of only the matches that pass the bar. What this workflow does Triggers on a schedule (or manually via the Firecrawl Execute trigger) Queries LinkedIn (via HTTP + Firecrawl), Indeed, WeAreDevelopers, Stepstone, and Xing in parallel Scrapes each job listing with Firecrawl to extract full content Processes posting dates with an AI agent to normalise inconsistent formats Scores each listing 0–100 against your applicant profile using an OpenAI agent with a structured output parser Filters out anything below 60% suitability Runs detailed research on high-scoring jobs for additional context Sends an HTML email with your curated job list — or a "no jobs" email if nothing passed the threshold Setup Configure your applicant profile — edit the sticky note JSON in the workflow with your role, stack, experience, salary minimum, location preference, and deal-breakers Connect OpenAI credentials — used for suitability scoring and date parsing Connect Firecrawl credentials — used to scrape job board listings Configure email — set your SMTP credentials and recipient address in both email nodes (Job Mail and No Jobs Mail) Adjust the schedule — set how frequently the crawler runs (daily recommended) Activate the workflow How to customize this workflow to your needs Change the scoring threshold** — the filter is set to 60% by default; raise it to 75%+ for tighter results or lower it if you're early in your search Add or remove job boards** — each source (LinkedIn, Indeed, etc.) is modular; you can disable boards by bypassing their query node Modify the applicant profile** — update the JSON sticky note at any time to reflect a new role or changed requirements; no other changes needed Adjust the detail research** — the "Detailed Research" agent runs on high-scoring jobs; you can expand or simplify what it looks for Change the output** — swap the email nodes for a Notion database write, a Slack message, or a Google Sheet if you prefer a different delivery format Filter by posting age** — the max_listing_age_days field in the profile JSON controls how recent listings must be

Purpose This solution enables you to manage all your Notion and Todoist tasks from different workspaces as well as your calendar events in a single place. All tasks can be managed in Todoist and additionally Fantastical can be used to manage scheduled tasks & events all together. Demo & Explanation How it works The realtime sync consists of two workflows, both triggered by a registered webhook from either Notion or Todoist To avoid overwrites by lately arriving webhook calls, every time the current task is retrieved from both sides. Redis is used to prevent from endless loops, since an update in one system triggers another webhook call again. Using the ID of the task, the trigger is being locked down for 15 seconds. Depending on the detected changes, the other side is updated accordingly. Generally Notion is treaded as the main source. Using an "Obsolete" Status, it is guaranteed, that tasks never get deleted entirely by accident. The Todoist ID is stored in the Notion task, so they stay linked together An additional full sync workflow daily fixes inconsistencies, if any of them occurred, since webhooks cannot be trusted entirely. Since Todoist requires a more complex setup, a tiny workflow helps with activating the webhook. Another tiny workflow helps generating a global config, which is used by all workflows for mapping purposes. Mapping (Notion >> Todoist) Name: Task Name Priority: Priority (1: do first, 2: urgent, 3: important, 4: unset) Due: Date Status: Section (Done: completed, Obsolete: deleted) <page_link>: Description (read-only) Todoist ID: <task_id> Current limitations Changes on the same task cannot be made simultaneously in both systems within a 15-20 second time frame Subtasks are not linked automatically to their parent yet Recurring tasks are not supported yet Tasks names do not support URL’s yet Prerequisites Notion A database must already exist (get a basic template here) with the following properties (case matters!): Text: "Name" Status: "Status", containing at least the options "Backlog", "In progress", "Done", "Obsolete" Select: "Priority", containing the options "do first", "urgent", "important" Date: "Due" Checkbox: "Focus" Text: "Todoist ID" Todoist A project must already exist with the same sections like defined as Status in Notion (except Done and Obsolete) Redis Create a Free Redis Cloud instance or self-host Setup The setup involves quite a lot of steps, yet many of them can be automated for business internal purposes. Just follow the video or do the following steps: Setup credentials for Notion (access token), Todoist (access token) and Redis - you can also create empty credentials and populate these later during further setup Clone this workflow by clicking the "Use workflow" button and then choosing your n8n instance - otherwise you need to map the credentials of many nodes. Follow the instructions described within the bundle of sticky notes on the top left of the workflow How to use You can apply changes (create, update, delete) to tasks both in Notion and Todoist which then get synced over within a couple of seconds (this is handled by the differential realtime sync) The daily running full sync, resolves possible discrepancies in Todoist and sends a summary via email, if anything needed to be updated. In case that contains an unintended change, you can jump to the Task from the email directly to fix it manually.

What it is: An automation to plan→draft→finalize and publish your textual blog post ideas to your wordpress blog Works in stages and hand back control to you in between those You can use a Google Spreadsheet for planning topics and configuring LLM models and prompts What it does: plans→drafts→finalizes blog post topics you specify in a Google Spreadsheet using an LLM with prompts that also ar configured in that spreadsheet (even which model to use) savs the results in the corresponding columns of the "Schedule" sheet in the spreadsheet hands control back to the user for inspecting or changing the results and for setting the next "Action" for th workflow Finally publishes the blog post to your Wordpress instance Limitations Probably slightly over-engineered ;-) No media generation yet some LLM models don't work because of their output format How it works: The Workflow is triggered manually or scheduled every hour It ingests a Google Spreadsheet to get Config for prompts/context tc Blog-Topics and their status and next action Depending on each blog topics "Status" and "Action" it then either uses an LLM for th next action ("plan"→"draft"→"final" actions) or publishes the written content to your Wordpress instance ("publish" actions) Set up steps: Import the workflow Make your own copy of the Google Spreadsheet Update the credentials using your individual credentials for: Google Spreadsheets OpenRouter Edit the "Settings" node and enter your individual values for Your spreadsheet copy URL Your wordpress blog URL Your wordpress blog username Your wordpress blog app password (a 4x4 alphanumeric sequence), that you probably have to create first, for which your wordpress user has to have 2-factor-authentication enabled. In your own copy of the spreadsheet: individualize the "Config" sheet's "Value" column for the prompts/context/etc Populate the "Schedule" sheet with at least one line in which you specify a "Topic" a "Schedulded" date (YYYY-MM-DD HH:mm:ss) a "Status" of "idea" an "Action" of "plan" (to kick off that action)

🛡 CyberScan – AI-Powered Vulnerability Scanner with Nessus, OpenAI, and Google Sheets 👤 Who’s it for Security teams, DevOps engineers, vulnerability analysts, and automation builders who want to eliminate repetitive Nessus scan parsing, AI-based risk triage, and manual reporting. Designed for orgs following NIST CSF or CISA KEV compliance guidelines. ⚙️ How it works / What it does Runs scheduled or manual scans via the Nessus API. Processes scan results and extracts asset + vulnerability data. Uses a custom AI-based risk metric (LEV) to triage findings into: 🚨 Expert review ✅ Self-healing 🕵️ Monitoring Automatically sends email alerts for critical CVEs. Exports daily summaries to Google Sheets (or your own BI system). Maps to NIST CSF (Identify, Protect, Detect, Respond, Recover). 🧰 How to set up Nessus: Add your Nessus API credentials and instance URL. Google Sheets: Authenticate your Google account. OpenAI / LLM: Use your API key if adding LLM triage or rewrite prompts. Email: Update SMTP credentials and alert recipient address. Set your targets: Adjust asset ranges or scan UUIDs as needed. ⚠️ All setup steps are explained in sticky notes inside the workflow. 📋 Requirements Nessus Essentials (Free) or Nessus Pro with API access. SMTP service (e.g. Gmail, Mailgun, SendGrid). Google Sheets OAuth2 credentials. Optional: OpenAI or other LLM provider for LEV scoring and CVE insights. 🛠 How to customize the workflow Swap Google Sheets with Airtable, Supabase, or PostgreSQL. Change scan logic or asset list to fit your internal network scope. Adjust AI scoring logic to match internal CVSS thresholds or KEV tags. Expand alerting logic to include Slack, Discord, or webhook triggers. 🔒 No sensitive data included. All credentials and sheet links are placeholders.

How it works This template is a hands-on tutorial for one of the most advanced and powerful patterns in n8n: asynchronous parallel processing, also known as the Fan-Out/Fan-In model. When should you use this? Use this pattern when speed is your top priority and you have multiple independent, long-running tasks. Instead of running them one after another (which is slow), this workflow runs them all at the same time and waits for them all to finish. We use a Construction Project analogy to explain the architecture: The Main Workflow (Top):* This is the *Project Manager**. It defines the project, assigns all the tasks to specialist teams, and then pauses, waiting for a final report. The Sub-Workflow (Bottom):* This represents the *Specialist Teams**. It's a single, reusable workflow that can perform any task it's assigned. Static Data (The Brains):* A hidden *Project Dashboard** is used to track the status of every task in real-time. The process follows three key phases: Fan-Out: The Project Manager starts multiple sub-workflows at once without waiting for them to finish. Asynchronous Execution: Each Specialist Team works on its task independently and in parallel. When a team finishes, it updates its status on the Project Dashboard. Fan-In: The Project Manager, which has been paused by a Wait node, is only resumed when the Project Dashboard confirms that all tasks are complete. It then receives the aggregated results from all the parallel tasks. Set up steps Setup time: < 1 minute This workflow is a self-contained tutorial. The only setup required is to configure the AI model. Configure Credentials: Go to the The AI Specialist node in the sub-workflow (bottom flow). Select your desired AI credential (Gemini in that case). Execute the Workflow: Click the "Execute Workflow" button on the Start Project node. Explore and Learn: Follow the execution path to see how the main workflow fans out, and how the sub-workflow is called multiple times. Click on each node and read the detailed sticky notes to understand its specific role in this advanced pattern.

Automate Legal Document Generation with n8n, Apify, Google Drive, and AI This tutorial details an end-to-end automation solution for streamlining the lien filing process for Homeowners Associations (HOAs) using an n8n workflow. It significantly reduces manual effort and potential errors for legal professionals by automating document retrieval, information extraction, and document generation. Who's it for This template is ideal for legal professionals, law firms, and property management companies that frequently handle lien filings for Homeowners Associations. If you're looking to reduce manual document processing time, minimize errors, and improve efficiency in your legal operations, this workflow is for you. The Problem Legal professionals often allocate a significant portion of their time—up to 40%—to manual document processing tasks. The traditional process for filing a lien is particularly time-consuming (e.g., 15 minutes per case) and error-prone, involving steps like manual searching, downloading, extracting, and populating legal documents. The Automation Solution Overview This automation leverages an n8n workflow in conjunction with external services like Playwright (via Apify), Google Drive, Google Sheets, Gmail, and the Gemini API. The primary objective is to automate the legal document generation process—from initial data submission to final document generation and notification. Requirements Before importing and running the n8n workflow, you need the following: n8n Instance:** A running n8n instance (self-hosted or cloud). Google Account:** With access to Google Sheets, Google Drive, and Gmail. Google Sheets:** An Input Sheet to receive form responses (e.g., "Legal Automation Input Form (Responses)"). An Output/Review Sheet for extracted data and approval (e.g., "Automation Output data Sheet") with specific columns like "Timestamp", "Legal Description", "Association Name", "Debt", "Parcel", "Owner", "Doc link", "Approval", and "Created". Google Drive:** A main folder for n8n outputs (e.g., "N8N Folder"). A Google Docs Lien Template with placeholders (e.g., {{ASSOCIATION}}, {{DEBT}}, {{PROPERTY}}, {{MONTH}}, {{YEAR}}, {{DAY}}, {{PARCEL}}, {{OWNER}}). Google Gemini API Key:** For text and image processing. Apify Account & Playwright Actor:** An Apify account with access to a Playwright actor capable of scraping property information from your target county's website. Setup Steps n8n Credentials: Add Google Sheets, Google Drive, and Gmail credentials in your n8n instance. Add an HTTP Query Auth credential for your Gemini API key (named "Query Auth account" in the template). Ensure your Apify API token is configured within the Apify Playwright script to find property info node. Google Sheets Configuration: Link the Google Sheets Trigger node to your Input Sheet. Link the Google Sheets node (for appending data) and the Intermediate data received trigger to your Output/Review Sheet. Google Drive Configuration: Update the Create folder to output node with the ID of your "N8N Folder". Update the Make Copy of Template node with the ID of your Google Docs Lien Template. Email Addresses: Update the recipient email addresses in the Approve Through Email and Notify complete nodes to your desired notification email. Detailed Tutorial Steps and n8n Workflow Breakdown Summary This n8n workflow, "Legal Document Generator E2E", automates the process of generating legal lien documents, from initial data input to final document creation and notification. Initiate Workflow: The workflow starts with a Google Sheets Trigger node, which listens for new lien requests submitted via a form that populates a Google Sheet. Gather Property Data: An Apify Playwright script to find property info node fetches property details from county websites, and a Get file for property node downloads associated legal documents. Process and Store Document: The downloaded document is transformed to base64 using Transform to base64 and then uploaded to Google Drive via Upload legal doc for storage and further processing. Extract Information with AI: Call Gemini API for legal desc and Property metadata nodes leverage the Gemini API to extract the precise legal description, parcel number, and owner's name from the document. This extracted data is then structured by the Property Information Extractor. Review and Approve: The extracted information is appended to an intermediate Google Sheet by the first Google Sheets node, and an email is sent via Approve Through Email to the user for review and approval. Generate Documents on Approval: A second Intermediate data received Google Sheets Trigger node monitors the approval status in the sheet. Once "Approved", an If node allows the workflow to proceed. Create and Populate Documents: A new client-specific folder is created in Google Drive using Create folder to output. A blank lien template is copied (Make Copy of Template), and its custom variables are populated with the extracted data using Change Custom Variables. Finalize and Store Output: The populated document is converted to PDF (Generate PDF), and both the new PDF (Add PDF To Drive) and the original source document (Move file in Google Drive) are saved to the client's new folder. Update Records and Notify: The Update Creation Google Sheets node marks the document as "Created" in the tracking sheet and updates the document link. Finally, Notify complete sends a notification email about the completion. How to Customize the Workflow Adjust Input Form Fields:** Modify the column names in your initial Google Sheet and update the expressions in the Google Sheets Trigger and Apify Playwright script to find property info nodes to match your form. Change County Website/Scraper:** If you need to fetch data from a different county or property database, you will need to modify the Apify Playwright script to find property info node to call a different Apify actor or configure a new HTTP Request node to interact with your chosen data source. Customize Document Template:** Update the placeholders in your Google Docs Lien Template to match your specific document needs. Ensure corresponding replaceAll actions are updated in the Change Custom Variables node. Modify AI Prompts:** Refine the prompts within the Call Gemini API for legal desc and Property metadata nodes to improve the accuracy of information extraction based on your document types. Notification Preferences:** Adjust the sendTo email addresses and subject/message content in the Approve Through Email and Notify complete nodes. Benefits of this Automation This automation offers significant advantages for legal professionals: Streamlined Organization:** Ensures all relevant documents—original source files, editable templates, and final PDFs—are systematically organized, tracked, and easily accessible within Google Drive. Time-Saving and Efficiency:** Documents are quickly generated and ready for client sharing, leading to faster turnaround times and improved service delivery. Scalability:** Provides a scalable solution for handling a higher volume of document processing tasks without a proportional increase in manual effort. Learn more about Chill Labs and our services on our website: Chill Labs