Workflow Templates

Chat with thing This n8n template lets you build a smart AI chat assistant that can handle text, images, and PDFs — using OpenAI's GPT-4o multimodal model. It supports dynamic conversations and file analysis, making it great for AI-driven support bots, personal assistants, or embedded chat widgets. 🔍 How it Works The chat trigger node kicks off a session using n8n's hosted chat UI. Users can send text or upload images or PDFs — the workflow checks if a file was included. If an image is uploaded, the file is converted to base64 and analyzed using GPT-4o's vision capabilities. GPT-4o generates a natural language description of the image and responds to the user's question in context. A memory buffer keeps track of the conversation thread, so follow-up questions are handled intelligently. OpenAI’s chat model handles both text-only and mixed media input seamlessly. 🧪 How to Use You can embed this in a website or use it with your own webhook/chat interface. The logic is modular — just swap out the chatTrigger node for another input (e.g. form or API). To use with documents, you can modify the logic to pass PDF content to GPT-4 directly. You can extend it with action nodes, e.g. saving results to Notion, Airtable, or sending replies via email or Slack. 🔐 Requirements Your OpenAI GPT-4o API key Set File Upload on the chat 🚀 Use Cases PDF explainer bot Internal knowledge chat with media support Personal assistant for mixed content

What This Workflow Does This workflow automates the candidate nurturing process, solving the common problem of candidates losing interest or "ghosting" after an application. It keeps them engaged and informed by sending a personalized, multi-channel (WhatsApp & Gmail) sequence of follow-up messages over their first week. The automation triggers when a new candidate is added to your ATS (e.g., via a Recrutei webhook). It then uses AI to generate a custom 3-part message (for Day 1, Day 3, and Day 7) tailored to the candidate's age and the specific job they applied for, ensuring a professional and empathetic experience that strengthens your employer brand. How it Works Trigger: A Webhook node captures the new candidate data from your Applicant Tracking System (ATS) or form. Data Preparation: Two Code nodes clean the incoming data. The first (Separating information) extracts key fields and formats the phone number. The second (Extract age) calculates the candidate's age from their birthday to be used by the AI. AI Content Generation: The workflow sends the candidate's details (name, age, job title) to an AI model (AI Recruitment Assistant). The AI has a detailed system prompt to generate three distinct messages for Day 1 (Thank You), Day 3 (Friendly Reminder), and Day 7 (Final Reinforcement), adapting its tone based on the candidate's age. Split Messages: A Code node (Separating messages per days) receives the single text block from the AI and splits it into three separate variables (day1, day3, day7). Day 1 Send: The workflow immediately sends the day1 message via both Gmail and WhatsApp (configured for Evolution API). Day 3 Send: A "Wait" node pauses the workflow for 2 days, after which it sends the day3 message. Day 7 Send: Another "Wait" node pauses for 4 more days, then sends the final day7 message, completing the 7-day nurturing sequence. Setup Instructions This workflow is plug-and-play once you configure the following 5 steps: Webhook Node: Copy the Test URL from the Webhook node and configure it in your ATS (e.g., Recrutei) or form builder to trigger whenever a new candidate is added. Run one test submission to make the data structure visible to n8n. AI Credentials: In the AI Recruitment Assistant node, select or create your OpenAI API credential. MCP Credential (Optional): If you use a Recrutei MCP, paste your endpoint URL into the MCP Recrutei node. Gmail Credentials: In all three Message Gmail nodes (Day 1, 3, 7), select or create your Gmail (OAuth2) credential. Optional: In the same nodes, go to Options and change the Sender Name from your_company to your actual company name. WhatsApp (Evolution API): This template is pre-configured for the Evolution API. In all three Message WhatsApp nodes (Day 1, 3, 7), you must: URL: Replace {server-url} and {instance} with your Evolution API details. Headers: In the "Header Parameters" section, replace your_api_key with your actual Evolution API key.

🔮 Mystic Tarot Bot — AI-Powered iMessage Readings This magical n8n template turns your iMessage inbox into a soulful tarot reading experience powered by Blooio and AI. Users can send in questions or photos of their tarot spreads, and the bot replies like a mystical oracle — interpreting symbols, offering gentle insights, and guiding with poetic warmth. ✨ Ideal for solo reflection, spiritual creators, or client-based guidance services — no tech knowledge needed. 🌟 Use Cases Offer intuitive, emotionally resonant tarot readings via iMessage Support coaching, wellness, and metaphysical businesses with AI-enhanced readings Accept photos of real tarot card spreads or plain text questions Great for automating daily card pulls, client responses, or onboarding into spiritual flows 🧠 Good to Know Built using Blooio’s iMessage API — supports image attachments and conversational replies Includes visual recognition and symbolic interpretation of real tarot card photos Responses generated by OpenAI with a custom “Mystic Tarot Reader” persona Onboards users if they say “Hi” or request a virtual card draw Responds in poetic, spiritually attuned language — no markdown, no tech-speak ⚙️ How it Works Trigger: iMessage webhook via Blooio receives user message or image Check: Bot ignores self-sent messages to prevent loops Detect: If a photo is attached, it’s passed to AI for card recognition Interpret: The AI agent gives a heartfelt, symbolic interpretation Respond: A final, warm tarot reading is sent back through iMessage 📝 How to Use Set Up Blooio: Sign up at https://blooio.com Choose a Dedicated or Enterprise plan (image support required) Copy your API key from Settings → API Keys Paste it into the Send Message HTTP node as a Bearer token Customize the Experience: Adjust the prompt for a different tone or deck style Add journaling prompts, affirmations, or follow-ups Use other workflows to track users, create reading logs, or offer upsells Try It Out: Text your Blooio-connected number with: “Hi” → get onboarding “Draw a card for me” → get a virtual pull A tarot photo + question → get a full, soulful reading ✅ Requirements Blooio Account & API Token (Dedicated plan or higher for images) Optional: Tarot images, user questions, or both 🔧 Customizing This Workflow Add personalized spreads (e.g. past/present/future layouts) Send AI-generated visuals of the pulled cards Route readings into Notion, Airtable, or Google Sheets Expand to WhatsApp, web, or email with Blooio’s multichannel support 🃏 Let the cards speak. Let the messages flow.

This n8n workflow helps eCommerce businesses (especially in the Cash on Delivery space) send real-time order events to the Meta (Facebook) Conversions API, ensuring accurate event tracking and better ad attribution. Features Webhook Listener**: Accepts incoming order data (name, phone, IP, user-agent, etc.) via HTTP POST/GET. Data Normalization**: Cleans and formats first_name, last_name, phone, and event_time according to Facebook's strict specs. Data Hashing**: Securely hashes sensitive user data (SHA256), as required by Meta. Full Custom Data Suppor**t: Pass order value, currency, and more. Ideal For: Shopify, WooCommerce, custom stores (Laravel, Node, etc.) Businesses using Meta Ads and needing high-quality server-side tracking Teams without access to full dev resources, but using n8n for automation How It Works: Receive Order from your store via Webhook or API. Format & Normalize fields to match Facebook’s expected structure. Encrypt Sensitive Fields using SHA256 (name, phone, email). Send to Facebook via the Conversions API endpoint. Requirements: A Meta Business Manager account with Conversions API access Your Access Token and Pixel ID set up in n8n credentials

Automate Actions After PDF Generation with PDFMonkey in n8n Overview This n8n workflow template allows you to automatically react to PDF generation events from PDFMonkey. When a new PDF is successfully created, this workflow retrieves the file and processes it based on your needs—whether it’s sending it via email, saving it to cloud storage, or integrating it with other apps. How It Works Trigger: The workflow listens for a PDFMonkey webhook event when a new PDF is generated. Retrieve PDF: It fetches the newly generated PDF file from PDFMonkey. Process & Action: Depending on the outcome: ✅ On success: The workflow downloads the PDF and can distribute or store it. ❌ On failure: It handles errors accordingly (e.g., sending alerts, retrying, or logging the issue). Configuration To set up this workflow, follow these steps: Copy the Webhook URL generated by n8n. Go to your PDFMonkey Webhooks dashboard and paste the URL in the appropriate field to define the callback URL. Save your settings and trigger a test to ensure proper integration. 📖 For detailed setup instructions, visit: PDFMonkey Webhooks Documentation Use Cases This workflow is ideal for: Automating invoice processing (e.g., sending PDFs to customers via email). Archiving reports** in cloud storage (e.g., Google Drive, Dropbox, or AWS S3). Sending notifications** via Slack, Microsoft Teams, or WhatsApp when a new PDF is available. Logging generated PDFs** in Airtable, Notion, or a database for tracking. Customization You can customize this workflow to: Add conditional logic** (e.g., different actions based on the document type). Enhance security** (e.g., encrypting PDFs before sharing). Extend integrations** by connecting with CRM tools, task managers, or analytics platforms. Need Help? If you need assistance setting up or customizing this workflow, feel free to reach out to us via chat on pdfmonkey.io—we’ll be happy to help! 🚀

How it Works This n8n template makes it possible to send emails directly from your Obsidian notes. It leverages the power of the Obsidian Post Webhook plugin, allowing seamless integration between your notes and the email workflow. What it does: Receives note content and metadata from Obsidian via a Webhook. Parses YAML frontmatter to define email recipients, subject, and more. Automatically processes attachments, encoding them into an email-friendly format. Sends emails via Gmail and confirms the status back to Obsidian. Includes a testing feature to verify everything works before going live. Set-up Steps Webhook Configuration: Set your n8n POST Webhook URL in the Obsidian Obsidian Post Webhook plugin settings. Email Integration: Submit the Gmail credentials in n8n email nodes. Test the Workflow: Run a test from Obsidian to ensure the template functions correctly. Activate and Enjoy: Start sending customized emails with attachments from your notes in no time!

Brief Overview This workflow integrates Linear, Scrapeless, and Claude AI to create an AI research assistant that can respond to natural language commands and automatically perform market research, trend analysis, data extraction, and intelligent analysis. Simply enter commands such as /search, /trends, /crawl in the Linear task, and the system will automatically perform search, crawling, or trend analysis operations, and return Claude AI's analysis results to Linear in the form of comments. How It Works Trigger: A user creates or updates an issue in Linear and enters a specific command (e.g. /search competitor analysis). n8n Webhook: Listens to Linear events and triggers automated processes. Command identification: Determines the type of command entered by the user through the Switch node (search/trends/unlock/scrape/crawl). Data extraction: Calls the Scrapeless API to perform the corresponding data crawling task. Data cleaning and aggregation: Use Code Node to unify the structure of the data returned by Scrapeless. Claude AI analysis: Claude receives structured data and generates summaries, insights, and recommendations. Result writing: Writes the analysis results to the original issue as comments through the Linear API. Features Multiple commands supported /search: Google SERP data query /trends: Google Trends trend analysis /unlock: Unlock protected web content (JS rendering) /scrape: Single page crawling /crawl: Whole site multi-page crawling Claude AI intelligent analysis Automatically structure Scrapeless data Generate executable suggestions and trend insights Format optimization to adapt to Linear comment format Complete automation process Codeless process management based on n8n Multi-channel parallel logic distribution + data standardization processing Support custom API Key, regional language settings and other parameters Requirements Scrapeless API Key**: Scrapeless Service request credentials. Log in to the Scrapeless Dashboard Then click "Setting" on the left -> select "API Key Management" -> click "Create API Key". Finally, click the API Key you created to copy it. n8n Instance**: Self-hosted or n8n.cloud account. Claude AI**: Anthropic API Key (Claude Sonnet 3.7 model recommended) Installation Log in to Linear and get a Personal API Token Log in to n8n Cloud or a local instance Import the n8n workflow JSON file provided by Scrapeless Configure the following environment variables and credentials: Linear API Token Scrapeless API Token Claude API Key Configure the Webhook URL and bind to the Linear Webhook settings page Usage This automated job finder agent is ideal for: | Industry / Role | Use Case | | --------------------------------- | -------------------------------------------------------------------------------------------------- | | SaaS / B2B Software | | | Market Research Teams | Analyze competitor pricing pages using /unlock, and feature pages via /scrape. | | Content & SEO | Discover trending keywords and SERP data via /search and /trends to guide content topics. | | Product Managers | Use /crawl to explore product documentation across competitor sites for feature benchmarking. | | AI & Data-Driven Teams | | | AI Application Developers | Automate info extraction + LLM summarization for building intelligent research agents. | | Data Analysts | Aggregate structured insights at scale using /crawl + Claude summarization. | | Automation Engineers | Integrate command workflows (e.g., /scrape, /search) into tools like Linear to boost productivity. | | E-commerce / DTC Brands | | | Market & Competitive Analysts | Monitor competitor sites, pricing, and discounts with /unlock and /scrape. | | SEO & Content Teams | Track keyword trends and popular queries via /search and /trends. | | Investment / Consulting / VC | | | Investment Analysts | Crawl startup product docs, guides, and support pages via /crawl for due diligence. | | Consulting Teams | Combine SERP and trend data (/search, /trends) for fast market snapshots. | | Media / Intelligence Research | | | Journalists & Editors | Extract forum/news content from platforms like HN or Reddit using /scrape. | | Public Opinion Analysts | Monitor multi-source keyword trends and sentiment signals to support real-time insights. | Output

CallForge - AI-Powered Product Insights Processor from Sales Calls Automate product feedback extraction from AI-analyzed sales calls and store structured insights in Notion for data-driven product decisions. 🎯 Who is This For? This workflow is designed for: ✅ Product managers tracking customer feedback and feature requests. ✅ Engineering teams identifying usability issues and AI/ML-related mentions. ✅ Customer success teams monitoring product pain points from real sales conversations. It streamlines product intelligence gathering, ensuring customer insights are structured, categorized, and easily accessible in Notion for better decision-making. 🔍 What Problem Does This Workflow Solve? Product teams often struggle to capture, categorize, and act on valuable feedback from sales calls. With CallForge, you can: ✔ Automatically extract and categorize product feedback from AI-analyzed sales calls. ✔ Track AI/ML-related mentions to gauge customer demand for AI-driven features. ✔ Identify feature requests and pain points for product development prioritization. ✔ Store structured feedback in Notion, reducing manual tracking and increasing visibility across teams. This workflow eliminates manual feedback tracking, allowing product teams to focus on innovation and customer needs. 📌 Key Features & Workflow Steps 🎙️ AI-Powered Product Feedback Processing This workflow processes AI-generated sales call insights and organizes them in Notion databases: Triggers when AI sales call data is received. Detects product-related feedback (feature requests, bug reports, usability issues). Extracts key product insights, categorizing feedback based on customer needs. Identifies AI/ML-related mentions, tracking customer interest in AI-driven solutions. Aggregates feedback and categorizes it by sentiment (positive, neutral, negative). Logs insights in Notion, making them accessible for product planning discussions. 📊 Notion Database Integration Product Feedback** → Logs feature requests, usability issues, and bug reports. AI Use Cases** → Tracks AI-related discussions and customer interest in machine learning solutions. 🛠 How to Set Up This Workflow 1. Prepare Your AI Call Analysis Data Ensure AI-generated sales call insights are available. Compatible with Gong, Fireflies.ai, Otter.ai, and other AI transcription tools. 2. Connect Your Notion Database Set up Notion databases for: 🔹 Product Feedback (logs feature requests and bug reports). 🔹 AI Use Cases (tracks AI/ML mentions and customer demand). 3. Configure n8n API Integrations Connect your Notion API key** in n8n under “Notion API Credentials.” Set up webhook triggers** to receive AI-generated sales insights. Test the workflow** using a sample AI sales call analysis. 🔧 How to Customize This Workflow 💡 Modify Notion Data Structure – Adjust fields to align with your product team's workflow. 💡 Refine AI Data Processing Rules – Customize how feature requests and pain points are categorized. 💡 Integrate with Slack or Email – Notify teams when recurring product issues emerge. 💡 Expand with Project Management Tools – Sync insights with Jira, Trello, or Asana to create product tickets automatically. ⚙️ Key Nodes Used in This Workflow 🔹 If Nodes – Detect if product feedback, AI mentions, or feature requests exist in AI data. 🔹 Notion Nodes – Create and update structured feedback entries in Notion. 🔹 Split Out & Aggregate Nodes – Process multiple insights and consolidate AI-generated data. 🔹 Wait Nodes – Ensure smooth sequencing of API calls and database updates. 🚀 Why Use This Workflow? ✔ Eliminates manual sales call review for product teams. ✔ Provides structured, AI-driven insights for feature planning and prioritization. ✔ Tracks AI/ML mentions to assess demand for AI-powered solutions. ✔ Improves product development strategies by leveraging real customer insights. ✔ Scalable for teams using n8n Cloud or self-hosted deployments. This workflow empowers product teams by transforming sales call data into actionable intelligence, optimizing feature planning, bug tracking, and AI/ML strategy. 🚀

This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: ** **Apply filters to avoid generating embeddings for all site content. Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown Nodes : Set Fields Filter - Only Published & Unprotected Content HTML to Markdown These three nodes prepare the content for embedding by: Setting up the necessary fields for content embeddings and document metadata. Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: Supabase Vector Store Sub-nodes: Embeddings OpenAI Default Data Loader Token Splitter Aggregate Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: Newly added content** Updated content** Step 1: Retrieve WordPress Content with Regular CRON Nodes: CRON - Every 30 Seconds Postgres - Get Last Workflow Execution WordPress - Get Posts Modified After Last Workflow Execution WordPress - Get Pages Modified After Last Workflow Execution Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2 : Set fields, use filter Nodes : Set fields2 Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: Loop Over Items Postgres - Filter on Existing Documents Switch Route existing_documents (if documents with matching IDs are found in metadata): Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes. Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: When Chat Message Received Supabase - Retrieve Documents from Chat Input Embeddings OpenAI1 Aggregate Documents Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: The Supabase vector store retriever fetches documents that match the user’s question, including metadata. The Aggregate Documents node consolidates the retrieved data. Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: AI Agent** Sub-nodes: OpenAI Chat Model Postgres Chat Memories Respond to Webhook** This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

WordPress Blog to Google Sheets Sync Posts • Categories • Tags • Media 🧩 Overview This n8n workflow automatically syncs your WordPress website content — including posts, categories, tags, and media — into Google Sheets. It helps automate content reporting, SEO analysis, and data backups. The workflow can run on schedule or on demand via a webhook. 💡 Use cases Maintain a live database of blog posts in Google Sheets. Create dashboards in Google Data Studio or Looker Studio. Track new articles for newsletters or social media scheduling. Backup all WordPress content and media outside of your CMS. ⚙️ Prerequisites Before importing the workflow, ensure you have: A WordPress website with the REST API enabled (default in WP 4.7+). Authentication: either Application Passwords or Basic Auth credentials. A Google Sheet with the following tabs: Posts Categories Tags Media The following credentials configured in n8n: HTTP Basic Auth (for WordPress) Google Sheets OAuth2 🚀 Setup instructions Import the workflow into your n8n instance. Replace all example WordPress API URLs with your domain, for example: https://yourdomain.com/wp-json/wp/v2/ Connect your HTTP Basic Auth credentials (WordPress username + Application Password). Connect your Google Sheets OAuth2 account. Update the spreadsheet ID in each Google Sheets node with your own. Adjust the Schedule Trigger (e.g. run daily at 2:00 AM). Run once manually to verify data sync. 🧠 Workflow structure | Section | Description | |----------|--------------| | Schedule / Webhook Trigger | Starts the workflow manually or automatically | | Variables & Loop Vars | Initialize pagination for REST API requests | | Get Posts → Split Out → Update Posts | Fetch and update all WordPress posts | | Get Categories → Update Categories | Sync WordPress categories | | Get Tags → Update Tags | Sync WordPress tags | | Get Media → Split Out → Update Media | Sync media library (images, videos, etc.) | | IF Loops | Handles pagination logic until all items are retrieved | ⚠️ Notes & Limitations Works with standard WordPress REST API endpoints only. Custom post types require editing endpoint URLs. The per_page value defaults to 10; increase for faster syncs. For large sites, consider increasing n8n memory or adding execution logs. Avoid running the workflow too frequently to prevent API rate limits. 🎥 Video Tutorial A step-by-step setup guide is available here: 👉 https://www.youtube.com/watch?v=czSMWyD6f-0 Please subscribe to my YouTube channel to support me: 👉 https://www.youtube.com/@gregfrolovpersonal 👨‍💻 Author Created by: Grigory Frolov SEO & Automation Specialist — helping businesses integrate WordPress, AI, and data tools with n8n. 🧾 License This workflow is provided under the MIT License. Feel free to use, modify, and share improvements with the community.

Update 19-04-2025 Change from OpenAI to Claude 3.7 Sonnet module Adding the Think Tool The update enables significantly better results to be achieved. This is particularly noticeable during longer meetings! What this workflow does This workflow retrieves the Zoom meeting data from the last 24 hours. The transcript of the last meeting is then retrieved, processed, a summary is created using AI and sent to all participants by email. AI is then used to create tasks and follow-up appointments based on the content of the meeting. Important: You need a Zoom Workspace Pro account and must have activated Cloud Recording/Transcripts! This workflow has the following sequence: manual trigger (Can be replaced by a scheduled trigger or a webhook) retrieval of of Zoom meeting data filter the events of the last 24 hours retrieval of transcripts and extract of the text creating a meeting summary, format to html and send per mail create tasks and follow-up call (if discussed in the meeting) in ClickUp/Outlook (can be replaced by Gmail, Airtable, and so forth) via sub workflow Requirements: Zoom Workspace (via API and HTTP Request): Documentation Microsoft Outlook: Documentation ClickUp: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access data (for sending the mail) You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools. You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g. “Analytics_Domain_5). Feel free to contact me via LinkedIn, if you have any questions!

Using PostBin to Test Webhooks Without Changing WEBHOOK_URL How it Works Many new n8n users struggle with testing webhooks when running n8n on localhost, as external services cannot reach localhost. This workflow introduces a technique using PostBin, which provides a temporary, publicly accessible URL to receive webhook requests. Generates a temporary webhook endpoint via PostBin. Uses this endpoint in place of localhost to test webhooks. Captures and displays the incoming webhook request data. Enables debugging and iterating without modifying the WEBHOOK_URL environment variable. Set Up Steps Estimated time:** ~5–10 minutes Create a PostBin instance to generate a publicly accessible webhook URL. Copy the PostBin URL and use it as the webhook destination in n8n. Trigger the webhook from an external service or manually. Inspect the request payload in PostBin to verify data reception. (EXAMPLE) Using PostBin for Webhook Testing in a BambooHR Integration How it Works In this example, we apply the PostBin technique to a BambooHR integration. Instead of manually configuring a webhook in BambooHR, this workflow automates webhook registration using the BambooHR API. The workflow: Uses the BambooHR API to programmatically register the PostBin URL as a webhook. Retrieves the most recent webhook calls made by BambooHR to the PostBin URL. (Optional) Sends a personalized Slack message for new employees using OpenAI. Set Up Steps Estimated time:** ~15–20 minutes Set up PostBin using the steps from the first section. Log into BambooHR to generate an API key for authentication. Run the workflow to register the PostBin URL as a webhook in BambooHR via the API. Retrieve recent webhook calls from PostBin to validate data reception. (Optional) Send a Slack notification using the processed data.