Workflow Templates

TOTP Validation with Function Node This template allows you to verify if a 6-digit TOTP code is valid using the corresponding TOTP secret. It can be used in an authentication system. The inputs need to be: a base32 totp secret (String) a 6 digits code (String) ++Important:++ The 6-digit code must be in text format. If the code starts with zeros and is treated as a number, it could cause validation issues. The function node will generate a 6-digit code from the TOTP secret, then compare it with the provided code. If they match, it will return 1 otherwise, it will return 0. Example usage: You retrieve the user's TOTP secret from a database, then you want to verify if the 2FA code provided by the user is valid. Setup Guidelines You only need the ==TOTP VALIDATION== node. You will need to modify lines 39 and 40== of the node with the correct values for your specific context. Testing the Template You can define a sample secret and code in the EXAMPLE FIELDS node of the template, then click "Test Workflow". If the code is valid for the provided secret, the flow will proceed to the true branch of the IF CODE IS VALID node. Otherwise, it will go to the false branch.

🎨 AI Image Editor with Form Upload + Telegram Delivery 🚀 Who’s it for? 👥 This workflow is built for content creators, social media managers, designers, and agencies who need fast, AI-powered image editing without the hassle. Whether you're batch-editing for clients or spicing up personal projects, this tool gets it done — effortlessly. What it does 🛠️ A seamless pipeline that: 📥 Accepts uploads + prompts via a clean form ☁️ Saves images to Google Drive automatically 🧠 Edits images with OpenAI’s image API 📁 Converts results to downloadable PNGs 📬 Delivers the final image instantly via Telegram Perfect for AI-enhanced workflows that need speed, structure, and simplicity. How it works ⚙️ User Uploads: Fill a form with an image + editing prompt Cloud Save: Auto-upload to your Google Drive folder AI Editing: OpenAI processes the image with your prompt Convert & Format: Image saved as PNG Telegram Delivery: Final result sent straight to your chat 💬 You’ll need ✅ 🔑 OpenAI API key 📂 Google Drive OAuth2 setup 🤖 Telegram bot token & chat ID ⚙️ n8n instance (self-hosted or cloud) Setup in 4 Easy Steps 🛠️ 1. Connect APIs Add OpenAI, Google Drive, and Telegram credentials to n8n Store keys securely (avoid hardcoding!) 2. Configure Settings Set Google Drive folder ID Add Telegram chat ID Tweak image size (default: 1024×1024) 3. Deploy the Form Add a Webhook Trigger node Test with a sample image Share the form link with users 🎯 Fine-Tune Variables In the Set node, customize: 📐 Image size 📁 Folder path 📲 Delivery options ⏱️ Timeout duration Want to customize more? 🎛️ 🖼️ Image Settings Change size (e.g. 512x512 or 2048x2048) Update the model (when new versions drop) 📂 Storage Auto-organize files by date/category Add dynamic file names using n8n expressions 📤 Delivery Swap Telegram with Slack, email, Discord Add multiple delivery channels Include image prompt or metadata in messages 📝 Form Upgrades Add fields for advanced editing Validate file types (e.g. PNG/JPEG only) Show a progress bar for long edits ⚡ Advanced Features Add error handling or retry flows Support batch editing Include approvals or watermarking before delivery ⚠️ Notes & Best Practices ✅ Check OpenAI credit balance 🖼️ Test with different image sizes/types ⏱️ Adjust timeout settings for larger files 🔐 Always secure your API keys

Video Guide I prepared a comprehensive guide demonstrating how to build a multi-level retrieval AI agent in n8n that smartly narrows down search results first by file descriptions, then retrieves detailed vector data for improved relevance and answer quality. Youtube Link Who is this for? This workflow suits developers, AI enthusiasts, and data engineers working with vector stores and large document collections who want to enhance the precision of AI retrieval by leveraging metadata-based filtering before deep content search. It helps users managing many files or documents and aiming to reduce noise and input size limits in AI queries. What problem does this workflow solve? Performing vector searches directly on large numbers of document chunks can degrade AI input quality and introduce noise. This workflow implements a two-stage retrieval process that first searches file descriptions to filter relevant files, then runs vector searches only within those files to fetch precise results. This reduces irrelevant data, improves answer accuracy, and optimizes performance when dealing with dozens or hundreds of files split into multiple pieces. What this workflow does This n8n workflow connects to a Supabase vector store to perform: Multi-level Retrieval:** File Description Search: Calls a Supabase RPC function to find files whose descriptions (metadata) best match the user query. It filters and limits the number of relevant files based on similarity scores. Document Chunk Retrieval: Uses retrieved file IDs to perform a second RPC call fetching detailed vector pieces only within those files, again filtered by similarity thresholds. OpenAI Integration:** The filtered document chunks and associated metadata (like file names and URLs) are passed to an OpenAI message node that includes system instructions to guide the AI in leveraging the knowledge base and linked resources for comprehensive responses. Custom Code Functions:** Two code nodes interact with Supabase stored procedures match_files and match_documents to perform the semantic searches with multiline metadata filtering unavailable in default vector filters. Helper Flows and SQL Setup:** Templates and SQL scripts prepare database tables and functions, with additional flows to generate embeddings from file description summaries using OpenAI. N8N Workflow Preparation: Create or verify Supabase account with vector store capability. Set up necessary database tables and RPC functions (match_files and match_documents) using provided SQL scripts. Replace all credentials in n8n nodes to connect to your Supabase and OpenAI accounts. Optionally upload document files and generate their vector embeddings and description summaries in a separate helper workflow. Main Workflow Logic: Code Function Node #1: Receives user query and calls the match_files RPC to retrieve file IDs by searching file descriptions with vector similarity thresholds and file limits. Code Function Node #2: Takes filtered file IDs, invokes match_documents RPC to fetch vector document chunks only from those files with additional similarity filtering and count limits. OpenAI Message Node: Combines fetched document pieces, their metadata (file URLs, similarity scores), and system prompts to generate precise AI-powered answers referencing the documents. This multi-tiered retrieval process improves search relevance and AI contextual understanding by smartly limiting vector search scope first to relevant files, then to specific document chunks, refining user query results.

📄 What this workflow does This workflow turns TikTok videos into high-quality marketing insights and social-ready posts using Dumpling AI and GPT-4. It takes a TikTok URL, keyword, and product name, then automatically extracts the video transcript, analyzes the content for key marketing insights (pain points, outcomes, triggers), and rewrites it as a social media post that positions your product as the solution. Everything is logged to Google Sheets for use by your content or product team. 👤 Who is this for Product marketers doing UGC research Copywriters repurposing TikTok into content Founders or VAs turning viral clips into assets Agencies building research-based social proof ⚙️ How to set up ✅ Requirements Dumpling AI**: For TikTok transcript extraction OpenAI GPT-4 or GPT-4o-mini**: For analysis and rewriting Google Sheets**: To log the results n8n Form Trigger**: To input TikTok URL, Keyword, and Product 🔧 Setup Instructions Google Sheets Create a sheet with the following columns: Video URL, Original Transcription, Pain points, Desired outcomes, Triggers or motivating events, Interesting direct quotes, New Script Update the sheet ID and tab in the Google Sheets node Credentials Add your Dumpling AI key using HTTP Header Auth Use GPT-4 via OpenAI credentials Connect your Google Sheets using OAuth2 Customization (Optional) You can modify the GPT-4 prompts in the LangChain nodes to change tone, output structure, or content depth 🧠 How it works A form is submitted with a TikTok URL, keyword, and product Dumpling AI fetches and returns the TikTok transcript The VTT format is cleaned into plain text GPT-4 (via LangChain agent) extracts: Pain points Desired outcomes Motivating events Direct quotes GPT-4 then rewrites the transcript into a compelling marketing post Results are saved to Google Sheets for further use 🛠️ Customization ideas Push insights to Notion or Airtable instead of Sheets Use Claude or Gemini instead of GPT-4 Automatically generate image prompts to pair with the rewritten script Add notification email or Slack post when draft is ready This workflow gives marketers and founders a fast way to convert real social content into reusable copy, backed by authentic user voice and GPT-powered insights.

This template can be used to generate research questions from PDF documents (e.g. research papers, market reports) based on the content gaps found in text using the InfraNodus knowledge graph GraphRAG knowledge graph representation. Simply upload several PDF files (research papers, corporate or market reports, etc) and generate a research question / AI prompt in seconds. The template is useful for: generating research questions generating AI prompts that drive research further finding blind spots in any discourse and generating ideas that address them. avoiding the generic bias of LLM models and focusing on what's important in your particular context Using Content Gaps for Generating Research Questions Knowledge graphs represent any text as a network: the main concepts are the nodes, their co-occurrences are the connections between them. Based on this representation, we build a graph and apply network science metrics to rank the most important nodes (concepts) that serve as the crossroads of meaning and also the main topical clusters that they connect. Naturally, some of the clusters will be disconnected and will have gaps between them. These are the topics (groups of concepts) that exist in this context (the documents you uploaded) but that are not very well connected. Addressing those gaps can help you see which groups of concepts you could connect with your own ideas. This is exactly what InfraNodus does: builds the structure, finds the gaps, then uses the built-in AI to generate research questions that bridge those gaps. How it works 1) Step 1: First, you upload your PDF files using an online web form, which you can run from n8n or even make publicly available. 2) Steps 2-4: The documents are processed using the Code and PDF to Text nodes to extract plain text from them. 3) Step 5: This text is then sent to the InfraNodus GraphRAG node that creates a knowledge graph, identifies structural gaps in this graph, and then uses built-in AI to research questions / prompts. 4) Step 6: The ideas are then shown to the user in the same web form. Optionally, you can hook this template to your own workflow and send the question generated to an InfraNodus expert or your own AI model / agent for further processing. If you'd like to sync this workflow to PDF files in a Google Drive folder, you can copy our Google Drive PDF processing workflow for n8n. How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key. Add this key into the InfraNodus GraphRAG HTTP node(s) you use in this workflow. You do not need any OpenAI keys for this to work. Optionally, you can change the settings in the Step 4 of this workflow and enforce it to always use the biggest gap it identifies. Requirements An InfraNodus account and API key Note: OpenAI key is not required. You will have direct access to the InfraNodus AI with the API key. Customizing this workflow You can use this same workflow with a Telegram bot or Slack (to be notified of the summaries and ideas). You can also hook up automated social media content creation workflows in the end of this template, so you can generate posts that are relevant (covering the important topics in your niche) but also novel (because they connect them in a new way). Check out our n8n templates for ideas at https://n8n.io/creators/infranodus/ Also check the full tutorial with a conceptual explanation at https://support.noduslabs.com/hc/en-us/articles/20454382597916-Beat-Your-Competition-Target-Their-Content-Gaps-with-this-n8n-Automation-Workflow Also check out the video introduction to InfraNodus to better understand how knowledge graphs and content gaps work: For support and help with this workflow, please, contact us at https://support.noduslabs.com

n8n Workflow: Invoice PDF Generator This n8n workflow captures invoice data and generates a PDF invoice, ready to be sent or saved. It uses a webhook to trigger the process, preprocesses the invoice data, and converts it to a PDF using HTML and custom styling. @custom-js/n8n-nodes-pdf-toolkit Features: Webhook Trigger**: Receives incoming data, including invoice details. Preprocessing**: Transforms the invoice data into HTML format. HTML to PDF Conversion**: Converts the preprocessed HTML into a styled PDF document. Response**: Sends the generated PDF back to the webhook response. Notice Community nodes can only be installed on self-hosted instances of n8n. Requirements Self-hosted** n8n instance A CustomJS API key for website screenshots. Invoice data** for PDF generation Workflow Steps: Webhook Trigger: Accepts incoming data (e.g., invoice number, recipient details, itemized list). This data is passed to the next node for processing. Set Data Node: Configures initial values for the invoice, including the recipient, sender, invoice number, and the items on the invoice. The invoice details include information like description, unit price, and quantity. Preprocess Node: Processes the raw data to format it correctly for HTML. This includes splitting addresses and converting the items into an HTML table format. HTML to PDF Conversion: Converts the generated HTML into a PDF document. The HTML includes a header, a detailed invoice table, and a footer with contact information. Respond to Webhook: Returns the generated PDF as a response to the initial webhook request. Setup Guide: 1. Configure CustomJS API Sign up at CustomJS. Retrieve your API key from the profile page. Add your API key as n8n credentials. 2. Design Workflow Create a Webhook: Set up a webhook to trigger the workflow when invoice data is received. Prepare Data: Ensure the incoming request contains fields like "Invoice No", "Bill To", "From", and "Details" (list of items with price and quantity). Customize the HTML: The HTML template for the invoice includes custom styling to give the invoice a professional look. Convert to PDF: The HTML to PDF node is configured with the data generated from the preprocessing step to convert the invoice HTML to a PDF format. Example Invoice Data: { "Invoice No": "1", "Bill To": "John Doe\n1234 Elm St, Apt 567\nCity, Country, 12345", "From": "ABC Corporation\n789 Business Ave\nCity, Country, 67890", "Details": [ { "description": "Web Hosting", "price": 150, "qty": 2 }, { "description": "Domain", "price": 15, "qty": 5 } ], "Email": "support@mycompany.com" } Result PDF File

This n8n demonstrates how to build your own Github MCP server to personalise it to your organisation's repositories, issues and pull requests. This n8n implementation, though not as fully featured as the official MCP server offered by Github, allows you to control precisely what access and/or functionality is granted to users which can make MCP use simpler and in some cases, more secure. The use-case in this template is to simply view and comment on issues within a specific repository but can be extended to meet the needs of your team. This MCP example is based off an official MCP reference implementation which can be found here https://github.com/modelcontextprotocol/servers/tree/main/src/github How it works A MCP server trigger is used and connected to 3 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task. Behind these tools are regular Github nodes although preconfigured with credentials and targeted repository. The "Get Issue Comments" and "Create Issue Comment" tools depend on obtaining an Issue Number first. The agent should call the "Get Latest Issues" tool for this. How to use This Github MCP server allows any compatible MCP client to view and comment on Github Issues. You will need to have a Github account and repository access available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Can you get me the latest issues about MCP?" "What is the current progress on Issue 12345?" "Please can you add a comment to Issue 12345 that they should try installing the latest version and see if that works?" Requirements Github for account and repository access. The repository need not be your own but you'll still need to ensure you have the correct permissions. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Extend this template to interactive with pull requests or workflows within your own company's Github repositories. Alternatively, pull in metrics and generate reports for programme managers. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditiona OCR because: It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! How it works You can use the example bank statement created specifically for this workflow here: https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. Because multimodal LLMs do not accept PDFs directly, well have to convert the PDF to a series of images. We can achieve this by using a tool such as Stirling PDF. Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a "user message" of type binary (data) which is how we add our image data as an input. Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLMs ability to accurately read the page. Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. Requirements Google Gemini API for Multimodal LLM. Google Drive access for document storage. Stirling PDF instance for PDF to Image conversion Customising the workflow At time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini however you can switch to other multimodal LLMs such as OpenAI GPT or Antrophic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. Not parsing any bank statements any time soon? This template also works for Invoices, inventory lists, contracts, legal documents etc.

Who is this template for? This template is for learners, researchers, students and professionals who want to quickly capture the essence of a YouTube video. Steps in the workflow: Gets the transcript from any YouTube video through Supadata. Process the result from Supadata to one text Process the text with AI (any LLM of your choice) Final result: Produces a summary accompanied with the most important lessons and interesting facts mentioned in the video. The workflow automatically creates a new Google Doc wiht this output, in a folder of your choice on your Google Drive. (If you want to convert the markdown text to real markup after the Google Doc is created: just select all text (Ctrl-A or CMD-A), Cut the text (Ctrl-X or CMD-X and then go to Edit > Paste from Markdown.) Setup Edit your Supadata credentials in the second node (you can start for free) Choose your favourite LLM for AI processing Edit your Google Drive credentials. How to adjust it to your needs If you want the outcome to be different, edit the Prompt in "Proces transcript to summary template". The file name is a combination of ‘transcript ‘ and the date and time. You can change this to whatever you need in the Google Drive node. Supadata offers more details and options (or even translation) when working with transcripts. Check the options here: https://supadata.ai/documentation/youtube/get-transcript

This workflow converts any n8n workflow outputs into Markdown notes that are accessible in your Obsidian Vault through Google Drive synchronization. Setup Requirements Create a designated folder in Google Drive (Desktop). Create a symbolic link between this folder and a new target folder in your Obsidian Vault. Configure Google Drive n8n node settings. Send the output of any workflow to the trigger, and the notes will appear in your Vault folder. Optional Features You can use AI agents to: Write notes in your preferred format (e.g., Zettelkasten). Compose YAML front matter. Suggest tags. Use Cases Convert RSS feed items to notes. Create notes from YouTube video transcripts. Transform tasks in Slack messages into Obsidian tasks. (Requires setting up a corresponding workflow, e.g., RSS trigger, YouTube transcriber, or Slack bot.)

Use Case Transform and optimize images for web use: You need to host local images online You want to reduce image file sizes automatically You need image URLs for web content You want to generate and optimize AI-created images What this Workflow Does The workflow processes images through two services: Uploads images to ImgBB for hosting and URL generation (free but need API key) Optimizes images using ReSmush.it to reduce file size (free) Optional: Creates images using OpenAI's image generation Returns optimized image URLs ready for use Setup Create an ImgBB account and get your API key Add your ImgBB API key to the HTTP Request node (key parameter) Optional: Configure OpenAI credentials for image generation Connect your image input source How to Adjust it to Your Needs Skip OpenAI nodes if using your own image files Adjust image optimization parameters Customize image hosting settings Modify output format for your needs More templates and n8n workflows >>> @simonscrapes

This workflow is designed to automate the generation and updating of SEO meta titles and descriptions for WooCommerce products using n8n. It leverages Google Sheets for data input, a FREE language model (Gemini 2.0 Flash Exp. via OpenRouter) for generating SEO-optimized meta tags, and WooCommerce for updating product details. How It Works: Trigger: The workflow can be triggered manually or on a schedule. The manual trigger allows for testing, while the schedule trigger can be set to run at regular intervals (e.g., every few minutes) to process new products. Data Retrieval: The workflow starts by retrieving product IDs from a Google Sheets document. It looks for products that do not yet have meta titles or descriptions. Using the retrieved product ID, the workflow fetches the corresponding product details from WooCommerce, including the product name, description, short description, and categories. Meta Tag Generation: The product details are passed to a language model (Gemini 2.0 Flash Exp) via OpenRouter. The model generates SEO-optimized meta titles and descriptions based on the provided content. The generated meta tags are structured and validated to ensure they meet SEO best practices, such as character limits and keyword inclusion. Update WooCommerce: The generated meta title and description are then updated in the WooCommerce product metadata using the Yoast SEO fields. Update Google Sheets: Finally, the workflow updates the Google Sheets document with the newly generated meta tags, along with the product URL, title, and the timestamp of the update. Set Up Steps: Google Sheets Setup: Create a copy of the provided Google Sheets template and insert WooCommerce product IDs in column "B". Ensure the Google Sheets document has columns for METATITLE, METADESCRIPTION, URL, TITLE POST, and DATA (timestamp). n8n Workflow Configuration: Google Sheets Node: Configure the "Get product ID" node to connect to your Google Sheets document. Use OAuth2 for authentication. WooCommerce Node: Set up the WooCommerce nodes to connect to your WooCommerce store using the WooCommerce API credentials. OpenRouter Node: Configure the "Gemini 2.0 Flash Exp" node with your OpenRouter API credentials to access the language model. Structured Output Parser: Ensure the output parser is set to handle the structured data format for meta titles and descriptions. Workflow Execution: Trigger the workflow manually to test the process or set up a schedule trigger to automate the workflow at regular intervals. Monitor the workflow execution to ensure that meta tags are generated and updated correctly in both WooCommerce and Google Sheets. Validation: After the workflow runs, verify that the meta titles and descriptions in WooCommerce are correctly updated and that the Google Sheets document reflects the changes. This workflow streamlines the process of optimizing WooCommerce product pages for SEO, saving time and ensuring consistency in meta tag generation. Need help customizing? Contact me for consulting and support or add me on Linkedin.