Workflow Templates

This n8n template offers a simple yet capable chatbot assistant who can answer course enquiries over SMS. Given the right access to data, AI Agents are capable of planning and performing relatively complex research tasks to get their answers. In this example, the agent must first understand the database schema, retrieve lists of values before generating it's own query to search over the database. Checkout the example database here - https://airtable.com/appO5xvP1aUBYKyJ7/shr8jSFDaghubDOrw How it works A Twilio trigger gives us the ability to receive SMS input into our workflow via webhook. The message is then directed to our AI agent who is instructed to assist the user and use the course database as reference. The database is an Airtable base. The agent autonomously figures out which tool it needs to use and generates it's own "filter_by_formula" query to search over the available courses. On successful search results, the Agent can then use this information to answer the user's query. The Agent's output is logged in a second sheet of the Airtable base. We can use this later for analysis and lead gen. Finally, the response is sent back to the user through SMS using Twilio. How to use Ensure your Twilio number is set to forward messages to this workflow's webhook URL. Configure and update the course database as required. If you're not interested in courses, you can swap this out for inventory, deliveries or any other data relevant to your business. Ask questions like: "Can you help me find suitable courses to fill my Wednesday mornings?" "Which courses are being instructed by profession Lee?" "I'm interested in creative arts. What courses are available which could be relevant to me?" Requirements Twilio for SMS receiving and sending OpenAI for LLM and Agent Airtable for Course Database Customising this workflow Add additional tools and expand the range of queries the agent is able to answer or assist with. Not using Airtable? This technique also works with SQL databases like PostgreSQL.

This workflow creates a Slack thread when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as replies to the thread in Slack. Prerequisites Zendesk account and Zendesk credentials. Slack account and Slack credentials. Slack channel to create threads in. How it works The workflow listens for new tickets in Zendesk. When a new ticket is created, the workflow creates a new thread/message in Slack. The Slack thread ID is then saved in one of the ticket's fields called "Slack thread ID". The next time a comment is added to the ticket, the workflow retrieves the Slack thread ID from the ticket's field and adds the comment to the thread/message in Slack as a reply. Setup This workflow requires that you set up a webhook in Zendesk. To do so, follow the steps below: In the workflow, open the On new Zendesk ticket node and copy the webhook URL. In Zendesk, navigate to Admin Center > Apps and integrations > Webhooks > Actions > Create Webhook. Add all the required details which can be retrieved from the On new Zendesk ticket node. The webhook URL gets added to the “Endpoint URL” field, and the “Request method” should match what is shown in n8n. Save the webhook. In Zendesk, navigate to Admin Center > Objects and rules > Business rules > Triggers > Add trigger. Give trigger a name such as “New tickets”. Under “Conditions” in “Meet ALL of the following conditions”, add “Status is New”. Under “Actions”, select “Notify active webhook” and select the webhook you created previously. In the JSON body, add the following: { "id": "{{ticket.id}}", "comment": "{{ticket.latest_comment_html}}" } Save the Zendesk trigger. You will also need to set up a field in Zendesk to store the Slack thread ID. To do so, follow the steps below: In Zendesk, navigate to Admin Center > Objects and rules > Tickets > Fields > Add field. Use the text field option and give the field a name such as “Slack thread ID”. Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.

What this workflow does This n8n workflow collects client feedback through a form (Tally, Typeform, or Google Forms) and uses AI to analyze it. It automatically generates a summary of the positive points, highlights areas for improvement, and drafts a short social media post based on the feedback. Ideal for: Freelancers Customer support teams Online service providers Coaches and educators Setup steps Connect your form tool to the Webhook node (POST method) and make sure it sends a feedback field. Add your DeepSeek (or other GPT-compatible) API key to the AI request node. Configure the email node with your SMTP credentials and desired recipient address. Replace the Telegram node with Slack, Buffer, or another integration if you prefer. (Optional) Customize the prompt in the function node for different tone/language. 🕐 Estimated setup time: ~15 minutes 💬 Sticky notes are included and clearly positioned to guide you. Technologies used n8n Webhook node n8n Function node DeepSeek Chat or compatible AI API Email node (SMTP) Telegram node (or other integration) Sticky Notes for setup guidance Use cases Analyze feedback from onboarding or satisfaction surveys Create ready-to-publish social media content from real customer praise Help support or marketing teams act on feedback immediately

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. ❓ What Problem Does It Solve? Manual transcription and action planning from meeting notes is often error-prone, time-consuming, and inconsistent. Important tasks, decisions, or deadlines can be overlooked or delayed. This workflow solves these pain points by automatically analyzing notes using AI and turning them into actionable, structured data. It drastically reduces follow-up delays, miscommunications, and administrative effort, letting teams focus on execution instead. 💡 Why Use Google Meet Automation? Save Hours of Manual Work:** Automatically transform raw meeting notes into structured tasks and emails without lifting a finger. Ensure Accurate Follow-up:** Never miss important action items or decisions buried in text; everything is extracted and assigned clearly. Improve Team Collaboration:** Instantly distribute meeting summaries and next steps to attendees, keeping everyone aligned. Leverage Advanced AI:** Utilize Google Gemini’s powerful natural language processing tailored specifically for meetings. Fully End-to-End Automated:** From receiving notes to task creation and email dispatch — your post-meeting workflow is completely hands-free. ⚡ Who Is This For? Project Managers:** Streamline task delegation and keep project timelines on track. Team Leads:** Quickly communicate key takeaways and follow-ups to team members. Sales and Account Teams:** Document client meetings efficiently and automate follow-up outreach. Remote Teams:** Ensure clarity and continuity after virtual meetings. Executives:** Get concise summaries and important decision logs automatically. 🔧 What This Workflow Does ⏱ Trigger: Activated via a POST webhook receiving meeting notes, title, attendees, date, and duration. 📎 Step 2: Validates inputs; if missing required fields, sends an error response. 🔍 Step 3: Extracts and formats meeting data into structured variables for processing. 🤖 Step 4: Sends meeting notes to Google Gemini AI for advanced analysis to identify action items, decisions, summaries, follow-ups, and dates. 💌 Step 5: Splits AI responses to create Google Tasks from action items and send personalized follow-up emails via Gmail. 🗂 Step 6: Generates a Google Docs meeting summary document and finally returns a success response with all processed results. 🔐 Setup Instructions Import the provided Google Meet Automation.json file into your n8n instance. use Payload example Set up credentials for: Google OAuth2 API (Google Tasks, Google Docs) Gmail OAuth2 API for sending emails Google Palm API (for Google Gemini AI access) Customize workflow parameters: Webhook URL and access permissions Google Tasks project or folders if applicable Email templates if desired (subject line, branding) Update any API endpoints or credential references to match your account setup. Thoroughly test with sample meeting note payloads to ensure smooth execution. 🧩 Pre-Requirements Active n8n instance (Cloud or Self-hosted) Google Cloud Platform project with: Google Tasks API enabled Google Docs API enabled Gmail API enabled Google Palm API access (Google Gemini AI) Valid OAuth2 credentials configured in n8n for above services API quota and permissions for sending emails, creating docs, and tasks 🛠️ Customize It Further Integrate with calendar apps (Google Calendar, Outlook) to auto-schedule next meetings. Add Slack or Microsoft Teams notifications for real-time alerts. Extend AI prompt for deeper insights like sentiment analysis or risk flags. Customize email templates with branding, signatures, or attachments. Connect task outputs with project management tools like Asana, Trello, or Jira. 📞 Support Made by: khaisa Studio Tag: automation, google meet, meeting notes, AI, google tasks, gmail, google docs Category: Productivity Need a custom? Contact Us

How it works This workflow demonstrates a fundamental pattern for securing a webhook by requiring an API key. It acts as a gatekeeper, checking for a valid key in the request header before allowing the request to proceed. Incoming Request: The Secured Webhook node receives an incoming POST request. It expects an API key to be sent in the x-api-key header. API Key Verification: The Check API Key node takes the key from the incoming request's header. It then makes an internal HTTP request to a second webhook (Get API Key) which acts as a mock database. This second webhook retrieves a list of registered API keys (from the Registered API Keys node) and filters it to find a match for the key that was provided. Conditional Response: If a match is found, the API Key Identified node routes the execution to the "success" path, returning a 200 OK response with the identified user's ID. If no match is found, it routes to the "unauthorized" path, returning a 401 Unauthorized error. This pattern separates the public-facing endpoint from the data source, which is a good security practice. Set up steps Setup time: ~2 minutes This workflow is designed to be a self-contained example. Set up Credentials: This workflow uses "Header Auth" for its internal communication. Go to Credentials and create a new Header Auth credential. You can use any name and value (e.g., Name: X-N8N-Auth, Value: my-secret-password). Select this credential in all four webhook/HTTP Request nodes. Add Your API Keys: Open the Registered API Keys node. This is your mock database. Edit the array to include the user_id and api_key pairs you want to authorize. Activate the workflow. Test it: Use the Test Secure Webhook node to send a request. Try it with a valid key from your list to see the success response. Change the x-api-key header to an invalid key to see the 401 Unauthorized error. For Production: Replace the mock database part of this workflow (the Get API Key webhook and Registered API Keys node) with a real database node like Supabase, Postgres, or Baserow to look up keys.

🤖 Smart Interview Assistant: Tailored Questions Based on CV, JD, and Round Watch the demo video below: 📌 Who’s it for This workflow is designed for: Recruiters* and *Talent Acquisition Specialists** who want to automate candidate interview prep. Hiring Managers** conducting multiple interviews and needing personalized question sets. Technical Interviewers** who want to save time and be well-prepared with relevant questions. ⚙️ How it works / What it does The Smart Interview Assistant automates the interview preparation process in a few clicks: Accepts: Multiple resumes (PDFs) Selected job role Chosen interview round Extracts structured data from: The candidate’s CV The corresponding Job Description (JD) Uses GPT-4 to analyze: Candidate profile Role requirements Interview round context Generates: Tailored interview questions Expected answers A summarized interview prep report Sends the report directly to the hiring team via email (SMTP) 📁 Google Drive Structure 📂 Root Folder ├── 📁 jd/ # Stores all job descriptions in PDF format │ ├── Backend_Engineer.pdf │ ├── Azure_DevOps_Lead.pdf │ └── ... └── 📄 Positions (Google Sheet) # Maps Job Role ↔ JD File Link 📝 Sample Mapping Sheet: Positions Sheet Columns: Job Role Job Description File URL (pointing to PDF in jd/ folder) 🛠️ How to Set Up Step 1: Configure API Integrations ✅ Connect your OpenAI GPT-4 API Key ✅ Enable Google Cloud APIs: Google Sheets API (to read job roles) Google Drive API (to access CV and JD files) ✅ Set up SMTP credentials (for email delivery) Step 2: Prepare Google Drive & Mapping Sheet Create a root folder on Google Drive Inside the root folder: Create a folder named /jd/ and upload all job descriptions (PDFs) Create a Google Sheet named Positions with the following format: | Job Role | Job Description File URL | |-----------------------------|--------------------------------------------| | Azure DevOps Engineer | https://drive.google.com/xxx/jd1.pdf | | Full-Stack Developer (.NET) | https://drive.google.com/xxx/jd2.pdf | Step 3: Build the Application Form Use any form tool (e.g., Typeform, Tally, or custom HTML) that collects: 📎 Resume file (PDF) 🧾 Job Role (dropdown) 🔄 Interview Round (dropdown) Step 4: Resume & JD Extraction 🔍 Use Extract from PDF to parse the resume content 📄 Retrieve the JD link from the Positions sheet based on the selected Job Role 🔗 Use Download file to pull the PDF for processing Step 5: Analyze with GPT-4 Run both Resume and JD through a Profile Analyzer Agent (GPT-4 with JSON output) Merge results Add manual input or mapping for the Interview Round metadata Step 6: Generate Interview Report Use a second GPT-4 agent (e.g., HR Expert Agent) to: Generate 6–8 tailored interview questions Include expected answers and rationale Step 7: Deliver Final Report Format the content as: 📄 PDF (optional) 📨 Email body Send the report to the recruiter, hiring manager, or interviewer via SMTP ✅ Requirements 🔑 OpenAI GPT-4 API Key 📁 Google Drive (for resume and JD storage) 📊 Google Sheet (job role mapping) 📬 SMTP credentials (host, username, password) 🧰 n8n self-hosted or cloud instance with: PDF Parser Google Sheets node HTTP Download node Email node ✏️ How to Customize the Workflow | Part | Customization Options | |----------------------------|-------------------------------------------------------------| | Form UI | Modify the design, dropdown options, or input validations | | Job Description Source | Replace Google Sheet with Notion, Airtable, or database | | Interview Metadata | Add job level, region, or language preference | | AI Prompt Tuning | Adjust prompt phrasing or temperature in GPT nodes | | Report Format | Generate PDF instead of email body using PDF node | | Delivery Method | Add internal HR portal webhook or generate downloadable link |

CV Extractor: Google Drive to Sheet + Slack Update for Recruiters Watch the demo video below: > This workflow automatically processes resumes (PDFs) uploaded or updated in a Google Drive folder. It extracts and structures the candidate’s information using AI, then updates or inserts the data into a Google Sheet, acting as a central talent database. Finally, it notifies the hiring team via Slack with a summary. Perfect for HR and TA teams, this automation eliminates the repetitive task of manually copying candidate details from CVs into spreadsheets, saving hours of admin work every week and keeping your hiring pipeline clean, fast, and up to date. 👤 Who’s it for This workflow is designed for: Recruiters* and *HR coordinators** who manage candidate profiles via Google Drive. Talent Acquisition teams** who want to automate CV parsing, enrichment, and database updating. Companies or hiring agencies** using spreadsheets for candidate tracking and CRM-like HR ops. ⚙️ How it works / What it does This smart and fully automated workflow: Monitors a Google Drive folder for any uploaded or updated resumes (PDFs). Downloads and extracts resume content using PDF parsing. Sends the raw text to GPT-4, which returns a structured profile (name, title, experience, skills, etc.). Verifies the profile and transforms it into a clean, row-based format. Upserts the candidate profile into a Google Sheet (insert or update by email). Notifies the hiring team in Slack or email that a profile was added or updated. This is a no-touch pipeline to keep your candidate data clean, current, and centralized. 🛠️ How to set up Step 1: Prepare your Google Drive folder Create a folder like /SmartHR/cv/ Upload sample resumes in .pdf format Step 2: Create your Google Sheet Columns to include: Email, FullName, JobTitle, Phone, Location, Experience, Education, Skills, etc. Optional: Add conditional formatting to highlight updates Step 3: Connect the n8n workflow Use the Google Drive Trigger: fileCreated → new profile uploaded fileUpdated → existing profile modified Use Google Drive (Download file) to fetch the resume Use Extract From PDF to get raw content Step 4: Configure GPT-4 node Use the structured system prompt to extract profile information Use json parser node to ensure safe formatting for next steps Step 5: Transform & Save Use a Function node to map fields to Google Sheet columns Use Append or update row (based on email as unique key) Optionally send Slack or email message to notify hiring team ✅ Requirements 🔑 OpenAI GPT-4 API key 🟩 n8n Cloud or Self-hosted with: Google Drive integration Google Sheets integration Email/Slack credentials (optional) 📄 Resume files in readable PDF format 📊 Google Sheet prepared with relevant headers ✏️ How to customize the workflow | Part | Customization Options | |----------------------------|----------------------------------------------------------------------------------------| | GPT Prompt | Tune for different job levels or fields (e.g., engineers vs marketers) | | Field Mapping | Update transform node to include other profile fields (LinkedIn, portfolio, etc.) | | Notification | Switch to Microsoft Teams, Telegram, or email alerts instead of Slack | | Data Store | Replace Google Sheet with Airtable, Notion, or database system | | Trigger Source | Trigger from email attachments or webhook instead of Google Drive if needed | | Output Format | Generate PDF profile cards or summary documents using HTML → PDF node |

Automating your marketing campaign management process can streamline your workflow and save you valuable time. With the combination of Baserow and n8n, you can efficiently handle your campaign data and seamlessly publish content to your Shopify store. In this workflow template, I demonstrate how to leverage Baserow as a centralized platform for organizing your marketing campaign assets, including copy and images. By utilizing n8n, we automate the process of fetching images and campaign descriptions from Baserow and uploading them directly to your Shopify store. With this automated solution, you can expedite the publishing process, ensuring that your campaigns are launched swiftly across your sales channels. Additionally, this workflow serves as a foundational step towards further automation in campaign management, allowing you to dynamically generate and upload content to your Shopify store with ease. This template will help you: Use n8n to get images for marketing campaigns from Baserow and upload them to your Shopify media library Dynamically inject data from Baserow into a template file Upload a template file to your Shopify theme This template will demonstrate the follwing concepts in n8n: use the Webhook node use the IF node to control the execution flow of the workflow do time calculation using expressions and javascript use the GraphQL node to upload images to your Shopify media files create a dynamic template file for your Shopify theme use the HTTP Reqest node to upload your template file to your Shopify store How to get started? Create a custom app in Shopify get the credentials needed to connect n8n to Shopify This is needed for the Shopify Trigger Create Shopify Acces Token API credentials n n8n for the Shopify trigger node Create Header Auth credentials: Use X-Shopify-Access-Token as the name and the Acces-Token from the Shopify App you created as the value. The Header Auth is neccessary for the GraphQL nodes. You will need a running Baserow instance for this. You can also sign up for a free account at https://baserow.io/ Please make sure to read the notes in the template. For a detailed explanation please check the corresponding video: https://youtu.be/Ky-dYlljGiY

Overview Receive a URL via Webhook, submit it to urlscan.io, wait ~30 seconds for artifacts (e.g., screenshot), then email a clean summary with links to the result page, screenshot, and API JSON. What this template does Ingests a URL from a POST request. Submits the URL to urlscan.io and captures the scan UUID. Waits 30s** to give urlscan time to generate the screenshot and result artifacts. Sends a formatted HTML email via Gmail with all relevant links. Nodes used Webhook** (POST /urlscan) urlscan.io → Perform a scan** Wait** (30 seconds; configurable) Gmail → Send a message** Input { "url": "https://example.com" }

This is an example of how we can build a slack bot in a few easy steps Before you can start, you need to o a few things Create a copy of this workflow Create a slack bot Create a slash command on slack and paste the webhook url to the slack command Note Make sure to configure this webhook using a https:// wrapper and don't use the default http://localhost:5678 as that will not be recognized by your slack webhook. Once the data has been sent to your webhook, the next step will be passing it via an AI Agent to process data based on the queries we pass to our agent. To have some sort of a memory, be sure to set the slack token to the memory node. This way you can refer to other chats from the history. The final message is relayed back to slack as a new message. Since we can not wait longer than 3000 ms for slack response, we will create a new message with reference to the input we passed. We can advance this using the tools or data sources for it to be more custom tailored for your company. Usage To use the slackbot, go to slack and click on your set slash command eg /Bob and send your desired message. This will send the message to your endpoint and get return the processed results as the message. If you would like help setting this up, feel free to reach out to zacharia@effibotics.com

This n8n template demonstrates how to automate the creation of high-quality visual content using AI. The workflow takes simple titles from a Google Sheets spreadsheet, generates detailed artistic prompts using AI, creates photorealistic images, and manages the entire process from data input to final delivery. Use cases are many: Perfect for digital marketers, content creators, social media managers, e-commerce businesses, advertising agencies, and anyone needing consistent, high-quality visual content for marketing campaigns, social media posts, or brand materials! Good to know The Gemini 2.0 Flash Exp image generation model used in this workflow may have geo-restrictions. The workflow processes one image at a time to ensure quality and avoid rate limiting. Each generated image maintains high consistency with the source prompt and shows minimal AI artifacts. How it works Automated Trigger: A schedule trigger runs every minute to check for new entries in your Google Sheets spreadsheet. Data Retrieval: The workflow fetches rows from your Google Sheets document, specifically looking for entries with "pending" status. AI Prompt Generation: Using Google Gemini, the workflow takes simple titles and transforms them into detailed, artistic prompts for image generation. The AI considers: Specific visual elements, styles, and compositions Natural poses, interactions, and environmental context Lighting conditions and mood settings Brand consistency and visual appeal Proper aspect ratios for different platforms Text Processing: A code node ensures proper JSON formatting by escaping newlines and maintaining clean text structure. Image Generation: Gemini's advanced image generation model creates photorealistic images based on the detailed prompts, ensuring high-quality, consistent results. File Management: Generated images are automatically uploaded to a designated folder in Google Drive with organized naming conventions. Public Sharing: Images are made publicly accessible with read permissions, enabling easy sharing and embedding. Database Update: The workflow completes by updating the Google Sheets with the generated image URL and changing the status from "pending" to "posted", creating a complete audit trail. How to use Setup: Ensure you have the required Google Sheets document with columns for ID, prompt, status, and imageUrl. Configuration: Update the Google Sheets document ID and folder IDs in the respective nodes to match your setup. Activation: The workflow is currently inactive - activate it in n8n to start processing. Data Input: Simply add new rows to your Google Sheets with titles and set status to "pending" - the workflow will automatically process them. Monitoring: Check the Google Sheets for updated status and image URLs to track progress. Requirements Google Gemini API** account for LLM and image generation capabilities Google Drive** for file storage and management Google Sheets** for data input and tracking n8n instance** with proper credentials configured Customizing this workflow Content Variations: Try different visual styles, seasonal themes, or trending designs by modifying the AI prompt in the LangChain agent. Output Formats: Adjust the aspect ratio or image specifications for different platforms (Instagram, Pinterest, TikTok, Facebook ads, etc.). Integration Options: Replace the schedule trigger with webhooks for real-time processing, or add notification nodes for status updates. Batch Processing: Modify the limit node to process multiple items simultaneously, though be mindful of API rate limits. Quality Control: Add additional validation nodes to ensure generated images meet quality standards before uploading. Analytics: Integrate with analytics platforms to track image performance and engagement metrics. This workflow provides a complete solution for automated visual content creation, perfect for businesses and creators looking to scale their visual content production while maintaining high quality and consistency across all marketing materials.

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