Workflow Templates

This sample workflow allows you to forward alerts from TheHive 5 to SIGNL4 in order to send reliable alerts to your team. There are two nodes for testing the TheHive connection ("TheHive Read Alerts" and "TheHive Create Alert"). The node "TheHive Webhook Request" will receive requests for new alerts from TheHive. You need to configure the webhook and the notifications in TheHive accordingly. The node "SIGNL4 Send Alert" sends the alert to SIGNL4 and the node "SIGNL4 Resolve Alert" will close the alert in SIGNL4 in case it has been closed in TheHive.

This workflow generates CSV files containing a list of 10 random users with specific characteristics using OpenAI's GPT-4 model. It then splits this data into batches, converts it to CSV format, and saves it to disk for further use. The execution of the workflow begins from here when triggered manually. "OpenAI" Node. This uses the OpenAI API to generate random user data. The input to the OpenAI API is a fixed string, which asks for a list of 10 random users with some specific attributes. The attributes include a name and surname starting with the same letter, a subscription status, and a subscription date (if they are subscribed). There is also a short example of the JSON object structure. This technique is called one-shot prompting. "Split In Batches" Node. This node is used to handle the OpenAI responses one by one. "Parse JSON" Node. This node converts the content of the message received from the OpenAI node (which is in string format) into a JSON object. "Make JSON Table" Node. This node is used to convert the JSON data into a tabular format, which is easier to handle for further data processing. "Convert to CSV" Node. This node converts the table format data received from the "Make JSON Table" node into CSV format and assigns a file name. "Save to Disk" Node. This node is used to save the CSV generated in the previous node to disk in the ".n8n" directory. The workflow is designed in a circular manner. So, after saving the file to disk, it goes back to the "Split In Batches" node to process the OpenAI output, until all batches are processed.

This is an example workflow that imports an XML file into an SQL database. The ReadBinaryFiles node loads the XML file from the server. Then the Code node extracts the file content from the binary buffer. Afterwards, an XML node converts the XML string into a JSON structure. Finally, in the MySQL node inserts the data records into the SQL table. In the upper part of the workflow there is another MySQL node that is disabled. This node creates a new table with all the required variables based on the sample SQL database: https://www.mysqltutorial.org/mysql-sample-database.aspx

Automated Daily AI Summaries from WhatsApp Groups using a Custom AI Agent Transform your WhatsApp group conversations into actionable business intelligence through automated AI analysis and daily reporting. This workflow eliminates manual conversation monitoring by capturing messages in real-time, processing voice notes, and delivering structured insights directly to your team. Overview This workflow provides complete conversation intelligence automation from message capture to insight delivery. It eliminates manual monitoring, analysis, and reporting by using Evolution API integration, OpenAI transcription, and advanced LLM analysis for hands-free business intelligence that scales your team's awareness of important discussions. Core Function: Autonomous conversation analysis that transforms WhatsApp group chatter into structured business insights with zero manual intervention, maintaining consistent daily reporting while capturing emerging opportunities and trends before your competition. Key Capabilities Real-time message capture - Monitors multiple WhatsApp groups simultaneously with instant processing and smart filtering Voice message transcription - Automatic conversion of audio messages to searchable text using OpenAI Whisper AI-powered insight extraction - Advanced LLM analysis identifies trends, opportunities, and actionable information while filtering noise Automated daily reporting - Scheduled intelligence summaries delivered directly to your team via WhatsApp Multi-group organization - Separate tracking and analysis for different communities with unified reporting Smart content filtering - AI agent trained to focus on business-relevant discussions (AI, automation, tech trends, opportunities) Tools Used n8n: Workflow orchestration managing the entire intelligence pipeline from capture to delivery Evolution API: WhatsApp Business API integration for real-time message monitoring and sending OpenAI Whisper: Voice message transcription ensuring no important audio content is missed OpenRouter/GPT-4.1: Advanced AI analysis for intelligent insight extraction and content filtering Google Sheets: Organized message storage with timestamps and metadata for historical analysis Custom AI Agent: "WhatsOn" - specialized business intelligence detective for tech and automation insights How to Install Import the Workflow: Download the JSON file and import into your n8n instance Configure Evolution API: Set up WhatsApp integration and webhook endpoints for message capture API Credentials Setup: Add OpenAI, OpenRouter, and Google Sheets credentials in n8n Group Configuration: Update group IDs in the "Set Info" node with your monitored groups Google Sheets Setup: Create organized spreadsheet with separate tabs for each group Schedule Configuration: Set your preferred daily summary delivery time Test Execution: Run manual test to verify message capture and AI analysis work correctly Use Cases Business Intelligence Automation: Stay informed about industry discussions without manual monitoring Opportunity Detection: Identify emerging trends, tools, and business opportunities in real-time Team Knowledge Sharing: Automated distribution of relevant insights from multiple communities Competitive Intelligence: Monitor industry discussions to stay ahead of market developments Community Management: Track engagement patterns and important conversations across groups Voice Message Processing: Ensure audio-based insights aren't lost in team communications Setup Requirements Evolution API account: WhatsApp Business integration with webhook capabilities OpenAI API: Voice transcription access through Whisper API OpenRouter account: Access to GPT-4.1 for advanced conversation analysis Google Sheets: Message storage and organization with proper permissions configured WhatsApp Groups: Access to business or professional groups with relevant discussions Total setup time: 15-20 minutes once all API accounts are properly configured. How to Customize Analysis Focus: Modify the AI agent's system prompt to target your industry or specific topics. Adjust keyword priorities, conversation themes, or insight categories based on your business needs. Group Management: Add additional groups by extending the Switch node logic, creating new Google Sheets tabs, and updating group ID variables. Scale from 3 to unlimited group monitoring. Delivery Schedule: Change summary frequency from daily to weekly, multiple times per day, or custom schedules. Add multiple delivery destinations for different team segments. AI Intelligence: Customize the "WhatsOn" agent personality, adjust insight priorities, modify filtering criteria, or add sentiment analysis for deeper conversation understanding. Storage & Organization: Modify Google Sheets structure, add custom metadata fields, integrate with other databases, or connect to business intelligence dashboards for advanced analytics. Advanced Features Smart Voice Processing Automatically transcribes voice messages to text using OpenAI's Whisper API, ensuring critical audio-based discussions are captured and analyzed alongside text conversations. Intelligent Content Filtering The AI agent is specifically trained to identify valuable business insights while filtering out casual conversation, ensuring your daily summaries focus on actionable information that drives decisions. Multi-Fragment Delivery System Large intelligence summaries are automatically broken into properly formatted WhatsApp messages with natural pacing to avoid delivery issues and improve readability. Historical Analysis Capability All conversations are stored with full metadata in Google Sheets, enabling historical trend analysis, keyword tracking, and long-term pattern recognition for strategic planning. Ready to transform group conversations into competitive intelligence? This template converts casual WhatsApp discussions into structured business insights delivered automatically to your team, ensuring you never miss important industry developments or opportunities. Google Sheets Template The workflow includes a pre-configured structure for tracking: Message timestamps and sender information Full conversation content with voice transcriptions Group-specific organization and categorization Daily summary delivery logs and performance metrics Was this helpful? Let me know! I truly hope this WhatsApp intelligence system helps streamline your team's awareness of important conversations. Your feedback helps me create better automation resources for the n8n community. Ready to Build Something Great? If you're looking to take your n8n skills or business automation to the next level, I can help. 🎓 n8n Coaching: Want to become an n8n pro? I offer one-on-one coaching sessions to help you master workflows, tackle specific problems, and build with confidence. ➡️ Book a Coaching Session 💼 n8n Consulting: Have a complex project, an integration challenge, or need a custom workflow built for your business? Let's work together to create a powerful automation solution. ➡️ Inquire About Consulting Services Stay Updated on Automation For more content automation strategies, AI workflow tips, and business automation insights: Follow me on LinkedIn Happy Automating! Daniel Lianes

This n8n workflow automatically fetches the latest CVE data at scheduled intervals, extracts relevant security details, and creates a corresponding Security Incident in ServiceNow for each new vulnerability. Schedule Trigger – Runs at predefined intervals. Jina Fetch – Retrieves the latest CVE feed. Information Extractor (OpenAI Chat Model) – Processes and extracts key details from the CVE data. Split Out – Separates each CVE entry for individual processing. Create Incident – Generates a ServiceNow Security Incident with the extracted CVE details. Ideal for security teams to ensure timely tracking and remediation of new vulnerabilities without manual monitoring.

Overview This workflow automates the process of notifying users about new emails via Telegram and temporarily hosting the email content on a secret HTML page. It is ideal for users who need immediate notifications and a secure, temporary web view of their email content. Use Cases Immediate notification of new emails in Telegram with the ability to preview the email content in a secure, temporary HTML format. Automation for users who need to keep track of their emails without constantly checking their email client. Useful for teams or individuals who require instant updates on critical emails and prefer a quick preview through a web interface. My Personal Use Case: Secure Subscription Sharing From time to time, I find myself wanting to share my paid subscriptions with friends, but giving out OTP codes manually or sharing my email isn't a good idea due to security concerns. I attempted to use the IMAP node to integrate with Telegram secret channel for this purpose but encountered numerous problems, such as difficulties in scraping content from emails. Additionally, the Telegram API sometimes rejects certain special characters found within email contents. After facing these challenges, I discovered that rendering emails as HTML pages and sharing them directly is the most effective solution. This approach bypasses the issues with character limitations and content scraping, providing a seamless way to share subscription benefits securely. Services/APIs Used | Service/API | Node Type | Description | |----------------------|-------------------------|------------------------------------------------------| | IMAP Email Server | Email Trigger (IMAP) | Triggers the workflow on receiving a new email. | | Telegram API | Telegram | Sends notifications and manages messages in Telegram.| | GitHub Gist API | HTTP Request (Github Gist) | Temporarily hosts email content on GitHub Gist. | | GitHub Gist API (Deletion) | HTTP Request (Github Gist ‌) | Deletes the temporary GitHub Gist after a specified time. | | Wait | Wait | Delays the workflow for a specified period. | Configuration Steps Email Trigger (IMAP): Configure your IMAP email credentials to enable the workflow to check for new emails. Telegram Nodes: Insert your Telegram bot's API credentials and your chat ID in both Telegram nodes to send and delete messages. Github Gist Nodes: Provide your GitHub API credentials to create and delete Gists. Ensure the GitHub token has the necessary permissions to create and delete Gists. Wait Node: Adjust the wait time according to your preference for how long the email content should be accessible via the HTML page. Screenshot Additional Notes Ensure all credentials are securely stored and have the minimum necessary permissions for the workflow to function. Test the workflow with non-sensitive emails to ensure it operates as expected before using it with critical email accounts. Consider the privacy and security implications of temporarily hosting email content on GitHub Gist. For any questions or issues, refer to the respective API documentation for each service used or consult the n8n community for support.

Streamline data from an n8n form into Google Sheet Airtable and and Email Sending Video for workflow process This workflow facilitates efficient data collection and management by leveraging the capabilities of various nodes within the n8n platform. It commences with the n8n Form Trigger node, where users provide their name, location, and email address. Subsequently, the data seamlessly flows through nodes like Google Sheets, Code, Set, Airtable, Gmail, and Gmail1 for processing and storage. n8n Form Trigger:** Gathers user input data, including Name, City, and Email. Google Sheets:** Manages data operations related to Google Sheets. Code:** Executes JavaScript code to manipulate data fields. Set:** Formats and sets data values for further processing. Airtable:** Facilitates data operations specific to Airtable. Gmail:** Sends custom emails to the provided Email address. Gmail:** Sends additional emails using different templates. Each node within the workflow performs specialized tasks such as extracting date and time fields, formatting data, appending it to Google Sheets and Airtable, and sending personalized emails to the submitter. This streamlined process ensures effective handling of collected information and enhances overall data management efficiency. Workflow Description: n8n Form Trigger: A trigger node that initiates the workflow upon form submission. Captures essential user details like Name, City, and Email. Extracting Date and Time Fields from 'submittedAt' Field: Utilizes a code node to extract Date and Time information from the submitted data. Format the Fields: Standardizes the format of extracted fields (Name, City, Date, Time, Email) for consistency. Airtable: Creates a new record in Airtable with the formatted data. Includes columns for Name, City, Email, Time, and Date. Google Sheets: Appends the formatted data to a designated Google Sheet. Includes columns for Name, City, Email, Date, and Time. Gmail: Sends an email to the provided Email address with a customized message. Subject: "Testing Text Message Delivery" Message: Personalized content with a Name placeholder. Gmail1: Sends another email using a different template. Subject incorporates the Date field for variation. Message content tailored to the subject line. Workflow Connections: n8n Form Trigger -> Extracting Date and Time Fields -> Format the Fields -> Google Sheets & Airtable -> Gmail Google Sheets -> Gmail1 This comprehensive workflow efficiently collects user data, processes it to extract Date and Time fields, stores the formatted information in Google Sheets and Airtable, and delivers tailored emails to the recipients. Copy these templates to get started : Google Sheet Airtable Links to Node Documentation: n8n Form Trigger Documentation Code Node Documentation Set Node Documentation Airtable Node Documentation Google Sheets Node Documentation Gmail Node Documentation

Transforming Emails into Podcasts 🎙️ Check out this channel for example. The n8n workflow described here aims to revolutionize the way users engage with promotional emails by converting them into entertaining audio podcasts. This innovative project leverages automation through n8n to streamline tasks and enhance user experience. Project Benefit 🎧🌟 The primary goal of this project is to transform "CATEGORY_PROMOTIONS" emails into engaging audio content. By converting text into speech, users can enjoy promotional material hands-free, making it easier to consume information while on the go or relaxing. The workflow consists of several key steps orchestrated seamlessly to deliver a delightful experience to users. How to Use the Workflow: Gmail trigger Node: Initiates the workflow by fetching "CATEGORY_PROMOTIONS" emails at regular intervals. The Gmail Trigger node in your N8N workflow is set to poll for new emails every minute and is configured to filter emails with the label "CATEGORY_PROMOTIONS" before triggering the workflow. Steps to Use Filters Inside the Gmail Trigger Node: Configure Gmail Trigger Node: Set "Poll Times" to "Every Minute" to check for new emails at regular intervals. Enable the "Simple" toggle if you want to simplify the node interface. Under "Filters", specify the label IDs you want to filter by. In this case, it's set to "CATEGORY_PROMOTIONS". Adjust any additional options as needed. // Configure Gmail Trigger node pollTimes: { item: [ { mode: "everyMinute" } ] }, simple: false, filters: { labelIds: [ "CATEGORY_PROMOTIONS" ] }, options: {} Save and Execute: Save your workflow and execute it to start monitoring your Gmail account for new emails with the specified label filter. By following these steps, your workflow will effectively trigger based on new emails that match the "CATEGORY_PROMOTIONS" label in your Gmail account. Get message content Node: Extracts the email content for processing. Summarization Chain Node: Generates concise summaries using advanced methods for better readability. Delete the unnecessary items Node: Removes irrelevant details from the email content. Text to Free TTS Node: Converts the summary text into speech using Free TTS technology. Convert from base64 to File Node: Transforms the audio data into a compatible file format. Merge Text with Audio Node: Combines the text and audio components seamlessly. Aggregate in same cell Node: Gathers all processed data for finalization. Send Message to Telegram Node: Dispatches the audio message along with a caption to a designated Telegram chat ID. By automating these tasks, the workflow ensures efficient communication and delivers content in a more engaging format, fostering a positive user experience. Configuration Instructions: The configuration of this workflow involves setting up the necessary nodes and establishing connections between them. Each node performs a specific function crucial to the overall operation of the workflow. Additionally, credentials need to be provided for accessing Gmail and OpenAI services to enable seamless data processing and summarization. Utilizing Text-to-Speech API 🎧 In addition to n8n automation, an external Text-to-Speech API plays a pivotal role in generating audio content from text data. By sending a POST request with JSON data containing the text and voice preferences, users can quickly receive audio files of the converted content. The API offers a straightforward interface for text-to-speech conversion, making it ideal for creating audio clips efficiently. To access this API, simply submit the desired text and voice selection to receive the generated speech audio file. The API endpoint can be accessed at https://tiktok-tts.weilnet.workers.dev/api/generation or through https://tiktokvoicegenerator.com/. In conclusion, this n8n workflow coupled with a Text-to-Speech API presents a powerful solution for transforming emails into captivating podcasts, enhancing user engagement and communication effectiveness. By embracing automation and innovative technologies, this project aims to improve user experience and streamline content delivery processes. 🌈✨🚀

This workflow enriches new Pipedrive organization's data by adding a note to the organization object in Pipedrive. It assumes there is a custom "website" field in your Pipedrive setup, as data will be scraped from this website to generate a note using OpenAI. Then, a notification is sent in Slack. ⚠️ Disclaimer This workflow uses a scraping API. Before using it, ensure you comply with the regulations regarding web scraping in your country or state. Important Notes The OpenAI model used is GPT-4o, chosen for its large input token capacity. However, it is not the cheapest model if cost is very important to you. The system prompt in the OpenAI Node generates output with relevant information, but feel free to improve or modify it according to your needs. How It Works Node 1: Pipedrive Trigger - An Organization is Created This is the trigger of the workflow. When an organization object is created in Pipedrive, this node is triggered and retrieves the data. Make sure you have a "website" custom field in Pipedrive (the name of the field in the n8n node will appear as a random ID and not with the Pipedrive custom field name). Node 2: ScrapingBee - Get Organization's Website's Homepage Content This node scrapes the content from the URL of the website associated with the Pipedrive Organization created in Node 1. The workflow uses the ScrapingBee API, but you can use any preferred API or simply the HTTP request node in n8n. Node 3: OpenAI - Message GPT-4o with Scraped Data This node sends HTML-scraped data from the previous node to the OpenAI GPT-4o model. The system prompt instructs the model to extract company data, such as products or services offered and competitors (if known by the model), and format it as HTML for optimal use in a Pipedrive Note. Node 4: Pipedrive - Create a Note with OpenAI Output This node adds a Note to the Organization created in Pipedrive using the OpenAI node output. The Note will include the company description, target market, selling products, and competitors (if GPT-4o was able to determine them). Node 5 & 6: HTML To Markdown & Code - Markdown to Slack Markdown These two nodes format the HTML output to Slack Markdown. The Note created in Pipedrive is in HTML format, as specified by the System Prompt of the OpenAI Node. To send it to Slack, it needs to be converted to Markdown and then to Slack Markdown. Node 7: Slack - Notify This node sends a message in Slack containing the Pipedrive Organization Note created with this workflow.

n8n Workflow: Exponential Backoff for Google APIs Overview This n8n workflow implements an Exponential Backoff mechanism to handle retries when interacting with Google APIs. It ensures that failed API requests are retried with increasing delays, up to a specified maximum retry count. This approach helps mitigate transient errors (e.g., rate limits or temporary network issues) while maintaining workflow efficiency. Key Features: Exponential Backoff Logic**: Dynamically increases wait time between retries based on the retry count. Error Handling**: Stops the workflow and raises an error after a specified number of retries. Dynamic Waiting**: Waits for a calculated duration before each retry. Scalable Design**: Modular nodes for easy debugging and customization. Workflow Details Nodes in the Workflow: Trigger (When clicking "Test Workflow"): Manually starts the workflow for testing. Loop Over Items: Iterates over multiple input items to process Google API requests row by row. Google API Node (Example: Update Sheet): Sends a request to a Google API endpoint (e.g., updating a row in Google Sheets). On success: Moves to the next item in the loop. On error: Passes the error to the Exponential Backoff node. Exponential Backoff: Calculates the delay for the next retry based on the retry count. Logic: const retryCount = $json["retryCount"] || 0; const maxRetries = 5; const initialDelay = 1; // in seconds if (retryCount < maxRetries) { const currentDelayInSeconds = initialDelay * Math.pow(2, retryCount); return { json: { retryCount: retryCount + 1, waitTimeInSeconds: currentDelayInSeconds, status: 'retrying', } }; } else { return { json: { error: 'Max retries exceeded', retryCount: retryCount, status: 'failed' } }; } Wait: Dynamically waits for the waitTimeInSeconds value calculated in the Exponential Backoff node. Configuration: Resume: After Time Interval Wait Amount: {{ $json["waitTimeInSeconds"] }} Unit: Seconds Check Max Retries: Evaluates whether the retry count has exceeded the maximum limit. Routes the workflow: True: Passes to the Stop and Error node. False: Loops back to the Google API node for retry. Stop and Error: Stops the workflow and logs the error when the maximum retry count is reached. Parameters Configurable Settings: Max Retries: Defined in the Exponential Backoff node (const maxRetries = 5). Adjust this value based on your requirements. Initial Delay: The starting wait time for retries, defined as 1 second. Google API Configuration: Ensure your Google API node is properly authenticated and configured with the desired endpoint and parameters. How to Use Import the Workflow: Copy the workflow JSON and import it into your n8n instance. Configure Google API Node: Set up the Google API node with your credentials and target API endpoint (e.g., Google Sheets, Gmail, etc.). Test the Workflow: Manually trigger the workflow and observe the retry behavior in case of errors. Monitor Logs: Use the console logs in the Exponential Backoff node to debug retry timings and status. Example Scenarios Scenario 1: Successful Execution The Google API processes all requests without errors. Workflow completes without triggering the retry logic. Scenario 2: Transient API Errors The Google API returns an error (e.g., 429 Too Many Requests). The workflow retries the request with increasing wait times. Scenario 3: Maximum Retries Exceeded The workflow reaches the maximum retry count (e.g., 5 retries). An error is raised, and the workflow stops. Considerations Jitter: This workflow does not implement jitter (randomized delay) since it's not required for low-volume use cases. If needed, jitter can be added to the exponential backoff calculation. Retry Storms: If multiple workflows run simultaneously, ensure your API quotas can handle potential retries. Error Handling Beyond Max Retries: Customize the Stop and Error node to notify stakeholders or log errors in a centralized system. Customization Options Adjust the maximum retry limit and delay calculation to suit your use case. Add additional logic to handle specific error codes differently. Extend the workflow to notify stakeholders when an error occurs (e.g., via Slack or email). Troubleshooting Retry Not Triggering**: Ensure the retryCount variable is passed correctly between nodes. Confirm that the error output from the Google API node flows to the Exponential Backoff node. Incorrect Wait Time**: Verify the Wait node is referencing the correct field for waitTimeInSeconds. Request for Feedback We are always looking to improve this workflow. If you have suggestions, improvements, or ideas for additional features, please feel free to share them. Your feedback helps us refine and enhance this solution!

Workflow Description Your workflow is an intelligent chatbot, using ++OpenAI assistant++, integrated with a backend that supports WhatsApp Business, designed to handle various use cases such as sales and customer support. Below is a breakdown of its functionality and key components: Workflow Structure and Functionality Chat Input (Chat Trigger) The flow starts by receiving messages from customers via WhatsApp Business. Collects basic information, such as session_id, to organize interactions. Condition Check (If Node) Checks if additional customer data (e.g., name, age, dependents) is sent along with the message. If additional data is present, a customized prompt is generated, which includes this information. The prompt specifies that this data is for the assistant's awareness and doesn’t require a response. Data Preparation (Edit Fields Nodes) Formats customer data and the interaction details to be processed by the AI assistant. Compiles the customer data and their query into a single text block. AI Responses (OpenAI Nodes) The assistant’s prompt is carefully designed to guide the AI in providing accurate and relevant responses based on the customer’s query and data provided. Prompts describe the available functionalities, including which APIs to call and their specific purposes, helping to prevent “hallucinated” or irrelevant responses. Memory and Context (Postgres Chat Memory) Stores context and messages in continuous sessions using a database, ensuring the chatbot maintains conversation history. API Calls The workflow allows the use of APIs with any endpoints you choose, depending on your specific use case. This flexibility enables integration with various services tailored to your needs. The OpenAI assistant understands JSON structures, and you can define in the prompt how the responses should be formatted. This allows you to structure responses neatly for the client, ensuring clarity and professionalism. Make sure to describe the purpose of each endpoint in the assistant’s prompt to help guide the AI and prevent misinterpretation. Customer Response Delivery After processing and querying APIs, the generated response is sent to the backend and ultimately delivered to the customer through WhatsApp Business. Best Practices Implemented Preventing Hallucinations** Every API has a clear description in its prompt, ensuring the AI understands its intended use case. Versatile Functionality** The chatbot is modular and flexible, capable of handling both sales and general customer inquiries. Context Persistence** By utilizing persistent memory, the flow maintains continuous interaction context, which is crucial for longer conversations or follow-up queries. Additional Recommendations Include practical examples in the assistant’s prompt, such as frequently asked questions or decision-making flows based on API calls. Ensure all responses align with the customer’s objectives (e.g., making a purchase or resolving technical queries). Log interactions in detail for future analysis and workflow optimization. This workflow provides a solid foundation for a robust and multifunctional virtual assistant 🚀

Workflow Description This workflow automates the process of retrieving emails from a food delivery platform, extracting key order details, and sending notifications to a Slack channel. Additionally, the Slack message includes a Moze accounting app URL scheme link for quick expense tracking. Key Features Manual Trigger: Allows the workflow to be executed manually for immediate testing. Gmail Integration: Retrieves emails containing specific keywords in the subject line (e.g., "透過 Uber Eats 系統送出的訂單"). (You can adjust the keywords to fit your language.) Data Extraction: Parses the email content to extract key details such as: Order price Shop name Order date and time Slack Notification: Sends a notification to a specified Slack channel using a structured block format, including a link to record the expense in the Moze accounting app. Node Configurations 1. Manual Trigger Purpose**: Starts the workflow manually for testing or immediate execution. Configuration**: No setup needed. 2. Gmail Trigger Purpose**: Automatically polls Gmail for new emails matching specific subject keywords. Configuration**: Filters: q: subject:透過 Uber Eats 系統送出的訂單 (You can adjust the keywords to fit your language.) Polling Frequency: Every hour at 30 minutes past the hour. Credentials: Linked Gmail account. 3. Extract Price, Shop, Date, Time Purpose**: Extracts key information from the email content using regular expressions. Extracted Data**: price: Order price (e.g., $200). shop: Shop name (e.g., "店名"). date: Order date (e.g., 2024.01.01). time: Order time converted to 24-hour format (e.g., 14:30). 4. Slack Notification Purpose**: Sends a formatted message to a Slack channel with extracted order details. Message Content**: Text: Ubereat 訂餐資訊: 商家: {{ shop }} 金額: {{ price }} 日期: {{ date }} Moze App Link: Includes a clickable button in the Slack message with a pre-filled Moze app URL scheme: moze3://expense?amount={{ price }}&account=信用卡&subcategory=外送&store={{ shop }}&date={{ date }}&time={{ time }}&project=生活開銷 Channel: Slack channel ID associated with food delivery notifications. Additional Notes Customization**: Adjust the email subject filter (subject) to match other types of food delivery platforms or services. Error Handling**: Ensure regular expressions for data extraction match the email format. Test with sample emails before deployment. Moze URL Scheme Reference**: Learn more about Moze app URL schemes for customization by visiting the Moze Documentation. This workflow is ideal for automating expense tracking and centralizing notifications for food delivery orders, streamlining personal or team expense management. Image: UberEat Gmail with order information Slack text with button Click the button will call moze url scheme 工作流程描述 此工作流程自動化從外送平台獲取郵件，提取關鍵訂單詳細資訊，並將通知發送到指定的 Slack 頻道。此外，Slack 消息中包含一個 Moze 記帳 App URL Scheme 的連結，方便快速記帳。 主要功能 Manual Trigger：允許手動執行工作流程，方便測試。 Gmail Integration：從 Gmail 中提取包含特定關鍵字（例如：「透過 Uber Eats 系統送出的訂單」）的郵件。 資料提取：解析郵件內容，提取以下關鍵資訊： 訂單金額 商家名稱 訂單日期與時間 Slack 通知：將結構化的通知發送到指定的 Slack 頻道，並包含一個連結供用戶快速記帳。 節點設定 1. Manual Trigger 用途**：手動啟動工作流程以進行測試或即時執行。 設定**：無需額外設定。 2. Gmail Trigger 用途**：自動檢查 Gmail 中是否有符合特定主題關鍵字的新郵件。 設定**： 篩選條件： q: subject:透過 Uber Eats 系統送出的訂單 檢查頻率：每小時的 30 分。 憑證：已連結的 Gmail 帳號。 3. Extract Price, Shop, Date, Time 用途**：使用正則表達式從郵件內容中提取關鍵資訊。 提取的資料**： price：訂單金額（例如：$200）。 shop：商家名稱（例如：「店名」）。 date：訂單日期（例如：2024.01.01）。 time：訂單時間（24 小時制，例如：14:30）。 4. Slack 通知 用途**：將訂單詳細資訊以格式化消息發送到 Slack。 消息內容**： 文字： Ubereat 訂餐資訊: 商家: {{ shop }} 金額: {{ price }} 日期: {{ date }} Moze App 連結：Slack 消息中包含一個可點擊按鈕，預填 Moze App URL Scheme： moze3://expense?amount={{ price }}&account=信用卡&subcategory=外送&store={{ shop }}&date={{ date }}&time={{ time }}&project=生活開銷 頻道：與外送通知相關的 Slack 頻道。 補充說明 自訂化**：可調整郵件主題篩選條件（subject），以匹配其他外送平台或服務。 錯誤處理**：確保正則表達式匹配郵件格式。在部署前使用樣本郵件進行測試。 Moze URL Scheme 參考**：了解更多關於 Moze App URL Scheme 的客製化資訊，請參閱 Moze 官方文件。 此工作流程適合自動化費用記帳以及集中管理外送訂單通知，提升個人或團隊的費用管理效率。