Workflow Templates

Automated Financial Tracker: Telegram Invoices to Notion with AI Summaries & Reports Tired of manually logging every expense? Streamline your financial tracking with this powerful n8n workflow! Snap a photo of your invoice in Telegram, and let AI (powered by Google Gemini) automatically extract the details, record them in your Notion database, and even send you a quick summary. Plus, get scheduled weekly reports with charts to visualize your spending. Automate your finances, save time, and gain better insights with this easy-to-use template! Transform your expense tracking from a chore into an automated breeze. Try it out! Overview: This workflow revolutionizes how you track your finances by automating the entire process from invoice capture to reporting. Simply send a photo of an invoice or receipt to a designated Telegram chat, and this workflow will: Extract Data with AI: Utilize Google Gemini's capabilities to perform OCR on the image, understand the content, and extract key details like item name, quantity, price, total, date, and even attempt to categorize the expense. Store in Notion: Automatically log each extracted transaction into a structured Notion database. Instant Feedback: Send a summary of the processed transaction back to your Telegram chat. Scheduled Reporting: Generate and send a visual summary of your expenses (e.g., weekly spending by category) as a chart to your preferred Telegram chat or group. This workflow is perfect for individuals, freelancers, or small teams looking to effortlessly manage their expenses without manual data entry. Key Features & Benefits: Effortless Expense Logging:** Just send a picture – no more typing! AI-Powered Data Extraction:** Leverages Google Gemini for intelligent invoice processing. Centralized Data in Notion:** Keep all your financial records neatly organized in a Notion database. Automated Categorization:** AI helps in categorizing your expenses (e.g., Food & Beverage, Transportation). Instant Summaries:** Get immediate confirmation and a summary of what was recorded. Visual Reporting:** Receive scheduled charts (e.g., bar charts of spending by category) directly in Telegram. Customizable:** Easily adapt the workflow to your specific needs, categories, and reporting preferences. Time-Saving:** Drastically reduces the time spent on manual financial administration. How It Works (Workflow Breakdown): The workflow is divided into two main parts: Part 1: Real-time Invoice Processing & Logging (## Auto Notes Transaction with Telegram and Notion database) Telegram Trigger (Telegram Trigger | When recive photo): Activates when a new photo is sent to the configured Telegram chat. Get Photo Info (Get Info Photo from telegram chat): Retrieves the details of the received photo. Get Image Info (Get Image Info): Prepares the image data. AI Data Extraction (Google Gemini Chat Model & Basic LLM Chain): The image data is sent to the Google Gemini Chat Model. A specific prompt instructs the AI to extract details (date, ID, name, quantity, price, total, category, tax) in a JSON array format and provide a summary message. The categories include Food & Beverage, Transportation, Utilities, Shopping, Healthcare, Entertainment, Housing, and Education. Parse AI Output (Parse To your object | Table): Structures the AI's JSON output for easier handling. Split Transactions (Split Out | data transaction): If an invoice contains multiple items, this node splits them into individual records. Record to Notion (Record To Notion Database): Each transaction item is added as a new page/entry in your specified Notion database, mapping fields like Name, Quantity, Price, Total, Category, Date, and Tax. Send Telegram Summary (Sendback to chat and give summarize text): The summary message generated by the AI is sent back to the original Telegram chat. Part 2: Scheduled Financial Reporting (## Schedule report to send on chanel or private message) Schedule Trigger (Schedule Trigger | for send chart report): Runs at a predefined interval (e.g., every week) to generate reports. Get Recent Data from Notion (Get Recent Data from Notions): Fetches transaction data from the Notion database for a specific period (e.g., the past week). Summarize Data (Summarize Transaction Data): Aggregates the data, for example, by summing up the 'total' amount for each 'category'. Prepare Chart Data (Convert Data to JSON chart payload): Transforms the summarized data into a JSON format suitable for generating a chart (e.g., labels for categories, data for spending amounts). Generate Chart (Generate Chart): Uses the QuickChart node to create a visual chart (e.g., a bar chart) from the prepared data. Send Chart to Telegram (Send Chart Image to Group or Private Chat): Sends the generated chart image to a specified Telegram chat ID or group. Nodes Used (Key Nodes): Telegram Trigger & Telegram Node:** For receiving images and sending messages/images. Google Gemini Chat Model (Langchain):** For AI-powered OCR and data extraction from invoices. Basic LLM Chain (Langchain):** To interact with the language model using specific prompts. Output Parser Structured (Langchain):** To structure the output from the language model. Notion Node:** For reading from and writing to your Notion databases. Schedule Trigger:** To automate the reporting process. Summarize Node:** To aggregate data for reports. Code Node:** Used here to format data for the chart. QuickChart Node:** For generating charts. SplitOut Node:** To process multiple items from a single invoice. Setup Instructions: Credentials: Telegram: Create a Telegram bot and get its API token. You'll also need the Chat ID where you'll send invoices and where reports should be sent. Google Gemini (PaLM) API: You'll need an API key for Google Gemini. Notion: Create a Notion integration and get the API key. Create a Notion database with properties corresponding to the data you want to save (e.g., Name (Title), Quantity (Number), Price (Number), Total (Number), Category (Select), Date (Text or Date), Tax (Number)). Share this database with your Notion integration. Configure Telegram Trigger: Add your Telegram Bot API token. When you first activate the workflow or test the trigger, send /start to your bot in the chat you want to use for sending invoices. n8n will then capture the Chat ID. Configure Google Gemini Node (Google Gemini Chat Model): Select or add your Google Gemini API credentials. Review the prompt in the Basic LLM Chain node and adjust if necessary (e.g., date format, categories). Configure Notion Nodes: Record To Notion Database: Select or add your Notion API credentials. Select your target Notion Database ID. Map the properties from the workflow (e.g., ={{ $json.name }}) to your Notion database columns. Get Recent Data from Notions: Select or add your Notion API credentials. Select your target Notion Database ID. Adjust the filter if needed (default is "past_week"). Configure Telegram Node for Reports (Send Chart Image to Group or Private Chat): Select or add your Telegram Bot API token. Enter the Chat ID for the group or private chat where you want to receive the reports. Configure Schedule Trigger (Schedule Trigger | for send chart report): Set your desired schedule (e.g., every Monday at 9 AM). Test: Send an image of an invoice to your Telegram bot and check if the data appears in Notion and if you receive a summary message. Wait for the scheduled report or manually trigger it to test the reporting functionality. Sticky Note Text for Your n8n Template: (These are suggestions. You would place these directly into the sticky notes within your n8n workflow editor.) Existing High-Level Sticky Notes: ## Auto Notes Transaction with Telegram and Notion database ## Schedule report to send on chanel or private message Specific Sticky Notes to Add: On Telegram Trigger | When recive photo:** 📸 INVOICE INPUT 📸 Bot listens here for photos of your receipts/invoices. Ensure your Telegram Bot API token is set in credentials. Near Google Gemini Chat Model & Basic LLM Chain:** 🤖 AI MAGIC HAPPENS HERE 🧠 Image is sent to Google Gemini for data extraction. Check 'Basic LLM Chain' to customize the AI prompt (e.g., categories, output format). Requires Google Gemini API credentials. On Parse To your object | Table:** ✨ STRUCTURING AI DATA ✨ Converts the AI's text output into a usable JSON object. Check the schema if you modify the AI prompt significantly. On Record To Notion Database:** 📝 SAVING TO NOTION 📝 Extracted transaction data is saved here. Configure with your Notion API key & Database ID. Map fields correctly to your database columns! On Sendback to chat and give summarize text:** 💬 TRANSACTION SUMMARY 💬 Sends a confirmation message back to the user in Telegram with a summary of the recorded expense. On Schedule Trigger | for send chart report:** 🗓️ REPORTING SCHEDULE 🗓️ Set how often you want to receive your spending report (e.g., weekly, monthly). On Get Recent Data from Notions:** 📊 FETCHING DATA FOR REPORT 📊 Retrieves transactions from Notion for the report period. Default: "Past Week". Adjust filter as needed. Requires Notion API credentials & Database ID. On Summarize Transaction Data:** ➕ SUMMARIZING SPENDING ➕ Aggregates your expenses, usually by category, to prepare for the chart. On Convert Data to JSON chart payload (Code Node):** 🎨 PREPARING CHART DATA 🎨 This Code node formats the summarized data into the JSON structure needed by QuickChart. On Generate Chart (QuickChart Node):** 📈 GENERATING VISUAL REPORT 📈 Creates the actual chart image based on your spending data. You can customize chart type (bar, pie, etc.) here. On Send Chart Image to Group or Private Chat:** 📤 SENDING REPORT TO TELEGRAM 📤 Delivers the generated chart to your chosen Telegram chat/group. Set the correct Chat ID and Bot API token. General Sticky Note (Place where relevant):** 🔑 CREDENTIALS NEEDED 🔑 Remember to set up API keys/tokens for: Telegram Google Gemini Notion General Sticky Note (Place where relevant):** 💡 CUSTOMIZE ME! 💡 Adjust AI prompts for better accuracy. Change Notion database structure. Modify report frequency and content. `

This workflow creates/updates ClickUp tasks when Notion database pages are created/updated. All fields in the Notion database are mapped to a ClickUp property. Notion database will require setup before the workflow can be used. See the list of fields available in the setup below. Prerequisites Notion account and Notion credentials. ClickUp account and ClickUp credentials. How it works When a new database page is created in Notion, the workflow creates a new task in ClickUp with all required fields. The new ClickUp task's ID is saved in the Notion database page's "ClickUp ID" field. Then, when the database page is updated in Notion, the workflow updates the specific ClickUp task identified by the "ClickUp ID" field in Notion. Setup This workflow requires that you set up a Notion database. To do so, follow the steps below: In Notion, create a new database. Add the following columns to the database: Task name (renamed from "Name") Status (with type "Select" with the following options: "to do", "in progress", "review", "revision", "complete") Deadline (with type "Date") ClickUp ID (with type "Text") Add any other fields you require. Share the database to n8n. By default, the workflow will fill all the fields provided above, except for any other additional fields you add.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights This template demonstrates the Survey Insights scenario where survey participant responses can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, researchers can save days and even weeks of work breaking down cohorts of participants and identify frequently mentioned positives and negatives. Sample Output: https://docs.google.com/spreadsheets/d/e/2PACX-1vT6m8XH8JWJTUAfwojc68NAUGC7q0lO7iV738J7aO5fuVjiVzdTRRPkMmT1C4N8TwejaiT0XrmF1Q48/pubhtml# How it works All survey questions and responses are imported from a Google Sheet. Responses are then inserted into a Qdrant collection carefully tagged with the question and survey metadata. For each question, all relevant response are put through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups. Each group is looped to fetch the payloads of the points and feed them to the AI agent to summarise and generate insights for. The resulting insights and raw responses are then saved to the Google Spreadsheet for further analysis by the researcher. Requirements Survey data and format as shown in the attached google sheet. Qdrant Vectorstore for storing embeddings. OpenAI account for embeddings and LLM. Customising the Template Adjust clustering parameters which make sense for your data. Add more clusters for open-ended questions and less clusters when responses are multiple choice.

This n8n workflow automatically fetches trending news articles based on your chosen country, category, and keyword — then enriches the data with AI-powered business insights before posting a concise summary to Slack. Ideal for sales teams, executives, marketers, or anyone who wants fast, actionable news briefings directly in their Slack workspace. ⸻ Who it’s for Executives, analysts, sales teams, or marketing professionals who want curated, AI-enhanced news summaries tailored to business opportunities, risks, and trends — delivered automatically to Slack. ⸻ How it works / What it does A Schedule Trigger runs on a daily, weekly, or custom frequency. It queries the NewsAPI to retrieve top headlines by country, category, or keyword. Headlines are formatted and enriched with your configured query context. The AI model (GPT-4) analyzes articles and summarizes key insights, categorizing them as Opportunities, Risks, or Trends. Finally, the summarized insights are posted directly into a Slack channel of your choice. ⸻ How to set up Set your schedule frequency in the Schedule Trigger node. Configure your preferred country, category, and keyword in the Inject Config node. Add your NewsAPI Key inside the Fetch News Articles node. Connect your Slack credentials in the Post to Slack node. Optional: Adjust the AI prompt for more tailored analysis. ⸻ Requirements A NewsAPI account to fetch headlines. An OpenAI API key for GPT-4 summarization. A Slack workspace and connected credentials via n8n. ⸻ How to customize the workflow Change the country, category, or keyword in the Inject Config to focus on specific markets or sectors. Adjust the AI prompt in the GPT node to prioritize certain insights like ESG factors, M&A activity, or market sentiment. Extend the workflow to log results to Google Sheets, email summaries, or send SMS alerts. Replace the Schedule Trigger with a Webhook if you want to trigger summaries on demand. This template is designed to be modular, making it easy to adapt for competitive intelligence, investment tracking, or industry news curation.

SEO Blog Generator with GPT-4o, Perplexity, and Telegram Integration This workflow helps you automatically generate SEO-optimized blog posts using Perplexity.ai, OpenAI GPT-4o, and optionally Telegram for interaction. 🚀 Features 🧠 Topic research via Perplexity sub-workflow ✍️ AI-written blog post generated with GPT-4o 📊 Structured output with metadata: title, slug, meta description 📩 Integration with Telegram to trigger workflows or receive outputs (optional) ⚙️ Requirements ✅ OpenAI API Key (GPT-4o or GPT-3.5) ✅ Perplexity API Key (with access to /chat/completions) ✅ (Optional) Telegram Bot Token and webhook setup 🛠 Setup Instructions Credentials: Add your OpenAI credentials (openAiApi) Add your Perplexity credentials under httpHeaderAuth Optional: Setup Telegram credentials under telegramApi Inputs: Use the Form Trigger or Telegram input node to send a Research Query Subworkflow: Make sure to import and activate the subworkflow Perplexity_Searcher to fetch recent search results Customization: Edit prompt texts inside the Blog Content Generator and Metadata Generator to change writing style or target industry Add or remove output nodes like Google Sheets, Notion, etc. 📦 Output Format The final blog post includes: ✅ Blog content (1500-2000 words) ✅ Metadata: title, slug, and meta description ✅ Extracted summary in JSON ✅ Delivered to Telegram (if connected) Need help? Reach out on the n8n community forum

Purpose This workflow enables you to listen to your recent favorites in very hight quality offline without sacrificing all of your storage. How it works This workflow automatically creates a playlist in Spotify named "Downloads" which periodically gets updated so it always contains only a defined amount of the latest liked songs. This enables only the Downloads playlist to set for automatic downloading and thus free up space on the device. Setup The workflow is ready to go. Just select your Spotify credentials and activate the workflow. In Spotify just enable automatic downloads on the automatically created Downloads folder after the first workflow run. Current limitations This setup currently supports a maximum of 50 songs in the Downloads Playlist. This is due to the paylod limits defined by Spotify encountered in the Get liked songs node. Implementing batching would solve the issue.

"Ade Technical Analyst" is a dual-workflow AI system combining conversational intelligence with visual chart analysis through Telegram. The system features 11 primary nodes for conversation management and 8 secondary nodes for chart generation and analysis. Core Components: Telegram Integration: Message handling with dynamic typing indicators AI Personality: "Ade" - a financial analyst with 50+ years NYSE/LSE experience using Claude 3.5 Sonnet Chart Generation: TradingView integration via Chart-IMG API with MACD and volume indicators Visual Analysis: GPT-4O vision for technical pattern recognition Memory System: Session-based conversation context retention Target Users Individual traders seeking professional-grade analysis without subscription costs Financial advisors wanting 24/7 AI-powered client support Investment educators needing interactive learning tools Fintech companies requiring white-label analysis solutions Setup Requirements Critical Security Fix Needed: Remove hardcoded API key from Chart-IMG node immediately Store all credentials securely in n8n credential manager Required APIs: OpenRouter (Claude 3.5 Sonnet) OpenAI (GPT-4O vision) Chart-IMG API Telegram Bot Token Technical Prerequisites: n8n version 1.7+ with Langchain nodes Webhook configuration for Telegram Dual-workflow setup with proper ID referencing Workflow Requirements Security Compliance: Never hardcode API keys in workflow JSON files Use n8n credential manager for all sensitive data Implement proper session isolation for user data Include mandatory financial disclaimers Performance Specifications: Model temperature: 0.8 for balanced responses Token limit: 500 for optimized performance Dark theme charts with professional indicators Session-based memory management Need help customizing? Contact me for consulting and support or add me on LinkedIn

🔥 Upgrade to V3 Longer blogs, Higher SEO ranking with images, charts and tables We’ve released Version 3 of our AI-Powered Blog Automation workflow. We heard your complains and made a complete redesign built for serious content creators. 📝 Read the New Articles Generated by v3 🛒 View the workflow on n8n.io ✅ Longer Blog contents 3-Agent AI Architecture as the Planner, Writer, Editor: simulate a full content team for structure, writing, and QC. A more continuous flow 📈 2x bump in SEO ranking SEO Scoring System so every article is graded on keyword density, readability, structure, backlinks, and uniqueness. IF quality doesnt meet threshold, we revise the content again. 🖼️ Smarter Visuals In-blog images via Leonardo, charts via QuickChart, tables and web scraped outbound links 🕸️ Multi-Platform Publishing Auto-posts to WordPress, Twitter (X), and Dev.to 🕵️‍♂️ Research Agent Adds quotes, stats, facts, outbound links, entities, and references to improve article credibility Content Farming V2 AI Powered Blog Automation for WordPress This workflow automatically generates and publishes 10 blog posts per day to a WordPress site. It collects tech-related news articles, filters and analyzes them for relevance, expands them with research, generates SEO-optimized long-form articles using AI, creates a matching image using Leonardo AI, and publishes them via the WordPress REST API. Every step is tracked and stored in MongoDB for reference and performance tracking. You can see the demo results for the AI based articles here: Emp0 Articles How it works A scheduler runs daily to fetch the latest news from RSS feeds including BBC, TechCrunch, Wired, MIT Tech Review, HackerNoon, and others. The RSS data is normalized and filtered to include only articles published within the past 24 hours. Each article is passed through an OpenAI-powered classifier to check for relevance to predefined user topics like AI, robotics, or tech policy. Relevant articles are then aggregated, researched, and summarized with supporting sources and citations. An AI agent generates five long-tail SEO blog title ideas, ranks them by uniqueness and performance score, and selects the top one. A blog outline is created including H1 and H2 headers, keyword targeting, content structure, and featured snippet optimization. A full-length article (1000 to 1500 words) is generated based on the outline, with analogies, citations, examples, and keyword density maintained. SEO metadata is produced including meta title, description, image alt text, slug, and a readability audit. An AI-generated image is created based on the blog theme using Leonardo AI, enhanced for emotional storytelling and visual consistency. The blog article, metadata, and image are uploaded to WordPress as a draft, the image is attached, Yoast SEO metadata is set, and the article is published. All outputs including article versions, metadata, generation steps, and final blog URLs are stored in MongoDB to allow for future analytics and feedback. Requirements To run this project, you need accounts and API access for the following: | Tool | Purpose | Notes | |--------------|------------------------------------------------------------------|-----------------------------------------------------------------------| | OpenAI | Used for blog classification, generation, summarization, SEO | Around $0.20 per day, using GPT-4o-mini. Estimated monthly: $6 | | MongoDB | Stores data flexibly including drafts, titles, metadata, logs | Free tier on MongoDB Atlas offers 512 MB, enough for 64,000 articles | | Leonardo AI | Generates featured images for blog articles | $9 for 3500 credits, $5 monthly top-up needed for 300 images | | WordPress | Final publishing platform via REST API | Hosted on Hostinger for $15/year including domain | Setup Instructions Import the provided JSON file into your n8n instance. Configure these credentials in n8n: OpenAI API key MongoDB Atlas connection string HTTP Header Auth for Leonardo AI WordPress REST API credentials Modify the classifier and prompt nodes to reflect your preferred content themes. Adjust scheduler nodes if you want to change post frequency or publishing times. Run the n8n instance continuously using Docker, PM2, or hosted automation platform. Cost Estimate | Component | Daily Usage | Monthly Cost Estimate | |---------------|------------------------------|------------------------| | OpenAI | 10 posts per day | ~$6 | | Leonardo AI | 10 images per day (15 credits each) | ~$14 (9 base + 5 top-up) | | MongoDB | Free up to 512 MB | $0 | | WordPress | Hosting and domain | ~$1.25 | | Total | | ~$21/month | Observations and Learnings This system can scale daily article publishing with zero manual effort. However, current limitations include inconsistent blog length and occasional coherence issues. To address this, I plan to build a feedback loop within the workflow: An SEO Commentator Agent will assess keyword strength, structure, and discoverability. An Editor-in-Chief Agent will review tone, clarity, and narrative structure. Both agents will loop back suggestions to the content generator, improving each draft until it meets human-level standards. The final goal is to consistently produce high-quality, readable, SEO-optimized content that is indistinguishable from human writing.

Who is this for? Marketing, content, and enablement teams that need a quick, human-readable summary of every new video published by the YouTube channels they care about—without leaving Slack. What problem does this workflow solve? Manually checking multiple channels, skimming long videos, and pasting the highlights into Slack wastes time. This template automates the whole loop: detect a fresh upload from your selected channels → pull subtitles → distill the key take-aways with GPT-4o-mini → drop a neatly-formatted digest in Slack. What this workflow does Schedule Trigger fires every 10 min, then grabs a list of YouTube RSS feeds from a Google Sheet. HTTP + XML fetch & parse each feed; only brand-new videos continue. YouTube API fetches title/description, RapidAPI grabs English subtitles. Code nodes build an AI payload; OpenAI returns a JSON summary + article. A formatter turns that JSON into Slack Block Kit, and Slack posts it. Processed links are appended back to the “Video Links” sheet to prevent dupes. Setup Make a copy of this Google Sheet and connect a Google Sheets OAuth2 credential with edit rights. Slack App: create → add chat:write, channels:read, app_mention; enable Event Subscriptions; install and store the Bot OAuth token in an n8n Slack credential. RapidAPI key for https://yt-api.p.rapidapi.com/subtitles (300 free calls/mo) → save as HTTP Header Auth. OpenAI key → save in an OpenAI credential. Add your RSS feed URLs to the “RSS Feed URLs” tab; press Execute Workflow. How to customise Adjust the schedule interval or freshness window in “If newly published”. Swap the OpenAI model or prompt for shorter/longer digests. Point the Slack node at a different channel or DM. Extend the AI payload to include thumbnails or engagement stats. Use-case ideas Product marketing**: Instantly brief sales & CS teams when a competitor uploads a feature demo. Internal learning hub**: Auto-summarise conference talks and share bullet-point notes with engineers. Social media managers**: Get ready-to-post captions and key moments for re-purposing across platforms.

This automated workflow scrapes and processes the monthly "Who is Hiring" thread from Hacker News, transforming raw job listings into structured data for analysis or integration with other systems. Perfect for job seekers, recruiters, or anyone looking to monitor tech job market trends. How it works Automatically fetches the latest "Who is Hiring" thread from Hacker News Extracts and cleans relevant job posting data using the HN API Splits and processes individual job listings into structured format Parses key information like location, role, requirements, and company details Outputs clean, structured data ready for analysis or export Set up steps Configure API access to [Hacker News](https://github.com/HackerNews/API ) (no authentication required) Follow the steps to get your cURL command from https://hn.algolia.com/ Set up desired output format (JSON structured data or custom format) Optional: Configure additional parsing rules for specific job listing information Optional: Set up integration with preferred storage or analysis tools The workflow transforms unstructured job listings into clean, structured data following this pattern: Input: Raw HN thread comments Process: Extract, clean, and parse text Output: Structured job listing data This template saves hours of manual work collecting and organizing job listings, making it easier to track and analyze tech job opportunities from Hacker News's popular monthly hiring threads.

Who is this for? This workflow is for everyone who wants to have easier access to their Odoo sales data without complex queries. Use Case To have a clear overview of your sales data in Odoo you typically needs to extract data from it manually to analyse it. This workflow uses OpenAI's language models to create an intelligent chatbot that provides conversational access to your Odoo sales opportunity data. How it works Creates a summary of all Odoo sales opportunities using OpenAI Uses that summary as context for the OpenAI chat model Keeps the summary up to date using a schedule trigger Set up steps: Configure the Odoo credentials Configure OpenAI credentials Toggle "Make Chat Publicly Available" from the Chat Trigger node.

This workflow is a building block designed to be called from other workflows via an Execute workflow node. When called from another workflow, and given the JSON input of a "pulse" field with the ID to pull from monday, this workflow will return: The items name and ID All column data, indexable by the column name All column data, indexable by the column's ID string All board relation columns, with their data and column values All subitems, with their data and column values For example: ++Prerequisites++ A monday.com account and credential A workflow that needs to get detailed data from a monday.com row The pulse id of the monday.com row to retreive data from. ++Setup++ Import the workflow Configure all monday nodes with your credentials and save the workflow Copy the workflow ID from it's URL In a different workflow, add an Edit Fields node, to output the field "pulse", with the monday item you want to retrieve. Feed the Edit Fields node with your pulse into an Execute workflow node, and paste the workflow ID from above into it This "pulse" field will tell the workflow what pulse to retreive. This can be populated by an expression in your workflow There is an example of the Edit Fields and Execute Workflow nodes in the template