Workflow Templates

This n8n template introduces the Dynamic Prompts AI workflow pattern which are incredible for certain types of data extraction tasks where attributes are unknown or need to remain flexible. The general idea behind this pattern is that the prompts for requested attributes to be extracted live outside the template and so can be changed at any time - without needing to edit the template. This seriously cuts down on maintainance requirements and is reusable for any number of tables at little cost. Check out the n8n Studio Episode here: https://www.youtube.com/watch?v=_fNAD1u8BZw Community post here: https://community.n8n.io/t/dynamic-prompts-with-n8n-baserow-and-airtable/72052 Looking for the Airtable Version? https://n8n.io/workflows/2771-ai-data-extraction-with-dynamic-prompts-and-airtable/ How it works Given we have an "input" field for context and a number of fields for the data we want to extract, this template will run in the background to react to any changes to either the "input" or fields and automatically update the rows accordingly. The key is that Baserow fields have a special property called the "field description". In this pattern, we use this property to allow the user to store a simple prompt describing the data that should exist in the column. Our n8n template reads these column descriptions aka "prompts" to use as instructions to perform tasks on the "input". In this template, the "input" is a PDF of a resume/CV and the columns are attributes a HR person would want to extract from it - such as full name, address, last position, years of experience etc. How to use First publish this template and ensure it's accessible via webhook URL. You then have to complete the "create Baserow webhooks" steps to configure your baserow to send change events to the n8n template. Baserow webhooks are created in the Baserow web interface. Check the template for more instructions. Requirements Baserow for Tables/Database OpenAI for LLM and extraction. Feel free to choose another LLM if preferred. Customising this workflow If you're not using files, you can replace the "input" field with anything you like. For example, the "input" could be single line text.

How it works: Webhook URL that responds to Requests with an AI generated Image based on the prompt provided in the URL. Setup Steps: Ideate your prompt URL Encode The Prompt (as shown in the Template) Authenticate with your OpenAI Credentials Put together the Webhook URL with your prompt and enter into a webbrowser In this way you can expose a public url to users, employee's etc. without exposing your OpenAI API Key to them. Click here to find a blog post with additional information.

Video Guide I prepared a detailed guide that demonstrates the complete process of building a trading agent automation using n8n and Telegram, seamlessly integrating various functions for stock analysis. Youtube Link Who is this for? This workflow is perfect for traders, financial analysts, and developers looking to automate stock analysis interactions via Telegram. It’s especially valuable for those who want to leverage AI tools for technical analysis without needing to write complex code. What problem does this workflow solve? Many traders desire real-time analysis of stock data but lack the technical expertise or tools to perform in-depth analysis. This workflow allows users to easily interact with an AI trading agent through Telegram for seamless stock analysis, chart generation, and technical evaluation, all while eliminating the need for manual interventions. What this workflow does This workflow utilizes n8n to construct an end-to-end automation process for stock analysis through Telegram communication. The setup involves: Receiving messages via a Telegram bot. Processing audio or text messages for trading queries. Transcribing audio using OpenAI API for interpretation. Gathering and displaying charts based on user-specified parameters. Performing technical analysis on generated charts. Sending back the analyzed results through Telegram. Setup Prepare Airtable: Create simple table to store tickers. Prepare Telegram Bot: Ensure your Telegram bot is set up correctly and listening for new messages. Replace Credentials: Update all nodes with the correct credentials and API keys for services involved. Configure API Endpoints: Ensure chart service URLs are correctly set to interact with the corresponding APIs properly. Start Interaction: Message your bot to initiate analysis; specify ticker symbols and desired chart styles as required.

Compare Local Ollama Vision Models for Image Analysis using Google Docs Process images using locally hosted Ollama Vision Models to extract detailed descriptions, contextual insights, and structured data. Save results directly to Google Docs for efficient collaboration. Who is this for? This workflow is ideal for developers, data analysts, marketers and AI enthusiasts who need to process and analyze images using locally hosted Ollama Vision Language Models. It’s particularly useful for tasks requiring detailed image descriptions, contextual analysis, and structured data extraction. What problem is this workflow solving? / Use Case The workflow solves the challenge of extracting meaningful insights from images in exhaustive detail, such as identifying objects, analyzing spatial relationships, extracting textual elements, and providing contextual information. This is especially helpful for applications in real estate, marketing, engineering, and research. What this workflow does This workflow: Downloads an image file from Google Drive. Processes the image using multiple Ollama Vision Models (e.g., Granite3.2-Vision, Gemma3, Llama3.2-Vision). Generates detailed markdown-based descriptions of the image. Saves the output to a Google Docs file for easy sharing and further analysis. Setup Ensure you have access to a local instance of Ollama. https://ollama.com/ Pull the Ollama vision models. Configure your Google Drive and Google Docs credentials in n8n. Provide the image file ID from Google Drive in the designated node. Update the list of Ollama vision models Test the workflow by clicking ‘Test Workflow’ to trigger the process. How to customize this workflow to your needs Replace the image source with another provider if needed (e.g., AWS S3 or Dropbox). Modify the prompts in the "General Image Prompt" node to suit specific analysis requirements. Add additional nodes for post-processing or integrating results into other platforms like Slack or HubSpot. Key Features: Detailed Image Analysis**: Extracts comprehensive details about objects, spatial relationships, text elements, and contextual settings. Multi-Model Support**: Utilizes multiple vision models dynamically for optimal performance. Markdown Output**: Formats results in markdown for easy readability and documentation. Google Drive Integration**: Seamlessly downloads images and saves results to Google Docs.

This workflow template, "Chinese Translator via Line x OpenRouter (Text & Image)" is designed to provide seamless Chinese translation services directly within the LINE messaging platform. By integrating with OpenRouter.ai and advanced language models like Qwen, this workflow translates text or images containing Chinese characters into pinyin and English translations, making it an invaluable tool for language learners, travelers, and businesses operating in multilingual environments. This template is ideal for: Language Learners: Who want to practice Chinese by receiving instant translations of text or images. Travelers: Looking for quick translations of Chinese signs, menus, or documents while abroad. Educators: Teaching Chinese language courses and needing tools to assist students with translations. Businesses: Operating in multilingual markets and requiring efficient communication tools. Automation Enthusiasts: Seeking to build intelligent chatbots that can handle language translation tasks. What Problem Does This Workflow Solve? Translating Chinese text or images into English and pinyin can be challenging, especially for beginners or those without access to reliable translation tools. This workflow solves that problem by: Automatically detecting and translating text or images containing Chinese characters. Providing accurate translations in both pinyin and English for better comprehension. Supporting multiple input formats (text, images) to cater to diverse user needs. Sending replies directly to users via the LINE messaging platform , ensuring accessibility and ease of use. What This Workflow Does 1) Receive Messages via LINE Webhook The workflow is triggered when a user sends a message (text, image, or other types) to the LINE bot. 2) Display Loading Animation A loading animation is displayed to reassure the user that their request is being processed. 3) Route Input Types The workflow uses a Switch node to determine the type of input (text, image, or unsupported formats). If the input is text , it is sent to the OpenRouter.ai API for translation. If the input is an image , the workflow extracts the image content, converts it to base64, and sends it to the API for translation. Unsupported formats trigger a polite response indicating the limitation. 4) Translate Content Using OpenRouter.ai The workflow leverages Qwen models from OpenRouter.ai to generate translations: For text inputs, it provides Chinese characters , pinyin , and English translations . For images, it extracts and translates using the qwen-VL model which can take images 5) Reply with Translations The translated content is formatted and sent back to the user via the LINE Reply API. Setup Guide Pre-Requisites Access to the LINE Developers Console to configure your webhook and channel access token. An OpenRouter.ai account with credentials to access Qwen models. Basic knowledge of APIs, webhooks, and JSON formatting. Step-by-Step Setup 1) Configure the LINE Webhook: Go to the LINE Developers Console and set up a webhook to receive incoming messages. Copy the Webhook URL from the Line Webhook node and paste it into the LINE Console. Remove any "test" configurations when moving to production. 2) Set Up OpenRouter.ai: Create an account on OpenRouter.ai and obtain your API credentials. Connect your credentials to the OpenRouter nodes in the workflow. 3) Test the Workflow: Simulate sending text or images to the LINE bot to verify that translations are processed and replied correctly. How to Customize This Workflow to Your Needs Add More Languages: Extend the workflow to support additional languages by modifying the API calls. Enhance Image Processing: Integrate more advanced OCR tools to improve text extraction from complex images. Customize Responses: Modify the reply format to include additional details, such as grammar explanations or cultural context. Expand Use Cases: Adapt the workflow for specific industries, such as tourism or e-commerce, by tailoring the translations to relevant vocabulary. Why Use This Template? Real-Time Translation: Provides instant translations of text and images, improving user experience and accessibility. Multimodal Support: Handles both text and image inputs, catering to diverse user needs. Scalable: Easily integrate into existing systems or scale to support multiple users and workflows. Customizable: Tailor the workflow to suit your specific audience or industry requirements.

Auto Source LinkedIn Candidates with GPT-4 Boolean Search & Google X-ray How It Works: User Input: The user pastes a job description or ideal candidate specifications into the workflow. Boolean Search String Generation: OpenAI processes the input and generates a precise LinkedIn Boolean search string formatted as: site:linkedin.com/in ("Job Title" AND "Skill1" AND "Skill2") This search string is optimized to find relevant LinkedIn profiles matching the provided criteria. Google Sheet Creation: A new Google Sheet is automatically created within a specified document to store extracted LinkedIn profile URLs. Google Search Execution: The workflow sends a search request to Google using an HTTP node with the generated Boolean string. Iterative Search & Data Extraction: The workflow retrieves the first 10 results from Google. If the desired number of LinkedIn profiles has not been reached, the workflow loops, fetching the next set of 10 results until the if condition is met. Data Storage: The workflow extracts LinkedIn profile URLs from the search results and saves them to the newly created Google Sheet for further review. Setup Steps: 1. API Key Configuration Under "Credentials", add your OpenAI API key from your OpenAI account settings. This key is used to generate the LinkedIn Boolean search string. 2. Adjust Search Parameters Navigate to the "If" node and update the condition to define the desired number of LinkedIn profiles to extract. The default is 50, but you can set it to any number based on your needs. 3. Establish Google Sheets Connection Connect your Google Sheets account** to the workflow. Create a document** to store the sourced LinkedIn profiles. The workflow automatically creates a new sheet for each new search, so no manual setup is needed. 4. Authenticate Google Search Google search requires authentication** for better results. Use the Cookie-Editor browser extension to export your header string and enable authenticated Google searches within the workflow. 5. Run the Workflow Execute* the workflow and monitor the *Google Sheet** for newly added LinkedIn profiles. Benefits: ✅ Automates profile sourcing, reducing manual search time. ✅ Generates precise LinkedIn Boolean search strings tailored to job descriptions. ✅ Extracts and saves LinkedIn profiles efficiently for recruitment efforts. This solution leverages OpenAI and advanced search techniques to enhance your talent sourcing process, making it faster and more accurate! 🚀

Summary Engage multiple, uniquely configured AI agents (using different models via OpenRouter) in a single conversation. Trigger specific agents with @mentions or let them all respond. Easily scalable by editing simple JSON settings. Overview This workflow is for users who want to experiment with or utilize multiple AI agents with distinct personalities, instructions, and underlying models within a single chat interface, without complex setup. It solves the problem of managing and interacting with diverse AI assistants simultaneously for tasks like brainstorming, comparative analysis, or role-playing scenarios. It enables dynamic conversations with multiple AI assistants simultaneously within a single chat interface. You can: Define multiple unique AI agents. Configure each agent with its own name, system instructions, and LLM model (via OpenRouter). Interact with specific agents using @AgentName mentions. Have all agents respond (in random order) if no specific agents are mentioned. Maintain conversation history across multiple turns. It's designed for flexibility and scalability, allowing you to easily add or modify agents without complex workflow restructuring. Key Features Multi-Agent Interaction:** Chat with several distinct AI personalities at once. Individual Agent Configuration:** Customize name, system prompt, and LLM for each agent. OpenRouter Integration:** Access a wide variety of LLMs compatible with OpenRouter. Mention-Based Triggering:** Direct messages to specific agents using @AgentName. All-Agent Fallback:** Engages all defined agents randomly if no mentions are used. Scalable Setup:** Agent configuration is centralized in a single Code node (as JSON). Conversation Memory:** Remembers previous interactions within the session. How to Set Up Configure Settings (Code Nodes): Open the Define Global Settings Code node: Edit the JSON to set user details (name, location, notes) and add any system message instructions that all agents should follow. Open the Define Agent Settings Code node: Edit the JSON to define your agents. Add or remove agent objects as needed. For each agent, specify: "name": The unique name for the agent (used for @mentions). "model": The OpenRouter model identifier (e.g., "openai/gpt-4o", "anthropic/claude-3.7-sonnet"). "systemMessage": Specific instructions or persona for this agent. Add OpenRouter Credentials: Locate the AI Agent node. Click the OpenRouter Chat Model node connected below it via the Language Model input. In the 'Credential for OpenRouter API' field, select or create your OpenRouter API credentials. How to Use Start a conversation using the Chat Trigger input. To address specific agents, include @AgentName in your message. Agents will respond sequentially in the order they are mentioned. Example: "@Gemma @Claude, please continue the count: 1" will trigger Gemma first, followed by Claude. If your message contains no @mentions, all agents defined in Define Agent Settings will respond in a randomized order. Example: "What are your thoughts on the future of AI?" will trigger Chad, Claude, and Gemma (based on your default settings) in a random sequence. The workflow will collect responses from all triggered agents and return them as a single, formatted message. How It Works (Technical Details) Settings Nodes: Define Global Settings and Define Agent Settings load your configurations. Mention Extraction: The Extract mentions Code node parses the user's input (chatInput) from the When chat message received trigger. It looks for @AgentName patterns matching the names defined in Define Agent Settings. Agent Selection: If mentions are found, it creates a list of the corresponding agent configurations in the order they were mentioned. If no mentions are found, it creates a list of all defined agent configurations and shuffles them randomly. Looping: The Loop Over Items node iterates through the selected agent list. Dynamic Agent Execution: Inside the loop: An If node (First loop?) checks if it's the first agent responding. If yes (true path -> Set user message as input), it passes the original user message to the Agent. If no (false path -> Set last Assistant message as input), it passes the previous agent's formatted output (lastAssistantMessage) to the next agent, creating a sequential chain. The AI Agent node receives the input message. Its System Message and the Model in the connected OpenRouter Chat Model node are dynamically populated using expressions referencing the current agent's data from the loop ({{ $('Loop Over Items').item.json.* }}). The Simple Memory node provides conversation history to the AI Agent. The agent's response is formatted (e.g., AgentName:\n\nResponse) in the Set lastAssistantMessage node. Response Aggregation: After the loop finishes, the Combine and format responses Code node gathers all the lastAssistantMessage outputs and joins them into a single text block, separated by horizontal rules (---), ready to be sent back to the user. Benefits Scalability & Flexibility:** Instead of complex branching logic, adding, removing, or modifying agents only requires editing simple JSON in the Define Agent Settings node, making setup and maintenance significantly easier, especially for those managing multiple assistants. Model Choice:** Use the best model for each agent's specific task or persona via OpenRouter. Centralized Configuration:** Keeps agent setup tidy and manageable. Limitations Sequential Responses:** Agents respond one after another based on mention order (or randomly), not in parallel. No Direct Agent-to-Agent Interaction (within a turn):* Agents cannot directly call or reply to each other *during the processing of a single user message. Agent B sees Agent A's response only because the workflow passes it as input in the next loop iteration. Delayed Output:* The user receives the combined response only *after all triggered agents have completed their generation.

This workflow creates a documentation system for n8n instances using Docsify.js. It serves a dynamic documentation website that allows users to: View an overview of all workflows in a tabular format Filter workflows by tags Access automatically generated documentation for each workflow Edit documentation with a live Markdown preview Visualize workflow structures using Mermaid.js diagrams > 📺 Check out the short 2-min demonstration on LinkedIn. Don't forget to connect! 🔧 Key Components Main Documentation Portal Serves a Docsify-powered website Provides a navigation sidebar with workflow tags Displays workflow status, creation date, and documentation links Documentation Generator Uses GPT model to auto-generate workflow descriptions Creates Mermaid.js diagrams of workflow structures Maintains consistent documentation format Live Editor Split-screen Markdown editor with preview Real-time Mermaid diagram rendering Save/Cancel functionality ⚙️ Technical Details Environment Setup Requires write access to the specified project directory Uses environment variables for n8n instance URL configuration Implements webhook endpoints for serving documentation ⚠️ Security Considerations > Note: The current implementation doesn't include authentication for editing. Consider adding authentication for production use. Dependencies Docsify.js for documentation rendering Mermaid.js for workflow visualization OpenAI GPT for documentation generation 🔍 Part of the n8n Observability Series This workflow is part of a broader series focused on n8n instance observability. Check out these related workflows: Workflow Dashboard - Get comprehensive analytics of your n8n instance Visualize Your n8n Workflows with Mermaid.js - Create beautiful workflow visualizations Each workflow in this series helps you better understand and manage your n8n automation ecosystem!

📈 Get daily and on-demand Tesla (TSLA) trading signals via Telegram—powered by GPT-4.1 and real-time market data. This is the central AI supervisor that orchestrates seven sub-agents for technical analysis, price pattern recognition, and news sentiment. Reports are delivered in structured Telegram-ready HTML, optimized for traders seeking fast, intelligent decision-making signals. ⚠️ This master agent requires 7 connected sub-workflows to function. One of them, the News & Sentiment Agent, also requires a DeepSeek Chat API key for language processing. 🔌 Required Sub-Workflows You must download and publish the following workflows: Tesla Financial Market Data Analyst Tool Tesla News and Sentiment Analyst Tool (Requires DeepSeek Chat API Key) Tesla 15min Indicators Tool Tesla 1hour Indicators Tool Tesla 1day Indicators Tool Tesla 1hour & 1day Klines Tool Tesla Quant Technical Indicators Webhooks Tool (Requires Alpha Vantage Premium API Key) 📍 See all tools at: 🔗 https://n8n.io/creators/don-the-gem-dealer/ 🔍 What This Agent Does Listens to your trading query via Telegram Calls the Financial Analyst and News & Sentiment Analyst These agents aggregate: RSI, MACD, BBANDS, SMA, EMA, ADX Candlestick pattern + volume divergence analysis News summaries and sentiment scoring via DeepSeek Chat GPT-4.1 composes the final structured TSLA trade report with: Spot and leverage setups Signal rationale Confidence score Sentiment tag News summary 🧠 Output Example TSLA Trading Report (Daily Summary) Spot Trade • Action: Buy • Entry: 172.45 • TP: 182.00 • SL: 169.80 • Signal: RSI bounce + Bullish Engulfing • Sentiment: Neutral Leveraged Position • Position: Long • Leverage: 3x • TP: 186 • SL: 170 • Confidence: High (83/100) 📰 Top News • Tesla Model Y delivery surge – Electrek • Options market pricing in upside – Bloomberg • FSD delayed in Canada – TeslaNorth 🛠️ Setup Instructions 1. Import All 8 Workflows Ensure all sub-agents above are published in your n8n instance. 2. Create Your Telegram Bot Use @BotFather to generate the token and connect to the trigger/send nodes. 3. Connect OpenAI GPT-4.1 Add your OpenAI credentials for GPT-4.1 in the designated node. 4. Add DeepSeek Chat API Key Sign up at https://deepseek.com and insert your DeepSeek Chat credentials in the News Agent. 5. Add Alpha Vantage Premium API Key Sign up at https://www.alphavantage.co/premium/ Use HTTP Header Auth for webhook-based indicator fetchers. 6. Replace Telegram ID Update the placeholder <<replace your ID here>> with your actual Telegram numeric ID in the auth node. 📌 Included Sticky Notes ✅ Telegram Bot Setup ✅ Agent Routing & Memory ✅ Financial vs. Sentiment Trigger Flow ✅ Report Formatting (HTML) ✅ API Requirements (GPT-4.1, DeepSeek, Alpha Vantage) ✅ Troubleshooting & Licensing 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted. 🔗 For support: LinkedIn – Don Jayamaha 🚀 Deploy the Tesla Quant Trading AI system with GPT-4.1, DeepSeek Chat, and Alpha Vantage Premium—right into Telegram. All 8 workflows are required. 🎥 Tesla Quant AI Agent – Live Demo Experience the power of the Tesla Quant Trading AI Agent in action.

Manage Calendar with Voice & Text Commands using GPT-4, Telegram & Google Calendar This n8n workflow transforms your Telegram bot into a personal AI calendar assistant, capable of understanding both voice and text commands in Romanian, and managing your Google Calendar using the GPT-4 model via LangChain. Whether you want to create, update, fetch, or delete events, you can simply speak or write your request to your Telegram bot — and the assistant takes care of the rest. 🚀 Features Voice command support using Telegram voice messages (.ogg) Transcription using OpenAI Whisper Natural language understanding with GPT-4 via LangChain Google Calendar integration: ✅ Create Events 🔁 Update Events ❌ Delete Events 📅 Fetch Events Responses sent back via Telegram 🛠️ Step-by-Step Setup Instructions 1. Create a Telegram Bot Go to @BotFather on Telegram. Send /newbot and follow the instructions. Save the Bot Token. 2. Configure Telegram Trigger Node Paste the Telegram token into the Telegram Trigger and Telegram nodes. Set updates to ["message"]. 3. Set up OpenAI Credentials Get an OpenAI API key from https://platform.openai.com Create a credential in n8n for OpenAI. This is used for both transcription and AI reasoning. 4. Set up Google Calendar In Google Cloud Console: Enable Google Calendar API Set up OAuth2 credentials Add your n8n redirect URI (usually https://yourdomain/rest/oauth2-credential/callback) Create a credential in n8n using Google Calendar OAuth2 Grant access to your calendar (e.g., "Family" calendar). ⚙️ Customization Options 🗣️ Change Language or Locale The transcription node uses "en" for English. Change to another locale if needed. ✏️ Edit Prompt You can modify the prompt in the AI Agent node to include your name, work schedule, or specific behavior expectations. 📆 Change Calendar Logic Adjust time ranges or filters in the Get Events node Add custom logic before Create Event (e.g., validation, conflict checks) 📚 Helpful Tips Make sure n8n has HTTPS enabled to receive Telegram updates. You can test the flow first using only text, then voice. Use AI memory or vector stores (like Supabase) if you want context-aware planning in the future.

Overview This template is designed for Amazon sellers and advertisers who want to automate their campaign performance analysis and bidding strategy. It solves the common challenge of manually reviewing Sponsored Products reports and guessing how to adjust keywords, placements, and budgets. By combining Amazon Advertising reports with OpenAI's GPT-4o, this workflow delivers real-time, personalized optimization instructions — automatically. Features 📥 Automatically downloads Sponsored Products reports from Google Drive 🧠 Uses AI to analyze campaign, keyword, placement, targeting, and budget performance 📊 Supports both .csv and .xlsx report formats 🔁 Handles multiple ASINs and scales easily across ad accounts 📧 Sends structured optimization recommendations to your inbox via Gmail 🗂 Built-in logic to normalize filenames and correctly map reports 🧹 Includes error handling and formatting cleanup for AI-ready input Requirements To use this workflow, you’ll need: An Amazon Ads account with access to Sponsored Products reports A Google Drive folder where Amazon Ads reports are delivered (manually or via Gmail automation) A Gmail account (for sending summaries) An OpenAI API key with access to GPT-4o Optional: a developer account for the Amazon Ads API to fully automate report generation in the future Setup Instructions 📂 Connect your Amazon Ads reports folder in the Google Drive node 🔐 Add your credentials to the OpenAI and Gmail nodes 📝 Schedule five reports in the Amazon Ads Console: Search Term Report → Detailed Targeting Report → Detailed Campaign Report → Summary Placement Report → Summary Budget Report → Summary Use “Last 30 Days”, “Daily”, and .xlsx or .csv format 🔁 (Optional) Automate report ingestion using Gmail + Drive workflows 🧪 Test with one account, then replicate across additional ad accounts as needed ⏱️ Setup time: 15–30 minutes 📌 All field-specific guidance is included in workflow notes`

Notion Weekly Journal AI Summary This workflow will run on a weekly schedule and retrieve your Notion Daily Journal pages for the past week and aggregate them into a ChatGPT generated concise summary. It will save that weekly summary back to your Notion as a new Note in addition to posting to a personal Discord channel. Additionally it will also retrieve all of the Tasks you've completed in the past week and provide a quick total with a congratulatory message to a Discord channel as well. Requirements/Setup: You need Notion setup w/ a Notes database If you want the Discord messages, setup a Discord webhook for your channel as well, or simply delete the Discord nodes. One of the properties for the Notes db should be Type with a value of Journal The contents of your daily Journal pages can be whatever you want I've found what works best for me is the format of "What was a highlight of the day?", "What was a low point of the day?", and "What decisions did I delegate, delay, or dodge?" You should also create an additional Type for your Weekly summary page that gets created - in this case I used simply Weekly Automate this to run weekly on your day of choice. I tend to only journal on weekdays so I've set mine up to run every Friday retrieving the past week's Journal entries. Options: You don't have to use Discord, feel free to swap out with Slack or remove altogether. You also don't need to use the Tasks summary bottom half, simply remove that if you don't want it or need it. You can easily reuse this workflow to aggregate your Weekly Summary notes (that this workflow auto generates/saves) to generate a Quarterly or even Yearly summary!