Workflow Templates

Who’s it for Teams that care about space-weather impact—SRE/infra, satellite ops, aviation, power utilities, researchers—or anyone who wants timely, readable alerts when NASA publishes significant solar events. How it works / What it does Every 30 minutes a Cron trigger runs, the NASA DONKI node fetches the past 24 hours of space-weather notifications, and a code step de-duplicates, labels event types, and assigns a severity (CRITICAL / HIGH / OTHER). A Switch routes items: CRITICAL/HIGH** → an LLM (“AI Agent”) produces a concise Japanese alert → Slack posts with local time and source link. OTHER** → an LLM creates a short summary for record-keeping → a small merge step prepares fields → Google Sheets appends a new row. Sticky notes in the canvas explain the schedule, data source, and overall flow. How to set up Add credentials for Slack, Google Sheets, and OpenAI (or compatible LLM). Open the Slack nodes and select your workspace + target channel. Select your Google Sheet and worksheet for logging. (Optional) Adjust the Cron interval and the NASA lookback window. Test with a manual execution, then activate. Requirements Slack Bot with permission to post to the chosen channel Google account with access to the target Sheet OpenAI (or API-compatible) credentials for the LLM nodes Internet access to NASA DONKI (no API key required) How to customize the workflow Tweak severity rules inside the Analyze & Prioritize code node. Edit prompt tone/length in each AI Agent node. Change Slack formatting or mention style (@channel vs none). Add filters (e.g., alert only on CME/FLR) or extend logging fields in the merge step.

Managing contracts manually is time-consuming and prone to human error, especially when documents need to be shared, tracked, and stored across different tools. This workflow automates the entire process by capturing contract PDFs and Words uploaded to Slack, extracting key information with GPT, and organizing the data into a structured format inside Google Sheets. Essential fields such as client, service provider, contract value, and important dates are automatically parsed and logged, eliminating repetitive manual entry. Once the data is saved, a confirmation message is posted back to Slack so your team can quickly verify that everything has been recorded accurately. Who’s it for This workflow is ideal for operations teams, legal departments, or growing businesses that manage multiple contracts and want to maintain accuracy without spending hours on administration. By integrating Slack, GPT, and Google Sheets, you gain a simple but powerful contract management system that reduces risk, improves visibility, and keeps everyone aligned. Instead of scattered files and manual spreadsheets, you have a single automated pipeline that ensures your contract data is always up to date and accessible. How it works The workflow is triggered when a contract in PDF or Word format is shared in the designated Slack channel. The uploaded file is automatically retrieved for processing. Its content is extracted and converted into plain text. If the file is not in PDF or Word format, an error message is sent. GPT interprets the extracted text and structures the essential fields (e.g., Client, Service Provider, Effective Date, Expiration Date, Signature Date, Contract Value). The structured contract information is appended as a new row in the contract tracker spreadsheet on Google Sheets. A summary of the saved data is posted back to Slack for quick validation. How to set up You need to import this workflow into your n8n instance. You must authenticate your Slack account and select the target channel for contract submissions. You should link your Google account and specify the spreadsheet where the contract data will be stored. In this template, the required columns are Client, Service Provider, Effective Date, Expiration Date, Signature Date, and Contract Value. You can adjust the GPT parsing prompt to match the specific fields that your organization requires. You upload a sample contract in PDF or Word format to Slack and verify that the extracted data is correctly recorded in Google Sheets. Requirements You must have an active n8n instance in the cloud. You need a Slack account with permission to upload files and send messages. You must use a Google Sheets account with edit access to the target spreadsheet. You need a GPT integration (e.g., OpenAI) to enable AI-powered text parsing. How to customize the workflow You can modify this workflow to fit your organization’s unique contract needs. For example, you may update the GPT parsing prompt to capture additional fields, change the target Google Sheets structure, or integrate notifications into other tools. You have full flexibility to expand or simplify the steps so the workflow matches your team’s processes and compliance requirements.

J.A.R.V.I.S. Multimodal AI assistant on Telegram with OpenAI This workflow transforms your Telegram bot into J.A.R.V.I.S., a powerful, multimodal AI assistant. It can understand and process text, voice messages, images, and documents. The assistant can search the web, scrape websites, generate images, perform calculations, and reference uploaded documents to provide comprehensive and context-aware responses in either text or audio format. 🧑‍💻 Who’s it for This workflow is for developers, AI enthusiasts, and businesses who want to create an advanced, interactive AI assistant on Telegram. It’s perfect for automating customer support, creating a personal AI helper, or exploring the capabilities of multimodal large language models (LLMs) in a practical application. ⚙️ How it works The workflow begins when a message is received by your Telegram bot. A Switch node then directs the data based on the message type: Text:** The message is formatted and sent directly to the main AI agent. Voice:** The audio file is downloaded from Telegram and transcribed into text using the OpenAI API. Image:** The image is downloaded and analyzed by an OpenAI vision model to understand its content. Document:** The file is downloaded and its content is stored in a temporary vector store, making it searchable for the AI. The processed input is then passed to the core "J.A.R.V.I.S." Agent node. This agent uses an OpenAI model, conversational memory, and a suite of tools (Google Search, Web Scraper, Image Generator, Calculator, and the document vector store) to formulate a response. Finally, the workflow checks if the initial message was a voice note; if so, it generates an audio response. Otherwise, it sends the answer as a text message back to the user. 🛠️ How to set up Telegram: Create a Telegram Bot - Use @BotFather to create a bot and obtain your bot token; Add Telegram API credentials in n8n with your bot token to the Receive Message Trigger node and all other Telegram nodes. In the Receive Message node, enter the chatId of the user or group authorized to interact with the bot. OpenAI: Add your OpenAI API credentials to all OpenAI, AI Agent, and AI tool nodes. SerpAPI: Add your SerpAPI credentials to the Basic Google Search node to enable web search functionality. Jina AI: Add your Jina AI API key to the Setup Node - The API Key is used on the Webpage Scraper node. ✅ Requirements Telegram Bot API credentials and Bot token. OpenAI API credentials. SerpAPI API credentials. Jina.ai API credentials 🎨 How to customize the workflow Change the AI model:** You can select a different OpenAI model in the OpenAI Chat Model node (e.g., switch from gpt-4.1 to gpt-4o) or in the Analyze Image and Transcribe nodes. Modify the AI's personality:** Edit the system prompt in the J.A.R.V.I.S. Agent node to change its name, tone, instructions, or default language. Expand its tools:** Connect more tools to the J.A.R.V.I.S. Agent node to extend its capabilities, such as connecting to a database or another third-party API. Adjust the response format:** Modify the If Audio Response node to change the conditions for sending text or audio messages. For example, you could configure it to always respond with text. 💬 Need Help? Join the Discord or ask in the Forum

This n8n workflow automates the creation of high-quality, SEO-optimized blog posts using AI. It pulls keyword data from Google Sheets, conducts research via Perplexity AI, generates structured content (title, introduction, key takeaways, body, conclusion, and FAQs) with OpenAI and Anthropic models, assembles the post, performs final edits, converts to HTML, and publishes directly to WordPress. Ideal for content marketers, bloggers, or agencies looking to scale content production while maintaining relevance and engagement. Key Features Keyword-Driven Generation: Fetches primary keywords, search intent, and related terms from a Google Sheets spreadsheet to inform content strategy. AI Research & Structuring: Uses Perplexity for in-depth topic research and OpenAI/Anthropic for semantic analysis, outlines, and full content drafting. Modular Content Creation: Generates sections like introductions, key takeaways, outlines, body, conclusions, and FAQs with tailored prompts for tone, style, and SEO. Assembly & Editing: Combines sections into a cohesive Markdown post, adds internal/external links, and applies final refinements for readability and flow. Publishing Automation: Converts Markdown to styled HTML and posts drafts to WordPress. Customization Points: Easily adjust AI prompts, research depth, or output formats via Code and Set nodes. Requirements Credentials: OpenAI API (for GPT models), Perplexity API (for research), Google Sheets OAuth2 (for keyword input), WordPress API (for publishing). Setup: Configure your Google Sheets with columns like "keyword", "search intent", "related keyword", etc. Ensure the sheet is shared with your Google account. Dependencies: No additional packages needed; relies on n8n's built-in nodes for AI, HTTP, and data processing. How It Works Trigger & Input: Start manually or schedule; pulls keyword data from Google Sheets. Research Phase: Uses Perplexity to gather topic insights and citations from reputable sources. Content Generation: AI nodes create title, structure, intro, takeaways, outline, body, conclusion, and FAQs based on research and SEO guidelines. Assembly & Refinement: Merges sections, embeds links, edits for polish, and converts to HTML. Output: Publishes as a WordPress draft or outputs the final HTML for manual use. Benefits Time Savings: Automate 80-90% of content creation, reducing manual writing from hours to minutes. SEO Optimization: Incorporates primary/related keywords naturally, aligns with search intent, and includes semantic structures for better rankings. Scalability: Process multiple keywords in batches; perfect for content calendars or high-volume blogging. Quality Assurance: Built-in editing ensures engaging, error-free content with real-world examples and data-backed insights. Versatility: Adaptable for any niche (e.g., marketing, tech, finance) by tweaking prompts or sheets. Potential Customizations Add more AI models (e.g., via custom nodes) for varied tones. Integrate image generation or social sharing for full content pipelines. Filter sheets for specific topics or add notifications on completion.

How It Works This template is an n8n workflow that integrates with Jira to provide automated replies. When a ticket is assigned to a user, the workflow analyzes the ticket content, retrieves relevant knowledge from a vector database, and generates a response. By continuously enriching the knowledge base, the system improves response quality in Jira. Prerequisites A Jira account with API access A Pinecone account and credentials (API key and environment settings) An AI provider credential (e.g., OpenAI API key) Setup Instructions Jira Credentials Create Jira credentials in n8n (API token and email). In the Jira node, select the registered Jira account ID. Vector Database Setup (Pinecone) Register your Pinecone credentials (API key and environment variables) in n8n. Ensure that your knowledge base is indexed in Pinecone. AI Assistant Node Configure the OpenAI (or other LLM) node with your API key. Provide a system prompt that explains how to respond to Jira tickets using retrieved knowledge. Workflow Execution The workflow runs only via the Scheduled Trigger node at defined intervals. When Jira tickets are assigned, their summary, description, and latest comments are retrieved. These details are passed to the AI assistant, which queries Pinecone and generates a response. The generated response is then posted as a Jira comment. Step by Step Scheduled Trigger The workflow is executed at regular intervals using the Scheduled Trigger node. Jira Trigger (Issue Assigned) Retrieves the summary, description, and latest comments of assigned tickets. AI Assistant Sends ticket details to the AI assistant, which searches and summarizes relevant knowledge from Pinecone. Response Generation / Ticket Update The AI generates a response and automatically posts it as a Jira comment. (Optionally, the workflow can update the ticket status or mention the assignee.) Notes Keep your Pinecone knowledge base updated to improve accuracy. You can customize the AI assistant’s behavior by adjusting the system prompt. Configure the Scheduled Trigger frequency carefully to avoid API rate limits. Further Reference For a detailed walkthrough (in Japanese), see this article: 👉 Automating Jira responses with n8n, AI, and Pinecone (Qiita) You can find the template file on GitHub here: 👉 Template File on GitHub

Auto-detect news from n8n and turn into a human-approved LinkedIn post. gotoHuman is used to keep a human in the loop. There you can manually edit the AI draft of the post or request to regenerate it. How it works The workflow is triggered each day to fetch the latest version of https://blog.n8n.io. It then fetches each article, checks if it was published in the last 24 hours and uses an LLM to summarize it. An LLM then drafts a related LinkedIn post which is sent to gotoHuman for approval. In gotoHuman, the reviewer can manually edit it or ask to regenerate it with the option to even edit the prompt (Retries loop back to the AI Draft LinkedIn Post node) Approved Posts are automatically published to LinkedIn How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Set up your credentials for gotoHuman, OpenAI, and LinkedIn In gotoHuman, select and create the pre-built review template "Blog scraper agent" or import the ID: sMxevC9tSAgdfWsr6XIW Select this template in the gotoHuman node Requirements You need accounts for gotoHuman (human supervision) OpenAI (summary, draft) LinkedIn How to customize Change the blog URL to monitor. Adapt to its' HTML structure Provide the AI Draft LinkedIn Post with examples of previous posts so it picks up your writing style (consider adding gotoHuman's dataset of approved examples) Use the workflow to target other publications, like your newsletter, blog or other socials

How it works This workflow automates customer feedback management by capturing reviews through a form, analyzing them with AI for sentiment and insights, and then creating structured tasks across Monday.com, ClickUp, and HubSpot. It ensures that customer concerns are categorized, prioritized, and assigned to the right teams with actionable metadata. Step-by-step Trigger & Input The workflow starts when a customer submits the Feedback Form containing their Name, Message, Rating, and Product/Service. The submitted data is pre-processed with a Code node to cleanly extract fields for analysis. AI Analysis & Processing The extracted review is sent to OpenAI GPT-4 for analysis. AI identifies sentiment, sentiment score, category (e.g., product, service, support, delivery, pricing), department, priority, required actions, keywords, and suggested response tone. A Data Processing node enriches the output with due dates, task titles, structured descriptions, and fallback handling in case of parsing issues. Structured Output Generation An AI Agent and OpenAI Chat model transform the enriched data into a strict JSON format that is compatible with Monday.com, ClickUp, and HubSpot. This ensures consistent field order, formatting, and metadata for all downstream integrations. Task Creation in Platforms The structured task data is automatically pushed to: Monday.com → Creates an item in a specified board. ClickUp → Creates a task with mapped fields and priority. HubSpot → Creates an engagement task in CRM with due date and priority. Benefits Automates end-to-end customer feedback analysis and task creation. Ensures structured, AI-driven insights for actionable responses. Reduces manual work in categorizing and assigning reviews. Keeps customer feedback synchronized across multiple platforms (Monday.com, ClickUp, HubSpot). Improves response time by prioritizing high-impact feedback with due dates.

🚀 AI Marketing Campaign Generator Upload product image + details → Get complete professional marketing campaign with 5 custom-generated assets automatically. 🤖 AI Model GPT-4o Mini (OpenAI) - For campaign strategy | Prompt Image generation GPT Image-1 (OpenAI) - For visual asset generation 🔑 Required API Keys OpenAI API - AI analysis & image generation Google Drive API - Asset storage & organization 🎯 What It Generates 5 Marketing Assets: Instagram Post, Instagram Story, Website Banner, Ad Creative, Testimonial Graphic Brand Strategy: Colors, tone, positioning from your product image Campaign Strategy: Messaging, target audience, objectives Visual Analysis: Extracts colors, materials, styling from uploaded image ⚙️ Setup Import JSON to n8n Add OpenAI & Google Drive credentials Configure Google Drive folder for asset storage Deploy form webhook Test with product image upload 📱 How It Works Upload product image → AI analyzes visual + text → Generates complete campaign → Creates 5 custom marketing assets → Saves to Google Drive

How It Works User sends a reminder request via Telegram (e.g., “Remind me to clean the garage tomorrow at 12 pm”). The request is parsed by AI Agent and stored in Airtable with a unique reminder code. The reminder workflow checks Airtable at scheduled intervals and sends a Telegram notification when the reminder is due. Each reminder includes a unique cancel code (e.g., Reply 4936 to stop this reminder). If the user replies with the code, the bot searches Airtable, deletes the reminder, and confirms the deletion in Telegram. If the code doesn’t exist, the bot replies “Code not found.” ⚡ Setup Steps Create a Telegram Bot Use BotFather on Telegram. Run /newbot and copy your bot token. Add the token in your Telegram Trigger and Telegram Send nodes in n8n. Set Up Airtable Create an Airtable base called REMINDER-TABLE. Add a table with fields: title (Text) – reminder text due_at (Date/Time) – when the reminder is due chat_id (Text) – user’s Telegram chat ID code (Number/Text) – unique cancel code Generate an API key / Personal Access Token and connect it in n8n. Import This Workflow In n8n, click Import Workflow. Paste the JSON template. Connect your Telegram and Airtable credentials. Activate the Workflow Start the workflow in n8n Cloud or Self-Hosted. Send a test reminder in Telegram (e.g., “Remind me in 5 minutes to call mom”). When notified, reply with the cancel code to test deletion. Optional Customizations Modify reminder frequency (Every 5 minutes node). Change reminder message formatting in the Format Message node. Add logging/analytics by connecting Google Sheets or another DB. ⚡ Result: You now have a fully automated AI-powered Telegram Reminder Bot with Airtable storage, cancel codes, and real-time notifications!

How it works: This template turns rows in a Google Sheet into polished newsletter drafts in Notion using an AI writing agent. You click Execute workflow in n8n. It fetches all rows marked N8n Status = Pending, generates a draft from Newsletter Title and About the Newsletter, creates a Notion page, writes the full draft, then flips the sheet row to Done before moving to the next one. Before you start (use your own credentials): Create and select your credentials in n8n: Google Sheets (OAuth2 or Service Account) with access to the target spreadsheet. Notion (Internal Integration) with access to the target database. OpenAI (API Key) for the Chat Model. Replace any placeholders in nodes: Spreadsheet ID/URL and sheet/tab name (e.g., newsletter). Notion Database ID / Parent and any page or block IDs used by HTTP Request nodes. OpenAI model name if you prefer a different model. Give the Notion integration access to the database (Share → Invite the integration). Do not hard-code secrets in nodes. Store them in n8n Credentials. Step-by-step: Manual Trigger Start the run with When clicking ‘Execute workflow’. Fetch pending input (Google Sheets → Get row(s) in sheet) Read the newsletter tab and pull only rows where N8n Status = Pending. Iterate (Split In Batches → Loop Over Items) Process one sheet row at a time for stable memory use and pacing. Generate the newsletter (AI Agent + OpenAI Chat Model) AI Agent loads the “System Role Instructions” that define style, sections, and format. Pass Newsletter Title and About the Newsletter to the OpenAI Chat Model to produce the draft. Create a Notion page (Notion → Create Page) Create a page in your Newsletter Automation database with the page title set from Newsletter Title. Prepare long content for Notion (Code) Split the AI output into \~1,800-character chunks and wrap as Notion paragraph blocks to avoid payload limits. Write content blocks to Notion (HTTP Request → UpdateNotionBlock) Send a PATCH request to append all generated blocks so the full draft appears on the page. Mark the sheet row as done (Google Sheets → Update row in sheet) Update N8n Status = Done for the processed Newsletter Title. Continue the loop Return to Split In Batches for the next pending row until none remain. Tools integration: Google Sheets** — input queue and status tracking (Pending → Done) OpenAI** — LLM that writes the draft from provided fields Notion** — destination database for each draft page n8n Code + HTTP Request** — chunking and Notion API block updates Want auto-runs? Add a Cron trigger before step 2 and keep the flow unchanged.

Instantly access live Bybit Spot Market data in Telegram! This workflow integrates the Bybit REST v5 API with Telegram and optional GPT-4.1-mini formatting, delivering real-time crypto market insights such as latest prices, order books, trades, and candlesticks — all presented in clean, structured Telegram messages. 🔎 How It Works A Telegram Trigger node listens for incoming user requests. User Authentication checks the Telegram ID against an allowlist. A Session ID is created from chat.id for lightweight memory across interactions. The Bybit AI Agent orchestrates multiple API requests via HTTP nodes: Latest Price & 24h Stats (/v5/market/tickers?category=spot&symbol=BTCUSDT) Order Book Depth (/v5/market/orderbook?category=spot&symbol=BTCUSDT&limit=50) Best Bid/Ask Snapshot (from order book top levels) Candlestick Data (Klines) (/v5/market/kline?category=spot&symbol=BTCUSDT&interval=15&limit=200) Recent Trades (/v5/market/recent-trade?category=spot&symbol=BTCUSDT&limit=100) Utility Nodes process and format the response: Calculator → computes spreads, mid-prices, % changes. Think → transforms JSON into human-readable reports. Simple Memory → stores symbol, sessionId, and previous inputs. Message Splitter ensures responses over 4000 characters are broken into chunks. Final results are sent back to Telegram in structured, readable format. ✅ What You Can Do with This Agent Get real-time Bybit prices & 24h statistics. Retrieve spot order book depth and liquidity snapshots. Analyze candlesticks (OHLCV) across multiple timeframes. View recent trades for market activity. Monitor bid/ask spreads & mid-prices with calculated values. Receive Telegram-ready reports, cleanly formatted and auto-split when long. 🛠️ Setup Steps Create a Telegram Bot Use @BotFather to create a bot and get a token. Configure in n8n Import Bybit AI Agent v1.02.json. Update the User Authentication node with your Telegram ID. Add your Telegram API credentials (bot token). Add OpenAI API key (Optional) Add Bybit API key if you want AI-enhanced formatting. Deploy and Test Activate the workflow in n8n. Send a message like BTCUSDT to your bot. Instantly receive Bybit Spot data inside Telegram. 📺 Setup Video Tutorial Watch the full setup guide on YouTube: ⚡ Unlock Bybit Spot Market insights in Telegram — fast, structured, and API-key free. 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted. 🔗 For support: Don Jayamaha – LinkedIn

📚 Telegram RAG Chatbot with PDF Document & Google Drive Backup An upgraded Retrieval-Augmented Generation (RAG) chatbot built in n8n that lets users ask questions via Telegram and receive accurate answers from uploaded PDFs. It embeds documents using OpenAI and backs them up to Google Drive. 👤 Who’s it for Perfect for: Knowledge workers who want instant access to private documents Support teams needing searchable SOPs and guides Educators enabling course material Q&A for students Individuals automating personal document search + cloud backup ⚙️ How it works / What it does 💬 Telegram Chat Handling User sends a message Triggered by the Telegram bot, the workflow checks if the message is text. Text message → OpenAI RAG Agent If the message is text, it's passed to a GPT-powered document agent. This agent: Retrieves relevant info from embedded documents using semantic search Returns a context-aware answer to the user Send answer back The bot sends the generated response back to the Telegram user. Non-text input fallback If the message is not text, the bot replies with a polite unsupported message. 📄 PDF Upload and Embedding User uploads PDFs manually A manual trigger starts the embedding flow. Default Data Loader Reads and chunks the PDF(s) into text segments. Insert to Vector Store (Embedding) Text chunks are embedded using OpenAI and saved for retrieval. Backup to Google Drive The original PDF is uploaded to Google Drive for safekeeping. 🛠️ How to set up Telegram Bot Create via BotFather Connect it to the Telegram Trigger node OpenAI Use your OpenAI API key Connect the Embeddings and Chat Model nodes (GPT-3.5/4) Ensure both embedding and querying use the same Embedding node Google Drive Set up credentials in n8n for your Google account Connect the “Backup to Google Drive” node PDF Ingestion Use the “Upload your PDF here” trigger Connect it to the loader, embedder, and backup flow ✅ Requirements Telegram bot token OpenAI API key (GPT + Embeddings) n8n instance (self-hosted or cloud) Google Drive integration PDF files to upload 🧩 How to customize the workflow | Feature | How to Customize | |-------------------------------|-------------------------------------------------------------------| | Auto-ingest from folders | Add Google Drive/Dropbox watchers for new PDFs | | Add file upload via Telegram | Extend Telegram bot to receive PDFs and run the embedding flow | | Track user questions | Log Telegram usernames and questions to a database | | Summarize documents | Add summarization step on upload | | Add Markdown or HTML support | Format replies for better Telegram rendering | Built with 💬 Telegram + 📄 PDF + 🧠 OpenAI Embeddings + ☁️ Google Drive + ⚡ n8n