Workflow Templates

📋Instant Proposal Generator Automatically convert sales call transcripts into professional client proposals by extracting key details with AI, dynamically populating Google Slides templates, and tracking progress in Airtable, all in one seamless workflow. 🎯 What does this workflow do? This end-to-end automation creates client-ready proposals by: Taking call transcripts via chat interface The AI analyzes the transcript to extract key details like company name, goals, budget, and requirements, then structures this data as JSON for seamless workflow integration. Generating customized documents using Google Slides template with dynamic variables, Auto populating {Company_Name}, {Budget}, etc. from extracted data. Delivering finished proposals: Sharing final document with client, and Updating CRM status automatically. ⚙️ How it works User input: Paste call transcript in chat trigger node AI analysis: OpenAI node processes text to extract structured JSON, Identifies company name, goals, budget, requirements, etc. Document copy: it copies the file from Google Drive, and name it {company name} proposal. Variables replacement: Replaces all template variables ({Company_Name}, {Budget}, etc.) with extracted data from ChatGPT. Delivery & tracking: Shares finalized proposal with client via email, an Updates Airtable "Lead Status" to "Proposal Sent". 🔑 Required setup OpenAI API Key: Create a key from here Google Cloud Credentials: Setup here Required scopes: Google Slides edit + file creation Airtable Access Token: Create one from here

Why use this? This workflow turns any event-related text into a new event on your calendar. Poster for a concert you want to go to? Snap a photo [with your iPhone] and boom city, it's in your calendar. † Parent-Teacher conference you can't forget? Forward that email to the webhook. † Appointment card from the doctor? Snap it in, baby! † How it works (Very, very simple) Data received by webhook. Ai Agent prompted to parse the text into structured event data. Create event in NextCloud cal (or Zoho, or GoogleCal). (Optional, intended use case) Set up the iOS Shortcut (linked in workflow) to turn your iPhone into the trigger for this workflow. Say "Siri, Add Event To Calendar," and she opens the camera, OCRs the text in the photo and sends that to the webhook. Boom city. Expected input structure [ { "body": { "cal": "work", <- this is optional for deciding among calendars "eventInfo": "Join us for Betty-Jean's 98th birthday! (Yes, we celebrate every year now...) It's October 11th at 2:30pm, at Betty-Jean's house. Come after lunch 'cause her kitchen hasn't been used in 20 years. She mellows out pretty early these days so plan for the party to end by 4:00pm." } } ] Extras Includes multiple calendar nodes.** Whether you're using NextCloud, Zoho or Google Cal, you can swap in the node you need. iOS Shortcut linked in workflow.** I also set up a Shortcut for the iPhone. The first time you use the Shortcut, you'll need to give it some permissions, and paste in your production webhook URL. Expansion option: Accept images**. iPhone has a native OCR feature but this isn't always an option. To make this workflow more versatile, consider building out a second branch to send an image to an Agent to parse the event data from the image directly. Expansion option: Multiple triggers**. You could add more triggers to receive event-related text from other sources, like an IMAP node reading your email (pro tip: set up a designated folder and give the IMAP access only to that folder). † Workflow begins with a webhook which can receive correctly-formatted data from anywhere on the web --- mailhook, webform, iOS Shortcut, etc. Direct data to this webhook from your source of text to use this workflow.

What it's for: This is a base template for anyone trying to develop a telegram AI Agent. This base allows for multiple inputs (Voice, Picture, Video, and Text inputs) to be processed by an AI model of their choosing to a get a User started. From here, the User may connect any tools that they see fit to the AI Agent for their n8n workflows. How it works: Input: Telegram message to a bot chat n8n Processing: Switch node determines the type: Voice Message Picture Message Video Message Text Message (Currently uses OpenAI and Gemini to analyze Voice/Photo/Video content but feel free to change these nodes with other models) AI Agent Proccessing: LLM of your choosing examines message and based on system prompt, generates an output Output: AI Output is sent back in telegram Message How to use: Create your chat bot and generate access token -> Search Bot father in telegram -> Type "/newbot" -> follow instructions and create access token -> Copy access token Create Credentials in n8n -> Open telegram trigger node -> Click create credential -> Paste access token -> Save Create LLM access token (Different per LLM but search your LLM + API in google) -> (will have to create an account with the LLM platform) -> buy credits to use LLM API -> Generate Access token -> Paste token in LLM node Requirements: Telegram Bot Access Token Google Gemini Access Token (For Picture and Video messages) OpenAI Access Token (For Voice messages) LLM Access Token (Your preference for the AI Agent) Customizing this workflow: To personalize the AI Output, adjust the system prompt (give context or directions on the AI's role) Add tools to the AI agent to give it more utility besides a personalied LLM (Example: Calendars, Databases, etc).

Who’s it for Teams building health/fitness apps, coaches running check-ins in chat, and anyone who needs quick, structured nutrition insights from food photos—without manual logging. What it does / How it works This workflow accepts a food image (URL or Base64), uses a vision-capable LLM to infer likely ingredients and rough gram amounts, estimates per-ingredient calories, and returns a strict JSON summary with total calories and a short nutrition note. It normalizes different payloads (e.g., Telegram/LINE/Webhook) into a common format, handles transient errors with retries, and avoids hardcoded secrets by using credentials/env vars. Requirements Vision-capable LLM credentials (e.g., gpt-4o or equivalent) One input channel (Webhook, Telegram, or LINE) Environment variables for model name/temperature and optional request validation How to set up Connect your input channel and enable the Webhook (copy the test URL). Add LLM credentials and set LLM_MODEL and LLM_TEMPERATURE (e.g., 0.3). Turn on the workflow, send a sample payload with imageUrl, and confirm the strict JSON output. (Optional) Configure a reply node (Telegram/Slack or HTTP Response) and a logger (Google Sheets/Notion). How to customize the workflow Outputs**: Add macros (protein/fat/carb) or micronutrient fields. Units**: Convert portion descriptions (piece/slice) to grams with your own mapping. Languages**: Toggle multilingual output (ja/en). Policies**: Tighten validation (reject low-confidence parses) or add manual review steps. Security**: Use signed/temporary URLs for private images; mask PII in logs. Data model (strict JSON) { "dishName": "string", "ingredients": [{ "name": "string", "amount": 0, "calories": 0 }], "totalCalories": 0, "nutritionEvaluation": "string" } Notes Rename all nodes clearly, include sticky notes explaining the setup, and never commit real IDs, tokens, or API keys.

Sometimes you need the rich text field to be in HTML instead of Markdown. This template either syncs a single record or all records at once. Youtube tutorial

Sometimes you need the rich text field to be in HTML instead of Markdown. This template either syncs a single record or all records at once. Youtube tutorial

HOW IT WORKS : Automates SaaS operations by consolidating user management, AI-driven support triage, analytics, and billing into one unified system. User signups flow through registration, support requests route via OpenAI prioritization, billing events trigger confirmations, and daily analytics feed dashboards. The AI Business Logic layer orchestrates real-time decisions, enriches data, and triggers Gmail notifications. Four data streams converge into centralized routing for customer onboarding, ticket triage, metrics aggregation, and revenue automation. SETUP STEPS: Add OpenAI API credentials for chat model routing Authenticate Gmail with app password for notifications Connect Data Table Tool for user/support/billing storage Configure workflow settings: priority thresholds and routing rules Test each branch with sample data to verify all integrations PREREQUISITES: OpenAI API key, Gmail account with app password, MCP Server access, Data Table Tool credentials USE CASES: Manage SaaS customer lifecycle end-to-end; Route critical support instantly CUSTOMIZATION: Extend AI prompts for different support categories; add Slack/Teams notifications BENEFITS: Reduces manual overhead 70%, routes tickets 10x faster, centralizes customer data

Analyze productivity metrics from Google Calendar and Todoist to Slack This workflow acts as an automated personal productivity coach. It aggregates data from your daily tools (Google Calendar, Todoist, and Slack) to provide AI-driven insights into your work habits. It runs daily to log metrics to Google Sheets and sends a summary to Slack. Additionally, every Friday, it generates a comprehensive strategic weekly review. Who is this for? Remote Workers & Freelancers** who want to track their focus time and meeting load. Productivity Enthusiasts** looking to automate their "Quantified Self" data collection. Managers** who want a high-level overview of their weekly throughput and communication volume without manual tracking. What it does Daily Trigger: Runs automatically every weekday morning (default: 8 AM). Data Collection: Fetches today's meetings from Google Calendar. Retrieves high-priority and overdue tasks from Todoist. Analyzes recent message activity from Slack. AI Analysis: Uses OpenAI to analyze the data, identifying focus blocks and potential overload risks. Logging: Saves raw metrics (meeting hours, task counts, message volume) to a Google Sheet for historical tracking. Reporting: Sends a "Daily Productivity Summary" to Slack with actionable advice. On Fridays, it pulls the last 7 days of data from Google Sheets to generate and send a Weekly Strategic Report to Slack. Requirements n8n** (Self-hosted or Cloud) Google Cloud Console** project with Calendar and Sheets APIs enabled. Todoist** account. Slack** workspace. OpenAI** API Key (GPT-4 is recommended for better analysis). How to set up Configure Credentials: Set up your credentials in n8n for Google (OAuth2), Todoist, Slack, and OpenAI. Prepare Google Sheet: Create a new Google Sheet. Create the following header columns in the first row: date, meetingHours, tasksCount, slackMessages. Update Nodes: Log Daily Metrics node: Select your Spreadsheet and Sheet name. Fetch Last 7 Days Data node: Select the same Spreadsheet. Slack nodes: Select the channel where you want to receive reports. Activate: Toggle the workflow to Active. How to customize Adjust Schedule:* Change the *Schedule Daily Execution node to fit your preferred reporting time. Modify AI Persona:* Edit the system prompt in the *AI Analysis node to change the tone of the report (e.g., make it more strict or more encouraging). Add Data Sources:* You can easily chain additional nodes (like GitHub or Jira) into the *Aggregate Data code node to include coding or project management metrics.

📘 Description This workflow automates AI-driven Facebook Messenger product inquiry handling, connecting Facebook DMs with Airtable inventory and returning instant automated replies based on product availability. It runs hourly, fetches new messages, extracts the latest customer query, uses GPT-4o to identify the product and intent, merges this with the Airtable inventory dataset, performs an AI-assisted product match, and replies automatically inside the same Facebook conversation. Invalid or malformed messages are logged to Google Sheets for review. ⚙️ What This Workflow Does (Step-by-Step) ▶️ Trigger – Fetch New Facebook Messages (Every Hour) Schedules hourly polling of new conversations from Facebook Messenger. 🟦 Fetch Facebook Conversation List (Graph API) Retrieves conversation threads from the connected Facebook Page. 💬 Fetch Facebook Conversation Messages (Graph API) Loads message details (content, sender, timestamp) for the selected conversation. 📩 Extract Latest Facebook Message (Code) Sorts all messages and picks the latest one → this is the message analyzed by AI. 🔍 Validate Record Structure (IF) Ensures the incoming message has required fields. Valid → AI analysis Invalid → logged to Google Sheets. 📄 Log Invalid Records to Google Sheet Stores malformed or unprocessable messages for audit and correction. 🧠 Configure GPT-4o — Message Classification Model Defines AI model used to extract product details and intent from the customer’s message. 🤖 AI – Extract Product & Customer Intent AI identifies: product name (standardized) customer intent (availability, pricing, inquiry) cleaned query always returns structured JSON No inventory lookup happens here. 📦 Fetch Inventory Records from Airtable Pulls complete product inventory list to cross-match with customer request. 🔁 Merge AI Output With Inventory Dataset Combines: AI-interpreted message data Airtable inventory records This prepares a unified object for product lookup. 📝 Build Combined AI + Inventory Payload (Code) Constructs { ai: {...}, inventory: [...] } for the product-matching AI agent. 🧠 Configure GPT-4o — Product Matching Model Sets strict rules for identifying whether the requested product exists in inventory. 🤖 AI – Match Requested Product in Inventory AI checks: exact / close match to product name whether item exists generates structured JSON reply text + confidence score. 🧹 Parse AI Product Match JSON (Code) Ensures the AI output is valid JSON before making decisions. 🔍 Check If Product Exists (IF) If found → sends “product available” reply If not → sends “product not found” reply. 📨 Send Facebook Reply — Product Found (Graph API) Sends a personalized Messenger reply including matched product details. 📨 Send Facebook Reply — Product Not Found (Graph API) Replies politely informing customer that the product is not available. 🧩 Prerequisites Facebook Graph API access token Airtable API token Azure OpenAI GPT-4o credentials Google Sheets OAuth 💡 Key Benefits ✔ Fully automated Facebook DM handling ✔ AI-powered product identification even with typos or unclear wording ✔ Real-time product availability responses ✔ Unified Airtable-driven catalog lookup ✔ Automatic fallback for invalid messages ✔ Zero manual intervention for customer support 👥 Perfect For Ecommerce stores Catalog-based product businesses Teams handling large volumes of Facebook DM inquiries Businesses wanting instant customer replies without agents

📊 Description Automate your eCommerce content workflow by generating AI-optimized product descriptions directly from Airtable. 🛍️🤖 This automation checks for pending products every 15 minutes, processes them in batches, and uses GPT-4o-mini to create structured long descriptions, short answer blocks, bullet features, and feature tables. All AI-generated fields are then written back into the same Airtable record, ensuring clean, consistent, and SEO-friendly product copy. Perfect for stores looking to scale product listings without manual writing. ✨📄 🔁 What This Template Does 1️⃣ Triggers every 15 minutes to look for products marked as “pending”. ⏰ 2️⃣ Fetches product data from Airtable for processing. 📦 3️⃣ Splits results into batches to avoid API rate limits. 🔁 4️⃣ Sends product attributes to GPT-4o-mini for AI-generated content. 🤖 5️⃣ Uses a structured JSON parser to ensure clean, valid AI output. 📐 6️⃣ Converts the AI JSON into Airtable-friendly fields using a Code node. 🧩 7️⃣ Updates the original Airtable product record with generated descriptions. ✍️ 8️⃣ Marks each item as “done” with a timestamp. ✔️ ⭐ Key Benefits ✅ Creates consistent, high-quality product descriptions automatically ✅ Produces AI-friendly content for search engines & answer engines ✅ Eliminates manual copywriting for large product catalogs ✅ Ensures structured, valid, and predictable output every time ✅ Runs reliably on schedule with zero human oversight ✅ Ideal for eCommerce teams scaling product listings 🧩 Features Scheduled automation (every 15 minutes) Airtable integration for fetching & updating records Batch processing to prevent rate-limit issues GPT-4o-mini AI content generation Structured output parser for clean JSON Code node formatting for Airtable Automatic status + timestamp updating 🔐 Requirements Airtable Personal Access Token OpenAI API key (GPT-4o-mini) Airtable base + table with required fields n8n with LangChain nodes enabled 🎯 Target Audience eCommerce teams managing product catalogs Marketplace sellers needing scalable content Operations teams automating product listings Agencies generating optimized product copy for clients

Overview This template is ideal for photographers, graphic designers, and creative professionals who manage large volumes of visual assets. It is also perfect for Digital Asset Managers looking for a customizable, automated solution to organize files without manual tagging. What it does When a new image is uploaded to a designated "Inbox" folder in Google Drive, the workflow performs the following actions: AI Analysis**: Uses GPT-4o to analyze the image content, generating a description, extracting dominant colors, and determining the category (e.g., Portrait vs. Landscape). Safety Check**: Runs an AI-based NSFW filter. If inappropriate content is detected, the process stops, and a warning is sent to Slack. Smart Sorting**: Automatically moves the file into the correct subfolder based on its category. Contextual Tagging**: Generates specific tags (e.g., "smile, natural light" for portraits) and updates the file metadata. Archiving**: Creates a comprehensive entry in a Notion Database with the image link, tags, and description. Notification**: Sends a success alert to Slack with a summary of the archived asset. How to set up This workflow is designed to be plug-and-play using a central configuration node. Credentials: Connect your Google Drive, OpenAI, Notion, and Slack accounts in n8n. Set Variables: Open the node named "Workflow Configuration". Replace the placeholder IDs with your actual Folder IDs (for Inbox, Portraits, and Landscapes), Notion Database ID, and Slack Channel ID. Prepare Notion: Create a Database in Notion with the following properties: Category (Select) Description (Rich Text) Image URL (URL) Tags (Rich Text) Date (Date) Requirements n8n Version**: 1.0 or later. OpenAI API: Access to the **gpt-4o model is recommended for accurate vision analysis. Google Drive**: A specific folder structure (Inbox, Portraits, Landscapes). Notion**: A dedicated database for the portfolio. Slack**: A channel for notifications. How to customize Add Categories**: You can expand the "Category Router" (Switch node) to include more specific genres like "Architecture," "Macro," or "Street," and add corresponding paths. Adjust Prompts**: Modify the system prompts in the AI nodes to change the language of the output or the style of the generated tags. Change Output**: Connect to Airtable or Excel instead of Notion if you prefer a different database system.

Run an AI-powered degree audit for each senior student. This template reads student rows from Google Sheets, evaluates completed courses against hard-coded program requirements, and writes back an AI Degree Summary of what's still missing (major core, Gen Eds, major electives, and upper-division credits). It's designed for quick advisor/registrar review and SIS prototypes. Trigger: Manual — When clicking "Execute workflow" Core nodes: Google Sheets, OpenAI Chat Model, (optional) Structured Output Parser Programs included: Computer Science BS, Business Administration BBA, Psychology BA, Mechanical Engineering BS, Biology BS (Pre-Med), English Literature BA, Data Science BS, Nursing BSN, Economics BA, Graphic Design BFA Who's it for Registrars & advisors** who need fast, consistent degree checks Student success teams** building prototype dashboards SIS/EdTech builders** exploring AI-assisted auditing How it works Read seniors from Google Sheets (Senior_data) with: StudentID, Name, Program, Year, CompletedCourses. AI Agent compares CompletedCourses to built-in requirements (per program) and computes Missing items + a short Summary. Write back to the same sheet using "Append or update" by StudentID (updates AI Degree Summary; you can also map the raw Missing array to a column if desired). Example JSON (for one student): { "StudentID": "S001", "Program": "Computer Science BS", "Missing": [ "GEN-REMAIN | General Education credits remaining | 6", "CS-EL-REM | CS Major Electives (200+ level) | 6", "UPPER-DIV | Additional Upper-Division (200+ level) credits needed | 18", "FREE-EL | Free Electives to reach 120 total credits | 54" ], "Summary": "All core CS courses are complete. Still need 6 Gen Ed credits, 6 CS electives, and 66 total credits overall, including 18 upper-division credits — prioritize 200/300-level CS electives." } Setup (2 steps) 1) Connect Google Sheets (OAuth2) In n8n → Credentials → New → Google Sheets (OAuth2) and sign in. In the Google Sheets nodes, select your spreadsheet and the Senior_data tab. Ensure your input sheet has at least: StudentID, Name, Program, Year, CompletedCourses. 2) Connect OpenAI (API Key) In n8n → Credentials → New → OpenAI API, paste your key. In the OpenAI Chat Model node, select that credential and a model (e.g., gpt-4o or gpt-5). Requirements Sheet columns:** StudentID, Name, Program, Year, CompletedCourses CompletedCourses format:** pipe-separated IDs (e.g., GEN-101|GEN-103|CS-101). Program labels:** should match the built-in list (e.g., Computer Science BS). Credits/levels:** Template assumes upper-division ≥ 200-level (adjust the prompt if your policy differs). Customization Change requirements:** Edit the Agent's system message to update totals, core lists, elective credit rules, or level thresholds. Store more output:** Map Missing to a new column (e.g., AI Missing List) or write rows to a separate sheet for dashboards. Distribute results:** Email summaries to advisors/students (Gmail/Outlook), or generate PDFs for advising folders. Add guardrails:** Extend the prompt to enforce residency, capstone, minor/cognate constraints, or per-college Gen Ed variations. Best practices (per n8n guidelines) Sticky notes are mandatory:** Include a yellow sticky note that contains this description and quick setup steps; add neutral sticky notes for per-step tips. Rename nodes clearly:** e.g., "Get Seniors," "Degree Audit Agent," "Update Summary." No hardcoded secrets:** Use credentials—not inline keys in HTTP or Code nodes. Sanitize identifiers:** Don't ship personal spreadsheet IDs or private links in the published version. Use a Set node for config:** Centralize user-tunable values (e.g., column names, tab names). Troubleshooting OpenAI 401/429:** Verify API key/billing; slow concurrency if rate-limited. Empty summaries:** Check column names and that CompletedCourses uses |. Program mismatch:** Align Program labels to those in the prompt (exact naming recommended). Sheets auth errors:** Reconnect Google Sheets OAuth2 and re-select spreadsheet/tab. Limitations Not an official audit:** It infers gaps from the listed completions; registrar rules can be more nuanced. Catalog drift:** Requirements are hard-coded in the prompt—update them each term/year. Upper-division heuristic:** Adjust the level threshold if your institution defines it differently. Tags & category Category: Education / Student Information Systems Tags: degree-audit, registrar, google-sheets, openai, electives, upper-division, graduation-readiness Changelog v1.0.0 — Initial release: Senior_data in/out, 10 programs, AI Degree Summary output, append/update by StudentID. Contact Need help tailoring this to your catalog (e.g., per-college Gen Eds, capstones, minors, PDFs/email)? 📧 rbreen@ynteractive.com 📧 robert@ynteractive.com 🔗 Robert Breen — https://www.linkedin.com/in/robert-breen-29429625/ 🌐 ynteractive.com — https://ynteractive.com