Workflow Templates

Overview Confused which credit card to actually get or swipe? With 100+ cards in the market, hidden caps, and milestone rules, most people end up leaving rewards, perks, and cashback on the table. This workflow uses n8n + GPT + Google Sheets + Telegram to recommend the best credit card for each user’s lifestyle in under 3 seconds, while keeping the logic transparent with a ₹-value breakdown. What does this workflow do? This workflow: Captures User Inputs – Users answer a 7-question lifestyle quiz via Telegram. Stores Responses – Google Sheets logs all answers for resumption & deduplication. Scores Answers – n8n Function nodes map single & multi-select inputs into scores. Generates Recommendations – GPT analyses profile vs. 30+ card dataset. Breaks Down Value – Outputs a transparent table of rewards, milestones, lounge value. Delivers Results – Top 3 card picks returned instantly on Telegram. Why is this useful? Most card comparison tools only list features — they don’t personalise or calculate actual value. This workflow builds a decision engine: 🔍 Personalised → matches lifestyle to best-fit cards 💸 Transparent → shows value in real currency (rewards, milestones, lounges) ⏱ Fast → answers in under 3 seconds 🗂 Organised → Google Sheets keeps audit trail of every user + dedupe Tools used n8n (Orchestrator): Orchestration + logic branching Telegram: User-facing quiz bot Google Sheets: Database of credit cards + logs of user answers OpenAI (GPT): Analyses user profile & generates recommendations Who is this for? 🧑‍💻 Fintech product builders → see how AI can power recommendation engines 💳 Cardholders → understand which card fits their lifestyle best ⚙️ n8n makers → learn how to combine Sheets + GPT + chat interface into one workflow 🌍 How to adapt it for your country/location This workflow uses a credit card dataset stored in Google Sheets. To make it work for your country: Build your dataset → scrape or collect card details from banks, comparison sites, or official portals Fields to include: Fees, Reward rate, Lounge access, Forex markup, Reward caps, Milestones, Eligibility. You can use web crawlers (e.g., Apify, PhantomBuster) to automate data collection. Update the Google Sheet → replace the India dataset with your country’s cards. Adjust scoring logic → modify Function nodes if your cards use different reward structures (e.g., cashback %, miles, points value). Run the workflow → GPT will analyse against the new dataset and generate recommendations specific to your country. This makes the workflow flexible for any geography. Workflow Highlights ✅ End-to-end credit card recommendation pipeline (quiz → scoring → GPT → result) ✅ Handles single + multi-select inputs fairly with % match scoring ✅ Transparent value breakdown in local currency (rewards, milestones, lounge access) ✅ Google Sheets for persistence, dedupe & audit trail ✅ Delivers top 3 cards in <3 seconds on Telegram ✅ Fully customisable for any country by swapping the dataset

Multi-Agent Cold Email Campaign Generator with O3 Director & GPT-4.1 Specialists 🌍 Overview This workflow simulates a virtual sales & marketing team where each AI agent has a role: A Director Agent (O3) who manages strategy. Multiple Specialist Agents (GPT-4.1-mini) for research, writing, personalization, deliverability, sequencing, and analytics. Everything is triggered automatically when a new chat message request comes in. 🟢 Section 1: Entry & Director 🔗 Nodes: 1️⃣ When chat message received (Trigger) 💬 Starts the workflow when a new request arrives (e.g., “Create a cold email campaign for SaaS CTOs”). 2️⃣ Outreach Director Agent (O3 model) 🎯 The “manager” agent. Decides what kind of campaign is needed and assigns tasks. 3️⃣ Think (Planning Node) 🧠 Helps the Director structure thoughts before delegating. 💡 Why useful? Director uses O3 (strong reasoning model) only where strategy is needed → reduces cost. Provides a single point of control to coordinate all other agents. 🔵 Section 2: Specialist Agents Each is powered by GPT-4.1-mini (cheaper + faster). 🔍 Prospect Research Specialist → researches target companies, roles, pain points. ✍️ Cold Email Copywriter → drafts subject lines, hooks, and persuasive body copy. 🎯 Personalization Specialist → inserts custom variables for each recipient. 📅 Email Sequence Strategist → designs follow-ups, timing, nurture flows. 📬 Email Deliverability Expert → ensures emails land in inbox, not spam. 📊 Outreach Analytics Specialist → tracks performance, runs A/B tests, optimizes campaigns. 💡 Why useful? Each agent is a specialist → just like a real marketing team. Parallel execution** in n8n means faster results. Modular → you can remove or add more specialists. 🟣 Section 3: Execution Flow Request comes in via chat trigger Director (O3) interprets and delegates → calls specialists as tools Specialists generate their pieces (research → copy → personalization → sequence → deliverability → analytics) Director integrates results into a cohesive cold email campaign 🟡 Section 4: Documentation & Notes There are two Sticky Notes inside the workflow: Header Note** → Support info + tutorials (YouTube & LinkedIn by Yaron Been) Main Note** → Full documentation (overview, use cases, cost optimization, tags) 📊 Final Overview | Section | What Happens | Why It’s Useful | | -------------- | ------------------------ | --------------------------- | | 🟢 Director | Trigger + O3 strategy | Ensures smart coordination | | 🔵 Specialists | GPT-4.1-mini agents | Faster, cheaper execution | | 🟣 Flow | Delegation + Integration | Automated campaign building | | 🟡 Docs | Sticky Notes | In-workflow guide for users | 🚀 Benefits ✅ AI-powered cold email team without hiring humans ✅ Cost-optimized (O3 only for strategy, GPT-4.1-mini for tasks → \~90% cheaper) ✅ End-to-end coverage (research → writing → personalization → sequencing → analytics) ✅ Scalable: can run multiple campaigns in parallel ✅ Customizable: swap models, add tools, or expand team

Overview This workflow automates static security analysis for JavaScript, PHP, and Python codebases. It’s designed for bug bounty hunters and security researchers who need fast, structured, and AI-assisted vulnerability detection across multiple sources. Features 🤖 AI-Powered Analysis: Specialized agents for each language: AI JavaScript Expert AI PHP Expert AI Python Expert Each agent detects only exploitable vulnerabilities (AST + regex heuristics). Returns strict JSON with: { "results": [ { "url": "file or URL", "code": "lines + snippet", "severity": "medium|high|critical", "vuln": "vulnerability type" } ] } 🧩 Post-Processing: Cleans, formats, and validates JSON results. Generates HTML tables with clear styling for quick visualization. Output ✅ JSON vulnerability reports per file. 📊 HTML table summaries grouped by language and severity. Usage Import the workflow into n8n. Configure credentials: OpenAI API key GitHub API Key Google Drive API Key Run via the provided webhook form. Select analysis mode and input target. View structured vulnerability reports directly in n8n or Google Drive. Notes Performs static analysis only (no code execution). Detects exploitable findings only; ignores low-impact issues.

Supercharge your sales and marketing efforts with this powerful automation that transforms a list of LinkedIn profiles into a fully enriched, personalized outreach campaign. This workflow is designed for sales teams, growth marketers, and business development professionals looking to scale their lead generation without sacrificing personalization. It seamlessly integrates LinkedIn scraping, email enrichment with Hunter.io, AI-powered message generation with OpenAI, and data organization in Google Sheets. How It Works Start with Leads: The workflow begins with a list of target LinkedIn profile URLs. Scrape Profile Data: It automatically scrapes each LinkedIn profile to extract key professional information such as name, title, company, and location. A built-in delay helps manage rate limits. Find Verified Emails: Using the scraped company and name, the workflow queries ==Hunter.io to find a verified work email address== for the lead. AI-Powered Personalization: If an email is found, the lead's data is sent to OpenAI (GPT-4), which generates a highly personalized, conversational outreach message based on their role, company, and your value proposition. Sync to CRM/Sheet: Finally, all the enriched data—including the custom AI message—is neatly organized and saved as a new row in your designated Google Sheet. Stop wasting hours on manual lead research and generic outreach. Implement this automated workflow to focus on building relationships and closing deals.

Template presentation This template generates a sales follow-up presentation in Google Slides after a sales call recorded in Claap. The workflow is simplified to showcase the main use case. You can customize and enrich this workflow by connecting to the CRM, researching data online or adding more files in the presentation. The presentation template used in this workflow is available here. Workflow configuration Create a webhook in Claap, by following this article. Edit the labels that trigger the workflow and route on the relevant presentation. Fill your Open AI credentials by creating an API Key in OpenAI Platform Edit the presentation personalization details (user set as editor, content, title) Fill your Slack credentials by following steps in this video.

This n8n workflow pulls campaign data from Google Sheets, summarizes it using OpenAI, and sends a performance recap via Outlook email. ✅ Step 1: Connect Google Sheets In n8n, go to Credentials → click New Credential Select Google Sheets OAuth2 API Log in with your Google account and authorize Use a spreadsheet with: Column names in the first row Data in rows 2–100 Example format: 📄 Sample Marketing Sheet ✅ Step 2: Connect OpenAI Go to OpenAI API Keys Make sure you have a payment method set under Billing In n8n, create a new OpenAI API credential Paste your API key and save 📬 Need Help? Feel free to contact me if you run into issues: 📧 robert@ynteractive.com 🔗 LinkedIn

How This Works This automation automatically scrapes leads from Apollo using the Apify scraper, filters out those who do not have an Email or URL included, scrapes the leads' website content and writes personalised Icebreakers and subject lines based on the website's content. Set Up (Step-by-Step) Connect the API keys from the Apify scraper mentioned in the workflow sticky note. Insert Apollo URL and the amount of leads you want to scrape. Connect your Slack account (if needed) Reach Out To Me Send me an Email if you need further assistance: richard@advetica-systems.com

Who's it for Digital marketing agencies and Meta Ads managers who need to generate comprehensive performance reports across multiple client accounts automatically. Perfect for agencies handling 5+ Meta Ads accounts who want to save hours on manual reporting while delivering AI-powered insights to their teams. What it does Pulls performance data from multiple Meta Ads accounts for a specified time period (last 7, 14, or 30 days) Uses Claude AI with Pipeboard's Meta Ads MCP to analyze campaign performance, identify trends, and generate actionable insights Generates professional reports with AI-driven recommendations for optimization Automatically delivers formatted reports to your Slack channels Runs on a schedule (weekly/daily) or triggered manually How to set up Set up Claude AI integration (requires Anthropic API key) Configure Pipeboard's Meta Ads MCP connection Connect Slack to n8n via OAuth2 Create a list of client account IDs in the workflow configuration Customize your reporting template and Slack delivery settings Requirements n8n version 1.109.2 or newer. Claude AI API access (Anthropic) Pipeboard account Slack workspace access How to customize the workflow Adjust the date range and metrics to track Modify the AI prompts for different types of insights Configure multiple Slack channels for different clients Set up custom scheduling intervals Add email delivery as an additional output channel

clients kept booking meetings during my prayer times. i'd either miss a prayer or scramble to reschedule. the problem wasn't the clients — it was that my calendar had no blocked windows for salah. i needed a way to block those times every day, automatically, without thinking about it. what this does i built this so i can send /salah to my Telegram bot and it instantly fetches today's prayer times from IslamicAPI (using the Egyptian General Authority of Survey method for Cairo), calculates a realistic time window for each prayer, blocks all five prayers directly in Google Calendar, logs everything to a Google Sheet, then sends me a confirmation message on Telegram with the exact time windows. prayer times shift daily — this accounts for that automatically. what's inside Telegram Trigger — listens for incoming messages from the bot Validate the Sender — blocks anyone who isn't me from running this Check if it's Text — filters out non-text messages like stickers or photos Check for Triggers — only proceeds if the message is /salah Send an Action — sends a typing indicator while the workflow runs Get Prayer Times — fetches today's prayer times via IslamicAPI Map Prayer Data — extracts and formats the raw API response Code in JavaScript — calculates start and end windows per prayer Book for Fajr / Dhuhr / Asr / Maghrib / Isha — creates five Google Calendar events Map Outputs — bundles all calendar IDs and times into one object Store Prayer Bookings — appends a row to Google Sheets for tracking Send the Message — sends a formatted confirmation back to me on Telegram Error Trigger — catches failures and logs them to a separate sheet APIs used IslamicAPI — fetches today's accurate prayer times by coordinates Google Calendar — creates and holds the five prayer block events Google Sheets — logs each booking run with full timestamps and event IDs Telegram Bot API — receives the /salah command and sends the confirmation estimated cost per run free — IslamicAPI is free forever, Google and Telegram APIs have no per-call cost at this usage level. Created by Gurey AI other workflows i've built Full Email inbox managing system Client Onboarding Form Rag AI Agent more workflows coming soon — follow along Need Help? Email me with your questions my email or ask in the Forum! Happy Hacking!

Generate Invoices and Send Reminders for Customers with Jotform, QuickBooks and Outlook This workflow automates the entire process of receiving a product/service order, checking or creating a customer in QuickBooks Online (QBO), generating an invoice, emailing it — all triggered by a form submission (via Jotform), and sending invoice reminders. How It Works Receive Submission Triggered when a user submits a form. Collects data like customer details, selected product/service, etc. Check If Customer Exists Searches QBO to determine if the customer already exists. If Customer Exists:* *Update** customer details (e.g., billing address). If Customer Doesn’t Exist:* *Create** a new customer in QBO. Get The Item Retrieves the selected product or service from QBO. Create The Invoice Generates a new invoice for the customer using the item selected. Send The Invoice Automatically sends the invoice via email to the customer. Store The Invoice In DB Stores the needed invoice details in the DB. Send Reminders Every day at 8 AM, the automation checks each invoice to decide whether to: send a reminder email, skip and send it later, or delete the invoice from the DB (if it's paid or all reminders have been sent). Who Can Benefit from This Workflow? Freelancers** Service Providers** Consultants & Coaches** Small Businesses** E-commerce or Custom Product Sellers** Requirements Jotform webhook setup, more info here QuickBooks Online credentials, more info here Email setup, update email nodes (Send reminder email & Send reminders sent summary), more info about Outlook setup here Create data table with the following columns: invoiceId (string) remainingAmount (number) currency (string) remindersSent (number) lastSentAt (date time) Update Add reminders config node so update the data table id and intervals in days (default is after 2 days, then after 3 days and finally after 5 days ) LLM model credentials

Who's it for Developers building AI-powered workflows who want to ensure their agents work reliably. If you need to validate AI outputs, test agent behavior systematically, or build maintainable automation, this template shows you how. What it does This subworkflow extracts structured meeting details (title, date, time, location, links, attendees) from natural language messages using an AI agent. It demonstrates production-ready patterns: Structured output validation**: JSON schema enforcement prevents malformed responses Error handling**: Graceful failures with full execution traceability Automated evaluation**: Test agent accuracy against expected outputs using Google Sheets Dual execution modes**: Normal extraction + evaluation/testing mode The AI resolves relative time ("tomorrow", "next Friday") using timezone context and handles incomplete data gracefully. How to set it up Connect OpenAI API credential to the AI agent node Copy the test data sheet: https://docs.google.com/spreadsheets/d/1U89nPsasM2WNv1D7gEYINhDwylyxYw7BOd_i8ipFC0M/edit?usp=sharing Update Google Sheet IDs in load_eval_data and record_eval_output nodes Test normal mode: Execute workflow "from trigger" Test evaluation mode: Execute workflow "from load_eval_data" Requirements OpenAI API key Google Sheets OAuth credential Why subworkflow architecture? Reusability: Wrap AI agents in subworkflows to call them from multiple parent workflows. Extract meetings from Slack, email, or webhooks—same agent, consistent results. Testability: This pattern enables isolated testing for each AI component. Set up evaluation datasets, run automated tests, and validate accuracy before deploying to production. You can't do this easily with inline agents. Maintainability: Update the agent logic once, and all parent workflows benefit. Error handling and validation are built-in, so failures are traceable with execution IDs. This framework includes: Dual-trigger pattern (normal + evaluation modes) Output validation that catches silent AI failures Error bubbling with execution metadata for debugging Evaluation framework with semantic/exact matching Proper routing that returns output to parent workflows Following this pattern for other agents To adapt this for any AI task (contact extraction, invoice processing, sentiment analysis, etc.): Replace extract_meeting_details with your AI agent (add tools, memory, etc. as needed) Update Structured Output Parser schema to match your data structure Modify evaluate_match prompt for your validation criteria Create test cases in Google Sheets with your inputs/expected outputs Adjust normalize_eval_data timezone/reference time if needed The validation, error handling, and evaluation infrastructure stays the same regardless of what your agent does.

Overview Tired of cookie-cutter “AI LinkedIn post generators”? This workflow goes beyond just text generation — it orchestrates the entire lifecycle of a LinkedIn post. From idea capture to deduplication, from GPT-powered drafting to automatic image generation and link storage, it creates ready-to-publish posts while keeping your content unique and audit-friendly. What does this workflow do? This workflow: Captures Ideas & Briefs – Inputs are logged in Google Sheets with audience, goals, and angles. Deduplicates Smartly – Avoids repeating hooks or ideas with fuzzy GPT-based dedupe + GSheet logs. Generates Posts – GPT (OpenAI) drafts sharp LinkedIn-ready posts based on your brief. Creates Images – Post hook + body is sent to an Image Gen model (DALL·E / SDXL) → PNG asset. Stores & Links – Final text + image uploaded to Google Drive with shareable links. Audit Trail – GSheets keeps full history: raw idea, draft, final post, assets, notes. Why is this useful? Most “AI post generators” just spit out text. This workflow builds a real publishing pipeline: 🔄 No duplicates → keeps posts fresh & original. 🖼 Images included → auto-generated visuals increase engagement on LinkedIn. 📊 Audit-ready → every post has a traceable log in Sheets. ⚡ Fast iteration → from half-baked thought → polished post in minutes. Tools used n8n (Orchestrator): Automates triggers, merges, retries, and Google connectors. OpenAI (LLM): Idea generation, drafting, fuzzy dedupe, and voice conformity. Google Sheets: Source of truth — stores ideas, dedupe logs, audit trail. Google Drive: Stores rendered images and shares links for publishing. Image Generation (DALL·E / SDXL): Creates header graphics from hook + body. Who is this for? 🧑‍💻 Product Managers / Founders who want to post consistently but don’t have time. 🎨 Creators who want to add unique visuals without hiring a designer. ⚙️ n8n Builders who want to see how AI + automation + storage can be stitched into one pipeline. Workflow Highlights ✅ Full content pipeline (ideas → images → final copy). ✅ GPT-based fuzzy dedupe to avoid repetition. ✅ Auto-generated images for higher engagement. ✅ Clean logs in Google Sheets for future reuse & audits. ✅ Ready-to-publish LinkedIn post in minutes.