# templates

name: Google Sheets send SMS with Twilio - Bulk SMS Automation Simplified!
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-09-17T13:57:22.232Z
updated_at: 2025-09-17T13:57:22.232Z
url: https://n8n.io/workflows/8684
scraped_at: 2025-12-24 13:02:43
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  Overview

  This workflow automates sending personalized SMS messages directly from a Google Sheet using Twilio. Simply update a row's status to "To send" and the workflow automatically sends the text message, then updates the status to "Success" or "Error" based on delivery results. Perfect for event reminders, bulk notifications, appointment confirmations, or any scenario where you need to send customized messages to multiple recipients.

  Key Features

  Simple trigger mechanism**: Change the status column to "To send" to queue messages
  Personalization support**: Use [First Name] and [Last Name] placeholders in message templates
  Automatic status tracking**: The workflow updates your spreadsheet with delivery results
  Error handling**: Failed deliveries are clearly marked, making it easy to identify issues like invalid phone numbers
  Runs every minute**: The workflow polls your sheet continuously when active

  Setup Instructions

  Step 1: Copy the Template Spreadsheet

  Make a copy of the Google Sheets template by going to File → Make a copy. You must use your own copy so the workflow has permission to update status values.

  Step 2: Connect Your Accounts

  Google Sheets: Add your Google account credentials to the 'Monitor Google Sheet for SMS Queue' trigger node
  Twilio: Sign up for a free Twilio account (trial works for testing). From your Twilio dashboard, get your Account SID, Auth Token, and Twilio phone number, then add these credentials to the 'Send SMS via Twilio' node

  Step 3: Configure the Workflow

  In the Config node, update:
  sheet_url: Paste the URL of your copied Google Sheet
  from_number: Enter your Twilio phone number (include country code, e.g., +1234567890)

  Step 4: Activate and Test

  Activate the workflow using the toggle in the top right corner. Add a row to your sheet with the required information (ID, First Name, Phone Number, Message Template) and set the Status to "To send". Within one minute, the workflow will process the message and update the status accordingly.

nodes_used:
  ["n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.twilio","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4368 characters]


name: Categorize SEO Keywords & Create Content Strategy with AI and Airtable
author_name: Gloria
author_username: gloria
categories: []
created_at: 2025-09-18T13:27:50.277Z
updated_at: 2025-09-18T13:27:50.277Z
url: https://n8n.io/workflows/8721
scraped_at: 2025-12-24 13:02:41
description:
  Premium n8n Workflow: SMART AI Keyword Categorization & Content Strategy

  This n8n workflow transforms raw keyword data into actionable content intelligence using advanced AI categorization and clustering. It creates a comprehensive content strategy with ready-to-use titles and descriptions for your blog posts.

  🚀 Features

  🧠 AI-Powered Categorization  
  Automatically sorts keywords into strategic buckets — Quick Wins, Authority Builders, Emerging Topics, Intent Signals, and Semantic Topics — for targeted content creation 🎯.  

  🔗 Semantic Clustering  
  Identifies meaningful relationships between keywords to create logically grouped content clusters 🕸️.  

  📑 Content Blueprint Generation  
  Creates compelling titles and descriptions for each keyword and cluster to streamline your content creation process 📝.  

  🌐 Hub & Spoke Strategy Builder  
  Develops a complete site architecture plan with main hub articles and supporting spoke content 🏗️.  

  ⚡ Airtable Integration  
  Organizes all outputs in a structured database for seamless integration with your content workflow.  

  🤖 n8n AI Agents  
  Leverages advanced AI capabilities to analyze keyword intent and potential without manual intervention.  

  👥 This Workflow is Perfect For:

  Content strategists 📊  
  SEO professionals 🔍  
  Content marketing teams 👥  
  Blog managers 💻  
  Digital publishers 📰  
  Website owners (E-Commerce) 🏢  
  Anyone using the SMART AI Keyword Research Workflow 🔄  

  Stop struggling with manual keyword grouping and transform your raw keyword data into a comprehensive content strategy.  
  👉 Get this premium workflow today!

  📝 What's Included?

  ⚙️ n8n Workflow Template  
  Ready-to-use workflow with AI-powered nodes for keyword analysis and categorization.  

  📊 Airtable Database Structure  
  Pre-configured tables for categorized keywords, content ideas, and hub-spoke relationships.  

  🧩 Keyword Categorization System  
  Automated logic to sort keywords by opportunity, competition, and relevance.  

  📋 Content Titles and Descriptions  
  AI prompts to create optimized titles and descriptions for each content piece.  

  🌐 Hub & Spoke Mapper  
  Logic to identify main topics and supporting content opportunities.  

  📚 Documentation  
  Step-by-step instructions for importing keyword data and running the workflow.  

  🏆 Why Choose This Workflow?

  ⏱️ Save Massive Time: Automate what would take days of manual analysis and planning.  
  📈 Improve Content ROI: Create content that targets the right keywords in the right way.  
  🔄 Seamless Integration: Works perfectly with the AI Keyword Research and Blog Writing workflows available on my profile.  
  🏗️ Build Site Authority: Create topically relevant content clusters that boost domain expertise.  
  🎯 Strategic Focus: Stop guessing which keywords to target and how to organize your content.  

  🛠️ How It Works

  1️⃣ Import the provided n8n workflow into your n8n instance 📥.  
  2️⃣ Connect to your Airtable base containing keyword research data ⚙️.  
  3️⃣ Configure the AI agents with your preferred content parameters 🤖.  
  4️⃣ Run the workflow to automatically categorize, cluster, and create content briefs 🔄.  
  5️⃣ Use the generated content strategy to guide your blogging or feed directly into the Multi-Agent Blog Writing System available on my profile 📝.  


  Additional detailed instructions are provided in the workflow.  

  🏁 What You Need to Get Started

  🔹 Access to n8n (self-hosted or cloud) ☁️  
  🔹 An Airtable account with keyword data (ideally from the AI Keyword Research Workflow on my profile) 📊  
  🔹 OpenAI API credentials for the AI categorization and title generation 🔑  
  🔹 Basic understanding of content strategy and n8n workflows 🧠  

  💡 You can also connect this workflow with my SEO Keyword Research Automation using DataForSEO and Airtable and my Multi-Agent SEO Optimized Blog Writing System with Hyperlinks for E-Commerce, both available on my profile, to build a fully automated, end-to-end SEO content machine.

nodes_used:
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workflow_json_compressed:
  [encoded data, 12036 characters]


name: Automated Daily Stock Market Report with Bright Data, GPT-4.1, Airtable/Gmail
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-09-18T14:02:05.882Z
updated_at: 2025-09-18T14:02:05.882Z
url: https://n8n.io/workflows/8724
scraped_at: 2025-12-24 13:02:41
description:
  📘 Workflow Documentation – Stock Market Daily Digest

  👋 Introduction
  Wake up to a clean, analyst-style stock digest in your inbox—top gainers/losers, a readable performance table, 3–5 insights, and upcoming events—no spreadsheets, no manual scraping, no copy-paste.  
  This article explains, step by step, how to build a robust, daily, end-to-end automation that collects market data (Bright Data), waits until scraping is done, aggregates results, asks an AI model (OpenAI) to draft a styled HTML email, logs everything to Airtable, and finally sends the report via Gmail.

  You’ll find a friendly but technical tour of every single node, so you can rebuild or adapt the same pipeline with confidence.

  🎯 Who is this workflow for?
  Investors & traders** who want a quick, readable daily summary.  
  Finance/Product teams** building data-driven alerts/digests.  
  Consultants & agencies** sending recurring client updates.  
  Automation builders** prototyping finance ops quickly.

  🧰 Tools you’ll need
  Bright Data** — dataset triggers & snapshots for reliable web data.  
  OpenAI (GPT)** — to generate a professional HTML digest.  
  Airtable** — store daily rows for history, filters, dashboards.  

  Example Airtable Table: Daily Stocks

  | Ticker | Company                  | Price   | Change % | Sentiment | Date                |
  |--------|--------------------------|---------|----------|-----------|---------------------|
  | AAPL   | Apple Inc.               | 225.80  | +1.4%    | 🟢 Positive | 2025-09-18 09:00    |
  | MSFT   | Microsoft Corporation    | 415.20  | -0.7%    | 🔴 Negative | 2025-09-18 09:00    |
  | NVDA   | NVIDIA Corporation       | 124.55  | +2.1%    | 🟢 Positive | 2025-09-18 09:00    |
  | TSLA   | Tesla Inc.               | 260.00  | -3.0%    | 🔴 Negative | 2025-09-18 09:00    |
  | META   | Meta Platforms Inc.      | 310.45  | +0.5%    | 🟡 Neutral  | 2025-09-18 09:00    |



  Gmail** — deliver the final HTML email to stakeholders.  
  n8n** — the automation engine that orchestrates every step.

  &gt; Keep API keys in n8n Credentials (never hard-code secrets).

  🗺️ Architecture at a glance
  Schedule fires daily  
  Seed list of tickers  
  Split into one item per stock  
  Prepare keyword for scraping  
  Launch Bright Data job  
  Poll progress with a wait-loop  
  Fetch snapshot data  
  Aggregate for the AI  
  Generate HTML summary (GPT)  
  Save rows to Airtable  
  Send email via Gmail

  ⚙️ Step-by-step — Every node explained

  ⏰ Daily Run Trigger (Schedule Trigger)

  Purpose
  Start the automation at a precise time each day so nobody needs to push a button.

  Parameters (English)
  Trigger Type**: Time Interval or Cron  
  Every X**: 1 Day (or your preferred cadence)  
  Timezone**: UTC (or your own)  
  Start Time**: optional (e.g., 09:00)

  📝 Set Stock List (Set Node – SAMPLE DATA)

  Purpose
  Define the universe of stocks to monitor. This acts as the seed data for scraping.

  Parameters
  Values to Set**: Fixed JSON (array of objects)  
  Keep Only Set**: true  
  Fields per item**: ticker, name, market_cap (you may add sector, isin, etc.)

  🔀 Split Stocks (Split Out)

  Purpose
  Turn the array into individual items so each ticker is processed independently (scraping, polling, results).

  Parameters
  Operation**: Split Out Items  
  Field to Split**: the array defined in the previous Set node

  🏷 Prepare Stock Keyword (Set Node)

  Purpose
  Create a keyword field (typically equal to ticker) for Bright Data discovery.

  Parameters
  Values to Set**: Add Field  
  Field Name**: keyword  
  Value**: use an expression referencing the current item’s ticker (e.g., ` {{ $json.ticker }} `)

  🕸 Bright Data Scraper (HTTP Request)

  Purpose
  Trigger the Bright Data dataset to start collecting information for the keyword. Returns a snapshot_id to poll later.

  Parameters
  Method**: POST  
  Endpoint**: https://api.brightdata.com/datasets/v1/trigger  
  Authentication**: Authorization: Bearer &lt;token&gt; (header)  
  Body Fields**:  
    dataset_id: your Bright Data dataset ID  
    discover_by: usually keyword  
    keyword: the value prepared above

  &gt; Add a retry/backoff policy on 429/5xx in node options.

  🔄 Check Scraper Progress (HTTP Request)

  Purpose
  Poll Bright Data to see whether the snapshot is running or ready.

  Parameters
  Method**: GET  
  Endpoint**: https://api.brightdata.com/datasets/v1/snapshots/{snapshot_id}  
  Authentication**: Authorization: Bearer &lt;token&gt;  
  Expected Output**: a status field (running, ready)

  ⏳ Wait for Data (Wait Node)

  Purpose
  Pause between progress checks to avoid rate limits and give Bright Data time to finish.

  Parameters
  Mode**: Wait a fixed amount of time  
  Time**: e.g., 30 seconds (tune to your dataset size)

  🔀 Scraper Status Switch (Switch Node)

  Purpose
  Route logic based on the polled status.

  Parameters
  Value to Check**: status  
  Rules**:  
    Equals running → go to Wait for Data (then re-check)  
    Equals ready → proceed to Fetch Scraper Results

  &gt; Loop pattern: Check → Wait → Check, until ready.

  📥 Fetch Scraper Results (HTTP Request)

  Purpose
  Download the completed snapshot data once Bright Data marks it ready.

  Parameters
  Method**: GET  
  Endpoint**: https://api.brightdata.com/datasets/v1/snapshots/{snapshot_id}/data  
  Authentication**: Authorization: Bearer &lt;token&gt;  
  Query**: format=json  
  Output**: array of rows per ticker (price, change %, any fields your dataset yields)

  &gt; Normalize fields with a Set/Code node if needed.

  📊 Aggregate Stock Data (Aggregate Node)

  Purpose
  Combine all individual items into one consolidated object so the AI can analyze the entire market snapshot.

  Parameters
  Mode**: Aggregate (merge to a single item)  
  Fields to Include**: ticker, name, price, change, sentiment (plus any extra fields captured)  
  Output**: one JSON item containing an array/map of the day’s stocks

  🤖 Generate Daily Summary (AI Node – OpenAI)

  Purpose
  Ask the model to convert raw data into a styled HTML email: headline, top movers, table, insights, and (optional) upcoming events.

  Parameters
  Model**: gpt-4.1 
  Input**: the aggregated JSON from the previous node  
  Prompt guidelines**:  
    Output HTML only with inline styles (email-safe)  
    Include a table (Ticker, Company, % Change with ↑/↓ & color, Market Cap, Sentiment icon)  
    Highlight top 2 gainers & 2 losers with short reasoning if present  
    Provide 3–5 insights (sector rotation, volatility, outliers)  
    Add upcoming events when available (earnings, launches, macro)  
    Footer: “Generated automatically by your AI-powered stock monitor”  
  Output field**: confirm the exact property that contains the HTML (e.g., output, message, text)

  🗂 Save to Airtable (Airtable – Create Record)

  Purpose
  Log each item (or the roll-up) to Airtable for history, filtering, and dashboards.

  Parameters
  Operation**: Create Record  
  Base ID**: from your Airtable URL  
  Table**: e.g., Daily Stocks  
  Field Mapping**:  
    Ticker ← ` {{ $json.ticker }} `  
    Company ← ` {{ $json.name }} `  
    Price ← ` {{ $json.price }} `  
    Change % ← ` {{ $json.change }} `  
    Sentiment ← ` {{ $json.sentiment }} `  
    Date ← ` {{ $now.toISO() }} `

  &gt; Use a Single-Select for Sentiment (🟢 / 🟡 / 🔴) to build clean Airtable views.

  📧 Send Report via Gmail (Gmail Node)

  Purpose
  Deliver the AI-generated HTML digest to your recipients.

  Parameters
  Operation**: Send Email  
  Send To**: one or more recipients (e.g., investor@domain.com)  
  Subject**: Daily Stock Market Digest – {{ $now.format("yyyy-MM-dd") }}  
  Message (HTML)**: reference the AI node’s HTML property (e.g., ` {{ $('Generate Daily Summary').first().json.output }} `)  
  Options: set **Append Attribution to false (keep the email clean)

  &gt; Test in Gmail, Outlook, and mobile to validate inline CSS.

  🧪 Error handling & reliability tips
  Backoff on Bright Data* — If scraping many tickers, increase *Wait** or batch requests.  
  Guard against empty results** — If a snapshot returns 0 rows, branch to a fallback email (“No data today”).  
  AI guardrails** — Enforce “HTML-only” and skip missing sections gracefully.  
  Airtable normalization** — Strip %, cast numbers to float before insert.  
  Observability* — Add a final Slack/Email *On Fail** node with run ID and error message.

  🧩 Customization ideas
  Sector deep-dives**: add sector fields and a second AI paragraph on sector rotation.  
  CSV attachment**: generate & attach a CSV for power users.  
  Multiple lists**: run parallel branches for Tech, Healthcare, or regions.  
  Other asset classes**: Crypto, ETFs, Indices, FX.  
  Audience targeting**: different “To” lists and slightly different prompts per audience.

  ✅ Why this workflow is powerful
  Hands-off** — the report simply shows up every day.  
  Analyst-grade** — clean HTML, top movers, tidy table, actionable insights.  
  Auditable** — rows archived in Airtable for history and dashboards.  
  Composable** — swap scrapers, LLMs, storage, or email service.  
  Scalable** — start with 10 tickers, grow to many lists using the same loop.

  For advanced no-code & AI projects, see 0vni – Agence automatisation. 

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workflow_json_compressed:
  [encoded data, 10804 characters]


name: Streamline Document Signatures in Telegram with SignNow
author_name: Lenouar
author_username: aureusr
categories: []
created_at: 2025-09-19T10:58:42.493Z
updated_at: 2025-09-19T10:58:42.493Z
url: https://n8n.io/workflows/8749
scraped_at: 2025-12-24 13:02:46
description:
  Digital Signature Agent with SignNow + Telegram  

  Solve the #1 Bottleneck in Closing Deals  
  Tired of chasing clients for signed NDAs and contracts? This workflow eliminates the email back-and-forth and lets your clients sign instantly inside Telegram.  

  Faster deal flow** → Cut signing time from days to minutes.  
  No lost opportunities** → Eliminate delays that kill urgency in sales.  
  Professional & secure** → Legally binding signatures, fully trackable.  
  Hands-off automation** → Bot collects details, generates the signing link, and confirms when done.  

  Who Benefits Most  
  ✅ Brokers & consultants: Collect NDAs instantly before sharing sensitive offers.  
  ✅ Agencies & freelancers: Sign contracts with clients on the spot.  
  ✅ SMEs & startups: Speed up sales and onboarding with zero admin overhead.  
  ✅ HR teams: Seamlessly manage employee forms, waivers, and agreements.  

  What It Does for You  
  Triggered by a Telegram chat → no more clunky portals or attachments.  
  Collects name, email, and company in-chat.  
  Copies your SignNow template (NDA, contract, waiver, or any doc).  
  Prefills smart fields automatically.  
  Generates a one-click signing link.  
  Sends it via Telegram as a button (“🖊 Open NDA”).  
  Tracks completion with SignNow webhook → you get notified instantly.  

  Why This Template Instead of Building It Yourself  
  Save 20–30 hours** of research, API testing, and debugging.  
  Avoid headaches connecting SignNow’s API with Telegram.  
  Get a production-ready workflow you can deploy in minutes.  
  Customize easily → swap the NDA for any legal doc, add email/CRM integrations, or adjust the bot’s tone.  
  Peace of mind**: we’ve already tested it end-to-end (template copy, smart fields, webhook confirmation).  

  Requirements  
  n8n (self-hosted or cloud).  
  Active SignNow developer account with API key.  
  A Telegram bot via @BotFather.  
  HTTPS domain/server for webhook callbacks.  

  Bottom Line 💡  
  This workflow pays for itself the first time it helps you close a deal faster.  
  Instead of waiting 48 hours for someone to check email, they sign within 5 minutes in Telegram—so you can focus on closing, not chasing.  

  👉 By purchasing, you get:  
  Full workflow JSON file.  
  Email delivery with setup guidelines.  
  Access to a step-by-step Loom walkthrough video.  
  Contact Us** via services@quantumti.ae.  

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  ["n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.noOp","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegramTool"]

workflow_json_compressed:
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name: Manage WooCommerce Store with Natural Language using GPT-4.1 and MCP Server
author_name: Rohit Dabra
author_username: rohitdabra
categories: []
created_at: 2025-09-20T06:30:22.933Z
updated_at: 2025-09-20T06:30:22.933Z
url: https://n8n.io/workflows/8769
scraped_at: 2025-12-24 13:02:43
description:
  WooCommerce AI Agent — n8n Workflow (Overview)

  Description: Turn your WooCommerce store into a conversational AI assistant — create products, place orders, run reports and manage coupons using natural language via n8n + an MCP Server.

  Key features
  Natural-language commands mapped to WooCommerce actions (products, orders, reports, coupons).  
  Structured JSON outputs + lightweight mapping to avoid schema errors.  
  Calls routed through your MCP Server for secure, auditable tool execution.  
  Minimal user prompts — agent auto-fetches context and asks only when necessary.  
  Extensible: add new tools or customize prompts/mappings easily.

  Demo of the workflow:

  Youtube Video

  🚀 Setup Guide: WooCommerce + AI Agent Workflow in n8n

  1. Prerequisites
  Running n8n instance  
  WooCommerce store with REST API keys  
  OpenAI API key  
  MCP server (production URL)  

  2. Import Workflow
  Open n8n dashboard  
  Go to Workflows → Import  
  Upload/paste the workflow JSON  
  Save as WooCommerce AI Agent  

  3. Configure Credentials

  OpenAI
  Create new credential → OpenAI API  
  Add your API key → Save & test  

  WooCommerce
  Create new credential → WooCommerce API  
  Enter Base URL, Consumer Key & Secret → Save & test  

  MCP Client
  In MCP Client node, set Server URL to your MCP server production URL  
  Add authentication if required  

  4. Test Workflow
  Open workflow in editor  
  Run a sample request (e.g., create a test product)  
  Verify product appears in WooCommerce  

  5. Activate Workflow
  Once tested, click Activate in n8n  
  Workflow is now live 🎉  

  6. Troubleshooting
  Schema errors** → Ensure fields match WooCommerce node requirements  
  Connection issues** → Re-check credentials and MCP URL  

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: 🤖 Automate Lead Qualification & Multi-Channel Follow-up with AI (BANT)
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2025-09-20T09:54:57.584Z
updated_at: 2025-09-20T09:54:57.584Z
url: https://n8n.io/workflows/8773
scraped_at: 2025-12-24 13:02:41
description:
  How it works

  This workflow is your automated sales assistant, designed to intelligently qualify incoming leads and route them to the most appropriate follow-up channel. It uses the powerful BANT (Budget, Authority, Need, Timing) framework, powered by Google Gemini AI, to score leads as 'hot', 'mid', or 'cold', ensuring your sales team focuses on the most promising opportunities.

  Here's a step-by-step breakdown:

  Lead Capture: A public Form Trigger collects essential lead information, including their name, email, what they want to build, their budget, desired start time, and job role. These questions are specifically designed to gather BANT data.
  AI Lead Scoring: The collected data is sent to Google Gemini. A detailed prompt instructs the AI to act as a Lead Scoring Expert, evaluating each BANT component individually and then assigning an overall 'hot', 'mid', or 'cold' score based on predefined criteria.
  Intelligent Routing: A Switch node acts as the central router. Based on the AI's 'hot', 'mid', or 'cold' score, the workflow directs the lead down one of three distinct follow-up paths.
  Hot Leads (Calendar Booking): For highly qualified 'hot' leads, the workflow immediately redirects them to your calendar booking link, making it easy for them to schedule a direct conversation.
  Mid Leads (WhatsApp Engagement): For 'mid' priority leads, Google Gemini generates a personalized, pre-filled WhatsApp message summarizing their inquiry. The lead is then redirected to a WhatsApp chat with your sales team, allowing for quick, informal engagement.
  Cold Leads (Nurturing Email): For 'cold' leads who might be in an early research phase, Google Gemini crafts a helpful, non-salesy follow-up email. This email provides valuable resources (like templates or community links) and is sent via Gmail, keeping them engaged without pressure.

  Set up steps

  Setup time: ~10-15 minutes

  This workflow requires connecting your Google AI and Gmail accounts, and customizing several nodes to fit your sales process.

  Get Your Google AI API Key:
      Visit Google AI Studio at aistudio.google.com/app/apikey.
      Click "Create API key in new project" and copy the key.
      In the workflow, select the Score Lead node. Click the Credential dropdown and select + Create New Credential.
      Paste your key into the API Key field and Save.
      Repeat this for the Write Placeholder WA Message and Write Follow up Email nodes, selecting the credential you just created.

  Connect Your Gmail Account:
      Select the Send Follow up Email with Gmail node.
      Click the Credential dropdown and select + Create New Credential to connect your Google account. Follow the prompts to grant n8n access.

  Customize Lead Scoring Criteria:
      Go to the Score Lead node.
      In the Text parameter, carefully review and adapt the BANT Criteria Mapping and Scoring Logic to align with your specific sales process and ideal customer profile. This is crucial for accurate lead qualification.

  Configure Follow-up Channels:
      Hot Leads: Select the Calendar Booking Link node. Update the redirectUrl parameter with your personal or team's calendar booking link (e.g., Calendly, Chili Piper).
      Mid Leads: Select the Phone Number node. Set the whatsapp_phone value to your company's WhatsApp phone number (e.g., +15551234567). You can also customize the pre-filled WhatsApp message by adjusting the prompt in the Write Placeholder WA Message node.
      Cold Leads: Select the Redirect to Website node. Update the redirectUrl parameter to your company's main website or a relevant resource page (e.g., a free templates page, a blog post). You can also customize the email content and resources shared by adjusting the prompt in the Write Follow up Email node.

  Activate and Test:
      Activate the workflow using the toggle at the top right.
      Go to the Lead Contact Form node and click the "Open Form URL" button.
      Submit several test applications with different answers (e.g., one "hot" lead, one "mid", one "cold") to ensure the AI scores them correctly and they are routed to the appropriate follow-up action.

  Start qualifying and engaging your leads more effectively!

nodes_used:
  ["n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.form","n8n-nodes-base.form","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.form","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger"]

workflow_json_compressed:
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name: 🤖 Self Improving Email AI Support with Human-in-the-Loop
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2025-09-20T15:06:17.309Z
updated_at: 2025-09-20T15:06:17.309Z
url: https://n8n.io/workflows/8779
scraped_at: 2025-12-24 13:02:42
description:
  How it works

  This workflow creates a sophisticated, self-improving customer support system that automatically handles incoming emails. It's designed to answer common questions using an AI-powered knowledge base and, crucially, to learn from human experts when new or complex questions arise, continuously expanding its capabilities.

  Think of it like having an AI assistant with a smart memory and a human mentor. Here's the step-by-step process:

  New Email Received: The workflow is triggered whenever a new email arrives in your designated support inbox (via Gmail).
  Classify Request: An AI model (Google Gemini 2.5 Flash Lite) first classifies the incoming email to ensure it's a genuine support request, filtering out irrelevant messages.
  Retrieve Knowledge Base: The workflow fetches all existing Question and Answer pairs from your dedicated Google Sheet knowledge base.
  AI Answer Attempt: A powerful AI model (Google Gemini 2.5 Pro) analyzes the customer's email against the entire knowledge base. It attempts to find a highly relevant answer and drafts a complete HTML email response if successful.
  Decision Point: An IF node checks if the AI found a confident answer.
      If Answer Found: The AI-generated HTML response is immediately sent back to the customer via Gmail.
      If No Answer Found (Human-in-the-Loop):
          Escalate to Human: The customer's summarized question and original email are forwarded to a human expert (you or your team) via Gmail, requesting their assistance.
          Human Reply & AI Learning: The workflow waits for the human expert's reply. Once received, another AI model (Google Gemini 2.5 Flash) processes both the original customer question and the expert's reply to distill them into a new, generic, and reusable Question/Answer pair.
          Update Knowledge Base: This newly created Q&A pair is then automatically added as a new row to your Google Sheet knowledge base, ensuring the system can answer similar questions automatically in the future.

  Set up steps

  Setup time: ~10-15 minutes

  This workflow requires connecting your Gmail and Google Sheets accounts, and obtaining a Google AI API key. Follow these steps carefully:

  Connect Your Gmail Account:
      Select the On New Email Received node.
      Click the Credential dropdown and select + Create New Credential to connect your Gmail account. Grant the necessary permissions.
      Repeat this for the Send AI Answer and Ask Human for Help nodes, selecting the credential you just created.

  Connect Your Google Sheets Account:
      Select the Get Knowledge Base node.
      Click the Credential dropdown and select + Create New Credential to connect your Google account. Grant the necessary permissions.
      Repeat this for the Add to Knowledge Base node, selecting the credential you just created.

  Set up Your Google Sheet Knowledge Base:
      Create a new Google Sheet in your Google Drive.
      Rename the first sheet (tab) to QA Database.
      In the first row of QA Database, add two column headers: Question (in cell A1) and Answer (in cell B1).
      Go back to the Get Knowledge Base node in n8n. In the Document ID field, select your newly created Google Sheet. Do the same for the Add to Knowledge Base node.

  Get Your Google AI API Key (for Gemini Models):
      Visit Google AI Studio at aistudio.google.com/app/apikey.
      Click "Create API key in new project" and copy the key.
      In the workflow, go to the Google Gemini 2.5 Pro node, click the Credential dropdown, and select + Create New Credential.
      Paste your key into the API Key field and Save.
      Repeat this for the Google Gemini 2.5 Flash Lite and Google Gemini 2.5 Flash nodes, selecting the credential you just created.

  Configure Human Expert Email:
      Select the Ask Human for Help node.
      In the Send To field, replace the placeholder email address with the actual email address of your human expert (e.g., your own email or a team support email).

  Activate the Workflow:
      Once all credentials and configurations are set, activate the workflow using the toggle switch at the top right of your n8n canvas.

  Start Learning!
  Send a test email to the Gmail account connected to the On New Email Received node. Observe how the AI responds, or how it escalates to your expert email and then learns from the reply. Check your Google Sheet to see new Q&A pairs being added!

nodes_used:
  ["n8n-nodes-base.if","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.gmailTrigger","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.noOp"]

workflow_json_compressed:
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name: 🛠️ Re-Access Binary Data from Any Previous Node
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2025-09-20T15:28:27.172Z
updated_at: 2025-09-20T15:28:27.172Z
url: https://n8n.io/workflows/8780
scraped_at: 2025-12-24 13:02:42
description:
  How it works

  Ever had binary data (like images, PDFs, or files) disappear in your n8n workflow after an intermediate node processed it? This workflow provides a powerful solution by demonstrating how to re-access and re-attach binary data from any previous node, even if it was dropped along the way. Think of it like having a reliable backup copy of your file always available, no matter what happens to the original as it moves through your workflow.

  Here's how this template works step-by-step:

  Initial Binary Fetch: The workflow starts by fetching a binary image (the n8n logo) from a URL using an HTTP Request node. This is our original binary data.
  Simulated Data Loss: A Set node then processes this data. Crucially, by default, Set nodes (and many others) do not pass binary data to subsequent nodes. This step intentionally simulates a common scenario where your binary data might seem to "disappear" from the workflow's output.
  Re-Access and Re-Attach: The core of the solution is a Code node. It uses a specific n8n expression ($(nodeName).item) to reach back to the original node that produced the binary data (Get n8n Logo (Binary)). It then retrieves that binary data and uses this.helpers.prepareBinaryData() to correctly re-attach it to the current item, making it available for all subsequent nodes.

  Set up steps

  Setup time: 0 minutes!

  This is a self-contained tutorial workflow, so no external accounts or credentials are required.

  Simply click the "Execute Workflow" button to run it.
  Observe the output of the Re-Access Binary Data from Previous Node to see the binary data successfully re-attached.
  Important for Customization: If you adapt this technique to your own workflows, remember to update the previousNodeName variable within the Re-Access Binary Data from Previous Node (Code node) to match the exact name of the node that originally produced the binary data you wish to retrieve.

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Create Fact-Based Articles from Knowledge Sources with Lookio and OpenAI GPT
author_name: Guillaume Duvernay
author_username: duv
categories: []
created_at: 2025-09-20T17:13:30.944Z
updated_at: 2025-09-20T17:13:30.944Z
url: https://n8n.io/workflows/8782
scraped_at: 2025-12-24 13:02:45
description:
  Move beyond generic AI-generated content and create articles that are high-quality, factually reliable, and aligned with your unique expertise.
  This template orchestrates a sophisticated "research-first" content creation process. Instead of simply asking an AI to write an article from scratch, it first uses an AI planner to break your topic down into logical sub-questions.

  It then queries a Lookio assistant—which you've connected to your own trusted knowledge base of uploaded documents—to build a comprehensive research brief. Only then is this fact-checked brief handed to a powerful AI writer to compose the final article, complete with source links. This is the ultimate workflow for scaling expert-level content creation.

  Who is this for?

  Content marketers & SEO specialists:** Scale the creation of authoritative, expert-level blog posts that are grounded in factual, source-based information.
  Technical writers & subject matter experts:** Transform your complex internal documentation into accessible public-facing articles, tutorials, and guides.
  Marketing agencies:** Quickly generate high-quality, well-researched drafts for clients by connecting the workflow to their provided brand and product materials.

  What problem does this solve?

  Reduces AI "hallucinations":** By grounding the entire writing process in your own trusted knowledge base, the AI generates content based on facts you provide, not on potentially incorrect information from its general training data.
  Ensures comprehensive topic coverage:** The initial AI-powered "topic breakdown" step acts like an expert outliner, ensuring the final article is well-structured and covers all key sub-topics.
  Automates source citation:** The workflow is designed to preserve and integrate source URLs from your knowledge base directly into the final article as hyperlinks, boosting credibility and saving you manual effort.
  Scales expert content creation:** It effectively mimics the workflow of a human expert (outline, research, consolidate, write) but in an automated, scalable, and incredibly fast way.

  How it works

  This workflow follows a sophisticated, multi-step process to ensure the highest quality output:

  Decomposition: You provide an article title and guidelines via the built-in form. An initial AI call then acts as a "planner," breaking down the main topic into an array of 5-8 logical sub-questions.
  Fact-based research (RAG): The workflow loops through each of these sub-questions and queries your Lookio assistant. This assistant, which you have pre-configured by uploading your own documents, finds the relevant information and source links for each point.
  Consolidation: All the retrieved question-and-answer pairs are compiled into a single, comprehensive research brief.
  Final article generation: This complete, fact-checked brief is handed to a final, powerful AI writer (e.g., GPT-4o). Its instructions are clear: write a high-quality article using only the provided information and integrate the source links as hyperlinks where appropriate.

  Building your own RAG pipeline VS using Lookio or alternative tools

  Building a RAG system natively within n8n offers deep customization, but it requires managing a toolchain for data processing, text chunking, and retrieval optimization. 

  An alternative is to use a managed service like Lookio, which provides RAG functionality through an API. This approach abstracts the backend infrastructure for document ingestion and querying, trading the granular control of a native build for a reduction in development and maintenance tasks.

  Implementing the template

  1. Set up your Lookio assistant (Prerequisite):
  Lookio is a platform for building intelligent assistants that leverage your organization's documents as a dedicated knowledge base.

  First, sign up at Lookio. You'll get 50 free credits to get started.
  Upload the documents you want to use as your knowledge base.
  Create a new assistant and then generate an API key.
  Copy your Assistant ID and your API Key for the next step.

  2. Configure the workflow:
  Connect your AI provider (e.g., OpenAI) credentials to the two Language Model nodes.
  In the Query Lookio Assistant (HTTP Request) node, paste your Assistant ID in the body and add your Lookio API Key for authentication (we recommend using a Bearer Token credential).

  3. Activate the workflow:
  Toggle the workflow to "Active" and use the built-in form to generate your first fact-checked article!

  Taking it further

  Automate publishing:* Connect the final *Article result* node to a *Webflow* or *WordPress** node to automatically create a draft post in your CMS.
  Generate content in bulk:* Replace the *Form Trigger* with an *Airtable* or *Google Sheet** trigger to automatically generate a whole batch of articles from your content calendar.
  Customize the writing style:* Tweak the system prompt in the final *New content - Generate the AI output** node to match your brand's specific tone of voice, add SEO keywords, or include specific calls-to-action.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.formTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest"]

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name: Build an Intelligent Q&A Bot with Lookio Knowledge Base and GPT
author_name: Guillaume Duvernay
author_username: duv
categories: []
created_at: 2025-09-20T19:53:32.637Z
updated_at: 2025-09-20T19:53:32.637Z
url: https://n8n.io/workflows/8787
scraped_at: 2025-12-24 13:02:45
description:
  Build a powerful AI chatbot that provides precise answers from your own company's knowledge base. This template provides a smart AI agent that connects to Lookio, a platform where you can easily upload your documents (from Notion, Jira, Slack, etc.) to create a dedicated knowledge source.

  What makes this agent "smart" is its efficiency. It's configured to handle simple greetings and small talk on its own, only using its powerful (and paid) knowledge retrieval tool when a user asks a genuine question. This cost-saving logic makes it perfect for building production-ready internal helpdesks, customer support bots, or any application where you need accurate, source-based answers.

  Who is this for?

  Customer support teams:** Build internal bots that help agents find answers instantly from your support documentation and knowledge bases.
  Product & engineering teams:** Create a chatbot that can answer technical questions based on your product documentation or internal wikis.
  HR departments:** Deploy an internal assistant that can answer employee questions based on company handbooks, policies, and procedures.
  Any business with a knowledge base:** Provide an interactive, conversational way for employees or customers to access information locked away in your documents.

  What problem does this solve?

  Provides accurate, grounded answers:** Ensures the AI agent's responses are based on your trusted, private documents, not the open internet, which prevents factual errors and "hallucinations."
  Makes your knowledge accessible:** Transforms your static documents and knowledge bases into an interactive, 24/7 conversational resource.
  Optimizes for cost and efficiency:** The agent is intelligent enough to handle simple small talk without making unnecessary API calls to your knowledge base, saving you credits and money.
  Simplifies RAG setup:** Provides a ready-to-use template for a common RAG (Retrieval-Augmented Generation) pattern, with the complexities of document management and retrieval handled by the Lookio platform.

  How it works

  First, build your knowledge base in Lookio: The process starts on the Lookio platform. You upload your documents (from Notion, Jira, PDFs, etc.) and create an "assistant" which becomes your secure, queryable knowledge base.
  A user asks a question: The n8n workflow begins when a user sends a message via the Chat Trigger.
  The agent makes a decision: The AI Knowledge Agent, guided by its system prompt, analyzes the user's message. If it's a simple greeting like "hi," it will respond directly. If it's a substantive question that requires specific knowledge, it decides to use its "Query knowledge base" tool.
  Query the Lookio knowledge base: The agent passes the user's question to the HTTP Request Tool. This tool securely calls the Lookio API with your specific Assistant ID and API key.
  Deliver the fact-based answer: Lookio searches your documents, synthesizes a precise answer, and sends it back to the workflow. The n8n agent then presents this answer to the user in the chat interface.

  Architectural Approaches to RAG in n8n with Lookio

  From a workflow perspective, integrating RAG natively in n8n involves orchestrating multiple nodes for data handling, embedding, and vector searches. This method provides high visibility and control over each step. 

  An alternative architectural pattern is to use an external RAG service like Lookio, which consolidates these steps into a single HTTP Request node. This simplifies the workflow's structure by abstracting the multi-stage RAG process into one API endpoint.

  Setup

  Set up your Lookio assistant (Prerequisite): First, go to Lookio, sign up (you get 50 free credits), create an assistant with your documents, and from your settings, copy your API Key and Assistant ID.
  Configure the Lookio tool: In the Query knowledge base (HTTP Request Tool) node:
      Replace the &lt;your-assistant-id&gt; placeholder with your actual Assistant ID.
      Replace the &lt;your-lookio-api-key&gt; placeholder with your actual API Key.
  Connect your AI model: In the OpenAI Chat Model node, connect your AI provider credentials.
  Activate the workflow. Your smart knowledge base agent is now live and ready to chat!

  Taking it further

  Adjust retrieval quality:* In the *Query knowledge base** node, you can change the query_mode from flash (fastest) to deep for higher quality but slightly slower answers, depending on your needs.
  Add more tools:** Enhance your agent by giving it other tools, like a web search for when the internal knowledge base doesn't have an answer, or a calculator for performing computations.
  Deploy it anywhere:* Swap the *Chat Trigger* for a *Slack* or *Discord** trigger to deploy your agent right where your team works.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Jira Project Management Automation with Google Gemini & MCP Server
author_name: Rohit Dabra
author_username: rohitdabra
categories: []
created_at: 2025-09-22T16:26:50.376Z
updated_at: 2025-09-22T16:26:50.376Z
url: https://n8n.io/workflows/8838
scraped_at: 2025-12-24 13:02:45
description:
  Jira MCP Server Integration with n8n

  Overview

  Transform your Jira project management with the power of AI and automation! This n8n workflow template demonstrates how to create a seamless integration between chat interfaces, AI processing, and Jira Software using MCP (Model Context Protocol) server architecture.

  What This Workflow Does

  Chat-Driven Automation**: Trigger Jira operations through simple chat messages
  AI-Powered Issue Creation**: Automatically generate detailed Jira issues with descriptions and acceptance criteria
  Complete Jira Management**: Get issue status, changelogs, comments, and perform full CRUD operations
  Memory Integration**: Maintain context across conversations for smarter automations
  Zero Manual Entry**: Eliminate repetitive data entry and human errors

  Key Features

  ✅ Natural Language Processing: Use Google Gemini to understand and process chat requests  
  ✅ MCP Server Integration: Secure, efficient communication with Jira APIs  
  ✅ Comprehensive Jira Operations: Create, read, update, delete issues and comments  
  ✅ Smart Memory: Context-aware conversations for better automation  
  ✅ Multi-Action Workflow: Handle multiple Jira operations from a single trigger  

  Demo Video

  🎥 Watch the Complete Demo: Automate Jira Issue Creation with n8n & AI | MCP Server Integration

  Prerequisites

  Before setting up this workflow, ensure you have:

  n8n instance** (cloud or self-hosted)
  Jira Software** account with appropriate permissions
  Google Gemini API** credentials
  MCP Server** configured and accessible
  Basic understanding of n8n workflows

  Setup Guide

  Step 1: Import the Workflow

  Copy the workflow JSON from this template
  In your n8n instance, click Import &gt; From Text
  Paste the JSON and click Import

  Step 2: Configure Google Gemini

  Open the Google Gemini Chat Model node
  Add your Google Gemini API credentials
  Configure the model parameters:
     Model: gemini-pro (recommended)
     Temperature: 0.7 for balanced creativity
     Max tokens: As per your requirements

  Step 3: Set Up MCP Server Connection

  Configure the MCP Client node:
     Server URL: Your MCP server endpoint
     Authentication: Add required credentials
     Timeout: Set appropriate timeout values

  Ensure your MCP server supports Jira operations:
     Issue creation and retrieval
     Comment management
     Status updates
     Changelog access

  Step 4: Configure Jira Integration

  Set up Jira credentials in n8n:
     Go to Credentials &gt; Add Credential
     Select Jira Software API
     Add your Jira instance URL, email, and API token

  Configure each Jira node:
     Get Issue Status: Set project key and filters
     Create Issue: Define issue type and required fields
     Manage Comments: Set permissions and content rules

  Step 5: Memory Configuration

  Configure the Simple Memory node:
     Set memory key for conversation context
     Define memory retention duration
     Configure memory scope (user/session level)

  Step 6: Chat Trigger Setup

  Configure the When Chat Message Received trigger:
     Set up webhook URL or chat platform integration
     Define message filters if needed
     Test the trigger with sample messages

  Usage Examples

  Creating a Jira Issue

  Chat Input:
  Can you create an issue in Jira for Login Page with detailed description and acceptance criteria?

  Expected Output:
  New Jira issue created with structured description
  Automatically generated acceptance criteria
  Proper labeling and categorization

  Getting Issue Status

  Chat Input:
  What's the status of issue PROJ-123?

  Expected Output:
  Current issue status
  Last updated information
  Assigned user details

  Managing Comments

  Chat Input:
  Add a comment to issue PROJ-123: "Ready for testing in staging environment"

  Expected Output:
  Comment added to specified issue
  Notification sent to relevant team members

  Customization Options

  Extending Jira Operations

  Add more Jira operations (transitions, watchers, attachments)
  Implement custom field handling
  Create multi-project workflows

  AI Enhancement

  Fine-tune Gemini prompts for better issue descriptions
  Add custom validation rules
  Implement approval workflows

  Integration Expansion

  Connect to Slack, Discord, or Teams
  Add email notifications
  Integrate with time tracking tools

  Troubleshooting

  Common Issues

  MCP Server Connection Failed
  Verify server URL and credentials
  Check network connectivity
  Ensure MCP server is running and accessible

  Jira API Errors
  Validate Jira credentials and permissions
  Check project access rights
  Verify issue type and field configurations

  AI Response Issues
  Review Gemini API quotas and limits
  Adjust prompt engineering for better results
  Check model parameters and settings

  Performance Tips

  Optimize memory usage for long conversations
  Implement rate limiting for API calls
  Use error handling and retry mechanisms
  Monitor workflow execution times

  Best Practices

  Security: Store all credentials securely using n8n's credential system
  Testing: Test each node individually before running the complete workflow
  Monitoring: Set up alerts for workflow failures and API limits
  Documentation: Keep track of custom configurations and modifications
  Backup: Regular backup of workflow configurations and credentials

  Happy Automating! 🚀

  This workflow template is designed to boost productivity and eliminate manual Jira management tasks. Customize it according to your team's specific needs and processes.

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.stickyNote"]

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name: Transform Product Photos into Studio-Quality Visuals with Nano Banana & Telegram
author_name: SuS
author_username: sulieman-said
categories: []
created_at: 2025-09-22T22:53:36.260Z
updated_at: 2025-09-22T22:53:36.260Z
url: https://n8n.io/workflows/8843
scraped_at: 2025-12-24 13:02:44
description:
  Turn a single product photo into scroll-stopping, studio-quality visuals in minutes.  
  Perfect for Shopify / WooCommerce / Amazon / Etsy sellers, agencies, and UGC creators who want consistent, on-brand images without costly photoshoots or endless editing.

  Why you’ll love it
  Sell more, faster:** Professional, consistent visuals boost trust and conversions.
  Save time & money:** No studio, no photographer, no retouching marathon.
  Multiple looks instantly:* Get up to *3 variants** per product (great for A/B testing).
  On-brand results:* Subtle *brand color grading** for a polished, consistent look.
  Zero friction delivery:* Images are sent straight to your *Telegram** for instant review.

  Perfect for
  D2C brands, marketplace sellers, and dropshippers  
  Agencies producing UGC for clients  
  Creators who need ad-ready product images today  
  Teams that want reliable results without learning complex tools

  What’s included
  n8n workflow (template)** that automates: form input → prompt creation → image rendering → delivery
  Built-in guidance** (sticky notes) to keep you moving fast
  Prompt logic** tuned for product realism (camera, lighting, materials, subtle brand grading)

  Step-by-Step — How it works

  Open the form  
     Enter:
     Product Name & Product Description (material, finish, color, USP)  
     Product Image (required)  
     Optional: Background Image, or describe it (Background Type + Background Description)  
     Choose Camera Angle, Lighting Style, Aperture (Depth of Field)  
     Set Aspect Ratio, Variants (1–3), and your TG_Chat ID (Telegram target)

  Click “Submit”  
     The workflow automatically:
     Uploads your images  
     Writes photography-accurate prompts (focal length, aperture, lighting, surface/shadows)  
     Renders each variant into clean PNG results

  Receive your images  
     Finished images land in your Telegram chat/group for instant feedback and use

  That’s it. No manual editing, no waiting, no hassle.

  Real problems solved
  Inconsistent brand look?** Get uniform, polished visuals across your store and ads.  
  No time for shoots?** Generate studio/lifestyle shots from one upload.  
  Need options fast?** Produce multiple variants (A/B/C) in a single run.  
  Limited budget?** Cut studio and retouching costs to nearly zero.

  Customize it to your stack
  Swap Telegram for email, Slack, Google Drive, Dropbox, or S3
  Save prompts & URLs to Notion/Airtable for tracking
  Add an approval step before delivery
  Map aspect ratios to fixed pixel sizes for ad platforms

  Requirements
  n8n** (self-hosted or cloud)  
  Access to OpenAI and Kie.ai (API creds)  
  A Telegram bot added to your target chat (for delivery)

  You control which assets and metadata are sent to external APIs. Always ensure you have rights to the images you upload.

  FAQs

  Do I need a background image?  
  No. If you skip it, the workflow builds a realistic scene from your background description.

  How many images do I get?  
  Up to 3 variants per run (you choose 1–3).

  Are the results on-brand?  
  Yes—subtle grading honors your brand colors without over-saturation.

  Can I change the delivery channel?  
  Absolutely. Replace Telegram with your preferred destination (email, storage, chat).

  Use cases
  PDP images, marketplace listings, social ads, story posts, influencer kits, quick mockups for pitch decks and client approvals.

  Ready to level up your product visuals?
  Add to cart, upload your first product image, and get studio-quality results—today.

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name: PDF RAG Agent with Telegram Chat & Auto-Ingestion from Google Drive
author_name: Meelioo
author_username: mookielian
categories: []
created_at: 2025-09-23T10:13:36.936Z
updated_at: 2025-09-23T10:13:36.936Z
url: https://n8n.io/workflows/8860
scraped_at: 2025-12-24 13:02:45
description:
  How it works

  This workflow creates an intelligent document assistant called "Mookie" that can answer questions based on your uploaded documents. Here's how it operates:

  Document Ingestion:** The system can automatically load PDF files from Google Drive or accept PDFs uploaded directly through Telegram, then processes and stores them in a PostgreSQL vector database using Mistral embeddings
  Smart Retrieval:** When users ask questions via Telegram or a web chat interface, the AI agent searches through the stored documents to find relevant information using vector similarity matching
  Contextual Responses:** Using GPT-4 and the retrieved document context, Mookie provides accurate answers based solely on the ingested documents, avoiding hallucination by refusing to answer questions not covered in the stored materials
  Memory & Conversation:** The system maintains conversation history for each user, allowing for natural follow-up questions and contextual discussions

  Set up steps

  Estimated setup time: 30-45 minutes
  You'll need to configure several external services and credentials:

  Set up a PostgreSQL database with PGVector extension for document storage
  Create accounts and API keys for Azure OpenAI (GPT-4), Mistral Cloud (embeddings), and Google Drive access
  	Connect your own LLM's if you don't have these credentials.
  Configure a Telegram bot and obtain API credentials for chat functionality
  Update webhook URLs throughout the workflow to match your n8n instance
  Test the document ingestion pipeline with sample PDFs
  Verify the chat interfaces (both Telegram and web) are responding correctly

  &gt;The workflow includes approval mechanisms for PDF ingestion and handles both automated bulk processing from Google Drive and real-time document uploads through Telegram. Read the sticky notes provided in the template code for clear instructions.

nodes_used:
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name: Product Review Analysis with BrowserAct & Gemini-Powered Recommendations
author_name: Madame AI Team | Kai
author_username: madame-ai
categories: []
created_at: 2025-09-23T11:08:47.001Z
updated_at: 2025-09-23T11:08:47.001Z
url: https://n8n.io/workflows/8862
scraped_at: 2025-12-24 13:02:44
description:
  Product Review Analysis with BrowserAct & Gemini-Powered Recommendations.

  This n8n template demonstrates how to perform product review sentiment analysis and generate improvement recommendations using an AI Agent.

  This workflow is perfect for e-commerce store owners, product managers, or marketing teams who want to automate the process of collecting feedback and turning it into actionable insights.

  How it works
  The workflow is triggered manually.
  An HTTP Request node initiates a web scraping task with the BrowserAct API to collect product reviews.
  A series of If and Wait nodes are used to check the status of the scraping task. If the task is not yet complete, the workflow pauses and retries until it receives the full dataset.
  An AI Agent node, powered by Google Gemini, then processes the scraped review summaries. It analyzes the sentiment of each review and generates actionable improvement recommendations.
  Finally, the workflow sends these detailed recommendations via a Telegram message and an Email to the relevant stakeholders.

  Requirements
  BrowserAct** API account for web scraping
  BrowserAct* "Product Review Sentiment Analysis*" Template
  Gemini** account for the AI Agent
  Telegram* and *SMTP** credentials for sending messages

  Need Help ?
  How to Find Your BrowseAct API Key & Workflow ID
  How to Connect n8n to Browseract
  How to Use & Customize BrowserAct Templates
  Workflow Guidance and Showcase

  How to INSTANTLY Get Product Improvement Ideas from Amazon Reviews | BrowserAct + n8n + Gemini

nodes_used:
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name: Indeed Job Matching to Telegram with BrowserAct, Gemini & Telegram
author_name: Madame AI Team | Kai
author_username: madame-ai
categories: []
created_at: 2025-09-23T12:22:44.828Z
updated_at: 2025-09-23T12:22:44.828Z
url: https://n8n.io/workflows/8864
scraped_at: 2025-12-24 13:02:45
description:
  Analyze job market data with AI to find matching jobs

  This n8n template helps you stay on top of the job market by matching scraped job offers with your resume using an AI Agent.

  This workflow is perfect for job seekers, recruiters, or market analysts who need to find specific job opportunities without manually sifting through countless listings.

  Steps to Take
  Create BrowserAct Workflow:* Set up the *Job Market Intelligence** template in your BrowserAct account.
  Add BrowserAct Token:* Connect your BrowserAct account credentials to the *HTTP Request** node.
  Update Workflow ID:* Change the workflow_id value in the *HTTP Request** node to match the one from your BrowserAct workflow.
  Connect Gemini:* Add your Google Gemini credentials and update your *resume* inside the prompt in the *AI Agent** node.
  Configure Telegram:* Connect your Telegram account and add your Channel ID to the *Send a text message** node.

  How it works
  The workflow is triggered manually by clicking "Execute workflow," but you can easily set it to run on a schedule.
  It uses an HTTP Request node to start a web scraping task via the BrowserAct API to collect the latest job offers.
  A series of If and Wait nodes monitor the scraping job, ensuring the full data is ready before proceeding.
  An AI Agent node, powered by Google Gemini, processes the job listings and filters them to find the best matches for your resume.
  A Code node then transforms the AI's output into a clean, readable format.
  Finally, the filtered job offers are sent directly to you via Telegram.

  Requirements
  BrowserAct** API account
  BrowserAct* *“Job Market Intelligence”** Template
  Gemini** account
  Telegram** credentials

  Need Help ?
  How to Find Your BrowseAct API Key & Workflow ID
  How to Connect n8n to Browseract
  How to Use & Customize BrowserAct Templates

  Workflow Guidance and Showcase

  Never Manually Search for a Job Again (AI Automation Tutorial)

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name: Automate Google Maps Lead Generation with Perplexity AI and Email Verification
author_name: Antxon Pous
author_username: verificaremails
categories: []
created_at: 2025-09-24T16:54:28.812Z
updated_at: 2025-09-24T16:54:28.812Z
url: https://n8n.io/workflows/8904
scraped_at: 2025-12-24 13:02:41
description:
  Overview ✨

  This template finds businesses on Google Maps → writes to Google Sheets → enriches + **verifies email contact so your outreach stays clean and deliverable. It includes Sticky Notes  to explain the flow and setup.

  What it does 🔎➡️📄➡️✅

  Search & save: Fetch places and append rows (Name, Address, Website, Rating, Phone) to your sheet.
  Enrich: Ask an LLM (Perplexity) for Email + a short company Background.
  Verify emails: For Email Verification we use VerificarEmails  to validate every email address before writing back (fewer bounces, less blocking).

  &gt; ℹ️ VerificarEmails node: install the community node @verificaremails/n8n-nodes-verificaremails (Settings → Community Nodes) and use it until it’s approved as a core node. ✨
  Youtube Video

  Requirements 🔐

  Use n8n Credentials for: Google Sheets, Perplexity (Bearer), and VerificarEmails.
  Keep prompts short and temperature: 0 to lower cost.
  Create a Google Sheet with the following headers: UUID, Name, Address, Website, Rating	Email, Valid, Email, Phone, Valid Phone, Summary

  How to run ▶️

  Set your search params (query, locale, pages).
  Run the flow: it saves results → enriches → verifies emails → updates the sheet.
  If the read/update step doesn’t run, ensure the finalization (Done) path and the correct sheet/tab are selected.

  Why verify? ✉️🧹

  Bad emails cause bounces, throttling, and blocks. Verifying first protects sender reputation and improves deliverability.
  Beyond emails, VerificarEmails also offers **phone, names. Check Email Validation API documentation to better understand verification results.

nodes_used:
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name: Automated Sprint Reports from Jira to Stakeholders via Gmail
author_name: Yassin Zehar
author_username: yassinzehar
categories: []
created_at: 2025-09-25T18:03:07.898Z
updated_at: 2025-09-25T18:03:07.898Z
url: https://n8n.io/workflows/8932
scraped_at: 2025-12-24 13:02:45
description:
  Description

  Automated workflow that generates a Sprint Report from Jira and delivers it by Gmail.

  The flow fetches sprint issues from Jira, validates and normalizes the data, calculates metrics (tickets, story points, blockers, completion rate), generates an HTML report, and sends it by email.

  Context

  This template helps teams keep stakeholders updated automatically of the current sprint.
  Instead of manually compiling Jira data, the report is generated and sent on schedule (e.g., every Friday at 17:00).

  It’s production-friendly, reusable, and works across Jira projects.

  Target Users

  Scrum Masters and Agile Coaches who need sprint reports for retrospectives.

  Product Owners who want a weekly overview of sprint progress.

  Project Managers tracking Jira delivery KPIs.

  Engineering teams wanting automated status reporting without extra overhead.

  Technical Requirements

  Jira Cloud project + API email + API token + permission to read issues.

  Gmail credential for notifications.

  Workflow Steps



  Trigger – Schedule (e.g., Friday at 17:00).

  Edit Fields – Configure Jira base URL, project key, email recipients.

  Get Many Issues – Fetch sprint issues with JQL (project = &lt;KEY&gt; AND sprint in openSprints()).

  Validation & Normalization – Clean/validate fields (status, assignee, priority, story points, sprint info).

  Metrics Calculation – Aggregate KPIs (done, in progress, blockers, story points, completion %).

  HTML Report Generation – Build a styled email-friendly HTML summary + detailed table.

  Send Gmail – Deliver report to stakeholders.

  Key Features

  Automated Sprint Reports: No manual copy-paste.

  Metrics overview: Tickets done vs total, blockers, story points.

  Detailed table: Issue key, summary, status, assignee, priority, SP.

  Email delivery: HTML report with Jira links sent to stakeholders.

  Fully customizable: Adjust fields, KPIs, and recipients easily.

  Expected Output

  📊 HTML Sprint Report with KPIs and issue table.
  ✅ Email delivered to stakeholders via Gmail.
  🔗 Jira links embedded for easy navigation.

  How it works

  ⏰ Trigger – Runs on schedule (e.g., every Friday at 17:00).
  🧾 Fetch Issues – JQL filters sprint tickets.
  📊 Metrics – Done vs total, SP progress, blockers.
  💻 Generate HTML – Clean, styled table and summary.
  ✉️ Notify – Send Gmail with full sprint report to stakeholders.

  Tutorial video:
  Watch the Youtube Tutorial video

  About me :
  I’m Yassin a Project & Product Manager  Scaling tech products with data-driven project management.
  📬 Feel free to connect with me on Linkedin

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workflow_json_compressed:
  [encoded data, 5064 characters]


name: Generate Comprehensive Website Audit Report with GPT-4o-mini & Claude Sonnet 4.5
author_name: Sensonar.com
author_username: sebastian
categories: []
created_at: 2025-09-25T21:33:32.382Z
updated_at: 2025-09-25T21:33:32.382Z
url: https://n8n.io/workflows/8934
scraped_at: 2025-12-24 13:02:44
description:
  🌐 Webpage Audit AI Agent – Automate Your Website Review

  This workflow contains a virtual digital consultant team to professionally analyze Landing Page, generate Audit Report and sent it via Gmail.

  At the center is the Editor-in-Chief AI Agent, who acts as the lead strategist.  
  When you submit a website URL, it is scraped and then sent to to a team of specialist AI Agents (each powered by GPT-4o-mini for speed) then after finishing their sub-porcess deliver outcome to the final Editor (Claude Sonnet for premium quality) which is responsible for generating professional analysis.

  The result: a consulting-style email audit report that combines technical accuracy with business clarity — delivered straight to your inbox.

  🟢 Section 1 – Entry & Leadership

  Nodes:

  🌍 Webhook node → Entry point where the website URL, email and language is entered. 
  📝 Editor-in-Chief Agent → Acts like the consulting team leader. Interprets the request and merges results.  
  💡 Think Tool → Helps structure the analysis, ensuring that findings are presented in a business-friendly way.  
  🧠 CRO/SEO/Tech/Semantic AI Agents → Ensures both efficiency (GPT-4o-mini for scanning large content quickly) and focus on specific task.  

  ✅ Beginner view: Think of this as submitting your website to a digital auditors. They decide what needs to be reviewed from each perspective, delegate to the right experts, and then together - as an AI Agents team - craft the final report.

  📊 Section 2 – Specialist Audit AI Agents

  Each specialist focuses on one critical layer of your website:

  🔧 Technical SEO Agent → Analyzes code structure, page speed, mobile responsiveness, metadata, and accessibility.  
  📝 Content SEO Agent → Reviews keyword strategy, text quality, readability, and semantic richness.  
  🎯 CRO & UX Agent → Checks calls-to-action, user flows, conversion points, and usability.  
  🤖 Semantic Agent → Checks if AI Agents aligns with semantic flows, marketing problems and risks.  
  📈 Analytics Agent → Validates tracking, funnels, and key performance indicators (optional).  

  ✅ Beginner view: This is like hiring a team of experts — SEO, content, UX, and analytics — all working in parallel on your website.

  📋 Section 3 – Flow of Execution

  You submit your website URL.  
  Then it is distributed to the team of AI Agents.  
  Each Agent runs its analysis in parallel.  
  Findings are sent to the Editor-in-Chief.
  The Editor compiles everything into a consulting-style newsletter report.  
  The final report is emailed to you automatically.  

  ✅ Beginner view: It’s like sending your site to a consulting firm, and within minutes you get a polished audit back in your inbox.

  📊 Summary Table

  | Section               | Key Roles                       | Models                     | Purpose                      | Beginner Benefit                          |
  |-----------------------|---------------------------------|----------------------------|------------------------------|-------------------------------------------|
  | 🟢 Entry & Leadership | Editor-in-Chief, Think Tool     | Anthropic Claude Sonnet 4| Organize and refine analysis | Works like a consulting team leader        |
  | 📊 Specialists        | SEO, Content, CRO, Semantic, Analytics    | GPT-4o-mini                | Detailed audits of each area | Each Agent = a specialized consultant      |
  | 📋 Execution Flow     | All connected                   | n8n workflow               | Collaboration and reporting  | Output = clear, professional audit email   |

  🌟 Why This Workflow Rocks

  Full digital consulting team in one workflow**  
  Parallel execution → speed without losing depth**  
  Strategic + tactical insights → both “what” and “how”**  
  Cost-optimized** ($0.40–$0.80 per run) → cheaper than a single human consultant review  
  Action-oriented output** → not just issues, but recommendations and next steps  
  Scalable** → easily extend with more audit dimensions (security, branding, accessibility, etc.)  

  👉 Example Use Case

  “Analyze my e-commerce site for SEO, CRO, technical and semantic issues.”

  Technical SEO Agent → Reports missing alt text, metadata, and slow mobile speed.  
  Content SEO Agent → Flags weak product descriptions, suggests keyword improvements.  
  CRO Agent → Finds unclear CTAs and checkout friction points. 
  Semantic Agent → Finds everything to improve searching by AI Agents.
  Editor-in-Chief Agent → Combines everything into a consulting-style email with prioritized fixes and estimated business impact.  

  ✅ Delivered directly to your inbox in under 5 minutes.

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workflow_json_compressed:
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name: AI Call Summary to HubSpot + Follow-Up Task
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:43:32.501Z
updated_at: 2025-09-26T15:43:32.501Z
url: https://n8n.io/workflows/8966
scraped_at: 2025-12-24 13:02:43
description:
  This n8n template turns raw call transcripts into clean HubSpot call logs and a single, actionable follow-up task—automatically. Paste a transcript and the contact’s email; the workflow finds the contact, summarizes the conversation in 120–160 words, proposes the next best action, and (optionally) updates missing contact fields.

  Perfect for reps and founders who want accurate CRM hygiene without the manual busywork.

  How it works

  A form trigger collects two inputs:

    Contact email
    Plain-text call transcript
  The workflow looks up the HubSpot contact by email to pull known properties.
  An AI agent reads the transcript (plus known fields) to:

    Extract participants, role, problem/opportunity, requirements, blockers, timeline, and metrics.
    Write a 120–160 word recap a teammate can skim.
    Generate one concrete follow-up task (title + body).
    Suggest updates for missing contact properties (city, country, job title, job function).
  The recap is logged to HubSpot as a completed Call engagement.
  The follow-up is created in HubSpot as a Task with subject and body.
  (Optional) The contact record is updated using AI-suggested values if the transcript clearly mentions them.

  How to use

  Connect HubSpot (OAuth2) on all HubSpot nodes.
  Connect OpenAI on the AI nodes.
  Open Form: Capture Transcript, submit the email + transcript.
  (Optional) In AI: Summarize Call & Draft Task, tweak prompt rules (word count, date normalization).
  (Optional) In Update Contact from Transcript, review the mapped fields before enabling in production.
  Activate the workflow and paste transcripts after each call.

  Requirements

  HubSpot** (OAuth2) for contact search, call logging, and tasks
  OpenAI** for summarization and task drafting

  Notes & customization ideas

  Swap the form for a Google Drive or S3 watcher to ingest saved transcripts.
  Add a speech-to-text step if you store audio recordings.
  Extend Update Contact to include additional fields (timezone, department, seniority).
  Post the summary to Slack or email the AE for quick handoffs.
  Gate updates with a confidence check, or route low-confidence changes for manual approval.

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workflow_json_compressed:
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name: AI Email Reply Based on HubSpot Data + Slack Approval
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:48:45.383Z
updated_at: 2025-09-26T15:48:45.383Z
url: https://n8n.io/workflows/8967
scraped_at: 2025-12-24 13:02:43
description:
  This n8n template drafts customer-ready email replies using Google Gemini, enriched with HubSpot context (contact, deals, companies, tickets). Each draft is routed to Slack for one-click approval before it’s sent from Gmail—so you move fast without losing control.

  Ideal for support and sales teams that want speedy, personalized responses while keeping humans in the loop.

  How it works

  Gmail Trigger** watches for new inbound emails.
  Sender filter** excludes internal domains (e.g., n8n.io) to avoid auto-replying to teammates.
  HubSpot contact lookup* finds the sender and fetches associated *deals/companies/tickets** via association + batch read.
  CRM context is normalized** into clean, LLM-friendly fields (no IDs or sensitive noise).
  Gemini (Google AI Studio)** generates a concise, friendly reply using:

    Sender name, subject, and message snippet
    Safe, relevant HubSpot context (e.g., top 1–2 deals or an open ticket)
    Style constraints (≤ \~150 words, single CTA, optional clarifying question)
  Slack approval* posts the draft to a channel; if *approved, n8n **replies via Gmail in the original thread.

  How to use

  Gmail: Connect the same account for the trigger and reply nodes.
  HubSpot: Connect OAuth on the search + HTTP request nodes.
  Gemini: Add your Google AI Studio API key to the Google Gemini Chat Model node.
  Slack: Connect and select the channel for draft approvals.
  (Optional) Filter: Adjust the Allowed Sender filter before going live.
  (Optional) Prompt: Edit “Draft Reply (AI Agent)” tone/length or how much CRM detail to include.
  Activate the workflow. New emails will produce Slack-approved replies automatically.

  Requirements

  Gmail** (trigger + send)
  HubSpot** (OAuth2) for contact + associations
  Slack** for approval step
  Google Gemini** (Google AI Studio API key)

  Notes & customization

  Safety rails:** The prompt avoids exposing IDs/raw JSON and caps CRM details to what’s useful.
  Auto-send mode:** Skip Slack if you want fully automated replies for specific senders/labels.
  Richer context:** Extend the batch read to pull more properties (e.g., next step, renewal date).
  Triage:** Branch on subject/labels to route billing vs. technical requests to different prompts.
  QA queue:* If the model asks a clarifying question, keep it to *one**—the node enforces that.

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workflow_json_compressed:
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name: Email Outreach Drafter Based on HubSpot Data
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:55:13.793Z
updated_at: 2025-09-26T15:55:13.793Z
url: https://n8n.io/workflows/8968
scraped_at: 2025-12-24 13:02:44
description:
  This n8n template turns a small, targeted HubSpot list into tailored outreach. It scans each contact’s recent Gmail conversations, builds a lightweight persona with AI (tone, goals, pain points, decision style), then drafts a concise sales email aligned to your offer—saved to Gmail as a reviewable draft.

  Perfect for SDRs and founders who want personalization at scale without writing from scratch.

  This template was originally created by Jim Le.

  How it works

  Manual trigger** starts a controlled run.
  HubSpot search** pulls a focused list of contacts (e.g., hs_buying_role = DECISION_MAKER).
  Batch loop** processes contacts one by one.
  Gmail fetch** grabs up to 20 recent threads from each contact.
  AI persona extraction** (Information Extractor + Gemini) analyzes messages to capture:

    decision-making style, communication preferences, goals/motivations
    pain points, work style, personality traits, buying behavior, values, market awareness
  Variables node* sets core fields (first name, last name, email) and the *offer** you want to pitch.
  AI email generation* (Gemini) mirrors the contact’s tone and priorities; outputs *subject* + *HTML body**.
  Gmail draft** is created for the contact so a rep can skim, tweak, and send.

  How to use

  Connect HubSpot on the “Get Contacts” node and refine the filter to your segment.
  Connect Gmail on both read and draft nodes (same account recommended).
  Add Gemini key to both Gemini nodes.
  In Variables, update product_to_sell with your offer and confirm the contact field mappings.
  (Optional) Tweak the persona attributes or the email prompt for tone/length/CTA.
  Click Test workflow. Review drafts in Gmail, edit if needed, then send.

  Requirements

  HubSpot** (OAuth2) for contact targeting
  Gmail** (read + draft)
  Google Gemini** (API key) for persona + copy generation

  Notes & customization

  Tighter targeting:** Change the HubSpot filter (e.g., industry, territory, lifecycle stage) to keep the list small and measurable.
  Richer inputs:** Increase Gmail limit or include received/sent filters to capture more context (mind rate limits).
  Brand voice:** Add a short style guide to the email generator’s system prompt (e.g., sentence length, jargon rules, sign-off).
  Offer variants:** Replace product_to_sell per segment, or branch by industry to load different value props.
  Compliance & privacy:** Limit stored outputs to essentials; avoid copying sensitive content from threads verbatim.
  Auto-send option:** Swap the draft step for “send email” if you want hands-off delivery for known segments.

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workflow_json_compressed:
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name: HubSpot Contact AI Enrichment
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:58:10.491Z
updated_at: 2025-09-26T15:58:10.491Z
url: https://n8n.io/workflows/8969
scraped_at: 2025-12-24 13:02:43
description:
  This n8n template auto-enriches brand-new HubSpot contacts with company details. Each day it finds contacts created in the last 24 hours (skipping free email domains), researches the company from the contact’s email domain, and writes back clean fields—no manual lookup needed.

  Perfect for GTM teams that want better segmentation and faster personalization from day one.

  How it works

  A daily schedule trigger starts the workflow.
  HubSpot: Get recently created/updated contacts** pulls the newest records.
  A filter keeps only contacts:

    created within the last 24 hours
    whose email domain doesn’t contain gmail.com (adjust as needed).
  An AI research agent (Gemini + SerpAPI):

    extracts the company domain from the contact’s email
    searches the web and returns structured JSON:

      company_name, industry, headquarters_city, headquarters_country, employee_count, website, linkedin, description
  HubSpot: Add company info** updates the contact with the enriched fields.

  How to use

  Connect HubSpot on both HubSpot nodes (OAuth2).
  Connect SerpAPI (paste your API key).
  Connect Google Gemini (Google AI Studio API key).
  (Optional) Edit the agent prompt to fetch more/different fields.
  (Optional) Tweak the filter to include/exclude other domains.
  Activate the workflow to run daily.

  Requirements

  HubSpot** (OAuth2) for reading/updating contacts
  SerpAPI** for web search results
  Google Gemini** for company profiling and structured output

  Notes & customization

  Free domains:** Add more exclusions (e.g., yahoo.com, outlook.com) to reduce false positives.
  Confidence gating:** Require website + LinkedIn before writing to HubSpot, or route low-confidence results for manual review.
  Field mapping:** Extend the update step with additional properties (e.g., industry tags, HQ timezone).
  Frequency:** Switch the trigger to hourly for faster enrichment on high-volume inbound.
  Data hygiene:** Normalize employee count ranges and country names to your CRM picklists.

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name: Weekly HubSpot Lead Report to Slack
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:59:48.711Z
updated_at: 2025-09-26T15:59:48.711Z
url: https://n8n.io/workflows/8970
scraped_at: 2025-12-24 13:02:43
description:
  This n8n template posts a quick weekly snapshot to Slack so your team sees last week’s funnel movement at a glance. It counts how many contacts entered the Lead stage in the past week—and optionally reports closed-won deals from the same period.

  Perfect for GTM standups and Monday pipeline reviews.

  How it works

  A weekly schedule (e.g., Mondays 07:00) kicks things off.
  HubSpot → Get all contacts** pulls contacts with hs_v2_date_entered_lead.
  A filter keeps only those whose lead date is within the last 7 days.
  Summarize** counts the resulting leads.
  Slack* posts: “Last week we generated *X** leads.”
  (Optional branch) Deals:

    HubSpot → Get all deals with hs_is_closed_won and hs_closed_won_date.
    Filter to closed-won last week.
    Summarize to report count and (if mapped) total amount.

  How to use

  Connect HubSpot on the contacts and (optional) deals nodes.
  Connect Slack and pick the destination channel.
  In Schedule the report, set your preferred day/time.
  (Optional) For the Deals branch, include the amount property on “Get all deals” and set Summarize → sum over amount.
  Activate the workflow.

  Requirements

  HubSpot** (OAuth2)
  Slack** (OAuth2)

  Notes & customization

  Time windows:** Adjust the filter to different periods (e.g., last 14 or 30 days).
  More stages:** Duplicate the contacts branch for MQL/SQL or custom lifecycle events.
  Richer Slack message:** Add emojis, trend vs. prior week, or a tiny leaderboard by owner.
  Segments:** Split by country/industry using HubSpot lists or additional filters.

nodes_used:
  ["n8n-nodes-base.hubspot","n8n-nodes-base.filter","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.hubspot","n8n-nodes-base.filter","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote"]

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name: Chat with Your HubSpot Data
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T16:03:26.575Z
updated_at: 2025-09-26T16:03:26.575Z
url: https://n8n.io/workflows/8971
scraped_at: 2025-12-24 13:02:44
description:
  This n8n template gives you a chat-style assistant that can search your HubSpot CRM on demand. Ask natural-language questions like “show me leads in Germany” or “what deals close next month,” and the agent translates your request into precise HubSpot searches—then answers in plain English.

  Great for founders, AEs, and ops folks who want quick answers without clicking through the CRM.

  How it works

  Chat trigger** starts a session from your n8n chat UI or embed.
  AI Agent (Gemini 2.5 Pro)** interprets the message and:

    Chooses the right HubSpot search (contacts or deals).
    Fills filter property, operator (EQ, NEQ, GT, GTE, LT, LTE, BETWEEN, IN, NOT\_IN), and value(s).
    Requests specific properties (email, name, lifecycle stage, owner, activity timestamps, etc.).
  HubSpot tools** execute live queries:

    Contacts: flexible property filter + free-text query.
    Deals: filters by owner and core deal fields (stage, amount, pipeline, close date).
  Memory buffer** keeps the last turns so you can say “now only show closed won over 10k” and the agent understands context.

  How to use

  Connect credentials

     HubSpot OAuth on both HubSpot Tool nodes.
     Google Gemini API key on the Gemini Chat Model.
  Open the chat (the “When chat message received” node).
  Ask questions like:

     “Find contacts named Hans created after Sept 1.”
     “Deals owned by me in Proposal with amount &gt; 10,000.”
     “Contacts with lead status = New and no email reply in the last 14 days.”
  Refine with follow-ups:

     “Sort by most recently contacted.”
     “Only Germany.”
     “Show top 5 with emails.”

  Requirements

  HubSpot** (OAuth2)
  Google Gemini** (API key)

  Notes & customization

  Property/operator control:** The contact search node lets the agent set both the property (e.g., email, lifecyclestage, hs_lead_status) and the operator (EQ, IN, BETWEEN in epoch ms for dates, etc.).
  Owner filtering for deals:** Uses hs_all_owner_ids; swap or extend to filter by pipeline/stage ranges.
  Guardrails:** Add allowlists for searchable properties or cap result counts to avoid noisy answers.
  Display format:** Have the agent return concise tables (name, email, stage, last activity, CTA).
  Handoffs:** Add Slack/Email actions—e.g., “post this list to #sales” or “export to CSV.”
  Telemetry:** Log queries for later dashboards (common searches, coverage gaps).

  Troubleshooting

  No results?** Loosen operators (use IN lists, broaden dates) or include a free-text query.
  Date filters:** Provide epoch ms for GT/GTE/LT/LTE/BETWEEN on time fields (the agent handles this; keep system time in UTC).
  Too chatty?** Reduce memory window or ask the agent to summarize to bullet points.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.hubspotTool","n8n-nodes-base.hubspotTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

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name: Import E.ON W1000 Energy Meter Data to Home Assistant with Spook Integration
author_name: András Farkas
author_username: netesfiu
categories: []
created_at: 2025-09-27T13:32:01.805Z
updated_at: 2025-09-27T13:32:01.805Z
url: https://n8n.io/workflows/9010
scraped_at: 2025-12-24 13:02:46
description:
  UPDATES:
  2025-12-03 fix JS code in calculate hourly sum node

  E.ON W1000 → n8n → Home Assistant (Spook) “Integration”

  This workflow processes emails from the E.ON portal containing 15-minute +A -A (import/export) data and daily 1.8.0  2.8.0 meter readings.  
  It extracts the required columns from the attached XLSX file, groups the 15-minute values by hour, then:

  updates the Spook/Recorder statistics under the IDs sensor.grid_energy_import and sensor.grid_energy_export, and  
  sets the current meter readings for the entities input_number.grid_import_meter and input_number.grid_export_meter.

  &gt; You may need to modify the workflow if there are changes in how E.ON sends scheduled exports. If the exported data format changes, please report it on Github!

  Requirements

  n8n (cloud or self-hosted)
    HACS addon available here: Rbillon59/home-assistant-addons
    Official n8n Docker Compose template
    Simplified n8n Docker Compose template available on Github
  (For Gmail) Gmail API authentication (OAuth2) read-only email access to the account receiving the messages  
    Setup guide available here
  (For IMAP) IMAP provider credentials
  Home Assistant access via Long-Lived Access Token or API key  
    Setup guide available here
  Spook integration  
    Documentation and installation guide available here

  E.ON Portal Setup

  Create a scheduled export on the E.ON portal with the following parameters:  
     Under the Remote Meter Reading menu, click on the + new scheduled export setting button.  
       Specify POD identifier(s): choose one of the PODs you want to query.  
       Specify meter variables: select the following:
         +A Active Power Consumption
         -A Active Power Feed-In
         DP_1-1:1.8.0*0 Active Power Consumption Daily Reading
         DP_1-1:2.8.0*0 Active Power Feed-In Daily Reading
       Export sending frequency: daily  
       Days of historical data to include: recommended 7 days to backfill missed data.  
       Email subject: by default, use [EON-W1000]. If processing multiple PODs with the workflow, give each a unique identifier.

  Home Assistant Preparation

  Create the following input_number entities in configuration.yaml or via Helpers:



  input_number:
    grid_import_meter:
      name: grid_import_meter
      mode: box
      initial: 0
      min: 0
      max: 9999999999
      step: 0.001
      unit_of_measurement: kWh
    grid_export_meter:
      name: grid_export_meter
      mode: box
      initial: 0
      min: 0
      max: 9999999999
      step: 0.001
      unit_of_measurement: kWh
  &gt; If you name the entities differently, make sure to reflect these changes in the workflow.

  Create the following template_sensor entities in configuration.yaml or via Helpers:


  input_number:
    grid_import_meter:
      name: grid_import_meter
      mode: box
      initial: 0
      min: 0
      max: 9999999999
      step: 0.001
      unit_of_measurement: kWh
    grid_export_meter:
      name: grid_export_meter
      mode: box
      initial: 0
      min: 0
      max: 9999999999
      step: 0.001
      unit_of_measurement: kWh
  &gt;If you name the entities differently, make sure to reflect these changes in the workflow.

  create the following template_sensor entities in config.yaml or via Helpers:

    

  template:
    sensor:
        name: "grid_energy_import"
          state: "{{ states('input_number.grid_import_meter') | float(0) }}"
          unit_of_measurement: "kWh"
          device_class: energy
          state_class: total_increasing
        name: "grid_energy_export"
          state: "{{ states('input_number.grid_export_meter') | float(0) }}"
          unit_of_measurement: "kWh"
          device_class: energy
          state_class: total_increasing
  &gt; If you name the entities differently, make sure to reflect these changes in the workflow.

  n8n import and authentication

  importing the workflow

     In n8n → Workflows → Import from File/Clipboard → paste the JSON.
     Select the downloaded JSON and paste it into a new workflow using Ctrl+V.
  Set up n8n Credentials
     The credentials must be configured in the Home Assistant and Gmail nodes. The setup process is described in the Requirements section.

nodes_used:
  ["n8n-nodes-base.extractFromFile","n8n-nodes-base.renameKeys","n8n-nodes-base.gmail","n8n-nodes-base.aggregate","n8n-nodes-base.gmail","n8n-nodes-base.renameKeys","n8n-nodes-base.renameKeys","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.merge","n8n-nodes-base.renameKeys","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.homeAssistant","n8n-nodes-base.homeAssistant","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.homeAssistant","n8n-nodes-base.homeAssistant","n8n-nodes-base.gmailTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.emailReadImap","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.dateTime","n8n-nodes-base.dateTime","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 10228 characters]


name: Manage Shopify Store via Conversational OpenAI Assistant with SmartCommerce
author_name: Rohit Dabra
author_username: rohitdabra
categories: []
created_at: 2025-09-27T15:01:31.229Z
updated_at: 2025-09-27T15:01:31.229Z
url: https://n8n.io/workflows/9014
scraped_at: 2025-12-24 13:02:44
description:
  Shopify MCP AI Agent Workflow for n8n

  Overview

  This n8n workflow showcases a full-featured AI-powered assistant connected to a Shopify store through a custom MCP (Multi-Channel Commerce Platform) Server toolkit. It empowers users to automate comprehensive Shopify store management by leveraging AI to interact conversationally with their data and operations. The workflow can create, fetch, search, update, and delete Shopify products and orders, all triggered via simple chat messages, making day-to-day store operations frictionless and highly efficient.

  Core capabilities include:

  Product and order management (CRUD) via chat commands.

  Smart retrieval: AI proactively fetches details instead of asking repeated questions.

  Contextual memory: AI uses n8n memory to provide context-aware, fluent responses.

  End-to-end automation: Connects Shopify, OpenAI, and n8n’s automation logic for seamless workflows.

  This solution is ideal for Shopify merchants, agencies, and developers aiming to reduce manual overhead and enable conversational, AI-powered commerce automation in their operations.

  🎬 Watch Demo Video on YouTube 

  Step-by-Step Setup Guide

  Follow these steps to import and configure the Shopify MCP AI Agent workflow in n8n:

  1. Import the Workflow File

  Download the workflow file from this Creator Hub listing.

  In your n8n instance, go to Workflows &gt; Import from File and upload the JSON.

  2. Prepare Shopify Access

  Log in to your Shopify admin.

  Create a Custom App or use an existing app and retrieve the Admin API Access Token.

  Storefront access: Ensure your app has relevant permissions for Products, Orders, Discounts, and Store Settings.

  3. Set Up Credentials in n8n

  In n8n, navigate to Credentials and add a new Shopify API credential using your Access Token.

  Name it something memorable (e.g., Shopify Access Token account) to match the credential used in the workflow nodes.

  4. Configure the MCP Server Connection

  Make sure your MCP Server is running and accessible with API endpoints for product/order management.

  Update any relevant connection endpoints in the workflow if you run your MCP Server locally or in a different location.

  5. Connect OpenAI or Other LLM Provider

  Provide your API key for OpenAI GPT or a compatible model.

  Link the credential to the OpenAI Chat Model node (replace with other providers if required).

  6. (Optional) Customize for Your Needs

  Tweak node logic, add new triggers, or extend memory features as required.

  Add, remove, or restrain the AI’s capabilities to fit your operational needs.

  Configure chat triggers for more personalized workflows.

  7. Testing

  Use the “When chat message received” trigger or send http requests to the workflow’s endpoint.

  Example: “Create an order for Jane Doe, 3 Black T-shirts” or “Show today’s fulfilled orders”.

  The workflow and AI Agent will handle context, fetch/store data, and reply accordingly.

  8. Ready to Automate!

  Begin leveraging conversational automation to manage your Shopify store.

  For additional tips, consult the workflow’s internal documentation and n8n’s official guides.

  Additional Notes

  This template includes all core Shopify product and order operations.

  The AI agent auto-resolves context, making routine admin tasks simple and quick.

  Extend or fork the workflow to suit niche scenarios—discounts, analytics, and more.

  Visual thumbnail and schematic are included for easy reference.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.stickyNote"]

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  [encoded data, 6624 characters]


name: Generate Stock Trading Signals with Gemini 2.5 Pro & TwelveData via Telegram Bot
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-09-27T18:06:06.375Z
updated_at: 2025-09-27T18:06:06.375Z
url: https://n8n.io/workflows/9015
scraped_at: 2025-12-24 13:02:45
description:
  Purpose and Audience
  This n8n workflow template creates an intelligent stock technical analysis system that delivers professional-grade trading signals directly to your Telegram. Designed for retail traders, investors, and financial professionals who want to combine technical analysis with AI-powered insights for better market timing decisions.

  Who Is It For
  Retail Traders: Looking to enhance their technical analysis with AI-driven insights
  Swing Traders: Need reliable signals for entry and exit timing
  Investment Professionals: Want automated technical screening for multiple stocks
  Financial Enthusiasts: Interested in combining traditional TA with modern AI tools
  Portfolio Managers: Seeking systematic approach to market timing

  How It Works
  The workflow operates as a comprehensive technical analysis engine:
  Data Collection: Fetches real-time stock prices and 90-day historical data from TwelveData API
  Technical Calculation: Computes key indicators including EMAs, RSI, ATR, and support/resistance levels
  AI Analysis: Uses Google Gemini 2.5 Pro to analyze confluence of indicators and generate single, high-probability signals
  Signal Generation: Produces clear BUY/SELL/NEUTRAL recommendations with specific entry, stop-loss, and take-profit levels
  Delivery: Sends formatted analysis and interactive charts via Telegram bot
  Risk Management: Includes built-in risk parameters and confidence scoring

  Setup Requirements
  Create Telegram bot via BotFather
  Set up Google Gemini credentials
  Configure additional nodes as you see in the sticky notes
  Deploy and test with stock symbols (e.g., "AAPL", "TSLA")

  Note: Complete setup guide is provided with the workflow file.

  Technical Indicators Used in Strategy
  Core Indicators (1-Day Timeframe):
  EMA 50 & EMA 200: Trend direction and momentum confirmation
  RSI (14-period): Momentum oscillator for overbought/oversold conditions
  ATR (14-period): Volatility measurement for risk sizing
  Support/Resistance: Dynamic levels calculated from 90-day price history.
  Fibonacci Retracements: Additional confluence levels.

  Important Disclaimers
  ⚠️ Analysis timeframe is set to 1-day by default - suitable for swing trading, not day trading
  ⚠️ Not a get-rich-quick system - This tool provides technical analysis, not financial advice
  ⚠️ Use with fundamental analysis - Technical signals work best when combined with fundamental research for timing entries and exits
  ⚠️ Not recommended for isolated use - Always consider fundamentals, market conditions, news, and risk management

  Key Features
  Single, clear signal per analysis (no conflicting recommendations; either buy or sell)
  Built-in confidence scoring system
  Automated chart generation and delivery
  Support for any US stock ticker
  Professional-grade risk management parameters

  Use Case Examples
  Pre-market Analysis: Send "AAPL" stock ticker as a message to your telegram bot and get comprehensive technical analysis with 1 Day candlesticks chart
  Swing Trading: Use signals to time entries on already-researched stocks
  Portfolio Review: Analyze multiple positions for exit timing
  Educational Tool: Learn technical analysis through AI explanations

  This template transforms complex technical analysis into accessible, actionable trading insights while maintaining professional risk management standards.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.telegramTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Automated Economic Calendar PDF Reports to Telegram via RapidAPI
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-09-27T21:54:26.736Z
updated_at: 2025-09-27T21:54:26.736Z
url: https://n8n.io/workflows/9025
scraped_at: 2025-12-24 13:02:46
description:
  Stay ahead of the markets with this fully automated n8n workflow that delivers AI-generated economic calendar events PDF updates directly to your Telegram or Discord.

  Powered by the Economic Events Calendar API via RapidAPI, this workflow is perfect for traders, investors, and financial community managers who want timely notifications about high- and medium-impact global economic events—no coding required.

  Key Features
  Automated PDF Generation: Receive medium and high impact upcoming economic calendar events schedule (for up to 7 days in future) straight to Telegram/Discord.
  Flexible Scheduling: Default update interval is every 7 days, adjustable to any custom frequency.
  Customizable Date Range: Easily set the window for economic event coverage (e.g., today to 7 days ahead).
  No Coding Needed: Simple setup—just plug in your API keys and Telegram credentials.

  Who Is This For?
  Retail and professional traders
  Financial influencers and Telegram group admins
  Community managers seeking hands-off, real-time macroeconomic alerts

  Why Choose This Workflow?
  Save Time: No more manual economic calendar checks. Get upcoming full week high and medium impact news events straight to your telegram in neatly formatted PDF.
  Stay Informed: Instantly know about key market-moving events.
  Easy Integration: Works out-of-the-box with n8n and Telegram.

  Get started today and never miss a critical economic event again!

  PDF View:

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name: AI Sales Assistant with GPT & Claude: Qualify Leads, Book Meetings
author_name: Hassan
author_username: sycorda
categories: []
created_at: 2025-09-27T22:06:33.630Z
updated_at: 2025-09-27T22:06:33.630Z
url: https://n8n.io/workflows/9026
scraped_at: 2025-12-24 13:02:41
description:
  Lead Qualification & Meeting Booking System

  This workflow creates a sophisticated AI-powered sales assistant that automatically qualifies website visitors, schedules meetings, and manages the entire lead-to-booking pipeline without human intervention. The system acts as "Hassan," a friendly sales representative who engages prospects through natural conversation and converts interest into booked appointments with remarkable efficiency.

  Benefits:

  Complete Sales Automation - Engages website visitors 24/7, qualifies leads through intelligent conversation, and books meetings automatically without any manual oversight

  AI-Powered Lead Qualification - Uses advanced conversational AI with SPIN Selling and Challenger Sale methodologies to identify high-value prospects and overcome objections naturally

  Smart Calendar Integration - Automatically checks availability, prevents double-bookings, and sends professional confirmation emails with meeting details

  Intelligent Data Management - Tracks all interactions, prevents duplicate outreach, and maintains comprehensive conversation summaries for follow-up optimization

  Scalable Conversion System - Processes unlimited website visitors simultaneously while maintaining personalized, human-like interactions for maximum conversion rates

  Revenue-Focused Approach - Specifically designed to identify prospects interested in AI automation services and guide them toward high-value consulting calls

  How It Works:

  Website Visitor Engagement:
  Captures incoming chat messages from website visitors in real-time
  Initiates conversations using a trained AI persona that feels authentically human
  Gradually qualifies interest in AI automation services through strategic questioning

  Intelligent Lead Processing:
  Collects essential contact information (name, email, company) within first few exchanges
  Cross-references visitor history to provide personalized follow-up experiences
  Updates comprehensive database with conversation summaries and lead scoring

  Advanced Qualification System:
  Uses proven sales methodologies (SPIN, Challenger) to overcome objections naturally
  Identifies pain points, budget indicators, and timeline requirements through conversation
  Accesses company knowledge base to answer specific questions about services and pricing

  Automated Meeting Booking:
  Seamlessly transitions qualified prospects to calendar booking when interest is confirmed
  Checks real-time calendar availability to prevent conflicts and optimize scheduling
  Creates calendar events with proper attendee management and meeting details

  Professional Follow-Up Automation:
  Sends branded confirmation emails with meeting links and company information
  Maintains conversation context across multiple touchpoints for consistency
  Provides detailed handoff information to sales team for optimal meeting preparation

  Required Database Setup:

  Before running this workflow, create a Google Sheets database with these exact column headers:

  Essential Columns:
  Name** - Prospect's full name (collected during qualification)
  Email** - Primary email address (used for matching and updates) 
  Summary** - Detailed conversation summary with key insights
  Date** - Timestamp of interaction for tracking and follow-up

  Setup Instructions:
  Create a new Google Sheet with these column headers in the first row
  Name the sheet "Web Chat Bot Convo Summary"
  Connect your Google Sheets OAuth credentials in n8n
  Update the document ID in the workflow nodes

  The merge logic uses the Email column to prevent duplicate entries and update existing records with new conversation data.

  Business Use Cases:

  Service-Based Businesses - Automatically qualify and book high-value consultation calls without hiring additional sales staff

  Digital Agencies - Scale lead generation for AI automation services while maintaining personalized prospect experiences

  Consultants & Coaches - Convert website traffic into booked discovery calls with intelligent qualification and objection handling

  B2B SaaS Companies - Identify enterprise prospects and schedule product demos through natural conversation flow

  Revenue Potential:

  This system can generate $10,000-$50,000+ monthly by converting website visitors into qualified meetings. A single automated booking for AI automation services typically ranges from $3,000-$15,000 in project value, making the ROI calculation extremely attractive.

  Conversion Metrics:
  Typically converts 15-25% of engaged website visitors into qualified meetings
  Saves 20+ hours weekly of manual lead qualification and follow-up
  Eliminates scheduling back-and-forth and missed appointment opportunities

  Difficulty Level: Advanced
  Estimated Build Time: 3-4 hours  
  Monthly Operating Cost: ~$50-100 (AI API usage + integrations)

  Set Up Steps:

  Configure AI Services:
  Add OpenAI API credentials for conversational AI and Claude Sonnet for specialized tasks
  Set up appropriate rate limiting and cost controls for sustainable operation
  Customize the AI persona and conversation flow for your specific business

  Database Configuration:
  Create Google Sheets database with provided column structure  
  Connect Google Sheets OAuth credentials for seamless data management
  Configure the merge logic for duplicate prevention and record updates

  Calendar Integration Setup:
  Connect Google Calendar OAuth with proper permissions for event creation
  Configure calendar checking logic to prevent double-bookings
  Set up meeting link generation and attendee management

  Email Automation:
  Connect Gmail OAuth for sending confirmation emails
  Customize the branded email template with your company information
  Test email delivery and formatting across different clients

  Conversation Optimization:
  Customize AI prompts for your specific industry and service offerings
  Adjust qualification questions to identify your ideal customer profile  
  Set up objection handling responses that align with your sales methodology

  Advanced Configuration:
  Configure memory management for consistent multi-session conversations
  Set up proper error handling and fallback responses
  Implement conversation logging and analytics for optimization

  Testing & Launch:
  Test the complete flow with sample conversations and edge cases
  Verify calendar integration, email delivery, and data logging
  Deploy with monitoring to track performance and optimize conversion rates

  Advanced Customization:

  Multi-Language Support** - Adapt conversations for international prospects
  Industry-Specific Qualification** - Customize questioning for different market segments  
  Integration Expansion** - Connect to CRM systems, Slack notifications, or other business tools
  Advanced Analytics** - Track conversion funnels, conversation quality, and ROI metrics
  A/B Testing Framework** - Test different conversation approaches and optimize for higher conversion

  This system transforms your website from a passive information source into an active sales machine that works around the clock to generate qualified meetings and drive revenue growth.

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name: Automate Shopify Abandoned Cart WhatsApp Reminders with Product Links via Rapiwa
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2025-09-28T05:00:04.423Z
updated_at: 2025-09-28T05:00:04.423Z
url: https://n8n.io/workflows/9031
scraped_at: 2025-12-24 13:02:43
description:
  Automate Shopify Abandoned Cart WhatsApp Reminders with Product Links via Rapiwa

  Who’s it for
  This n8n workflow automatically identifies customers who have abandoned their carts on your Shopify store, cleans and verifies their WhatsApp numbers, and sends them personalized reminders via the Rapiwa API. It also logs each interaction—whether the number was valid or not—into a connected Google Sheet. Designed for marketers, small business owners, freelancers, and support teams, this solution makes it easy to run bulk WhatsApp campaigns using just a Google Sheet and your own WhatsApp number—no need for the official WhatsApp Business API. It’s a budget-friendly, scalable, and easy-to-manage alternative for anyone looking to automate WhatsApp follow-ups without the tech hassle.

  How it Works / What It Does
  Reads rows from a Google Sheet where the Status column is marked as "pending".
  Cleans each phone number (removes special characters, spaces, etc.).
  Verifies whether the number is a WhatsApp user using the Rapiwa API.
  If valid:
    Sends the message via Rapiwa.
    Updates Status = sent and Verification = verified.
  If invalid:
    Skips sending.
    Updates Status = not sent and Verification = unverified.
  Waits a configurable delay between sends to avoid rate limits.
  Processes rows in small batches and repeats on schedule (default every 5 minutes).

  How to Set Up
  Duplicate the sample Google Sheet format and populate it with contacts and messages.
  Fill required columns such as WhatsApp No, Name, Message, Image URL, and set Status = pending.
  Connect Google Sheets in n8n and grant the required OAuth scopes.
  Create an HTTP Bearer credential in n8n and paste your Rapiwa API key.
  Configure the workflow nodes (Trigger, Google Sheets, Limit, SplitInBatches, Code, HTTP Request, If, Update Google Sheets, Wait).
  Enable the workflow in n8n.

  Requirements
  Google Sheets API credentials (OAuth2) configured in n8n
  Google Sheet matching the template (WhatsApp No, Name, Message, Status, Verification)
  Rapiwa account and Bearer token
  n8n instance with HTTP Request and Google Sheets nodes enabled

  How to Customize the Workflow
  Add or increase delay between messages using the Wait node (e.g., 5–10 seconds).
  Change message content or include media by editing the HTTP Request body.
  Personalize messages using sheet columns (Name, product details, coupon codes).
  Add error handling nodes to retry failed sends or log errors.
  Adjust the Limit node to control how many rows are processed per run.

  Workflow Highlights
  Triggered every 5 minutes using the Schedule Trigger node.
  Filters sheet rows where Status = pending.
  Cleans numbers and verifies WhatsApp existence before sending.
  Sends messages via Rapiwa (unofficial API).
  Updates Google Sheet rows with Status = sent/not sent and Verification = verified/unverified.
  Uses a Wait node to prevent rapid-fire sending.

  Setup in n8n

  1. Connect Google Sheets
  Add a Google Sheets node.
  Authenticate with your Google account.
  Select the document and worksheet.
  Use a filter or query to fetch rows with Status = pending.

  2. Loop Through Rows
  Use SplitInBatches to process rows in small chunks (e.g., 5 rows per batch).
  Add a Code node to clean phone numbers (remove non-digits).
  Add a Wait node to pause between individual sends (recommended 5 seconds).

  3. Send Message via HTTP Node
  Configure an HTTP Request node to POST to:
    https://app.rapiwa.com/api/send-message
  Use Bearer Token authentication with your Rapiwa API key.
  Example JSON body:
  {
    "number": "{{ $json['WhatsApp No'] }}",
    "message": "{{ $json['Message'] }}"
  }
  After the send, update the row in Google Sheets with the result.

  Sample Google Sheet Structure
   A Google Sheet formatted like this ➤ sample

  | name | number | order id | item name | coupon | item link | total price | validity | status |
  | -------------- | ------------- | -------------- | --------------------------------- | ------ | -------------------------------------------------------------------------------- | ------------ | ---------- | -------- |
  | Abdul Mannan | 8801400620056 | 39248398811454 | S25 Ultra 5XXXXX Price Cell Phone | | Product Link | 11500.00 BDT | unverified | not sent |
  | Abdul Mannan | 8801400620055 | 39248402153790 | S25 Ultra 5XXXXX Price Cell Phone | | Product Link | 11500.00 BDT | verified | sent |
  | Abdul Mannan | 8801400620055 | 39248403431742 | S25 Ultra 5XXXXX Price Cell Phone | | Product Link | 11500.00 BDT | verified | sent |

  Tips
  Ensure numbers are in proper format (e.g., 8801XXXXXXXXX — no +, no spaces).
  Use public image URLs if sending media.
  Store Rapiwa API key securely in n8n credentials.
  Test with a small batch before scaling up.
  Mark processed rows as "sent" to avoid duplicates.
  Increase Wait time or reduce batch size if you encounter rate limiting.

  Important Notes
  Avoid Duplicates: The Google Sheet serves as a ledger. Sent messages won’t be repeated in the next cycle.
  Rate Limit Awareness: Use Wait nodes to space out requests and avoid getting banned.
  Add Delay for Safety: If needed, increase wait times or limit message batches.
  Extendable: You can add product images, links, or even abandon cart discounts dynamically.

  Future Enhancements (Ideas)
  Add delay between batches (e.g., 60 messages/hour)
  Auto-update Shopify checkout status (via GraphQL API)
  Use more advanced templates or media (via Rapiwa)
  Add error handling to log failed API calls
  Use Telegram/Slack node to notify when process ends
  Useful Links
  Dashboard:** https://app.rapiwa.com
  Official Website:** https://rapiwa.com
  Documentation:** https://docs.rapiwa.com

  Support
  WhatsApp Support: Chat Now  
  Discord: Join SpaGreen Community  
  Facebook Group: SpaGreen Support  
  Website: https://spagreen.net  
  Developer Portfolio: Codecanyon SpaGreen

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name: AI Resume Screening with GPT-4o & Error Handling | Google Sheets & Drive Flow
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-09-28T13:41:49.656Z
updated_at: 2025-09-28T13:41:49.656Z
url: https://n8n.io/workflows/9048
scraped_at: 2025-12-24 13:02:42
description:
  GPT-4o Resume Screener with Error Handling - Google Sheets & Drive Pipeline

  How it works

  Enterprise-grade resume screening automation built for production environments. This workflow combines intelligent AI analysis with comprehensive error handling to ensure reliable processing of candidate applications. Every potential failure point is monitored with automatic recovery and notification systems.

  Core workflow steps:
  Intelligent Email Processing - Monitors Gmail with attachment validation and file type detection
  Robust File Handling - Multi-format support with upload verification and extraction validation
  Quality-Controlled AI Analysis - GPT-4o evaluation with output validation and fallback mechanisms
  Verified Data Extraction - Contact and qualification extraction with data integrity checks
  Dual Logging System - Success tracking in main dashboard, error logging in separate audit trail

  Error Recovery Features:
  Upload failure detection with retry mechanisms
  Text extraction validation with quality thresholds
  AI processing timeout protection and fallback responses
  Data validation before final logging
  Comprehensive error notification and tracking system

  Set up steps

  Total setup time: 25-35 minutes

  Core Credentials Setup (8 minutes)
  Gmail OAuth2 with attachment permissions
  Google Drive API with folder creation rights
  Google Sheets API with read/write access
  OpenAI API key with GPT-4o model access

  Primary Configuration (12 minutes)
  Configure monitoring systems - Set up Gmail trigger with error detection
  Establish file processing pipeline - Create Drive folders for resumes and configure upload validation
  Deploy dual spreadsheet system - Set up main tracking sheet and error logging sheet
  Initialize AI processing - Configure GPT-4o with structured output parsing and timeout settings
  Customize job requirements - Update role specifications and scoring criteria

  Error Handling Setup (10 minutes)
  Configure error notifications - Set administrator email for failure alerts
  Set up error logging spreadsheet - Create audit trail for failed processing attempts
  Customize timeout settings - Adjust processing limits based on expected file sizes
  Test error pathways - Validate notification system with sample failures

  Advanced Customization (5 minutes)
  Modify validation thresholds for resume quality
  Adjust AI prompt for industry-specific requirements
  Configure custom error messages and escalation rules
  Set up automated retry logic for transient failures

  Production-Ready Features:
  Comprehensive logging for compliance and auditing
  Graceful degradation when services are temporarily unavailable
  Detailed error context for troubleshooting
  Scalable architecture for high-volume processing

  Template Features

  Enterprise Error Management
  Multi-layer validation at every processing stage
  Automatic error categorization and routing
  Administrative alerts with detailed context
  Separate error logging for audit compliance
  Timeout protection preventing workflow hangs

  Advanced File Processing
  Upload success verification before processing
  Text extraction quality validation
  Resume content quality thresholds
  Corrupted file detection and handling
  Format conversion error recovery

  Robust AI Integration
  GPT-4o processing with output validation
  Structured response parsing with error checking
  AI timeout protection and fallback responses
  Failed analysis logging with manual review triggers
  Retry logic for transient API failures

  Production Monitoring
  Real-time error notifications via email
  Comprehensive error logging dashboard
  Processing success/failure metrics
  Failed resume tracking for manual review
  Audit trail for compliance requirements

  Data Integrity Controls
  Pre-logging validation of all extracted data
  Missing information detection and flagging
  Contact information verification checks
  Score validation and boundary enforcement
  Duplicate detection and handling

  Designed for HR departments and recruiting agencies that need reliable, scalable resume processing with enterprise-level monitoring and error recovery capabilities.

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name: Aggregate Twitter/X Content to Telegram Channel with BrowserAct & Google Gemini
author_name: Madame AI Team | Kai
author_username: madame-ai
categories: []
created_at: 2025-09-28T18:31:43.616Z
updated_at: 2025-09-28T18:31:43.616Z
url: https://n8n.io/workflows/9055
scraped_at: 2025-12-24 13:02:44
description:
  Automate social media content aggregation to a Telegram channel

  This n8n template automatically aggregates and analyzes key updates from your social media platforms Home Page, delivering them as curated posts to a Telegram channel.

  This workflow is perfect for digital marketers, brand managers, or data analysts and Busy people, seeking to monitor real-time trends and competitor activity without manual effort.

  How it works
  The workflow is triggered automatically on a schedule to aggregate the latest social media posts.
  A series of If and Wait nodes monitor the data processing job until the full data is ready.
  An AI Agent, powered by Google Gemini, refines the content by summarizing posts and removing duplicates.
  An If node checks for an image in the post to decide if a photo or a text message should be sent.
  Finally, the curated posts are sent to your Telegram channel as rich media messages.

  How to use
  Set up BrowserAct Template: In your BrowserAct account, set up “Twitter/X Content Aggregation” template.
   
  Set up Credentials: Add your credentials for BrowserAct In Run Node , Google Gemini in Agent Node, and Telegram in Send Node.

  Add Workflow ID: Change the workflow_id value inside the HTTP Request inside the Run Node, to match the one from your BrowserAct workflow.

  Activate Workflow: To enable the automated schedule, simply activate the workflow.

  Requirements
  BrowserAct** API account
  BrowserAct* *“Twitter/X Content Aggregation”** Template
  Gemini** account
  Telegram** credentials

  customizing this workflow
  This workflow provides a powerful foundation for social media monitoring. You could:
  Replace the Telegram node with an email or Slack node to send notifications to a different platform.
  Add more detailed prompts to the AI Agent for more specific analysis or summarization.
  customize BrowserAct Workflow to reach your desire.
  Need Help ?
  How to Find Your BrowseAct API Key & Workflow ID
  How to Connect n8n to Browseract
  How to Use & Customize BrowserAct Templates
  Workflow Guidance and Showcase

  Automate Your Social Media: Get All X/Twitter Updates Directly in Telegram!

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name: 📁 Extract and Clean PDF Data from Google Drive
author_name: EoCi - Mr.Eo
author_username: eocimreo
categories: []
created_at: 2025-09-28T22:59:45.724Z
updated_at: 2025-09-28T22:59:45.724Z
url: https://n8n.io/workflows/9061
scraped_at: 2025-12-24 13:02:41
description:
  🎯 What This Does
  Automatically finds PDF file in Google Drive and extracts information. Use it to pull out clean output. It then formats the output into a clean JSON object.

  🔄 How It Works
  1. Manual Trigger starts the process.
  2. 🔎Find File: "Google Drive" node finds the PDF file/files in a specified folder and downloads it/them.
  3. 📝Extract Raw Text: "Extract From File" node pulls the text content from the retrieval file/files.
  4. ✅Output Clean Data: "Code" node refines the extracted content and runs custom code for cleaning and final formatting.

  🚀Setup Guidelines
  Setup Requirements
  Google Drive Account**: A Google Drive with an empty folder or folder that contains PDF file/files that you want to process.
  API Keys**: Gemini, Google Drive.
  Set up steps
  Setup time: &lt; 5 minutes

  Add Credentials in n8n:
      Ensure your Google Drive OAuth2 and Google Gemini (PaLM) API credentials are created and connected. Go to Credentials &gt; New to add them if you haven't created yet.
          
  Configure the Search Node (Get PDF Files/File):
      Open the node and select your Google Drive credential.
      In the "Resource" field, choose File/Folder. In "Search Method" field, select "Search File/Folder Name", In "Search Query" type in *.pdf.
      Add on 2 filters, in "Folder" filter click on dropdown choose "From List" and connect to the created folder on your google drive. In "What to Search" filter, select file.
      Add on "Options" (optional): Click on "Add option", choose ("ID" and "Name")
  	
  Define Extraction Rules (Extract Files/File's Data):
      Select File Type: Open node and click on the dropdown below "Operation" section, choose "Extract From PDF". Next, in "Input Binary Field" section keep as default "data".
  	
  Clean & Format Data (Optional):
      Adjust the Get PDF Data Only node to keep only the fields you need and give them friendly names.
      Modify the Data Parser & Cleaner node if you need to perform custom transformation.
         
  Activate and Run:
      Save and Activate the workflow.
      Click "Execute Workflow" to run it manually and check the output.

  That’s it! Once configured, this workflow becomes your personal data assistant. Run it anytime you need to extract information quickly and accurately, saving you hours of manual work and ensuring your data is always ready to use.

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name: WordPress Blog to Google Sheets Sync (Posts, Categories, Tags, Media)
author_name: Grigory Frolov
author_username: gregfrolov
categories: []
created_at: 2025-09-29T05:30:28.515Z
updated_at: 2025-09-29T05:30:28.515Z
url: https://n8n.io/workflows/9064
scraped_at: 2025-12-24 13:02:41
description:
  WordPress Blog to Google Sheets Sync  
  Posts • Categories • Tags • Media

  🧩 Overview
  This n8n workflow automatically syncs your WordPress website content — including posts, categories, tags, and media — into Google Sheets.

  It helps automate content reporting, SEO analysis, and data backups.  
  The workflow can run on schedule or on demand via a webhook.

  💡 Use cases
  Maintain a live database of blog posts in Google Sheets.  
  Create dashboards in Google Data Studio or Looker Studio.  
  Track new articles for newsletters or social media scheduling.  
  Backup all WordPress content and media outside of your CMS.

  ⚙️ Prerequisites

  Before importing the workflow, ensure you have:

  A WordPress website with the REST API enabled (default in WP 4.7+).  
  Authentication: either Application Passwords or Basic Auth credentials.  
  A Google Sheet with the following tabs:
     Posts
     Categories
     Tags
     Media
  The following credentials configured in n8n:
     HTTP Basic Auth (for WordPress)
     Google Sheets OAuth2

  🚀 Setup instructions

  Import the workflow into your n8n instance.  
  Replace all example WordPress API URLs with your domain, for example: https://yourdomain.com/wp-json/wp/v2/ 
  Connect your HTTP Basic Auth credentials (WordPress username + Application Password).  
  Connect your Google Sheets OAuth2 account.  
  Update the spreadsheet ID in each Google Sheets node with your own.  
  Adjust the Schedule Trigger (e.g. run daily at 2:00 AM).  
  Run once manually to verify data sync.

  🧠 Workflow structure

  | Section | Description |
  |----------|--------------|
  | Schedule / Webhook Trigger | Starts the workflow manually or automatically |
  | Variables & Loop Vars | Initialize pagination for REST API requests |
  | Get Posts → Split Out → Update Posts | Fetch and update all WordPress posts |
  | Get Categories → Update Categories | Sync WordPress categories |
  | Get Tags → Update Tags | Sync WordPress tags |
  | Get Media → Split Out → Update Media | Sync media library (images, videos, etc.) |
  | IF Loops | Handles pagination logic until all items are retrieved |

  ⚠️ Notes & Limitations

  Works with standard WordPress REST API endpoints only.  
  Custom post types require editing endpoint URLs.  
  The per_page value defaults to 10; increase for faster syncs.  
  For large sites, consider increasing n8n memory or adding execution logs.  
  Avoid running the workflow too frequently to prevent API rate limits.

  🎥 Video Tutorial

  A step-by-step setup guide is available here:
  👉 https://www.youtube.com/watch?v=czSMWyD6f-0

  Please subscribe to my YouTube channel to support me: 👉 https://www.youtube.com/@gregfrolovpersonal

  👨‍💻 Author

  Created by: Grigory Frolov  
  SEO & Automation Specialist — helping businesses integrate WordPress, AI, and data tools with n8n.

  🧾 License

  This workflow is provided under the MIT License.  
  Feel free to use, modify, and share improvements with the community.

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name: End-to-End Ai Blog Research and Writer with Gemini AI, Supabase and Nano-Banana
author_name: Muhammad Asadullah
author_username: asadtech
categories: []
created_at: 2025-09-29T17:15:05.051Z
updated_at: 2025-09-29T17:15:05.051Z
url: https://n8n.io/workflows/9079
scraped_at: 2025-12-24 13:02:41
description:
  Blog Research and Writer n8n Workflow - Ai Blog Writer

  Fully automated blog creation system using n8n + AI Agents + Image Generation

  Example Blog

  Overview

  This workflow automates the entire blog creation pipeline—from topic research to final publication. Three specialized AI agents collaborate to produce publication-ready blog posts with custom images, all saved directly to your Supabase database.

  How It Works

  1. Research Agent (Topic Discovery)
  Triggers**: Runs on schedule (default: daily at 4 AM)
  Process**:
    Fetches existing blog titles from Supabase to avoid duplicates
    Uses Google Search + RSS feeds to identify trending topics in your niche
    Scrapes competitor content to find content gaps
    Generates detailed topic briefs with SEO keywords, search intent, and differentiation angles
  Output**: Comprehensive research document with SERP analysis and content strategy

  2. Writer Agent (Content Creation)
  Triggers**: Receives research from Agent 1
  Process**:
    Writes full blog article based on research brief
    Follows strict SEO and readability guidelines (no AI fluff, natural tone, actionable content)
    Structures content with proper HTML markup
    Includes key sections: hook, takeaways, frameworks, FAQs, CTAs
    Places image placeholders with mock URLs (https://db.com/image_1, etc.)
  Output**: Complete JSON object with title, slug, excerpt, tags, category, and full HTML content

  3. Image Prompt Writer (Visual Generation)
  Triggers**: Receives blog content from Agent 2
  Process**:
    Analyzes blog content to determine number and type of images needed
    Generates detailed 150-word prompts for each image (feature image + content images)
    Creates prompts optimized for Nano-Banana image model
    Names each image descriptively for SEO
  Output**: Structured prompts for 3-6 images per blog post

  4. Image Generation Pipeline
  Process**:
    Loops through each image prompt
    Generates images via Nano-Banana API (Wavespeed.ai)
    Downloads and converts images to PNG
    Uploads to Supabase storage bucket
    Generates permanent signed URLs
    Replaces mock URLs in HTML with real image URLs
  Output**: Blog HTML with all images embedded

  5. Publication
  Final blog post saved to Supabase blogs table as draft
  Ready for immediate publishing or review

  Key Features

  ✅ Duplicate Prevention: Checks existing blogs before researching new topics  
  ✅ SEO Optimized: Natural language, proper heading structure, keyword integration  
  ✅ Human-Like Writing: No robotic phrases, varied sentence structure, actionable advice  
  ✅ Custom Images: Generated specifically for each blog's content  
  ✅ Fully Structured: JSON output with all metadata (tags, category, excerpt, etc.)  
  ✅ Error Handling: Automatic retries with wait periods between agent calls  
  ✅ Tool Integration: Google Search, URL scraping, RSS feeds for research

  Setup Requirements

  1. API Keys Needed
  Google Gemini API**: For Gemini 2.5 Pro/Flash models (content generation/writing)
  Groq API (optional)**: For Kimi-K2-Instruct model (research/writing)
  Serper.dev API**: For Google Search (2,500 free searches/month)
  Wavespeed.ai API**: For Nano-Banana image generation
  Supabase Account**: For database and image storage

  2. Supabase Setup
  Create blogs table with fields:
    title, slug, excerpt, category, tags, featured_image, status, featured, content
  Create storage bucket for blog images
  Configure bucket as public or use signed URLs

  3. Workflow Configuration

  Update these placeholders:

  RSS Feed URLs**: Replace [your website's rss.xml] with your site's RSS feed
  Storage URLs**: Update Supabase storage paths in "Upload object" and "Generate presigned URL" nodes
  API Keys**: Add your credentials to all HTTP Request nodes
  Niche/Brand**: Customize Research Agent system prompt with your industry keywords
  Writing Style**: Adjust Writer Agent prompt for your brand voice

  Customization Options

  Change Image Provider
  Replace the "nano banana" node with:
  Gemini Imagen 3/4
  DALL-E 3
  Midjourney API
  Any Wavespeed.ai model

  Adjust Schedule
  Modify "Schedule Trigger" to run:
  Multiple times daily
  Specific days of week
  On-demand via webhook

  Alternative Research Tools
  Replace Serper.dev with:
  Perplexity API (included as alternative node)
  Custom web scraping
  Different search providers

  Output Format

  {
    "title": "Your SEO-Optimized Title",
    "slug": "your-seo-optimized-title",
    "excerpt": "Compelling 2-3 sentence summary with key benefits.",
    "category": "Your Category",
    "tags": ["tag1", "tag2", "tag3", "tag4"],
    "author_name": "Your Team Name",
    "featured": false,
    "status": "draft",
    "content": "...complete HTML with embedded images..."
  }

  Performance Notes

  Average runtime**: 15-25 minutes per blog post
  Cost per post**: ~$0.10-0.30 (depending on API usage)
  Image generation**: 10-15 seconds per image with Nano-Banana
  Retry logic**: Automatically handles API timeouts with 5-15 minute wait periods

  Best Practices

  Review Before Publishing: Workflow saves as "draft" status for human review
  Monitor API Limits: Track Serper.dev searches and image generation quotas
  Test Custom Prompts: Adjust Research/Writer prompts to match your brand
  Image Quality: Review generated images; regenerate if needed
  SEO Validation: Check slugs and meta descriptions before going live

  Workflow Architecture

  3 Main Phases:
  Research → Writer → Image Prompts (Sequential AI Agent chain)
  Image Generation → Upload → URL Replacement (Loop-based processing)
  Final Assembly → Database Insert (Single save operation)

  Error Handling:
  Wait nodes between agents prevent rate limiting
  Retry logic on agent failures (max 2 retries)
  Conditional checks ensure content quality before proceeding

  Result: Hands-free blog publishing that maintains quality while saving 3-5 hours per post.

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name: Automated Viral Content Engine for LinkedIn & X with AI Generation & Publishing
author_name: Diptamoy Barman
author_username: diptamoy-barman
categories: []
created_at: 2025-09-30T09:47:43.617Z
updated_at: 2025-09-30T09:47:43.617Z
url: https://n8n.io/workflows/9100
scraped_at: 2025-12-24 13:02:41
description:
  Social Media Spark (SMS) — Automated Viral Content Engine

  Automate your entire content workflow: discover viral ideas, generate posts in your tone, repurpose for X, and auto-publish — reducing 90% of manual effort.

  🚀 What it Does
  Scrapes competitors or niche profiles on LinkedIn to find high-performing posts.
  Classifies and saves evergreen content ideas for later use.
  Generates fresh posts in your own voice with matching images.
  Repurposes content for X (Twitter) in a platform-optimized style.
  Automatically publishes content to LinkedIn and X on your schedule.
  Allows on-demand commands via Telegram for research or instant content generation.

  🧩 Why Use It
  Save time:** no more manual scraping, idea collection, or formatting.
  Stay consistent:** maintain a daily content pipeline.
  Multi-platform leverage:** create once, adapt for LinkedIn and X.
  Creative control:** mix automation with optional human review.
  Scalable:** extend to more platforms, analytics, and workflows as you grow.

  🔧 Prerequisites & Setup
  Before importing or activating the workflow, prepare these:

  AI Provider (OpenAI / Gemini / OpenRouter)**  
    For classifying posts, generating new content, repurposing for X.

  Google Sheets**  
    Central database for competitors, ideas, generated posts, and posting status.

  Google Drive**  
    Stores generated images.

  Apify & Browseract**  
    Scrapes LinkedIn profiles, posts, and performs research tasks.

  LinkedIn API**  
    Needed for automated LinkedIn publishing.

  X (Twitter) API**  
    Requires OAuth 1.0a for image uploads and OAuth 2.0 for text posting.

  Telegram Bot**  
    Enables on-demand commands and notifications.  
    Set your Telegram User ID in the trigger node.

  &gt; 🔎 In each sub-workflow, look for nodes marked “Configure Me!” to replace example prompts, search keywords, sheet IDs, etc.

  ⚙️ How It Works (Simplified Flow)
  Scrape & Classify: Collect high-engagement posts → keep evergreen ones.  
  Generate Content: Rewrite ideas into new posts in your voice → create images.  
  Repurpose for X: Adapt LinkedIn posts for short-form, high-impact tweets.  
  Auto-Publish: Post daily on LinkedIn and X.  
  Control via Telegram: Manually trigger scraping, research, or post generation.

  💡 Best Practices & Tips
  Keep all API keys private — never share them in public repos or screenshots.
  Adjust cron schedules (e.g., scraping on weekends, posting on weekdays) to fit your content rhythm.
  Add Human-in-the-Loop review steps for brand-sensitive content.
  Extend to other platforms (Instagram, TikTok, YouTube Shorts) as needed.
  Experiment with prompt variations for different tones or creative styles.
  Add analytics logging (likes, comments, clicks) to measure content performance.

  🙋‍♂️ Who is This For
  Solo creators & founders** who want to post consistently but don’t have time for daily ideation.
  Small marketing teams** that need to keep up with trends without spending hours on research.
  Consultants & thought leaders** who want to amplify their personal brand on LinkedIn and X.
  Startups & bootstrapped businesses** that need a lean but reliable content engine.
  Content strategists** who want a data-driven, repeatable pipeline for finding and using what works.

  Or anyone who wants to boost social presence by 300%

  💡 Why SMS Stands Out
  Authentic voice:** Uses your own tone and style (defined in prompts and examples), so posts feel personal — not generic AI fluff.
  Data-driven:** Pulls from real, viral posts in your niche to inspire fresh content.
  Quality over quantity:** Focuses only on proven viral ideas instead of churning random posts.
  Consistent growth:** Keeps your posting regular, so you stay visible and relevant.
  Efficient workflow:** Minimizes manual work while letting you step in when needed (e.g., for approvals or special campaigns).

  &gt; ⚡ SMS combines real market data with your unique voice — so you post smarter, not just more often.

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