by Yusei Miyakoshi
Who’s it for Teams that start their day in Slack and want a concise, automated summary of yesterday’s emails—ops leads, PMs, founders, and anyone handling busy inboxes without writing code. What it does / How it works Runs every morning at 08:00 (cron 0 0 8 * * ), fetches all emails received *yesterday, and routes the result: if none were found, it posts a polite “no emails” notice; if emails exist, it aggregates them and asks an AI agent to produce a structured digest, then formats and posts to your chosen Slack channel. The flow uses **Gmail → If → Aggregate (Item Lists) → AI Agent (OpenRouter model with structured output) → Code (Slack formatter) → Slack. A set of sticky notes on the canvas explains each step and required inputs. How to set up Connect Gmail (OAuth2) and keep the default date window (yesterday → today at 00:00). Connect Slack (OAuth2) and select your target channel. Add OpenRouter credentials and pick a compact model (e.g., gpt-4o-mini). Keep the provided structured-output schema and formatter code. Adjust the schedule/timezone if needed (the fallback message includes an Asia/Tokyo example). Paste this description into the yellow sticky note at the top of the canvas. Requirements Gmail & Slack accounts with appropriate scopes OpenRouter API key stored in Credentials (no hard-coded keys) n8n Cloud or self-host with LangChain agent nodes enabled How to customize the workflow Narrow Gmail results with label/search filters (e.g., from:, subject:). Change the digest sections or tone in the AI Agent system prompt. Swap the model for cost/quality needs and tweak temperature/max tokens. Localize dates/timezones in the formatter code and Slack messages. Branch the output to email, Google Docs, or Sheets for archival. Security & publishing tips Rename all nodes clearly, do not hardcode API keys, remove real channel IDs/emails before sharing, and group end-user variables in a Set (Fields) node. Keep the sticky notes—they’re mandatory for reviewers.
by Muhammad Farooq Iqbal
Google NanoBanana Model Image Editor for Consistent AI Influencer Creation with Kie AI Image Generation & Enhancement Workflow This n8n template demonstrates how to use Kie.ai's powerful image generation models to create and enhance images using AI, with automated story creation, image upscaling, and organized file management through Google Drive and Sheets. Use cases include: AI-powered content creation for social media, automated story visualization with consistent characters, marketing material generation, and high-quality image enhancement workflows. Good to know The workflow uses Kie.ai's google/nano-banana-edit model for image generation and nano-banana-upscale for 4x image enhancement Images are automatically organized in Google Drive with timestamped folders Progress is tracked in Google Sheets with status updates throughout the process The workflow includes face enhancement during upscaling for better portrait results All generated content is automatically saved and organized for easy access How it works Project Setup: Creates a timestamped folder structure in Google Drive and initializes a Google Sheet for tracking Story Generation: Uses OpenAI GPT-4 to create detailed prompts for image generation based on predefined templates Image Creation: Sends the AI-generated prompt along with 5 reference images to Kie.ai's nano-banana-edit model Status Monitoring: Polls the Kie.ai API to monitor task completion with automatic retry logic Image Enhancement: Upscales the generated image 4x using nano-banana-upscale with face enhancement File Management: Downloads, uploads, and organizes all generated content in the appropriate Google Drive folders Progress Tracking: Updates Google Sheets with status information and image URLs throughout the entire process Key Features Automated Story Creation**: AI-powered prompt generation for consistent, cinematic image creation Multi-Stage Processing**: Image generation followed by intelligent upscaling Smart Organization**: Automatic folder creation with timestamps and file management Progress Tracking**: Real-time status updates in Google Sheets Error Handling**: Built-in retry logic and failure state management Face Enhancement**: Specialized enhancement for portrait images during upscaling How to use Manual Trigger: The workflow starts with a manual trigger (easily replaceable with webhooks, forms, or scheduled triggers) Automatic Processing: Once triggered, the entire pipeline runs automatically Monitor Progress: Check the Google Sheet for real-time status updates Access Results: Find your generated and enhanced images in the organized Google Drive folders Requirements Kie.ai Account**: For AI image generation and upscaling services OpenAI API**: For intelligent prompt generation (GPT-4 mini) Google Drive**: For file storage and organization Google Sheets**: For progress tracking and status monitoring Customizing this workflow This workflow is highly adaptable for various use cases: Content Creation**: Modify prompts for different styles (fashion, product photography, architectural visualization) Batch Processing**: Add loops to process multiple prompts or reference images Social Media**: Integrate with social platforms for automatic posting E-commerce**: Adapt for product visualization and marketing materials Storytelling**: Create sequential images for visual narratives or storyboards The modular design makes it easy to add additional processing steps, change AI models, or integrate with other services as needed. Workflow Components Folder Management**: Dynamic folder creation with timestamp naming AI Integration**: OpenAI for prompts, Kie.ai for image processing File Processing**: Binary handling, URL management, and format conversion Status Tracking**: Multi-stage progress monitoring with Google Sheets Error Handling**: Comprehensive retry and failure management systems
by Kirill Khatkevich
This workflow transforms raw Meta Ads data into actionable, expert-level insights. It acts as a virtual performance marketer, analyzing each creative's performance, comparing it against your historical benchmarks, and delivering clear recommendations on whether to scale, optimize, or stop the ad. By running parallel analyses with both OpenAI and Gemini, it provides a unique, dual-perspective evaluation. This template is the perfect sequel to our "Automation of Creative Testing" workflow but also works powerfully on its own. Use Case Manually sifting through ads manager reports is tedious, and identifying true winners from early data is challenging. This workflow solves these problems by automating the entire analysis pipeline. It's designed for performance marketing teams who need to: Make faster, data-driven decisions on which creatives to scale. Get objective, AI-powered second opinions on ad performance. Systematically evaluate creatives against consistent, pre-defined benchmarks. Maintain a central log in Google Sheets with both raw metrics and qualitative AI analysis. Save hours spent on manual data crunching and report generation. How it Works The workflow is structured into three logical stages: Configuration & Data Ingestion: A central ⚙️ Set parameters node holds all key variables: the data source (Meta or Sheets), campaign_id, and, most importantly, your historical performance benchmarks as a simple text block. An IF node directs the workflow to fetch data either directly from a Meta Ads campaign or from a specified Google Sheet (ideal for analyzing a curated list of ads). Data Processing & AI Analysis (Parallel Execution): After fetching raw performance data (spend, impressions, clicks, actions), the workflow splits into three parallel branches for maximum resilience: Branch 1 (Data Logging): Immediately writes or updates a row in Google Sheets with the raw metrics for the creative. This ensures no data is lost, even if the AI analysis fails. Branch 2 (OpenAI Analysis): Prepares a CSV string of the creative's data, sends it along with the benchmarks to an OpenAI model (e.g., GPT-4), and instructs it to return a structured JSON analysis. Branch 3 (Gemini Analysis): Performs the exact same process but using Google's Gemini model via a LangChain agent, providing a second, independent evaluation. Results Aggregation: The results from both AI models are received as structured JSON. Two final Google Sheets nodes take these results and update the original row (matching by AdID), adding the evaluation, significance, summary, and recommendation into separate columns. The final sheet contains a complete picture: raw data side-by-side with analyses from two different AIs. Setup Instructions Credentials: 1.1 Connect your Meta Ads account. 1.2 Connect your Google account (for Sheets). 1.3 Connect your OpenAI account. 1.4 Connect your Google Gemini (Palm) account. The ⚙️ Set parameters Node: This is the central control panel. Open this first Set node and customize it: source: Set to "Meta" to pull from a campaign or "sheets" to read from a Google Sheet. campaign_id: If source is "Meta", enter your Meta Campaign ID here. benchmarks_data: This is critical. Paste your own historical performance data here as a CSV-formatted text block. The template includes an example. For best results, use an export from Ads Manager of your top-performing creatives, including key metrics. Google Sheets Nodes: There are three Google Sheets nodes that write data. You need to configure all of them to point to the same spreadsheet and sheet. Ad metrics (for raw metrics): Select your spreadsheet and sheet. Ensure "Operation" is set to Append or Update. Ad data from OpenAI (for OpenAI results): Select the same spreadsheet/sheet. Set "Operation" to Update. Ad data from Gemini (for Gemini results): Select the same spreadsheet/sheet. Set "Operation" to Update. Make sure your sheet has columns for all the data fields, e.g., AdID, FileName, spend, impressions, evaluation, summary, recommendation, evaluation G, summary G, etc. Activate the Workflow: Set your desired frequency in the Schedule Trigger node. Save and activate the workflow. Further Ideas & Customization This powerful analysis engine can be extended even further: Add a "Decision" Node: After the AI analyses are logged, add a final step that compares their recommendations. If both AIs say "scale", automatically increase the ad's budget via the Meta Ads API. Create Summary Reports: Add a branch that, after all ads are processed, calculates an overall summary (e.g., "3 creatives recommended for scaling, 5 for stopping") and sends it to a Slack channel. Dynamic Benchmarks: Instead of pasting benchmarks into the Set node, create a step that reads them from a dedicated "Benchmarks" tab in your Google Sheet, making them even easier to update. Experiment with Prompts and Benchmarks: The quality of the AI analysis is highly dependent on the quality of your input. Don't be afraid to: -- Refine the prompts in the AI Agent and Message a model nodes to better match your specific business context and KPIs. -- Curate your benchmarks_data. Test different sets of benchmark data (e.g., "last 30 days top performers" vs. "all-time best") to see how it influences the AI's recommendations. Finding the right combination of prompt and data is key to unlocking the most effective insights.
by Trung Tran
📄 Auto Extract Contacts from Business Cards to Sheet With GPT4o > This smart workflow extracts names, phone numbers, emails, and more from uploaded name card photos using AI, then logs them neatly into your Google Sheet. No typing. No mess. Just upload and go. 👤 Who’s it for Sales & Business Development Teams Recruiters & Talent Acquisition Specialists Event Teams collecting business cards Admins who manage contact databases manually ⚙️ How it works / What it does This workflow automates the extraction of contact details from uploaded name card (business card) images and stores them in a structured Google Sheet for easy tracking and follow-up. Workflow Steps: User uploads one or more name card images through a web form. The uploaded files are saved to a Google Drive folder for archiving. A smart AI agent (with OCR and GPT capabilities) scans each image and extracts relevant contact data into structured JSON format. Data is transformed, cleaned (e.g., removing + from phone numbers), and filtered. Valid contacts are appended to a Google Sheet for central tracking and future use. 🛠 How to set up Create a Form Allow file upload (JPG/PNG format). Label it as “Name Card Uploader” with a clear description. Upload to Google Drive Use the Google Drive node to store uploaded images. Configure Smart Agent Use GPT-4o or similar model with OCR capability. Apply a structured output parser to extract contact fields like name, phone, email, company, etc. Transform Data Use the Code node to clean and structure contact info. Strip out unwanted characters from phone numbers (e.g., +). Filter Invalid Records Remove entries with no meaningful contact data. Append to Google Sheets Use the Google Sheets node with "Append Sheet Row". Map fields to columns like Name, Phone, Email, etc. ✅ Requirements n8n workflow environment Google Drive integration (for file storage) Google Sheets integration (for storing contacts) GPT-4o or any image-capable LLM Clear name card images (PNG/JPG, readable text) (Optional) Slack/email integration for notifications 🧩 How to customize the workflow CRM Sync**: Connect to platforms like HubSpot, Salesforce, or Zoho. Validation Logic**: Ensure records contain key fields like name or email before writing. Uploader Info**: Attach submitter metadata to each contact record. Language Adaptation**: Adjust extracted field labels/output to target your preferred language. Batch Upload**: Handle multiple cards in a single image or multiple uploads in one go.
by DevCode Journey
Who is this for? This workflow is designed for business founders, CMOs, marketing teams, and landing page designers who want to automatically analyze their landing pages and get personalized, unconventional, high-impact conversion rate optimization (CRO) recommendations. It works by scraping the landing page content, then leveraging multiple AI models to roast the page and generate creative CRO ideas tailored specifically for that page. What this Workflow Does / Key Features Captures a landing page URL through a user-friendly form trigger. Scrapes the landing page HTML content using an HTTP request node. Sends the scraped content to a LangChain AI Agent, which orchestrates various AI models (OpenAI, Google Gemini, Mistral, etc.) for deep analysis. The AI Agent produces a friendly, fun, and unconventional “roast” of the landing page, explaining what’s wrong in human tone. Generates 10 detailed, personalized, easy-to-implement, and 2024-relevant CRO recommendations with a “wow” factor. Delivers the analysis and recommendations via Telegram message, Gmail email, and WhatsApp (via Rapiwa). Utilizes multiple AI tools and search APIs to enhance the quality and creativity of the output. Requirements OpenAI API credentials configured in n8n. Google Gemini (PaLM) API credentials for LangChain integration. Mistral Cloud API credentials for text extraction. Telegram bot credentials for sending messages. Gmail OAuth2 credentials for email delivery. Rapiwa API credentials for WhatsApp notifications. Running n8n instance with nodes: Form Trigger, HTTP Request, LangChain AI Agent, Telegram, Gmail, and custom Rapiwa node. How to Use — step-by-step Setup 1) Credentials Add your OpenAI API key under n8n credentials (OpenAi account 2). Add Google Gemini API key (Google Gemini (PaLM) Api account). Add Mistral Cloud API key (Mistral Cloud account). Set up Telegram Bot credentials (Telegram account). Set up Gmail OAuth2 credentials (Gmail account). Add Rapiwa API key for WhatsApp messages (Rapiwa). 2) Configure the Form Trigger Customize the form title, description, and landing page URL input placeholder if desired. 3) Customize Delivery Nodes Modify the Telegram, Gmail, and Rapiwa nodes with your desired recipient info and messaging preferences. 4) Run the Workflow Open the form URL webhook and submit the landing page URL to get a detailed AI-powered CRO roast and recommendations sent directly to your communication channels. Important Notes The AI Agent prompt is designed to create a fun and unconventional roast to engage users emotionally. Avoid generic advice. All CRO recommendations are personalized and contextual based on the scraped content of the provided landing page. Ensure all API credentials are kept secure and not hard-coded. Use n8n credentials management. Adjust the delivery nodes to match your preferred communication channels and recipients. The workflow supports expansion with additional AI models or messaging platforms as needed. 🙋 For Help & Community 👾 Discord: n8n channel 🌐 Website: devcodejourney.com 🔗 LinkedIn: Connect with Shakil 📱 WhatsApp Channel: Join Now 💬 Direct Chat: Message Now
by Jitesh Dugar
Automatically qualify inbound demo requests, scrape prospect websites, and send AI-personalized outreach emails—all on autopilot. What This Workflow Does This end-to-end lead automation workflow helps SaaS companies qualify and nurture inbound leads with zero manual work until human approval. Key Features ✅ Smart Email Filtering - Automatically flags personal emails (Gmail, Yahoo, etc.) and routes them to a polite regret message ✅ Website Intelligence - Scrapes prospect websites and extracts business context ✅ AI Analysis - Uses OpenAI to score ICP fit, identify pain points, and find personalization opportunities ✅ Personalized Outreach - AI drafts custom emails referencing specific details from their website ✅ Human-in-the-Loop - Approval gate before sending to ensure quality control ✅ Professional Branding - Even rejected leads get a thoughtful response Perfect For B2B SaaS companies with inbound lead forms Sales teams drowning in demo requests Businesses wanting to personalize at scale Anyone needing intelligent lead qualification What You'll Need Jotform account (or any form tool with webhooks) Create your form for free on Jotform using this link OpenAI API key Gmail account (or any email service) n8n instance (cloud or self-hosted) Workflow Sections 📧 Lead Intake & Qualification - Capture form submissions and filter personal emails 🕷️ Website Scraping - Extract company information from their domain ❌ Regret Flow - Send polite rejection to unqualified leads 🤖 AI Analysis - Analyze prospects and draft personalized emails 📨 Approved Outreach - Human review + send welcome email Customization Tips: Update the AI prompt with your company's ICP and value proposition Modify the personal email provider list based on your market Adjust the regret email template to match your brand voice Add Slack notifications for high-value leads Connect your CRM to log all activities Time Saved: ~15-20 minutes per lead Lead Response: Under 5 minutes (vs hours/days manually)
by masaya kawabe
Who’s it for Marketers, creators, and social managers who want hands-off reposting of a specific X (Twitter) user’s videos — with on-brand AI captions and clean, deduplicated logs. What it does / How it works On a schedule, the workflow resolves a target user, fetches recent tweets with media, filters to video posts, and writes them to Google Sheets for tracking and dedupe. It then builds a shareable video URL, generates a short caption via an AI model (OpenRouter), posts to your X account, and updates the sheet with completion status. Sticky notes inside the workflow explain each step, setup tasks, and best practices. How to set up Add credentials: Twitter (X) OAuth2, Google Sheets OAuth2, OpenRouter. Replace the demo Google Sheet with your own (document ID & sheet name). Set the target X username (or parameterize it). Adjust the schedule (interval/cron) and run a test execution. Verify logs and posting format, then enable. Requirements Twitter (X) OAuth2 credential Google Sheets OAuth2 credential OpenRouter credential (choose an affordable model) How to customize Edit the caption prompt (tone, hashtags count, CTAs, compliance lines). Add filters (language, min/max tweet age, exclude replies/retweets, since_id). Extend logging (timestamps, posted text, account, errors). Introduce a dry-run boolean to skip posting while testing. Swap the caption model or add retry rules for robustness. Security: Don’t hardcode tokens in HTTP nodes. Use n8n Credentials only and remove personal IDs before publishing.
by Muhammad Farooq Iqbal
🔄 How It Works - LinkedIn Post with Image Automation Overview This n8n automation creates and publishes LinkedIn posts with AI-generated images automatically. It's a complete end-to-end solution that transforms simple post titles into engaging social media content. Step-by-Step Process 1. Content Trigger & Management Google Sheets Trigger** monitors a spreadsheet for new post titles Only processes posts with "pending" status Limits to one post at a time for controlled execution 2. AI Content Generation AI Agent** uses Google Gemini to create engaging LinkedIn posts Takes the post title and generates: Compelling opening hooks 3-4 informative paragraphs Engagement questions Relevant hashtags (4-6) Appropriate emojis Output is structured and formatted for LinkedIn 3. AI Image Creation Google Gemini Image Generation** creates custom visuals Uses the AI-generated post content as context Generates professional images featuring: Modern workspace with coding elements Flutter development themes Professional, LinkedIn-appropriate aesthetics 16:9 aspect ratio, high resolution No text or captions** in the generated image 4. Image Processing & Storage Generated images are uploaded to Google Drive Files are shared with public access permissions Image URLs are stored back in the spreadsheet for tracking 5. LinkedIn Publishing LinkedIn API integration** handles the posting process: Registers image uploads Uploads images to LinkedIn's servers Creates posts with text + image Publishes to your LinkedIn profile Updates spreadsheet status to "posted" Technical Architecture Google Sheets → AI Content → AI Image → Google Drive → LinkedIn API → Status Update ↓ ↓ ↓ ↓ ↓ ↓ Trigger Gemini LLM Gemini File Upload Posting Tracking Content Gen Image Gen Key Features ✅ Fully Automated - Runs continuously without manual intervention ✅ AI-Powered - Both content and images generated by AI ✅ Professional Quality - LinkedIn-optimized formatting and visuals ✅ Real-time Tracking - Monitor status and performance ✅ Scalable - Handle multiple posts and campaigns How to Use Setup Requirements Google Gemini API for content and image generation LinkedIn API credentials for posting Google Sheets for content management Google Drive for image storage n8n instance for workflow execution Content Management Add new post titles to your Google Sheet Set status to "pending" Automation automatically processes and publishes Status updates to "posted" upon completion Customization Options Modify AI prompts for different content styles Adjust image generation parameters Change posting frequency and timing Add multiple LinkedIn accounts Integrate with other content sources Use Cases �� Perfect for: Startups** wanting consistent LinkedIn presence Marketing teams** overwhelmed with content creation HR departments** building employer branding Agencies** managing multiple client accounts Solo entrepreneurs** needing professional social media presence Benefits ⏰ Time Savings: 20+ hours per week for content teams 📈 Consistency: Daily, professional posts without gaps 🎨 Quality: AI-optimized content and visuals 📊 Scalability: Handle unlimited content volume 💰 Cost Effective: Reduce manual content creation costs 🔄 The automation runs continuously, ensuring your LinkedIn presence stays active and engaging 24/7! For inquiries: mfarooqiqbal143@gmail.com
by Ronalds Palacis
🚀 AI-Powered LinkedIn Post Automation with Figma Templates 🧩 How It Works This workflow automatically generates professional, branded LinkedIn posts using your custom Figma designs. Perfect for marketers, agencies, content creators, and businesses who want to maintain consistent branding while automating social media content creation. Key Features: Design-first approach using Figma templates AI-powered content generation (optional) High-quality image generation from templates Automatic LinkedIn publishing Telegram notifications for success/failure tracking High-Level Workflow: Template Selection: Fetch your pre-designed Figma templates from Templated Content Preparation: Set static content or configure the prompts in the agents to generate with AI (ChatGPT/Claude) Image Generation: Create branded images with dynamic content via Templated MCP server LinkedIn Publishing: Automatically post text and image to your LinkedIn profile or company page (single image or carousel post) Notification: Receive Telegram alert on success/failure (optional) ⚙️ Set Up Steps (Quick Overview) 🕐 Estimated Setup Time: ~15 minutes Create Templated Account: Sign up at templated.cometai.eu, import Figma designs, generate API key Configure LinkedIn OAuth: Set up LinkedIn Developer app with OAuth2 credentials for automatic posting Connect Templated MCP: Add API key authentication to MCP server nodes for template and image generation Set Up Telegram (Optional): Create bot for workflow notifications Customize Content: Add static fields or configure AI nodes for dynamic content generation Schedule & Activate: Set posting schedule (daily, weekly, etc.) and activate workflow 💡 Important Notes Figma Integration**: Uses real Figma files as templates - maintain professional design quality without manual recreation Template Variables**: Supports dynamic placeholders in Figma text layers for content replacement Character Limits**: Respects maxLength settings to ensure text fits your design Rate Limits**: LinkedIn allows 25 posts/day (personal), 100/day (company pages) AI-Ready**: Easily integrate ChatGPT, Claude, or other AI models for content generation Batch Generation**: Generate multiple posts at once with different templates 🛠 Detailed Node Breakdown 1. Schedule Trigger Action**: Triggers the workflow on a schedule (daily, weekly, custom cron) Configuration**: Set your desired posting frequency Alternative**: Use manual trigger for on-demand posts 2. LinkedIn Post Writer (OpenAI Chat Model) Action**: AI generates engaging LinkedIn post content based on your topic/prompt Tools**: Simple Memory, Think, Date & Time, Search latest news Output**: Professional post text ready for publication 3. Carousel Ideator (Templated MCP Client) Action**: Connects to Templated MCP server to fetch available templates Configuration**: Uses your Templated API key for authentication Output**: Available template IDs and configurations for carousel generation 4. Generate the Carousel (POST to Templated MCP) Action**: Sends content to Templated server with template ID and field data Input**: Template selection, content fields (title, subtitle, etc.) Output**: Encoded carousel images generated from Figma templates 5. Extract from File Action**: Extracts generated image data from the MCP response Process**: Parses the encoded image string for LinkedIn upload 6. Get LinkedIn User Info (HTTP Request) Action**: Fetches your LinkedIn profile URN for post attribution Authentication**: OAuth2 LinkedIn credentials Output**: User ID required for posting 7. Initialize Upload URN Action**: Requests upload URL from LinkedIn for carousel document upload Process**: Prepares LinkedIn's upload mechanism for multi-image posts 8. Edit Fields Action**: Maps and formats data for LinkedIn API requirements Process**: Structures image data and post metadata correctly 9. Convert to Binary Action**: Converts image data to binary format Key**: Required format for LinkedIn document upload API 10. Upload Posts as Binary Action**: Uploads the carousel document to LinkedIn's servers Process**: Multi-part upload of generated images 11. Get Uploaded File URN Action**: Retrieves LinkedIn's asset ID for the uploaded content Output**: Asset URN needed for post creation 12. Switch (Conditional Logic) Action**: Handles success/error routing Routes**: Directs to LinkedIn post creation on success, error notification on failure 13. Create LinkedIn Post (HTTP Request) Action**: Creates the final LinkedIn post with carousel and text Authentication**: OAuth2 with LinkedIn posting permissions Result**: Published carousel post on your LinkedIn feed 14. Success/Error Notifications (Telegram) Action**: Sends notification about workflow execution status Success**: Confirms post published with details Error**: Alerts you to failures with error message and debugging info ⏱ Execution Time Breakdown Total Estimated Execution Time: ~10–30 seconds per workflow run Template Fetch: ~1–2 seconds Content Preparation: ~1–2 seconds Image Generation: ~5–15 seconds LinkedIn Post Upload: ~2–5 seconds Telegram Notification: ~1–2 seconds Note: AI content generation (if added) adds ~5-10 seconds 🚀 Ready to Get Started? What You'll Need: Free Templated account at templated.cometai.eu Figma designs with placeholder text LinkedIn Developer app (free) n8n instance (cloud or self-hosted) Quick Start: Import this workflow to your n8n instance Follow the setup guide in the workflow notes Test with a single post Schedule for automatic posting Sit back and watch your LinkedIn feed grow! 🎉 📝 Notes & Customizations Template Customization Create multiple templates in Figma for different content types (quotes, announcements, tips, etc.) Rotate templates for visual variety Use template descriptions to guide AI content generation Set character limits per placeholder to prevent overflow Content Generation Options Static Mode**: Define fields manually for recurring post types AI Mode**: Connect ChatGPT/Claude for dynamic, topic-based content Hybrid Mode**: Mix static brand elements with AI-generated copy Scheduled Variety**: Rotate between templates and content styles Advanced Features Multi-Platform**: Duplicate workflow for Twitter, Instagram, Facebook A/B Testing**: Track performance across different templates Content Calendar**: Pull scheduled posts from Notion/Airtable Analytics Integration**: Log post IDs for engagement tracking
by Dr. Firas
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Automate Social Media with HeyGen and GPT-5: Publish Videos to TikTok, YouTube & Instagram 👥 Who is this for? This workflow is designed for: Content creators who want to scale their short-form video production Marketing teams seeking consistent and automated publishing pipelines Agencies managing multiple social accounts for clients Entrepreneurs looking to save time by automating repetitive content tasks 💡 What problem is this workflow solving? Publishing on multiple platforms like YouTube Shorts, TikTok, and Instagram is often: Time-consuming (manual editing, caption writing, uploads) Inconsistent (different requirements for each platform) Prone to delays (switching between tools) This workflow solves these issues by creating a fully automated video pipeline powered by GPT-5, HeyGen, and Blotato. ⚙️ What this workflow does Capture voice idea via Telegram Transcribe voice to text using OpenAI Whisper Generate a catchy title and caption with GPT-5 Create an AI avatar video with HeyGen Save and organize assets in Google Drive and Google Sheets Upload final video to Blotato Auto-publish to: YouTube Shorts TikTok Instagram (Optional: Facebook, X/Twitter, LinkedIn, Pinterest, Threads, Bluesky) Update logs in Google Sheets Send a Telegram confirmation once published 🧰 Setup Before using this workflow, ensure you have: A Telegram Bot connected to n8n for voice input An OpenAI API key for transcription (Whisper) and GPT-5 processing A HeyGen account & API key for avatar video generation A Google Drive & Google Sheets integration for storing assets and logs A Blotato account (Pro plan) with API access enabled Verified Community Nodes enabled in n8n Admin Panel Blotato node installed and credentials configured 🛠️ How to customize this workflow Prompts** → Adjust GPT-5 prompts to match your brand voice and niche Avatars** → Use custom avatars or voices via HeyGen configuration Platforms** → Activate only the social nodes you need (e.g., focus on TikTok & YouTube Shorts) Approval steps** → Add Telegram or Slack confirmation before publishing Analytics** → Extend the workflow to track engagement data in Google Sheets, Airtable, or Notion This workflow turns a simple spoken idea into a viral-ready video — automatically generated, styled, and posted across your most important platforms. 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube
by vinci-king-01
Competitor Price Monitoring Dashboard with AI and Real-time Alerts 🎯 Target Audience E-commerce managers and pricing analysts Retail business owners monitoring competitor pricing Marketing teams tracking market positioning Product managers analyzing competitive landscape Data analysts conducting pricing intelligence Business strategists making pricing decisions 🚀 Problem Statement Manual competitor price monitoring is inefficient and often leads to missed opportunities or delayed responses to market changes. This template solves the challenge of automatically tracking competitor prices, detecting significant changes, and providing actionable insights for strategic pricing decisions. 🔧 How it Works This workflow automatically monitors competitor product prices using AI-powered web scraping, analyzes price trends, and sends real-time alerts when significant changes are detected. Key Components Scheduled Trigger - Runs the workflow at specified intervals to maintain up-to-date price data AI-Powered Scraping - Uses ScrapeGraphAI to intelligently extract pricing information from competitor websites Price Analysis Engine - Processes historical data to detect trends and anomalies Alert System - Sends notifications via Slack and email when price changes exceed thresholds Dashboard Integration - Stores all data in Google Sheets for comprehensive analysis and reporting 📊 Google Sheets Column Specifications The template creates the following columns in your Google Sheets: | Column | Data Type | Description | Example | |--------|-----------|-------------|---------| | timestamp | DateTime | When the price was recorded | "2024-01-15T10:30:00Z" | | competitor_name | String | Name of the competitor | "Amazon" | | product_name | String | Product name and model | "iPhone 15 Pro 128GB" | | current_price | Number | Current price in USD | 999.00 | | previous_price | Number | Previous recorded price | 1099.00 | | price_change | Number | Absolute price difference | -100.00 | | price_change_percent | Number | Percentage change | -9.09 | | product_url | URL | Direct link to product page | "https://amazon.com/iphone15" | | alert_triggered | Boolean | Whether alert was sent | true | | trend_direction | String | Price trend analysis | "Decreasing" | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets account with API access Slack workspace for notifications (optional) Email service for alerts (optional) Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai npm install n8n-nodes-slack 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure it's working 3. Set up Google Sheets Connection Add Google Sheets OAuth2 credentials Grant necessary permissions for spreadsheet access Create a new spreadsheet for price monitoring data Configure the sheet name (default: "Price Monitoring") 4. Configure Competitor URLs Update the websiteUrl parameters in ScrapeGraphAI nodes Add URLs for each competitor you want to monitor Customize the user prompt to extract specific pricing data Set appropriate price thresholds for alerts 5. Set up Notification Channels Configure Slack webhook or API credentials Set up email service credentials (SendGrid, SMTP, etc.) Define alert thresholds and notification preferences Test notification delivery 6. Configure Schedule Trigger Set monitoring frequency (hourly, daily, etc.) Choose appropriate time zones for your business hours Consider competitor website rate limits 7. Test and Validate Run the workflow manually to verify all connections Check Google Sheets for proper data formatting Test alert notifications with sample data 🔄 Workflow Customization Options Modify Monitoring Targets Add or remove competitor websites Change product categories or specific products Adjust monitoring frequency based on market volatility Extend Price Analysis Add more sophisticated trend analysis algorithms Implement price prediction models Include competitor inventory and availability tracking Customize Alert System Set different thresholds for different product categories Create tiered alert systems (info, warning, critical) Add SMS notifications for urgent price changes Output Customization Add data visualization and reporting features Implement price history charts and graphs Create executive dashboards with key metrics 📈 Use Cases Dynamic Pricing**: Adjust your prices based on competitor movements Market Intelligence**: Understand competitor pricing strategies Promotion Planning**: Time your promotions based on competitor actions Inventory Management**: Optimize stock levels based on market conditions Customer Communication**: Proactively inform customers about price changes 🚨 Important Notes Respect competitor websites' terms of service and robots.txt Implement appropriate delays between requests to avoid rate limiting Regularly review and update your monitoring parameters Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider legal implications of automated price monitoring 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Google Sheets permission errors: Check OAuth2 scope and permissions Price parsing errors: Review the Code node's JavaScript logic Rate limiting: Adjust monitoring frequency and implement delays Alert delivery failures: Check notification service credentials Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Google Sheets API documentation for advanced configurations Slack API documentation for notification setup
by Yaron Been
Amplify Social Media Presence with O3 and GPT-4 Multi-Agent Team 🌍 Overview This n8n workflow acts like a virtual social media department. A Social Media Director Agent coordinates multiple specialized AI agents (Instagram, Twitter/X, Facebook, TikTok, YouTube, and Analytics). Each agent creates or analyzes content for its platform, powered by OpenAI models. The result? A fully automated, cross-platform social media strategy—from content creation to performance tracking. 🟢 Section 1: Trigger & Director Setup 🔗 Nodes: When chat message received (Trigger)** → Starts the workflow whenever you send a request (e.g., “Plan a TikTok campaign for my product launch”). Social Media Director Agent* (connected to *OpenAI O3 model**) → Acts as the strategist. Think Tool** → Helps the Director “reason” before delegating. 💡 Beginner takeaway: This section makes your workflow interactive. You send a request → the Director decides the best approach → then it assigns tasks. 📈 Advantage: Instead of manually planning content per platform, you only send one command, and the AI Director handles the strategy. 🟦 Section 2: Specialized Social Media Agents 🔗 Nodes (each paired with GPT-4.1-mini): 📸 Instagram Content Creator → Visual storytelling, Reels, Hashtags 🐦 Twitter/X Strategist → Viral tweets, trends, engagement 👥 Facebook Community Manager → Audience growth, ads, group engagement 🎵 TikTok Video Creator → Short-form video, viral trends 🎬 YouTube Content Planner → Long-form strategy, SEO, thumbnails 📊 Analytics Specialist → Performance insights, cross-platform reporting 💡 Beginner takeaway: Each platform has its own AI expert. They receive the Director’s strategy and produce tailored content for their platform. 📈 Advantage: Instead of one-size-fits-all posts, you get optimized content per platform—increasing reach and engagement. 🟣 Section 3: Models & Connections 🔗 Nodes: OpenAI Chat Models** (O3 + multiple GPT-4.1-mini models) Each model is connected to its respective agent. 💡 Beginner takeaway: Think of these as the “brains” behind each specialist. The Director uses O3 for advanced reasoning, while the specialists use GPT-4.1-mini (cheaper, faster) for content generation. 📈 Advantage: This keeps costs low while maintaining quality output. 📊 Final Overview Table | Section | Nodes | Purpose | Benefit | | --------------------- | -------------------------------------------------------- | -------------------------------------- | ------------------------------ | | 🟢 Trigger & Director | Chat Trigger, Director, Think Tool | Capture requests & plan strategy | One command → full social plan | | 🟦 Specialists | Instagram, Twitter, Facebook, TikTok, YouTube, Analytics | Platform-specific content | Optimized posts per platform | | 🟣 Models | O3 + GPT-4.1-mini | Provide reasoning & content generation | High-quality, cost-efficient | 🚀 Why This Workflow is Powerful Multi-platform coverage**: All major platforms handled in one flow Human-like strategy**: Director agent makes real marketing decisions Scalable & fast**: Generate a full campaign in minutes Cost-effective**: O3 only for strategy, GPT-4.1-mini for bulk content Beginner-friendly**: Just type your request → get full campaign output