HTTP Request and Slack integration

📊 Description Automate short-term trading research by generating high-quality trade ideas using MCP (Market Context Protocol) signals and AI-powered analysis. 📈🤖 This workflow evaluates market context, catalysts, momentum, and risk factors to produce structured trade ideas with clear reasoning, confidence scores, and execution notes. Designed for fast-moving markets, it helps traders and analysts spot actionable opportunities consistently—without manual screening or emotional bias. Perfect for systematic traders, research teams, and AI-driven trading desks. ⚡📊 🔁 What This Template Does 1️⃣ Ingests market context and asset data from MCP inputs. 📥 2️⃣ Normalizes symbols, sectors, and time horizons for analysis. 🧾 3️⃣ Evaluates short-term momentum and catalyst strength. 🚀 4️⃣ Analyzes risk, volatility, and downside exposure. ⚠️ 5️⃣ Uses AI to generate a structured trade thesis. 🤖 6️⃣ Assigns a confidence score and risk level to each idea. 📊 7️⃣ Produces clear entry logic, rationale, and trade direction. 🧠 8️⃣ Filters out low-quality or low-conviction setups. 🚫 9️⃣ Outputs clean JSON-ready trade ideas for downstream systems. 🔁 ⭐ Key Benefits ✅ Automates short-term trade idea generation ✅ Reduces emotional bias with AI-based scoring ✅ Combines market context, catalysts, and momentum ✅ Produces structured, repeatable trade logic ✅ Saves hours of manual chart and news analysis ✅ Ideal for fast-paced trading environments 🧩 Features MCP (Market Context Protocol) data ingestion AI-powered trade thesis generation Confidence and risk scoring logic Short-term momentum and catalyst analysis Strict structured output for automation reliability Easy integration into dashboards or execution systems 🔐 Requirements MCP-compatible market data source OpenAI API credentials (GPT-4o or GPT-4o-mini recommended) n8n instance with AI nodes enabled Defined schema for trade idea output 🎯 Target Audience Short-term and swing traders Quant and discretionary trading teams Market research analysts Fintech platforms generating trade signals Automation engineers building trading workflows

CV → Match → Screen → Decide, all automated This workflow automatically processes candidate CVs from email, intelligently matches them to job descriptions, performs AI-powered screening analysis, and sends actionable summaries to your team in Slack. Good to know Handles both PDF and Word document CVs automatically Two-stage JD matching: prioritizes role mentioned in candidate's email, falls back to CV analysis if needed Uses Google Gemini API for AI screening (generous free tier and rate limits, typically enough to avoid paying for API requests, but check latest pricing at Google AI Pricing) All CVs stored in Google Drive with standardized naming (candidate name + date/time) Complete audit trail logged in Google Sheets Who's it for Hiring teams and recruiters who want to automate first-round CV screening while maintaining quality. Perfect for companies receiving high volumes of applications across multiple roles, especially in tech, sales, or automation-focused positions. How it works Gmail monitors inbox for CVs with specific label and downloads attachments Detects file type (PDF or Word) and converts/standardizes format for text extraction AI agent matches candidate to best-fit job description by analyzing email context first (if candidate mentioned a role), or CV content as fallback (selects up to 3 potential JD matches) If multiple JDs matched, second AI agent selects the single best fit AI recruiter agent analyzes CV against selected JD and generates structured screening report (strengths, weaknesses, risk/reward factors, overall fit score 0-10 with justification) Extracts candidate details (name, email) from CV text Logs complete analysis to Google Sheets tracker Sends formatted summary to Slack with Proceed/Reject action buttons for instant team decisions Requirements Gmail account with API access Google Drive account (OAuth2) Google Sheets account (OAuth2) Slack workspace with bot permissions Google Gemini API key (Get free key here) Google Drive folders: one for CVs, one for Job Descriptions (as PDFs or Google Docs) How to set up Add credentials: Gmail OAuth2, Google Drive OAuth2, Google Sheets OAuth2, Slack OAuth2, Google Gemini API Create Gmail label (e.g., "CV-Screening") for incoming candidate emails In "Receive CV via Email" node: select your Gmail label for filtering Create two Google Drive folders: "Candidate CVs" and "Job Descriptions" In "Upload CV - PDF" and "Stream Doc/Docx File" nodes: update folder ID to your "Candidate CVs" folder In "Access JD Files" node: update folder ID to your "Job Descriptions" folder Create Google Sheet named "AI Candidate Screening" with columns matching the sample AI Candidate Screening sheet In "Append row in sheet" node: select your Google Sheet In "Send Candidate Screening Confirmation" node: select your Slack channel Activate workflow Customizing this workflow Change JD matching logic: Edit "JD Matching Agent" node prompt to adjust how CVs are matched to roles (e.g., weight technical skills vs. experience). Change "Company Description" in AI prompts: Insert your "Company Description" in System Message sections in "JD Matching Agent" and "Detailed JD Matching Agent" nodes Modify screening criteria: Edit "Recruiter Scoring Agent" node system message to focus on specific qualities (culture fit, leadership, technical depth, etc.) Add more storage locations: Add nodes to save CVs to other systems (Notion, Airtable, ATS platforms) Customize Slack message: Edit "Send Candidate Screening Confirmation" node to change formatting, add more context, or include additional candidate data Auto-proceed logic: Add IF node after screening to auto-proceed candidates with fit score above threshold (e.g., 8+/10) Add email responses: Connect nodes to automatically email candidates (confirmation, rejection, interview invite) Add human-in-the-loop: Sub-workflow triggered by Slack response or email response, to update Sheet with approve/reject status Add candidate email responses + interview scheduling**: For approved candidates, trigger email to candidate with Cal.com or Calendly link so they can book their interview Quick Troubleshooting No CVs being processed: Check Gmail label is correctly set in "Receive CV via Email" node and emails are being labeled Word documents failing: Verify "Stream Doc/Docx File" node has correct parent folder ID and Google Drive credentials authorized JD matching returns no results: Check "Access JD Files" node folder ID points to your Job Descriptions folder, and JD files are named clearly (e.g., "Marketing Director JD.pdf") JD matching is not relevant for my company: Update the "Company Description" in the System Messages in the "JD Matching Agent" and "Detailed JD Matching Agent" nodes "Can't find matching JD": Ensure candidate's email mentions role name OR their CV clearly indicates relevant experience for available JDs Google Sheets errors: Verify sheet name is "AI Candidate Screening" and column headers exactly match workflow expectations (Submission ID, Date, CV, First Name, etc.) Slack message not appearing: Re-authorize Slack credentials and confirm channel ID in "Send Candidate Screening Confirmation" node Missing candidate name/email: CV text must be readable - check PDF extraction quality or try converting complex CVs to simpler format 401/403 API errors: Re-authorize all OAuth2 credentials (Gmail, Google Drive, Google Sheets, Slack) AI analysis quality issues: Edit system prompts in "JD Matching Agent" and "Recruiter Scoring Agent" nodes to refine screening criteria Sample Outputs Google Sheets - AI Candidate Screening - sample Slack confirmation message Acknowledgments This workflow was inspired by Nate Herk's YouTube demonstration on building a resume analysis system. This implementation builds upon that foundation by adding dynamic job description matching (initial + detailed JD matching agents), Slack Block Kit integration with interactive buttons, updated Google Drive API methods for document handling, and enhanced candidate data capture in Google Sheets.

Overview Slack quietly released an update to their API that allows developers to build "AI Apps & Agents", which is a special classification of apps that have access to several special capabilities including: Multiple simultaneous chat threads with one user Loading "three dots" UI while your agent is thinking Option for users to pin your app to their top bar for quick chat access This workflow demonstrates how to build a Slack agent that takes advantage of all of these features. For a full video walkthrough of this workflow, watch this YouTube tutorial. Setup Instructions All of the below steps are required for this workflow to function properly unless otherwise noted. Create a Slack App Visit api.slack.com and click "Your Apps" Create a new app from scratch and follow the setup instructions In the Agents & AI Apps tab, enable the toggle and give your app a brief description In the OAuth & Permissions tab, enable the following bot token scopes: assistant:write chat:write channels:read im:history Install the app into your workspace and grant the requested permissions In your Slack workspace, right click your app's name in the sidebar, click "View app details", and make note of your apps Channel ID - you'll need this later. Copy your app's Bot User OAuth Token - you'll need that to create your n8n credentials In the Event Subscriptions tab, enable events and paste the workflows PRODUCTION webhook url (from this workflow's trigger node) into the input. In the same tab under "Susbcribe to bot events", select message.im Create a Postgres database In order to save the chat history and give your agent a working memory, you'll need your own Postgres database. You can use Supabase, Neon, or any other Postgres database provider. Once you've added your database's credentials to n8n, you can select those credentials in the Postgres Chat Memory node. This worklow saves all chat history in a table called chat_histories, but you name the table whatever you want. Create n8n Credentials You'll need to create the following credentials: Slack API. Use your Bot User OAuth Token referenced above. Bearer Auth. Use the same Bot User OAuth Token. Postgres. Use the connection string or config from your database provider. OpenRouter (or any other LLM model for the agent's model node) Wire Everything Up Now that you've created your Slack app, have your Postgres database, and have created credentials, follow these steps to wire up your workflow: In the "On Message Received" trigger, use your Slack API credential and enter your apps Channel ID in the "Channel To Watch" field. In the "Set Thinking Status" node, use your Bearer Auth credential. In the "Postgres Chat Memory" node, use your Postgres credential. In the "Send Reply" node, use your Slack API credential. Using the Chatbot Once you've completed the setup process and added in your credentials, you'll have a fully functional Slack chatbot complete with threads, loading UI, and the ability to pin your app to your workspace's top bar. Taking the Next Steps Now that this skeleton app is in place, it's up to you to add horsepower to the AI agent at the center of it all. Customize the prompts and add whatever tools you'd like. The sky is the limit! If you have any questions or feedback about this workflow, or would like me to build custom workflows for your business, email me at [email protected].

Workflow Overview This cutting-edge n8n automation is a sophisticated market research and intelligence gathering tool designed to transform web content discovery into actionable insights. By intelligently combining web crawling, AI-powered filtering, and smart summarization, this workflow: Discovers Relevant Content: Automatically crawls target websites Identifies trending topics Extracts comprehensive article details Intelligent Content Filtering: Applies custom keyword matching Filters for most relevant articles Ensures high-quality information capture AI-Powered Summarization: Generates concise, meaningful summaries Extracts key insights Provides quick, digestible information Seamless Delivery: Sends summaries directly to Slack Enables instant team communication Facilitates rapid information sharing Key Benefits 🤖 Full Automation: Continuous market intelligence 💡 Smart Filtering: Precision content discovery 📊 AI-Powered Insights: Intelligent summarization 🚀 Instant Delivery: Real-time team updates Workflow Architecture 🔹 Stage 1: Content Discovery Scheduled Trigger**: Daily market research FireCrawl Integration**: Web content crawling Comprehensive Site Scanning**: Extracts article metadata Captures full article content Identifies key information sources 🔹 Stage 2: Intelligent Filtering Keyword-Based Matching** Relevance Assessment** Custom Domain Optimization**: AI and technology focus Startup and innovation tracking 🔹 Stage 3: AI Summarization OpenAI GPT Integration** Contextual Understanding** Concise Insight Generation**: 3-point summary format Captures essential information 🔹 Stage 4: Team Notification Slack Integration** Instant Information Sharing** Formatted Insight Delivery** Potential Use Cases Market Research Teams**: Trend tracking Innovation Departments**: Technology monitoring Startup Ecosystems**: Competitive intelligence Product Management**: Industry insights Strategic Planning**: Rapid information gathering Setup Requirements FireCrawl API Web crawling credentials Configured crawling parameters OpenAI API GPT model access Summarization configuration API key management Slack Workspace Channel for insights delivery Appropriate app permissions Webhook configuration n8n Installation Cloud or self-hosted instance Workflow configuration API credential management Future Enhancement Suggestions 🤖 Multi-source crawling 📊 Advanced sentiment analysis 🔔 Customizable alert mechanisms 🌐 Expanded topic tracking 🧠 Machine learning refinement Technical Considerations Implement robust error handling Use exponential backoff for API calls Maintain flexible crawling strategies Ensure compliance with website terms of service Ethical Guidelines Respect content creator rights Use data for legitimate research Maintain transparent information gathering Provide proper attribution Workflow Visualization [Daily Trigger] ⬇️ [Web Crawling] ⬇️ [Content Filtering] ⬇️ [AI Summarization] ⬇️ [Slack Delivery] Connect With Me Ready to revolutionize your market research? 📧 Email: [email protected] 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your information gathering with intelligent, automated workflows! #AIResearch #MarketIntelligence #AutomatedInsights #TechTrends #WebCrawling #AIMarketing #InnovationTracking #BusinessIntelligence #DataAutomation #TechNews

This n8n workflow automatically fetches trending news articles based on your chosen country, category, and keyword — then enriches the data with AI-powered business insights before posting a concise summary to Slack. Ideal for sales teams, executives, marketers, or anyone who wants fast, actionable news briefings directly in their Slack workspace. ⸻ Who it’s for Executives, analysts, sales teams, or marketing professionals who want curated, AI-enhanced news summaries tailored to business opportunities, risks, and trends — delivered automatically to Slack. ⸻ How it works / What it does A Schedule Trigger runs on a daily, weekly, or custom frequency. It queries the NewsAPI to retrieve top headlines by country, category, or keyword. Headlines are formatted and enriched with your configured query context. The AI model (GPT-4) analyzes articles and summarizes key insights, categorizing them as Opportunities, Risks, or Trends. Finally, the summarized insights are posted directly into a Slack channel of your choice. ⸻ How to set up Set your schedule frequency in the Schedule Trigger node. Configure your preferred country, category, and keyword in the Inject Config node. Add your NewsAPI Key inside the Fetch News Articles node. Connect your Slack credentials in the Post to Slack node. Optional: Adjust the AI prompt for more tailored analysis. ⸻ Requirements A NewsAPI account to fetch headlines. An OpenAI API key for GPT-4 summarization. A Slack workspace and connected credentials via n8n. ⸻ How to customize the workflow Change the country, category, or keyword in the Inject Config to focus on specific markets or sectors. Adjust the AI prompt in the GPT node to prioritize certain insights like ESG factors, M&A activity, or market sentiment. Extend the workflow to log results to Google Sheets, email summaries, or send SMS alerts. Replace the Schedule Trigger with a Webhook if you want to trigger summaries on demand. This template is designed to be modular, making it easy to adapt for competitive intelligence, investment tracking, or industry news curation.

Who is this for? This workflow is ideal for n8n self-hosted users, DevOps engineers, and automation developers who want to automatically back up their n8n workflows to GitHub on a regular basis. What problem is this workflow solving Manually backing up n8n workflows can be time-consuming and prone to human error. This workflow automates the backup process, ensuring that all workflows are safely stored in a version-controlled GitHub repository every 24 hours. What this workflow does This automation runs daily to back up all workflows from your n8n instance to a specified GitHub repository. Each workflow is saved as a .json file using its unique ID, organized into a folder path defined by repo_path. The workflow is designed to manage memory usage efficiently by recursively calling itself. Once the backup is complete, it optionally sends a Slack notification to confirm success. Setup Configure the Config node in the subworkflow to set: GitHub Repo Owner GitHub Repo Name Main folder path (repo_path) Connect your GitHub and (optionally) Slack credentials. Set the workflow to run on a daily cron schedule. Test the workflow manually to confirm the GitHub integration works. Sign up for a free consultation and find out how n8n can help you.

✅ What problem does this workflow solve? Automatically monitor multiple websites every 5 minutes, log downtime, notify your team instantly via multiple channels, and track uptime/downtime in a Google Sheet—without relying on expensive monitoring tools. ⚙️ What does this workflow do? Triggers every 5 minutes to monitor website health. Fetches a list of website URLs from a Google Sheet. Checks the status of each website one by one. Sends instant alerts if a website is down (Email, Slack, Telegram, Voice Call). Logs downtime events in Google Sheets. Tracks when websites are back up and updates the log. Sends recovery notifications when a site is live again (Email, Slack, Telegram). 🔧 Setup 📄 Google Sheets Setup Sheet 1: List of website URLs to monitor. Sheet 2: Log to store uptime/downtime records. Sample Format: https://docs.google.com/spreadsheets/d/1_VVpkIvpYQigw5q0KmPXUAC2aV2rk1nRQLQZ7YK2KwY/edit?usp=sharing ✉️ Gmail, Slack & Telegram Setup Connect Gmail, Slack, and Telegram to n8n. Configure each service with proper credentials or OAuth. 📞 Vapi (Voice Call) Setup Create a Vapi account. Generate an API key. Configure API Parameters (vapi_api_key, assistant_id, number, phone_number_id) on VAPI Node. Insert the First Message specified in the Workflow. 🧠 How it Works ⏱ 1. Scheduled Monitoring A Schedule Trigger runs the workflow every 5 minutes. It reads the list of URLs from the Google Sheet and loops through each one. 🌍 2. Website Health Check Each website is pinged to check if it’s online. 🔴 3. If Website is Down: It verifies if a downtime record already exists. If not, it: Adds a new row in the Google Sheet with the timestamp. Sends notifications via: 📧 Email 💬 Slack 📲 Telegram 📞 Voice Call via Vapi 🟢 4. If Website is Back Up: It fetches the matching downtime record. Updates the sheet with: ✅ Uptime timestamp ⏱ Total downtime duration Sends recovery notifications via: 📧 Email 💬 Slack 📲 Telegram (No phone call is made for uptime.) 👤 Who can use it? This is perfect for: 🚀 Startups 👨‍💻 Freelance Developers 🛠 SaaS Product Owners 🖥 IT/DevOps Teams If you're looking to replace tools like UptimeRobot, Pingdom, or StatusCake, this no-code solution gives you full control, customization, and cost-efficiency.

This workflow creates an automated Product Intelligence Engine that continuously collects signals from multiple product sources and generates structured PRD updates using AI. It ingests conversations, feedback, support tickets, analytics, and design comments, standardizes them, analyzes them with an AI PRD Agent, and automatically updates a Google Doc with structured PRD recommendations. Instead of manually reviewing Slack threads, Zoom calls, Jira comments, support tickets, and customer forms, this workflow centralizes everything into one intelligent PRD analysis system. High-Level Architecture - The workflow runs in 4 layers: Signal Ingestion Layer Captures product signals from: • Slack (channel messages + app mentions) • Customer Form submissions • Zoom recordings (scheduled) • Jira comments (scheduled) • Zendesk tickets (scheduled) • Figma comments (file updates) • Platform analytics via webhook • (Extendable to Salesforce / HubSpot) Standardization Layer Each source passes through a Format Node that: • Extracts relevant text • Normalizes metadata • Adds timestamps • Labels source type All inputs are converted into a unified "product signal" object. Intelligence Layer (AI PRD Agent) All signals are merged into a single stream using a Merge node. The PRD Analysis Agent then: • Extracts feature requests • Detects scope changes • Identifies risks and constraints • Evaluates priority signals • Detects target user shifts • Generates structured PRD updates PRD Governance Layer - output in a Google Doc The structured AI output is appended to a Google Doc, which is fully traceable. This creates a living PRD that continuously evolves based on real product signals. Required Credentials (And How To Add Them): You will need to configure the following credentials in n8n: Slack Used for Slack Trigger. Steps: Create a Slack App at api.slack.com Enable: app_mentions:read channels:history chat:write (optional if you want replies) Install app to workspace Copy Bot OAuth Token In n8n → Create Slack API credential Paste token Reference - https://www.youtube.com/watch?v=qk5JH6ImK0I Zoom (OAuth2) Used to fetch recordings. Steps: Create an OAuth App in Zoom Marketplace Add the Redirect URL from n8n Copy Client ID + Secret Add Zoom OAuth2 credential in n8n Connect account Reference - https://www.youtube.com/watch?v=BC6O_3LYgac Google Docs (OAuth2) Used to update PRD document. Steps: Create Google Cloud Project Add Doc URl to n8n Replace the example Google Doc URL with your own PRD document. Reference - https://www.youtube.com/watch?v=iieEHvu93dc Jira (Cloud) Steps: Generate API token from Atlassian Create Jira Software Cloud credential Enter: Email API token Domain Reference - https://www.youtube.com/watch?v=T4z7lzqSZDY Zendesk Steps: Generate API token Add Zendesk credential Enter: Subdomain Email API token Figma Steps: Generate a personal access token in Figma Add Figma credentials with the team ID Paste token Platform Analytics Webhook Replace: <PLACEHOLDER_VALUE__your_analytics_api_endpoint> With your real analytics endpoint. You can: • Send Mixpanel exports • Send Amplitude exports • Or POST custom JSON What Makes This Powerful • Eliminates product signal silos • Creates AI-driven PRD governance • Ensures traceability of decisions • Enables continuous PRD evolution • Scales across teams

🧠 Google Ads Monthly Performance Optimization (Channable + Google Ads + Relevance AI) 🚀 Overview This workflow automatically analyzes your Google Ads performance every month, identifies top-performing themes and categories, and regenerates optimized ad copy using Relevance AI — powered by insights from your Channable product feed. It then saves the improved ads to Google Sheets for review and sends a detailed performance report to your Slack workspace. Ideal for marketing teams who want to automate ad optimization at scale with zero manual intervention. 🔗 Integrations Used Google Ads** → Fetch campaign and ad performance metrics using GAQL. Relevance AI** → Analyze performance data and regenerate ad copy using AI agents and tools. Channable** → Pull updated product feeds for ad refresh cycles. Google Sheets** → Save optimized ad copy for review and documentation. Slack** → Send a 30-day performance report to your marketing team. 🧩 Workflow Summary | Step | Node | Description | | ---- | --------------------------------------------------- | --------------------------------------------------------------------------- | | 1 | Monthly Schedule Trigger | Runs automatically on the 1st of each month to review last 30 days of data. | | 2 | Get Google Ads Performance Data | Fetches ad metrics via GAQL query (impressions, clicks, CTR, etc.). | | 3 | Calculate Performance Metrics | Groups results by ad group and theme to find top/bottom performers. | | 4 | AI Performance Analysis (Relevance AI) | Generates human-readable insights and improvement suggestions. | | 5 | Update Knowledge Base (Relevance AI) | Saves new insights for future ad copy training. | | 6 | Get Updated Product Feed (Channable) | Retrieves the latest catalog items for ad regeneration. | | 7 | Split Into Batches | Splits the feed into groups of 50 to avoid API rate limits. | | 8 | Regenerate Ad Copy with Insights (Relevance AI) | Rewrites ad copy with the latest product and performance data. | | 9 | Save Optimized Ads to Sheets | Writes output to your “Optimized Ads” Google Sheet. | | 10 | Generate Performance Report | Summarizes the AI analysis, CTR trends, and key insights. | | 11 | Email Performance Report (Slack) | Sends report directly to your Slack channel/team. | 🧰 Requirements Before running the workflow, make sure you have: A Google Ads account with API access and OAuth2 credentials. A Relevance AI project (with one Agent and one Tool setup). A Channable account with API key and project feed. A Google Sheets document for saving results. A Slack webhook URL for sending performance summaries. ⚙️ Environment Variables Add these environment variables to your n8n instance (via .env or UI): | Variable | Description | | -------------------------------- | ------------------------------------------------------------------- | | GOOGLE_ADS_API_VERSION | API version (e.g., v17). | | GOOGLE_ADS_CUSTOMER_ID | Your Google Ads customer ID. | | RELEVANCE_AI_API_URL | Base Relevance AI API URL (e.g., https://api.relevanceai.com/v1). | | RELEVANCE_AGENT_PERFORMANCE_ID | ID of your Relevance AI Agent for performance analysis. | | RELEVANCE_KNOWLEDGE_SOURCE_ID | Knowledge base or dataset ID used to store insights. | | RELEVANCE_TOOL_AD_COPY_ID | Relevance AI tool ID for generating ad copy. | | CHANNABLE_API_URL | Channable API endpoint (e.g., https://api.channable.com/v1). | | CHANNABLE_COMPANY_ID | Your Channable company ID. | | CHANNABLE_PROJECT_ID | Your Channable project ID. | | FEED_ID | The feed ID for product data. | | GOOGLE_SHEET_ID | ID of your Google Sheet to store optimized ads. | | SLACK_WEBHOOK_URL | Slack Incoming Webhook URL for sending reports. | 🔐 Credentials Setup in n8n | Credential | Type | Usage | | ----------------------------------------------- | ------- | --------------------------------------------------- | | Google Ads OAuth2 API | OAuth2 | Authenticates your Ads API queries. | | HTTP Header Auth (Relevance AI & Channable) | Header | Uses your API key as Authorization: Bearer <key>. | | Google Sheets OAuth2 API | OAuth2 | Writes optimized ads to Sheets. | | Slack Webhook | Webhook | Sends monthly reports to your team channel. | 🧠 Example AI Insight Output { "insights": [ "Ad groups using 'vegan' and 'organic' messaging achieved +23% CTR.", "'Budget' keyword ads underperformed (-15% CTR).", "Campaigns featuring 'new' or 'bestseller' tags showed higher conversion rates." ], "recommendations": [ "Increase ad spend for top-performing 'vegan' and 'premium' categories.", "Revise copy for 'budget' and 'sale' ads with low CTR." ] } 📊 Output Example (Google Sheet) | Product | Category | Old Headline | New Headline | CTR Change | Theme | | ------------------- | -------- | ------------------------ | -------------------------------------------- | ---------- | ------- | | Organic Protein Bar | Snacks | “Healthy Energy Anytime” | “Organic Protein Bar — 100% Natural Fuel” | +12% | Organic | | Eco Face Cream | Skincare | “Gentle Hydration” | “Vegan Face Cream — Clean, Natural Moisture” | +17% | Vegan | 📤 Automation Flow Run Automatically on the first of every month (cron: 0 0 1 * *). Fetch Ads Data → Analyze & Learn → Generate New Ads → Save & Notify. Every iteration updates the AI’s knowledge base — improving your campaigns progressively. ⚡ Scalability The flow is batch-optimized (50 items per request). Works for large ad accounts with up to 10,000 ad records. AI analysis & regeneration steps are asynchronous-safe (timeouts extended). Perfect for agencies managing multiple ad accounts — simply duplicate and update the environment variables per client. 🧩 Best Use Cases Monthly ad creative optimization for eCommerce stores. Marketing automation for Google Ads campaign scaling. Continuous learning ad systems powered by Relevance AI insights. Agencies automating ad copy refresh cycles across clients. 💬 Slack Report Example 30-Day Performance Optimization Report Date: 2025-10-01 Analysis Period: Last 30 days Ads Analyzed: 842 Top Performing Themes Vegan: 5.2% CTR (34 ads) Premium: 4.9% CTR (28 ads) Underperforming Themes Budget: 1.8% CTR (12 ads) AI Insights “Vegan” and “Premium” themes outperform baseline by +22% CTR. “Budget” ads underperform due to lack of value framing. Next Optimization Cycle: 2025-11-01 🛠️ Maintenance Tips Update your GAQL query occasionally to include new metrics or segments. Refresh Relevance AI tokens every 90 days (if required). Review generated ads in Google Sheets before pushing them live. Test webhook and OAuth connections after major n8n updates. 🧩 Import Instructions Open n8n → Workflows → Import from File / JSON. Paste this workflow JSON or upload it. Add all required environment variables and credentials. Execute the first run manually to validate connections. Once verified, enable scheduling for automatic monthly runs. 🧾 Credits Developed for AI-driven marketing teams leveraging Google Ads, Channable, and Relevance AI to achieve continuous ad improvement — fully automated via n8n.

💼 LinkedIn Content Machine – AI-Powered Post Generator & Scheduler for X and LinkedIn How it works: This end-to-end workflow automates your personal or brand content strategy by: 🧠 Using Google Gemini or OpenAI to generate engaging LinkedIn/X content from a title or trending posts. 🗓️ Posting directly to LinkedIn and X (formerly Twitter). 📊 Pulling high-performing LinkedIn posts to inspire new ideas. ✍️ Saving AI-generated drafts to Google Sheets for review. 🔔 Notifying your team on Slack when drafts are ready. Steps to set up: Add your API keys for Google Gemini or OpenAI. Set up your LinkedIn, X (Twitter), Google Sheets, and Slack credentials. Customize prompt logic or post filters if needed. Schedule the idea generation module or trigger it manually. Start generating and posting consistent, high-quality content with zero manual effort!

This n8n workflow automates the process of collecting, storing, and summarizing customer reviews from the Apple App Store for multiple apps. It fetches daily reviews, stores them in a Pinecone vector database, and generates a weekly summary using OpenAI, which is then posted to a Slack channel. Key Features Fetches daily customer reviews for a list of Apple App Store apps using the App Store Connect API. Stores reviews in Pinecone, with separate namespaces for each app and automatic weekly cleanup. Uses OpenAI to generate a summary of reviews, including positive/negative highlights and average star rating. Posts the summary to a specified Slack channel every week. How to use Set your Apple App Store app IDs and names in the provided Set nodes. Configure your Apple API, Pinecone, OpenAI, and Slack credentials. Adjust the schedule triggers as needed for daily fetching and weekly summarization. Deploy the workflow to automate review monitoring and reporting for your apps.

📘 Description This workflow automates the entire process of taking a raw user-submitted script, rewriting it into a polished HeyGen-ready narration, generating a full avatar video, handling all processing checks, retrieving the final MP4, uploading it to Google Drive, and notifying the user through email and Slack. It turns an unstructured script into a complete, ready-to-share video package without any manual intervention. The system rewrites scripts into structured sections (intro → narration sections → closing), ensures the final narration fits HeyGen’s free-tier limits, submits the script to HeyGen’s video API, initiates a polling loop until the video is fully rendered, downloads the finished file, uploads it to Drive with metadata, and generates clean notification messages containing the file links. Errors at any point trigger automated Slack alerts for debugging. This workflow replaces manual rewriting, video rendering, download/upload, and team communication—providing a fully-managed script-to-video pipeline. ⚙️ What This Workflow Does (Step-by-Step) 🟢 Receive Script Request via Webhook Captures the raw script payload submitted by the user. 🧹 Parse Incoming Script Payload Converts stringified JSON body into structured data for processing. ✍️ Rewrite & Structure Video Script Using AI Transforms the raw script into: • Video title • Intro • Multiple sections (title + narration) • Closing • Combined full_script optimized for HeyGen’s ~300–350 word limit Ensures natural, human narration with smooth transitions. 🧠 Script Writing Memory + LLM Engine Maintains writing consistency and uses GPT-4.1-mini to generate polished narration. 🧪 Validate Structured Script Output Enforces strict JSON schema for titles, sections, closing, and full_script. 🎞️ Submit Video Generation Request to HeyGen Sends structured script to HeyGen API with avatar, voice, and background settings for 1280×720 export. ⏳ Wait Before Polling Loop Initial delay before the first status check. 📡 Check HeyGen Video Status Queries the HeyGen API to check progress of the video generation. ⏳ Wait Before Rechecking Adds timed spacing between repeated polls. 🔍 Evaluate Video Completion If status = completed → proceed If still processing → loop back to polling sequence 📥 Download Generated HeyGen Video Retrieves the completed MP4 using the returned video_url. ☁️ Upload Video to Google Drive Uploads the MP4 to a specific Google Drive folder, returning: • File name • View link • Download link • Timestamp • Size metadata 📣 Generate Completion Notifications Using AI Creates two outputs: • A friendly short email summary • A concise Slack message Both containing the Google Drive file link. 🧠 Notification Memory + LLM Engine Maintains tone and ensures consistent messaging across channels. 🧪 Validate Notification Output JSON Ensures email_subject, email_body, and slack_message follow schema. 💬 Send Video Ready Slack Notification Posts the final Slack message containing file details and Drive link. 📧 Send Video Ready Email Notification Emails the summary and file link to the designated recipient. 🚨 Error Handler Trigger → Slack Alert Any workflow failure sends a detailed diagnostic alert with node name, message, and timestamp. 🧩 Prerequisites • HeyGen API key • Google Drive OAuth connection • Slack API credentials • Gmail OAuth connection • Valid webhook endpoint to receive scripts 💡 Key Benefits ✔ Converts raw script → structured HeyGen-ready narration automatically ✔ Produces full HD avatar video with zero manual steps ✔ Polls HeyGen until the video is fully rendered ✔ Securely uploads final MP4 to Google Drive ✔ Auto-generates polished email + Slack notifications ✔ Ensures narrative consistency using memory context ✔ Provides real-time error alerts via Slack 👥 Perfect For HR teams automating onboarding videos Marketing teams producing quick avatar explainers Training content creators Founders creating rapid how-to or announcement videos Anyone needing instant script-to-video automation

How it works Runs every morning at 8:00 using the Schedule Trigger. Sets a stale_days value and queries Salesforce for Opportunities where Stage_Unchanged_Days__c equals that value and the stage is not Closed Won / Closed Lost. For each “stale” Opportunity, loads full deal details and sends them to an OpenAI model. The model uses the query_soql tool to pull recent Notes, the primary Contact, and the Opportunity Owner, then returns a single JSON object with: a personalized follow-up email for the client, a short SMS template, a concise Slack summary for the sales team, and a ready-to-use Task payload for Salesforce. n8n parses that JSON, sends the email via SMTP, posts the Slack message to your chosen channel, and creates a Salesforce Task assigned to the Opportunity Owner so every stalled deal has a clear next step. Setup steps Estimated setup time: ~30–45 minutes if your Salesforce, OpenAI, SMTP and Slack credentials are ready. Create Stage_Unchanged_Days__c on Opportunity (Salesforce) Field Type: Formula (Number, 0 decimal places) Formula: IF( ISBLANK(LastStageChangeDate), TODAY() - DATEVALUE(CreatedDate), TODAY() - DATEVALUE(LastStageChangeDate) ) This field tracks how many days the Opportunity has been in the current stage. Connect credentials in n8n Salesforce OAuth2 for the Salesforce nodes and the query_soql HTTP Tool. OpenAI (or compatible) credential for the “Message a model” node. SMTP credential for the customer email node. Slack credential for the internal notification node. Configure your follow-up rules In Edit Fields (Set), set stale_days to the threshold that defines a stalled deal (e.g. 7, 14, 30). In Perform a query, optionally refine the SOQL (record types, owners, minimum amount, etc.) to match your pipeline. Update the Send Email SMTP Customer node with your real “from” address and tweak the wording if needed. Point Send Message To Internal Team (Slack) to the right channel or user. Test safely Turn off the Schedule Trigger and run the workflow manually with a few test Opportunities. Inspect the AI output in Message a model and Parse JSON to confirm the structure (email, sms, slack, task.api_body). Check that the email and Slack messages look good and that Salesforce Tasks are created, assigned to the right Owner, and linked to the correct Opportunity. Go live Re-enable the Schedule Trigger. Monitor the first few days to confirm that follow-ups, Slack alerts, and Tasks all behave as expected, then let the automation quietly keep your pipeline clean and moving.

LinkedIn Profile Tracker Automation Who is this for? This template is ideal for sales teams, recruiters, business development professionals, and relationship managers who need to monitor changes in their network's LinkedIn profiles. Perfect for agencies tracking client personnel changes, HR teams monitoring talent movements, sales professionals staying updated on prospect job changes, and content teams tracking influencer activity. What problem does this workflow solve? Manually checking LinkedIn profiles for updates like job changes, status modifications, profile edits, or latest posts is extremely time-consuming and easy to miss. This automation eliminates the need for constant manual monitoring while ensuring you never miss important changes that could signal new business opportunities, relationship updates, or content engagement opportunities. What this workflow does This workflow automatically monitors a list of LinkedIn profiles on a weekly schedule, detects any changes in: Personal information** (name, headline, summary) Job status** (hiring/open to work flags) Latest work experience** (new positions, company changes) Recent posts** (latest content activity) When changes are detected, it immediately sends Slack notifications with before/after comparisons and updates your tracking database to maintain historical records of all profile evolution. Setup Create a Ghost Genius API account and get your API key for LinkedIn profile scraping Configure HTTP Request nodes with Header Auth credentials using your Ghost Genius API key Set up your Google Sheets database with columns: Firstname, Lastname, LinkedIn URL, ID Tagline, Summary, Latest experience Open to work?, Hiring?, Latest post Configure Slack webhook integration for real-time notifications Set up credentials for Google Sheets and Slack following n8n documentation Add LinkedIn profile URLs to your Google Sheet to start monitoring How to customize this workflow Modify the schedule trigger** to check profiles daily, bi-weekly, or monthly based on your monitoring needs Customize Slack notification messages** to include additional context, mentions, or custom formatting Add email notifications** alongside Slack alerts for critical changes like job transitions Set up filtered notifications** to only alert on specific types of changes (e.g., job changes only, posts from key influencers) Add post content analysis** to detect mentions of your company or competitors Integrate with CRM systems** to automatically update lead records when profile changes occur

Revolutionize your recruitment process with intelligent AI-driven candidate screening that evaluates resumes, scores applicants, and automatically routes them based on fit - saving 10-15 hours per week on initial screening. 🎯 What This Workflow Does Transforms your hiring pipeline from manual resume review to intelligent automation: 📝 Captures Applications - Jotform intake with resume upload 🤖 AI Resume Analysis - OpenAI parses skills, experience, education, and red flags 🎯 Intelligent Scoring - Evaluates candidates against job requirements with structured scoring (0-100) 🚦 Smart Routing - Automatically routes based on AI recommendation: Strong Yes (85-100): Instant Slack alert → Interview invitation Maybe/Yes (60-84): Manager review → Approval workflow No (<60): Polite rejection email 📊 Analytics Tracking - All data logged to Google Sheets for hiring insights ✨ Key Features AI Resume Parsing**: Extracts structured data from any resume format Intelligent Scoring System**: Multi-dimensional evaluation (skills match, experience quality, cultural fit) Structured Output**: Consistent JSON schema ensures reliable data for decision-making Automated Communication**: Personalized emails for every candidate outcome Human-in-the-Loop**: Manager approval for borderline candidates Comprehensive Analytics**: Track conversion rates, average scores, and hiring metrics Customizable Job Requirements**: Easy prompt editing to match any role 💼 Perfect For Startups & Scale-ups**: Processing 50+ applications per week HR Teams**: Wanting to reduce time-to-hire by 40-60% Technical Recruiters**: Screening engineering, product, or design roles Growing Companies**: Scaling hiring without scaling headcount 🔧 What You'll Need Required Integrations Jotform** - Application intake form (free tier works) Create your form for free on Jotform using this link OpenAI API** - GPT-4o-mini for cost-effective AI analysis (~$0.15 per candidate) Gmail** - Automated candidate communication Google Sheets** - Hiring database and analytics Optional Integrations Slack** - Instant alerts for hot candidates Linear/Asana** - Task creation for interview scheduling Calendar APIs** - Automated interview booking 🚀 Quick Start Import Template - Copy JSON and import into n8n Create Jotform - Use provided field structure (name, email, resume upload, etc.) Add API Keys - OpenAI, Jotform, Gmail, Google Sheets Customize Job Requirements - Edit AI screening prompt with your role details Personalize Emails - Update templates with your company branding Test & Deploy - Submit test application and verify all nodes 🎨 Customization Options Adjust Scoring Thresholds**: Change routing logic based on your needs Multiple Positions**: Clone workflow for different roles with unique requirements Add Technical Assessments**: Integrate HackerRank, CodeSignal, or custom tests Interview Scheduling**: Connect Calendly or Google Calendar for auto-booking ATS Integration**: Push data to Lever, Greenhouse, or BambooHR Diversity Tracking**: Add demographic fields and analytics Reference Checking**: Automate reference request emails 📈 Expected Results 90% reduction** in manual resume review time 24-hour response time** to all candidates Zero missed applications** - every candidate gets feedback Data-driven hiring** - track what works with comprehensive analytics Better candidate experience** - fast, professional communication Consistent evaluation** - eliminate unconscious bias with structured AI scoring 🏆 Use Cases Technology Companies Screen 100+ engineering applications per week, identify top 10% instantly, schedule interviews same-day. Agencies & Consultancies Evaluate consultant candidates across multiple skill dimensions, route to appropriate practice areas. High-Volume Hiring Process retail, customer service, or sales applications at scale with consistent quality. Remote-First Teams Evaluate global candidates 24/7, respond instantly regardless of timezone. 💡 Pro Tips Train Your AI**: After 50+ applications, refine prompts based on false positives/negatives A/B Test Thresholds**: Experiment with score cutoffs to optimize for your needs Build Talent Pipeline**: Keep "maybe" candidates in CRM for future roles Track Source Effectiveness**: Add UTM parameters to measure which job boards deliver best candidates Continuous Improvement**: Weekly review of AI assessments to calibrate accuracy 🎓 Learning Resources This workflow demonstrates: AI Agents with structured output Multi-stage conditional routing Human-in-the-loop automation Binary data processing (resume files) Email automation with HTML templates Real-time notifications Analytics and data logging Perfect for learning advanced n8n automation patterns! Ready to transform your hiring process? Import this template and start screening candidates intelligently in under 30 minutes. Questions or customization needs? The workflow includes detailed sticky notes explaining each section.

Who is this for? Public-facing professionals (developer advocates, founders, marketers, content creators) who get bombarded with LinkedIn messages that aren't actually for them - support requests when you're in marketing, sales inquiries when you're a devrel, partnership pitches when you handle content, etc. What problem is this workflow solving? When you're visible online, people assume you handle everything at your company. You end up spending hours daily playing human router, forwarding messages like "How do I reset my password?" or "What's your enterprise pricing?" to the right teams. This LinkedIn automation workflow stops you from being your company's unofficial customer service representative. What this workflow does This AI-powered LinkedIn DM management workflow automatically assesses incoming LinkedIn messages and routes them intelligently: Automated Message Assessment: Receives inbound LinkedIn messages via UniPile and looks up sender details from both personal and company LinkedIn profiles. Smart Route Matching: Compares the message content against your message routing workflow table in Notion, which contains: Question: "How can I become an n8n ambassador?" Description: "Route here when a user is requesting to become an n8n ambassador. Also when they're asking how they could do more to evangelize n8n in their city, or to start organizing n8n meetups and events in their city." Action: "Tell the user to open the following notion page which has details on ambassador program including how to apply, as well as perks of the program: https://www.notion.so/n8n-Ambassador-Program-d883b2a130e5448faedbebe5139187ea?pvs=21" AI Response Generation: When a message matches an existing route, this AI assistant generates a personalized response draft based on the "Action" instructions from your routing table. Human-in-the-Loop Approval: Sends the draft response to Slack with approve/reject buttons, so you maintain control while saving time. Draft can be edited from within Slack on desktop and mobile. Automated LinkedIn Responses: Once approved, sends the reply back via LinkedIn and marks the original message as handled. The result: You stop being a human switchboard and can focus on your actual job while people still get helpful, timely responses through automated customer service. You can also add routes for things you do handle but get asked about daily (like 'How do I join your beta?' or 'What's your content strategy?') to standardize your responses. Setup Sign up for a UniPile account and create a webhook under the Messaging section Set the callback URL to this workflow's production URL Generate a UniPile API key with all required scopes and store it in your n8n credentials Create a Slack app and enable interactive message buttons and webhooks Here is a slack App manifest template for easy deployment in slack: { "display_information": { "name": "Request Router", "description": "A bot that alerts when a new linkedin question comes in.", "background_color": "#12575e" }, "features": { "bot_user": { "display_name": "Request Router", "always_online": false } }, "oauth_config": { "scopes": { "bot": [ "chat:write", "chat:write.customize", "chat:write.public", "links:write", "im:history", "im:read", "im:write" ] } }, "settings": { "interactivity": { "is_enabled": true, "request_url": "Your webhook url here" }, "org_deploy_enabled": false, "socket_mode_enabled": false, "token_rotation_enabled": false } } Set up your Notion database with the three-column structure (Question, Description, Action) Configure the AI node with your preferred provider (OpenAI, Gemini, Ollama etc) Replace placeholder LinkedIn user and organization IDs with your own How to customize this workflow to your needs Database Options**: Swap Notion with Google Sheets, Airtable, or another database Filtering Logic**: Add custom filters based on keywords, message length, follower count, or business logic AI Customization**: Adjust the system prompt to match your brand tone and response goals Approval Platform**: Replace Slack with email, Discord, or another review platform Team Routing**: Use Slack metadata to route approvals to specific team members based on message category Enrichment**: Add secondary data enrichment using tools like Clearbit or FullContact Response Rules**: Create conditional logic for different response types based on sender profile or message content Perfect for anyone who's tired of being their company's accidental customer service department while trying to do their real job. This LinkedIn automation template was inspired by a live build done by Max Tkacz and Angel Menendez for The Studio.

What is This? This automation simulates Scrum Master role on daily meetings. Essentially it is an AI Scrum Master using different sources of data. As intelligent support system for Scrum Masters that leverages data from Asana, Slack, and direct developer responses for comprehensive sprint status analysis and identification of areas requiring intervention. As such it is usable for Scrum Masters (of course) but Scrum Team aswell, Product Owner and possibly Business Owner. Who is it For? This automation is designed for Agile teams to support the Scrum Master role by collecting and analyzing data from various sources to identify potential impediments and support the team in sprint delivery. How Does It Work? The workflow has four main data entry points, that are launched either on-click or on workdays. First is collecting project section information from Asana. The automation retrieves project structure, available sections, and their organization, allowing the AI to understand the team's work context. Second is getting recently modified tasks in the Asana project. The system tracks changes in tasks, their status, assignments, and updates to detect potential delays or issues. Third is obtaining communication in the team's Slack channel. The flow collects data about recent conversations, discussion threads, and team communication to identify warning signals or areas requiring attention. Fourth is directly collecting responses from developers about the current sprint - their progress, impediments, concerns, and support needs. All collected data is passed to an AI model that analyzes it within the Scrum methodology context and identifies: Potential impediments in sprint delivery Areas requiring Scrum Master intervention Recommendations for team support Warning signals regarding Sprint Goal achievement Output is being pushed to Slack channel so it can be potentially used by another iteration of same flow itself via Slack channel history. Requirements You need Asana oAuth credentials You need OpenAI / alternative AI for processing data You need to have Slack app with proper permissions channels:history chat:write groups:history im:history mpim:history users.profile:write users:write Configuration Set up node "Asana Project and Slack Channel". Provide Asana project ID and Slack Channel ID (optional) Set up node "Get Scrum Master Answers". There are daily questions/answers that are being sent to channel. Alternative use You can get rid of the whole "Ask Users Daily ScrumMaster Questions" part if you don't want to do it simirarly as "daily Scrum standups". In such case whole flow is essentially changed to static analyzer of project status based on Slack and Asana. Extensions and Customizations There are many possibilities to extend this automation depending on team needs. For example, you can add integration with additional project management tools, implement different notification schemes based on detected issue criticality, or adjust data collection frequency to match the team's work rhythm. Disclaimers and Notes Whole automation has one important assumption: project is run on single Slack channel and on single Asana board. Of cource this can be extended, but is beyond currently designed scope. Adding new sources for AI to analyze should be fairly easy - just add another branch of data and push it to AI prompt. This automation represents a proof-of-concept and should not replace an actual Scrum Master. The Scrum Master role extends far beyond data collection and analysis - it requires deep understanding of team dynamics, business context, and interpersonal skills. As Scrum.org emphasizes, the Scrum Master doesn't need to be present during Daily Scrum, and their role is to ensure the meeting happens, but developers are responsible for conducting the meeting. Mindlessly executing daily questions without proper context analysis can lead to situations where the Scrum Master becomes a team manager instead of a self-organization facilitator. A real Scrum Master analyzes much more data than what's collected by automation - they observe team dynamics, understand business context, identify deeper root causes of problems, and support the team in developing self-organization skills. AI can be a valuable support tool, but it cannot replace the human intuition, empathy, and experience essential in this role. The automation should be treated as a tool supporting the Teams's work, providing additional insights and helping identify areas requiring attention, but always under the supervision and interpretation of an experienced Scrum practitioner.

LinkedIn Post Automation with AI Generation (Gpt-4o) & Slack Approval How it Works This workflow automates the creation and publishing of LinkedIn posts with AI-generated content and human approval via Slack, using Google Sheets, OpenAI (GPT-4), Slack Interactive Messages, and the LinkedIn API. Whether you're a social media manager, content creator, or marketing professional, this workflow helps you maintain consistent LinkedIn presence and scale content creation while keeping human oversight — all managed from a simple spreadsheet. 🎯 Use Case Ideal for: Content Marketers** managing multiple LinkedIn accounts Personal Brand Builders** maintaining regular posting schedule Agencies** handling client social media presence Teams** requiring content approval workflows Setup Instructions Prepare the Spreadsheet File name: Linkedin Post Main sheet structure: | ID | Linkedin Post Title | Status | Image URL | Groups sheet structure: | GroupIds | Add post topics and set their Status as Pending Configure Google Sheets Nodes Connect your Google account to: Linkedin-Post-Topic (Trigger node) Update-Status Get-Group-id Add API Credentials OpenAI API Key** → for GPT-4 content generation Slack OAuth Token** → for approval messages LinkedIn Access Token** → for posting content HTTP Header Auth** → Bearer token for LinkedIn API Configure Webhooks Set up Slack Interactive Components webhook Point Request URL to your N8N webhook endpoint Enable interactive messages in Slack app Activate the Workflow Once live, the workflow will: Monitor spreadsheet for new topics every minute Generate LinkedIn post using GPT-4 Send to Slack for approval/editing Upload image to LinkedIn Publish to profile and groups Update spreadsheet status to Posted 🔁 Workflow Logic Trigger: New/updated row with Pending status Generate: AI creates engaging LinkedIn post Approve: Slack message for review/edit Process: Handle approval response Upload: Register and upload image Publish: Post to LinkedIn profile & groups Update: Mark as Posted in sheet 🧩 Node Descriptions | Node Name | Description | |-----------|-------------| | Linkedin-Post-Topic | Monitors spreadsheet for new post topics | | Validate-Status | Filters only 'Pending' items | | Limit | Processes one item at a time | | Linedin-Post-Creator | Generates post content using GPT-4 | | Format-Content | Prepares content for Slack display | | Approval-on-Slack | Sends interactive approval message | | Webhook | Receives Slack button responses | | Format-Response | Extracts edited content from Slack | | Set-Final-Message | Prepares approved content | | Linkedin-User-Detail | Fetches LinkedIn user info | | Register Image | Initiates LinkedIn image upload | | Upload Image | Uploads image to LinkedIn | | Linkedin-post | Publishes to personal profile | | Get-Group-id | Retrieves LinkedIn group IDs | | Post-Linkedin-Groups | Posts to multiple groups | | Update-Status | Marks as completed in sheet | 🛠️ Customization Tips Adjust AI prompt for brand voice and hashtags Change Slack approver or add multiple reviewers Modify posting schedule with delay nodes Add analytics tracking with additional API calls Filter groups based on specific criteria Include URL shortening for tracking 📒 Suggested Sticky Notes for Workflow | Node/Section | Sticky Note Content | |--------------|---------------------| | Linkedin-Post-Topic | "Triggers every minute for new posts in your spreadsheet" | | Validate-Status | "Filters to process only 'Pending' items - prevents duplicates" | | Linedin-Post-Creator | "Uses GPT-4 to generate LinkedIn content - customize prompt for your brand voice" | | Approval-on-Slack | "Sends for human review - edit the Slack user to change approver" | | Webhook | "Receives approval responses - ensure URL is configured in Slack app" | | Register Image | "Initiates LinkedIn media upload - requires valid image URL from sheet" | | Linkedin-post | "Publishes to your profile - update with your LinkedIn credentials" | | Post-Linkedin-Groups | "Posts to multiple groups - add group IDs in Groups sheet" | | Update-Status | "Marks as 'Posted' to prevent reprocessing" | 💡 AI Prompt Configuration The workflow uses a sophisticated prompt that: Creates compelling hooks Includes 3-4 informative paragraphs Adds engagement questions Inserts relevant emojis Generates 4-6 hashtags Formats with proper spacing 🔒 Security & Permissions LinkedIn App Requirements: r_liteprofile - Read profile data r_emailaddress - Access email w_member_social - Post content rw_organization_admin - Group posting Slack Bot Permissions: chat:write - Send messages im:write - Direct messages users:read - User information 📎 Required Components | Component | Purpose | |-----------|---------| | Google Sheet | Store post topics and status | | LinkedIn App | API access for posting | | Slack App | Interactive approval flow | | OpenAI Account | GPT-4 content generation | | N8N Instance | Workflow execution | 🧪 Testing Tips Start with one test topic marked as Pending Verify Slack message appears correctly Test both "Approve" and "Edit" buttons Check image upload completes Confirm post appears on LinkedIn Verify status updates to Posted ⚠️ Common Issues & Solutions LinkedIn API Errors: Token expiration → Refresh access token Rate limits → Add delays between posts Group restrictions → Check posting permissions Slack Integration: Missing responses → Verify webhook URL Button not working → Check interactive components User not found → Confirm Slack user ID Image Upload Failures: Invalid URL → Validate image accessibility Size limits → Compress images under 10MB Format issues → Use JPEG or PNG only 📊 Workflow Benefits Time Savings**: 80% reduction in content publishing time Consistency**: Regular posting schedule maintained Quality Control**: Human review ensures brand standards Scalability**: Handle multiple accounts and groups Flexibility**: Easy to modify and extend 📎 Required Files | File Name | Purpose | |-----------|---------| | Linkedin Post | Google Sheet to hold post topics and status | 🏷 Suggested Tags & Categories #LinkedIn #AI #ContentAutomation #SocialMedia #Slack #GPT4 #Marketing #WorkflowAutomation

📘 Description This workflow is an AI-powered roto matte generation and first-pass compositing pipeline designed for VFX production. It transforms structured roto requests into multiple high-precision matte passes using AI, performs automated quality evaluation, generates production-ready Nuke scripts, and delivers fully organized assets and review tasks to the team—bridging the gap between manual rotoscoping and scalable AI-assisted matte generation. ⚙️ Step-by-Step Flow The workflow begins with a Google Sheets trigger that continuously monitors for new roto requests, acting as the intake layer for structured production data such as plate image URLs, subject descriptions, shot codes, subject type, and complexity. Once a new entry is detected, the system moves into a validation and extraction stage, where input fields are normalized, defaults are assigned, and essential attributes like sequence codes, frame ranges, QC thresholds, and project metadata are structured for downstream processing. The pipeline then performs prompt engineering and expands the request into four specialized roto passes: an alpha matte pass for clean silhouette extraction, an edge detail pass for fine hair and motion blur accuracy, a foreground isolation pass for compositing-ready subject separation, and a motion tracking reference pass enriched with tracking markers and velocity overlays. Each variation includes detailed instructions aligned with shot context and technical constraints to ensure precise AI-driven outputs. At the core of the workflow, an image-to-video generation layer constructs structured API requests and submits each variation as an independent job to the Seedance AI model, always using the plate image as a reference for accurate subject extraction. A polling mechanism continuously monitors each job at fixed intervals, ensuring that processing proceeds only after successful completion. Once rendering is complete, a metadata and automated QC layer evaluates each pass by assigning a quality score based on resolution and predefined thresholds, classifying outputs as either AI-approved or requiring manual intervention. A decision routing layer then determines the next stage—approved passes continue through the pipeline, while failed outputs trigger immediate Slack alerts for senior artist review and manual roto assignment. For approved outputs, the system generates a fully production-ready Nuke roto template, automatically configuring nodes for plate input, AI matte extraction, alpha application, edge refinement, and artist-level roto adjustments, along with structured EXR output paths and naming conventions. The pipeline then proceeds to asset handling, where generated videos are downloaded and distributed via email to requesters, while simultaneously creating Jira review tasks to ensure pipeline tracking and accountability. An aggregation layer consolidates all roto passes into a unified structured summary, combining QC results, video links, task references, and technical metadata. Finally, a team notification system delivers a comprehensive summary to the roto team via Telegram, including per-pass QC status, preview links, task tracking, output paths, and artist notes highlighting areas requiring refinement—ensuring seamless collaboration between AI outputs and human artists. 🚨 Error Handling • Global error trigger across the workflow • Instant Slack alerts with failure details • Prevents silent failures and ensures pipeline reliability 🧩 Prerequisites • Seedance API (AI video generation) • Google Sheets OAuth2 (trigger input) • Jira API (task tracking) • Gmail OAuth2 (asset delivery) • Slack OAuth2 (alerts & QC failures) • Telegram Bot (team notifications) 💡 Key Benefits ✔ Automated roto matte generation with AI-assisted precision ✔ Multiple specialized passes for production flexibility ✔ Built-in QC scoring and intelligent decision routing ✔ Auto-generated Nuke scripts with pre-configured nodes ✔ Integrated task tracking via Jira ✔ Seamless delivery via email and team notifications ✔ Scalable pipeline for high-volume roto workflows 👥 Perfect For Roto and paint teams in VFX studios Compositors working with Nuke pipelines Film and post-production environments AI-assisted rotoscoping workflows Studios handling high-volume shot processing

Automated Databricks Data Querying & SQL Insights via Slack with AI Agent & Gemini Node-by-Node Explanation This workflow is divided into three functional phases: Initialization, AI Processing, and Response Delivery. | Node Name | Category | What it does | | :--- | :--- | :--- | | When Slack Message Received | Trigger | Monitors a Slack channel for @mentions. It captures the user's question and the thread ID to keep the conversation organized. | | Set Databricks Config | Configuration | A "helper" node where you hardcode your Databricks warehouse_id and target_table. This makes it easy to update settings in one place. | | Fetch Databricks Schema | Data Retrieval | Sends a DESCRIBE command to the Databricks API. It learns what columns exist (e.g., "price", "date", "store_id") so the AI knows what it can query. | | Parse Table Schema | Data Transformation | Uses JavaScript to clean up the raw Databricks response. it converts complex technical data into a simple list that the AI can easily read. | | SQL Data Analyst Agent | AI Brain | The "manager" of the workflow. It takes the user's question and the table schema, decides which SQL query to write, and interprets the results. | | Gemini Model | LLM Engine | Provides the actual intelligence (using Google Gemini 3.1 Flash). This is what "thinks" and generates the SQL and conversational text. | | Redis Chat Memory | Memory | Stores previous messages in the thread. This allows you to ask follow-up questions (e.g., "Now show me only the top 5") without repeating the whole context. | | Run Primary SQL Query | AI Tool | An HTTP tool given to the Agent. The Agent "calls" this node to actually run the generated SQL on Databricks and get the real data back. | | If Output Valid | Logic Gate | A safety check. It verifies if the Agent successfully produced a message for Slack or if something went wrong during the process. | | Post to Slack Channel | Output (Success) | Sends the final answer (e.g., "The total revenue for Q3 was $4.2M") back to the user in Slack. | | Post Error to Slack | Output (Failure) | If the SQL fails or the AI hits a wall, this node sends an error message to the user so they aren't left waiting. | How the "Agent" Loop Works Unlike a standard linear workflow, the SQL Data Analyst Agent doesn't just move to the next step. It performs a "Reasoning" loop: Observe: "The user wants to know sales for March." Think: "I have a table called 'franchises' with a 'sale_date' column. I should run a SUM query." Act: It triggers the Run Primary SQL Query node. Observe Results: "The query returned 150,000." Final Response: "The total sales for March were 150,000."

Monitor CRM accounts for hiring spikes by enriching HubSpot companies with PredictLeads job data and alerting your team via Slack. This workflow pulls all companies from your HubSpot CRM, checks each one against the PredictLeads Job Openings API for target roles (sales, engineering, marketing, product, data), compares the current count to historical data stored in Google Sheets, and flags any company where hiring jumped more than 50%. Flagged companies get updated in HubSpot with a hiring signal and trigger a Slack alert so your sales team can act fast. How it works: Schedule trigger runs the workflow daily at 9 AM. Retrieves all companies from HubSpot CRM (domain, name, ID). Loops through each company and fetches job openings from PredictLeads. Filters jobs to target roles (sales, engineering, marketing, product, data). Reads the previous job count for that company from Google Sheets. Calculates percentage change between current and historical counts. If hiring increased more than 50%, flags it as a spike. Updates the HubSpot company record with a hiring signal property. Sends a Slack alert with the company name, role count, and percentage change. Updates Google Sheets with the latest count regardless of spike status. Setup: Connect your HubSpot CRM (OAuth2) with company read/write access. Create a Google Sheet with a "HistoricalCounts" tab containing columns: domain, company_name, job_count, previous_count, percent_change, check_date. Connect a Slack bot to the channel where you want hiring alerts. Add your PredictLeads API credentials (X-Api-Key and X-Api-Token headers). Requirements: HubSpot CRM account with OAuth2 credentials. Google Sheets OAuth2 credentials. Slack OAuth2 credentials (bot with chat:write permission). PredictLeads API account (https://docs.predictleads.com). Notes: The 50% spike threshold can be adjusted in the IF node. Target roles are configured in the Filter Target Roles code node -- add or remove roles as needed. The workflow updates historical data on every run, so spike detection improves over time. PredictLeads Job Openings API docs: https://docs.predictleads.com

How It Works This workflow automates end-to-end concert ticket booking validation and fan experience management using two coordinated AI agents. It is designed for ticketing platforms, event operators, and venue operations teams that must prevent fraud, manage inventory accurately, and deliver seamless customer communication at scale. The workflow solves key challenges such as duplicate bookings, bot-driven purchases, payment failures, overbooking, and inconsistent refund handling. When a booking request enters via webhook, inventory data is fetched and normalized. The Ticket Validation Agent analyzes structured booking, payment authorization, pricing tier rules, seat allocation, resale restrictions, and fraud signals to produce a standardized risk classification. Based on risk level, the workflow routes transactions for auto-approval, conditional handling, or escalation. The Fan Experience Orchestration Agent then manages confirmations, ticket system updates, waitlist activation, refund or compensation logic, and SLA-driven escalation to operations. All outcomes are merged and logged into an audit trail, ensuring compliance, transparency, and consistent service enforcement while minimizing manual review. Setup Steps Add OpenAI/Nvidia API credentials in n8n. Configure ticketing system HTTP endpoint. Connect Gmail for confirmations. Connect Slack for escalation alerts. Connect Google Sheets for audit logging. Define risk thresholds and SLA rules. Prerequisites n8n account, OpenAI API key, ticketing API access, Gmail, Slack, Google Sheets Use Cases Concert ticket sales, festival booking systems, venue seat management, VIP allocation handling Customization Adjust fraud thresholds, add SMS notifications, integrate CRM, extend loyalty logic Benefits Reduces fraud, automates fan communication, enforces ticketing policies

📊 Description Automate your inbound lead qualification pipeline by enriching raw lead data, scoring it with AI, and instantly creating follow-up tasks for your sales team. 🎯🤖 This workflow receives new leads via webhook, enriches them using RocketReach to fetch verified emails and contact details, applies confidence-based filtering, and uses GPT-4o-mini to score each lead based on seniority, company fit, and data quality. High-quality leads automatically generate Asana tasks and Slack notifications, while low-quality leads are safely filtered out — ensuring your team focuses only on the best opportunities. 🚀📋 🔁 What This Template Does 1️⃣ Receives inbound lead data via webhook (name, company, job title, LinkedIn URL). 🌐 2️⃣ Normalizes incoming data into a clean, structured format. 🧾 3️⃣ Enriches lead profiles using RocketReach to fetch emails and phone numbers. 📇 4️⃣ Calculates email confidence scores based on quality grades (A–D). 📊 5️⃣ Filters out low-confidence leads automatically. 🚫 6️⃣ Uses AI (GPT-4o-mini) to score qualified leads from 1–10. 🤖 7️⃣ Assigns priority levels (High / Medium / Low) with reasoning. 🧠 8️⃣ Creates an Asana task for leads scoring above the threshold. ✅ 9️⃣ Sends Slack notifications for both qualified and disqualified leads. 💬 🔟 Returns a structured webhook response with scoring results. 🔁 1️⃣1️⃣ Sends Gmail alerts automatically if any workflow error occurs. 🚨 ⭐ Key Benefits ✅ Eliminates manual lead enrichment and qualification ✅ Uses verified contact data from RocketReach ✅ AI scoring ensures consistent, unbiased lead evaluation ✅ Automatically routes high-quality leads to sales via Asana ✅ Keeps teams informed with real-time Slack notifications ✅ Prevents sales effort from being wasted on low-quality leads 🧩 Features Webhook-based inbound lead intake RocketReach profile enrichment Email confidence scoring logic AI lead scoring with strict JSON output Configurable quality and score thresholds Automatic Asana task creation Slack alerts for qualified and disqualified leads Gmail-based error handling Frontend-ready webhook responses 🔐 Requirements RocketReach API key OpenAI API key (GPT-4o-mini recommended) Asana OAuth2 credentials Slack API credentials Gmail OAuth2 credentials (for error alerts) Webhook client sending lead details (first_name, last_name, company, job_title, linkedin_url) 🎯 Target Audience B2B sales teams handling inbound leads Revenue operations (RevOps) teams SaaS companies optimizing lead qualification Lead generation agencies Automation teams building AI-driven sales pipelines

Automate your Instagram growth strategy by generating and posting viral Reels using AI and Creatomate. This workflow plans content topics based on trends, generates video assets, and handles the approval and posting process—all without manual video editing. How it works Schedule Trigger: Runs every day at 9:00 AM. Topic Planning: Checks past topics from Google Sheets to avoid duplicates, then uses OpenAI (GPT-4o) to generate a new quiz-style content plan. Video Generation: Uses Creatomate to generate a video based on a template, dynamically inserting the AI-generated text and images. Approval Loop: Sends the generated video to Slack for human review. Posting: Once approved in Slack, the workflow automatically uploads the Reel to Instagram. Logging: Saves the new topic to Google Sheets and notifies Slack upon successful publication. Setup steps Configure Credentials: OpenAI: For generating content plans. Creatomate: For video rendering. Google Sheets: For tracking past topics. Slack: For approval notifications. Facebook Graph API: For Instagram publishing. Google Sheets Setup: Create a Google Sheet with columns: Question, Answer, Title, Date. Update the Get Past Topics and Save New Topic nodes with your Sheet ID. Creatomate Setup: Create a template in Creatomate or use an existing one. Update the Generate Video node with your template_id in the JSON body. Slack Setup: Create a channel for approvals. Update the Slack Approval Request and Slack Notification nodes with your Channel ID. Activate: Turn on the workflow to start automating your content pipeline!

This workflow automatically collects customer reviews from Google, Facebook, Trustpilot, and Yelp, analyzes their sentiment using AI, sends real-time alerts for negative feedback, and generates a weekly summary report. It is ideal for businesses that want to monitor their online reputation across multiple platforms and respond quickly to customer concerns. How It Works Daily Schedule**: Triggers the workflow every day at 09:00. Review Collection**: Fetches new reviews from Google, Facebook, Trustpilot, and Yelp using their official APIs. Data Normalization**: Merges and standardizes all reviews into a unified format. AI Sentiment Analysis**: Uses GPT-4 to analyze the sentiment and extract key insights from each review. Negative Review Alerts**: Sends a Slack notification to managers if a negative review is detected. Logging**: Saves all reviews and their analysis to a Google Sheet for record-keeping. Weekly Reporting**: Every Monday, aggregates the past week’s reviews, generates an AI-powered summary, and emails it to management. Configuration API Credentials: Google My Business API: Create a project in Google Cloud, enable the My Business API, and generate OAuth credentials. Facebook Graph API: Create a Facebook App, request the necessary permissions, and obtain a Page Access Token. Trustpilot API: Register for a Trustpilot Business account and generate an API key. Yelp Fusion API: Sign up for Yelp Fusion, create an app, and get your API key. OpenAI API: Create an account at OpenAI, generate an API key for GPT-4 access. Slack API: Create a Slack App, enable Incoming Webhooks, and get the webhook URL. Google Sheets API: Enable the Google Sheets API in Google Cloud and create OAuth credentials. Set Up Environment Variables: Add all API keys, tokens, and configuration values in the Workflow Configuration node or as n8n credentials. Google Sheet Setup: Create a Google Sheet with columns for review data and share it with your service account email. Slack Channel: Set up a Slack channel for alerts and add the webhook URL in the configuration. Email Settings: Configure the recipient email address for weekly reports in the workflow. Usage Activate the workflow after configuration. Reviews will be collected and analyzed daily. Negative reviews trigger instant Slack alerts. Every Monday, a summary report is sent via email. Use Case: Monitor and respond to customer feedback across Google, Facebook, Trustpilot, and Yelp, automate sentiment analysis, and keep management informed with actionable weekly insights.

This workflow automatically scores and categorizes new GoHighLevel contacts using AI (GPT-4), then tags and assigns them to the appropriate team member based on their score. Hot leads also trigger a Slack notification for immediate follow-up. What does it do? Triggers when a new contact is added in GoHighLevel. Fetches full contact details and recent engagement data. Uses AI (GPT-4) to analyze and score the lead (1-100), categorize it (Hot, Warm, Cold), and provide an explanation. Tags the contact in GoHighLevel based on the score. Assigns the lead to the correct sales or nurturing team member. Sends a Slack alert for Hot leads to ensure fast response. Use case Use this workflow to automate lead qualification and assignment in sales teams using GoHighLevel. It helps prioritize high-quality leads, ensures fast follow-up, and reduces manual work. How to configure GoHighLevel API: Set your GoHighLevel API URL and API key in the Workflow Configuration node. Update user IDs for assignment as needed. Slack Integration: Add your Slack webhook URL or credentials in the Slack Notify Hot Lead node. AI Provider: Configure your OpenAI (or compatible) credentials in the AI Lead Scoring (GPT-4) node. Adjust thresholds: If needed, change the score thresholds in the IF nodes to match your business logic. Activate the workflow: Once configured, activate the workflow to start processing new leads automatically. Tip: You can further customize the workflow to fit your sales process, add more notifications, or integrate with other tools as needed.

Analyze Furusato Nozei trends from Google News to Slack This workflow acts as a specialized market analyst for Japan's "Furusato Nozei" (Hometown Tax) system. It automates the process of monitoring related news, validating keyword popularity via search trends, and delivering a concise, strategic report to Slack. By combining RSS feeds, AI agents, and real-time search data, this template helps marketers and municipal researchers stay on top of the highly competitive Hometown Tax market without manual searching. 👥 Who is this for? Municipal Government Planners:** To track trending return gifts and competitor strategies. E-commerce Marketers:** To identify high-demand keywords for Furusato Nozei portals. Content Creators:** To find trending topics for blogs or social media regarding tax deductions. Market Researchers:** To monitor the seasonality and shifting interests in the Hometown Tax sector. ⚙️ How it works News Ingestion: The workflow triggers on a schedule and fetches the latest "Furusato Nozei" articles from Google News via RSS. AI Analysis & Extraction: An AI Agent (using OpenRouter) summarizes the news cluster and identifies the most viable search keyword (e.g., "Scallops," "Travel Vouchers," or specific municipalities). Data Validation: The workflow queries the Google Trends API (via SerpApi) to retrieve search volume history for the extracted keyword in Japan. Strategic Reporting: A second AI Agent analyzes the search trend data alongside the keyword to generate a market insight report. Delivery: The final report is formatted and sent directly to a Slack channel. 🛠️ Requirements To use this workflow, you will need: n8n** (Version 1.0 or later recommended). OpenRouter API Key** (or you can swap the model nodes for OpenAI/Anthropic). SerpApi Key** (Required to fetch Google Trends data programmatically). Slack Account** (with permissions to post to a channel). 🚀 How to set up Configure Credentials: Add your OpenRouter API key to the Chat Model nodes. Add your SerpApi key to the Google Trends API node. Connect your Slack account in the Send a message node. Check the RSS Feed: The RSS Read node is pre-configured for "Furusato Nozei" (ふるさと納税). You can leave this as is. Regional Settings: The workflow is pre-set for Japan (jp / ja). If you need to change this, check the Workflow Configuration and Google Trends API nodes. Schedule: Enable the Schedule Trigger node to run at your preferred time (default is 9:00 AM JST). 🎨 How to customize Change the Topic:** While this is optimized for Furusato Nozei, you can change the RSS feed URL to track other Japanese market trends (e.g., NISA, Inbound Tourism). Swap AI Models:** The template uses OpenRouter, but you can easily replace the "Chat Model" nodes with OpenAI (GPT-4) or Anthropic (Claude) depending on your preference. Adjust AI Prompts:** The AI prompts are currently in Japanese to match the content. You can modify the system instructions in the AI Agent nodes if you prefer English reports.

Description This workflow is designed for B2B/SaaS teams who want to secure renewals before it’s too late. It runs every day, identifies all accounts whose licenses are up for renewal in J–30, enriches them with CRM, product usage and support data, computes an internal churn risk level, and then triggers the appropriate playbook: HIGH risk** → full escalation (tasks, alerts, emails) MEDIUM risk** → proactive follow-up by Customer Success LOW risk** → light renewal touchpoint / monitoring Everything is logged into a database table so that you can build dashboards, run analysis, or plug additional automations on top. How it works Daily detection (J–30 renewals) A scheduled trigger runs every morning and queries your database (Postgres / Supabase) to fetch all active subscriptions expiring in 30 days. Each row includes the account identifier, name, renewal date and basic commercial data. Data enrichment across tools For each account, the workflow calls several business systems to collect context: HubSpot → engagement history Salesforce → account profile and segment Pipedrive → deal activities and associated products Analytics API → product feature usage and activity trends Zendesk → recent support tickets and potential friction signals All of this is merged into a single, unified item. Churn scoring & routing An internal scoring step evaluates the risk for each account based on multiple signals (engagement, usage, support, timing). The workflow then categorizes each account into one of three risk levels: HIGH – strong churn signals → needs immediate attention MEDIUM – some warning signs → needs proactive follow-up LOW – looks healthy → light renewal reminder A Switch node routes each account to the relevant playbook. Automated playbooks 🔴 HIGH risk Create a Trello card on a dedicated “High-Risk Renewals” board/list Create a Jira ticket for the CS / AM team Send a Slack alert in a designated channel Send a detailed email to the CSM and/or account manager 🟠 MEDIUM risk Create a Trello card in a “Renewals – Follow-up” list Send a contextual email to the CSM to recommend a proactive check-in 🟢 LOW risk Send a soft renewal email / internal note to keep the account on the radar Logging & daily reporting For every processed account, the workflow prepares a structured log record (account, renewal date, risk level, basic context). A Postgres node is used to insert the data into a churn_logs table. At the end of each run, all processed accounts are aggregated and a daily summary email is sent (for example to the Customer Success leadership team), listing the renewals and their risk levels. Requirements Database A table named churn_logs (or equivalent) to store workflow decisions and history. Example fields: account_id, account_name, end_date, riskScore, riskLevel, playbook, trello_link, jira_link, timestamp. External APIs HubSpot (engagement data) Salesforce (account profile) Pipedrive (deals & products) Zendesk (support tickets) Optional: product analytics API for usage metrics Communication & task tools Gmail (emails to CSM / AM / summary recipients) Slack (alert channel for high-risk cases) Trello (task creation for CS follow-up) Jira (escalation tickets for high-risk renewals) Configuration variables Thresholds are configured in the Init config & thresholds node: days_before_renewal churn_threshold_high churn_threshold_medium These parameters let you adapt the detection window and risk sensitivity to your own business rules. Typical use cases Customer Success teams who want a daily churn watchlist without exporting spreadsheets. RevOps teams looking to standardize renewal playbooks across tools. SaaS companies who need to prioritize renewals based on real risk signals rather than gut feeling. Product-led organizations that want to combine usage data + CRM + support into one automated process. 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

Turn your favorite podcast episodes into engaging social media content automatically. This workflow fetches new episodes from an RSS feed, transcribes the audio using OpenAI Whisper, generates a concise summary using GPT-4o, and drafts a tweet. It then sends the draft to Slack for your review before posting it to X (Twitter). Who is this for Content creators, social media managers, and podcast enthusiasts who want to share insights without manually listening to and typing out every episode. Key Features Large File Support:** Includes a custom logic to download audio in chunks, ensuring stability even with long episodes (preventing timeouts). Human-in-the-Loop:** Nothing gets posted without your approval. You can review the AI-generated draft in Slack before it goes live. High-Quality AI:** Uses OpenAI's Whisper for accurate transcription and GPT-4o for intelligent summarization. How it works Monitor: Checks the Podcast RSS feed daily for new episodes. Process: Downloads the audio (handling large files via chunking) and transcribes it. Draft: AI summarizes the transcript into bullet points and formats it for X (Twitter). Approve: Sends the draft to a Slack channel. Publish: Once approved by you, it posts the tweet to your X account. Requirements OpenAI API Key Slack Account & App (Bot Token) X (Twitter) Developer Account (OAuth2) Setup instructions RSS Feed: The template defaults to "TED Talks Daily" for demonstration. Open the [Step 1] RSS node and replace the URL with your target podcast. Connect Credentials: Set up your credentials for OpenAI, Slack, and X (Twitter) in the respective nodes. Slack Channel: In the [Step 12] Slack node, select the Channel ID where you want to receive the approval request.

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

Automated Email Verification & Digital Health Card Generator Overview Transform your clinic's patient check-in process with this fully automated pre-registration system. When patients submit their appointment information through your website, this workflow instantly verifies their email, generates a professional digital health card with a scannable QR code, stores it securely in Google Drive, and sends personalized notifications to both the patient and your reception team—all in under 20 seconds. What This Workflow Does This comprehensive automation handles the entire patient pre-check-in journey: Receives Patient Data - Webhook captures form submissions from your website or app with patient details (name, email, phone, appointment date/time, symptoms, age, gender) Validates & Cleans Data - Automatically validates required fields, cleans input data, and generates a unique patient ID with timestamp for tracking Verifies Email Address - Uses VerifiEmail API to ensure email deliverability by checking RFC compliance, MX records, and filtering out disposable/spoof emails Generates QR Code - Creates a unique verification URL and scannable QR code for instant patient identification at reception Builds Professional Health Card - Generates a beautiful, responsive HTML health card featuring: Patient information grid (name, ID, email, phone, age/gender, appointment) Chief complaints/symptoms section Embedded QR code for quick check-in Important appointment instructions Modern gradient design with mobile-responsive layout Converts to PNG Image - Uses HTMLCSSToImg API to convert the HTML card into a high-quality PNG image (900x1200px) Stores in Google Drive - Uploads the health card to an organized "Patients record" folder with patient ID-based naming for easy retrieval Emails Patient - Sends a beautifully formatted email to the patient containing: Their health card as a PNG attachment Appointment details and confirmation Google Drive link for backup access Check-in instructions and preparation tips Notifies Reception Team - Sends real-time Slack message to clinic reception with patient details, verification status, and Drive link Logs to Database - Records complete patient information, timestamps, verification status, and file links in Google Sheets for tracking and analytics Returns Success Response - Sends JSON response back to the website form with patient ID, confirmation, and Drive link Key Features ✅ Email Verification - VerifiEmail API integration prevents failed deliveries and fake emails ✅ Unique Patient IDs - Timestamp-based IDs ensure no duplicates (format: PAT-{timestamp}-{random}) ✅ QR Code Generation - Free QR Server API creates scannable codes for instant check-in ✅ Professional Design - Modern, gradient-styled health cards with responsive layout ✅ Multi-format Output - PNG image format for easy viewing on any device ✅ Cloud Storage - Secure Google Drive storage with organized folder structure ✅ Multi-channel Notifications - Email to patient + Slack to staff for complete coverage ✅ Comprehensive Logging - Google Sheets database for analytics and record-keeping ✅ Error Handling - Graceful failure for invalid emails with user notification ✅ Webhook Response - Real-time feedback to website form for seamless UX ✅ Indian Locale Support - Date/time formatting in Indian format with 12-hour time ✅ Mobile Responsive - Health cards look great on both desktop and mobile devices Perfect For 🏥 Medical Clinics & Healthcare Providers - Streamline patient pre-registration and reduce waiting times 🦷 Dental Practices - Digital check-in for appointments with patient history 💉 Diagnostic Centers - Pre-appointment verification for lab tests and scans 👨‍⚕️ Specialist Doctors - Organized patient records with symptoms documentation 🏃 Physiotherapy Clinics - Track patient visits and treatment history 💆 Wellness Centers & Spas - Appointment management with customer details 🐕 Veterinary Clinics - Pet owner pre-registration system 📋 Any Appointment-Based Business - Adaptable to salons, consultancies, or service providers Business Benefits 💰 Reduced No-Shows - Email verification ensures valid contact information ⏱️ Time Savings - Eliminates manual data entry at reception 📊 Better Analytics - Automated logging provides insights into patient flow ✨ Professional Image - Modern, branded health cards improve patient experience 🔒 Secure Records - Cloud storage with organized folder structure 📱 Contactless Check-in - QR codes enable touch-free reception process 🎯 Improved Communication - Multi-channel notifications keep everyone informed 🚀 Scalable System - Handles high volumes without additional staff Required Services & Credentials VerifiEmail API - Email verification service Sign up at: https://verifi.email HTMLCSSToImg API - HTML to image conversion Sign up at: https://htmlcsstoimg.com Google Drive - Cloud file storage Requires: Google Account with Drive access Gmail - Email delivery Requires: Google Account Slack - Team notifications Requires: Slack workspace Google Sheets - Database logging Requires: Google Account Customization Options Change Health Card Design: Edit the "Build Health Card HTML" node Modify CSS styles, colors, layout, fonts Add clinic logo by including `` tag in header Adjust Email Template: Edit the "Email Health Card to Patient" node Customize subject line, message content, styling Add clinic branding and contact information Modify Slack Message: Edit the "Notify Reception Team" node Change message format, add emojis, include additional fields Integrate with different channels Add PDF Generation: Insert an additional HTTP Request node after "Build Health Card HTML" Use a PDF conversion API (like PDFMunk or Puppeteer) Upload both PNG and PDF to Google Drive Add SMS Notifications: Insert Twilio or similar SMS node after email verification Send appointment confirmation via SMS Include patient ID and appointment time Multi-language Support: Modify the HTML template to support multiple languages Add language detection based on patient input Translate email and Slack messages Troubleshooting Guide Email Verification Fails: Check VerifiEmail API key is correct Verify API quota hasn't been exceeded Test with known valid email address Image Generation Fails: Check HTMLCSSToImg API credentials Verify HTML content is valid (no syntax errors) Check API rate limits Google Drive Upload Fails: Re-authenticate Google Drive OAuth2 credentials Check folder permissions Verify folder ID is correct Email Not Sending: Re-authenticate Gmail OAuth2 credentials Check email attachment size limits Verify "Less secure app access" if using password auth Slack Message Not Posting: Check Slack app permissions Verify channel exists and bot is invited Re-authenticate Slack credentials Google Sheets Not Logging: Re-authenticate Google Sheets credentials Verify sheet name and column headers match exactly Check sheet permissions Performance & Scalability Expected Performance: Single execution: 15-20 seconds Concurrent executions: Supports multiple parallel workflows API rate limits: Respects all third-party API limits Volume Handling: Small clinics: <50 patients/day - Perfect Medium practices: 50-200 patients/day - Excellent Large hospitals: 200+ patients/day - Consider API tier upgrades Security & Compliance ✅ Data Privacy - Patient data transmitted securely via HTTPS ✅ Access Control - OAuth2 authentication for all Google services ✅ Secure Storage - Files stored in private Google Drive folders ✅ Audit Trail - Complete logging in Google Sheets with timestamps ✅ Email Verification - Prevents data leakage to invalid addresses ✅ No Data Storage in n8n - Patient data passes through, not stored Tags healthcare, medical, clinic, patient-management, appointment, email-verification, qr-code, google-drive, gmail, slack, automation, workflow, pre-checkin, health-card, verifi-email, htmlcsstoimg, medical-records, patient-portal, healthcare-automation, clinic-management Category Healthcare & Medical Subcategory Patient Management & Appointment Systems License MIT License - Free to use, modify, and distribute with attribution

This workflow provides a comprehensive weather reporting system with two main functionalities: a scheduled daily summary and an interactive AI agent for dynamic queries. Who's it for? This template is ideal for anyone who wants to stay updated on the weather, from individuals planning their day to teams needing automated daily briefings. It's also a great example for developers and n8n users who want to explore the capabilities of AI Agents and integrating external APIs in their workflows. What it does / How it works This workflow operates in two distinct modes: Scheduled Daily Summary: The workflow triggers automatically every day at 9:00 AM (customizable) or can be run manually. It fetches the latest weather data for a specified location (default is near Chiba, Japan) from the Open-Meteo API. It then formats a message with the day's maximum and minimum temperatures. Finally, it sends this summary to a designated Gmail address and a Slack channel. AI-Powered Forecasts via Chat: When you send a message to the Chat Trigger webhook, it activates the AI Agent. The AI Agent, powered by an OpenAI model, understands natural language questions like "What's the humidity right now?" or "Will it be warmer tomorrow?". The agent uses an HTTP Request tool to fetch real-time, specific data from the weather API to answer your question. The generated answer is then sent back to you via both Gmail and Slack. How to set up Configure Credentials: Add your credentials for OpenAI, Gmail, and Slack in the credentials menu. Set Your Location: In the Fetch Weather Data and HTTP Request Tool for AI nodes, update the latitude and longitude in the URL to your desired location. Update Email and Slack: In the Send Email Summary and Send AI Response via Email nodes, change the recipient email address to your own. In the Send Slack Summary and Send AI Response via Slack nodes, select your desired Slack channel. Adjust the Schedule: Modify the Schedule Trigger node to change the time or frequency of the daily summary. Activate the Workflow: Click the "Active" toggle in the top-right corner to enable the workflow. How to customize the workflow Change the Message:** You can easily customize the notification message in the Format Daily Summary node (for the summary) and in the Send... nodes for both flows. Add More Data:** The Open-Meteo API provides a wealth of data. You can fetch additional information like precipitation, wind speed, or UV index by modifying the URL in the HTTP Request nodes. Integrate Other Services:** Add other notification nodes like Discord, Telegram, or Microsoft Teams to send the weather reports to more platforms.

Introduction Automates scholarship tracking by scraping university sites, assessing eligibility via AI, and publishing results to WordPress or Slack. Eliminates manual searches for students, counselors, and education platforms, enabling scalable curation and timely notifications. How it Works Webhook triggers parallel scraping of NUS, NTU, SIT, SUTD → merge data → AI evaluates eligibility → aggregate qualified scholarships → generate summaries → post to WordPress/Slack → send email notifications with appeal options. Setup Steps Configure OpenAI credentials and eligibility prompt template Update HTTP requests with university URLs and selectors Add WordPress site URL and API credentials Create Slack webhook and notification channel Configure Gmail/SMTP for email notifications Workflow Webhook → Scrape 4 Universities (Parallel) → Merge Data → Prepare Context → AI Eligibility Check → Aggregate Results → Generate Summary → Check Status → Publish Slack/Email/WordPress → Handle Appeals Workflow Steps Scraping: Fetch scholarship pages from four universities simultaneously Merge: Combine data into a unified dataset AI Processing: Analyze eligibility criteria, deadlines against student profile Aggregation: Consolidate qualified scholarships with match scores Publishing: Post to WordPress, send Slack/email with results Appeals: Webhook handles rejection appeals with AI review Prerequisites OpenAI API key, WordPress site with REST API, Slack workspace with webhook, Gmail/SMTP credentials, student profile data (GPA, citizenship, major) Use Cases Counselors automating recommendations for 100+ students, financial aid offices aggregating departmental opportunities Customization Add universities (SMU, SUSS, international institutions), include government schemes (MOE, Edusave, Mendaki) Benefits Saves 10+ hours weekly per counselor, monitors 50+ scholarships automatically, provides AI eligibility matching (85%+ accuracy)

Introduction Automates flight deal discovery and intelligent analysis for travel bloggers and deal hunters. Scrapes live pricing, enriches with weather data, applies AI evaluation, and auto-publishes to WordPress—eliminating manual research and accelerating content delivery. How It Works User submits route via form, scrapes real-time flight prices and weather data, AI analyzes deal quality considering weather conditions, formats results, publishes to WordPress, sends Slack notification—fully automated from input to publication. Workflow Template Form Input → Extract Data → Scrape Flight Prices → Extract Pricing → Fetch Weather → Parse Weather → Prepare AI Input → AI Analysis → Parse Output → Format Results → Publish WordPress → Slack Alert → User Response Setup Instructions Form Setup: Configure user input fields for flight routes and preferences APIs: Connect Google Flights scraping endpoint, weather API credentials, OpenAI/Chat Model API key Publishing: Set WordPress credentials, target blog category, Slack webhook URL AI Configuration: Define analysis prompts, output structure, parser rules Workflow Steps Data Collection: Form captures route, scrapes Google Flights pricing, fetches destination weather via API AI Processing: Enriches flight data with weather context, analyzes deal quality using OpenAI/Chat Model with structured output parsing Publishing: Formats analysis results, creates WordPress post, sends Slack notification, delivers response to user Prerequisites n8n instance, Google Flights access, weather API key, OpenAI/compatible AI service, WordPress site with API access, Slack workspace Use Cases Travel blog automation, flight deal newsletters, price comparison services, seasonal travel planning, destination weather analysis, automated social media content Customization Modify AI analysis criteria, adjust weather impact weighting, customize WordPress post templates, add email distribution, integrate additional data sources, expand to hotel/rental deals Benefits Eliminates manual price checking, combines multiple data sources automatically, delivers AI-enhanced insights, accelerates publishing workflow, scales across unlimited routes, provides weather-aware recommendations

How It Works The system collects real-time wearable health data, normalizes it, and uses AI to analyze trends and risk scores. It detects anomalies by comparing with historical patterns and automatically triggers alerts and follow-up actions when thresholds are exceeded. Setup Steps Configure Webhook Endpoint - Set up webhook to receive data from wearable devices Connect Database - Initialize storage for health metrics and historical data Set Normalization Rules - Define data standardization parameters for different devices Configure AI Model - Set up health score calculation and risk prediction algorithms Define Thresholds - Establish alert triggers for critical health metrics Connect Notification Channels - Configure email and Slack integrations Set Up Reporting - Create automated report templates and schedules Test Workflow - Run end-to-end tests with sample health data Workflow Template Webhook → Store Database → Normalize Data → Calculate Health Score → Analyze Metrics → Compare Previous → Check Threshold → Generate Reports/Alerts → Email/Slack → Schedule Follow-up Workflow Steps Ingest real-time wearable data via webhook, store and standardize it, and use GPT-4 for trend analysis and risk scoring. Monitor metrics against thresholds, trigger SMS, email, or Slack alerts, generate reports, and schedule follow-ups. Setup Instructions Configure webhook, database, GPT-4 API, notifications, calendar integration, and customize alert thresholds. Prerequisites Wearable API, patient database, GPT-4 key, email SMTP, optional Slack/Twilio, calendar integration. Use Cases Monitor glucose for diabetics, track elderly vitals/fall risk, assess corporate wellness, and post-surgery recovery alerts. Customization Adjust risk algorithms, add metrics, integrate telemedicine. Benefits Early intervention reduces readmissions and automates 80% of monitoring tasks.

Introduction Automates gold market tracking using AI forecasting by collecting live prices, financial news, and macro indicators (inflation, interest rates, employment) to produce real-time insights and trend predictions for analysts and investors. How It Works Every 6 hours, the system fetches market data and news → runs AI sentiment and trend analysis → generates a concise forecast report → publishes it to WordPress → and alerts users via Slack or email. Workflow Template Trigger → Fetch → Format → Merge → AI Analyze → Report → Publish → Notify Workflow Steps Schedule: Executes automatically every 6 hours using a Cron trigger. Fetch: Retrieves live gold prices (MetalPriceAPI), financial headlines (NewsAPI), and macroeconomic indicators (FRED). Format & Merge: Cleans, normalizes, and merges all data into a single structured dataset for AI analysis. AI Analyze (OpenAI): Performs sentiment, trend, and correlation analysis to forecast short-term gold price movements. Report Generation: Creates a concise summary report with forecasts, insights, and confidence metrics. Publish & Notify: Automatically posts to WordPress and sends alerts via Slack and Email to keep analysts updated. Setup Add API keys: MetalPrice, NewsAPI, FRED, OpenAI, WordPress, Slack, Gmail. Configure scheduling interval, API endpoints, and authentication in n8n. Predefine WordPress post format and Slack message templates for smooth automation. Prerequisites n8n v1.0+, API keys, OAuth credentials, and internet access. Use Cases Investment forecasting, financial newsletter automation, or market monitoring dashboards. Customization Add cryptocurrency or stock tracking, modify AI prompts, or route summaries to Telegram, Notion, or Google Sheets. Benefits Saves analyst time, ensures consistent insights, enhances accuracy, and delivers timely, AI-driven financial intelligence.

Introduction Automates travel planning by aggregating flights, hotels, activities, and weather via APIs, then uses AI to generate professional itineraries delivered through Gmail and Slack. How It Works Webhook receives requests, searches APIs (Skyscanner, Booking.com, Kiwi, Viator, weather), merges data, AI builds itineraries, scores options, generates HTML emails, delivers via Gmail/Slack. Workflow Template Webhook → Extract → Parallel Searches (Flights/Hotels/Activities/Weather) → Merge → Build Itinerary → AI Processing → Score → Generate HTML → Gmail → Slack → Response Workflow Steps Trigger & Extract: Receives destination, dates, preferences, extracts parameters. Data Gathering: Parallel APIs fetch flights, hotels, activities, weather, merges responses. AI Processing: Analyzes data, creates itinerary, ranks recommendations. Delivery: Generates HTML email, sends via Gmail/Slack, confirms completion. Setup Instructions API Configuration: Add keys for Skyscanner, Booking.com, Kiwi, Viator, OpenWeatherMap, OpenRouter. Communication: Connect Gmail OAuth2, Slack webhook. Customization: Adjust endpoints, AI prompts, HTML template, scoring criteria. Prerequisites API keys: Skyscanner, Booking.com, Kiwi, Viator, OpenWeatherMap, OpenRouter Gmail account Slack workspace n8n instance Use Cases Corporate travel planning Vacation itinerary generation Group trip coordination Customization Add sources (Airbnb, TripAdvisor) Filter by budget preferences Add PDF generation Customize Slack format Benefits Saves 3-5 hours per trip Real-time pricing aggregation AI-powered personalization Automated multi-channel delivery

Introduction Automate peer review assignment and grading with AI-powered evaluation. Designed for educators managing collaborative assessments efficiently. How It Works Webhook receives assignments, distributes them, AI generates review rubrics, emails reviewers, collects responses, calculates scores, stores results, emails reports, updates dashboards, and posts analytics to Slack. Workflow Template Webhook → Store Assignment → Distribute → Generate Review Rubric → Notify Slack → Email Reviewers → Prepare Response → Calculate Score → Store Results → Check Status → Generate Report → Email Report → Update Dashboard → Analytics → Post to Slack → Respond to Webhook Workflow Steps Receive & Store: Webhook captures assignments, stores data. Distribute & Generate: Assigns peer reviewers, AI creates rubrics. Notify & Email: Alerts via Slack, sends review requests. Collect & Score: Gathers responses, calculates peer scores. Report & Update: Generates reports, emails results, updates dashboard. Analyze & Alert: Posts analytics to Slack, confirms completion. Setup Instructions Webhook & Storage: Configure endpoint, set up database. AI Configuration: Add OpenAI key, customize rubric prompts. Communication: Connect Gmail, Slack credentials. Dashboard: Link analytics platform, configure metrics. Prerequisites OpenAI API key Gmail account Slack workspace Database or storage system Dashboard tool Use Cases University peer review assignments Corporate training evaluations Research paper assessments Customization Multi-round review cycles Custom scoring algorithms Benefits Eliminates manual distribution Ensures consistent evaluation

Introduction Automate candidate evaluation from CV submission to interview booking. Perfect for HR teams and recruiters. How It Works Webhook receives CVs, extracts Airtable data, AI assesses qualifications, filters candidates, sends emails, schedules Google Calendar interviews, and updates records. Workflow Template Webhook → Airtable (Get Data) → AI Extract CV → AI Assessment → Filter Qualified → Generate Email → Send Email → Filter Interview Candidates → Schedule Calendar → Update Airtable → Slack Notification → Respond Workflow Steps Receive & Store: Webhook receives CVs, saves to Airtable. Fetch & Download: Gets job criteria, downloads CVs. AI Assessment: Parses skills, scores candidates. Filter & Email: Routes qualified, sends messages. Schedule & Update: Books interviews, updates Airtable. Notify: Alerts via Slack, confirms status. Setup Instructions Webhook & Airtable: Set URL, create tables, add credentials. AI Configuration: Add OpenAI key, define schema, customize scoring. Communication: Connect Gmail, Calendar, and Slack. Prerequisites Airtable account OpenAI API key Gmail and Google Calendar Slack workspace (optional) Customization Multi-stage scheduling ATS integration (Greenhouse, Lever) Benefits Reduces screening time by 90% Ensures uniform evaluation Cuts time-to-hire by 60%

Automatically fetch, curate, and distribute Reddit content digests using AI-powered filtering. This workflow monitors multiple subreddits, ranks posts by relevance, removes spam and duplicates, then delivers beautifully formatted digests to Telegram, Discord, or Slack. Who's it for Perfect for content creators tracking trends, marketers monitoring discussions, researchers following specific topics, and community managers staying informed. Anyone who wants high-quality Reddit updates without manually browsing multiple subreddits. How it works The workflow fetches top posts from your chosen subreddits using Reddit's JSON API (no authentication required). Posts are cleaned, deduplicated, and filtered by upvote threshold and custom keywords. An AI model (Google Gemini, OpenAI, or Claude) then ranks remaining posts by relevance, filters out low-quality content, and generates a formatted digest. The final output is delivered to your preferred messaging platform on a schedule or on-demand. Setup requirements n8n version 1.0+ AI provider API key (Google Gemini recommended - has free tier) At least one messaging platform configured: Telegram bot token + chat ID Discord webhook URL Slack OAuth token + channel access How to set up Open the Configuration node and edit subreddit list, post counts, and keywords Configure the Schedule Trigger or use manual execution Add your AI provider credentials in the AI Content Curator node Enable and configure your preferred delivery platform (Telegram/Discord/Slack) Test with manual execution, then activate the workflow Customization options Subreddits**: Add unlimited subreddits to monitor (comma-separated) Time filters**: Choose from hour, day, week, month, year, or all-time top posts Keywords**: Set focus keywords to prioritize and exclude keywords to filter out Post count**: Adjust how many posts to fetch vs. how many appear in final digest AI prompt**: Customize ranking criteria and output format in the AI node Schedule**: Use cron expressions for hourly, daily, or weekly digests Output format**: Modify the formatting code to match your brand style Add email notifications, database storage, or RSS feed generation by extending the workflow with additional nodes.

🛍️ Google Shopping Feed Optimization with Channable + Relevance AI + Google Merchant API 🚀 Automate, Optimize & Sync Your Product Feeds at Scale 🧩 Overview This workflow automates Google Shopping Feed Optimization using Channable, Relevance AI, and the Google Merchant API. It runs daily, enhancing product titles and descriptions, validating feed quality, assigning custom campaign labels, and syncing the optimized feed with Google Merchant Center. The system ensures every product listing meets Google’s content standards, is SEO-friendly, and ready for high-performance Shopping campaigns. 🧠 Key Benefits ✅ Automated daily product feed optimization ✅ AI-enhanced titles and descriptions (via Relevance AI) ✅ Google Merchant API integration (latest version) ✅ Quality scoring and error detection before sync ✅ Custom campaign labels for segmented bidding ✅ Slack alerts for issues and daily summaries ✅ 100% no-code deployment with scalable batch processing ⚙️ System Architecture | Component | Purpose | | ----------------------------- | -------------------------------------------------------- | | n8n | Workflow automation and orchestration | | Channable | Product feed source (can replace with any eCommerce API) | | Relevance AI | AI title and description optimization | | Google Merchant API (NEW) | Product publishing and validation | | Slack | Alerts and reporting | | Cron Trigger | Daily schedule (6 AM sync) | 🧭 Workflow Logic (Visual Summary) Daily Trigger (06:00 AM) ⬇️ 1️⃣ Get Product Feed (Channable) Fetches product data for optimization. ⬇️ 2️⃣ Data Quality Checks Validates titles, GTINs, pricing, categories, and descriptions. Assigns quality scores. ⬇️ 3️⃣ Split Products Breaks the all_products array into single items for AI processing. ⬇️ 4️⃣ Optimize Title (Relevance AI Tool) Enhances product titles for SEO, clarity, and Google compliance. ⬇️ 5️⃣ Generate Description (Relevance AI Tool) Creates 300–400 character, benefit-focused product descriptions. ⬇️ 6️⃣ Assign Custom Labels Adds five segmentation labels: margin, performance, seasonality, stock level, and category. ⬇️ 7️⃣ Aggregate Products Combines optimized items into one unified dataset. ⬇️ 8️⃣ Upload to Merchant Center (NEW Merchant API) Publishes products via Google’s latest /products endpoint. ⬇️ 9️⃣ Check Product Status Verifies successful uploads and identifies disapprovals. ⬇️ 🔍 Analyze Product Issues Summarizes errors and warnings from Merchant API results. ⬇️ ⚖️ IF Disapprovals Found → 🚨 Send Slack alert for issues → ✅ Otherwise, post success summary 🧩 Environment Variables Set these under n8n → Settings → Variables → Add Variable | Variable | Example | Purpose | | ----------------------------------- | -------------------------------------------------- | -------------------------------- | | CHANNABLE_API_URL | https://api.channable.com/v1 | Channable API base | | CHANNABLE_COMPANY_ID | 12345 | Company ID in Channable | | CHANNABLE_PROJECT_ID | abcd | Project ID | | FEED_ID | shopping-feed | Feed endpoint | | RELEVANCE_AI_API_URL | https://api-f1db6c.stack.tryrelevance.com/latest | Relevance AI API base | | RELEVANCE_TOOL_TITLE_OPTIMIZER_ID | tQy48Ld8n0zp | Relevance AI Title Tool ID | | RELEVANCE_TOOL_DESCRIPTION_ID | hJ9bT01r8Lqf | Relevance AI Description Tool ID | | MERCHANT_API_URL | https://merchantapi.googleapis.com/content/v2.1 | Google Merchant API base | | MERCHANT_ACCOUNT_ID | 123456789 | Merchant Center account ID | | SLACK_CHANNEL | #shopping-feed-automation | Slack channel for reports | 🔑 Credential Setup | Service | Type | Setup | | ------------------- | ---------------- | -------------------------------------------------------------- | | Relevance AI | HTTP Header Auth | Header → Authorization: Bearer {{$env.RELEVANCE_AI_API_KEY}} | | Channable | HTTP Header Auth | Header → Authorization: Bearer {{$env.CHANNABLE_API_TOKEN}} | | Google Merchant | Google OAuth2 | Scopes: • https://www.googleapis.com/auth/content | | Slack | Slack API | Add chat:write Bot Token Scope | 🧱 Node-by-Node Breakdown | Node | Description | Key Action | | ----------------------------- | ---------------------------------------- | ----------------------------------------------------------- | | Daily Trigger (6 AM) | Starts workflow every morning | cron: 0 6 * * * | | Get Product Feed | Fetches products from Channable | GET {{$env.CHANNABLE_API_URL}}/.../feeds/{{$env.FEED_ID}} | | Data Quality Checks | Validates GTINs, titles, pricing, images | Returns quality_score + all_products | | Split Products | Splits array into individual products | Operation: splitOut, Field: all_products | | Optimize Title | Calls Relevance AI title tool | /tools/{{$env.RELEVANCE_TOOL_TITLE_OPTIMIZER_ID}}/trigger | | Generate Description | Calls Relevance AI description tool | /tools/{{$env.RELEVANCE_TOOL_DESCRIPTION_ID}}/trigger | | Assign Custom Labels | Adds 5 Smart Bidding Labels | Margin, performance, seasonality, stock, category | | Aggregate Products | Combines optimized product data | For batch upload | | Upload to Merchant Center | Posts via NEW Merchant API | /accounts/{id}/products | | Check Product Status | Retrieves upload results | Lists disapproved or pending items | | Analyze Product Issues | Summarizes product disapprovals | Returns disapproval_count and warnings | | IF Disapprovals Found | Conditional routing | Sends alert or success message | | Slack - Alert | Sends error summary to Slack | Includes product name and issue detail | | Slack - Success Summary | Posts daily completion message | Includes counts and optimizations applied | 🧰 Testing Procedure 1️⃣ Temporarily disable the cron schedule 2️⃣ Run manually using “Execute Workflow” 3️⃣ Start with 3–5 products 4️⃣ Check: Slack → Success message Google Merchant → Updated products n8n Execution logs → No failed nodes Once validated → Re-enable the 6 AM trigger 🧾 Example Output Slack Success Message ✅ Shopping Feed Optimization Complete 📊 Summary: • Total Products Processed: 135 • Products with Quality Issues: 12 • Disapprovals: 0 • Warnings: 3 🎯 Optimizations Applied: • Titles optimized for SEO • Descriptions enhanced • Custom labels added API: NEW Merchant API (merchantapi.googleapis.com) Next Run: Tomorrow 6 AM Timestamp: 2025-10-22T06:00:00Z Slack Alert Message 🚨 Merchant Center Disapprovals Alert Total Disapprovals: 5 Total Warnings: 2 Critical Issues: • Product: Wireless Headphones (ID: 4829) Issue: Missing GTIN • Product: Yoga Mat Eco (ID: 7350) Issue: Invalid price Action Required: Review disapproved products in Merchant Center. Timestamp: 2025-10-22T06:00:00Z 📊 Success Metrics | Metric | Goal | | ---------------------------- | ------------------- | | Feed approval rate | ≥ 90% | | AI optimization success rate | ≥ 95% | | Manual review reduction | 80% | | Daily automation uptime | 99.9% | | Scalable throughput | 5,000+ products/day | 🧩 Maintenance Schedule | Frequency | Task | | ------------- | ------------------------------ | | Daily | Monitor Slack alerts | | Weekly | Check disapproval logs | | Monthly | Refresh API tokens | | Quarterly | Tune AI prompts and thresholds | 🪜 Next Steps ✅ Deploy workflow in production 📈 Connect to your performance dashboard 🌍 Extend to multi-language feeds (Relevance AI translations) 💡 Add conversion optimization loop in Google Ads 🔗 References n8n Documentation Relevance AI Documentation Google Merchant API Docs Channable Help Center 🎉 Conclusion You now have a production-grade, AI-driven Shopping Feed Optimization workflow built on: Channable** for structured data ingestion Relevance AI** for content intelligence Google Merchant API** for publishing n8n** as the automation engine 💡 Result: A fully autonomous product feed system that self-improves daily, keeping your listings compliant, optimized, and performing at scale.

🚀 Channable + Google Ads + Relevance AI: Scalable AI Workflow for Automated Ad Copy Generation & Publishing 🧩 Overview This workflow automates the entire ad creation process for Google Ads by integrating product data, AI-generated copy, compliance checks, and publication into your marketing pipeline. It connects n8n, Relevance AI, Google Sheets, and optionally Channable to: Fetch product data from your catalog Generate Google Text Ad headlines and descriptions using Relevance AI Validate character limits and ensure Google Ads compliance Route non-compliant ads to a Slack review channel Save compliant, ready-to-publish ads in Google Sheets Notify your marketing team automatically after each generation cycle 🧠 Key Benefits ✅ 100% automated ad copy pipeline ✅ AI-generated, human-quality Google Ads text ✅ Built-in compliance verification (Google Ads policy) ✅ Google Sheet integration for team review ✅ Daily automatic schedule (zero manual effort) ✅ Slack alerts for QA and transparency ✅ Modular design — extendable for Shopping and Performance Optimization ✅ Scalable for 10 → 10,000+ product ads ⚙️ System Architecture Tech Stack n8n** – Automation Orchestrator Relevance AI** – AI tools for copy generation and policy compliance Google Sheets** – Data storage and team collaboration Slack** – Real-time alerts and notifications (Optional) Channable – Product feed integration 🧭 Workflow Logic Daily Trigger (00:00) ⬇️ 1️⃣ Get Product Feed (Channable or custom API) ⬇️ 2️⃣ Split Into Batches (50 products each) ⬇️ 3️⃣ Generate Ad Copy (Relevance AI tool → Claude 3.5 prompt) ⬇️ 4️⃣ Validate Character Limits (JS node: max 30 headline / 90 description) ⬇️ 5️⃣ Compliance Check (Relevance AI agent → Google Ads policies) ⬇️ 6️⃣ IF Compliant → CSV / Google Sheets ↳ ❌ Non-Compliant → Slack Alert ⬇️ 7️⃣ Aggregate Batches + Generate CSV ⬇️ 8️⃣ Save to Google Sheets (“Generated Ads” tab) ⬇️ 9️⃣ Slack Notification → Summary Report 📋 Environment Variables Set these in n8n → Settings → Variables → Add Variable Copy-paste from your ENVIRONMENT_VARIABLES_CORRECTED.txt. Includes: ✅ Relevance AI region, API key, tool & agent IDs ✅ Google Ads, Merchant Center, and Sheets credentials ✅ Slack channel name ✅ Optional Channable endpoint Example: RELEVANCE_AI_API_URL=https://api-f1db6c.stack.tryrelevance.com/latest RELEVANCE_TOOL_AD_COPY_ID=bueQG8io04dw RELEVANCE_AGENT_COMPLIANCE_ID=xT29mQ4QKsl GOOGLE_SHEET_ID=1q2w3e4r5t6y7u8i9o0p SLACK_CHANNEL=#google-ads-automation 🏗️ Node-by-Node Breakdown | Node | Description | Endpoint / Logic | | -------------------------------------- | ----------------------------------------------- | ----------------------------------------------------------------------------- | | 🕓 Schedule Trigger | Runs daily at 00:00 | Cron 0 0 * * * | | 📦 Get Product Feed | Pulls product data from Channable or custom API | GET {{$env.CHANNABLE_API_URL}}/v1/projects/{{$env.PROJECT_ID}}/items | | 🧮 Split Into Batches | Processes 50 products at a time | Avoids rate limits | | ✍️ Generate Ad Copy (Relevance AI) | Calls AI tool for each product | POST {{$env.RELEVANCE_AI_API_URL}}/tools/google_text_ad_copy_generator/run | | 🔍 Validate Character Limits | JS validation (≤30 headline / ≤90 description) | Truncates smartly | | 🧠 Compliance Check Agent | Verifies Google Ads compliance | POST {{$env.RELEVANCE_AI_API_URL}}/agents/google_ads_compliance_checker/run | | ⚖️ IF Compliant | Routes APPROVED vs REJECTED | "contains 'APPROVED'" | | 💾 Format for CSV | Formats compliant ads for export | Maps ID, headline, desc, URLs | | 📊 Aggregate Batches | Combines all results | Merges datasets | | 🧱 Generate CSV File | Converts JSON → CSV | Escaped string-safe format | | 📑 Save to Google Sheets | Saves reviewed ads | Sheet: Generated Ads | | 📢 Slack Notification (Success) | Posts completion summary | Shows ad count, timestamp | | 🚨 Slack Alert (Non-Compliant) | Notifies team for review | Includes issues, category | 🔑 API Authentication Setup 🔹 Relevance AI Create “HTTP Header Auth” credential Header Name: Authorization Header Value: Bearer {{$env.RELEVANCE_AI_API_KEY}} 🔹 Google Sheets Credential type: “Google OAuth2 API” Scopes: https://www.googleapis.com/auth/spreadsheets https://www.googleapis.com/auth/drive.file 🔹 Slack Create Slack App → Add Bot Token Scopes → chat:write Paste token in n8n “Slack API” credential. 🔹 (Optional) Channable Header Auth: Bearer {{$env.CHANNABLE_API_TOKEN}} 🧩 Google Sheet Template Sheet name: Generated Ads Columns: | product_id | headline | description | final_url | display_url | generated_at | Optional: Add compliance_status or notes columns for QA. ⚙️ Testing Procedure Manual Trigger: Disable the schedule → click “Execute Workflow”. Batch Size: Start small (3 products). Expected Output: ✅ Ad copy generated ✅ Character limits validated ✅ Slack alerts for rejects ✅ Google Sheet filled Check logs in Executions for errors. Re-enable the cron trigger after successful validation. 🧾 Example Output | product_id | headline | description | final_url | display_url | generated_at | | ---------- | ------------------ | --------------------------------------------- | ------------------------------------------------ | ----------- | -------------------- | | 12243 | “Eco Bamboo Socks” | “Soft, breathable comfort for everyday wear.” | https://shop.com/socks | shop.com | 2025-10-22T00:00:00Z | 📬 Slack Alert Templates ✅ Success Notification ✅ Google Ads Generation Complete 📊 Summary: • Total Ads Generated: 50 • Saved to Google Sheets: Generated Ads • Timestamp: 2025-10-22T00:00:00Z ⚠️ Non-Compliant Alert ⚠️ Non-Compliant Ad Flagged Product: Bamboo Socks Issues: Contains “Free Shipping” Headline too long Timestamp: 2025-10-22T00:00:00Z 🧰 Maintenance & Monitoring | Frequency | Task | | --------- | -------------------------------- | | Daily | Check Slack alerts for rejects | | Weekly | Review ad performance metrics | | Monthly | Update Relevance AI prompts | | Quarterly | Refresh API tokens and variables | 📊 Success Metrics ✅ Compliance approval rate: >85% 🚫 Disapproval rate: <5% 📈 CTR improvement: +15–25% ⏱️ Time saved: 10–15 hours/week 🌐 Scalable: 1,000+ ads/day 🪜 Next Steps Deploy and monitor for 7 days. After 30 days → activate Workflow 2: Performance Optimization Loop. Extend to Shopping Feed Optimization. Add multi-language generation using Relevance AI. Integrate Google Ads API publishing (full automation). 🔗 Resources n8n Docs Relevance AI Docs Google Ads API Merchant API Channable Help 🎉 Conclusion You now have a production-ready, scalable AI-powered ad generation system integrating Channable, Google Ads, and Relevance AI — built entirely on n8n. This delivers: 💡 AI creativity at scale ✅ Google Ads policy compliance ⚙️ Hands-free daily automation 📊 Transparent reporting and collaboration > Start small → validate → scale to 10,000+ ads per day. > Within weeks, you’ll have a self-learning, always-on ad pipeline driving consistent performance.

This workflow is the AI analysis and alerting engine for a complete social media monitoring system. It's designed to work with data scraped from X (formerly Twitter) using a tool like the Apify Tweet Scraper, which logs the data into a Google Sheet. The workflow then automatically analyzes new tweets with Google Gemini and sends tailored alerts to Slack. How it works This workflow automates the analysis and reporting part of your social media monitoring: tweet Hunting:** It finds tweets for the query entered in the set node and passes the data to the google sheets Fetches New Tweets:** It gets all new rows from your Google Sheet that haven't been processed yet (it looks for "Notmarked" in the 'action taken' column). Prepares for AI:** It combines the data from all new tweets into a single, clean prompt for the AI to analyze. AI Analysis with Gemini:* It sends the compiled data to Google Gemini, asking for a full summary report *and a separate, machine-readable JSON list of any urgent items. Splits the Response:** The workflow intelligently separates the AI's text summary from the JSON data for urgent alerts. Sends Notifications:** The high-level summary is sent to a general Slack channel (e.g., #brand-alerts). Each urgent item is sent as a separate, detailed alert to a high-priority Slack channel (e.g., #urgent). Set up steps It should take about 5-10 minutes to get this workflow running. Prerequisite - Data Source: Ensure you have a Google Sheet being populated with tweet data. For a complete automation, you can set up a new google sheet with the same structure for saving the tweets data and run the Tweet Scraper on a schedule. Configure Credentials: Make sure you have credentials set up in your n8n instance for Google Sheets, Google Gemini (PaLM) API, and Slack. Google Sheets Node ("Get row(s) in sheet"): Select your Google Sheet containing the tweet data. Choose the specific sheet name from the dropdown. Ensure your sheet has a column named action taken so the filter works correctly. Google Gemini Chat Model Node: Select your Google Gemini credential from the dropdown. Slack Nodes ("Send a message" & "Send a message1"): In the first Slack node, choose the channel for the summary report. In the second Slack node, choose the channel for urgent alerts. Save and Activate: Once configured, save your workflow and turn it on!

This workflow tracks week-over-week changes in Google Search Console performance and highlights the top movers across keyword segments like brand, nonbrand, and content categories. Instead of providing a routine check, it focuses on significant movements by: Sending a Slack alert only if a query crosses a defined movement threshold. Emailing a structured report with the Top 25 increases and Top 25 decreases for clicks, including % changes and linked URLs It’s designed to surface the most important shifts, helping SEO teams catch big wins, losses, or anomalies early. How it works Runs weekly (e.g. every Monday) to compare last week’s GSC data to the week prior. Segments traffic based on query and page (e.g. brand terms, category page URLs, etc.). Calculates delta and % change for clicks, CTR, impressions, and position. Filters and flags top movers with large shifts (default: ±200 clicks and ±30%). Sends Slack alerts only if meaningful changes are detected. Emails a full HTML table report showing the Top 25 up/down queries per segment. Setup steps Requires a connected Google Search Console account. Slack alert is included by default (can be replaced with email, webhook, or other tools). Customize your brand terms and URL filters to match your segments (e.g. recipes, blog, category pages). Typical setup time: 15–25 minutes depending on the number of segments and filters you want. Note: “Recipes” is used in the example to show how to segment by content type. You can update this to reflect your own site’s structure.