Workflow Templates

Generate AI viral videos with NanoBanana & VEO3, shared on socials via Blotato Who is this for? This workflow is designed for content creators, marketers, and entrepreneurs who want to automate their video production and social media publishing process. If you regularly post promotional or viral-style content on platforms like TikTok, YouTube Shorts, Instagram Reels, LinkedIn, and more, this template will save you hours of manual work. What problem is this workflow solving? / Use case Creating viral short-form videos is often time-consuming: You need to generate visuals, write scripts, edit videos, and then manually upload them to multiple platforms. Staying consistent across TikTok, YouTube Shorts, Instagram Reels, LinkedIn, Twitter/X, and others requires constant effort. This workflow solves the problem by automating the entire pipeline from idea → video creation → multi-platform publishing. What this workflow does Collects an idea and image from Telegram. Enhances visuals with NanoBanana for user-generated content style. Generates a complete video script with AI (OpenAI + structured prompts). Creates the final video with VEO3 using your custom prompt and visuals. Rewrites captions with GPT to be short, catchy, and optimized for social platforms. Saves metadata in Google Sheets for tracking and management. Auto-uploads the video to all major platforms via Blotato (TikTok, YouTube, Instagram, LinkedIn, Threads, Pinterest, X/Twitter, Bluesky, Facebook). Notifies you on Telegram with a preview link once publishing is complete. Setup Connect your accounts: Google Sheets (for video tracking) Telegram (to receive and send media) Blotato (for multi-platform publishing) OpenAI API (for captions, prompts, and image analysis) VEO3 API (for video rendering) Fal.ai (for NanoBanana image editing) Google Drive (to store processed images) Set your credentials in the respective nodes. Adjust the Google Sheet IDs to match your own sheet structure. Insert your Telegram bot token in the Set: Bot Token (Placeholder) node. How to customize this workflow to your needs Platforms**: Disable or enable only the Blotato social accounts you want to post to. Video style**: Adjust the master prompt schema in the Set Master Prompt node to fine-tune tone, camera style, or video format. Captions**: Modify the GPT prompt in the Rewrite Caption with GPT-4o node to control length and tone. Notifications**: Customize the Telegram nodes to notify team members, not just yourself. Scheduling**: Add a Cron trigger if you want automatic posting at specific times. ✨ With this workflow, you go from idea → AI-enhanced video → instant multi-platform publishing in just minutes, with almost no manual work. 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube

💥 Automate YouTube thumbnail creation from video links (with templated.io) Who is this for? This workflow is designed for content creators, YouTubers, and automation enthusiasts who want to automatically generate stunning YouTube thumbnails and streamline their publishing workflow — all within n8n. If you regularly post videos and spend hours designing thumbnails manually, this automation is built for you. What problem is this workflow solving? Creating thumbnails is time-consuming — yet crucial for video performance. This workflow completely automates that process: No more manual design. No more downloading screenshots. No more repetitive uploads. In less than 2 minutes, you can refresh your entire YouTube thumbnail library and make your channel look brand new. What this workflow does Once activated, this workflow can: ✅ Receive YouTube video links via Telegram ✅ Extract metadata (title, description, channel info) via YouTube API ✅ Generate a custom thumbnail automatically using Templated.io ✅ Upload the new thumbnail to Google Drive ✅ Log data in Google Sheets ✅ Send email and Telegram notifications when ready ✅ Create and publish AI-generated social posts on LinkedIn, Facebook, and Twitter via Blotato Bonus: You can re-create dozens of YouTube covers in minutes — saving up to 5 hours per week and around $500/month in manual design effort. Setup 1️⃣ Get a YouTube Data API v3 key from Google Cloud Console 2️⃣ Create a Templated.io account and get your API key + template ID 3️⃣ Set up a Telegram bot using @BotFather 4️⃣ Create a Google Drive folder and copy the folder ID 5️⃣ Create a Google Sheet with columns: Date, Video ID, Video URL, Title, Thumbnail Link, Status 6️⃣ Get your Blotato API key from the dashboard 7️⃣ Connect your social media accounts to Blotato 8️⃣ Fill all credentials in the Workflow Configuration node 9️⃣ Test by sending a YouTube URL to your Telegram bot How to customize this workflow Replace the Templated.io template ID with your own custom thumbnail layout Modify the OpenAI node prompts to change text tone or style Add or remove social platforms in the Blotato section Adjust the wait time (default: 5 minutes) based on template complexity Localize or translate the generated captions as needed Expected Outcome With one Telegram message, you’ll receive: A professional custom thumbnail An instant email + Telegram notification A Google Drive link with your ready-to-use design And your social networks will be automatically updated — no manual uploads. Credits Thumbnail generation powered by Templated.io Social publishing powered by Blotato Automation orchestrated via n8n 👋 Need help or want to customize this? 📩 Contact: LinkedIn 📺 YouTube: @DRFIRASS 🚀 Workshops: Mes Ateliers n8n 🎥 Watch This Tutorial 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube / 🚀 Mes Ateliers n8n

🚀 Create Pro-Level Social Media Carousels & Auto-Publish with Blotato By @nocodehack Who is this for? This workflow is built for e-commerce brands, social media managers, marketing agencies, dropshippers, content creators, and automation builders who need to produce professional carousel posts at scale. Perfect for anyone running product marketing, brand campaigns, multi-platform social media, affiliate content, or any business that publishes carousel posts regularly and wants to eliminate design costs entirely. What problem is this workflow solving? / Use case Creating professional carousel posts is: Slow** — designing even one carousel takes 30-60 minutes manually Expensive** — Fiverr/Upwork designers charge $50-100 per carousel Inconsistent** — AI-generated slides never visually match each other Unscalable** — managing multiple brands multiplies every problem Tedious** — exporting, uploading, scheduling, and publishing is repetitive busywork This workflow solves: ❌ Manual carousel design (Canva, Photoshop, Figma) ❌ Paying designers per post ❌ AI images that look obviously AI-generated ❌ Visually inconsistent slides that don't match ❌ Manual copywriting for captions and hashtags ❌ Manual uploading and publishing to each platform ❌ Managing multiple brands with different visual identities It turns one Google Sheet row into a fully designed, published carousel — across Instagram, Facebook, and X — for approximately 5 cents. What this workflow does This automation system acts as a complete AI-powered carousel design studio and publishing pipeline. Step-by-step pipeline: Step 1 — Data Pipeline (Google Sheet) Runs on a schedule (configurable interval) Pulls the next unprocessed row from Google Sheets Each row = one carousel (one brand, one product, one post) Marks the row as "Processing" to prevent duplicate execution Checks if product description and images are provided — if missing, auto-scrapes from the product URL using Jina.ai (free, no account needed) Merges all data into one clean payload for the AI Step 2 — AI Creative Direction (Claude) Sends all product data (description, images as base64, brand logo, creative specifications) to Claude via the Anthropic API Claude acts as an executive creative director — not just generating content, but building a complete visual identity first: Color palette (2-3 hex colors) Typography style and hierarchy Lighting direction and mood Signature design element Background texture concept Then generates for each slide: headline, body copy, layout approach, and a detailed 80+ word image prompt A 2000-word system prompt with banned elements list eliminates the generic AI look (no waves, no scattered leaves, no flat backgrounds, no Canva-style templates) Every image prompt ends with a negative prompt / AVOID block — same concept as Stable Diffusion negative prompts, applied to Gemini Output is structured JSON via a parser — no freeform text that could break the pipeline Also generates the Instagram caption and hashtags Step 3 — Image Generation with Visual Consistency Loop This is the core innovation of the workflow** Slides are generated sequentially, NOT in parallel — this is critical For slide 1: Gemini generates the image from the prompt + product reference images For slide 2+: The workflow fetches all previously generated slides, converts them to base64, and attaches them as reference images alongside the current prompt The text prompt explicitly instructs: "Match the exact typography, color palette, and lighting from the attached previous slides" This creates a double enforcement system — visual reference + written instruction Result: every slide in the carousel shares the same visual identity without using templates or presets Images are generated via NanoBanana Pro (Gemini image generation API) Each generated slide is uploaded to Blotato media storage and saved to a global memory array for the next iteration Uses $getWorkflowStaticData('global') to persist slide URLs across loop iterations Step 4 — Publishing & Status Update Collects all uploaded slide URLs in correct order Reads the "Socials" field from the Google Sheet (comma-separated: instagram, facebook, x) Routes to the correct platform via a Switch node Publishes via Blotato API — supports immediate posting or scheduled posting (ISO 8601 format) One row can publish to all three platforms simultaneously Updates the Google Sheet row: Status → "Published" + direct Post URL If anything breaks: Status → "Failed" with error details ➡️ Result: One Google Sheet row in, one fully designed and published multi-platform carousel out. 5 cents. 5 minutes. Setup Required accounts & API keys: Google Sheets** — read/write access to your content spreadsheet Anthropic** — Claude API key (creative direction + copywriting) Google AI / Gemini** — API key for image generation via NanoBanana Pro ($300 free credit per new Gmail) Blotato** — API key for media upload + multi-platform publishing Jina.ai** — free web scraping, no account required (10M tokens free) Google Sheet structure: Column Description Brand Logo URL Direct link to your brand logo — placed on every slide automatically Product URL Link to product page — used for auto-scraping if description/images are empty Product Description Optional — write it yourself for best results, or leave blank to auto-scrape Product Images URL Direct links to product photos (comma-separated for multiple) Specification Creative direction hint (e.g. "dark cinematic luxury" or "bright playful minimal") — leave empty for AI to decide Post Date YYYY-MM-DD format — workflow only picks up rows matching today's date Post Hour now for immediate publish, or 14:00 / 2pm for scheduled Socials Comma-separated platforms: instagram, facebook, x Status Leave empty — auto-filled: Processing → Published / Failed Post URL Leave empty — auto-filled with direct link to live post Configuration steps: Import the workflow JSON into n8n Add all required API credentials in n8n's credential manager Create your Google Sheet using the template provided (link in resources) Set your Blotato profile IDs in each publishing node (one per platform) Map platform outputs in the Switch node Verify the Gemini image generation endpoint in the NanoBanana Pro node Test with one row before activating production mode Recommended hosting: n8n is free and open source but needs a server. A VPS with at least 2GB RAM handles image generation and multiple API calls without issues. The workflow runs 24/7 on schedule. How to customize Change AI model:** Swap Claude for GPT-4o or Gemini in the LLM Chain node — the structured output parser works with any model Change slide count:** Edit the system prompt and user prompt (currently locked to 3 slides) Change visual style:** Edit the creative direction in the system prompt — modify banned elements, change composition approaches, adjust the quality standard Add platforms:** Add new outputs to the Switch node + new Blotato publish nodes (Blotato supports TikTok, LinkedIn, Pinterest, Threads, YouTube, Bluesky) Add approval step:** Insert a Wait node before publishing to manually review before posting Change image hosting:** Swap Blotato Upload for Cloudinary or any S3-compatible storage Change scraper:** Swap Jina.ai for any other web scraping tool Adjust scheduling:** Modify the Schedule Trigger interval and use the Post Hour column for per-post timing Multi-brand setup:** Each row can have a different brand logo and creative specification — the AI generates a fresh visual identity per row Cost breakdown per carousel (approx.) Component Cost Claude (creative direction + copy, ~8K tokens) ~$0.02 Gemini (3 slide images via NanoBanana Pro) ~$0.03 Jina.ai (web scraping) Free Blotato (publishing) Per plan Total per carousel ~$0.05 Compare: Fiverr/Upwork designers charge $50-100 per carousel post. This workflow does it for 5 cents. Gemini offers $300 free credit per new Gmail account — enough for thousands of carousels before spending anything. Expected outcome You get a fully automated carousel production system that can: Generate agency-quality carousel designs from a spreadsheet Maintain visual consistency across all slides without templates Handle multiple brands with completely different visual identities Publish to Instagram, Facebook, and X simultaneously Schedule content weeks in advance Scale from 1 carousel/day to dozens without additional effort Eliminate design costs almost entirely Typical use cases E-commerce product marketing (daily product carousels) Brand awareness campaigns across multiple platforms Affiliate marketing content at scale Social media agency client deliverables Dropshipping product promotion Multi-brand social media management Content calendar automation A/B testing different creative directions for the same product Watch the full step-by-step walkthrough. 🎥 Video Tutorial 👋 Need help or want to customize? 📩 Contact: LinkedIn 📺 YouTube: @nocodehack 🌐 Resources & Downloads: nocodehack.io

This workflow automates the post-publish process for YouTube videos, combining advanced SEO optimization, cross-platform promotion, and analytics reporting. It is designed for creators, marketers, and agencies who want to maximize the reach and performance of their YouTube content with minimal manual effort. Features SEO Automation** Fetches video metadata and analyzes competitor and trending data. Uses AI to generate SEO-optimized titles, descriptions, and tags. Calculates an SEO score and applies A/B testing logic to select the best title. Updates the video metadata on YouTube automatically. Cross-Platform Promotion** Generates platform-specific promotional content (LinkedIn, X/Twitter, Instagram, Facebook, etc.) using AI. Publishes posts to each connected social channel. Extracts video clips and analyzes thumbnails for enhanced promotion. Engagement Monitoring & Analytics** Monitors YouTube comments, detects negative sentiment, and drafts AI-powered replies. Logs all key data (videos, comments, analytics) to Google Sheets for tracking and reporting. Runs a weekly analytics job to aggregate performance, calculate engagement/viral indicators, and email a detailed report. Notifications & Alerts** Sends Slack alerts when a new video is published or when viral potential/negative comments are detected. How It Works Trigger The workflow starts automatically when a new YouTube video is published (via webhook) or on a weekly schedule for analytics. Video Intake & SEO Fetches video details (title, description, tags, stats). Gathers competitor and trending topic data. Uses AI to generate improved SEO assets and calculates an SEO score. Selects the best title (A/B test) and updates the video metadata. Clip & Thumbnail Processing If the video is long enough, runs thumbnail analysis and extracts short clips for social media. Cross-Platform Promotion Generates and formats promotional posts for each social platform. Publishes automatically to enabled channels. Engagement & Comment Monitoring Fetches comments, detects negative sentiment, and drafts AI-powered replies. Logs comments and responses to Google Sheets. Analytics & Reporting Aggregates weekly analytics, calculates engagement and viral indicators. Logs insights and sends a weekly report via email. Notifications Sends Slack alerts for new video publications and viral/negative comment detection. Setup Instructions Connect YouTube Set up YouTube API credentials and required IDs in the Workflow Configuration node. Connect OpenAI Add your OpenAI credentials for AI-powered content generation. Connect Slack Configure Slack credentials and specify alert channels. Connect Google Sheets Set up service account credentials for logging video, comment, and analytics data. Configure Social Platforms Add credentials for LinkedIn, Twitter (X), Instagram, and Facebook as needed. Test the Workflow Publish a test video and verify that metadata updates, social posts, logging, and weekly reports are working as expected. Use Cases YouTube Creators:** Automate SEO, promotion, and analytics to grow your channel faster. Marketing Teams:** Streamline multi-channel video campaigns and reporting. Agencies:** Deliver consistent, data-driven YouTube growth for multiple clients. Requirements YouTube API credentials OpenAI API key Slack API token Google Sheets service account (Optional) LinkedIn, Twitter, Instagram, Facebook API credentials Limitations Requires valid API credentials for all connected services. AI-powered features depend on OpenAI API access. Social posting is limited to platforms with available n8n nodes and valid credentials. Tip: You can easily customize prompts, scoring logic, and enabled platforms to fit your channel’s unique needs.

AI Agent Lead Funnel for AI Agencies An End-to-End Automation That Turns Demos Into Booked Calls This n8n workflow is a full inbound → outbound hybrid funnel designed for AI agencies. It captures warm leads through instant AI value, then automatically follows up with personalized, context-aware outreach and reminders until the lead either replies or books a call. No cold scraping. No manual follow-ups. Just leverage + timing. 🚀 How the Workflow Works 📋 PART 1 — Lead Capture & Instant Value 1 — Share High-Impact AI Image Edits You post before/after examples using the NanoBanna / Gemini image-editing model on social platforms. Each post includes a link to a lightweight form. The visual results do the selling for you. 2 — Lead Submits Image & Details The form collects: Image upload Edit instructions Name Email Company name This filters for high-intent prospects only. 3 — AI Edits the Image Instantly Once submitted, the workflow: Sends the image + instructions to the AI image editor Preserves lighting and camera angle unless specified Generates a polished result in seconds 4 — Result Delivered via Email The edited image is emailed directly to the user with: A friendly confirmation message Soft positioning for future work This establishes trust before any sales motion happens. 5 — Lead Is Logged Automatically All lead data is saved to Google Sheets: Name Company Email Timestamp This becomes your live CRM of warm inbound leads. 🤖 PART 2 — AI-Driven Personalized Outreach 6 — AI Analyzes the Lead An AI sales agent: Looks at the company name + context Reviews a library of proven automation ideas Either selects the best fit or creates a simple custom one 7 — AI Writes a Personalized Outreach Email The agent generates a short email that: Mentions a specific automation already built States you can help implement it quickly Invites them to book a call via your calendar No marketing fluff. No generic pitches. Every email feels hand-written. 8 — Outreach Email Is Sent Automatically The email is sent from your inbox (Outlook, Gmail, SMTP, etc.) and includes: Their name Their company A clear calendar booking link 📬 PART 3 — Smart Follow-Up System 9 — Wait 48 Hours The workflow pauses to give the lead time to respond naturally. 10 — Check for a Reply After 48 hours: If the lead replied → they are tagged as Interested If no reply → continue to follow-up (Current reply detection is placeholder logic and can be swapped for a live inbox listener.) 11 — AI Writes a Polite Follow-Up If there’s no response, an AI agent writes: A short, non-pushy follow-up Referencing the original automation idea Under 60 words 12 — Follow-Up Email Is Sent The follow-up goes out automatically and keeps the conversation alive without manual effort. 📈 Why This Workflow Converts So Well Instant Value First Leads experience AI results before being pitched anything. Context-Aware Outreach Every email is personalized based on the lead, not a template. Built-In Persistence The system follows up automatically — no leads fall through the cracks. Fully Automated Once live, this workflow handles: Lead capture AI delivery Outreach Follow-ups CRM updates You just keep posting content. 🔧 Setup Requirements To deploy this workflow, connect: Google Gemini API** (image editing + agents) Email provider** Outlook Gmail SMTP Google Sheets** Columns: Name, Company, Email, Time, Status Calendar booking link** Example: https://cal.com/your-link All credentials are modular and easily swappable. 🎯 Summary This n8n automation turns attention into action by: Delivering immediate AI value Following up with relevant, personalized ideas Nudging leads toward a booked call — automatically It’s not just a lead funnel. It’s an AI sales assistant that runs 24/7.

Who is this for? This workflow is for Product Managers, Indie Hackers, and Customer Success teams who collect feature requests but struggle to notify specific users when those features actually ship. It helps you turn old feedback into customer loyalty and potential upsells. What it does This workflow creates a "Semantic Memory" of user requests. Instead of relying on exact keyword tags, it uses Vector Embeddings to understand the meaning of a request. For example, if a user asks for "Night theme," and months later you release "Dark Mode," this workflow understands they are the same thing, finds that user, and drafts a personal email to them. How it works Listen: Receives new requests via Tally Forms, vectorizes the text using Nomic Embed Text (via Ollama or OpenAI), and stores them in Supabase. Watch: Monitors your Changelog (RSS) or waits for a manual trigger when you ship a new feature. Match: Performs a Vector Similarity Search in Supabase to find users who requested semantically similar features in the past. Notify: An AI Agent drafts a hyper-personalized email connecting the user's specific past request to the new feature, saving it as a Gmail Draft (for safety). Requirements Supabase Project:** You need a project with the vector extension enabled. AI Model:* This template is pre-configured for *Ollama (Local)** to keep it free, but works perfectly with OpenAI. Tally Forms & Gmail:** For input and output. Setup steps Database Setup (Crucial): Copy the SQL script provided in the workflow's Red Sticky Note and run it in your Supabase SQL Editor. This creates the necessary tables and the vector search function. Credentials: Add your credentials for Tally, Supabase, and Gmail. URL Config: Update the HTTP Request node with your specific Supabase Project URL. SQL Script Open your Supabase SQL Editor and paste this script to set up the tables and search function: -- 1. Enable Vector Extension create extension if not exists vector; -- 2. Create Request Table (Smart Columns) create table feature_requests ( id bigint generated by default as identity primary key, content text, metadata jsonb, embedding vector(768), -- 768 for Nomic, 1536 for OpenAI created_at timestamp with time zone default timezone('utc'::text, now()), user_email text generated always as (metadata->>'user_email') stored, user_name text generated always as (metadata->>'user_name') stored ); -- 3. Create Search Function create or replace function match_feature_requests ( query_embedding vector(768), match_threshold float, match_count int ) returns table ( id bigint, user_email text, user_name text, content text, similarity float ) language plpgsql as $$ begin return query select feature_requests.id, feature_requests.user_email, feature_requests.user_name, feature_requests.content, 1 - (feature_requests.embedding <=> query_embedding) as similarity from feature_requests where 1 - (feature_requests.embedding <=> query_embedding) > match_threshold order by feature_requests.embedding <=> query_embedding limit match_count; end; $$; ⚠️ Dimension Warning: This SQL is set up for 768 dimensions (compatible with the local nomic-embed-text model included in the template). If you decide to switch the Embeddings node to use OpenAI's text-embedding-3-small, you must change all instances of 768 to 1536 in the SQL script above before running it. How to customize Change Input:** Swap the Tally node for Typeform, Intercom, or Google Sheets. Change AI:** The template includes notes on how to swap the local Ollama nodes for OpenAI nodes if you prefer cloud hosting. Change Output:** Swap Gmail for Slack, SendGrid, or HubSpot to notify your sales team instead of the user directly.

{ "name": "IoT Sensor Data Aggregation with AI-Powered Anomaly Detection", "nodes": [ { "parameters": { "content": "## How it works\nThis workflow monitors IoT sensors in real-time. It ingests data via MQTT or a schedule, normalizes the format, and removes duplicates using data fingerprinting. An AI Agent then analyzes readings against defined thresholds to detect anomalies. Finally, it routes alerts to Slack or Email based on severity and logs everything to Google Sheets.\n\n## Setup steps\n1. Configure the MQTT Trigger with your broker details.\n2. Set your specific limits in the Define Sensor Thresholds node.\n3. Connect your OpenAI credential to the Chat Model node.\n4. Authenticate the Gmail, Slack, and Google Sheets nodes.\n5. Create a Google Sheet with headers: timestamp, sensorId, location, readings, analysis.", "height": 484, "width": 360 }, "id": "298da7ff-0e47-4b6c-85f5-2ce77275cdf3", "name": "Main Overview", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -2352, -480 ] }, { "parameters": { "content": "## 1. Data Ingestion\nCaptures sensor data via MQTT for real-time streams or runs on a schedule for batch processing. Both streams are merged for unified handling.", "height": 488, "width": 412, "color": 7 }, "id": "4794b396-cd71-429c-bcef-61780a55d707", "name": "Section: Ingestion", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -1822, -48 ] }, { "parameters": { "content": "## 2. Normalization & Deduplication\nSets monitoring thresholds, standardizes the JSON structure, creates a content hash, and filters out duplicate readings to prevent redundant API calls.", "height": 316, "width": 884, "color": 7 }, "id": "339e7cb7-491e-44c9-b561-983e147237d8", "name": "Section: Processing", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -1376, 32 ] }, { "parameters": { "content": "## 3. AI Anomaly Detection\nAn AI Agent evaluates sensor data against thresholds to identify anomalies, assigning severity levels and providing actionable recommendations.", "height": 528, "width": 460, "color": 7 }, "id": "ebcb7ca3-f70c-4a90-8a2a-f489e7be4c73", "name": "Section: AI Analysis", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -422, 24 ] }, { "parameters": { "content": "## 4. Routing & Archiving\nRoutes alerts based on severity (Critical = Email+Slack, Warning = Slack) and archives all data points to Google Sheets for historical analysis.", "height": 756, "width": 900, "color": 7 }, "id": "7f2b32a5-d3b2-4fea-844f-4b39b8e8a239", "name": "Section: Alerting", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ 94, -196 ] }, { "parameters": { "topics": "sensors/+/data", "options": {} }, "id": "bc86720b-9de9-4693-b090-343d3ebad3a3", "name": "MQTT Sensor Trigger", "type": "n8n-nodes-base.mqttTrigger", "typeVersion": 1, "position": [ -1760, 88 ] }, { "parameters": { "rule": { "interval": [ { "field": "minutes", "minutesInterval": 15 } ] } }, "id": "1c38f2d0-aa00-447e-bdae-bffd08c38461", "name": "Batch Process Schedule", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.2, "position": [ -1760, 280 ] }, { "parameters": { "mode": "chooseBranch" }, "id": "f9b41822-ee61-448b-b324-38483036e0e1", "name": "Merge Triggers", "type": "n8n-nodes-base.merge", "typeVersion": 3, "position": [ -1536, 184 ] }, { "parameters": { "mode": "raw", "jsonOutput": "{\n \"thresholds\": {\n \"temperature\": {\"min\": -10, \"max\": 50, \"unit\": \"C\"},\n \"humidity\": {\"min\": 20, \"max\": 90, \"unit\": \"%\"},\n \"pressure\": {\"min\": 950, \"max\": 1050, \"unit\": \"hPa\"},\n \"co2\": {\"min\": 400, \"max\": 2000, \"unit\": \"ppm\"}\n },\n \"alertConfig\": {\n \"criticalChannel\": \"#iot-critical\",\n \"warningChannel\": \"#iot-alerts\",\n \"emailRecipients\": \"ops@example.com\"\n }\n}", "options": {} }, "id": "308705a8-edc7-4435-9250-487aa528e033", "name": "Define Sensor Thresholds", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -1312, 184 ] }, { "parameters": { "jsCode": "const items = $input.all();\nconst thresholds = $('Define Sensor Thresholds').first().json.thresholds;\nconst results = [];\n\nfor (const item of items) {\n let sensorData;\n try {\n sensorData = typeof item.json.message === 'string' \n ? JSON.parse(item.json.message) \n : item.json;\n } catch (e) {\n sensorData = item.json;\n }\n \n const now = new Date();\n const reading = {\n sensorId: sensorData.sensorId || sensorData.topic?.split('/')[1] || 'unknown',\n location: sensorData.location || 'Main Facility',\n timestamp: now.toISOString(),\n readings: {\n temperature: sensorData.temperature ?? null,\n humidity: sensorData.humidity ?? null,\n pressure: sensorData.pressure ?? null,\n co2: sensorData.co2 ?? null\n },\n metadata: {\n receivedAt: now.toISOString(),\n source: item.json.topic || 'batch',\n thresholds: thresholds\n }\n };\n \n results.push({ json: reading });\n}\n\nreturn results;" }, "id": "a2008189-5ace-418b-b0db-d51d63dcf2d8", "name": "Parse Sensor Payload", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1088, 184 ] }, { "parameters": { "type": "SHA256", "value": "={{ $json.sensorId + '-' + $json.timestamp + '-' + JSON.stringify($json.readings) }}", "dataPropertyName": "dataHash" }, "id": "bf8db555-a10e-4468-a44a-cdc4c97e5b80", "name": "Generate Data Fingerprint", "type": "n8n-nodes-base.crypto", "typeVersion": 1, "position": [ -864, 184 ] }, { "parameters": { "compare": "selectedFields", "fieldsToCompare": "dataHash", "options": {} }, "id": "a45405e2-d211-449d-84d7-4538eaf56fcd", "name": "Remove Duplicate Readings", "type": "n8n-nodes-base.removeDuplicates", "typeVersion": 1, "position": [ -640, 184 ] }, { "parameters": { "text": "=Analyze this IoT sensor reading and determine if there are any anomalies:\n\nSensor ID: {{ $json.sensorId }}\nLocation: {{ $json.location }}\nTimestamp: {{ $json.timestamp }}\n\nReadings:\n- Temperature: {{ $json.readings.temperature }}°C (Normal: {{ $json.metadata.thresholds.temperature.min }} to {{ $json.metadata.thresholds.temperature.max }})\n- Humidity: {{ $json.readings.humidity }}% (Normal: {{ $json.metadata.thresholds.humidity.min }} to {{ $json.metadata.thresholds.humidity.max }})\n- CO2: {{ $json.readings.co2 }} ppm (Normal: {{ $json.metadata.thresholds.co2.min }} to {{ $json.metadata.thresholds.co2.max }})\n\nProvide your analysis in this exact JSON format:\n{\n \"hasAnomaly\": true/false,\n \"severity\": \"critical\"/\"warning\"/\"normal\",\n \"anomalies\": [\"list of detected issues\"],\n \"reasoning\": \"explanation of your analysis\",\n \"recommendation\": \"suggested action\"\n}", "options": { "systemMessage": "You are an IoT monitoring expert. Analyze sensor data and detect anomalies based on the provided thresholds. Be precise and provide actionable recommendations. Always respond in valid JSON format." } }, "id": "b60194ba-7b99-44e0-b0d7-9f1632dce4d4", "name": "AI Anomaly Detector", "type": "@n8n/n8n-nodes-langchain.agent", "typeVersion": 1.7, "position": [ -416, 184 ] }, { "parameters": { "jsCode": "const item = $input.first();\nconst originalData = $('Remove Duplicate Readings').first().json;\n\nlet aiAnalysis;\ntry {\n const responseText = item.json.output || item.json.text || '';\n const jsonMatch = responseText.match(/\\{[\\s\\S]*\\}/);\n aiAnalysis = jsonMatch ? JSON.parse(jsonMatch[0]) : {\n hasAnomaly: false,\n severity: 'normal',\n anomalies: [],\n reasoning: 'Unable to parse AI response',\n recommendation: 'Manual review required'\n };\n} catch (e) {\n aiAnalysis = {\n hasAnomaly: false,\n severity: 'normal',\n anomalies: [],\n reasoning: 'Parse error: ' + e.message,\n recommendation: 'Manual review required'\n };\n}\n\nreturn [{\n json: {\n ...originalData,\n analysis: aiAnalysis,\n alertLevel: aiAnalysis.severity,\n requiresAlert: aiAnalysis.hasAnomaly && aiAnalysis.severity !== 'normal'\n }\n}];" }, "id": "a145a8c7-538c-411a-95c6-9485acdcb969", "name": "Parse AI Analysis", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -64, 184 ] }, { "parameters": { "rules": { "values": [ { "conditions": { "options": { "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "critical", "operator": { "type": "string", "operation": "equals" }, "leftValue": "={{ $json.alertLevel }}", "rightValue": "critical" } ] }, "renameOutput": true, "outputKey": "Critical" }, { "conditions": { "options": { "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "warning", "operator": { "type": "string", "operation": "equals" }, "leftValue": "={{ $json.alertLevel }}", "rightValue": "warning" } ] }, "renameOutput": true, "outputKey": "Warning" } ] }, "options": { "fallbackOutput": "extra" } }, "id": "1ab9785d-9f7f-4840-b1e9-0afc62b00b12", "name": "Route by Severity", "type": "n8n-nodes-base.switch", "typeVersion": 3.2, "position": [ 160, 168 ] }, { "parameters": { "sendTo": "={{ $('Define Sensor Thresholds').first().json.alertConfig.emailRecipients }}", "subject": "=CRITICAL IoT Alert: {{ $json.sensorId }} - {{ $json.analysis.anomalies[0] || 'Anomaly Detected' }}", "message": "=CRITICAL IoT SENSOR ALERT\n\nSensor: {{ $json.sensorId }}\nLocation: {{ $json.location }}\nTime: {{ $json.timestamp }}\n\nReadings:\n- Temperature: {{ $json.readings.temperature }}°C\n- Humidity: {{ $json.readings.humidity }}%\n- CO2: {{ $json.readings.co2 }} ppm\n\nAI Analysis:\n{{ $json.analysis.reasoning }}\n\nDetected Issues:\n{{ $json.analysis.anomalies.join('\\n- ') }}\n\nRecommendation:\n{{ $json.analysis.recommendation }}", "options": {} }, "id": "28201a6c-10b5-4387-be89-10a57c634622", "name": "Send Critical Email", "type": "n8n-nodes-base.gmail", "typeVersion": 2.1, "position": [ 384, -80 ], "webhookId": "35b9f8fa-4a50-456e-b552-9fd20a25ccc5" }, { "parameters": { "select": "channel", "channelId": { "__rl": true, "mode": "name", "value": "#iot-critical" }, "text": "=🚨 CRITICAL IoT ALERT\n\nSensor: {{ $json.sensorId }}\nLocation: {{ $json.location }}\n\nReadings:\n• Temperature: {{ $json.readings.temperature }}°C\n• Humidity: {{ $json.readings.humidity }}%\n• CO2: {{ $json.readings.co2 }} ppm\n\nAI Analysis: {{ $json.analysis.reasoning }}\nRecommendation: {{ $json.analysis.recommendation }}", "otherOptions": {} }, "id": "c5a297be-ccef-40ba-9178-65805262efba", "name": "Slack Critical Alert", "type": "n8n-nodes-base.slack", "typeVersion": 2.2, "position": [ 384, 112 ], "webhookId": "19113595-0208-4b37-b68c-c9788c19f618" }, { "parameters": { "select": "channel", "channelId": { "__rl": true, "mode": "name", "value": "#iot-alerts" }, "text": "=⚠️ IoT Warning\n\nSensor: {{ $json.sensorId }} | Location: {{ $json.location }}\nIssue: {{ $json.analysis.anomalies[0] || 'Threshold approaching' }}\nRecommendation: {{ $json.analysis.recommendation }}", "otherOptions": {} }, "id": "5c3d7acf-0211-44dd-9f4b-a43d3796abb1", "name": "Slack Warning Alert", "type": "n8n-nodes-base.slack", "typeVersion": 2.2, "position": [ 384, 400 ], "webhookId": "37abfb19-f82f-4449-bd69-a65635b99606" }, { "parameters": {}, "id": "6bcbb42f-ec14-4f00-a091-babcc2d2d5c4", "name": "Merge Alert Outputs", "type": "n8n-nodes-base.merge", "typeVersion": 3, "position": [ 608, 184 ] }, { "parameters": { "operation": "append", "documentId": { "__rl": true, "mode": "list", "value": "" }, "sheetName": { "__rl": true, "mode": "list", "value": "" } }, "id": "6243aa23-408d-4928-a512-811eeb3b5f9e", "name": "Archive to Google Sheets", "type": "n8n-nodes-base.googleSheets", "typeVersion": 4.5, "position": [ 832, 184 ] }, { "parameters": { "model": "gpt-4o-mini", "options": { "temperature": 0.3 } }, "id": "61081e8a-ebc9-465f-8beb-88af225e59f2", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "typeVersion": 1.2, "position": [ -344, 408 ] } ], "pinData": {}, "connections": { "MQTT Sensor Trigger": { "main": [ [ { "node": "Merge Triggers", "type": "main", "index": 0 } ] ] }, "Batch Process Schedule": { "main": [ [ { "node": "Merge Triggers", "type": "main", "index": 1 } ] ] }, "Merge Triggers": { "main": [ [ { "node": "Define Sensor Thresholds", "type": "main", "index": 0 } ] ] }, "Define Sensor Thresholds": { "main": [ [ { "node": "Parse Sensor Payload", "type": "main", "index": 0 } ] ] }, "Parse Sensor Payload": { "main": [ [ { "node": "Generate Data Fingerprint", "type": "main", "index": 0 } ] ] }, "Generate Data Fingerprint": { "main": [ [ { "node": "Remove Duplicate Readings", "type": "main", "index": 0 } ] ] }, "Remove Duplicate Readings": { "main": [ [ { "node": "AI Anomaly Detector", "type": "main", "index": 0 } ] ] }, "AI Anomaly Detector": { "main": [ [ { "node": "Parse AI Analysis", "type": "main", "index": 0 } ] ] }, "Parse AI Analysis": { "main": [ [ { "node": "Route by Severity", "type": "main", "index": 0 } ] ] }, "Route by Severity": { "main": [ [ { "node": "Send Critical Email", "type": "main", "index": 0 }, { "node": "Slack Critical Alert", "type": "main", "index": 0 } ], [ { "node": "Slack Warning Alert", "type": "main", "index": 0 } ], [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Send Critical Email": { "main": [ [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Slack Critical Alert": { "main": [ [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Slack Warning Alert": { "main": [ [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Merge Alert Outputs": { "main": [ [ { "node": "Archive to Google Sheets", "type": "main", "index": 0 } ] ] }, "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "AI Anomaly Detector", "type": "ai_languageModel", "index": 0 } ] ] } }, "active": false, "settings": { "executionOrder": "v1" }, "versionId": "", "meta": { "instanceId": "15d6057a37b8367f33882dd60593ee5f6cc0c59310ff1dc66b626d726083b48d" }, "tags": [] }

Who’s it for This workflow is for hotel managers, travel agencies, and hospitality teams who receive booking requests via email. It eliminates the need for manual data entry by automatically parsing emails and attachments, assigning booking cases to the right teams, and tracking performance metrics. What it does This workflow goes beyond simple automation by including enterprise-grade logic and security: 🛡️ Gatekeeper:** Watches your Gmail and filters irrelevant emails before spending money on AI tokens. 🧠 AI Brain:** Uses OpenAI (GPT-5-mini) to extract structured data from unstructured email bodies and PDF attachments. ⚖️ Business Logic:** Automatically routes tasks to different teams based on urgency, room count, and VIP status. 🔒 Security:** Catches PII (like credit card numbers) and scrubs them before they hit your database. 🚨 Safety Net:** If anything breaks, a dedicated error handling path logs the issue immediately so no booking is lost. 📈 ROI Tracking:** Calculates the time saved per booking to prove the value of automation. How to set up Create your Google Sheet: Create a new sheet and rename the tabs to: Cases, Team Assignments, Error Logs, Success Metrics. Add Credentials: Go to n8n Settings → Credentials and add your Gmail (OAuth2), Google Sheets, and OpenAI API keys. Configure User Settings: Open the "Configuration: User Settings" node at the start of the workflow. Paste your specific Google Sheet ID and Admin Email there. Adjust Business Rules: Open the "Apply Business Rules" node (Code node) to adjust the logic for team assignment (e.g., defining what counts as a "VIP" booking). Customize Templates: Modify the email templates in the Gmail nodes to match your hotel's branding. Test: Send a sample booking email to yourself to verify the filters and data extraction. Setup requirements Gmail account (OAuth2 connected) Google Sheets (with the 4 tabs listed below) OpenAI API key (GPT-5-mini recommended) n8n Cloud or self-hosted instance How to customize Filter Booking Emails:** Update the trigger node keywords to match your specific email subjects (e.g., "Reservation", "Booking Request"). Apply Business Rules:** Edit the Javascript in the Code node to fit your company’s internal logic (e.g., changing priority thresholds). New Metrics:** Add new columns in the Google Sheet (e.g., “Revenue Metrics”) and map them in the "Update Sheet" node. AI Model:** Switch to GPT-5 if you need higher reasoning capabilities for complex PDF layouts. Google Sheets Structure Description This workflow uses a Google Sheets document with four main tabs to track and manage hotel booking requests. 1. Cases This is the main data log for all incoming booking requests. case_id:** Unique identifier generated by the workflow. processed_date:** Timestamp when the workflow processed the booking. travel_agency / contact_details:** Extracted from the email. number_of_rooms / check_in_date:** Booking details parsed by the AI. special_requests:** Optional notes (e.g., airport transfer). assigned_team / priority:** Automatically set based on business rules. days_until_checkin:** Dynamic field showing urgency. 2. Team Assignments Stores internal routing and assignment details. timestamp:** When the case was routed. case_id:** Link to the corresponding record in the Cases tab. assigned_team / team_email:** Which department handles this request. priority:** Auto-set based on room count or urgency. 3. Error Log A critical audit trail that captures details about any failed processing steps. error_type:** Categorization of the failure (e.g., MISSING_REQUIRED_FIELDS). error_message:** Detailed technical explanation for debugging. original_sender / snippet:** Context to help you manually process the request if needed. 4. Success Metrics Tracks the results of your automation to prove its value. processing_time_seconds:** The time savings achieved by the automation (run time vs. human time). record_updated:** Confirmation that the database was updated. 🙋 Support If you encounter any issues during setup or have questions about customization, please reach out to our dedicated support email: foivosautomationhelp@gmail.com

Overview Manual financial reconciliation is tedious and prone to error. This workflow functions as an AI Financial Controller, automatically monitoring your inbox for invoices, receipts, and bills, extracting the data using OCR, and syncing it to Google Sheets for approval. Unlike simple scrapers, this workflow uses a "Guardrail" AI agent to filter out non-financial emails (like newsletters) before they are processed, ensuring only actual transactions are recorded. Who is it for? Finance Teams:** To automate the collection of vendor invoices. Freelancers:** To track expenses and receipts for tax season. Operations Managers:** To monitor budget spend and categorize costs automatically. How it works Ingest: The workflow watches a specific Gmail label (e.g., "INBOX") for new emails. Guardrail: A Gemini-powered AI agent analyzes the email text to determine if it is a valid financial transaction. If not, the workflow stops. Extraction (OCR): If an attachment exists: An AI Agent (GPT-4o) extracts data from the PDF. If no attachment: An AI Agent extracts data directly from the email body. Validation: Code nodes check for missing fields or invalid amounts. Business Logic: The system automatically assigns General Ledger (GL) categories (e.g., "Uber" -> "Travel") and sets approval statuses based on amount thresholds. Sync: Validated data is logged to Google Sheets, and a confirmation email is sent. Errors are logged to a separate error sheet. How to set up Google Sheets: Copy this Google Sheet template to your drive. It contains the necessary tabs (Invoices, Error Logs, Success Metrics). Configure Workflow: Open the node named "Configuration: User Settings". Paste your Google Sheet ID (found in the URL of your new sheet). Enter the Admin Email address where you want to receive error notifications. Credentials: Connect your Gmail account. Connect your Google Sheets account. Connect your OpenAI (for OCR) and Google Gemini/PaLM (for Guardrails) accounts. Requirements n8n version 1.0 or higher. Gmail account. OpenAI API Key. Google Gemini (PaLM) API Key.

Who’s it for Marketing, growth, and analytics teams who want a decision-ready GA4 summary—automatically calculated, clearly color-coded, and emailed as a polished HTML report. How it works / What it does Get Client (Form Trigger)* collects *GA4 Property ID (“Account ID”), **Key Event, date ranges (current & previous), Client Name, and recipient email. Overall Metrics This Period / Previous Period (GA4 Data API)** pull sessions, users, engagement, bounce rate, and more for each range. Form Submits This Period / Previous Period (GA4 Data API)** fetch key-event counts for conversion comparisons. Code** normalizes form dates for API requests. AI Agent* builds a *valid HTML email**: Calculates % deltas, applies green for positive (#10B981) and red for negative (#EF4444) changes. Writes summary and recommendations. Produces the final HTML only. Send a message (Gmail)** sends the formatted HTML report to the specified email address with a contextual subject. How to set up 1) Add credentials: Google Analytics OAuth2, OpenAI (Chat), Gmail OAuth2. 2) Ensure the form fields match your GA4 property and event names; “Account ID” = GA4 Property ID. Property ID - https://take.ms/vO2MG Key event - https://take.ms/hxwQi 3) Publish the form URL and run a test submission. Requirements GA4 property access (Viewer/Analyst) • OpenAI API key • Gmail account with send permission. Resources Google OAuth2 (GA4) – https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/ OpenAI credentials – https://docs.n8n.io/integrations/builtin/credentials/openai/ Gmail OAuth2 – https://docs.n8n.io/integrations/builtin/credentials/google/ GA4 Data API overview – https://developers.google.com/analytics/devguides/reporting/data/v1

🚀 Daily IndieHackers Reddit Trend Analysis to Slack > Transform Reddit chaos into actionable startup intelligence > Get AI-powered insights from r/indiehackers delivered to your Slack every morning 🎯 Who's It For This template is designed for startup founders, growth teams, and product managers who need to: Stay ahead of indie hacker trends without manual Reddit browsing Understand what's working in the entrepreneurial community Get actionable insights for product and marketing decisions Keep their team informed about emerging opportunities Perfect for teams building products for entrepreneurs or anyone wanting to leverage community intelligence for competitive advantage. ✨ What It Does Transform your morning routine with automated intelligence gathering that delivers structured, AI-powered summaries of the hottest r/indiehackers discussions directly to your Slack channel. 🧠 Smart Analysis Features | Feature | Description | |---------|-------------| | 🔥 Hotness Scoring | Calculates engagement scores using time-decay algorithms | | 📊 Topic Extraction | Identifies key themes and trending subjects | | 💰 Traction Signals | Spots revenue, metrics, and growth indicators | | 🎯 Theme Clustering | Groups posts into actionable categories | | ⚡ Action Items | Generates specific recommendations for your team | 📱 Slack Integration Receive beautifully formatted messages with: Executive summaries and key takeaways Top 3 hottest posts with engagement metrics Interactive buttons for deeper exploration Team discussion prompts ⚙️ How It Works graph LR A[🕐 Daily 8AM Trigger] --> B[📱 Fetch Reddit Posts] B --> C[🔄 Process Data] C --> D[🤖 Gemini AI Analysis] D --> E[✨ Groq Slack Formatting] E --> F[💬 Deliver to Slack] 🔄 The Complete Process Step 1: Automated Trigger Every morning at 8 AM, the workflow springs into action Step 2: Reddit Data Collection Fetches the latest 5 posts from r/indiehackers with full metadata Step 3: Data Processing Structures raw Reddit data for optimal AI analysis Step 4: AI-Powered Analysis Gemini AI performs deep analysis calculating hotness scores, extracting topics, and identifying patterns Step 5: Slack Formatting Groq AI Agent transforms insights into beautiful Slack Block Kit messages Step 6: Team Delivery Your designated Slack channel receives the formatted analysis 🛠️ Requirements You'll need API access for: Reddit (OAuth2), Google Gemini, Groq, and Slack (OAuth2). All have free tiers available. 🚀 Setup Guide 1️⃣ Configure Your Credentials Add these credentials in n8n: Reddit OAuth2, Google Gemini, Groq, and Slack OAuth2. The workflow will guide you through each setup. 2️⃣ Customize the Schedule Default: Daily at 8:00 AM To modify: Edit the "Daily Schedule" cron trigger node // Example: Run at 9:30 AM { "triggerTimes": { "item": [{ "hour": 9, "minute": 30 }] } } 3️⃣ Set Your Slack Destination Open the "Send to Slack" node Select your target channel Configure notification preferences 4️⃣ Adjust Analysis Parameters Post Limit: Change from default 5 posts // In "Get many posts" Reddit node "limit": 10 // Recommended: 3-10 posts Context Customization: { "channel_type": "team", "audience": "Growth, Product, and Founders", "cta_link": "https://your-dashboard.com", "timeframe_label": "This Week" } 🎨 Customization Options 🔍 Analysis Focus Areas Transform the workflow for different insights: SaaS-Focused Analysis Add to Gemini prompt: "Focus on SaaS and B2B insights, prioritizing recurring revenue and product-market fit signals" Geographic Targeting Add: "Prioritize posts relevant to [your region/market]" Stage-Specific Insights Add: "Focus on [early-stage/growth-stage] startup challenges" 📈 Hotness Algorithm Tweaking Default Formula: (ups + 2*num_comments) * freshness_decay Emphasize Comments: (ups + 3*num_comments) * freshness_decay Include Upvote Ratio: (ups * upvote_ratio + 2*num_comments) * freshness_decay 🌐 Multi-Subreddit Analysis Expand beyond r/indiehackers: Additional Communities: r/startups r/entrepreneur r/SideProject r/buildinpublic r/nocode 💾 Data Storage Extensions Enhance with historical tracking: | Node Type | Purpose | Benefit | |-----------|---------|---------| | Google Sheets | Trend storage | Historical analysis | | Airtable | Advanced data management | Rich analytics | | Webhook | External analytics | Custom dashboards | 📊 Expected Output 📱 Daily Slack Message Structure 🚀 IndieHackers Trends — This Week 📋 TL;DR: [One-sentence key insight] 🔥 Hot Posts (Top 3) [Post Title] (Hotness: 8.7) Topics: SaaS launch, pricing strategy 💬 23 comments | 👍 156 ups | 📅 Posted 4 hours ago [Open Reddit Button] 🧭 Themes Summary Go-to-market tactics — 3 posts, hotness: 24.1 Product launches — 2 posts, hotness: 18.3 ✅ What to Do Now Test pricing page variations based on community feedback Consider cold email strategies mentioned in hot posts Validate product ideas using discussed frameworks [Open Dashboard Button] 💡 Pro Tips for Success 🎯 Optimization Strategies Week 1-2: Baseline Monitor output quality and team engagement Note which insights generate the most discussion Week 3-4: Refinement Adjust AI prompts based on feedback Fine-tune hotness scoring for your needs Month 2+: Advanced Usage Add historical trend analysis Create custom dashboards with stored data Build feedback loops for continuous improvement 🚨 Common Pitfalls to Avoid | Issue | Solution | |-------|---------| | API Rate Limits | Reduce post count or increase time intervals | | Poor Insight Quality | Refine prompts with specific examples | | Team Engagement Drop | Rotate focus areas and encourage thread discussions | | Information Overload | Limit to top 3 posts and key themes only | 🔧 Troubleshooting ❌ Common Issues & Solutions "Model not found" Error Cause: Gemini regional availability Fix: Check supported regions or switch to alternative AI model Slack Formatting Broken Cause: Invalid Block Kit JSON Fix: Validate JSON structure in AI Agent output Missing Reddit Data Cause: API credentials or rate limits Fix: Verify OAuth2 setup and check usage quotas AI Timeouts Cause: Too much data or complex prompts Fix: Reduce post count or simplify analysis requests ⚡ Performance Optimization Keep analysis under 10 posts for optimal speed Monitor execution times in n8n logs Add error handling nodes for production reliability Use webhook timeouts for external API calls 🌟 Advanced Use Cases 📈 Competitive Intelligence Modify prompts to track specific competitors or market segments mentioned in discussions 🎯 Product Validation Focus analysis on posts related to your product category for market research 📝 Content Strategy Use trending topics to inform your content calendar and thought leadership 🤝 Community Engagement Identify opportunities to participate in discussions and build relationships Ready to transform your startup intelligence gathering? 🚀 Deploy this workflow and start receiving actionable insights tomorrow morning!

🤖 AI Business Automation Opportunity Finder Turn automation audits into high-ticket sales with this ROI-focused n8n workflow powered by 4 specialized AI agents that identify and quantify automation opportunities in any business. What It Does This workflow analyzes any business and delivers a comprehensive automation blueprint with concrete ROI calculations in under 60 seconds. Perfect for agencies, consultants, and automation experts looking to generate qualified leads and close high-value deals. Unlike generic automation advice, this delivers personalized, quantified opportunities ranked by return on investment - making it incredibly easy for prospects to say yes. 🤖 Four Specialized AI Agents Business Analyst - Deep analysis of business model, workflows, pain points, tech stack, and scalability challenges Process Mapper - Maps all repetitive processes, calculates time waste, identifies bottlenecks across the entire operation Automation Architect - Designs 15+ specific automation solutions with tools, complexity ratings, and implementation steps ROI Calculator - Calculates detailed ROI for each automation, ranks top 10, creates 90-day implementation roadmap ✨ Key Features Concrete Dollar Savings**: Every automation shows exact time saved, labor cost saved, and payback period Top 10 Ranked by ROI**: Opportunities prioritized by impact vs. effort with detailed financial analysis 90-Day Implementation Roadmap**: Month-by-month plan showing progressive savings milestones Comprehensive Process Mapping**: Identifies inefficiencies they didn't even mention Tool-Specific Recommendations**: Exact tools and platforms needed (n8n, Zapier, Make, etc.) Beautiful HTML Reports**: Professional, conversion-focused email with 3-tier pricing built in Multiple CTAs**: Strategically placed conversion points throughout the report 📊 What Gets Analyzed Business Analysis Business model and revenue streams Operational workflows and processes Current tech stack assessment Team capacity and resource allocation Growth stage and scalability blockers Industry-specific automation patterns Process Mapping Comprehensive workflow documentation Time waste analysis (hours per month) Bottleneck identification Process dependencies and integration opportunities Quick win vs. strategic project categorization Automation Architecture For each of 15+ automation opportunities: Clear description of what it automates Specific tools required Step-by-step implementation flow Complexity rating (Easy/Medium/Hard) Prerequisites and requirements Additional benefits beyond time savings Real-world use case examples ROI Calculations For each automation: Time saved per week/month/year Labor cost savings (calculated from team size/industry) One-time implementation cost Ongoing monthly costs Payback period in months 12-month net savings ROI percentage Priority score (0-10) 💼 Perfect For Automation Agencies**: High-value lead magnet that pre-sells your services Business Consultants**: Demonstrate ROI before engagement No-Code Developers**: Show concrete value of your expertise Digital Transformation Consultants**: Quantify the opportunity SaaS Companies**: Lead gen for automation/workflow tools Freelancers**: Land bigger clients with data-driven proposals 🚀 Why This Converts Better Than Other Lead Magnets Traditional Lead Magnets: Generic advice ("You should automate") Subjective benefits ("Save time") No clear next steps Conversion rate: 5-10% This Workflow: Specific to their business** (personalized analysis) Quantified in dollars** ($50K+ annual savings) Prioritized action plan** (top 10 ranked by ROI) Clear implementation path** (90-day roadmap) Conversion rate: 20-30%** to strategy call 40-50% of calls close** to paid engagement 📈 Expected Business Results Per 100 Form Submissions: 25-30 strategy calls booked** (25-30% conversion) 10-15 deals closed** (40-50% call-to-close rate) $12K-18K in initial revenue** (mix of Tier 1 & 2) 2-4 retainer clients** ($30K-60K annual value) Total potential: $42K-78K** from 100 leads Why It Works: Self-qualifying**: Detailed form filters serious prospects Pre-sold**: They see the value before the call ROI-focused**: Speaks CFO language (dollars, not features) Urgency**: Shows money being wasted daily Social proof**: Built-in testimonials and case studies 📋 What You Need Required n8n instance (self-hosted or cloud) Anthropic API key (Claude Sonnet 4.5) Gmail account or SMTP provider Optional Enhancements CRM integration (HubSpot, Salesforce, Pipedrive) Slack notifications for high-value leads Calendly for automatic call booking Zapier/Make for additional workflows Analytics tracking (Mixpanel, Segment) ⚙️ Technical Details AI Model**: Claude Sonnet 4.5 (4 sequential agents) Average Runtime**: 50-70 seconds Cost Per Analysis**: ~$0.20-0.30 Form Fields**: 9 (business description, industry, team size, tasks, tools, bottleneck, revenue, email, name) Output**: Comprehensive HTML email with all analyses, pricing, and CTAs 🎨 Customization Options The workflow is fully customizable and includes detailed documentation: Adjust ROI calculation parameters (labor rates by industry) Modify agent prompts for specific niches Customize pricing tiers and packages Add/remove form fields White-label the entire report Integrate with your CRM/marketing stack Segment responses by company size or revenue Add video walkthroughs or personalized messages Create industry-specific versions 📊 Form Fields Explained The 9-field form is strategically designed to gather intelligence: Business Description (textarea): Core operations and offerings Industry/Niche (text): Context for automation patterns Team Size (dropdown): Affects ROI calculations and tool recommendations Repetitive Tasks (textarea): Gold mine for automation opportunities Current Tools (textarea): Integration points and tech stack assessment Biggest Bottleneck (textarea): Primary pain point for targeting Monthly Revenue (optional dropdown): For accurate ROI estimates and lead scoring Email (required): For report delivery Name (required): For personalization 🔧 Setup Difficulty Basic - Requires basic n8n knowledge and API configuration Setup Steps Import workflow JSON to n8n Add Anthropic API credentials Configure Gmail/SMTP credentials Customize branding and pricing in email template Test with sample business scenarios Deploy form on your website Set up follow-up sequences (recommended) 📚 Included Documentation Comprehensive sticky notes** for every component Setup instructions** with prerequisites Customization guide** for different industries Pricing strategy** breakdown and alternatives Conversion optimization** tips Follow-up sequence** recommendations Sales script** suggestions for strategy calls Marketing promotion** ideas 🌟 Advanced Use Cases 1. Lead Magnet Embed on website to capture qualified automation leads continuously 2. Discovery Tool Use during sales calls to demonstrate immediate value and build credibility 3. Content Marketing Offer in LinkedIn posts, email campaigns, YouTube videos for viral growth 4. Partner Program White-label for partners/affiliates to generate leads in their networks 5. Upsell Sequence For existing clients, identify additional automation opportunities 6. Industry Templates Create versions for specific industries (real estate, e-commerce, agencies) 7. Competitive Intelligence Analyze competitor operations and position your services ⚡ Why This Workflow Stands Out Compared to Generic Automation Audits: ✅ Quantified in dollars vs. vague "save time" claims ✅ Personalized to their business vs. generic templates ✅ Prioritized by ROI vs. random feature lists ✅ Actionable roadmap vs. overwhelming possibilities ✅ Tool-specific vs. theoretical concepts Compared to Manual Analysis: ✅ 60 seconds vs. 2-3 hours of consultant time ✅ $0.25 cost vs. $300-500 in labor ✅ Consistent quality vs. variable analyst experience ✅ Scalable vs. bottlenecked by human capacity ✅ 24/7 available vs. business hours only 🤝 Support & Community 📖 Website: https://evervise.ai/ ✨ Support: mark.marin@evervise.com N8N Link 🎁 Bonus Resources Included Follow-up email sequence** (3 emails over 10 days) Sales call script** for strategy calls Objection handling** guide Pricing calculator** spreadsheet Marketing assets** (social media templates) Case study template** for testimonials Tags automation lead-generation roi-calculator business-analysis process-mapping ai-agents anthropic claude workflow-automation business-consulting no-code n8n-workflows high-ticket-sales conversion-optimization saas-tools Ready to turn automation audits into recurring revenue? Import this workflow and start attracting qualified leads who can see the exact dollar value you provide before they even talk to you. Average user results: $42K-78K revenue from first 100 form submissions.