Workflow Templates

Lead Scoring Pipeline with Telegram and Box This workflow ingests incoming lead data from a form submission webhook, enriches each lead with external data sources, applies a custom scoring algorithm, and automatically stores the enriched record in Box while notifying your sales team in Telegram. It is designed to give you a real-time, end-to-end lead-qualification pipeline without writing any glue code. Pre-conditions/Requirements Prerequisites n8n instance (self-hosted or n8n.cloud) ScrapeGraphAI community node installed (not directly used in this template but required by marketplace listing rules) Telegram Bot created via BotFather Box account (Developer App or User OAuth2) Publicly accessible URL (for the Webhook trigger) Optional: Enrichment API account (e.g., Clearbit, PDL) for richer scoring data Required Credentials | Credential | Scope | Purpose | |------------|-------|---------| | Telegram Bot Token | Bot | Send scored-lead alerts | | Box OAuth2 Credentials | App Level | Upload enriched lead JSON/CSV | | (Optional) Enrichment API Key | REST | Append firmographic & technographic data | Environment Variables (Recommended) | Variable | Example | Description | |---------|----------|-------------| | LEAD_SCORE_THRESHOLD | 75 | Minimum score that triggers a Telegram alert | | BOX_FOLDER_ID | 123456789 | Destination folder for lead files | How it works This workflow listens for new form submissions, enriches each contact with external data, calculates a lead score based on configurable criteria, and routes the lead through one of two branches: high-value leads trigger an instant Telegram alert and are archived to Box, while low-value leads are archived only. Errors are captured by an Error Trigger for post-mortem analysis. Key Steps: Webhook Trigger**: Receives raw form data (name, email, company, etc.). Set node – Normalization**: Renames fields and initializes default values. HTTP Request – Enrichment**: Calls an external enrichment API to augment data. Merge node**: Combines original and enriched data into a single object. Code node – Scoring**: Runs JavaScript to calculate a numeric lead score. If node – Qualification Gate**: Checks if score ≥ LEAD_SCORE_THRESHOLD. Telegram node**: Sends alert message to your sales channel for high-scoring leads. Box node**: Uploads the enriched JSON (or CSV) file into a specified folder. Error Trigger**: Captures any unhandled errors and notifies ops (optional). Sticky Notes**: Explain scoring logic and credential placement (documentation aids). Set up steps Setup Time: 15-25 minutes Create Telegram Bot & Get Token Talk to BotFather → /newbot → copy the provided token. Create a Box Developer App Enable OAuth2 → add https://api.n8n.cloud/oauth2-credential/callback (or your own) as redirect URI. Install Required Community Nodes From n8n editor → “Install” → search “ScrapeGraphAI” → install. Import the Workflow JSON Click “Import” → paste the workflow file → save. Configure the Webhook URL in Your Form Tool Copy the production URL generated by the Webhook node → add it as form action. Set Environment Variables In n8n (Settings → Environment) add LEAD_SCORE_THRESHOLD and BOX_FOLDER_ID. Fill in All Credentials Telegram: paste bot token. Box: complete OAuth2 flow. Enrichment API: paste key in the HTTP Request node headers. Activate Workflow Toggle “Activate”. Submit a test form to verify Telegram/Box outputs. Node Descriptions Core Workflow Nodes: Webhook** – Entry point; captures incoming JSON payload from the form. Set (Normalize Fields)** – Maps raw keys to standardized ones (firstName, email, etc.). HTTP Request (Enrichment)** – Queries external service for firmographic data. Merge (Combine Data)** – Merges the two JSON objects (form + enrichment). Code (Scoring)** – Calculates lead score using weighted attributes. If (Score Check)** – Branches flow based on the score threshold. Telegram** – Sends high-score alerts to a specified chat ID. Box** – Saves a JSON/CSV file of the enriched lead to cloud storage. Error Trigger** – Executes if any preceding node fails. Sticky Notes** – Inline documentation for quick reference. Data Flow: Webhook → Set → HTTP Request → Merge → Code → If If (true) → Telegram If (always) → Box Error (from any node) → Error Trigger Customization Examples Change Scoring Logic // Inside the Code node const { jobTitle, companySize, technologies } = items[0].json; let score = 0; if (jobTitle.match(/(CTO|CEO|Founder)/i)) score += 50; if (companySize > 500) score += 20; if (technologies.includes('AWS')) score += 10; // Bonus: subtract points if free email domain if (items[0].json.email.endsWith('@gmail.com')) score -= 30; return [{ json: { ...items[0].json, score } }]; Use a Different Storage Provider (e.g., Google Drive) // Replace Box node with Google Drive node { "node": "Google Drive", "operation": "upload", "fileName": "lead_{{$json.email}}.json", "folderId": "1A2B3C..." } Data Output Format The workflow outputs structured JSON data: { "firstName": "Ada", "lastName": "Lovelace", "email": "ada@example.com", "company": "Analytical Engines Inc.", "companySize": 250, "jobTitle": "CTO", "technologies": ["AWS", "Docker", "Node.js"], "score": 82, "qualified": true, "timestamp": "2024-04-07T12:34:56.000Z" } Troubleshooting Common Issues Telegram messages not received – Ensure the bot is added to the group and chat_id/token are correct. Box upload fails with 403 – Check folder permissions; verify OAuth2 tokens have not expired. Webhook shows 404 – The workflow is not activated or the URL was copied in “Test” mode instead of “Production”. Performance Tips Batch multiple form submissions using the “SplitInBatches” node to reduce API-call overhead. Cache enrichment responses (Redis, n8n Memory) to avoid repeated lookups for the same domain. Pro Tips: Add an n8n “Wait” node between enrichment calls to respect rate limits. Use Static Data to store domain-level enrichment results for even faster runs. Tag Telegram alerts with emojis based on score (🔥 Hot Lead for >90). This is a community-contributed n8n workflow template provided “as-is.” Always test thoroughly in a non-production environment before deploying to live systems.

Automated Solana News Tracker with AI-Powered Weekly Summaries Never miss important Solana ecosystem updates again. This production-ready workflow automatically scrapes crypto news daily, intelligently filters duplicates, stores everything in Google Sheets, and generates AI-powered weekly summaries every Monday—completely hands-free. 🎯 What It Does: This intelligent automation runs on autopilot to keep you informed about Solana developments without manual monitoring. Every day at 8 AM PT, it fetches the latest Solana news from CryptoPanic, checks for duplicates against your existing database, and stores only new articles in Google Sheets. On Mondays, it takes an extra step: reading all accumulated articles from the past week and using GPT-4.1-mini to generate a concise, factual summary of key developments and investor takeaways. Daily News Collection**: Automatically fetches latest Solana articles from CryptoPanic API Smart Duplicate Detection**: Compares incoming articles against existing database to prevent redundancy Data Validation**: Filters out incomplete articles to ensure data quality Organized Storage**: Maintains clean Google Sheets database with timestamps and descriptions Weekly AI Summaries**: Analyzes accumulated news every Monday and generates 2-3 sentence insights Historical Archive**: Builds searchable database of both raw articles and weekly summaries 💼 Perfect For: Crypto traders tracking market-moving news • SOL investors monitoring ecosystem growth • Blockchain researchers building historical datasets • Content creators sourcing newsletter material • Portfolio managers needing daily briefings • Anyone wanting Solana updates without information overload 🔧 How It Works: The workflow operates in two distinct modes based on the day of the week. During the daily collection phase (Tuesday-Sunday), it runs at 8 AM PT, fetches the latest Solana news from CryptoPanic, formats the data to extract titles, descriptions, and timestamps, checks each article against your Google Sheets database to identify duplicates, filters out any articles that already exist or have missing data, and appends only valid new articles to your "Raw Data" sheet. On Mondays, the workflow performs all daily tasks plus an additional summarization step. After storing new articles, it retrieves all accumulated news from the "Raw Data" sheet, aggregates all article descriptions into a single text block, sends this consolidated information to GPT-4.1-mini with instructions to create a factual, spartan-toned summary highlighting key investor takeaways, and saves the AI-generated summary with a timestamp to the "Weekly Summary" sheet for historical reference. ✨ Key Features: Schedule-based execution**: Runs automatically at 8 AM PT every day without manual intervention Intelligent deduplication**: Title-based matching prevents storing the same article multiple times Data quality control**: Validates required fields before storage to maintain clean dataset Dual-sheet architecture**: Separate sheets for raw articles and weekly summaries for easy access Cost-effective AI**: Uses GPT-4.1-mini (~$0.001 per summary) for extremely low operating costs Scalable storage**: Google Sheets handles thousands of articles with free tier Customizable cryptocurrency**: Easily adapt to track Bitcoin, Ethereum, or any supported coin Flexible scheduling**: Modify trigger time and summary frequency to match your needs 📋 Requirements: CryptoPanic account with free API key (register at cryptopanic.com) Google Sheets with two sheets: "Raw Data" (columns: date, title, descripton, summary) and "Weekly Summary" (columns: Date, Summary) OpenAI API key for GPT-4.1-mini access (~$0.05/month cost) n8n Cloud or self-hosted instance with schedule trigger enabled ⚡ Quick Setup: Register for a free CryptoPanic API key and replace [your token] in the "Get Solana News" HTTP Request node URL. Create a new Google Spreadsheet with two sheets: one named "Raw Data" with columns for date, title, descripton (note the typo in template), and summary; another named "Weekly Summary" with columns for Date and Summary. Connect your Google Sheets OAuth2 credential to all Google Sheets nodes in the workflow. Add your OpenAI API credential to the "Summarize News" node. Test the workflow manually to ensure it fetches news and stores it correctly. Activate the workflow to enable daily automatic execution. 🚨 Please note, that you're not able to get news in real-time with a FREE CryptoPanic API. Consider their pro plan or another platform for real-time news scraping You'll get new that's up to date as of yesterday. 🎁 What You Get: Complete end-to-end automation with concise sticky note documentation at each workflow stage, pre-configured duplicate detection logic, AI summarization with investor-focused prompts optimized for factual analysis without hype, dual-sheet Google Sheets structure for raw data and summaries, flexible schedule trigger you can adjust to any timezone, example data in pinned format showing expected API responses, customization guides for different cryptocurrencies and summary frequencies, and troubleshooting checklist for common setup issues. 💰 Expected Costs & Performance: CryptoPanic API is free with reasonable rate limits for personal use. OpenAI GPT-4.1-mini costs approximately $0.001 per summary, totaling about $0.05 per month for weekly summaries. The workflow typically processes 20-50 articles daily and generates one summary weekly from 140-350 accumulated articles. Daily executions complete in 5-10 seconds, while Monday runs with AI summarization take 15-20 seconds. Google Sheets provides free storage for up to 5 million cells, easily handling years of news data. 🔄 Customization Ideas: Track different cryptocurrencies by changing the currencies parameter (btc, eth, ada, doge, etc.). Adjust the schedule trigger to run at different times matching your timezone. Modify the Monday check condition to generate summaries on different days or multiple times per week. Connect Slack, Discord, or Email nodes to receive instant notifications when summaries are generated. Edit the AI prompt to change tone, detail level, or focus on specific aspects like price action, development updates, or partnerships. Add conditional logic to send alerts only when certain keywords appear in news (like "hack," "partnership," or "upgrade").

👔 Recruitment Office WhatsApp Automation Automate WhatsApp communication for recruitment agencies with an interactive, structured customer experience. This workflow handles pricing inquiries, request submissions, tracking, complaints, and human escalation while maintaining full session tracking and media support. Good to know Uses WhatsApp Interactive List Messages for user selection and navigation. Includes session-state logic and memory across messages. Includes a 5-minute cooldown to avoid spam and repeated triggers. Supports logging for all interaction types including media files. Includes both a global bot shutdown switch and per-user override. How it works A customer sends a message to the official WhatsApp number. The workflow replies with an interactive menu containing 8 service options: 💰 Pricing by nationality (8 supported countries) 📝 New recruitment request submission 🔍 Tracking existing applications via Google Sheets lookup 🔁 Worker transfer link distribution 🌍 Translation service information 📄 Required documents and instructions ⚠️ Complaint submission and routing 👤 Request a human agent The workflow retrieves or stores data based on the selection using Google Sheets and Data Tables. If the customer requests human help or the logic detects uncertainty, the workflow: Pauses automation for that user Notifies a designated staff member All interactions are logged including files, text, timestamps, and selections. Features 📋 Structured WhatsApp service menu 📄 CRM-style recruitment request logging ✨ Pricing logic with nationality mapping 🔍 Lookup-based status tracking 📎 Support for media uploads (PDF, images, audio, documents) 🧠 Session tracking with persistent user state 🤝 Human escalation workflow with internal notifications 🛑 Anti-spam and cooldown control 🎚 Bot master switch (global + per-user) Technology stack | Component | Usage | |----------|-------| | n8n | Automation engine | | WhatsApp Business API | Messaging and interactive UX | | Google Sheets | CRM and logs | | Data Tables | State management | | JavaScript | Custom logic and routing | Requirements WhatsApp Business API account with active credentials n8n Cloud or self-hosted instance Google Sheets for CRM storage Data Tables enabled for persistent session tracking How to use The workflow uses a Webhook trigger compatible with common WhatsApp API providers. Modify menu content, pricing, optional steps, and escalation flows as needed. Link your Google Sheets and replace test sheet IDs with production values. Configure human escalation to notify team members or departments. Customising this workflow Replace Google Sheets with Airtable, HubSpot, or SQL storage. Add expiration and reminder messages for missing documents. Add AI-powered response logic for common questions. Enable multi-country support (Saudi/UAE/Jordan/Qatar/Kuwait/etc.) Connect to dashboards for reporting and staff performance analytics.

🧾An intelligent automation system that turns WhatsApp into your personal receipt manager — integrating Meta WhatsApp Cloud API, Google Drive, Google Sheets, and OpenAI GPT-4o-mini via n8n. 🎥 Demo: Watch the Loom walkthrough ⚙️ What It Does The AI-Powered WhatsApp Receipt Bot automates the complete invoice handling process through a conversational interface. Workflow Summary: User sends a receipt image via WhatsApp. The bot automatically downloads the media using the WhatsApp Cloud API. The image is uploaded to a Google Drive “Invoices” folder. The file is shared publicly, generating a shareable URL. The receipt is analyzed using OpenAI GPT-4o-mini to extract structured data: Store name Items purchased Payment method Total amount The extracted details are appended to a Google Sheet for record-keeping. The bot sends a human-readable summary back to WhatsApp with emojis and the invoice link. Output Example: 🏬 Store: Big Bazaar 📝 Items: Rice, Detergent, Snacks 💳 Payment: Card 💰 Total: ₹1520.75 🔗 Link: https://drive.google.com/file/d/1abcXYZ/view This system eliminates manual expense tracking, improves accuracy through OCR, and provides a seamless way to manage receipts in real time. 💡 Use Cases | Scenario | Description | | ------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | | Personal Expense Management | Automatically store and categorize receipts from daily purchases. | | Business Accounting | Collect employee expense receipts through WhatsApp and centralize them in Google Sheets. | | Freelancer or Consultant Tracking | Keep a digital record of client reimbursements or software purchase receipts. | | Family Budgeting | Family members send receipts to one shared WhatsApp number, all data gets logged centrally. | | E-commerce / Delivery Teams | Drivers or delivery agents send invoices from the field to WhatsApp; data automatically goes to the accounting sheet. | 🔧 Setup 1. Accounts and Tools Needed | Tool | Purpose | Link | | -------------------------- | ------------------------------------------- | -------------------------------------------------------------------------------------------- | | Meta Developer Account | To access WhatsApp Business Cloud API | https://developers.facebook.com/apps | | Google Cloud Account | For enabling Drive and Sheets APIs | https://console.cloud.google.com | | n8n Instance | Workflow automation engine (local or cloud) | https://app.n8n.cloud | | OpenAI API Key | For GPT-4o-mini model OCR + reasoning | https://platform.openai.com/account/api-keys | 2. Meta Developer Setup (WhatsApp Cloud API) Go to Meta Developer Dashboard → My Apps → Create App → Business type. Add WhatsApp product under your app. Retrieve the following from WhatsApp > Configuration: Permanent Access Token Phone Number ID WhatsApp Business Account ID Add these credentials in n8n → Credentials → WhatsApp API. Use the same credentials for WhatsApp Trigger and Send Message nodes. Verify webhook in Meta with your n8n webhook URL. Important: In your HTTP Node, set the header as: Authorization: Bearer <access_token> Replace <access_token> with your WhatsApp Cloud API permanent token. Without this, the workflow will fail to send or receive WhatsApp messages properly. 3. Google Drive Setup Create a folder named Invoices on your Google Drive. Copy the Folder ID (found in the Drive URL). In Google Cloud Console → APIs & Services → Enable APIs: Enable Google Drive API Enable Google Sheets API Go to Credentials → Create Credentials → OAuth 2.0 Client ID. Download the credentials.json file. Upload this to n8n → Credentials → Google Drive OAuth2 API. Authorize the connection on first workflow run. 4. Google Sheets Setup Create a new Google Sheet titled Invoices. Add the following headers in Row 1: store name | discription | image_url | payment | total Copy the Sheet ID (from the URL). Add the ID under the Google Sheets Append node in n8n. Map each field to its corresponding value extracted from the OCR result. 5. OpenAI Setup Generate an API key from https://platform.openai.com/account/api-keys. Add it to n8n → Credentials → OpenAI API. Use model gpt-4o-mini in the “Analyze Image” node. Can upgrade to gpt-4o for better OCR accuracy if account supports it. 6. n8n Workflow Setup Import the provided n8n workflow JSON. Configure credentials for: WhatsApp API Google Drive OAuth2 Google Sheets OAuth2 OpenAI API Activate workflow and set webhook in Meta Developer console. Send a test receipt image to your WhatsApp Business number. The bot will automatically: Download → Upload → Extract → Log → Summarize → Reply 📊 Example Google Sheet Record | store name | discription | image_url | payment | total | | ---------- | ----------------------- | -------------------------------------------------------------------------------------------- | ------- | ------- | | Big Bazaar | Rice, Detergent, Snacks | https://drive.google.com/file/d/1abcXYZ/view | Card | 1520.75 | 🧠 Result A fully automated AI pipeline that transforms WhatsApp into a smart expense-tracking interface — integrating vision, automation, and natural language processing for zero-manual financial documentation. Support & Contact: If you face any issues during setup or execution, contact: 📧 Email: atharvapj5@gmail.com 🔗 LinkedIn: Atharva Jaiswal

ETL Monitoring & Alert Automation: Jira & Slack Integration This workflow automatically processes ETL errors, extracts important details, generates a preview, creates a log URL, classifies the issue using AI and saves the processed data into Google Sheets. If the issue is important or needs attention, it also creates a Jira ticket automatically. The workflow reduces manual debugging effort, improves visibility and ensures high-severity issues are escalated instantly without human intervention. Quick Start – Implementation Steps Connect your webhook or ETL platform to trigger the workflow. Add your OpenAI, Google Sheets and Jira credentials. Enable the workflow. Send a sample error to verify Sheets logging and Jira ticket creation. Deploy and let the workflow monitor ETL pipelines automatically. What It Does This workflow handles ETL errors end-to-end by: Extracting key information from ETL error logs. Creating a short preview for quick understanding. Generating a URL to open the full context log. Asking AI to identify root cause and severity. Parsing the AI output into clean fields. Saving the processed error to Google Sheets. Creating a Jira ticket for medium/high-severity issues. This creates a complete automated system for error tracking, analysis and escalation. Who’s It For DevOps & engineering teams monitoring data pipelines. ETL developers who want automated error reporting. QA teams verifying daily pipeline jobs. Companies using Jira for issue tracking. Teams needing visibility into ETL failures without manual log inspection. Requirements to Use This Workflow n8n account or self-hosted instance. ETL platform capable of sending error payloads (via webhook). OpenAI API Key. Google Sheets credentials. Jira Cloud API credentials. Optional: log storage URL (S3, Supabase, server logs). How It Works & Setup Steps 1. Get ETL Error (Webhook Trigger) Receives ETL error payload and starts the workflow. 2. Prepare ETL Logs (Code Node) Extracts important fields and makes a clean version of the error.Generates a direct link to open the full ETL log. 3. AI Severity Classification (OpenAI / AI Agent) AI analyzes the issue, identifies cause and assigns severity. 4. Parse AI Output (Code Node) Formats AI results into clean fields: severity, cause, summary, recommended action. 5. Prepare Data for Logging (Set / Edit Fields) Combines all extracted info into one final structured record. 6. Save ETL Logs (Google Sheets Node) Logs each processed ETL error in a spreadsheet for tracking. 7. Create Jira Ticket (Jira Node) Automatically creates a Jira issue when severity is Medium, High or Critical. 8. ETL Failure Alert (Slack Node) Sends a Slack message to notify the team about the issue. 9. ETL Failure Notify (Gmail Node) Sends an email with full error details to the team. How to Customize Nodes ETL Log Extractor Add/remove fields based on your ETL log structure. AI Classification Modify the OpenAI prompt for custom severity levels or deep-dive analysis. Google Sheets Logging Adjust columns for environment, job name or log ID. Jira Fields Customize issue type, labels, priority and assignees. Add-Ons (Extend the Workflow) Send Slack or Teams alerts for high severity issues Store full logs in cloud storage (S3, Supabase, GCS) Add daily/weekly error summary reports Connect monitoring tools like Datadog or Grafana Trigger automated remediation workflows Use Case Examples Logging all ETL failures to Google Sheets Auto-creating Jira tickets with AI-driven severity Summarizing large logs with AI for quick analysis Centralized monitoring of multiple ETL pipelines Reducing manual debugging effort across teams Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | Sheets not updating | Wrong Sheet ID or missing permission | Reconnect and reselect the sheet | | Jira ticket fails | Missing required fields or invalid project key | Update Jira mapping | | AI output empty | Invalid OpenAI key or exceeded usage | Check API key or usage limits | | Severity always “low” | Prompt too broad | Adjust AI prompt with stronger rules | | Log preview empty | Incorrect error field mapping | Verify the structure of the ETL error JSON | Need Help? For assistance setting up this workflow, customizing nodes or adding additional features, feel free to contact our n8n developers at WeblineIndia. We can help configure, scale or build similar automation workflows tailored to your ETL and business requirements.

Overview Use your voice or text to command a Telegram-based AI agent that scrapes leads or generates detailed research reports—instantly. This workflow turns your Telegram bot into a full-blown outbound machine. Just tell it what type of leads you need, and it’ll use Apollo to find and save them into a spreadsheet. Or drop in a LinkedIn profile, and it’ll generate a personalized research dossier with info like job title, company summary, industry insights, and more. It handles voice messages too—just speak your request and get the results sent back like magic. Who’s it for Cold emailers and growth marketers Solo founders running outbound SDRs doing daily prospecting Agencies building high-quality lead lists or custom research for clients How it works Triggered by a message (text or voice) in Telegram If it’s voice, it transcribes using OpenAI Whisper Uses an AI agent to interpret intent: scrape leads or research a person For lead scraping: Gathers criteria (e.g., location, job title) via Telegram Calls the Apollo API to return fresh leads Saves the leads to Google Sheets For research reports: Takes a LinkedIn profile link Uses AI and lead data tools to create a 1-page professional research report Sends it back to the user via email Example outputs Lead scraping**: Populates a spreadsheet with names, roles, LinkedIn links, company info, emails, and more Research report**: A formatted PDF-style brief with summary of the person, company, and key facts How to set up Connect your Telegram bot to n8n Add your OpenAI credentials (for Whisper + Chat agent) Plug in your Apollo API key or scraping tool Replace the example spreadsheet with your own Customize the prompts for tone or data depth (Optional) Add PDF generation or CRM sync Requirements Telegram Bot Token OpenAI API Key Apollo (or other scraping API) credentials LinkedIn URLs for research functionality How to customize Replace Apollo with Clay, People Data Labs, or another scraping tool Add a CRM push step (e.g. Airtable, HubSpot, Notion) Add scheduling to auto-scrape daily Reformat the research report as a downloadable PDF Change the agent’s tone or role (e.g. “Outreach Assistant,” “Investor Scout,” etc.)

Quick Overview This workflow runs daily (or manually) to pull the last 24 hours of AI and blockchain headlines from Google News via SerpAPI, uses Groq Llama 3.3 to draft Twitter/X and LinkedIn posts, generates a matching image with Pollinations, then saves everything to Google Sheets and notifies Slack. How it works Runs on a daily 8 AM schedule (or via a manual trigger for testing). Queries Google News through SerpAPI for the last day’s AI, artificial intelligence, blockchain, and crypto headlines. Builds a numbered news digest and stops with an error if no results are returned. Sends the digest to Groq (Llama 3.3 70B) to generate a Twitter/X post and a LinkedIn post in JSON format. Generates a 1024×1024 social image with Pollinations based on the Twitter/X post, then uploads it to Google Drive and makes it publicly accessible. Appends the generated posts and public image URL to Google Sheets, logs the successful run to a separate sheet, and posts a success notification to a Slack channel. Setup Add a SerpAPI credential using HTTP Query Auth and provide your API key as the api_key query parameter. Add a Groq API credential and ensure the Groq Llama 3.3 chat model is selected. Add a Pollinations Bearer token credential (or adjust the request to use the anonymous tier if applicable). Connect Google Drive OAuth and choose the destination folder (or leave it as My Drive) for image uploads. Connect Google Sheets OAuth and select the target spreadsheet and sheet tabs for both the content append and the run log append steps. Connect Slack OAuth and set the channel where the workflow posts the success notification.

Quick Overview This workflow listens for a Telegram message containing an idea or URL, extracts any linked page text, uses OpenAI to generate LinkedIn/X/Instagram captions plus an image prompt, creates an illustration via AtlasCloud (Grok Imagine), then publishes the posts with the image to LinkedIn, X, and Instagram using Blotato. How it works Receives a Telegram message with an idea, topic, or URL. If the message includes a link, fetches the web page and extracts a cleaned text snippet to use as reference context. Uses OpenAI (via an AI agent with conversation memory) to generate a LinkedIn post, an X caption, an Instagram caption, and a detailed image-generation prompt in the specified brand voice. Sends the image prompt to AtlasCloud’s image generation API (Grok Imagine) and polls the prediction endpoint until the image is completed or errors. Extracts the generated image URL and publishes the image with the platform-specific captions to LinkedIn, X, and Instagram via Blotato. Sends a confirmation message back to Telegram including the generated image URL. Setup Create and connect Telegram bot credentials, then message the bot using the Telegram Trigger webhook. Add an OpenAI API credential and ensure the selected chat model is available in your OpenAI account. Add an AtlasCloud API key as HTTP Header Auth and confirm the configured model name, aspect ratio, and resolution match your AtlasCloud account capabilities. Install and configure the Blotato community node, connect your Blotato API credential, and replace the LinkedIn/X/Instagram account IDs in each publish step with your own.

Trend2Content This n8n workflow (named Trend2Content) takes a short topic input from a small web form, scrapes recent/top social content for that topic (via an Apify act), aggregates the raw text, passes that aggregated content into a LangChain AI agent (Google Gemini in this flow) which returns a structured content output (topic summary, blog post title ideas, tweet hooks), formats that output, and appends the results into a Google Sheet. It’s a lightweight: Topic → Trending Content → AI Ideas → Sheet pipeline for fast content ideation. How It Works (Step-by-Step) On Form Submission The user fills a single field Topic (webhook/form trigger). X Scraper (HTTP Request) Calls an Apify act run-sync-get-dataset-items with: searchTerms: [{{ $json.Topic }}] maxItems: 20 to fetch social posts for that topic. Edit Fields (Set) Extracts fullText from each scraped item and stores it in a Content field. Aggregate Aggregates the Content field so the AI agent receives one combined input rather than many separate items. Google Gemini Chat Model (LM) + AI Agent (LangChain Agent Node) The agent uses a templated system prompt + the aggregated content to generate a structured response with: Topic summary Blog title ideas Tweet hooks The agent is connected to a Structured Output Parser node to force a predictable JSON schema. Code in JavaScript Transforms the structured JSON into sheet-friendly strings (joins arrays with bullets). Append Row in Sheet (Google Sheets) Appends the generated blog_post_titles and tweet_hooks to the target Google Sheet. (Optional) Sticky notes and internal meta nodes exist for documentation and board organization. Quick Setup Guide 👉 Demo & Setup Video 👉 Sheet Template 👉 Course Nodes of Interest You Can Edit 1. On Form Submission (formTrigger) Edit form fields (add author, language, region, or filters). Change webhook behaviour or require authentication. 2. X Scraper (HTTP Request) URL:** Change to another Apify act or another scraping API. jsonBody:** Change maxItems, sort (Top/Recent), or modify searchTerms (e.g., topic + hashtag). Headers:** Set the Authorization: Bearer token (Apify). Add pagination or query parameters if switching scraper APIs. 3. Edit Fields (Set) Map additional fields (author, date, source URL). Add filtering logic (remove short posts, retweets, duplicates). 4. Aggregate Customize aggregation strategy: Concatenate Sample top N Deduplicate before combining 5. Google Gemini Chat Model / AI Agent / Structured Output Parser Edit systemMessage and prompt template (tone, format, extra outputs). Tune LM parameters (temperature, max tokens). Update schema to request: Sentiment Key quotes Additional formats 6. Code in JavaScript Modify formatting (CSV-ready, add timestamp). Add metadata columns. Add deduplication or length checks before write. 7. Append Row in Sheet (Google Sheets) Change spreadsheet ID or sheet name. Add more columns. Switch from Append to Upsert. Configure batch appends. What You’ll Need (Credentials) 1. Apify API Token Used in the HTTP Request node. Set in header: Authorization: Bearer YOUR_APIFY_TOKEN 2. Google Sheets OAuth2 Credentials Must include spreadsheets scope. Required for appending rows. 3. Google / PaLM / Google Gemini API Credentials Used by the LangChain / Google Gemini node. Optional n8n webhook URL (for mounting the form). Monitoring credentials (Slack webhook, Sentry, etc.) for alerts. Recommended Settings & Best Practices Enable workflow only after testing (active: true). Limit maxItems (20–50 recommended). Sanitize & dedupe content before sending to the AI. Always use a Structured Output Parser for reliable JSON. Set low temperature (0.0–0.6) for consistent results. Add retries and exponential backoff for external APIs. Add logging or Slack alerts for failures. Keep execution log columns in the sheet (status, error_message, run_time). Store workflow JSON in version control. Monitor API rate limits (Apify + Google). Avoid writing scraped PII into public sheets. Customization Ideas Add output types: Instagram captions LinkedIn posts Video scripts Email subject lines Add sentiment / trend scoring. Add language detection & translation. Store aggregated content in a vector database (Pinecone / Chroma). Schedule runs using Cron trigger. Add multiple data sources (Reddit, RSS, HackerNews). Add approval workflow (Slack / Notion). Add metadata columns: source_urls top_authors most_shared Tags #content-ideation' #social #ai #google-gemini #apify #google-sheets #n8n

This workflow monitors brand mentions across multiple platforms (Twitter/X, Reddit, News) and automatically detects reputation crises based on sentiment analysis and trend detection. How it works Multi-platform monitoring: Every 10 minutes, scans Twitter/X, Reddit, and news sites for brand mentions Data normalization: Converts all platform data into unified format Smart filtering: Removes duplicates and already-analyzed mentions AI sentiment analysis: Analyzes each mention for: Sentiment score (positive/negative/neutral) Amplification factors (engagement, verified accounts, news sources) Crisis-level phrases and keywords Trend detection: Compares current sentiment to 24-hour baseline: Detects sharp sentiment drops Identifies negative mention spikes Calculates impact surge Crisis classification: Assigns severity (CRITICAL/HIGH/MEDIUM/LOW) Automated response: For crises, triggers immediate alerts: Executive crisis brief with action plan Slack alerts to crisis team Email to leadership and PR team JIRA ticket creation Crisis event logging Setup steps Connect platforms: Twitter/X: Add OAuth credentials to "Monitor Twitter/X" node Reddit: Add OAuth credentials to "Monitor Reddit" node News API: Get API key from newsapi.org and add to "Monitor News & Blogs" node Configure brand monitoring: Update brand name and handles in search queries Add additional platforms if needed (LinkedIn, Facebook, Instagram) Set up alerting: Slack: Add credentials and update channel names Email: Add SMTP settings and recipient lists JIRA: Add credentials and project ID Adjust sensitivity: Modify sentiment keyword dictionaries in "AI Sentiment Analysis Engine" Adjust crisis threshold scores Customize amplification factors Test thoroughly: Run manual execution with test data Verify alert routing and content Test false positive handling Activate: Enable for continuous 24/7 monitoring Key Features: Multi-platform monitoring** (every 10 minutes): Twitter/X, Reddit, and News sites Data normalization** that converts different platform formats into unified structure AI sentiment analysis** engine that evaluates: Sentiment keywords (critical, severe, moderate, mild negative/positive) Amplification factors (engagement, verified accounts, follower counts) Impact scoring based on reach and influence Baseline trend detection** that tracks 24-hour sentiment history and detects: Sharp sentiment drops (15+ points = crisis) Negative mention spikes (30%+ increase) Impact surges Automated crisis response workflow**: Aggregates crisis mentions into executive brief Generates detailed action plan based on severity Sends Slack alerts to crisis team Emails leadership with comprehensive brief Creates JIRA ticket for tracking Logs all events for analysis Two-path routing**: Crisis-level events trigger full response workflow, while routine mentions are logged for trend analysis

Quick overview This workflow collects a social media content brief via an n8n form, uses Google Sheets for client brand context, and runs a three-stage human-in-the-loop process powered by Google Gemini (strategy angles, draft writing, and quality audit) before logging the approved post back to Google Sheets. How it works Receives a new content request through an n8n Form that captures client, platform, content type, and optional topic and tone notes. Looks up the client’s brand fields in Google Sheets and combines them with the brief plus embedded brand and platform playbooks. Uses Google Gemini to generate three strategy angles, then pauses for a human to approve an angle or request revised angles with feedback. Uses Google Gemini to write a platform-specific draft (post copy, hashtags, and visual direction), then pauses for a human to approve or send revision notes to regenerate the draft. Sends the draft to Google Gemini for a structured quality audit, parses the returned JSON into readable fields, and pauses for a final human decision. If approved, updates a Google Sheets “Output” sheet with the published post; otherwise it routes feedback back to Marcus for revisions or back to Sofia to restart with new angles. Setup Connect Google Gemini (PaLM) credentials for the three Google Gemini chat/model steps. Connect a Google Sheets OAuth2 credential and update the spreadsheet ID and sheet/tab selections used for the client lookup and output update. Ensure your Google Sheet contains the expected brand fields (for example: brand_name, brand_voice, tone_linkedin/tone_x/tone_instagram, language_avoid, visual_style, key_messages) and an output sheet with a row_number and “Published Post” column. Review the three n8n Forms (brief intake, Sofia review, Marcus review, and Taylor review) and adjust labels/options to match your internal approval process before activating the workflow.

Quick overview This workflow takes a YouTube video URL, fetches its transcript via RapidAPI, uses Google Gemini to generate multiple repurposed content assets, stores everything in Google Sheets, and automatically publishes the LinkedIn post while logging posted/failed status and any errors. How it works Starts when a user submits a YouTube video URL through an n8n form. Requests the video transcript from the youtube-transcript3 RapidAPI endpoint and normalizes it into a single clean text block. Sends the transcript to Google Gemini to generate a JSON bundle containing a title, summary, blog article, LinkedIn post, X (Twitter) thread, newsletter, content ideas, hooks, and CTA suggestions. Parses and validates the Gemini response into consistent fields for downstream use. Appends the transcript and all generated content to Google Sheets (Sheet1) as a content database row. Publishes the generated LinkedIn post to your LinkedIn profile and updates the same Google Sheets row as Posted or Failed. Logs any errors from transcript fetching, AI generation/parsing, Google Sheets, or LinkedIn publishing to a Google Sheets “Errors” tab. Setup Create a RapidAPI subscription for youtube-transcript3 and replace the x-rapidapi-key value with your RapidAPI key. Add Google Gemini (PaLM) API credentials in n8n and connect them to the Google Gemini Chat Model node. Create a Google Sheets spreadsheet with “Sheet1” and an “Errors” sheet using the exact headers shown in the workflow notes, then set your Google Sheet ID in all Google Sheets nodes. Add Google Sheets OAuth2 credentials in n8n with access to that spreadsheet. Add LinkedIn OAuth2 credentials in n8n and confirm the workflow is permitted to post to the target LinkedIn account. Requirements I need an AI-powered content workflow that takes a YouTube URL, pulls the transcript automatically, and generates multiple content assets (blog, LinkedIn post, X thread, newsletter, hooks, CTAs) in one run. It should log all outputs into a Google Sheet as a structured content database and optionally auto-post to LinkedIn while tracking Posted/Failed status with error logs. Customization I want the system to be customizable for different niches and tones, so I can tweak prompts or add extra output formats without changing the overall workflow logic. It should also let me plug in my own API keys and credentials inside n8n, without hardcoding any secrets in the workflow JSON. Additional info This is built as an n8n workflow that starts from a form (YouTube URL input), calls a transcript API, uses a Gemini model node, and writes to Google Sheets plus LinkedIn. I’m comfortable editing nodes, expressions, and environment variables myself, so a clean template without bound credentials is actually preferred.