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Triage support tickets by GPT-4o sentiment and route them to Slack

Analyze customer support ticket sentiment and route to Slack channels Who is this for Customer support teams, customer experience managers, and operations teams that need to triage incoming support tickets in real time without manual classification. Ideal for SaaS companies, e-commerce businesses, and service providers handling high ticket volumes. What this workflow does This workflow automatically triages incoming customer support tickets by sentiment using OpenAI GPT-4o. A ticket arrives via webhook, is normalized, analyzed for anger intensity (scored 0-10), and routed to the correct Slack channel in real time. High-anger tickets are escalated immediately, mid-range tickets go to standard support, and low-anger tickets or feedback are logged separately. How to set up Add your OpenAI API credential to the AI sentiment analysis node Add your Slack OAuth2 credential to all three Slack nodes Update Slack channel names (#escalation / #support / #feedback) to match your workspace Activate the workflow and copy the webhook URL for your support form integration Send a test request to verify end-to-end execution Requirements OpenAI API account with GPT-4o access Slack workspace with OAuth2 app installed (chat:write scope) Three Slack channels: one for escalation, one for support, one for feedback How to customize Adjust the anger score thresholds in the Switch node to match your team's escalation policy. Modify the GPT-4o prompt to detect additional sentiment categories or extract custom fields from tickets. Add language detection for multilingual support. Node List | # | Node Name | Type | Role | |---|-----------|------|------| | 1 | When Ticket Received | Webhook | Trigger on incoming HTTP POST request | | 2 | Normalize Ticket Data | Code | Sanitize and validate ticket fields | | 3 | OpenAI Sentiment Analysis | OpenAI | AI anger scoring and sentiment tagging | | 4 | Parse Sentiment Output | Code | Parse JSON output and decide routing | | 5 | Route by Sentiment Score | Switch | 3-way branch: ≥8 / 4-7 / ≤3 | | 6 | Send to Slack #escalation | Slack | Alert for high-anger tickets | | 7 | Send to Slack #support | Slack | Notification for mid-range tickets | | 8 | Send to Slack #feedback | Slack | Log for low-anger tickets or feedback | | 9 | Respond with Routing Outcome | Respond to Webhook | Confirm result to the caller | Total: 9 nodes (+ 5 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Receive and normalize | White | Covers webhook reception and data normalization | | 3 | Analyze sentiment | White | Covers GPT-4o sentiment analysis | | 4 | Parse and route | White | Covers JSON parsing and routing logic | | 5 | Post to Slack channels | White | Covers all 3 Slack post nodes and webhook response | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Tags ai gpt-4 openai slack customer-support sentiment-analysis automation

BeginnerManual Trigger

by TakatoYamada

Analyze error logs with GPT-4o and create GitHub issues plus Slack alerts

Analyze error logs with AI and auto-create GitHub issues with fix suggestions Who is this for DevOps engineers, SREs, and development teams who want to automate error monitoring and reduce mean time to resolution (MTTR). Ideal for teams using GitHub for issue tracking and Slack for incident response. What this workflow does This workflow automates the entire error management lifecycle - from log ingestion to GitHub issue creation and Slack notification - using GPT-4o-powered root cause analysis. When an application error log is received, it parses the payload, checks for duplicates against existing GitHub issues, generates a structured root cause analysis with fix suggestions, creates a formatted GitHub Issue, and routes Slack notifications by severity. A 30-minute Wait node prevents notification flooding. How to set up Add your GitHub Personal Access Token (repo scope) credential Add your OpenAI API credential to the AI analysis node Add your Slack OAuth2 credential (chat:write scope) to all Slack nodes Configure n8n Variables: GITHUB_OWNER and GITHUB_REPO Update Slack channel names (#incident / #dev-alerts) to match your workspace Activate the workflow and copy the webhook URL for your application logger Requirements GitHub repository with Personal Access Token (repo scope) OpenAI API account with GPT-4o access Slack workspace with OAuth2 app installed Two Slack channels: one for critical incidents, one for general dev alerts How to customize Adjust the duplicate detection score threshold (default 60) in the Code node. Modify the GPT-4o prompt to focus on specific error categories. The Wait node duration (30 minutes) can be tuned to match your alerting policy. Key features Scoring-based duplicate detection (no extra API calls required) GPT-4o structured JSON output with graceful fallback parser Severity-based Slack routing (#incident vs #dev-alerts) Dynamic GitHub labels: bug, auto-generated, environment, and critical n8n Variables used for GitHub owner and repo - no hardcoded values Node List | # | Node Name | Type | Purpose | |---|-----------|------|---------| | 1 | Webhook for Error Logs | Webhook | Accepts error log payload via HTTP POST | | 2 | Parse and Enrich Log | Code | Normalizes level, extracts error type, builds duplicate search keyword | | 3 | Search GitHub Issues | GitHub | Fetches open issues labeled bug,auto-generated from the target repo | | 4 | Score GitHub Issue Duplicates | Code | Scores each issue for similarity; flags duplicates at threshold ≥ 60 | | 5 | Check for Duplicates | If | Routes to skip path (true) or analysis path (false) | | 6 | Notify Duplicate to Slack | Slack | Posts link to existing issue when duplicate detected | | 7 | Respond with Duplicate Status | Respond to Webhook | Returns 200 OK JSON acknowledgment for duplicate path | | 8 | OpenAI Error Analysis | OpenAI | GPT-4o analyzes root cause and returns structured JSON fix suggestions | | 9 | Build GitHub Issue Body | Code | Parses AI JSON and builds Markdown issue body with tables and code blocks | | 10 | Create GitHub Issue | GitHub | Creates GitHub issue with title, body, and dynamic labels | | 11 | If Critical Error | If | Checks isCritical flag to route Slack notification channel | | 12 | Post Critical Alert to Slack | Slack | Posts @here alert to #incident with full error details | | 13 | Post Error Summary to Slack | Slack | Posts summary to #dev-alerts with GitHub issue link | | 14 | Wait 30 Minutes | Wait | Enforces 30-minute cooldown to prevent notification flooding | Total: 14 nodes (+ 6 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Receive and parse log | White | Covers webhook reception and log parsing | | 3 | Check for duplicate GitHub issues | White | Covers GitHub search and duplicate scoring | | 4 | Handle duplicates and notify | White | Covers duplicate branch (notification + webhook response) | | 5 | Analyze error and suggest fixes | White | Covers AI analysis and issue creation | | 6 | Send alerts and summarize | White | Covers severity check, Slack notifications, and wait | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Webhook payload schema { "service": "payment-api", "level": "CRITICAL", "message": "NullPointerException at PaymentProcessor.java:142", "stack_trace": "java.lang.NullPointerException...", "environment": "production", "timestamp": "2025-01-15T09:23:45Z", "trace_id": "abc-123-xyz", "endpoint": "/api/v2/payments", "http_method": "POST", "status_code": 500, "user_id": "user_98765" } Required fields: service, level, message Optional fields: All others - missing values are handled gracefully with fallbacks. How duplicate detection works | Match condition | Points | |-----------------|--------| | Service name found in issue title | +40 | | Error type found in issue title | +40 | | Keyword overlap (words > 4 chars) | +5 per word (max +20) | Threshold: Score ≥ 60 → duplicate detected → skip issue creation, notify Slack. Tags ai gpt-4 openai github slack error-monitoring devops automation

Review GitHub pull requests with GPT-4o and send feedback and Slack alerts

Automatically review pull requests with AI and post feedback as GitHub comments Who is this for Engineering teams, code reviewers, and tech leads who want to automate code review feedback. Ideal for repositories with high PR volume where consistent first-pass review is valuable. What this workflow does This workflow automates pull request code review using GPT-4o and routes findings to GitHub and Slack. When a pull request is opened, it fetches the raw diff, sends it to GPT-4o for analysis, and posts a formatted Markdown comment with severity rating, categorized issues, suggestions, and an overall score. Critical findings trigger immediate Slack alerts. How to set up Add your GitHub OAuth2 credential to the trigger and comment nodes Add your OpenAI API credential to the GPT-4o node Add your Slack OAuth2 credential to both Slack notification nodes Configure n8n Variables: GITHUB_OWNER, GITHUB_REPO, and GITHUB_TOKEN Activate the workflow - the GitHub webhook registers automatically Open a test pull request to verify end-to-end execution Requirements GitHub repository with OAuth2 credentials and Personal Access Token OpenAI API account with GPT-4o access Slack workspace with OAuth2 app installed Two Slack channels: one for critical alerts, one for review summaries How to customize Adjust the diff truncation limit (default 12,000 chars) in the Format PR Diff node. Modify the GPT-4o system prompt to focus on specific code quality concerns like security or performance. Update Slack channel routing based on your team's notification policy. Node List | # | Node Name | Type | Purpose | |---|-----------|------|---------| | 1 | When PR Opened | GitHub Trigger | Receives pull_request webhook events | | 2 | Check PR Open | If | Passes only action = opened to avoid duplicates | | 3 | Fetch PR Diff | HTTP Request | Fetches the raw unified diff from GitHub | | 4 | Format PR Diff | Code | Extracts PR metadata; truncates diff to 12,000 chars | | 5 | AI Code Review with GPT-4 | OpenAI | Sends diff to GPT-4o; returns structured JSON review | | 6 | Build Comment for GitHub | Code | Parses JSON; assembles Markdown comment | | 7 | Post Comment to GitHub | GitHub | Posts the AI review comment on the pull request | | 8 | Check Critical Severity | If | Routes based on CRITICAL vs non-critical severity | | 9 | Alert Critical Issues to Slack | Slack | Sends urgent alert to #incident | | 10 | Share Review Summary on Slack | Slack | Sends summary notification to #code-reviews | Total: 10 nodes (+ 5 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Trigger and filter PR | White | Covers GitHub trigger and PR filter | | 3 | Fetch and format diff | White | Covers diff retrieval and formatting | | 4 | AI review and comment | White | Covers AI review and comment building | | 5 | Severity check and alerts | White | Covers severity routing and Slack notifications | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Tags ai gpt-4 openai github slack code-review devops automation

Save business card contacts from LINE photos to Google Sheets using Gemini

Save business card contacts from LINE photos to Google Sheets with Gemini Who is this for Sales professionals, account managers, and anyone who exchanges business cards regularly. Designed especially for LINE users (Japan, Taiwan, Thailand, etc.) who want to eliminate manual data entry after networking events or client meetings. What this workflow does When a user sends a business card photo to a LINE bot, Gemini AI automatically extracts contact information (name, company, job title, phone number, email, postal address) and saves it to Google Sheets. Duplicate entries are detected by email address and skipped automatically. The team receives a Slack notification for every new contact, and the user gets an instant LINE confirmation. How to set up Create a LINE Messaging API channel and obtain a Channel Access Token Prepare a Google Sheet with columns: Timestamp, Name, Company, Title, Phone, Email, Address, LINE_UID Get a Google Gemini API key from Google AI Studio Set up a Slack Incoming Webhook URL Enter all credentials in the Set Configuration Parameters node Register the n8n Webhook URL in LINE Developer Console Requirements LINE Messaging API account (free tier available) Google Sheets (any Google account) Google Gemini API key (free tier available) Slack workspace with Incoming Webhooks enabled How to customize Adjust Google Sheets columns to match your CRM fields. Add a HubSpot or Salesforce node after the Sheets node to sync contacts directly to your CRM. Replace Slack with Microsoft Teams or Discord for team notifications. Node List | # | Node Name | Type | Role | |---|-----------|------|------| | 1 | Set Configuration Parameters | Set | Centralizes all credentials and config values | | 2 | When LINE Event Received | Webhook | Receives LINE Webhook events (POST) | | 3 | If Image Message | If | Checks whether the message contains an image | | 4 | Fetch Image from LINE API | HTTP Request | Downloads image from LINE Content API | | 5 | Encode Image as Base64 | Code | Converts image binary to Base64 string | | 6 | Setup Gemini for Card Data | Gemini Chat Model | Gemini 1.5 Flash model definition | | 7 | Extract Card Info with Gemini | LLM Chain | Extracts contact info from card image as JSON | | 8 | Parse Gemini Output Data | Code | Parses Gemini response into structured data | | 9 | Read Contacts from Sheets | Google Sheets | Searches existing records by email address | | 10 | If Contact Exists | If | Checks whether a duplicate record exists | | 11 | Append Contact to Sheets | Google Sheets | Appends new contact to Google Sheets | | 12 | Post New Contact to Slack | HTTP Request | Sends Slack notification for new contact | | 13 | Send Success Reply via LINE | HTTP Request | Sends registration confirmation via LINE | | 14 | Send Duplicate Reply via LINE | HTTP Request | Sends duplicate notification via LINE | | 15 | Respond Not Image to LINE | Respond to Webhook | Responds to non-image messages | | 16 | Acknowledge OK Webhook | Respond to Webhook | Returns HTTP 200 on success | | 17 | Acknowledge Duplicate Webhook | Respond to Webhook | Returns HTTP 200 on duplicate | Total: 17 nodes (+ 8 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Configuration setup | White | Covers credentials and config | | 3 | Webhook event handling | White | Covers LINE webhook reception and image check | | 4 | Image download and conversion | White | Covers image fetch and Base64 encoding | | 5 | Card data extraction | White | Covers Gemini extraction and parsing | | 6 | Duplicate check and contact saving | White | Covers Sheets read/write and duplicate logic | | 7 | Messaging and webhook response | White | Covers Slack post, LINE replies, and webhook responses | | 8 | Gemini configuration | White | Covers Gemini model setup | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Setup Guide 1. Create a LINE Messaging API channel Log in to LINE Developers Console Create a new provider (or select an existing one) Create a Messaging API channel Issue a long-lived Channel Access Token and copy it Set the Webhook URL after configuring n8n 2. Prepare Google Sheets Create a new spreadsheet Add the following headers in row 1: | A | B | C | D | E | F | G | H | |---|---|---|---|---|---|---|---| | Timestamp | Name | Company | Title | Phone | Email | Address | LINE_UID | Copy the spreadsheet ID from the URL (between /d/ and /edit) 3. Set up Slack Incoming Webhook Create a new app at Slack API Enable Incoming Webhooks Select a channel and copy the Webhook URL 4. Get a Google Gemini API key Go to Google AI Studio Create an API key Add it as a Google PaLM API credential in n8n 5. Configure the n8n workflow Import the workflow JSON into n8n Open the Set Configuration Parameters node and enter: LINE_CHANNEL_ACCESS_TOKEN GOOGLE_SHEET_ID SLACK_WEBHOOK_URL Set up Google Sheets OAuth2 credentials Set up Gemini Chat Model credentials 6. Register the LINE Webhook URL Return to LINE Developers Console Enter the n8n Webhook URL Enable Webhook usage Disable auto-reply messages 7. Test Activate the workflow Send a business card photo to your LINE bot Confirm the contact is saved in Google Sheets Confirm the Slack notification is received Confirm the LINE confirmation message is received Tags ai gemini line google-sheets slack crm image-recognition automation

Log meal nutrition from LINE food photos to Google Sheets with Gemini AI

Log meal nutrition from LINE food photos to Google Sheets using Gemini AI Who is this for Health-conscious individuals, people on a diet, and anyone who wants to track daily nutrition without manual data entry. Designed especially for LINE users (Japan, Taiwan, Thailand, etc.) who want an effortless way to monitor calories and macronutrients from meal photos. What this workflow does Send a meal photo to a LINE bot and Gemini 1.5 Flash automatically identifies the food and estimates calories, protein, fat, and carbohydrates. Each meal is logged to Google Sheets with a timestamp and user ID. The workflow calculates the running daily calorie total and warns when the personal limit is exceeded. Every Monday morning, a weekly nutrition summary with AI-generated advice is pushed via LINE automatically. How to set up Create a LINE Messaging API channel and copy the Channel Access Token Copy your LINE User ID for weekly Push messages Set up a Google Sheet with columns: Timestamp, LINE_UID, Food_Name, Meal_Type, Calories, Protein, Fat, Carbs, Confidence Get a Google Gemini API key (free tier available) Configure CALORIE_LIMIT (default 2000) and LINE_USER_ID in the Set Config Fields node Register the n8n Webhook URL in LINE Developer Console Requirements LINE Messaging API account (free tier) Google Sheets (any Google account) Google Gemini API key (free tier available) How to customize Adjust CALORIE_LIMIT in the Set Config Fields node for different dietary goals. Add a Slack notification node to share weekly reports with a fitness accountability group. Modify the Gemini prompt to track additional nutrients like fiber or sodium. Node List | # | Node Name | Type | Role | |---|-----------|------|------| | 1 | Set Config Fields | Set | Centralizes LINE token, Sheet ID, calorie limit, and user ID | | 2 | When LINE Event Received | Webhook | Receives LINE Webhook (POST) | | 3 | If Image Message | If | Branches on image vs text message | | 4 | If Report Command | If | Checks whether text is a report command | | 5 | Send Help Reply via LINE | HTTP Request | Sends usage guide as reply | | 6 | Fetch LINE Image Data | HTTP Request | Downloads image from LINE Content API | | 7 | Encode Image to Base64 | Code | Converts image binary to Base64 string | | 8 | Gemini Food Analysis Config | Gemini Chat Model | Gemini 1.5 Flash model for food analysis | | 9 | Process Food Analysis | LLM Chain | Estimates nutrition info from meal image as JSON | | 10 | Extract Nutrition Data | Code | Extracts and parses JSON from Gemini response | | 11 | Append Meal to Sheets | Google Sheets | Appends nutrition data to spreadsheet | | 12 | Read Today's Total from Sheets | Google Sheets | Retrieves all records for today | | 13 | Compute Daily Calorie Total | Code | Calculates total calories for the day | | 14 | If Over Calorie Limit | If | Checks whether daily limit is exceeded | | 15 | Send Calorie Warning via LINE | HTTP Request | Sends calorie warning reply via LINE | | 16 | Send Nutrition Info via LINE | HTTP Request | Sends nutrition info and daily total via LINE | | 17 | Weekly 9AM Schedule | Schedule Trigger | Triggers weekly report every Monday at 9 AM JST | | 18 | Read Weekly Data from Sheets | Google Sheets | Retrieves records from the past 7 days | | 19 | Summarize Weekly Stats | Code | Aggregates weekly totals, averages, and peak day | | 20 | Gemini Weekly Report Config | Gemini Chat Model | Gemini 1.5 Flash model for weekly comment | | 21 | Create Weekly Comment with LLM | LLM Chain | Generates personalized nutrition advice | | 22 | Deliver Weekly Report via LINE | HTTP Request | Sends weekly report via LINE Push | | 23 | Send Webhook Response OK | Respond to Webhook | Returns HTTP 200 to Webhook | Total: 23 nodes (+ 9 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Set configuration fields | White | Covers configuration setup | | 3 | Receive and verify message type | White | Covers LINE webhook and message type checks | | 4 | Download and convert image | White | Covers image fetch and Base64 encoding | | 5 | Analyze image and parse data | White | Covers Gemini analysis and data parsing | | 6 | Log and calculate nutrition | White | Covers meal logging and daily total calculation | | 7 | Notify via LINE based on calorie | White | Covers calorie warning and nutrition info LINE replies | | 8 | Weekly report scheduling and stats | White | Covers schedule trigger and weekly aggregation | | 9 | Respond to LINE webhook | White | Covers webhook response | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Setup Guide 1. Create a LINE Messaging API channel Log in to LINE Developers Create a new provider and a Messaging API channel Issue a long-lived Channel Access Token and copy it Copy your User ID from the channel basic settings 2. Prepare Google Sheets Create a new spreadsheet Add the following headers in row 1: Timestamp | LINE_UID | Food_Name | Meal_Type | Calories | Protein | Fat | Carbs | Confidence Copy the spreadsheet ID from the URL (between /d/ and /edit) 3. Get a Google Gemini API key Go to Google AI Studio Create an API key (free tier available) Register it as a Google PaLM API credential in n8n 4. Configure the n8n workflow Import the workflow JSON into n8n Open Set Config Fields and enter: LINE_CHANNEL_ACCESS_TOKEN GOOGLE_SHEET_ID CALORIE_LIMIT (default: 2000) LINE_USER_ID Set up Google Sheets OAuth2 and Google PaLM API credentials 5. Register the Webhook URL Activate the workflow in n8n Copy the Webhook URL Paste it into LINE Developers Console → Messaging API settings Enable Webhook and verify the connection 6. Test Send a meal photo to your LINE bot → confirm nutrition info is returned Send "report" as text → confirm weekly summary is returned Send other text → confirm help message is returned Tags ai gemini line google-sheets health nutrition-tracking image-recognition automation

Collect SOC 2 AWS IAM evidence to Google Sheets with Gmail alerts

Your automated compliance assistant that pulls, formats, and logs AWS infrastructure evidence to Google Sheets on a recurring schedule. Stop manually exporting IAM user lists and CloudTrail logs for your auditors. This workflow orchestrates the collection of critical infrastructure evidence, validates the data integrity, and appends it to your compliance source of truth automatically. You maintain the cloud; it maintains the proof. This is not a generic "checklist" template. It is a functional operational pipeline designed to generate empirical evidence for Trust Services Criteria (TSC) compliance. How the workflow works The workflow executes in four strategic stages: Initialize & Schedule: The workflow runs on a quarterly cron schedule (or manual trigger). It initializes the audit metadata, including a precise collection timestamp and the specific Trust Services Criteria (TSC) category being addressed. Native AWS Extraction: Using the native AWS IAM node, the workflow queries your global identity directory. It fetches the "Get Many" user list, ensuring a clean capture of every active identity currently possessing access to your infrastructure. Data Normalization: A specialized code node parses the raw AWS response. It extracts critical auditor data—including Usernames, ARNs, and Account Creation Dates—and injects a "Review Required" status to facilitate human-in-the-loop compliance verification. Evidence Logging & Reporting: Success Path: Evidence is appended to your master Google Sheet. A summarizer node calculates the total user count and fires a professional HTML Executive Summary to your inbox. Failure Path: If authentication fails (e.g., Signature Mismatch) or the directory is empty, a high-priority warning email is sent with specific troubleshooting steps to ensure you never miss a compliance window. Benefits Auditor-Ready Structure**: Does not just dump data; it organizes it by ARN and Creation Date with a built-in "Review Status" column for your compliance team. Self-Healing Logic**: Built-in SignatureDoesNotMatch detection. If the AWS connection drops, you get a detailed troubleshooting alert instead of a silent failure. Zero Data Loss**: Uses n8n's native credential encryption to securely handle AKIA keys, ensuring your most sensitive infrastructure metadata is never exposed in logs. Eliminates "Audit Panic"**: By running on a quarterly schedule, you build a continuous trail of evidence, avoiding the 40-hour "last-minute scramble" before the auditor arrives. Executive Visibility**: Every run generates a formatted HTML report. You don't have to check a spreadsheet to know you're compliant; you'll see the green "Success" header in your inbox. Target Audience CTOs & DevOps Leads** at startups preparing for their first SOC 2 Type I or Type II audit. Compliance Officers** who need an automated "System of Record" for identity access. Security Engineers** looking to replace manual AWS exports with immutable, timestamped logs. Managed Service Providers (MSPs)** managing compliance for multiple client AWS accounts. Required APIs AWS IAM Credentials**: Access Key (AKIA) and Secret Key with iam:ListUsers permissions. Note: Set region to us-east-1 in n8n credentials. Google Sheets OAuth2**: To append evidence to your master compliance spreadsheet. Gmail OAuth2**: To deliver the Executive Summary and Failure Alert reports. Easy Customization Adjust Cadence**: Change the Cron expression in the Schedule node to run weekly or monthly for higher-fidelity evidence. Notification Channels**: Swap Gmail for Slack, Discord, or Microsoft Teams to fit your team's communication stack. Output Destination**: Easily replace Google Sheets with Airtable, PostgreSQL, or Supabase for enterprise-grade data handling.

Analyze daily stock news sentiment with Gemini AI, Google Sheets and Gmail

Stock News Sentiment Automation using Gemini AI, Google Sheets & Gmail This workflow automatically fetches daily stock market news, analyzes sentiment using Gemini AI, calculates impact scores, sends alerts for high-impact news and stores structured results in Google Sheets. Quick Implementation Steps Import the workflow into your n8n account Configure the Market News API (HTTP Request node) Add your Google Gemini API credentials Connect Google Sheets and create required columns Configure Gmail node (add recipient email) Activate the workflow What It Does This workflow runs every day at 8 AM and automatically collects the latest stock market news from a public API. It filters, cleans and processes the data to ensure only relevant and unique articles are analyzed. Each news article is sent to Google Gemini AI, which classifies the sentiment as bullish, bearish or neutral, along with a confidence score and reasoning. The workflow then calculates an impact score based on sentiment and confidence. High-impact news is immediately flagged and sent via email alerts, while all processed results are stored in a structured format in Google Sheets for tracking and analysis. Who It's For Stock traders and investors Financial analysts Algo trading developers FinTech startups Anyone tracking market sentiment automatically Requirements To use this workflow, you need: n8n account (cloud or self-hosted) Public Market News API (already used in workflow) Google Gemini API credentials Google Sheets account Gmail account for sending alerts How It Works & Setup Guide 1. Trigger Setup The workflow starts using Schedule Trigger Runs daily at 8 AM 2. Fetch Market News HTTP Request node calls: https://api.tradient.org/v1/api/market/news No authentication required (public API) 3. Select & Limit News Code node extracts: data.latest_news Limits to top 2 articles You can change this limit in: return output.slice(0, 2); 4. Normalize Data Ensures consistent fields: news_object.title news_object.text stock_name 5. Remove Duplicate News Deduplicates using title Prevents repeated analysis 6. Process One-by-One Uses SplitInBatches Ensures controlled processing 7. Rate Limiting Wait node prevents API overuse Avoids hitting Gemini limits 8. Generate AI Prompt Creates structured prompt like: Title Content Expected JSON output 9. AI Sentiment Analysis Uses Gemini model: models/gemini-3.1-flash-lite-preview Returns: Sentiment Confidence Reason 10. Parse AI Response Removes markdown formatting Converts response into valid JSON Handles errors safely 11. Calculate Impact Score Formula logic: Bullish → confidence × 1.2 Bearish → confidence × -1.2 Neutral → confidence × 0.3 12. High Impact Filter Conditions: Impact > 60 Impact < -60 13. Email Alert Setup You MUST configure: Recipient email in Gmail node (sendTo)** Example message: Stock: XYZ Title: News title Sentiment: bullish Impact: 75 Reason: Strong earnings report 14. Google Sheets Setup Create a sheet with EXACT column names: Title Stock Sentiment Reason Important: Column names must match exactly (case-sensitive) Do NOT rename fields Sheet must be connected in the node How To Customize Nodes Change Number of News Articles Modify: return output.slice(0, 2); Adjust Impact Threshold In IF node: Change: > 60 < -60 Modify AI Prompt Edit Generate Sentiment Prompt node to: Add more context Include additional fields Add More Sheet Columns Update Google Sheets node mapping: Add new fields like: confidence impact_score Add-ons (Enhancements) Slack/Telegram alerts instead of Gmail Dashboard (Power BI / Looker Studio) Store historical trends in database Add stock price correlation Multi-language news analysis Increase batch size with queue system Use Case Examples Daily Trading Signals Identify high-impact bullish/bearish news instantly Portfolio Monitoring Track sentiment around owned stocks Market Intelligence Systems Build automated research dashboards Algo Trading Input Feed sentiment into trading algorithms Financial News Aggregators Classify and rank news automatically And many more custom use cases depending on your strategy. Troubleshooting Guide | Issue | Possible Cause | Solution | | ------------------------------ | ------------------------ | --------------------------------------------------- | | No data in Google Sheets | Incorrect column names | Ensure exact match: Title, Stock, Sentiment, Reason | | Emails not sending | Gmail not configured | Add credentials + recipient email | | AI response parsing error | Invalid JSON from Gemini | Check Parse node logs | | Duplicate news still appearing | Title mismatch | Improve deduplication logic | | Workflow not triggering | Schedule inactive | Activate workflow | | API returns empty data | API downtime | Test endpoint manually | Need Help? If you need help with: Setting up this workflow Customizing AI prompts Adding dashboards or alerts Scaling automation systems Building advanced trading workflows Reach out to our n8n workflow development experts at WeblineIndia for advanced assistance. 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Send birthday and anniversary reminders with Groq AI gift ideas to Telegram

Birthday & Anniversary Reminder with AI Gift Ideas — Telegram Never forget a birthday again. This workflow checks your contacts daily and sends you a Telegram alert 7 days before each birthday and on the day itself — with a warm, AI-generated personal message and 3 gift ideas tailored to the person's interests. How It Works Fill a Google Sheet ("People" tab) with: name, birthday, relationship, interests The workflow runs daily at 8 AM (configurable) Checks if any birthday is today → sends 🎂 "TODAY" alert Checks if any birthday is in 7 days → sends 📅 "REMINDER" alert AI (Groq, free tier) writes a warm, personalized birthday message based on the person's name, age, relationship, and interests AI suggests 3 specific gift ideas with price estimates ($20-100) Everything sent to your Telegram in one clean message What You'll Receive on Telegram 🎂 TODAY — Khalil Mansour turns 34! 👤 friend 📅 2026-04-26 💡 Interests: football, gaming, tech gadgets 💌 MESSAGE: Happy Birthday Khalil! 🎉 Wishing you an epic 34th year filled with incredible goals, clutch victories, and exciting new tech to geek out over. You deserve all the best — enjoy your day! 🎁 GIFT IDEAS: FIFA 26 Ultimate Edition ($60) Razer BlackShark V2 gaming headset ($45) Anker portable phone charger for match days ($30) Workflow Architecture ▶️ Manual Test ──→ 📖 Read People → 📅 Check Dates → 🎁 Format Messages ⏰ Daily 8 AM ──↗ ↓ 🤖 AI Personalize ← Groq Chat Model ↓ 📝 Combine Message ↓ 📲 Send Telegram 9 nodes total. Each node has a clear, single responsibility. Google Sheet Format Create a Google Sheet with a tab named "People" and these columns: | name | birthday | relationship | interests | |---|---|---|---| | Mom | 1965-08-20 | family | gardening, cooking | | Ahmad | 1990-05-15 | friend | photography, hiking | | Sarah | 1985-12-03 | colleague | books, cooking | | Boss | 1978-11-02 | work | golf, wine, travel | What You Need Google Sheet** with your contacts (format above) Telegram bot** — create via @BotFather in 2 minutes, then send /start to your bot Groq API key** — free, no credit card needed (console.groq.com) Setup (10 minutes) Import the workflow JSON into n8n Open 📖 Read People → select your Google Sheet and set tab name to "People" Open 📲 Send Telegram → add your Telegram credential and set your Chat ID (get it from @userinfobot on Telegram) Open Groq Chat Model → add your Groq API credential Click ▶️ Manual Test to verify everything works Activate the workflow — it runs automatically every day at 8 AM Features 7-day advance reminder** — enough time to buy and ship a gift Day-of alert** — never miss sending a birthday message AI-personalized messages** — warm, unique messages based on who the person is and what they like AI gift suggestions** — 3 specific ideas with prices, tailored to their interests Age calculation** — automatically calculates how old they're turning Year wrap-around** — correctly handles birthdays that already passed this year Graceful fallback** — if AI is unavailable, you still get the basic reminder Manual test button** — test the full workflow before activating Customizable Reminder window** — change from 7 days to any number (edit one line in the Check Dates node) Alert time** — change from 8 AM to any time (edit the cron in the Schedule trigger) AI model** — swap Groq model in the Chat Model node (default: llama-3.1-8b-instant) Multiple events** — works for birthdays, anniversaries, or any annual date — just add rows Monthly Cost $0. Google Sheets is free. Telegram Bot API is free. Groq free tier (14,400 requests/day) easily handles a few AI calls per day. Setup Time 10 minutes. Import → connect 3 credentials → fill your contacts sheet → activate. Done forever.

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