Redis and Slack integration

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

Run automated daily standups using Slack, Notion, and Redis 📌 Overview This workflow fully automates your team's daily standup process using Slack for communication, Notion for structured data storage, and Redis for real-time session management. It automatically sends standup questions to active team members, collects and stores their responses, manages conversation sessions, and generates structured summary reports for managers. Morning and evening standups run on schedule without manual intervention. Redis ensures fast and reliable session tracking, prevents duplicate standups, and maintains conversation state. All responses are securely stored in Notion for long-term reporting and tracking. This workflow eliminates manual follow-ups, improves reporting consistency, and gives managers full visibility into team progress, blockers, and attendance. ⚙️ How it works This workflow runs automatically based on configured schedules. Morning standup Fetches active team members from the Notion database Creates standup sessions in Redis Sends standup questions to each team member via Slack direct message Stores responses in Notion Tracks session state using Redis Automated reports The workflow automatically generates: Morning summary report showing attendance, responses, and blockers Evening summary report showing accomplishments, completion status, and help requests Both reports are automatically sent to the Slack admin channel. Redis ensures session tracking and prevents duplicate standups. 🛠 Setup steps Import this workflow into n8n Connect your Slack credentials Connect your Notion credentials Connect your Redis credentials Configure your Notion database IDs Configure your Slack admin channel ID Activate the workflow The workflow will run automatically based on the configured schedule. 🚀 Features Automated standup management Automatically sends standup questions Tracks team responses Stores responses securely in Notion Prevents duplicate standup sessions Automated reporting Attendance tracking Task completion tracking Blocker detection Missing response detection Automatic Slack summary reports 📋 Requirements You need the following accounts: n8n Slack Notion Redis 🎯 Benefits Fully automated standup system No manual follow-ups required Automatic attendance tracking Identifies blockers early Improves team visibility Saves management time 👨‍💻 Author BytezTech Pvt Ltd

Overview This n8n template monitors specified GitHub repositories. When a new release is published, it automatically fetches the information, uses AI (Google Gemini by default) to summarize and translate it into Chinese, and sends a formatted notification to a designated Slack channel. Core Features: Automated Monitoring**: Checks for updates on a predefined schedule. Intelligent Processing**: Uses AI to extract key information and translate. Error Handling**: Sends an error notification if fetching RSS for a single repository fails, without affecting others. Duplicate Prevention**: Remembers the last processed release ID using Redis to ensure only new content is pushed. Prerequisites Slack**: Configure your Slack app credentials in n8n. Redis**: Have an available Redis service and configure its credentials in n8n. AI Provider (Gemini)**: Configure credentials for Google Gemini (or your chosen AI model) in n8n. Configuration Instructions After importing the template, you need to modify the following key nodes: Cron Trigger: Adjust the Rule setting to change the update check frequency (default is 0 */10 9-23 * * *, checking every 10 minutes between 9 AM and 11 PM daily). GitHub Config (Repository List - Code Node): Edit the JavaScript array within this node's code area. Modify or add the repositories you want to follow. Each repository object needs a name (custom display name) and github (format: owner/repo). Example: { "name": "n8n", // Custom display name "github": "n8n-io/n8n" // GitHub path }, { "name": "LobeChat", "github": "lobehub/lobe-chat" } // ... add more repositories Redis and Redis2 (Redis Connection): Select your configured Redis credentials in both nodes. Gemini (AI Model): Select your configured Google Gemini credentials. (Optional) Replace with a different supported AI model node and select its credentials. Information Extractor (AI Processing & Translation): Main Configuration: Review the System Prompt. By default, it asks the AI to extract information and translate it into Chinese. Modify this prompt if you need a different language or summary style. Send Message and Send Error (Slack Notifications): Select your configured Slack credentials in both Slack nodes. Set the target Channel ID for notifications. Workflow Overview Start: Cron Trigger initiates the workflow on schedule. Load Config: GitHub Config provides the list of repositories to monitor. Loop: The Loop node iterates through each repository. Fetch & Check: The RSS node attempts to fetch the repository's releases feed. If No Error checks for success: Failure: Send Error posts an error to Slack, skips this repository. Success: Continues. Check for New Release: The Redis node retrieves the last recorded Release ID for this repository. The If New node compares the latest Release ID with the recorded ID: Different IDs (New Release): Proceeds to processing. Same ID (Already Processed): Skips this repository. Process & Notify (Only for New Releases): Information Extractor (with Gemini) extracts, summarizes, and translates the content. The Code node formats the information into Slack Block Kit. Send Message sends the formatted message to Slack. The Redis2 node stores the current Release ID in Redis. End: The workflow finishes after processing all repositories. Conclusion Once configured, this template automates GitHub release monitoring, uses AI to distill key information, and delivers it efficiently to your Slack workspace.

How It Works This workflow automates customer feedback processing by analyzing sentiment, identifying key issues, generating personalized responses, and escalating critical cases to support teams when required. Designed for customer success managers, support teams, and product managers, it enables scalable feedback handling without compromising response quality or urgency. The workflow eliminates manual triage and response drafting by normalizing incoming feedback, performing sentiment and topic analysis, generating context-aware AI responses, validating tone and intent, escalating high-risk or negative feedback, logging all interactions for traceability, and delivering automated replies via email. Setup Steps Configure webhook trigger URL for feedback form integration or email parsing Add OpenAI API key for sentiment analysis and response generation Connect Anthropic Claude API for alternative response generation and validation Set up Google Sheets integration for feedback logging and analytics tracking Configure Gmail OAuth2 credentials for automated customer response delivery Integrate support ticket system (Zendesk, Freshdesk) for escalation routing Prerequisites OpenAI API key, Anthropic Claude API key (optional), Google Workspace account (Sheets, Gmail) Use Cases Product feedback management, customer support automation Customization Adjust sentiment scoring thresholds per industry standards, modify response templates Benefits Responds to feedback 95% faster, maintains consistent response quality across all interactions

How it Works This workflow transforms natural language queries into research reports through a five-stage AI pipeline. When triggered via webhook (typically from Google Sheets using the companion google-apps-script.js (GitHub gist), it first checks Redis cache for instant results. For new queries, GPT-4o breaks complex questions into focused sub-queries, optimizes them for search, then uses Bright Data's MCP Tool to find the top 5 credible sources (official sites, news, financial reports). URLs are scraped in parallel, bypassing bot detection. GPT-4o extracts structured data from each source: answers, facts, entities, sentiment, quotes, and dates. GPT-4o-mini validates source credibility and filters unreliable content. Valid results aggregate into a final summary with confidence scores, key insights, and extended analysis. Results cache for 1 hour and output via webhook, Slack, email, and DataTable—all in 30-90 seconds with 60 requests/minute rate limiting. Who is this for? Research teams needing automated multi-source intelligence Content creators and journalists requiring fact-checked information Due diligence professionals conducting competitive intelligence Google Sheets power users wanting AI research in spreadsheets Teams managing large research volumes needing caching and rate limiting Setup Steps Setup time: 30-45 minutes Requirements: Bright Data account (Web Scraping API + MCP token) OpenAI API key (GPT-4o and GPT-4o-mini access) Redis instance Slack workspace (optional) SMTP email provider (optional) Google account (optional for Sheets integration) Core Setup: Get Bright Data Web Scraping API token and MCP token Get OpenAI API key Set up Redis instance Configure critical nodes: Webhook Entry: Add Header Auth token Bright Data MCP Tool: Add MCP endpoint with token Parallel Web Scraping: Add Bright Data API credentials Redis Nodes: Add connection credentials All GPT Nodes: Add OpenAI API key (5 nodes) Slack/Email: Add credentials if using Google Sheets Integration: Create Google Sheet Open Extensions → Apps Script Paste the companion google-apps-script.js code Update webhook URL and auth token Save and authorize Test: {"prompt": "What is the population of Tokyo?", "source": "Test", "language": "English"} Customization Guidance Source Count:** Change from 5 to 3-10 URLs per query Cache Duration:** Adjust from 1 hour to 24 hours for stable info Rate Limits:** Modify 60/minute based on usage needs Character Limits:** Adjust 400-char main answer to 200-1000 AI Models:** Swap GPT-4o for Claude or use GPT-4o-mini for all stages Geographic Targeting:** Add more regions beyond us/il Output Channels:** Add Notion, Airtable, Discord, Teams Temperature:** Lower (0.1-0.2) for facts, higher (0.4-0.6) for analysis Once configured, this workflow handles all web research, from fact-checking to complex analysis—delivering validated intelligence in seconds with automatic caching. Built by Daniel Shashko Connect on LinkedIn