Detect team burnout with Groq AI analysis of GitHub activity for wellness reports

Team Wellness - AI Burnout Detector Agent devex github 🎯 Demo !Demo sample report github action code alternative ( https://github.com/suarifymy/adk-samples/blob/main/.github/workflows/devex-ai-burnout-detector.yml) !output !output !output How it works 🎯 Overview A comprehensive n8n workflow that analyzes developer workload patterns from GitHub repositories to detect potential software engineering team burnout risks and provide actionable team wellness recommendations. This workflow automatically monitors team activity patterns, analyzes them using AI, and provides professional wellness reports with actionable recommendations which will automate GitHub issue creation and do email notifications for critical alerts. ✨ Features - Automated Data Collection: Fetches commits, pull requests, and workflow data from GitHub - Pattern Analysis: Identifies late-night work, weekend activity, and workload distribution - AI-Powered Analysis: Uses Groq's LLM for professional burnout risk assessment - Automated Actions: Creates GitHub issues and sends email alerts based on criticality - Professional Guardrails: Ensures objective, evidence-based analysis with privacy protection - Scheduled Monitoring: Weekly automated wellness checks 🏗️ Architecture 1. Data Collection Layer - GitHub Commits API: Fetches commit history and timing data - GitHub Pull Requests API: Analyzes collaboration patterns - GitHub Workflows API: Monitors CI/CD pipeline health 2. Pattern Analysis Engine - Work Pattern Signals: Late-night commits, weekend activity - Developer Activity: Individual contribution analysis - Workflow Health: Pipeline success/failure rates - Collaboration Metrics: PR review patterns and merge frequency 3. AI Analysis Layer - Professional Guardrails: Objective, evidence-based assessments - Risk Assessment: Burnout risk classification (Low/Medium/High) - Health Scoring: Team wellness score (0-100) - Recommendation Engine: Actionable suggestions for improvement 📊 Sample Output markdown 📊 Team Health Report 📝 Summary Overall, the team is maintaining a healthy delivery pace, but there are emerging signs of workload imbalance due to increased after-hours activity. 🔢 Health Score - Value: 68 / 100 - Confidence: 87% - Limitations: Based solely on commit and PR activity; meeting load and non-code tasks not captured. 🔍 Observed Patterns - ⏰ After-hours activity - 29% of commits occurred between 10pm–1am (baseline: 12%). - Confidence: 0.90 ⚠️ Systemic Risks - Sustained after-hours work may indicate creeping burnout risk. - Evidence: 3 consecutive weeks of elevated late-night commits. - Confidence: 0.85 ✅ Recommendations - 📌 Facilitate a team discussion on workload distribution and sprint commitments. (Priority: Medium) - 🔔 Introduce automated nudges discouraging late-night commits. (Priority: Low) - 🛠️ Rotate PR review responsibilities or adopt lightweight review guidelines. (Priority: High) 🚀 Quick Start Prerequisites - n8n instance (cloud or self-hosted) - GitHub repository with API access - Groq API key - Gmail account (optional, for email notifications) Setup Instructions 1. Import Workflow bash Import the workflow JSON file into your n8n instance 2. Configure Credentials - GitHub API: Create a personal access token with repo access - Groq API: Get your API key from Groq Console - Gmail OAuth2: Set up OAuth2 credentials for email notifications 3. Update Configuration json { "repoowner": "your-github-username", "reponame": "your-repository-name", "period": 7, "emailreport": "[email protected]" } 4. Test Workflow - Run the workflow manually to verify all connections - Check that data is being fetched correctly - Verify AI analysis is working 5. Schedule Automation - Enable the schedule trigger for weekly reports - Set up monitoring for critical alerts 🔧 Configuration Configuration Node Settings - repoowner: GitHub username or organization - reponame: Repository name - period: Analysis period in days (default: 7) - emailreport: Email address