Detect Team Burnout with Groq AI Analysis of GitHub Activity for Wellness Reports

Description

Team Wellness - AI Burnout Detector Agent devex github

🎯 Demo


sample report

github action code alternative
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

📊 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

Import Workflow
Import the workflow JSON file into your n8n instance

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

Update Configuration
{
"repoowner": "your-github-username",
"reponame": "your-repository-name",
"period": 7,
"emailreport": "[email protected]"
}

Test Workflow
Run the workflow manually to verify all connections
Check that data is being fetched correctly
Verify AI analysis is working

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 for critical alerts

AI Model Settings
Model**: openai/gpt-oss-120b (Groq)
Temperature**: 0.3 (for consistent analysis)
Max Tokens**: 2000
Safety Settings**: Professional content filtering

📈 Metrics Analyzed

Repository-Level Metrics
Total commits count
Pull requests opened/closed
Workflow runs and success rate
Failed workflow percentage

Work Pattern Signals
Late-night commits (10PM-6AM)
Weekend commits (Saturday-Sunday)
Work intensity patterns
Collaboration bottlenecks

Developer-Level Activity
Individual commit counts
Late-night activity per developer
Weekend activity per developer
Workload distribution fairness

🛡️ Privacy & Ethics

Professional Guardrails
Never makes personal judgments about individual developers
Only analyzes observable patterns in code activity data
Always provides evidence-based reasoning for assessments
Never suggests disciplinary actions or performance reviews
Focuses on systemic issues and team-level recommendations
Respects privacy and confidentiality of team members

Data Protection
No personal information is stored or transmitted
Analysis is based solely on public repository data and public data
All recommendations are constructive and team-focused
Confidence scores indicate analysis reliability
There is added redaction prompt. Note that LLM is not deterministic and usually, you will need to refine your own prompt to enhance difference level of criticality of privacy you need censored or displayed. In some cases ,you will need the engineer account names to help identify f2f conversation.

🔄 Workflow Nodes

Core Nodes
Schedule Trigger: Weekly automation (configurable)
Config: Repository and email configuration
Github Get Commits: Fetches commit history
Github Get Workflows: Retrieves workflow runs
Get Prs: Pulls pull request data
Analyze Patterns Developer: JavaScript pattern analysis
AI Agent: Groq-powered analysis with guardrails
Update Github Issue: Creates wellness tracking issues
Send a message in Gmail: Email notifications

Data Flow
Schedule Trigger → Config → Github APIs → Pattern Analysis → AI Agent → Actions

🚨 Alert Levels (Optional and Prompt configurable)

Critical Alerts (Health Score < 90)
GitHub Issue**: Automatic issue creation with detailed analysis
Email Notification**: Immediate alert to team leads
Slack Integration**: Critical team notifications

Warning Alerts (Health Score 90-95)
GitHub Issue**: Tracking issue for monitoring
Slack Notification**: Team awareness message

Normal Reports (Health Score > 95)
Weekly Report**: Comprehensive team health summary
Slack Summary**: Positive reinforcement message

🔧 Troubleshooting

Common Issues

GitHub API Rate Limits
Solution: Use authenticated requests, implement rate limiting
Check: API token permissions and repository access

AI Analysis Failures
Solution: Verify Groq API key, check model availability
Check: Input data format and prompt structure

Email Notifications Not Sending
Solution: Verify Gmail OAuth2 setup, check email permissions
Check: SMTP settings and authentication

Workflow Execution Errors
Solution: Check node connections, verify data flow
Check: Error logs and execution history
🤝 Contributing

Development Setup
Fork the repository link above demo part
Create a feature branch
Make your changes
Test thoroughly
Submit a pull request

Testing
Test with different repository types
Verify AI analysis accuracy
Check alert threshold sensitivity
Validate email and GitHub integrations

📄 License

This project is licensed under the MIT License

🙏 Acknowledgments

Groq**: For providing the AI analysis capabilities
GitHub**: For the comprehensive API ecosystem
n8n**: For the powerful workflow automation platform
Community**: For feedback and contributions

📞 Support

Getting Help
Issues**: Create a GitHub issue for bugs or feature requests
Discussions**: Use GitHub Discussions for questions
Documentation**: Check the comprehensive setup guides

Contact
Email**:[email protected]
LinkedIn**: SeanLon

⚠️ Important: This tool is designed for team wellness monitoring and should be used responsibly. Always respect team privacy and use the insights constructively to improve team health and productivity.