Developer Burnout Detection via GitHub Activity Analysis - n8n Workflow

• Engineering Managers and Directors seeking objective, data-driven insights into team workload.


• DevOps and SRE teams focused on improving development experience (DevEx).


• HR professionals needing quantifiable data on employee workload distribution.


• Users looking for advanced examples of an n8n workflow integrating AI agents with external tools.

Maintaining a healthy pace is crucial for long-term development success. This sophisticated n8n workflow provides a solution to proactively monitor developer workload patterns, detecting early signs of burnout such as excessive late-night or weekend commits. By leveraging the analytical power of a dedicated n8n AI Agent and high-speed LLMs (like those available via Groq), the system transforms raw GitHub data into objective, professional wellness reports complete with health scores and concrete recommendations.

This specific n8n templates demonstrates a powerful use case for combining core n8n node logic (HTTP requests, code execution) with modern AI tools. It ensures that critical wellness data is not just collected, but intelligently interpreted, allowing managers to intervene positively before issues escalate.

This n8n workflow executes on a regular schedule, acting as a proactive monitoring system.

1. Scheduled Trigger: The process begins with the Schedule Trigger n8n node, which initiates the monitoring cycle weekly.


2. Configuration: The Config n8n node sets critical variables, including the target repoowner, reponame, the period of analysis (defaulting to 7 days), and the emailreport recipient.


3. Data Collection: Three parallel operations fetch data from GitHub: Github Get Workflows, Github Get Commits, and Get Prs. These use HTTP Request and GitHub n8n nodes, configured with dynamic filtering to retrieve data only within the specified period.


4. Pattern Analysis (Code): The Analyze Patterns Developer n8n node, a Custom Code script, takes the raw data. It iterates through all commits to calculate metrics like total commits, failed workflow counts, and crucially, counts of latenightcommits (22:00–6:00) and weekend_commits. This structured data is prepared for the AI Agent.


5. AI Agent Execution: The AI Agent n8n node is the core intelligence. It receives the calculated patterns and is instructed with strict guidelines (e.g., focus on systemic issues, avoid personal judgments). It uses the Groq Chat Model Report as its underlying LLM.


6. Tool Utilization & Reporting: Based on the analysis, the n8n AI Agent determines the next action. It is configured to generate a comprehensive HTML report and use its available tools:

To deploy this n8n workflow template, follow these steps:

1. Import: Copy the provided JSON and import it into your n8n instance.


2. Credentials Setup: This n8n workflow requires three distinct credential sets:

1. Activation: Ensure all credentials are set and activate the n8n trigger (Schedule Trigger).

Schedule Trigger (n8n trigger): Sets the periodic execution of the n8n workflow, running weekly to provide regular wellness checks.
Config (Set n8n node): Acts as a global configuration source, storing repository details and the reporting email address.
Github Get Workflows / Github Get Commits / Get Prs (HTTP Request & GitHub n8n nodes): These nodes are responsible for data ingestion, retrieving the fundamental activity metrics required for burnout analysis.
Analyze Patterns Developer (Code n8n node): This is a custom script that performs the feature engineering. Key function: Calculating developer-level metrics, identifying late-night commits (22:00–6:00), and aggregating weekend activity into a clean JSON structure for the AI.
Groq Chat Model Report (LLM n8n node): Serves as the high-speed large language model used by the AI Agent for analysis, allowing for quick processing of complex data patterns.
AI Agent (n8n node): The core decision engine. It takes the statistical patterns, applies its programmed rules (strict guardrails against personal judgment), and uses available tools (Update Github Issue and Send a message in Gmail) to deliver the outcome.

• Send a message in Gmail (Tool n8n node): Used by the AI Agent to send the final, professionally formatted HTML report to the configured stakeholder email address.