Claude Code for Incident Metrics Workflow Tutorial

Tracking incident metrics is essential for any engineering team that wants to maintain reliable systems and continuously improve their incident response processes. With Claude Code, you can build powerful automation workflows that collect, analyze, and report on incident data without manual intervention. This tutorial walks you through creating a complete incident metrics workflow using Claude Code’s capabilities.

Why Automate Incident Metrics with Claude Code

Manual incident tracking consumes valuable engineering time and often results in incomplete or inconsistent data. By using Claude Code, you can create workflows that automatically capture incident data from multiple sources, calculate key metrics, and generate actionable reports. The benefits extend beyond time savings—automated workflows ensure consistency and provide real-time visibility into your system’s reliability.

Claude Code excels at this use case because it can interact with APIs, parse logs, execute shell commands, and generate formatted reports. This makes it ideal for building an incident metrics pipeline that pulls data from your incident management system, processes it, and delivers insights.

Setting Up Your Incident Metrics Workflow

Before building the workflow, ensure you have Claude Code installed and configured with access to your incident management tools. For this tutorial, we’ll assume you have access to a generic incident API or log files that contain incident data.

Create a new directory for your incident metrics project:

mkdir incident-metrics-workflow
cd incident-metrics-workflow

Initialize your workflow configuration file to store settings:

cat > config.json << 'EOF'
{
  "incident_sources": [
    {
      "name": "pagerduty",
      "api_endpoint": "https://api.pagerduty.com/incidents",
      "time_range_days": 30
    }
  ],
  "metrics": {
    "mttr_target_minutes": 30,
    "mttd_target_minutes": 5,
    "sla_threshold_minutes": 60
  },
  "output": {
    "format": "markdown",
    "destination": "./reports"
  }
}
EOF

This configuration establishes the foundation for your workflow. The incident_sources array defines where Claude Code should pull incident data, while metrics specifies your target thresholds for comparison.

Fetching and Processing Incident Data

The core of your workflow involves fetching incident data and calculating metrics. Create a Python script that Claude Code can invoke to handle the data processing:

#!/usr/bin/env python3
import json
import requests
from datetime import datetime, timedelta
from collections import defaultdict

class IncidentMetricsCalculator:
    def __init__(self, config_path):
        with open(config_path) as f:
            self.config = json.load(f)
    
    def fetch_incidents(self, source):
        """Fetch incidents from API or return mock data for testing"""
        # In production, replace with actual API call
        return self._generate_sample_data()
    
    def _generate_sample_data(self):
        """Generate sample incidents for demonstration"""
        now = datetime.now()
        incidents = []
        
        # Sample incident data
        sample_incidents = [
            {"id": "INC-001", "created_at": now - timedelta(hours=2),
             "resolved_at": now - timedelta(hours=1), "severity": "high"},
            {"id": "INC-002", "created_at": now - timedelta(hours=5),
             "resolved_at": now - timedelta(hours=4), "severity": "medium"},
            {"id": "INC-003", "created_at": now - timedelta(days=1),
             "resolved_at": now - timedelta(days=1, hours=1), "severity": "critical"},
        ]
        
        return sample_incidents
    
    def calculate_metrics(self, incidents):
        """Calculate key incident metrics"""
        total_incidents = len(incidents)
        
        mttr_total = 0
        mttd_total = 0
        
        for incident in incidents:
            created = incident['created_at']
            resolved = incident['resolved_at']
            mttr_total += (resolved - created).total_seconds() / 60
        
        avg_mttr = mttr_total / total_incidents if total_incidents > 0 else 0
        
        return {
            "total_incidents": total_incidents,
            "average_mttr_minutes": round(avg_mttr, 2),
            "period_start": min(i['created_at'] for i in incidents),
            "period_end": max(i['resolved_at'] for i in incidents)
        }
    
    def generate_report(self, metrics):
        """Generate markdown report"""
        report = f"""# Incident Metrics Report

## Summary
- **Total Incidents**: {metrics['total_incidents']}
- **Average MTTR**: {metrics['average_mttr_minutes']} minutes
- **Period**: {metrics['period_start']} to {metrics['period_end']}

## Key Insights
"""
        
        if metrics['average_mttr_minutes'] > self.config['metrics']['mttr_target_minutes']:
            report += "- ⚠️ MTTR exceeds target - consider improving response process\n"
        else:
            report += "- ✅ MTTR within acceptable range\n"
        
        return report

if __name__ == "__main__":
    calculator = IncidentMetricsCalculator("config.json")
    incidents = calculator.fetch_incidents({})
    metrics = calculator.calculate_metrics(incidents)
    report = calculator.generate_report(metrics)
    print(report)

This script handles the calculation logic, but Claude Code orchestrates the entire workflow. The key advantage is that Claude Code can interpret the results, explain the metrics in context, and take additional actions based on the findings.

Creating the Claude Code Workflow

Now create the main workflow file that Claude Code will execute:

# incident-metrics-workflow.md
# Claude Code workflow for incident metrics

## Configuration
- Define incident data sources (PagerDuty, Datadog, custom logs)
- Set metric targets (MTTR < 30min, MTTD < 5min)
- Configure reporting frequency (daily, weekly)

## Workflow Steps

### Step 1: Gather Incident Data
- Connect to incident management APIs
- Query incidents within the specified time range
- Validate data completeness

### Step 2: Calculate Metrics
- Compute Mean Time to Resolve (MTTR)
- Compute Mean Time to Detect (MTTD)
- Calculate incident frequency by severity
- Measure SLA compliance rates

### Step 3: Analyze Trends
- Compare current period to previous periods
- Identify recurring incident patterns
- Flag incidents exceeding severity thresholds

### Step 4: Generate Reports
- Create markdown-formatted reports
- Include visualizations where possible
- Distribute to relevant stakeholders

Running Your Workflow

Execute the workflow using Claude Code:

claude-code run incident-metrics-workflow.md

Claude Code will guide you through each step, providing context-aware suggestions and handling edge cases. For automated execution, configure scheduled runs:

# Add to crontab for daily execution
0 8 * * * claude-code run incident-metrics-workflow.md --output ./daily-report.md

Measuring Key Incident Metrics

Understanding which metrics matter most helps you build a focused workflow. Here are the essential metrics to track:

Mean Time to Resolve (MTTR) measures the average time from incident creation to resolution. Lower MTTR indicates faster incident resolution. Set realistic targets based on your team’s capabilities and incident complexity.

Mean Time to Detect (MTTD) tracks how quickly your team identifies incidents after they occur. This metric reveals gaps in your monitoring and alerting setup.

Incident Frequency counts the number of incidents over a period. A high frequency often indicates underlying system issues that need addressing.

SLA Compliance measures what percentage of incidents are resolved within your service level agreement timeframes.

Best Practices for Incident Metrics Automation

Keep your workflow maintainable by following these practices. First, version control your configuration and scripts—incident metrics logic should be as carefully tracked as production code. Second, validate incoming data before processing to avoid garbage-in-garbage-out scenarios. Third, implement alerts for anomalous metric values rather than relying solely on scheduled reports. Fourth, regularly review and adjust your targets as your team improves.

Conclusion

Building an incident metrics workflow with Claude Code transforms raw incident data into actionable insights. By automating data collection and calculation, your team gains consistent, real-time visibility into system reliability without manual overhead. Start with the basic workflow outlined here, then customize it to fit your specific tools and requirements.

The key is to begin simply, measure what matters most to your organization, and iteratively improve your workflow as your incident management matures.