Claude Code installed and configured Access to your incident management system (PagerDuty, OpsGenie, etc.) Metrics storage (Prometheus, Datadog, or a custom solution) Basic understanding of your incident data structure

Claude Code for Incident Metrics (2026)

Last updated: April 19, 2026

The scope here is incident metrics configuration and practical usage with Claude Code. This does not cover general project setup. For that foundation, see Mendeley Chrome Extension — Honest Review 2026.

Claude Code for Incident Metrics Workflow Tutorial

Incident management is a critical aspect of DevOps and Site Reliability Engineering (SRE). Tracking metrics like Mean Time to Detect (MTTD), Mean Time to Resolve (MTTR), and incident frequency helps teams improve their response capabilities. In this tutorial, you’ll learn how to use Claude Code to automate and streamline your incident metrics workflow.

What is Claude Code?

Claude Code is an AI-powered coding assistant that can interact with your development environment through a Model Context Protocol (MCP) server. It can execute commands, read and write files, and integrate with various tools in your workflow. For incident metrics, Claude Code can help you collect data, generate reports, and even trigger automated responses based on your metrics.

Setting Up Your Incident Metrics Environment

Before diving into the workflow, you need to set up your environment. Claude Code needs access to your incident management tools and metrics storage.

Prerequisites

Claude Code installed and configured
Access to your incident management system (PagerDuty, OpsGenie, etc.)
Metrics storage (Prometheus, Datadog, or a custom solution)
Basic understanding of your incident data structure

Configuration Steps

First, create a configuration file for your incident metrics:

incident_metrics_config.py
INCIDENT_SOURCES = {
 "pagerduty": {
 "api_key": "{{PAGERDUTY_API_KEY}}",
 "region": "us"
 },
 "datadog": {
 "api_key": "{{DATADOG_API_KEY}}",
 "app_key": "{{DATADOG_APP_KEY}}"
 }
}
METRICS_CONFIG = {
 "mttd_target": 300, # 5 minutes in seconds
 "mttr_target": 1800, # 30 minutes in seconds
 "incident_severity_levels": ["critical", "high", "medium", "low"]
}

Building the Incident Metrics Collector

The core of your workflow is an incident metrics collector. This script gathers data from your incident management tools and computes key metrics.

Creating the Collector Script

#!/usr/bin/env python3
"""Incident Metrics Collector using Claude Code MCP"""
import json
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import List, Optional
@dataclass
class Incident:
 incident_id: str
 title: str
 severity: str
 created_at: datetime
 resolved_at: Optional[datetime]
 assignee: str
 
 @property
 def mttd(self) -> Optional[int]:
 """Mean Time to Detect - from creation to first acknowledgment"""
 # Implementation depends on your incident data
 return None
 
 @property
 def mttr(self) -> Optional[int]:
 """Mean Time to Resolve - from creation to resolution"""
 if self.resolved_at:
 return int((self.resolved_at - self.created_at).total_seconds())
 return None
class IncidentMetricsCollector:
 def __init__(self, config: dict):
 self.config = config
 self.incidents: List[Incident] = []
 
 def fetch_incidents(self, days: int = 30) -> List[Incident]:
 """Fetch incidents from the past N days"""
 # This would integrate with your actual incident source
 # Using a placeholder implementation
 start_date = datetime.now() - timedelta(days=days)
 
 # Example: Fetch from PagerDuty API
 # response = self.pagerduty_client Incidents.list(
 # since=start_date.isoformat()
 # )
 
 return self.incidents
 
 def calculate_mttd(self) -> float:
 """Calculate Mean Time to Detect across all incidents"""
 detected_times = [i.mttd for i in self.incidents if i.mttd is not None]
 if not detected_times:
 return 0.0
 return sum(detected_times) / len(detected_times)
 
 def calculate_mttr(self) -> float:
 """Calculate Mean Time to Resolve across all incidents"""
 resolved_times = [i.mttr for i in self.incidents if i.mttr is not None]
 if not resolved_times:
 return 0.0
 return sum(resolved_times) / len(resolved_times)
 
 def generate_report(self) -> dict:
 """Generate a comprehensive metrics report"""
 return {
 "period": f"Last 30 days",
 "total_incidents": len(self.incidents),
 "mttd_seconds": self.calculate_mttd(),
 "mttr_seconds": self.calculate_mttr(),
 "severity_breakdown": self._severity_breakdown()
 }
 
 def _severity_breakdown(self) -> dict:
 breakdown = {}
 for incident in self.incidents:
 breakdown[incident.severity] = breakdown.get(incident.severity, 0) + 1
 return breakdown

Automating Metrics Collection with Claude Code

Now that you have the collector, let’s integrate it with Claude Code to create an automated workflow.

Using Claude Code for Daily Metrics

You can create a Claude Code skill that runs your metrics collection on a schedule:

.claude/skills/incident-metrics-skill.md
Skill: Incident Metrics Automation
Triggers
- Run daily at 9:00 AM
- On demand via "/incident-metrics"
Actions
Daily Metrics Collection
1. Execute incident_metrics_collector.py
2. Compare metrics against SLAs
3. If metrics exceed thresholds, create alert
4. Generate and save report to /reports/
Alert Conditions
- MTTD exceeds {{MTTD_THRESHOLD}} seconds
- MTTR exceeds {{MTTR_THRESHOLD}} seconds
- Critical incidents increase by >20% from previous week

Running Metrics Analysis

To run your metrics collection with Claude Code:

claude --dangerously-skip-permissions \
 --allowed-tools Read,Write,Bash \
 "Run the incident metrics collector and generate today's report"

Practical Examples and Use Cases

Example 1: Weekly Incident Review

Here’s how to set up a weekly incident review workflow:

def weekly_review_workflow():
 """Automated weekly incident review"""
 
 # Step 1: Collect week's incidents
 collector = IncidentMetricsCollector(config)
 incidents = collector.fetch_incidents(days=7)
 
 # Step 2: Calculate key metrics
 report = collector.generate_report()
 
 # Step 3: Compare with previous week
 previous_report = load_previous_report()
 changes = compare_reports(report, previous_report)
 
 # Step 4: Generate actionable insights
 insights = []
 if report['mttr_seconds'] > 1800:
 insights.append("MTTR exceeded 30-minute target")
 if changes['critical_incidents'] > 0.2:
 insights.append("Critical incidents increased by >20%")
 
 # Step 5: Format and save report
 formatted = format_slack_message(report, insights)
 save_report(formatted)
 
 return formatted

Example 2: Post-Incident Analysis

After each major incident, use Claude Code to perform a quick analysis:

def post_incident_analysis(incident_id: str):
 """Analyze a specific incident for improvements"""
 
 # Fetch incident details
 incident = fetch_incident(incident_id)
 
 # Calculate incident-specific metrics
 analysis = {
 "incident_id": incident_id,
 "time_to_acknowledge": incident.first_ack_time - incident.created_at,
 "time_to_resolve": incident.resolved_at - incident.created_at,
 "number_of_responders": len(incident.responders),
 "escalation_count": incident.escalation_count
 }
 
 # Identify improvements
 improvements = []
 if analysis['time_to_acknowledge'] > 300:
 improvements.append("Consider improving on-call notification")
 if analysis['escalation_count'] > 2:
 improvements.append("Review escalation policy")
 
 return analysis, improvements

Actionable Advice for Implementation

Start Small and Iterate

Begin with simple metrics like total incident count and MTTR. As your workflow matures, add more sophisticated metrics like:

Time to first response
Number of false positives
Incident recurrence rate
Customer impact duration

Automate Gradually

Don’t try to automate everything at once. Start with:

Automated data collection
Basic reporting
Threshold-based alerts
Advanced analytics

Integrate with Your Existing Tools

Claude Code works best when integrated with your existing workflow:

Slack: Send reports and alerts to your #incidents channel
Jira: Create tickets for identified improvements
PagerDuty: Enrich incidents with metrics context
Grafana: Push metrics to existing dashboards

Set Meaningful Targets

Define realistic targets based on your team’s historical performance:

SLA_TARGETS = {
 "mttd": {
 "critical": 300, # 5 minutes
 "high": 900, # 15 minutes
 "medium": 3600, # 1 hour
 },
 "mttr": {
 "critical": 1800, # 30 minutes
 "high": 7200, # 2 hours
 "medium": 28800, # 8 hours
 }
}

Conclusion

Claude Code can significantly streamline your incident metrics workflow by automating data collection, analysis, and reporting. Start with the basic collector script, integrate it with your incident management tools, and gradually add more automation as you see value.

Remember that the goal isn’t just to collect metrics, it’s to use them to improve your incident response process. Let Claude Code handle the routine work so your team can focus on what matters: resolving incidents quickly and preventing future occurrences.

Next Steps:

Customize the collector for your specific incident management system
Set up scheduled runs with Claude Code
Integrate with your team’s communication tools
Review and act on the insights generated

Happy incident managing!

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Last verified: April 2026. If this approach no longer works, check Mendeley Chrome Extension — Honest Review 2026 for updated steps.

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