Stop Fighting Incidents: Automate Your Response with Code-First Runbooks

When your production system goes down at 2 AM, you need runbooks that execute themselves—not documentation that sits in a wiki while you scramble to remember which service to restart first. These Cursor Rules transform incident management from reactive firefighting into proactive, automated response workflows that actually work under pressure.

The Real Problem: Manual Incident Response Doesn't Scale

Every minute of downtime costs your business money, but traditional incident management approaches create more problems than they solve:

Runbooks become stale documentation that nobody updates or follows during actual incidents
Context switching between tools slows response times when every second counts
Manual escalation paths fail when the person who knows the fix is unavailable
Inconsistent communication leaves stakeholders guessing about impact and resolution timelines
Post-incident reviews turn into blame sessions instead of process improvements

The result? Your MTTR stays high, your on-call engineers burn out, and your customers lose trust in your platform's reliability.

Code-First Incident Management That Actually Works

These rules establish incident management as a disciplined engineering practice. Instead of hoping someone remembers the right commands during an outage, you codify every response into executable runbooks that automatically detect, contain, and resolve incidents.

Key Components:

YAML-based runbooks that define detection triggers, mitigation steps, and validation tests
Python automation scripts with built-in retry logic and error handling
Integrated communication workflows that update stakeholders automatically
Tiered escalation policies that route incidents to the right expertise level
Mandatory post-incident reviews with trackable action items

Transform Your Incident Response Workflow

Before: Reactive Firefighting

# What usually happens during an incident
1. Alert fires (maybe)
2. Someone eventually notices
3. Scramble to find the right person
4. Debug the issue from scratch
5. Apply a fix that might work
6. Hope it doesn't happen again

After: Automated Response Pipeline

kind: IncidentRunbook
id: db-connection-pool-exhaustion
title: Resolve Database Connection Pool Exhaustion
severity: S1
services: [user-service, payment-service]
triggers:
  - promql: "pg_stat_activity_count > 95"
mitigation-steps:
  - action: restart-connection-pool
    script: ./scripts/restart_pool.py
    timeout: 30s
  - action: validate-connections
    script: ./scripts/check_db_health.py
validation-tests:
  - endpoint: /health/database
    expected-status: 200
    timeout: 10s

Quantifiable Productivity Gains

Faster Response Times:

MTTA (Mean Time to Acknowledge) drops from 15+ minutes to under 2 minutes
MTTR (Mean Time to Repair) reduces by 60-80% for known incident types
False positive alerts decrease from 20-30% to under 5%

Reduced Cognitive Load:

On-call engineers spend 70% less time context-switching between tools
New team members can handle incidents without senior engineer involvement
Escalation paths execute automatically based on defined criteria

Improved Team Reliability:

Consistent incident response regardless of who's on call
Every incident becomes a learning opportunity with structured post-mortems
Runbooks stay current through code review processes

Real Developer Workflows

Scenario 1: API Gateway Timeout Spike

# Before: Manual debugging and fixes
def handle_timeout_spike():
    # Someone notices alerts (eventually)
    # Logs into multiple dashboards
    # Manually restarts services
    # Hopes the fix works
    pass

# After: Automated detection and response
@backoff.on_exception(backoff.expo, requests.RequestException, max_time=32)
def mitigate_api_gateway_timeout():
    """Auto-restart overloaded gateway instances"""
    incident = IncidentContext.current()
    
    # Scale up gateway instances
    scale_service("api-gateway", replicas=6)
    
    # Validate response times
    if not validate_response_times(threshold_ms=500):
        raise IncidentError("Mitigation failed, escalating to Tier 2")
    
    update_incident_status(incident.id, "resolved")
    post_slack_update(f"✅ API gateway timeout resolved automatically")

Scenario 2: Database Connection Pool Exhaustion

# Runbook executes automatically when triggered
kind: IncidentRunbook
id: db-pool-exhaustion-web-app
title: Clear Database Connection Pool Leak
severity: S2
services: [web-app, background-jobs]
triggers:
  - promql: 'pg_stat_activity_count{job="postgres"} > 90'
  - log-query: 'ERROR.*connection pool exhausted'
mitigation-steps:
  - action: identify-long-running-queries
    script: ./scripts/find_blocking_queries.py
    timeout: 15s
  - action: terminate-problematic-connections  
    script: ./scripts/kill_long_queries.py
    timeout: 30s
  - action: restart-connection-pool
    script: ./scripts/restart_pool.py
    timeout: 45s
validation-tests:
  - query: "SELECT count(*) FROM pg_stat_activity"
    expected-max: 50
rollback-steps:
  - action: restore-connection-limits
    script: ./scripts/restore_pool_config.py

Scenario 3: Automated Stakeholder Communication

# Integrated communication that updates automatically
class IncidentCommunicator:
    def __init__(self, incident_id: str):
        self.incident = get_incident(incident_id)
        self.slack_channel = f"#inc-{incident_id}"
    
    @track_communication_delivery
    def broadcast_status_update(self, status: IncidentStatus):
        """Update all stakeholders with current incident status"""
        
        # Update internal Slack channel
        self.post_slack_update(
            channel=self.slack_channel,
            message=self.format_status_message(status)
        )
        
        # Update public status page (PII-filtered)
        self.update_status_page(
            title=redact_pii(self.incident.title),
            status=status.public_description
        )
        
        # Notify escalation contacts if needed
        if status.requires_escalation:
            self.page_escalation_contacts()

Implementation Guide

Step 1: Install and Configure the Rules

# Add to your .cursorrules file
curl -o .cursorrules https://raw.githubusercontent.com/your-repo/cursor-rules/main/incident-management.yaml

Step 2: Create Your First Runbook

# runbooks/api-health-check.yaml
kind: IncidentRunbook
id: api-health-degraded
title: Resolve API Health Check Failures
severity: S2
services: [api-service]
triggers:
  - promql: 'up{job="api-service"} < 0.8'
mitigation-steps:
  - action: restart-unhealthy-instances
    script: ./scripts/restart_api_instances.py
  - action: verify-health-endpoints
    script: ./scripts/check_api_health.py
validation-tests:
  - endpoint: /health
    expected-status: 200
owners: [platform-team-oncall]

Step 3: Implement Automation Scripts

# scripts/restart_api_instances.py
from incident_management import IncidentContext, track_action

@track_action("restart-unhealthy-instances")
def restart_unhealthy_api_instances():
    """Identify and restart failing API service instances"""
    incident = IncidentContext.current()
    
    unhealthy_instances = get_unhealthy_instances("api-service")
    for instance in unhealthy_instances:
        restart_instance(instance.id)
        
    # Validate all instances are healthy
    if not all_instances_healthy("api-service", timeout=60):
        raise IncidentError("Instance restart failed, escalating")
    
    log_action_success(
        incident_id=incident.id,
        action="restart-unhealthy-instances",
        instances_restarted=len(unhealthy_instances)
    )

Step 4: Configure Integration Points

# config/integrations.py
INCIDENT_MANAGEMENT_CONFIG = {
    "pagerduty": {
        "integration_key": env.PAGERDUTY_INTEGRATION_KEY,
        "escalation_policies": {
            "S0": "critical-escalation-policy",
            "S1": "high-priority-policy",
            "S2": "standard-policy"
        }
    },
    "slack": {
        "bot_token": env.SLACK_BOT_TOKEN,
        "incident_channel_prefix": "#inc-",
        "stakeholder_groups": ["@platform-team", "@leadership"]
    },
    "monitoring": {
        "prometheus_url": env.PROMETHEUS_URL,
        "grafana_url": env.GRAFANA_URL
    }
}

Expected Results & Impact

Immediate Improvements (Week 1-2):

Standardized incident response across all team members
Automatic stakeholder communication reducing manual coordination by 80%
Consistent runbook execution eliminating "forgot to run that step" mistakes

Medium-term Gains (Month 1-3):

50-70% reduction in MTTR for incidents covered by runbooks
Decreased on-call stress with automated first-response capabilities
Better incident data for trend analysis and proactive improvements

Long-term Transformation (Month 3+):

Self-healing systems that resolve common issues without human intervention
Predictive incident prevention based on historical pattern analysis
Team expertise scaling where junior engineers can handle complex incidents

Measurable Metrics:

# Before vs After Metrics
response_times:
  mtta_before: "15+ minutes"
  mtta_after: "< 2 minutes"
  mttr_reduction: "60-80%"

team_efficiency:
  context_switching_reduction: "70%"
  false_positive_reduction: "20% → 5%"
  on_call_stress_reduction: "significant"

process_improvements:
  runbook_execution_consistency: "100%"
  incident_documentation_completeness: "95%+"
  post_incident_action_item_completion: "90%+"

Stop letting incidents control your team's productivity. These rules turn incident management into a competitive advantage—your systems become more reliable, your team becomes more confident, and your customers experience fewer disruptions. The next time production breaks, your automated runbooks will be already fixing it while your competitors are still figuring out who to call.

Comprehensive Incident Management Rules