Incident runbook templates

Incident Runbook Templates

Production-ready templates for incident response runbooks covering detection, triage, mitigation, resolution, and communication.

Do not use this skill when

• The task is unrelated to incident runbook templates
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

• Creating incident response procedures
• Building service-specific runbooks
• Establishing escalation paths
• Documenting recovery procedures
• Responding to active incidents
• Onboarding on-call engineers

Core Concepts

1. Incident Severity Levels

Severity	Impact	Response Time	Example
SEV1	Complete outage, data loss	15 min	Production down
SEV2	Major degradation	30 min	Critical feature broken
SEV3	Minor impact	2 hours	Non-critical bug
SEV4	Minimal impact	Next business day	Cosmetic issue

2. Runbook Structure

1. Overview & Impact
2. Detection & Alerts
3. Initial Triage
4. Mitigation Steps
5. Root Cause Investigation
6. Resolution Procedures
7. Verification & Rollback
8. Communication Templates
9. Escalation Matrix

Runbook Templates

Template 1: Service Outage Runbook

# [Service Name] Outage Runbook

## Overview
**Service**: Payment Processing Service
**Owner**: Platform Team
**Slack**: #payments-incidents
**PagerDuty**: payments-oncall

## Impact Assessment
- [ ] Which customers are affected?
- [ ] What percentage of traffic is impacted?
- [ ] Are there financial implications?
- [ ] What's the blast radius?

## Detection
### Alerts
- `payment_error_rate > 5%` (PagerDuty)
- `payment_latency_p99 > 2s` (Slack)
- `payment_success_rate < 95%` (PagerDuty)

### Dashboards
- [Payment Service Dashboard](https://grafana/d/payments)
- [Error Tracking](https://sentry.io/payments)
- [Dependency Status](https://status.stripe.com)

## Initial Triage (First 5 Minutes)

### 1. Assess Scope

Check service health

kubectl get pods -n payments -l app=payment-service

Check recent deployments

kubectl rollout history deployment/payment-service -n payments

Check error rates

curl -s "http://prometheus:9090/api/v1/query?query=sum(rate(http_requests_total{status=~'5..'}[5m]))"

### 2. Quick Health Checks
- [ ] Can you reach the service? `curl -I https://api.company.com/payments/health`
- [ ] Database connectivity? Check connection pool metrics
- [ ] External dependencies? Check Stripe, bank API status
- [ ] Recent changes? Check deploy history

### 3. Initial Classification
| Symptom | Likely Cause | Go To Section |
|---------|--------------|---------------|
| All requests failing | Service down | Section 4.1 |
| High latency | Database/dependency | Section 4.2 |
| Partial failures | Code bug | Section 4.3 |
| Spike in errors | Traffic surge | Section 4.4 |

## Mitigation Procedures

### 4.1 Service Completely Down

Step 1: Check pod status

kubectl get pods -n payments

Step 2: If pods are crash-looping, check logs

kubectl logs -n payments -l app=payment-service --tail=100

Step 3: Check recent deployments

kubectl rollout history deployment/payment-service -n payments

Step 4: ROLLBACK if recent deploy is suspect

kubectl rollout undo deployment/payment-service -n payments

Step 5: Scale up if resource constrained

kubectl scale deployment/payment-service -n payments --replicas=10

Step 6: Verify recovery

kubectl rollout status deployment/payment-service -n payments

### 4.2 High Latency

Step 1: Check database connections

kubectl exec -n payments deploy/payment-service -- \ curl localhost:8080/metrics | grep db_pool

Step 2: Check slow queries (if DB issue)

psql -h $DB_HOST -U $DB_USER -c " SELECT pid, now() - query_start AS duration, query FROM pg_stat_activity WHERE state = 'active' AND duration > interval '5 seconds' ORDER BY duration DESC;"

Step 3: Kill long-running queries if needed

psql -h $DB_HOST -U $DB_USER -c "SELECT pg_terminate_backend(pid);"

Step 4: Check external dependency latency

curl -w "@curl-format.txt" -o /dev/null -s https://api.stripe.com/v1/health

Step 5: Enable circuit breaker if dependency is slow

kubectl set env deployment/payment-service \ STRIPE_CIRCUIT_BREAKER_ENABLED=true -n payments

### 4.3 Partial Failures (Specific Errors)

Step 1: Identify error pattern

kubectl logs -n payments -l app=payment-service --tail=500 | \ grep -i error | sort | uniq -c | sort -rn | head -20

Step 2: Check error tracking

Go to Sentry: https://sentry.io/payments

Step 3: If specific endpoint, enable feature flag to disable

curl -X POST https://api.company.com/internal/feature-flags \ -d '{"flag": "DISABLE_PROBLEMATIC_FEATURE", "enabled": true}'

Step 4: If data issue, check recent data changes

psql -h $DB_HOST -c " SELECT * FROM audit_log WHERE table_name = 'payment_methods' AND created_at > now() - interval '1 hour';"

### 4.4 Traffic Surge

Step 1: Check current request rate

kubectl top pods -n payments

Step 2: Scale horizontally

kubectl scale deployment/payment-service -n payments --replicas=20

Step 3: Enable rate limiting

kubectl set env deployment/payment-service \ RATE_LIMIT_ENABLED=true \ RATE_LIMIT_RPS=1000 -n payments

Step 4: If attack, block suspicious IPs

kubectl apply -f - <<EOF apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: name: block-suspicious namespace: payments spec: podSelector: matchLabels: app: payment-service ingress:

• from:
• ipBlock:

cidr: 0.0.0.0/0 except:

• 192.168.1.0/24 # Suspicious range

EOF

## Verification Steps

Verify service is healthy

curl -s https://api.company.com/payments/health | jq

Verify error rate is back to normal

curl -s "http://prometheus:9090/api/v1/query?query=sum(rate(http_requests_total{status=~'5..'}[5m]))" | jq '.data.result[0].value[1]'

Verify latency is acceptable

curl -s "http://prometheus:9090/api/v1/query?query=histogram_quantile(0.99,sum(rate(http_request_duration_seconds_bucket[5m]))by(le))" | jq

Smoke test critical flows

./scripts/smoke-test-payments.sh

## Rollback Procedures

Rollback Kubernetes deployment

kubectl rollout undo deployment/payment-service -n payments

Rollback database migration (if applicable)

./scripts/db-rollback.sh $MIGRATION_VERSION

Rollback feature flag

curl -X POST https://api.company.com/internal/feature-flags \ -d '{"flag": "NEW_PAYMENT_FLOW", "enabled": false}'

## Escalation Matrix

| Condition | Escalate To | Contact |
|-----------|-------------|---------|
| > 15 min unresolved SEV1 | Engineering Manager | @manager (Slack) |
| Data breach suspected | Security Team | #security-incidents |
| Financial impact > $10k | Finance + Legal | @finance-oncall |
| Customer communication needed | Support Lead | @support-lead |

## Communication Templates

### Initial Notification (Internal)

🚨 INCIDENT: Payment Service Degradation

Severity: SEV2 Status: Investigating Impact: ~20% of payment requests failing Start Time: [TIME] Incident Commander: [NAME]

Current Actions:

• Investigating root cause
• Scaling up service
• Monitoring dashboards

Updates in #payments-incidents

### Status Update

📊 UPDATE: Payment Service Incident

Status: Mitigating Impact: Reduced to ~5% failure rate Duration: 25 minutes

Actions Taken:

• Rolled back deployment v2.3.4 → v2.3.3
• Scaled service from 5 → 10 replicas

Next Steps:

• Continuing to monitor
• Root cause analysis in progress

ETA to Resolution: ~15 minutes

### Resolution Notification

✅ RESOLVED: Payment Service Incident

Duration: 45 minutes Impact: ~5,000 affected transactions Root Cause: Memory leak in v2.3.4

Resolution:

• Rolled back to v2.3.3
• Transactions auto-retried successfully

Follow-up:

• Postmortem scheduled for [DATE]
• Bug fix in progress

Template 2: Database Incident Runbook

# Database Incident Runbook

## Quick Reference
| Issue | Command |
|-------|---------|
| Check connections | `SELECT count(*) FROM pg_stat_activity;` |
| Kill query | `SELECT pg_terminate_backend(pid);` |
| Check replication lag | `SELECT extract(epoch from (now() - pg_last_xact_replay_timestamp()));` |
| Check locks | `SELECT * FROM pg_locks WHERE NOT granted;` |

## Connection Pool Exhaustion

-- Check current connections SELECT datname, usename, state, count() FROM pg_stat_activity GROUP BY datname, usename, state ORDER BY count() DESC;

-- Identify long-running connections SELECT pid, usename, datname, state, query_start, query FROM pg_stat_activity WHERE state != 'idle' ORDER BY query_start;

-- Terminate idle connections SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE state = 'idle' AND query_start < now() - interval '10 minutes';

## Replication Lag

-- Check lag on replica SELECT CASE WHEN pg_last_wal_receive_lsn() = pg_last_wal_replay_lsn() THEN 0 ELSE extract(epoch from now() - pg_last_xact_replay_timestamp()) END AS lag_seconds;

-- If lag > 60s, consider: -- 1. Check network between primary/replica -- 2. Check replica disk I/O -- 3. Consider failover if unrecoverable

## Disk Space Critical

Check disk usage

df -h /var/lib/postgresql/data

Find large tables

psql -c "SELECT relname, pg_size_pretty(pg_total_relation_size(relid)) FROM pg_catalog.pg_statio_user_tables ORDER BY pg_total_relation_size(relid) DESC LIMIT 10;"

VACUUM to reclaim space

psql -c "VACUUM FULL large_table;"

If emergency, delete old data or expand disk

Best Practices

Do's

• Keep runbooks updated - Review after every incident
• Test runbooks regularly - Game days, chaos engineering
• Include rollback steps - Always have an escape hatch
• Document assumptions - What must be true for steps to work
• Link to dashboards - Quick access during stress

Don'ts

• Don't assume knowledge - Write for 3 AM brain
• Don't skip verification - Confirm each step worked
• Don't forget communication - Keep stakeholders informed
• Don't work alone - Escalate early
• Don't skip postmortems - Learn from every incident

Resources

• Google SRE Book - Incident Management
• PagerDuty Incident Response
• Atlassian Incident Management

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.