incident-runbook-templates

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

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Repository: BEKO2210/Firstbrain
Letzte Quellaktivität: 17. Mai 2026 um 12:40
Erkannte Sprache von SKILL.md: Englisch
Sterne: 15
Forks: 2

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name	incident-runbook-templates
description	Production-ready templates for incident response runbooks covering detection, triage, mitigation, resolution, and communication.
type	skill
created	2026-02-27T00:00:00.000Z
domain	communication-content
category	email-comms
risk	critical
source	community
tags	["skill","communication-content","email-comms","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?



  (PagerDuty)
  (Slack)
  (PagerDuty)


 []()
 []()
 []()

# 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

# 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

# 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';"

# 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

# 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 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}'

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

🚨 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

📊 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

✅ 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

```markdown
# 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
```sql
-- 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';

-- 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

# 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](https://sre.google/sre-book/managing-incidents/)
- [PagerDuty Incident Response](https://response.pagerduty.com/)
- [Atlassian Incident Management](https://www.atlassian.com/incident-management)

## Connections

- **Domain:** [[Kommunikation & Content]]
- **Kategorie:** [[E-Mail & Messaging]]
- **Navigation:** [[Skills Uebersicht]], [[Home]]