Incident Runbook Templates

---
name: incident-runbook-templates
description: Create structured incident response runbooks with step-by-step procedures, escalation paths, and recovery actions. Use when building runbooks, responding to incidents, or establishing incident response procedures.
version: 1.1.0
model: sonnet
invoked_by: both
user_invocable: true
tools: [Read, Write, Bash]
best_practices:
  - Keep runbooks updated after incidents
  - Test runbooks regularly
  - Include rollback steps
  - Document assumptions
error_handling: graceful
streaming: supported
verified: true
lastVerifiedAt: 2026-02-22T00:00:00.000Z
source: builtin
trust_score: 100
provenance_sha: ff891c5f12206ab2
---

**Mode: Cognitive/Prompt-Driven** — No standalone utility script; use via agent context.

# Incident Runbook Templates

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

## When to Use This Skill

- 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

````markdown
# [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

```bash
# 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

```bash
# 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

```bash
# 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)

```bash
# 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

```bash
# 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

```bash
# 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

```bash
# 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

```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';
````

## Replication Lag

```sql
-- 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

```bash
# 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)

## Iron Laws

1. **ALWAYS** write and test runbooks before an incident occurs — an untested runbook written during an active P0 incident introduces errors when cognitive load is highest.
2. **NEVER** assume operator knowledge in a runbook — write every step as if for a new on-call engineer at 3 AM with no context; assumed knowledge creates fatal gaps under stress.
3. **ALWAYS** include a rollback step for every mitigation action — mitigation steps that cannot be reversed trap teams in a worse state when the fix makes the incident worse.
4. **NEVER** resolve an incident without updating the runbook if a step failed or was missing — unupdated runbooks repeat the same failures in the next incident.
5. **ALWAYS** define explicit escalation triggers with time bounds and contact owners — runbooks without escalation criteria leave operators guessing when to escalate, causing under-escalation or over-escalation.

## Anti-Patterns

| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Runbooks written during the incident | High stress + cognitive load = errors and gaps; untested steps fail | Write and game-day runbooks before incidents; update post-incident |
| Steps that assume domain knowledge | New on-call engineers fail; team knowledge is a single point of failure | Write each step as a complete command with expected output and failure signal |
| No rollback steps | Mitigation makes incident worse; team is stuck without recovery path | Every `change X` step must have a matching `revert X` step with commands |
| Runbook not updated after incident | Same failure mode repeated in next incident | Mandate runbook review as part of postmortem action items |
| Vague escalation criteria ("escalate if needed") | Under-escalation extends MTTR; over-escalation burns on-call | Define triggers: "escalate after 15 minutes without mitigation progress" |

## Memory Protocol (MANDATORY)

**Before starting:**
Read `.claude/context/memory/learnings.md`

**After completing:**
- New pattern -> `.claude/context/memory/learnings.md`
- Issue found -> `.claude/context/memory/issues.md`
- Decision made -> `.claude/context/memory/decisions.md`

> ASSUME INTERRUPTION: If it's not in memory, it didn't happen.
```