On-Call Runbook Skill

Produce a complete on-call runbook for a service — giving the on-call engineer everything they need to respond confidently to alerts at 3am, without having to ask anyone for help.

A good on-call runbook reduces mean time to resolution (MTTR) by eliminating the "what do I do first?" problem. It is written for the on-call engineer who has just been paged and needs to act, not for someone calmly reading documentation.

Required Inputs

Ask for these if not already provided:

• Service name and what it does
• Team and tech lead name
• Alert list — names of alerts that currently page on-call
• Monitoring setup — Datadog / Grafana / CloudWatch / PagerDuty / etc.
• Common failure modes — what breaks most often, and what fixes it
• Escalation contacts — who to call when on-call can't resolve it
• Deployment setup — can on-call roll back? How?
• Service dependencies — what does this service depend on, and what depends on it?

Output Format

On-Call Runbook: [Service Name]

Team: [Team name] | Tech lead: [Name] PagerDuty service: [Link] | Escalation policy: [Policy name] Last updated: [Date] | Next review: [Date + 90 days]

First time on-call for this service? Read the [developer onboarding doc] first — it covers the architecture and how things work. This runbook assumes you understand the service.

Quick Reference

Dashboard: [Link — the first thing to open when paged] Logs: [Link — where to find logs] Runbook index: Jump to the alert that paged you → [Alert list below] Can't resolve in 30 min? Escalate to: [Name] via [Slack / PagerDuty]

Rollback command (memorise this):

[rollback command — e.g. kubectl rollout undo deployment/[service-name]]

Escalation Matrix

Contacts:

Service Architecture (Quick View)

[Upstream callers]
        │
        ▼
[This Service]
        │
        ├──→ [Primary Database]
        ├──→ [Cache — e.g. Redis]
        └──→ [Downstream Service / Queue]

If this service is down, these are affected: [List downstream consumers] If these are down, this service is affected: [List upstream dependencies]

Alert Runbooks

ALERT: [Alert Name 1 — e.g. HighErrorRate]

What it means: [Plain English — e.g. "More than 5% of API requests are returning 5xx errors in the last 5 minutes"] Severity: P1 / P2 / P3 SLO impact: Yes / No — [If yes: this alert means the error budget is burning at [X]× rate]

Step 1 — Acknowledge and assess

# Check current error rate
[query or dashboard link]

# Check which endpoints are erroring
[query or command]

Step 2 — Check recent changes

# Any deploys in the last hour?
[command or link to deployment log]

# Recent config changes?
[where to check]

Step 3 — Check dependencies

# Is the database healthy?
[health check command or link]

# Is [downstream service] healthy?
[health check command or link]

Step 4 — Diagnose

Step 5 — Fix or mitigate

# If caused by bad deploy — roll back:
[rollback command]

# If caused by [specific issue]:
[fix command]

# If caused by upstream dependency:
[mitigation — e.g. enable circuit breaker, reduce traffic, etc.]

After resolving:

• Confirm error rate has returned to baseline
• Check no downstream services were affected
• If P1: open a post-incident review — see [incident-postmortem skill]
• Update #incidents with resolution summary

ALERT: [Alert Name 2 — e.g. HighLatency]

What it means: [e.g. "P99 response time has exceeded 1s for more than 3 consecutive minutes"] Severity: P1 / P2 / P3 SLO impact: Yes — latency SLO breach

Step 1 — Assess scope

# Check which endpoints are slow
[query or dashboard — broken down by endpoint]

# Check if latency is across all regions or localised
[query or command]

Step 2 — Common causes and fixes

Step 3 — Escalate if unresolved in 20 minutes Page [Tech lead] via PagerDuty / Slack.

ALERT: [Alert Name 3 — e.g. DatabaseConnectionPoolExhausted]

What it means: [e.g. "The service has used all available database connections — new requests will fail"] Severity: P1 SLO impact: Yes — will cause errors immediately

Immediate mitigation:

# Restart the service to flush stale connections
[restart command]

# Check current connection count
[DB connection query]

Diagnose root cause after stabilising:

# Check for long-running queries holding connections
[query]

# Check if a recent deploy changed connection pool config
[where to check]

Resolution: [e.g. "Increase pool size in config / kill long-running queries / scale the service"]

ALERT: [Alert Name 4 — e.g. QueueBacklogHigh / ConsumerLag]

What it means: [e.g. "The message queue backlog exceeds 10,000 messages — consumers are not keeping up"] Severity: P2 SLO impact: Depends — if queue backs up, downstream systems will receive delayed data

Step 1 — Check consumer health

# Are consumers running?
[command]

# Consumer error rate?
[dashboard or query]

Step 2 — Check message contents

# Are there poison messages causing retries?
[command to inspect dead-letter queue or failed messages]

Step 3 — Options

ALERT: [Add additional alerts following the same pattern]

Diagnostic Cheat Sheet

Common commands for quick diagnosis. Paste and run without modification.

# Service health
[health check command]

# Recent logs (last 100 lines)
[log command]

# Error logs only
[error log filter command]

# Current pod / instance status
[kubectl get pods / aws ecs describe-tasks / etc.]

# Restart the service
[restart command]

# Roll back to previous version
[rollback command]

# Database connection count
[DB query]

# Cache hit rate
[cache stats command]

# Current request rate
[metrics query]

Useful Dashboard Links