write-incident-runbook
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Esta Skill de Claude genera manuales de procedimientos estructurados para incidentes, con pasos de diagnóstico, procedimientos de resolución y rutas de escalado para estandarizar la respuesta. Se utiliza para documentar procedimientos de alertas recurrentes, reducir el MTTR y crear materiales de formación. La skill produce guías accionables para mejorar el manejo de incidentes en las rotaciones de guardia.
Instalación rápida
Claude Code
Recomendadonpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/write-incident-runbookCopia y pega este comando en Claude Code para instalar esta habilidad
Documentación
Write Incident Runbook
Create actionable runbooks guiding responders through incident diagnosis and resolution.
When Use
- Documenting response procedures for recurring alerts or incidents
- Standardizing incident response across on-call rotation members
- Reducing mean time to resolution (MTTR) with clear diagnostic steps
- Creating training materials for new team members on incident handling
- Establishing escalation paths and communication protocols
- Migrating tribal knowledge to written documentation
- Linking alerts to resolution procedures (alert annotations)
Inputs
- Required: Incident or alert name/description
- Required: Historical incident data and resolution patterns
- Optional: Diagnostic queries (Prometheus, logs, traces)
- Optional: Escalation contacts and communication channels
- Optional: Previous incident post-mortems
Steps
Step 1: Choose Runbook Template Structure
See Extended Examples for complete template files.
Select appropriate template based on incident type and complexity.
Basic runbook template structure:
# [Alert/Incident Name] Runbook
## Overview | Severity | Symptoms
## Diagnostic Steps | Resolution Steps
## Escalation | Communication | Prevention | Related
Advanced SRE runbook template (excerpt):
# [Service Name] - [Incident Type] Runbook
## Metadata
- Service, Owner, Severity, On-Call, Last Updated
## Diagnostic Phase
### Quick Health Check (< 5 min): Dashboard, error rate, deployments
### Detailed Investigation (5-20 min): Metrics, logs, traces, failure patterns
# ... (see EXAMPLES.md for complete template)
Key template components:
- Metadata: Service ownership, severity, on-call rotation
- Diagnostic Phase: Quick checks → detailed investigation → failure patterns
- Resolution Phase: Immediate mitigation → root cause fix → verification
- Escalation: Criteria and contact paths
- Communication: Internal/external templates
- Prevention: Short/long-term actions
Got: Template selected matches incident complexity. Sections appropriate for service type.
If err:
- Start with basic template, iterate based on incident patterns
- Review industry examples (Google SRE books, vendor runbooks)
- Adapt template based on team feedback after first use
Step 2: Document Diagnostic Procedures
See Extended Examples for complete diagnostic queries and decision trees.
Create step-by-step investigation procedures with specific queries.
Six-step diagnostic checklist:
-
Verify Service Health: Health endpoint checks and uptime metrics
curl -I https://api.example.com/health # Expected: HTTP 200 OKup{job="api-service"} # Expected: 1 for all instances -
Check Error Rate: Current error percentage and breakdown by endpoint
sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) * 100 # Expected: < 1% -
Analyze Logs: Recent errors and top error messages from Loki
{job="api-service"} |= "error" | json | level="error" -
Check Resource Utilization: CPU, memory, connection pool status
avg(rate(container_cpu_usage_seconds_total{pod=~"api-service.*"}[5m])) * 100 # Expected: < 70% -
Review Recent Changes: Deployments, git commits, infrastructure changes
-
Examine Dependencies: Downstream service health, database/API latency
Failure pattern decision tree (excerpt):
- Service down? → Check all pods/instances
- Error rate elevated? → Check specific error types (5xx, gateway, database, timeouts)
- When did it start? → After deployment (rollback), gradual (resource leak), sudden (traffic/dependency)
Got: Diagnostic procedures specific. Include expected vs actual values. Guide responder through investigation.
If err:
- Test queries in actual monitoring system before documenting
- Include screenshots of dashboards for visual reference
- Add "Common mistakes" section for frequently missed steps
- Iterate based on feedback from incident responders
Step 3: Define Resolution Procedures
See Extended Examples for all 5 resolution options with full commands and rollback procedures.
Document step-by-step remediation with rollback options.
Five resolution options (brief summary):
-
Rollback Deployment (fastest): For post-deployment errors
kubectl rollout undo deployment/api-serviceVerify → Monitor → Confirm resolution (error rate < 1%, latency normal, no alerts)
-
Scale Up Resources: For high CPU/memory, connection pool exhaustion
kubectl scale deployment/api-service --replicas=$((current * 3/2)) -
Restart Service: For memory leaks, stuck connections, cache corruption
kubectl rollout restart deployment/api-service -
Feature Flag / Circuit Breaker: For specific feature errors or external dependency failures
kubectl set env deployment/api-service FEATURE_NAME=false -
Database Remediation: For database connections, slow queries, pool exhaustion
-- Kill long-running queries, restart connection pool, increase pool size
Universal verification checklist:
- Error rate < 1%
- Latency P99 < threshold
- Throughput at baseline
- Resource usage healthy (CPU < 70%, Memory < 80%)
- Dependencies healthy
- User-facing tests pass
- No active alerts
Rollback procedure: Resolution worsens situation? → pause/cancel → revert → reassess
Got: Resolution steps clear. Include verification checks. Provide rollback options for each action.
If err:
- Add more granular steps for complex procedures
- Include screenshots or diagrams for multi-step processes
- Document command outputs (expected vs actual)
- Create separate runbook for complex resolution procedures
Step 4: Establish Escalation Paths
See Extended Examples for full escalation levels and contact directory template.
Define when and how to escalate incidents.
When to escalate immediately:
- Customer-facing outage > 15 minutes
- SLO error budget > 10% depleted
- Data loss/corruption or security breach suspected
- Unable to identify root cause within 20 minutes
- Mitigation attempts fail or worsen situation
Five escalation levels:
- Primary On-Call (5 min response): Deploy fixes, rollback, scale (up to 30 min solo)
- Secondary On-Call (auto after 15 min): Additional investigation support
- Team Lead (architectural decisions): Database changes, vendor escalation, incidents > 1 hour
- Incident Commander (cross-team coord): Multiple teams, customer comms, incidents > 2 hours
- Executive (C-level): Major impact (>50% users), SLA breach, media/PR, outages > 4 hours
Escalation process:
- Notify target with: current status, impact, actions taken, help needed, dashboard link
- Handoff if needed: share timeline, actions, access. Remain available
- Don't go silent: update every 15 min, ask questions, provide feedback
Contact directory: Maintain table with role, Slack, phone, PagerDuty for:
- Platform/Database/Security/Network teams
- Incident Commander
- External vendors (AWS, database vendor, CDN provider)
Got: Clear criteria for escalation. Contact information readily accessible. Escalation paths aligned with organizational structure.
If err:
- Validate contact information current (test quarterly)
- Add decision tree for when to escalate
- Include examples of escalation messages
- Document response time expectations for each level
Step 5: Create Communication Templates
See Extended Examples for all internal and external templates with full formatting.
Provide pre-written messages for incident updates.
Internal templates (Slack #incident-response):
-
Initial Declaration:
🚨 INCIDENT: [Title] | Severity: [Critical/High/Medium] Impact: [users/services] | Owner: @username | Dashboard: [link] Quick Summary: [1-2 sentences] | Next update: 15 min -
Progress Update (every 15-30 min):
📊 UPDATE #N | Status: [Investigating/Mitigating/Monitoring] Actions: [what we tried and outcomes] Theory: [what we think is happening] Next: [planned actions] -
Mitigation Complete:
✅ MITIGATION | Metrics: Error [before→after], Latency [before→after] Root Cause: [brief or "investigating"] | Monitoring 30min before resolved -
Resolution:
🎉 RESOLVED | Duration: [time] | Root Cause + Impact + Follow-up actions -
False Alarm: No impact, no follow-up needed
External templates (status page):
- Initial: Investigating, started time, next update in 15 min
- Progress: Identified cause (customer-friendly), implementing fix, estimated resolution
- Resolution: Resolved time, root cause (simple), duration, prevention measures
Customer email template: Timeline, impact description, resolution, prevention, compensation (if applicable)
Got: Templates save time during incidents. Ensure consistent communication. Reduce cognitive load on responders.
If err:
- Customize templates to match company communication style
- Pre-fill templates with common incident types
- Create Slack workflow/bot to populate templates automatically
- Review templates during incident retrospectives
Step 6: Link Runbook to Monitoring
See Extended Examples for complete Prometheus alert configuration and Grafana dashboard JSON.
Integrate runbook with alerts and dashboards.
Add runbook links to Prometheus alerts:
- alert: HighErrorRate
annotations:
runbook_url: "https://wiki.example.com/runbooks/high-error-rate"
dashboard_url: "https://grafana.example.com/d/service-overview"
incident_channel: "#incident-platform"
Embed quick diagnostic links in runbook:
- Service Overview Dashboard
- Error Rate Last 1h (Prometheus direct link)
- Recent Error Logs (Loki/Grafana Explore)
- Recent Deployments (GitHub/CI)
- PagerDuty Incidents
Create Grafana dashboard panel with runbook links (markdown panel listing all incident runbooks with on-call and escalation info)
Got: Responders can access runbooks directly from alerts or dashboards. Diagnostic queries pre-filled. One-click access to relevant tools.
If err:
- Verify runbook URLs accessible without VPN/login
- Use URL shorteners for complex Grafana/Prometheus links
- Test links quarterly to ensure they don't break
- Create browser bookmarks for frequently used runbooks
Check
- Runbook follows consistent template structure
- Diagnostic procedures include specific queries and expected values
- Resolution steps actionable with clear commands
- Escalation criteria and contacts current
- Communication templates provided for internal and external audiences
- Runbook linked from monitoring alerts and dashboards
- Runbook tested during incident simulation or actual incident
- Feedback from responders incorporated into runbook
- Revision history tracked with dates and authors
- Runbook accessible without authentication (or cached offline)
Pitfalls
- Too generic: Runbooks with vague steps like "check the logs" without specific queries not actionable. Be specific.
- Outdated information: Runbooks referencing old systems or commands become useless. Review quarterly.
- No verification steps: Resolution without verification leads to false positives. Always include "how to confirm it's fixed."
- Missing rollback procedures: Every action should have rollback plan. Don't trap responders in worse state.
- Assume knowledge: Runbooks for experts only exclude junior engineers. Write for least experienced person on rotation.
- No ownership: Runbooks without owners become stale. Assign team/person responsible for updates.
- Hidden behind auth: Runbooks inaccessible during VPN/SSO issues useless during crisis. Cache copies or use public wiki.
See Also
configure-alerting-rules- Link runbooks to alert annotations for immediate access during incidentsbuild-grafana-dashboards- Embed runbook links in dashboards and diagnostic panelssetup-prometheus-monitoring- Include diagnostic queries from Prometheus in runbook proceduresdefine-slo-sli-sla- Reference SLO impact in incident severity classification
Repositorio GitHub
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