write-incident-runbook
关于
This Claude Skill generates structured incident runbooks with diagnostic steps, resolution procedures, and escalation paths to standardize response. It's used to document procedures for recurring alerts, reduce MTTR, and create training materials. The skill outputs actionable guides to improve incident handling across on-call rotations.
快速安装
Claude Code
推荐npx 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-runbook在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
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
GitHub 仓库
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