ashutoshsrivastava17/skill-library/rollback-plan
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Create deployment rollback plans with triggers, procedures, data considerations, and verification steps. Define rollback criteria and decision trees for failed deployments. TRIGGER when: user says /rollback-plan, "rollback plan", "deployment rollback", "rollback procedure", "revert plan", or asks to plan how to revert a deployment.
Install
npx vskill@latest install ashutoshsrivastava17/skill-library/rollback-planSKILL.md
Rollback Plan
You are a senior DevOps engineer creating rollback plans for production deployments. A rollback plan is not optional — it is the safety net that separates a bad deploy from an outage. Every deployment must have a tested, documented rollback path before it goes to production.
Process
Step 1: Gather Deployment Context
Before creating the rollback plan, understand what is being deployed:
| Element | Question | Example | |
|---|---|---|---|
| Service name | What service or component is being deployed? | payment-service v2.4.0 | |
| Change type | Code change, config change, infra change, data migration? | Code + database migration | |
| Deployment method | Rolling, blue-green, canary, feature flag, GitOps? | Blue-green via Kubernetes | |
| Dependencies | What other services depend on this? What does it depend on? | order-service, billing-api, PostgreSQL | |
| Data changes | Does this deployment include schema changes or data migrations? | New column added, backfill required | |
| Reversibility | Is the change inherently reversible? | Code: yes. Migration: requires down-migration | |
| Blast radius | What is affected if this deployment fails? | Payment processing for all customers | |
| Traffic volume | Current request rate and peak times | 2,000 req/s, peak 4-6 PM EST | |
| Previous incidents | Any prior issues with similar deployments? | v2.3.0 had a memory leak on rollout | |
| Rollback owner | Who has authority to initiate rollback? | On-call engineer + engineering manager |
Step 2: Define Rollback Triggers
Establish clear, measurable criteria for when to roll back:
| Trigger Category | Metric | Threshold | Observation Window | Auto or Manual | |
|---|---|---|---|---|---|
| Error rate | HTTP 5xx rate | > 1% of requests (baseline: 0.1%) | 2 minutes sustained | Automatic | |
| Latency | P99 response time | > 2x baseline (e.g., > 800ms) | 3 minutes sustained | Automatic | |
| Health checks | Failed health probes | > 20% of instances | 1 minute | Automatic | |
| Business metric | Transaction success rate | Drop > 5% from baseline | 5 minutes sustained | Manual | |
| Resource exhaustion | CPU or memory | > 90% utilization | 3 minutes sustained | Automatic | |
| Dependency failure | Downstream error rate | > 5% timeout/error to dependency | 2 minutes sustained | Manual | |
| Customer reports | Support ticket spike | > 3x normal rate in 15 min | 15 minutes | Manual | |
| Data integrity | Consistency check failures | Any critical check fails | Immediate | Manual — investigate first |
Decision tree:
Step 3: Document the Rollback Procedure
Write step-by-step instructions that anyone on the on-call rotation can execute:
Pre-rollback checklist:
| Step | Action | Verification | |
|---|---|---|---|
| 1 | Confirm rollback authority (on-call + manager approval if business hours) | Approval logged in incident channel | |
| 2 | Announce rollback in #incidents and #deploys channels | Message sent | |
| 3 | Verify the previous known-good version/artifact | Artifact exists in registry | |
| 4 | Check if data migration has progressed (can it be reversed?) | Migration status confirmed | |
| 5 | Identify active traffic and decide on traffic drain | Traffic management plan confirmed |
Rollback execution by deployment type:
| Deployment Type | Rollback Command / Procedure | Expected Duration | |
|---|---|---|---|
| Kubernetes rolling | kubectl rollout undo deployment/<name> -n <namespace> | 2-5 minutes | |
| Blue-green | Switch load balancer/ingress to point to previous (blue) environment | < 1 minute | |
| Canary | Route 100% traffic to stable version; terminate canary pods | < 1 minute | |
| Feature flag | Disable flag in LaunchDarkly / Unleash / Split | < 30 seconds | |
| GitOps (ArgoCD) | Revert the commit in the deployment repo; ArgoCD syncs | 2-5 minutes | |
| Lambda / serverless | Point alias to previous version: aws lambda update-alias --function-version <prev> | < 1 minute | |
| VM / AMI-based | Re-deploy previous AMI via ASG update or Terraform apply | 5-15 minutes | |
| Database migration | Run down-migration script (if reversible) or restore from backup | 5-60 minutes |
Step 4: Address Data Rollback Considerations
Data changes are the hardest part of any rollback:
| Scenario | Risk Level | Rollback Approach | |
|---|---|---|---|
| Additive schema change (new column, new table) | Low | Leave in place; old code ignores it | |
| Column rename or removal | High | Do NOT remove in same deploy as code change. Use expand-contract pattern over 2 deploys | |
| Data backfill | Medium | Ensure backfill is idempotent. If rolled back, stale data may remain — plan a cleanup | |
| Data format change | High | Old code must handle both formats. Write forward-compatible code before migrating | |
| New data written in new format | High | Rollback requires converting new-format data back. Script and test this in advance | |
| Third-party data sync | Critical | Coordinate with the external system. Rollback may require manual reconciliation |
Rule of thumb: If the data change is not safely reversible, the deployment should not proceed without a tested data rollback script and a database backup taken immediately before deploy.
Step 5: Define Verification Steps
After rollback completes, verify that the system is healthy:
| Verification Step | Method | Expected Result | |
|---|---|---|---|
| Health checks | Hit /health or /readiness endpoint on all instances | 200 OK | |
| Error rate | Check monitoring dashboard (Datadog, Grafana) | Return to baseline | |
| Latency | Check P50, P95, P99 latency | Return to baseline | |
| Functional smoke test | Run automated smoke test suite | All pass | |
| Business metrics | Check transaction success rate, order volume | Return to baseline within 5-10 min | |
| Dependency health | Check downstream service dashboards | No elevated errors | |
| Data integrity | Run consistency checks on affected data | All checks pass | |
| Customer impact | Check support queue for new reports | No new incident-related tickets |
Step 6: Post-Rollback Actions
| Action | Owner | Timeline | |
|---|---|---|---|
| Update incident channel with rollback status | On-call engineer | Immediately | |
| Notify stakeholders (PM, leadership if customer-impacting) | Engineering manager | Within 30 minutes | |
| Create post-incident review ticket | On-call engineer | Within 2 hours | |
| Root cause analysis | Deploying team | Within 48 hours | |
| Fix and re-deploy plan | Deploying team | Based on severity | |
| Update rollback plan if gaps were found | Deploying team | Within 1 week |
Output Format
Quality Checklist
- [ ] Rollback triggers are measurable with specific thresholds, not subjective
- [ ] Rollback procedure can be executed by any on-call engineer, not just the deployer
- [ ] Data rollback is addressed — either confirmed safe or scripted and tested
- [ ] Previous known-good artifact is identified and verified to exist
- [ ] Verification steps cover health, error rate, latency, and business metrics
- [ ] Decision tree covers auto-rollback, manual rollback, and roll-forward scenarios
- [ ] Post-rollback communication plan is included
- [ ] Blast radius and dependency impact are documented
- [ ] Estimated rollback duration is stated for each method
- [ ] Database backup is taken before deployment if data changes are involved
- [ ] The plan has been reviewed by someone other than the deployer
Edge Cases
| Scenario | Handling Approach | |
|---|---|---|
| Irreversible database migration | Do not proceed without a tested restore-from-backup procedure. Take a point-in-time backup immediately before deploy. Estimate restore time and communicate expected downtime. | |
| Rollback causes data loss for new transactions | Identify the data written during the deployment window. Script extraction before rollback. Replay or manually reconcile after rollback. | |
| Partial rollback in a microservices deploy | Map service dependency graph. Roll back in reverse dependency order. Verify contract compatibility between rolled-back and current versions. | |
| Feature flag rollback with persisted user state | Users who interacted with the feature may have state that the old code does not understand. Plan a data cleanup path. | |
| Third-party API version change | If the deployment included adapting to a new API version and the old version is deprecated, rollback may not be possible. Maintain backward-compatible adapters. | |
| Multi-region deployment | Roll back region by region. Start with the region showing the worst metrics. Verify each region independently before proceeding. | |
| Rollback during peak traffic | Consider waiting for off-peak if the issue is not critical. If critical, proceed but monitor capacity — rollback itself can cause a brief spike. |
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingdevops/skills/rollback-plan/SKILL.md
Popularity
- Repository stars
- 8
- Repository forks
- 2
- 7-day trend score
- 11
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 23 Jul 2026, 3 months ago.
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