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deployment
Plan and execute safe deployments with rollback procedures, verification, and monitoring
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Plan and execute safe deployments with rollback procedures, verification, and monitoring
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | deployment |
| description | Plan and execute safe deployments with rollback procedures, verification, and monitoring |
Plan and execute safe deployments — rollback procedures, verification steps, and monitoring.
Before deploying:
| Strategy | When | Risk |
|---|---|---|
| Blue/Green | Need instant rollback | Low — traffic switch |
| Canary | Testing with real traffic | Low — limited blast radius |
| Rolling | Resource-constrained environments | Medium — gradual exposure |
| Direct | Non-critical services only | High — all-or-nothing |
# Example: Kubernetes blue/green
kubectl apply -f deployment-green.yaml
kubectl rollout status deployment/myapp-green
# Verify health
curl -f https://myapp-green.internal/health/ready
# Cut traffic
kubectl patch service myapp -p '{"spec":{"selector":{"version":"green"}}}'
/health/ready returns 200| Trigger | Action |
|---|---|
| Error rate > 1% for 5 minutes | Automated rollback to N-1 |
| Performance degradation > 50% | Manual rollback after investigation |
| Security vulnerability found | Emergency rollback immediately |
# Blue/Green rollback — switch selector back
kubectl patch service myapp -p '{"spec":{"selector":{"version":"blue"}}}'
| Signal | Action |
|---|---|
| Friday afternoon deploy | Postpone to Monday |
| No rollback plan | Define triggers and procedure first |
| Skipping staging | Run in staging first |
| 100% traffic on first deploy | Use canary or blue/green |
| Skill | When |
|---|---|
| e2e-testing | Smoke testing after deploy |
| secure-coding | Security review before release |
| logging | Verifying logs post-deploy |
Execute safe Git workflows — branching, committing, resolving conflicts, and managing PRs
Use when controlling AI spend, token budgets, model routing, or workflow efficiency before scaling usage
Use when handling incidents, outages, severe regressions, or operational emergencies before attempting broad fixes
Use when investigating latency, throughput, resource saturation, or performance regressions before changing implementation details
Use when reviewing code, preparing a PR for review, or processing review feedback
Use when diagnosing bugs, test failures, or unexpected behavior before attempting any fix