devops-rollout-plan

Generate comprehensive rollout plans with preflight checks, step-by-step deployment, verification signals, rollback procedures, and communication plans for infrastructure and application changes

github/awesome-copilot9.6k installsMITSynced Aug 27

Works with

Claude CodeCursorCodex CLIGitHub CopilotGemini CLI
---
name: devops-rollout-plan
description: Generate comprehensive rollout plans with preflight checks, step-by-step deployment, verification signals, rollback procedures, and communication plans for infrastructure and application changes
license: MIT
---

# DevOps Rollout Plan Generator

Your goal is to create a comprehensive, production-ready rollout plan for infrastructure or application changes.

## Input Requirements

Gather these details before generating the plan:

### Change Description
- What's changing (infrastructure, application, configuration)
- Version or state transition (from/to)
- Problem solved or feature added

### Environment Details
- Target environment (dev, staging, production, all)
- Infrastructure type (Kubernetes, VMs, serverless, containers)
- Affected services and dependencies
- Current capacity and scale

### Constraints & Requirements
- Acceptable downtime window
- Change window restrictions
- Approval requirements
- Regulatory or compliance considerations

### Risk Assessment
- Blast radius of change
- Data migrations or schema changes
- Rollback complexity and safety
- Known risks

## Output Format

Generate a structured rollout plan with these sections:

### 1. Executive Summary
- What, why, when, duration
- Risk level and rollback time
- Affected systems and user impact
- Expected downtime

### 2. Prerequisites & Approvals
- Required approvals (technical lead, security, compliance, business)
- Required resources (capacity, backups, monitoring, rollback automation)
- Pre-deployment backups

### 3. Preflight Checks
- Infrastructure health validation
- Application health baseline
- Dependency availability
- Monitoring baseline metrics
- Go/no-go decision checklist

### 4. Step-by-Step Rollout Procedure
**Phases**: Pre-deployment, deployment, progressive verification
- Specific commands for each step
- Validation after each step
- Duration estimates

### 5. Verification Signals
**Immediate** (0-2 min): Deployment success, pods/containers started, health checks passing
**Short-term** (2-5 min): Application responding, error rates acceptable, latency normal
**Medium-term** (5-15 min): Sustained metrics, stable connections, integrations working
**Long-term** (15+ min): No degradation, capacity healthy, business metrics normal

### 6. Rollback Procedure
**Decision Criteria**: When to initiate rollback
**Rollback Steps**: Automated, infrastructure revert, or full restore
**Post-Rollback Verification**: Confirm system health restored
**Communication**: Stakeholder notification

### 7. Communication Plan
- Pre-deployment (T-24h): Schedule and impact notice
- Deployment start: Commencement notice
- Progress updates: Status every X minutes
- Completion: Success confirmation
- Rollback (if needed): Issue notification

**Stakeholder Matrix**: Who to notify, when, via what method, with what content

### 8. Post-Deployment Tasks
- Immediate (1h): Verify criteria met, review logs
- Short-term (24h): Monitor metrics, review errors
- Medium-term (1 week): Post-deployment review, lessons learned

### 9. Contingency Plans
Scenarios: Partial failure, performance degradation, data inconsistency, dependency failure
For each: Symptoms, response, timeline

### 10. Contact Information
- Primary and secondary on-call
- Escalation path
- Emergency contacts (infrastructure, security, database, networking)

## Plan Customization

Adapt based on:
- **Infrastructure Type**: Kubernetes, VMs, serverless, databases
- **Risk Level**: Low (simplified), medium (standard), high (additional gates)
- **Change Type**: Code deployment, infrastructure, configuration, data migration
- **Environment**: Production (full plan), staging (simplified), development (minimal)

## Remember

- Always have a tested rollback plan
- Communicate early and often
- Monitor metrics, not just logs
- Document everything
- Learn from each deployment
- Never deploy on Friday afternoon (unless critical)
- Never skip verification steps
- Never assume "it should work"

More Deployment & CI/CD skills

azure-enterprise-infra-planner

microsoft/azure-skills

Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.

383.4k

azure-kubernetes-app-deploy

microsoft/azure-skills

Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.

376.4k

finetuning

microsoft/azure-skills

Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

323.2k

← All Deployment & CI/CD skills

Check your AI visibility

One URL in, a 0–100 score and the exact fixes out.

RUN THE CHECK

Browse all the tools

15 tools across six categories
13 of them never send your data anywhere

Free · No signup · No trial clock

SEE THE DIRECTORY