deployment-automation
>
Works with
---
name: deployment-automation
description: >
license: MIT
---
# Deployment Automation
## Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
## Overview
Establish automated deployment pipelines that safely and reliably move applications across development, staging, and production environments with minimal manual intervention and risk.
## When to Use
- Continuous deployment to Kubernetes
- Infrastructure as Code deployment
- Multi-environment promotion
- Blue-green deployment strategies
- Canary release management
- Infrastructure provisioning
- Automated rollback procedures
## Quick Start
Minimal working example:
```yaml
# helm/Chart.yaml
apiVersion: v2
name: myapp
description: My awesome application
type: application
version: 1.0.0
# helm/values.yaml
replicaCount: 3
image:
repository: ghcr.io/myorg/myapp
pullPolicy: IfNotPresent
tag: "1.0.0"
service:
type: ClusterIP
port: 80
targetPort: 3000
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "500m"
autoscaling:
// ... (see reference guides for full implementation)
```
## Reference Guides
Detailed implementations in the `references/` directory:
| Guide | Contents |
|---|---|
| [Helm Deployment Chart](references/helm-deployment-chart.md) | Helm Deployment Chart |
| [GitHub Actions Deployment Workflow](references/github-actions-deployment-workflow.md) | GitHub Actions Deployment Workflow |
| [ArgoCD Deployment](references/argocd-deployment.md) | ArgoCD Deployment |
| [Blue-Green Deployment](references/blue-green-deployment.md) | Blue-Green Deployment |
## Best Practices
### ✅ DO
- Use Infrastructure as Code (Terraform, Helm)
- Implement GitOps workflows
- Use blue-green deployments
- Implement canary releases
- Automate rollback procedures
- Test deployments in staging first
- Use feature flags for gradual rollout
- Monitor deployment health
- Document deployment procedures
- Implement approval gates for production
- Version infrastructure code
- Use environment parity
### ❌ DON'T
- Deploy directly to production
- Skip testing in staging
- Use manual deployment scripts
- Deploy without rollback plan
- Ignore health checks
- Use hardcoded configuration
- Deploy during critical hours
- Skip pre-deployment validation
- Forget to backup before deploy
- Deploy from local machinesMore 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.
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.
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).

