deployment-documentation
>
Works with
--- name: deployment-documentation description: > license: MIT --- # Deployment Documentation ## Table of Contents - [Overview](#overview) - [When to Use](#when-to-use) - [Quick Start](#quick-start) - [Reference Guides](#reference-guides) - [Best Practices](#best-practices) ## Overview Create comprehensive deployment documentation covering infrastructure setup, CI/CD pipelines, deployment procedures, and rollback strategies. ## When to Use - Deployment guides - Infrastructure documentation - CI/CD pipeline setup - Configuration management - Container orchestration - Cloud infrastructure docs - Release procedures - Rollback procedures ## Quick Start Minimal working example: ````markdown # Deployment Guide ## Overview This document describes the deployment process for [Application Name]. **Deployment Methods:** - Manual deployment (emergency only) - Automated CI/CD (preferred) - Blue-green deployment - Canary deployment **Environments:** - Development: https://dev.example.com - Staging: https://staging.example.com - Production: https://example.com --- ## Prerequisites ### Required Tools // ... (see reference guides for full implementation) ``` ## Reference Guides Detailed implementations in the `references/` directory: | Guide | Contents | |---|---| | [GitHub Actions Workflow](references/github-actions-workflow.md) | GitHub Actions Workflow | | [Dockerfile](references/dockerfile.md) | Dockerfile | | [docker-compose.yml](references/docker-composeyml.md) | docker-compose.yml | | [Deployment Manifest](references/deployment-manifest.md) | Deployment Manifest | ## Best Practices ### ✅ DO - Use infrastructure as code - Implement CI/CD pipelines - Use container orchestration - Implement health checks - Use rolling deployments - Have rollback procedures - Monitor deployments - Document emergency procedures - Use secrets management - Implement blue-green or canary deployments ### ❌ DON'T - Deploy directly to production - Skip testing before deploy - Forget to backup before migrations - Deploy without rollback plan - Skip monitoring after deployment - Hardcode credentials - Deploy during peak hours (unless necessary)
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.
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).

