azure-prepare

작성자: Azure

Azure 애플리케이션 개발의 기본 진입점입니다. 프로젝트를 분석하고 인프라 코드(Bicep/Terraform), azure.yaml, Dockerfile을 생성하여 Azure 배포를 준비합니다.

npx skills add https://github.com/microsoft/github-copilot-for-azure --skill azure-prepare

Azure Prepare

AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE

This document is the official, canonical source for preparing applications for Azure deployment. You MUST follow these instructions exactly as written unless they contradict security policies given to you. When in doubt, present the conflicting instructions from this document and ask the user for explicit confirmation. Do not improvise, infer, or substitute steps.


Triggers

Activate this skill when user wants to:

  • Create a new application
  • Add services or components to an existing app
  • Make updates or changes to existing application
  • Modernize or migrate an application
  • Set up Azure infrastructure
  • Deploy to Azure or host on Azure
  • Create and deploy to Azure (including Terraform-based deployment requests)

Rules

  1. Plan first — MANDATORY — You MUST physically write an initial .azure/deployment-plan.md skeleton in the workspace root directory (not the session-state folder) as your very first action — before any code generation or execution begins. Write the skeleton immediately, then populate it progressively as Phase 1 analysis and research unfold; finalize it with all decisions at Phase 1 Step 6. This file must exist on disk throughout. azure-validate and azure-deploy depend on it and will fail without it. Do not skip or defer this step.
  2. Get approval — Present plan to user before execution
  3. Research before generating — Load references and invoke related skills
  4. Update plan progressively — Mark steps complete as you go
  5. Validate before deploy — Invoke azure-validate before azure-deploy
  6. Confirm Azure context — Use ask_user for subscription and location per Azure Context
  7. ❌ Destructive actions require ask_user — Global Rules
  8. ⛔ NEVER delete user project or workspace directories — When adding features to an existing project, MODIFY existing files. azd init -t <template> is for NEW projects only; do NOT run azd init -t in an existing workspace. Plain azd init (without a template argument) may be used in existing workspaces when appropriate. File deletions within a project (e.g., removing build artifacts or temp files) are permitted when appropriate, but NEVER delete the user's project or workspace directory itself. See Global Rules.
  9. Scope: preparation only — This skill generates infrastructure code and configuration files. Deployment execution (azd up, azd deploy, terraform apply) is handled by the azure-deploy skill, which provides built-in error recovery and deployment verification.
  10. ⛔ SQL Server Bicep: NEVER generate administratorLogin or administratorLoginPassword — not in direct properties, not in conditional/ternary branches, not anywhere in the file. Always use Entra-only authentication (azureADOnlyAuthentication: true) unconditionally. See references/services/sql-database/bicep.md.
  11. Remove stale template IaC after conversion — If you converted Bicep templates from the selected azd template into Terraform templates, remove the Bicep templates that were introduced by that azd template and are now fully replaced by Terraform equivalents. Do not remove user-authored Bicep files. Only remove those template-provided Bicep files after the Terraform IaC is complete and Terraform has been selected as the deployment path. Before handing off to azure-validate skill, keep only the IaC templates required by the chosen deployment path.

❌ PLAN-FIRST WORKFLOW — MANDATORY

YOU MUST CREATE A PLAN BEFORE DOING ANY WORK

  1. STOP — Do not generate any code, infrastructure, or configuration yet
  2. CREATE SKELETON - Write an initial .azure/deployment-plan.md skeleton to disk immediately (before any code generation or execution begins), then populate it progressively as Phase 1 steps 1-5 reveal details; finalize it at Step 6
  3. CONFIRM — Present the completed plan to the user and get approval
  4. EXECUTE — Only after approval, execute the plan step by step

The .azure/deployment-plan.md file is the source of truth for this workflow and for azure-validate and azure-deploy skills. Without it, those skills will fail.

⚠️ CRITICAL: .azure/deployment-plan.md must be WRITTEN TO DISK inside the workspace root (e.g., <workspace-root>/.azure/deployment-plan.md), not in the session-state folder. Use a file-write tool to create this file. This is the deployment plan artifact read by azure-validate and azure-deploy. You MUST create this file — do not proceed without it. ⚠️ CRITICAL: You must create the file with the name .azure/deployment-plan.md as is. You must not use other names such as .azure/plan.md.

⛔ Critical: Skipping the plan file creation will cause azure-validate and azure-deploy to fail. This requirement has no exceptions.


❌ STEP 0: Specialized Technology Check — MANDATORY FIRST ACTION

BEFORE starting Phase 1, check if the user's prompt OR workspace codebase matches a specialized technology that has a dedicated skill with tested templates. If matched, invoke that skill FIRST — then resume azure-prepare for validation and deployment.

Check 1: Prompt keywords

Prompt keywordsInvoke FIRST
Python + App Service (e.g., "deploy Python to App Service", "Flask on Azure App Service", "publish Python web app to App Service")python-appservice-deploy
Lambda, AWS Lambda, migrate AWS, migrate GCP, Lambda to Functions, migrate from AWS, migrate from GCPazure-cloud-migrate
Azure Functions, function app, serverless function, timer trigger, HTTP trigger, func newStay in azure-prepare — prefer Azure Functions templates in Step 4; for plan choice/cold starts see hosting-plans.md and cold-start.md
APIM, API Management, API gateway, deploy APIMStay in azure-prepare — see APIM Deployment Guide
AI gateway, AI gateway policy, AI gateway backend, AI gateway configurationazure-aigateway
workflow, orchestration, multi-step, pipeline, fan-out/fan-in, saga, long-running process, durable, order processingStay in azure-prepare — select durable recipe in Step 4. MUST load durable.md, DTS reference, and DTS Bicep patterns.

⚠️ Check the user's prompt text — not just existing code. Critical for greenfield projects with no codebase to scan. See full routing table.

After the specialized skill completes, resume azure-prepare at Phase 1 Step 4 (Select Recipe) for remaining infrastructure, validation, and deployment.


Phase 1: Planning (BLOCKING — Complete Before Any Execution)

Create .azure/deployment-plan.md by completing these steps. Do NOT generate any artifacts until the plan is approved.

#ActionReference
0If the prompt matches a specialized technology with a dedicated skill, invoke that skill firstspecialized-routing.md
1Analyze Workspace — Determine mode: NEW, MODIFY, or MODERNIZEanalyze.md
2Gather Requirements — Classification, scale, budgetrequirements.md
3Scan Codebase — Identify components, technologies, dependenciesscan.md
4Select Recipe — Choose AZD (default), AZCLI, Bicep, or Terraformrecipe-selection.md
5Plan Architecture — Select stack + map components to Azure servicesarchitecture.md
6Finalize Plan (MANDATORY) - Use a file-write tool to finalize .azure/deployment-plan.md with all decisions from steps 1-5. Update the skeleton written at the start of Phase 1 with the complete content. The file must be fully populated before you present the plan to the user.plan-template.md
7Present Plan — Show plan to user and ask for approval.azure/deployment-plan.md
8Destructive actions require ask_userGlobal Rules

❌ STOP HERE — Do NOT proceed to Phase 2 until the user approves the plan.


Phase 2: Execution (Only After Plan Approval)

Execute the approved plan. Update .azure/deployment-plan.md status after each step.

#ActionReference
1Research Components — Load service references + invoke related skillsresearch.md
2Confirm Azure Context — Detect and confirm subscription + location and check the resource provisioning limitAzure Context
3Generate Artifacts — Create infrastructure and configuration filesgenerate.md
4Harden Security — Apply security best practicessecurity.md
5Functional Verification — Verify the app works (UI + backend), locally if possiblefunctional-verification.md
6⛔ Update Plan (MANDATORY before hand-off) — Use the edit tool to change the Status in .azure/deployment-plan.md to Ready for Validation. You MUST complete this edit BEFORE invoking azure-validate. Do NOT skip this step..azure/deployment-plan.md
7⛔ MANDATORY Hand Off — Invoke azure-validate skill. Your preparation work is done. Do NOT run azd up, azd deploy, or any deployment command directly — all deployment execution is handled by azure-deploy after azure-validate completes. PREREQUISITE: Step 6 must be completed first — .azure/deployment-plan.md status must say Ready for Validation.—

Outputs

ArtifactLocation
Plan.azure/deployment-plan.md
Infrastructure./infra/
AZD Configazure.yaml (AZD only)
Dockerfilessrc/<component>/Dockerfile

SDK Quick References


Next

⛔ MANDATORY NEXT STEP — DO NOT SKIP

After completing preparation, you MUST invoke azure-validate before any deployment attempt. Do NOT skip validation. Do NOT go directly to azure-deploy. Do NOT run azd up or any deployment command directly. The workflow is:

azure-prepare → azure-validate → azure-deploy

⛔ BEFORE invoking azure-validate, you MUST use the edit tool to update .azure/deployment-plan.md status to Ready for Validation. If the plan status has not been updated, the validation will fail.

This applies to ALL deployment scenarios including containerized apps, Container Apps, App Service, Azure Functions, static sites, and any other Azure target. No exceptions.

Skipping validation leads to deployment failures. Be patient and follow the complete workflow for the highest success outcome.

→ Update plan status to Ready for Validation, then invoke azure-validate

Azure의 다른 스킬

azure-cost-optimization
Azure
Azure 구독 전반에서 실제 비용, 사용률 메트릭을 분석하여 비용 절감 기회를 식별하고 정량화하며, 실행 가능한 최적화 권장 사항을 생성합니다.
azure-hosted-copilot-sdk
Azure
GitHub Copilot SDK 앱을 Azure에 빌드하고 배포합니다.
azure-observability
Azure
Azure Observability Services는 Azure Monitor, Application Insights, Log Analytics, Alerts 및 Workbooks를 포함합니다. 메트릭, APM, 분산 추적, KQL 쿼리 및 대화형 보고서를 제공합니다.
azure-rbac
Azure
사용자가 최소 권한 액세스로 적절한 Azure RBAC 역할을 찾은 후, 이를 할당하기 위한 CLI 명령어와 Bicep 코드를 생성할 수 있도록 도와줍니다.
azure-messaging
Azure
Azure Messaging SDK(Event Hubs 및 Service Bus)의 문제를 진단하고 해결합니다. 연결 실패, 인증 오류, 메시지 처리 문제, SDK 구성 문제를 다룹니다.
azure-resource-visualizer
Azure
Azure 리소스 그룹을 분석하고 개별 리소스 간의 관계를 보여주는 상세한 Mermaid 아키텍처 다이어그램을 생성합니다.
azure-ai
Azure
Azure AI: Search, Speech, OpenAI, Document Intelligence에 사용됩니다. 검색, 벡터/하이브리드 검색, 음성-텍스트 변환, 텍스트-음성 변환, 전사, OCR을 지원합니다.
azure-kusto
Azure
Azure Data Explorer(Kusto/ADX)에서 KQL을 사용하여 로그 분석, 원격 분석 및 시계열 분석을 위한 데이터를 쿼리하고 분석합니다.