Azure Prepare
MicrosoftOfficialCreated by Microsoft
- Skill
- Workflow
- Cloud and DevOps
- MIT
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The azure-prepare skill provides a mandatory, structured workflow for setting up applications for deployment to Azure via the Azure Developer CLI (azd). It enforces a plan-first approach, requiring the creation of a deployment plan file before any infrastructure generation or configuration occurs. The skill guides users through analyzing their workspace, selecting appropriate deployment recipes (Bicep or Terraform), and configuring necessary Azure services, while ensuring security best practices are followed. It acts as the initial phase in a three-part deployment pipeline: prepare, validate, and deploy.
What it does
- Generates azure.yaml configuration files for azd-based projects.
- Creates infrastructure-as-code templates using Bicep or Terraform.
- Enforces a mandatory .azure/deployment-plan.md file to track project state.
- Integrates with specialized skills for specific technologies like Azure Functions or API Management.
- Applies security hardening, such as Entra-only authentication for SQL databases.
- Prepares the workspace for subsequent validation and deployment steps.
When to use it
- When you need to prepare a new or existing project for deployment to Azure using azd.
- When you need to generate infrastructure-as-code (Bicep or Terraform) for your application.
- When you are modernizing an existing application to run on Azure.
- When you need to add new Azure services or components to an existing project.
Advantages
- Ensures consistent deployment configurations across projects.
- Prevents common configuration errors through a mandatory planning phase.
- Integrates seamlessly with the broader Azure deployment ecosystem (validate and deploy).
- Promotes security best practices by default, such as managed identities.
- Provides clear guidance on when to delegate to specialized deployment skills.
Usage examples
- “I want to deploy my new web application to Azure using azd.”
- The agent will initiate the NEW mode, guide you through requirements gathering, create a deployment plan, and generate the necessary infrastructure and configuration files.
- “Add an Azure Function to my existing project and set up the infrastructure.”
- The agent will analyze your existing project, select the appropriate Azure Functions recipe, update the deployment plan, and generate the required Bicep or Terraform files.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/azure-prepare/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip azure-prepare.zip -d .agents/skills
Documentation by Microsoft
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
Plan first — MANDATORY — You MUST physically write an initial
.azure/deployment-plan.mdskeleton 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.Get approval — Present plan to user before execution
Research before generating — Load references and invoke related skills
Update plan progressively — Mark steps complete as you go
Validate before deploy — Invoke azure-validate before azure-deploy
Confirm Azure context — Use
ask_userfor subscription and location per Azure Context❌ Destructive actions require
ask_user— Global Rules⛔ 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 runazd init -tin an existing workspace. Plainazd 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.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.⛔ SQL Server Bicep: NEVER generate
administratorLoginoradministratorLoginPassword— 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.Remove stale template IaC after conversion — If you converted Bicep templates from the selected
azdtemplate into Terraform templates, remove the Bicep templates that were introduced by thatazdtemplate 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
STOP — Do not generate any code, infrastructure, or configuration yet
CREATE SKELETON - Write an initial
.azure/deployment-plan.mdskeleton 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 6CONFIRM — Present the completed plan to the user and get approval
EXECUTE — Only after approval, execute the plan step by step
The
.azure/deployment-plan.mdfile 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.mdmust 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.mdas 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 keywords | Invoke 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 GCP | azure-cloud-migrate |
| Azure Functions, function app, serverless function, timer trigger, HTTP trigger, func new | Stay 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 APIM | Stay in azure-prepare — see APIM Deployment Guide |
| AI gateway, AI gateway policy, AI gateway backend, AI gateway configuration | azure-aigateway |
| workflow, orchestration, multi-step, pipeline, fan-out/fan-in, saga, long-running process, durable, order processing | Stay 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.
| # | Action | Reference |
|---|---|---|
| 0 | If the prompt matches a specialized technology with a dedicated skill, invoke that skill first | specialized-routing.md |
| 1 | Analyze Workspace — Determine mode: NEW, MODIFY, or MODERNIZE | analyze.md |
| 2 | Gather Requirements — Classification, scale, budget | requirements.md |
| 3 | Scan Codebase — Identify components, technologies, dependencies | scan.md |
| 4 | Select Recipe — Choose AZD (default), AZCLI, Bicep, or Terraform | recipe-selection.md |
| 5 | Plan Architecture — Select stack + map components to Azure services | architecture.md |
| 6 | Finalize 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 |
| 7 | Present Plan — Show plan to user and ask for approval | .azure/deployment-plan.md |
| 8 | Destructive actions require ask_user | Global 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.
| # | Action | Reference |
|---|---|---|
| 1 | Research Components — Load service references + invoke related skills | research.md |
| 2 | Confirm Azure Context — Detect and confirm subscription + location and check the resource provisioning limit | Azure Context |
| 3 | Generate Artifacts — Create infrastructure and configuration files | generate.md |
| 4 | Harden Security — Apply security best practices | security.md |
| 5 | Functional Verification — Verify the app works (UI + backend), locally if possible | functional-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
| Artifact | Location |
|---|---|
| Plan | .azure/deployment-plan.md |
| Infrastructure | ./infra/ |
| AZD Config | azure.yaml (AZD only) |
| Dockerfiles | src/<component>/Dockerfile |
SDK Quick References
Azure Developer CLI: azd
Azure Identity: Python | .NET | TypeScript | Java
App Configuration: Python | TypeScript | Java
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 upor any deployment command directly. The workflow is:
azure-prepare→azure-validate→azure-deploy⛔ BEFORE invoking azure-validate, you MUST use the
edittool to update.azure/deployment-plan.mdstatus toReady 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
Files
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references/research.md
Research Components
After architecture planning, research each selected component to gather best practices before generating artifacts.
Process
Identify Components — List all Azure services from architecture plan
Load Service References — For each service, load
services/<service>/README.mdfirst, then specific references as neededCheck Resource Naming Rules — For each resource type, check resource naming rules for valid characters, length limits, and uniqueness scopes
Load Recipe References — Load the selected recipe's guide (e.g., AZD) and its IAC rules, MCP best practices, and schema tools listed in its "Before Generation" table
Check Region Availability — Verify all selected services are available in the target region per region-availability.md
Check Provisioning Limits — Invoke azure-quotas skill to validate that the selected subscription and region have sufficient quota/capacity for all planned resources. Complete Step 6 of the plan template in two phases: (1) prepare resource inventory with deployment quantities, (2) fetch quotas and validate capacity using azure-quotas skill
Load Runtime References — For containerized apps, load language-specific production settings (e.g., Node.js)
Invoke Related Skills — For deeper guidance, invoke mapped skills from the table below
Document Findings — Record key insights in
.azure/deployment-plan.md
Service-to-Reference Mapping
| Azure Service | Reference | Related Skills |
|---|---|---|
| Hosting | ||
| Container Apps | Container Apps | azure-diagnostics, azure-observability, azure-nodejs-production |
| App Service | App Service | azure-diagnostics, azure-observability, azure-nodejs-production |
| Azure Functions | Functions | — |
| Static Web Apps | Static Web Apps | — |
| AKS | AKS | azure-networking |
| Data | ||
| Azure SQL | SQL Database | — |
| Cosmos DB | Cosmos DB | — |
| PostgreSQL | — | — |
| Storage (Blob/Files) | Storage | azure-storage |
| Messaging | ||
| Service Bus | Service Bus | — |
| Event Grid | Event Grid | — |
| Event Hubs | — | — |
| Integration | ||
| API Management | APIM | azure-aigateway (invoke for AI Gateway policies) |
| Logic Apps | Logic Apps | — |
| Workflow & Orchestration | ||
| Durable Functions | Durable Functions, Durable Task Scheduler | — |
| Durable Task Scheduler | Durable Task Scheduler | — |
| Security & Identity | ||
| Key Vault | Key Vault | azure-keyvault-expiration-audit |
| Managed Identity | — | entra-app-registration |
| Observability | ||
| Application Insights | App Insights | appinsights-instrumentation (invoke for instrumentation) |
| Log Analytics | — | azure-observability, azure-kusto |
| AI Services | ||
| Azure OpenAI | Foundry | microsoft-foundry (invoke for AI patterns and model guidance) |
| AI Search | — | azure-ai (invoke for search configuration) |
Research Instructions
Step 1: Load Internal References (Progressive Loading)
For each selected service, load the README.md first, then load specific files as needed:
Selected: Container Apps, Cosmos DB, Key Vault
→ Load: services/container-apps/README.md (overview)
→ If need Bicep: services/container-apps/bicep.md
→ If need Terraform: services/container-apps/terraform.md
→ If need scaling: services/container-apps/scaling.md
→ If need health probes: services/container-apps/health-probes.md
→ Load: services/cosmos-db/README.md (overview)
→ If need partitioning: services/cosmos-db/partitioning.md
→ If need SDK: services/cosmos-db/sdk.md
→ Load: services/key-vault/README.md (overview)
→ If need SDK: services/key-vault/sdk.mdStep 2: Invoke Related Skills (When Deeper Guidance Needed)
Invoke related skills for specialized scenarios:
| Scenario | Action |
|---|---|
| Using Azure Functions | Stay in azure-prepare — load selection.md → Follow composition.md algorithm |
| PostgreSQL with passwordless auth | Handle directly without a separate skill |
| Need detailed security hardening | Handle directly with service-specific security guidance and platform best practices |
| Setting up App Insights instrumentation | appinsights-instrumentation |
| Building AI applications | microsoft-foundry |
| Estimate a planned deployment's cost | cost-estimation from the separately installed azure-cost plugin |
Skill/Reference Invocation Pattern:
For Azure Functions:
Load: selection.md (decision tree)
Follow: composition.md (algorithm)
Result: Base template + recipe composition (never synthesize IaC)
For PostgreSQL:
Handle passwordless auth patterns directly without a separate skill
Step 3: Document in Plan
Add research findings to .azure/deployment-plan.md under a ## Research Summary section with source references and key insights per component.
Common Research Patterns
Web Application + API + Database (Cosmos DB)
Load: services/container-apps/README.md → bicep.md or terraform.md, scaling.md
Invoke:
azure-observability(monitoring setup)Review service-specific security guidance directly before generation
Container Apps + API + SQL Database
Load: services/container-apps/README.md → bicep.md or terraform.md, scaling.md
Review auth.md directly for Entra-only auth configuration
App Service + API + SQL Database
Review auth.md directly for Entra-only auth configuration
Serverless Event-Driven
Load: services/functions/README.md (contains mandatory composition workflow)
Load: services/event-grid/README.md or services/service-bus/README.md (if using messaging)
Load: services/storage/README.md (if using queues/blobs)
Invoke:
azure-observability(distributed tracing)
AI Application
Invoke:
microsoft-foundry(AI patterns and best practices)Load: services/container-apps/README.md → bicep.md or terraform.md
Load: services/cosmos-db/README.md → partitioning.md (vector storage)
Review Key Vault and Foundry references directly for API key management
After Research
Proceed to Generate Artifacts step with research findings applied.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Medium risk
- Instructions that use the network
- Downloads piped into a shell
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Microsoft
- Publisher
- Microsoft (Official)
- Source
- microsoft/skills · .github/plugins/azure-skills/skills/azure-prepare · commit 0696600
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from .github/plugins/azure-skills/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- microsoft/azure-skills
AutoGineer is not affiliated with or endorsed by Microsoft.
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-prepare.zip