AGENT SKILL

azure-deploy

Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.

License 许可
MIT
最近更新
2026-11-15
Stars
★ 1
Forks
0

01 前言 Front Matter

name
azure-deploy
license
MIT
metadata
{ "author": "Microsoft", "version": "1.0.21" }
description
Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.

02 资源文件 40

03 SKILL.md 正文

Azure Deploy

AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE

PREREQUISITE: The azure-validate skill MUST be invoked and completed with status Validated BEFORE executing this skill.

⛔ STOP — PREREQUISITE CHECK REQUIRED Before proceeding, verify BOTH prerequisites are met:

  1. azure-prepare was invoked and completed → .azure/deployment-plan.md exists
  2. azure-validate was invoked and passed → plan status = Validated

If EITHER is missing, STOP IMMEDIATELY:

  • No plan? → Invoke azure-prepare skill first
  • Status not Validated? → Invoke azure-validate skill first

⛔ DO NOT MANUALLY UPDATE THE PLAN STATUS

You are FORBIDDEN from changing the plan status to Validated yourself. Only the azure-validate skill is authorized to set this status after running actual validation checks. If you update the status without running validation, deployments will fail.

DO NOT ASSUME the app is ready. DO NOT SKIP validation to save time. Skipping steps causes deployment failures. The complete workflow ensures success:

azure-prepare → azure-validate → azure-deploy

Triggers

Activate this skill when user wants to:

  • Execute deployment of an already-prepared application (azure.yaml and infra/ exist)
  • Push updates to an existing Azure deployment
  • Run azd up, azd deploy, or az deployment on a prepared project
  • Ship already-built code to production
  • Deploy an application that already includes API Management (APIM) gateway infrastructure

Scope: This skill executes deployments. It does not create applications, generate infrastructure code, or scaffold projects. For those tasks, use azure-prepare.

APIM / AI Gateway: Use this skill to deploy applications whose APIM/AI gateway infrastructure was already created during azure-prepare. For creating or changing APIM resources, see APIM deployment guide. For AI governance policies, invoke azure-aigateway skill.

Rules

  1. Run after azure-prepare and azure-validate
  2. .azure/deployment-plan.md must exist with status Validated
  3. Pre-deploy checklist required — Pre-Deploy Checklist
  4. ⛔ Destructive actions require ask_user — global-rules
  5. Scope: deployment execution only — This skill owns execution of azd up, azd deploy, terraform apply, and az deployment commands. These commands are run through this skill's error recovery and verification pipeline.

Steps

# Action Reference
1 Check Plan — Read .azure/deployment-plan.md, verify status = Validated AND Validation Proof section is populated .azure/deployment-plan.md
2 Pre-Deploy Checklist — MUST complete ALL steps Pre-Deploy Checklist
3 Load Recipe — Based on recipe.type in .azure/deployment-plan.md recipes/README.md
4 RBAC Health Check — For Container Apps + ACR with managed identity: run azd provision --no-prompt, then verify AcrPull role has propagated before proceeding (see checklist) Pre-Deploy Checklist — Container Apps RBAC
5 Execute Deploy — Follow recipe steps Recipe README
6 Post-Deploy — Configure SQL managed identity and apply EF migrations if applicable Post-Deployment
7 Handle Errors — See recipe's errors.md —
8 Verify Success — Confirm deployment completed and endpoints are accessible Verification
9 Live Role Verification — Query Azure to confirm provisioned RBAC roles are correct and sufficient live-role-verification.md
10 Report Results — Present deployed endpoint URLs to the user as fully-qualified https:// links Verification

⛔ URL FORMAT RULE

When presenting endpoint URLs to the user, you MUST always use fully-qualified URLs with the https:// scheme (e.g. https://myapp.azurewebsites.net, not myapp.azurewebsites.net). Many Azure CLI commands return bare hostnames without a scheme — always prepend https:// before presenting them.

⛔ VALIDATION PROOF CHECK

When checking the plan, verify the Validation Proof section (Section 7) contains actual validation results with commands run and timestamps. If this section is empty, validation was bypassed — invoke azure-validate skill first.

SDK Quick References

MCP Tools

Tool Purpose
mcp_azure_mcp_subscription_list List available subscriptions
mcp_azure_mcp_group_list List resource groups in subscription
mcp_azure_mcp_azd Execute AZD commands
azure__role List role assignments for live RBAC verification (step 9)

References

04 同仓库更多 Skills

strmt7
strmt7/ai_skills_collection_benchmarked

webapp-testing

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

测试与质量前端开发
★ 1
strmt7
strmt7/ai_skills_collection_benchmarked

skill-creator

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Skill与智能体开发
★ 1
strmt7
strmt7/ai_skills_collection_benchmarked

playwright-skill

Complete browser automation with Playwright. Auto-detects dev servers, writes reusable test scripts, and supports screenshots, responsive checks, UX validation, login flows, link checks, and arbitrary browser automation. Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.

测试与质量Skill与智能体开发
★ 1
strmt7
strmt7/ai_skills_collection_benchmarked

web-artifacts-builder

Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.

前端开发
★ 1
strmt7
strmt7/ai_skills_collection_benchmarked

frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

设计师前端开发视觉设计
★ 1
strmt7
strmt7/ai_skills_collection_benchmarked

zarr-python

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

★ 1