Azure App Onboard

OrganizationPopular

microsoft

azure-app-onboard

End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use azure-cost), code readiness checks only (use azure-app-onboard-prereq).

Overview

Publisher	microsoft
Repository	`azure-skills`
Skill name	`azure-app-onboard`
Stars	1.5K
Forks	246
Bundled files	64
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
64 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Azure App Onboard AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/microsoft/azure-skills.git /tmp/azure-skills
mkdir -p .claude/skills
cp -r /tmp/azure-skills/skills/azure-app-onboard .claude/skills/azure-app-onboard

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure App Onboard in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure App Onboard on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure App Onboard is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Azure App Onboard

⛔ Every repo goes through the full pipeline (Steps 1–10). No exceptions. Do not skip steps, refuse, or short-circuit based on what you recognize. Follow the Workflow table below sequentially — read each step's references before acting.

Quick Reference

Property	Value
Best for	Developers who know what to build but not which Azure services to use
Inputs	Business idea or existing codebase, budget/scale preferences (optional)
Outputs	Architecture plan, cost estimate, IaC files, deployed Azure resources
Phases	Discover → Architect → Scaffold → Deploy (self-contained, no external skill calls)

When to Use This Skill

Deploy existing code without knowing which Azure services to use
Check if your existing code is ready to deploy to Azure
Move an existing app to Azure without rewriting or with minimal changes
Get cost estimates before committing to infrastructure
Understand architecture decisions and rejected alternatives
Get answers to Azure architecture or service selection questions (e.g., "What database should I use?")
Get guided Azure onboarding without prior experience

When NOT to Use

Scenario	Use Instead
Run `azd up` or execute an existing deployment	`azure-deploy`
Optimize existing Azure spend	`azure-cost`
Generate Bicep/Terraform for a known architecture	`azure-prepare`
Validate infrastructure or run preflight checks	`azure-validate`
Troubleshoot a running Azure deployment	`azure-diagnostics`
Deploy to or manage AKS/Kubernetes directly	`azure-kubernetes`
Look up or list existing Azure resources	`azure-resource-lookup`

Pipeline Rules

⛔ You MUST read references/pipeline-rules.md at the start of every AppOnboard session. It contains approval gates, phase lifecycle, session artifacts, deploy-as-is, and security baseline rules.

Workflow

⛔ Deploy recovery: After deploy gate approval OR before any az deployment/az webapp deploy/az acr build — if you haven't read deploy/SKILL.md, read .copilot-azure/sessions/{id}/deploy-checklist.md first, then deploy/SKILL.md. ⛔ NEVER invoke {"skill": "azure-deploy"} — that is a DIFFERENT skill for a DIFFERENT workflow.

⛔ Post-scaffold transition (MANDATORY): Immediately after scaffold-manifest.json is written, YOUR NEXT ACTION MUST be Step 8 (Deploy Approval Gate) — NOT a summary report, NOT a "here are the generated files" message, NOT a completion signal. Confirm context.json has completedPhases: [...,"scaffold"] + currentPhase: "deploy" (update it yourself if the scaffold subagent didn't). Re-read approval-gates.md § Deploy Gate if evicted from context (scaffold reference loading is heavy), then present the exact prompt: "🚀 Ready to deploy? (Yes / Run manually / Edit plan / Cancel)". This gate is the LAST content in your response — wait for the user's reply.

#	Step	Action	Reference
1	Session check + Azure login	Create/resume session, verify Azure CLI auth, resolve subscription + user identity	⛔ You MUST read session-protocol.md
2	Scope triage	Check azd markers, triage question. Empty workspace or code-only (no infra) → Step 3 directly.	⛔ Read intent-gathering.md § Scope Triage
3	Prereq scan	⛔ Skip if `completedPhases` includes `"prereq"`. Otherwise: invoke `{"skill": "azure-app-onboard-prereq"}`. Write `prereq-output.json`, update `context.json`. Halt if: `overallHealth: "blocked"` OR `routeToSkill` set.
4	Gather intent	Present prereq results, confirm stack + Azure services, ask remaining questions.	⛔ Read intent-gathering.md § After Prereq Returns
5	Plan architecture	Write `prepare-plan.json`.	⛔ You MUST read prepare/SKILL.md
6	Scaffold approval gate	Display plan for user approval BEFORE generating any files.	⛔ Read approval-gates.md § Scaffold Gate
7	Scaffold	Generate IaC, self-review. Write `scaffold-manifest.json`. Update `context.json`.	⛔ You MUST read scaffold/SKILL.md
8	Deploy approval gate	Display validation summary. ⛔ After approval: FIRST read deploy-checklist.md → deploy/SKILL.md. NEVER `{"skill": "azure-deploy"}`.	⛔ Read approval-gates.md § Deploy Gate
9	Deploy	Execute IaC, health-check. Write `deploy-result.json`.	⛔ You MUST read deploy/SKILL.md
10	Handoff	Surface deployment identity, cleanup commands, next steps.	⛔ You MUST read `handoff-protocol.md`

Error Handling

Error	Remediation
Phase fails	Halt, report phase + error. User decides: retry, skip, abort.
MCP server unavailable	Skip affected checks, add disclaimer to `costEstimate.assumptions[]` and every approval gate.
Missing RBAC	Report required role + `az role assignment` command.

Shared references: MCP tools (cross-phase tool parameters) | IaC resources (Azure resource docs for troubleshooting)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 4 more files.

Frequently asked questions

What does the Azure App Onboard AI skill do?

Why use Azure App Onboard on TypingMind?

Because you install it once and use it with any model. Azure App Onboard is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure App Onboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/azure-skills/tree/main/skills/azure-app-onboard. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Azure App Onboard?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure App Onboard?

As many as you like. As long as a model supports skills, you can use Azure App Onboard with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure App Onboard AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Mem Setup

thedotmack

CommunityPopular

This skill should be used when the user asks to "set up claude-mem", "pair claude-mem", "connect cmem", "add my cmem key", "set up cloud sync in Cowork", or provides cmem.ai Connect values (sync…

Wizard

Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI secrets, walking an unfamiliar…

Deployment Patterns

Flujos de trabajo de despliegue, patrones de pipeline CI/CD, contenedorización Docker, health checks, estrategias de rollback y listas de verificación de preparación para producción de aplicaciones…

Docker Patterns

Patrones de Docker y Docker Compose para desarrollo local, seguridad de contenedores, networking, estrategias de volúmenes y orquestación de múltiples servicios.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Azure App Onboard

Quick Reference

When to Use This Skill

When NOT to Use

Pipeline Rules

Workflow

Error Handling

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now