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Azure App Onboard Prereq

OrganizationPopular
microsoft
azure-app-onboard-prereq

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".

Overview

Publishermicrosoft
Repositoryazure-skills
Skill nameazure-app-onboard-prereq
Stars
1.5K
Forks
246
Bundled files
15
LicenseMIT
Links
  • 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.

  • 15 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 Prereq AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. 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.
2

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-prereq .claude/skills/azure-app-onboard-prereq
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure App Onboard Prereq 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 Prereq 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 Prereq 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 Prereq — Repository Evaluation

Evaluate a user's repository for build health, app completeness, and Azure deployment feasibility — before infrastructure planning. Produces per-component verdicts (PASS/WARN/FAIL) consumed by downstream phases.

Orchestrator relationship: Called by azure-app-onboard at Step 3, or standalone for code readiness checks. When called by orchestrator, return control to azure-app-onboard after writing artifacts — do NOT invoke downstream phases directly.

Phase 1 of 4 in AppOnboard pipeline. Session: .copilot-azure/sessions/{session-id}/. Reads context.json. Writes components[], repo{}, detectedInfra[]. Produces prereq-output.json. Schema: prereq-schemas.tsPrereqOutput, BuildRequirements. Direct entry supported.

When NOT to Use

SignalRedirect
Validate infrastructure (Bicep/TF/azure.yaml)azure-validate
Generate IaCazure-prepare
End-to-end idea-to-productionazure-app-onboard
Run azd up or deployazure-deploy

Rules

ABSOLUTE PROHIBITION — npm install, npm test, npx jest, pytest, and ALL install/build/test commands are NEVER allowed. Under NO circumstances may you run npm install, npm test, npx jest, pip install, pytest, dotnet build, dotnet restore, dotnet test, go mod download, cargo build, or ANY package-manager install, build, or test command during the prereq phase. Do NOT run test suites to verify code — check for test config files statically instead. The prereq phase is read-only evaluation + static-only verification. ONLY exception — two sanctioned contexts, both consent-gated: (a) code the agent modified during migration/remediation (see remediation-protocol.md step 6), or (b) code the agent wrote from scratch on the zero-code path (see zero-code-path.md). In either case, install/build/test runs ONLY via the user-confirmed build-validation gate (build-check.md Step 3), after the user answers that specific per-command consent prompt. General prior consent never counts.

  1. Full pipeline (Steps 1–8), no exceptions. All prompts → Step 1 directly. Answer specific questions AS PART OF findings (Step 5), not before.
  2. No sub-agents for evaluation. 3-axis evaluation is inline. Exception: zero-code-path scaffolding (Step 2).
  3. Code/destructive modifications require ask_user. Max 3 questions before results. Direct entry: don't repeat orchestrator's intent questions.

MCP Tools

ToolPurpose
mcp_azure_mcp_get_azure_bestpracticesValidate detected stack patterns against Azure best practices
mcp_azure_mcp_extension_cli_installCheck/install required CLI tools (az, azd, func)

Workflow

Step 1: Session Check

Orchestrator entry: Session exists — read context.json, proceed to Step 2.

Direct entry: Check .copilot-azure/sessions/active-session.json:

  • Exists → ⛔ read session-protocol.md for resume/fresh gate. Do NOT proceed until user answers.
  • Missing → create session: generate UUID, New-Item -ItemType Directory -Path ".copilot-azure/sessions/{uuid}" -Force, write context.json + active-session.json via create tool.

Then: az account show → merge {id, name, tenantId} into context.json.azure. ⛔ Session MUST exist on disk before any scanning.

Step 2: Scan Workspace

Scan for project files. Detect components, repo{}, detectedInfra[], detectedServices[]. Classify Terraform providers. Check CLI availability. Stack detection conflicts: user explicit statement wins (write to context.json, mark scan as override); scan-only → confirm with user; multiple stacks → show all and ask (see component-mapping.md); no code → zero-code-path.md.

If no project files, no Dockerfile, AND no index.html → ⛔ read zero-code-path.md.

Cloud SDK early gate. Grep for aws-sdk|@aws-sdk|boto3|google-cloud|@google-cloud|firebase. If functional deps found → read cloud-sdk-migration.md, then ask_user: "Redirect to Azure Cloud Migrate" (set routeToSkill: "azure-cloud-migrate") · "Continue evaluation anyway" (finish readiness eval + SDK→Azure mapping, then STOP at Step 8 — no plan until the deps are swapped) · "Cancel".

Step 3: Per-Component Evaluation

Sub-stepActionReference
3.1Build checkYou MUST read build-check.md
3.2Completeness checkYou MUST read completeness-check.md
3.3Deployability checkYou MUST read deployability-check.md
3.3aComponent mapping (conditional)Read component-mapping.md ONLY IF >1 project manifest found (monorepo)

Populate buildRequirements per component after evaluation. Verdict propagation, tier rules, and f1Viable aggregation are in readiness-gate.md and the individual check references.

Step 4: Write Artifacts + Readiness Gate

⛔ Verify context.json exists on disk. Read readiness-gate.md (verdicts, tiers, batch-then-approve, fast-track) then prereq-artifacts.md (write procedures, schemas).

Step 5: Present Findings

Per readiness-gate.md § Present Findings — show verdicts grouped by severity before proceeding.

Step 6: Remediation (conditional)

You MUST read remediation-protocol.md IF any ❌ FAIL verdict, 🔧 Recommended Fix, or ⚠️ WARN with fixPhase: "prereq" exists. Contains remediation loop, static verification, re-eval mandate, post-remediation artifact updates, and the build-validation consent gate. If all verdicts are ✅ PASS or ⚠️ WARN without fixPhase: "prereq", skip to Step 7.

Step 7: Write Final State

completedPhases already has "prereq" + currentPhase: null (from Step 4). Then:

Write lastScanCommit. Run git rev-parse HEAD and store the full 40-character SHA as context.json.repo.lastScanCommit. Required — staleness guard in Step 1 compares to HEAD on resume to detect changes.

Step 8: Route

Mandatory — do NOT skip this step.

Routing fields: All routing writes routeToSkill and routeReason to context.json.

Post-remediation context: If Step 6 ran, lead the routing prompt with: "Remediation complete — {N} issues fixed, your app is now {overallHealth}."

Evaluate rows top to bottom — first match wins.

#ConditionAction
1routeToSkill set (any entry)ask_user: "Redirect to {routeToSkill}" / "Not now". ⛔ Pipeline stops — do NOT proceed to architecture planning.
2cloudSdkFindings[] non-empty (user chose "Continue evaluation anyway")Present the cloud-SDK → Azure swap mapping as 🔶 blockers, then ask_user with this exact prompt: "🔶 Cloud SDK migration required — these dependencies must be swapped before this app can deploy to Azure. (Redirect to azure-cloud-migrate / Stop — swap manually and re-run)" — Redirect sets routeToSkill: "azure-cloud-migrate", Stop halts. ⛔ Pipeline stops — do NOT proceed to architecture planning, and do NOT offer a "continue to prepare" option; the app can't deploy until the deps are swapped.
3Orchestrator + no routeToSkillTell the user: "✅ Your app has been evaluated and is ready — let's plan your Azure deployment." Then invoke azure-app-onboard. ⛔ Do NOT stop, do NOT wait for user input, do NOT narrate internal handoffs. The user already consented to the full pipeline at scope triage.
4Direct + ready/readyWithCaveats + no Azure infraask_user: "Deploy to Azure (full pipeline)" → invoke azure-app-onboard / "Not now"
5Direct + ready/readyWithCaveats + existing Azure infraask_user: "Start fresh" → invoke azure-app-onboard / "Use existing infra" → invoke azure-prepare / "Not now"
6Direct + blockedReport blocker summary + "Fix and re-run."

Severity tiers (🛑🔶❌🔧⚠️✅) are defined in readiness-gate.md.

Outputs

ArtifactLocationConsumer
Session contextcontext.jsoncomponents[], repo{}, detectedInfra[], detectedServices[]All downstream phases
Prereq outputprereq-output.jsonprepare phase (via azure-app-onboard)
Readiness report.copilot-azure/sessions/{uuid}/readiness-report.mdUser (offline reference)

Bundled files

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

Frequently asked questions

What does the Azure App Onboard Prereq AI skill do?

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I shi...

Why use Azure App Onboard Prereq on TypingMind?

Because you install it once and use it with any model. Azure App Onboard Prereq 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 Prereq in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/azure-skills/tree/main/skills/azure-app-onboard-prereq. 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 Prereq?

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 Prereq?

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

Is the Azure App Onboard Prereq 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.

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