microsoft/azure-skills

azure-app-onboard-prereq

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work.

View source
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

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.ts`PrereqOutput, 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](references/build-check.md)
3.2Completeness checkYou MUST read [completeness-check.md](references/completeness-check.md)
3.3Deployability checkYou MUST read [deployability-check.md](references/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](references/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)
from this repository

More skills

All skills
microsoft
Official

microsoft-foundry

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end with azd. USE FOR: azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, fine-tuned model, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

installs
561,6K
GitHub stars
1,4K
Updated
31 ago
microsoft
Official

azure-diagnostics

Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, troubleshoot functions, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, service bus dead letter.

installs
554,8K
GitHub stars
1,4K
Updated
31 ago
microsoft
Official

azure-ai

Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.

installs
554,2K
GitHub stars
1,4K
Updated
31 ago
microsoft
Official

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.

installs
554,1K
GitHub stars
1,4K
Updated
31 ago