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 cost-optimization), code readiness checks only (use azure-app-onboard-prereq).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
⛔ 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.
| 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) |
| Scenario | Use Instead |
|---|---|
Run azd up or execute an existing deployment | azure-deploy |
| Optimize existing Azure spend | cost-optimization from the optional azure-cost plugin |
| 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 |
If cost-optimization is unavailable, explain that it is provided by the
optional azure-cost plugin and direct the user to their client's supported
plugin installation flow; do not imply the handoff completed.
⛔ You MUST read
references/pipeline-rules.mdat the start of every AppOnboard session. It contains approval gates, phase lifecycle, session artifacts, deploy-as-is, and security baseline rules.
⛔ Deploy recovery: After deploy gate approval OR before any
az deployment/az webapp deploy/az acr build— if you haven't readdeploy/SKILL.md, read.copilot-azure/sessions/{id}/deploy-checklist.mdfirst, thendeploy/SKILL.md. ⛔ NEVER invoke{"skill": "azure-deploy"}— that is a DIFFERENT skill for a DIFFERENT workflow.
⛔ Post-scaffold transition (MANDATORY): Immediately after
scaffold-manifest.jsonis 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. Confirmcontext.jsonhascompletedPhases: [...,"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 | 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)
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
日本語の概要は準備中です。原文の説明を表示しています。
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin).
日本語の概要は準備中です。原文の説明を表示しています。
Lookup documented AKS fixes only when the prompt includes an exact catalog signature and all of its qualifiers: VMCannotFitEphemeralOSDisk; NodePoolMcVersionIncompatible; 'NodeImageVersion is not accepted'; AKS SkuNotAvailable with size, location, and zone; ZonalAllocationFailed with insufficient zone capacity; OverconstrainedAllocationRequest with listed constraints; nested AKS vmssCSE/CSE VMExtensionError_OutboundConnFail, VMExtensionError_K8SAPIServerConnFail, or VMExtensionError_K8SAPIServerDNSLookupFail; or AllocationFailed with the full cataloged internal-error or insufficient-regional-capacity message. Never use for quota errors, code-only or bare wrappers, generic symptoms, incomplete signatures, or failures outside AKS; use aks-troubleshooting or azure-diagnostics.
日本語の概要は準備中です。原文の説明を表示しています。
Collects bounded packet captures from AKS nodes and Azure network configuration for wire-level evidence. WHEN: "capture packets on an AKS node", "take a pcap", "run tcpdump on AKS", "prove where packets drop". Use for explicit packet-capture intent after read-only diagnostics, not general AKS connectivity or ingress troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS or ingress failure, connectivity timeout, network policy, SNAT exhaustion, node-pool scaling blocked by QuotaExceeded or InsufficientVCPUQuota, upgrade stuck, spot or zone disruption, a bare VMExtensionProvisioningError or AllocationFailed wrapper, an uncataloged capacity symptom, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use azure-kubernetes); cost (use cost-analysis from the optional azure-cost plugin); pod rightsizing (use azure-kubernetes); a fully qualified documented AKS signature with every required nested qualifier (use aks-known-issues); standalone failures on non-AKS Azure resources (use azure-diagnostics). Unqualified errors and open-ended incidents stay here only for AKS.
日本語の概要は準備中です。原文の説明を表示しています。