Turn an app idea or existing code into an Azure deployment. The skill selects services, plans the architecture, estimates costs, and generates infrastructure code, with approval steps before file generation and deployment.
中文简介正在准备中。当前显示英语简介。
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).
安装前可以查看交给 AI 代理的指令内容。
⛔ 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)
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Create Python agents on Azure AI Foundry that retain conversation context and use multiple tools. The skill covers code execution, file and web search, external tool connections, streaming responses, and outputs organized into defined fields.
中文简介正在准备中。当前显示英语简介。
Set up AI Runway on an existing AKS cluster, from checking cluster health and GPU compatibility to installing model serving software, deploying a first AI model, and verifying that it runs.
中文简介正在准备中。当前显示英语简介。
Investigate GPU and KAITO failures in AKS using read-only evidence. Helps distinguish scheduling problems, memory shortages, workspace readiness issues, and scaling failures, then organizes findings with confidence levels and missing evidence.
中文简介正在准备中。当前显示英语简介。
Find documented fixes for known AKS errors by matching the exact error message and required conditions. Get an explanation of the cause, a proposed fix, a Microsoft Learn reference, and read-only verification steps.
中文简介正在准备中。当前显示英语简介。
Collect bounded packet captures from AKS nodes or pods alongside Azure network settings. Use the captured traffic as evidence when investigating where packets are lost, after initial checks that do not change the environment.
中文简介正在准备中。当前显示英语简介。
Investigate live Azure Kubernetes Service incidents through read-only checks of logs, events, and resource state. Trace workload, node, networking, or upgrade failures and produce an incident report with supporting evidence, confidence, and proposed remediation.
中文简介正在准备中。当前显示英语简介。