Fase 2 del pipeline azure-app-onboard: mapea los componentes de la app a servicios de Azure con estimación de costes y validación de cuota. No es invocable directamente.
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Funciona con cualquier agente que lea SKILL.md
npx -y skills add microsoft/azure-skills --skill prepare --agent claude-codeSe instala solo en este repositorio.
| Property | Value |
|---|---|
| Best for | Mapping app components to Azure services with cost estimation and quota validation |
| Inputs | prereq-output.json + context.json from .copilot-azure/sessions/{id}/ |
| Outputs | prepare-plan.json written to session directory |
| Parent | azure-app-onboard |
Invoked by the azure-app-onboard orchestrator at Phase 2 when prereq-output.json exists. Not directly user-routable.
Return to orchestrator: When complete, return control to
azure-app-onboard. Do NOT directly invoke scaffold or deploy.
| Scenario | Use Instead |
|---|---|
| Code readiness or prereq scanning | azure-app-onboard Step 3 (prereq) |
| IaC generation from a completed plan | azure-app-onboard Step 7 (scaffold) |
| Deploying resources to Azure | azure-app-onboard Step 9 (deploy) |
| Optimizing existing Azure spend | azure-cost |
| Estimating VM-specific costs | azure-compute |
| Enterprise landing zone architecture | azure-enterprise-infra-planner |
| Tool | Purpose |
|---|---|
mcp_azure_mcp_pricing / azure-pricing (router → command: pricing_get) |
Cost estimation (inline — see Step 6). Fallback: dispatch subagent-pricing.md |
mcp_azure_mcp_policy |
Subscription policy constraints |
az rest |
Quota validation (via sub-agent — see Step 5) |
mcp_azure_mcp_cloudarchitect → cloudarchitect_design |
WAF-aligned architecture design |
mcp_azure_mcp_wellarchitectedframework |
Per-service WAF guidance |
mcp_azure_mcp_advisor → advisor_recommendation_list |
Optimization recommendations |
| # | Step | Action | Reference |
|---|---|---|---|
| 1 | Read session state | Load prereq-output.json + context.json. Resolve subscription |
Cross-ref subscription-resolution.md if needed |
| 2 | Query policy constraints | Inline MCP: fetch policy + advisor recommendations | mcp_azure_mcp_policy + mcp_azure_mcp_advisor |
| 3 | Map components to services | Per-component Azure service selection, Dockerfile routing, deploy-as-is | ⛔ You MUST read service-mapping.md and deploy-strategy.md |
| 4 | Select SKUs + WAF analysis | Budget-aware SKU selection, inline WAF service guidance | ⛔ You MUST read sku-matrix.md |
| 5 | Validate quotas + region capacity | ⛔ Read subagent-quota.md → dispatch as task (NEXT action MUST be task, ⛔ agent_type: "task" — NEVER "general-purpose"). Copy the COMPLETE and UNMODIFIED template text into the task prompt between <<<TEMPLATE_START>>> / <<<TEMPLATE_END>>> delimiters — do NOT summarize. Append the caller-provided inputs listed in subagent-quota.md's Input table AFTER the template block. ⛔ After dispatching, proceed to Step 6 (cost estimation) while the subagent runs. Do NOT run quota checks yourself — the subagent handles it. Collect subagent results before Step 9 (write plan). |
⛔ You MUST read subagent-quota.md |
| 6 | Estimate costs | ⛔ You MUST read pricing-guide.md for methodology, then pricing-guide-services.md for per-service filters. Call the pricing router (mcp_azure_mcp_pricing/azure-pricing) with command: "pricing_get" + a parameters{} object inline per paid service. If MCP unavailable or fails → ⛔ Read subagent-pricing.md → dispatch as task (NEXT action MUST be task, ⛔ agent_type: "task" — NEVER "general-purpose"). Copy the COMPLETE and UNMODIFIED template text into the task prompt between <<<TEMPLATE_START>>> / <<<TEMPLATE_END>>> delimiters — do NOT summarize. Append data (services[], region, budget tier) AFTER the template block. Write results to prepare-plan.json.costEstimate. |
pricing-guide.md |
| 7 | Generate naming | Centralized naming: suffix, prefix, all resource names | ⛔ You MUST read naming-patterns.md |
| 8 | Determine IaC format | Existing non-Azure .tf → ask_user Bicep vs TF, write to overrides[].iacFormat. No .tf → default Bicep. |
(inline) |
| 9 | Write prepare-plan.json | Per PreparePlan schema. Include postDeployRecommendations, deploymentVariables |
⛔ You MUST read prepare-schemas.ts for PreparePlan schema |
| 10 | Return summary | Structured summary for orchestrator approval gate | (inline — 1 line) |
| 11 | Validate plan | 4-dimension check: Goal Alignment, WAF Alignment, Dependency Completeness, Deployment Viability. Fix inline on failure, document tradeoffs in assumptions[]. |
All must pass before writing |
⛔ NEVER present a region without checking quota first. Skipping quota validation causes cascading deploy failures and extended healing loops. ⛔ If plan includes PostgreSQL/MySQL, verify
offerRestrictionsVerified: true— if false/missing, region is blocked. Do NOT proceed to scaffold with unchecked DB services. ⛔ Free ≠ unlimited. Every compute SKU — including F1, Consumption, and Serverless tiers — has a per-subscription, per-region quota. Do NOT skip quota checks because a SKU is free. ⛔ After region fallback, update ALLservices[].regioninprepare-plan.json. Do not leave stale values.
| Error | Remediation |
|---|---|
| Pricing API 400 | Verify --sku included. Free tiers: skip API |
| MCP pricing unavailable | Dispatch subagent-pricing.md as task fallback (uses direct HTTP to prices.azure.com) |
| Prereq output missing | Trigger prereq backfill |
| Quota check fails | Fall back to best-effort estimate + disclaimer |
| Override conflicts | Re-run from Step 3 with new constraints |
Reproducido de microsoft/azure-skills bajo licencia MIT. Leer esta página en markdown.
13 archivos en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.
Requiere `prereq-output.json` y `context.json` en `.copilot-azure/sessions/{id}/`. No tiene frontmatter propio: es un sub-skill del pipeline.
Este repo incluye 41 skills. Si instalas uno, normalmente ya tienes los demás. Ver el pack azure-skills entero y su comando de instalación
Despliega, evalúa, ajusta y gestiona agentes de Foundry de punta a punta con azd: agentes hospedados, evaluación por lotes y continua, optimizador de prompts y fine-tuning (SFT/DPO/RFT).
Prepara proyectos Azure basados en azd para desplegar: genera azure.yaml, la infraestructura (Bicep o Terraform) y los Dockerfiles del flujo del Azure Developer CLI.
Depura incidencias de producción en Azure con AppLens, Azure Monitor, resource health y triaje seguro. Cubre App Service, Container Apps, Functions, AKS, VMs y mensajería.
Validación previa al despliegue en Azure. Comprueba en profundidad la configuración, la infraestructura (Bicep o Terraform), los roles RBAC, los permisos de identidad administrada y los prerrequisitos.
Orquestador de punta a punta: de una idea o una app existente hasta un despliegue en Azure, con estimación de costes y aprobación previa. Analiza tu app y detecta los servicios adecuados.
Skill unificado de despliegue de modelos de Azure OpenAI con enrutado por intención: despliegues rápidos con preset, despliegues personalizados y descubrimiento de capacidad entre regiones y proyectos.
Instala AI Runway sobre AKS, de un clúster desnudo a un modelo funcionando: verificación del clúster, instalación del controlador, evaluación de GPU, configuración del proveedor y primer despliegue.
Guía para instrumentar aplicaciones web con Azure Application Insights: patrones de telemetría, configuración del SDK y referencias. Para añadirlo a tu app, usa azure-prepare.
Orquestador de punta a punta: de una idea o una app existente hasta un despliegue en Azure, con estimación de costes y aprobación previa. Analiza tu app y detecta los servicios adecuados.
