Fase 4 del pipeline azure-app-onboard: ejecuta la IaC validada contra Azure y verifica la salud de los recursos desplegados. No es invocable directamente.
en todo el repo
0–100, la ruta de este skill
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Funciona con cualquier agente que lea SKILL.md
npx -y skills add microsoft/azure-skills --skill deploy --agent claude-codeSe instala solo en este repositorio.
| Property | Value |
|---|---|
| Best for | Executing validated IaC against Azure, health-checking deployed resources |
| Inputs | prepare-plan.json + scaffold-manifest.json from .copilot-azure/sessions/{id}/ |
| Outputs | deploy-result.json written to session directory |
| Parent | azure-app-onboard |
Invoked by the azure-app-onboard orchestrator at Phase 4 when scaffold-manifest.json exists with files[] and validationResult. Not directly user-routable.
Return to orchestrator: When complete, return control to
azure-app-onboardfor handoff (Step 10). Do NOT start new phases.
| Scenario | Use Instead |
|---|---|
| Plan architecture, map services, estimate costs | prepare |
| Generate IaC files from a plan | azure-app-onboard Step 7 (scaffold) |
Run azd up or execute existing deployment templates |
azure-deploy |
| Debug a running app after deployment | azure-diagnostics |
| Optimize existing Azure spending | azure-cost |
⛔ Sub-agent delegation is MANDATORY for Step 0. Read
subagent-preflight.md, then dispatch as ataskwith the COMPLETE and UNMODIFIED template text between<<<TEMPLATE_START>>>/<<<TEMPLATE_END>>>delimiters. Do NOT summarize or rewrite the template — the sub-agent needs every "Read [file]" instruction to produce a correctdeploy-checklist.md. Append session artifact data AFTER the template block. If your next action after reading the template is anything other thantask, you are executing it inline instead of delegating.
⛔ Healing loop: ask user after 3 attempts, then every 5 (counter =
healingAttempts[].length).
⛔ Region lock: Before
az deploymentretry, compare--locationagainstprepare-plan.json.deploymentVariables.location. If changed → re-approval gate required. Update plan after approval.
⛔ After compaction or any
az deployment/az webapp deploy/az acr build/failed health check: re-readdeploy-checklist.md. If missing → fill fromdeploy-checklist-template.md. On significant context loss: also re-read this SKILL.md.
| # | Step | Action | Artifact | Reference |
|---|---|---|---|---|
| 0 | Dispatch preflight sub-agent | ⛔ You MUST dispatch subagent-preflight.md as a task. ⛔ agent_type: "task" — NEVER "general-purpose". Read the template, then your NEXT action MUST be task. If after reading the template your next action is powershell, view, or anything other than task, STOP — you are executing inline instead of delegating. Writes deploy-checklist.md. view it immediately after return. |
deploy-checklist.md |
⛔ You MUST read subagent-preflight.md |
| 1 | Read upstream artifacts | Load prepare-plan.json + scaffold-manifest.json. Check validationResult. Resolve subscription + deployment variables. |
— | — |
| 3 | Preflight checks | Auth, mandatory what-if preview, RBAC, RG per deploy-checklist.md § Preflight. |
— | ⛔ You MUST read deploy-checklist.md (re-read if compaction occurred) |
| 4 | Deploy approval gate | Present cost + resource summary per deploy-checklist.md § Deploy approval gate format. |
— | — |
| 5b | Write deploy-result.json skeleton | ⛔ Read deploy-schemas.ts, write skeleton (status: "in-progress"). Must exist BEFORE first az command. |
deploy-result.json |
⛔ You MUST read deploy-schemas.ts |
| 6 | Execute deployment | ⛔ BEFORE az deployment sub create: Generate portal link — $dn="{deploymentName}"; $r="/subscriptions/{subId}/providers/Microsoft.Resources/deployments/$dn"; $l="https://portal.azure.com/#view/Microsoft_Azure_Resources/DeploymentDetails.MenuView/~/overview/id/$($r.Replace('/','%2F'))"; Write-Output "LINK=$l". ⛔ Auto-open link in browser: Start-Process $l 2>$null. Print bare URL in chat (ctrl-clickable).Auto-generate ALL @secure() params (openssl rand -base64 32 | tr -d '/+='), NEVER ask_user for passwords; on retry reuse from deploy-secrets.env or Key Vault — NEVER regenerate (see deploy-safety.md § Deploy Checklist). THEN deploy IaC. |
— | ⛔ You MUST read deploy-checklist.md § Execute deployment |
| 6b | Deploy application code | ⛔ Deploy code for EVERY service in prepare-plan.json.services[]. Follow deploy-checklist.md § Code deploy. |
— | ⛔ You MUST read deploy-checklist.md § Code deploy |
| 7 | Health-check + SCM re-disable | HTTP GET per endpoint (max 3 iterations). ⛔ Multi-service apps: Also inspect the response body for error patterns (connection refused, MODULE_NOT_FOUND, localhost, SET-IN-DEPLOY-PHASE) — HTTP 200 alone does not mean functional when the app depends on another service or KV secrets. Then ⛔ for EVERY App Service/Functions app run BOTH commands — no exceptions: az rest --method put --url "/subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.Web/sites/{app}/basicPublishingCredentialsPolicies/scm?api-version=2023-12-01" --headers "Content-Type=application/json" --body '{"properties":{"allow":false}}' then verify: az rest --method get --url "/subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.Web/sites/{app}/basicPublishingCredentialsPolicies/scm?api-version=2023-12-01" --query properties.allow -o tsv (must return false). |
deploy-result.json full |
⛔ You MUST read deploy-checklist.md § Health check |
| 8 | Finalize artifacts | ⛔ Read deploy-schemas.ts. ⛔ Re-read deploy-checklist.md § Artifact verification — follow ALL 5 checks. ⛔ No "live"/handoff message until you overwrite the skeleton deploy-result.json — flip status off "in-progress" (→ succeeded/failed) and fill healthStatus, endpoints, completedUtc, deploymentNames, healingAttempts. Write deployment-summary.md (status table + health + portal link(s) + cleanup commands — same content as your handoff message). Update context.json — add "deploy" to completedPhases, currentPhase: null, lastModifiedUtc. Read back to confirm status != "in-progress" and "deploy" ∈ completedPhases. ⛔ Then STOP — return to orchestrator. No further CLI commands. |
deploy-result.json final + deployment-summary.md + context.json update |
⛔ You MUST read deploy-schemas.ts + ⛔ Re-read deploy-checklist.md § Artifact verification |
| 9 | Error handling + healing | ⛔ Only if Steps 6/6b/7 returned nonzero exit code or health check failed. Skip entirely on clean deploys. Classify errors, healing loop, PLAN_LEVEL_CHANGE re-approval per deploy-checklist.md § During healing. ⛔ Even on unrecoverable failure: write deploy-result.json with status: "failed" and errorDetails before returning to orchestrator — the artifact must always exist. |
— | ⛔ You MUST read error-classification.md |
Reproducido de microsoft/azure-skills bajo licencia MIT. Leer esta página en markdown.
16 archivos en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.
Requiere `prepare-plan.json` y `scaffold-manifest.json` en el directorio de sesión. 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.
