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/azure-app-onboard

@b8a1c66
by microsoftmicrosoft/skills3.1k stars
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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).

Use this Skill: https://skilld.dev/gh/microsoft/skills/azure-app-onboard

This session only. Nothing lands on disk.

scaffoldreferencessubagent-review.md

≈899 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Subagent Template — Security + Adversarial Review (Steps 6–9)

Review generated IaC for security compliance and correctness. Follow the workflow below — each step specifies which reference to read and what to check.

Critical Rules

  • ⛔ Do NOT invoke ANY skills — no {"skill": "azure-validate"}, {"skill": "azure-deploy"}, {"skill": "azure-prepare"}, or any other skill call. Use the procedures in THIS file only.
  • ⛔ Do NOT run az deployment commands — review is read-only analysis of generated files.
  • ⛔ Do NOT modify IaC files — report findings only. The caller fixes issues.

Input (provided by caller)

Field Required
All generated IaC file contents (every .bicep or .tf file) YES
prepare-plan.json — services (service types, SKUs), naming, deploymentVariables sections YES
scaffold-manifest.json.files[] list YES
prereq-output.json.warnings[] — all prereq warnings that require IaC fixes YES

Output

Return JSON (≤1000 tokens):

{
  "findings": [
    { "layer": "L1|L2|L3|L4", "file": "modules/app.bicep", "claim": "...", "rating": "VERIFIED|PLAUSIBLE|FLAGGED", "detail": "..." }
  ],
  "summary": "N/N VERIFIED, N PLAUSIBLE, N FLAGGED"
}

Workflow

Step 1 — Read security patterns + run L1 security baseline

Read bicep-patterns-security.md and rbac-roles.md.

Do: Check every generated IaC file against ALL security checks defined in the reference file. The file contains the complete check table with FLAGGED conditions, edge cases, and Bicep code patterns. Do NOT guess checks from memory — use the reference file as the checklist.

Step 2 — Read checklist + run L2–L4 adversarial review

Read self-review-checklist.md.

Do: First run the cross-module reference trace from the checklist's § Cross-Module Reference Validation: parse every module call in main.bicep, read each target module's param/output declarations and secrets[] entries, then verify every reference resolves (params passed match params declared, outputs referenced exist, every CA secretRef has a matching KV secret resource). Then run L2–L4:

  • L2 (Pattern Validation): File structure matches main.bicep → modules/*.bicep, naming follows plan, Container Apps uses two-phase wiring, every files[] entry exists on disk, no azure.yaml, cross-module references all resolve
  • L3 (Hallucination Detection): Resource names match naming.resources[] exactly, API versions are real (verify via az bicep build), SKU names match plan, no invented resource types
  • L4 (WAF Alignment): Check per-pillar:
    • Reliability: zone redundancy (prod SKUs), health probes, GRS storage, min replicas ≥1
    • Security: managed identity, KV secrets, HTTPS+TLS 1.2, no public blob, no administratorLogin
    • Cost: SKU matches budget, scale-to-zero for dev/test CA, free grants applied
    • Ops: App Insights, 5 AppOnboard tags, all values parameterized
    • Performance: autoscale (prod), CDN for SPA, connection pooling, cache tier

Step 3 — Compile findings + return

Do: Merge L1–L4 results into the findings JSON. Apply rating per self-review-checklist.md § Rating System: VERIFIED (evidence confirms claim), PLAUSIBLE (no counter-evidence but unverified), FLAGGED (evidence contradicts or missing critical pattern). ⛔ FLAGGED at L1 (Security) or L3 (Hallucination) → caller must fix before deploy. Return to caller.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub3d

    This skill provides a comprehensive end-to-end orchestrator for deploying applications to Azure. It incorporates several security-focused patterns, such as secrets management via Azure Key Vault, managed identity integration, and robust preflight validation. There are some security considerations, such as a surface for indirect prompt injection during workspace analysis and broad default firewall rules, but these are managed through explicit user approval gates and documented trade-offs.

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  • Snyk3d

    Risk: LOW · No issues

Signed by skilld at b8a1c66. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

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metadata
{
  "author": "Microsoft",
  "version": "1.2.4"
}

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