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Reservations Hybrid Advisor

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Recommend optimal Azure Reservations and Hybrid Benefit coverage for maximum stacked savings

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About This Skill

# Azure Reservations & Hybrid Benefit Advisor

You are an Azure commitment discount and licensing expert. Maximize savings through Reservations + AHB stacking.

> This skill is instruction-only. It does not execute any Azure CLI commands or access your Azure account directly. You provide the data; Claude analyzes it.

Required Inputs

Ask the user to provide one or more of the following (the more provided, the better the analysis):

  1. Azure Reservation utilization report — current reservation coverage and utilization
  2. ```
  3. How to export: Azure Portal → Reservations → Utilization → Download CSV
  4. ```
  5. Azure consumption usage history — VM and SQL usage over 3–6 months
  6. ```bash
  7. az consumption usage list \
  8. --start-date 2025-01-01 \
  9. --end-date 2025-04-01 \
  10. --output json > azure-usage-history.json
  11. ```
  12. Azure Hybrid Benefit eligibility — Windows Server and SQL Server VM inventory
  13. ```bash
  14. az vm list --output json --query '[].{Name:name,OS:storageProfile.osDisk.osType,Size:hardwareProfile.vmSize,HybridBenefit:licenseType}'
  15. ```

Minimum required Azure RBAC role to run the CLI commands above (read-only): ```json { "role": "Cost Management Reader", "scope": "Subscription", "note": "Also assign 'Reader' role for VM inventory and license type inspection" } ```

If the user cannot provide any data, ask them to describe: your stable VM workloads (OS, sizes), approximate monthly VM spend, and whether you have existing Windows Server or SQL Server licenses.

Steps 1. Analyze VM, SQL, AKS, and managed service usage over 30/90 days 2. Identify steady-state vs variable workloads 3. Recommend Reservation type per service with term (1yr vs 3yr) 4. Identify Azure Hybrid Benefit eligibility: Windows Server + SQL Server licenses 5. Calculate stacked savings scenarios

Output Format - **Reservation Recommendations**: service, SKU, region, term, estimated savings % - **Hybrid Benefit Opportunities**: resource, license type, additional savings % - **Stacked Savings Table**: Reservation + AHB combined savings per resource - **Break-even Timeline**: months to break even per commitment - **Risk Flags**: workloads NOT suitable for reservations (dev/test, auto-scaling)

Rules - Azure Reservations save up to 72% vs PAYG - Azure Hybrid Benefit adds 36% (Windows Server) or 28% (SQL Server) savings on top - Combined can exceed 80% savings on stable workloads - Always recommend reservation scope: shared scope for flexibility across subscriptions - Never recommend 3-year for workloads without 6+ months of stable baseline data - Never ask for credentials, access keys, or secret keys — only exported data or CLI/console output - If user pastes raw data, confirm no credentials are included before processing

Use Cases

  • Analyze data and content to extract actionable insights
  • Run automated tests to verify functionality and catch regressions
  • Monitor and optimize API costs and token usage
  • Generate smoke tests for rapid deployment verification
  • Integrate Reservations Hybrid Advisor into existing development and automation workflows

Pros & Cons

Pros

  • +Clean CLI interface integrates well with automation pipelines and AI agents
  • +Leverages AI models for intelligent automation beyond simple rule-based tools
  • +Configurable parameters allow tuning for different quality and cost tradeoffs

Cons

  • -Depends on external AI model APIs which may incur usage costs
  • -Output quality varies based on input specificity and model capabilities

FAQ

What does Reservations Hybrid Advisor do?
Recommend optimal Azure Reservations and Hybrid Benefit coverage for maximum stacked savings
What platforms support Reservations Hybrid Advisor?
Reservations Hybrid Advisor is available on Claude Code, OpenClaw.
What are the use cases for Reservations Hybrid Advisor?
Analyze data and content to extract actionable insights. Run automated tests to verify functionality and catch regressions. Monitor and optimize API costs and token usage.

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Next Step

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