cost-optimization
Optimize existing Azure resources and analyze Reservations or Savings Plans. WHEN: "optimize Azure costs", "reduce cloud spending", "rightsize resources", "find idle resources", "orphaned disk", "deleted VM still charged", "public IP still charging", "reservation utilization", "Savings Plan coverage", "commitment recommendation", "why is pay-as-you-go still charged". DO NOT USE FOR: cost spikes, forecasts, pricing estimates, budgets, or governance.
- License 许可
- MIT
- 最近更新
- 2026-11-13
- Stars
- ★ 264
- Forks
- 225
01 前言 Front Matter
- name
- cost-optimization
- license
- MIT
- metadata
- { "author": "Microsoft", "version": "0.0.0-placeholder" }
- description
- Optimize existing Azure resources and analyze Reservations or Savings Plans. WHEN: "optimize Azure costs", "reduce cloud spending", "rightsize resources", "find idle resources", "orphaned disk", "deleted VM still charged", "public IP still charging", "reservation utilization", "Savings Plan coverage", "commitment recommendation", "why is pay-as-you-go still charged". DO NOT USE FOR: cost spikes, forecasts, pricing estimates, budgets, or governance.
02 资源文件 9
- 参考文档references/commitments.md2.9 KB
- 参考文档references/optimization.md3.0 KB
- 参考文档references/report-template.md1.7 KB
- 参考文档references/resource-graph.md3.3 KB
- 参考文档references/services/redis.md2.1 KB
- 参考文档references/services/storage-lifecycle.md1.7 KB
- 参考文档references/services/storage.md3.8 KB
- 参考文档references/tool-fallback.md1.4 KB
- 参考文档version.json55 B
03 SKILL.md 正文
Azure Cost Optimization
Quick Reference
| Intent | Workflow | Primary tools |
|---|---|---|
| Reduce waste or rightsize | Optimization | Cost and Resource Graph tools |
| Review commitments | Commitments | list_benefit_utilization, list_reservation_transactions, get_benefit_recommendations |
When to Use This Skill
Use for waste, rightsizing, and commitments.
MCP Tools
Use cost and Resource Graph tools for optimization and benefit tools for commitments. Validate queries. Fallback: API mappings.
Workflow
- Confirm scope, period, currency, and commitment intent.
- Load only the matching workflow above.
- Never transform MCP results in a shell or interpreter; request validated server-side projection, aggregation, or bounded follow-up queries.
- Separate measured cost, reported savings, and qualitative opportunities.
- Recommend changes only; do not delete, resize, purchase, or deploy resources.
Error Handling
| Error | Action |
|---|---|
| Access denied | Name the scope and permission. |
| Multiple currencies | Group and report each currency separately. |
| Missing evidence | State the gap; do not invent values. |
| Server error | Retry once; then report the trace ID. |
04 同仓库更多 Skills
azure-advisor-cost-optimization
Analyze Azure Advisor Cost/IdleResources findings and prevalidate deletion safety, including Service Groups. WHEN: "find unused Azure resources", "what resources am I paying for but no longer using", "idle-resource savings", "historical idle-resource savings", "are these idle resources safe to delete". Generic Cost, other subcategories, and cross-category requests use azure-advisor.
foundry-iq
Foundry IQ knowledge bases. WHEN: make local or Blob documents searchable; create/diagnose KBs; triage unsupported connectors or multi-source KB creation/reconfiguration (even with classic-search terms); connect existing KB to agents (including multi-source KBs); create/reuse a Search service; retrieve from an existing knowledge base with citations; no brand words needed. Read its procedure before query/target questions. NOT: other KB providers, repository-file search, classic Azure AI Search index/query/app work with no Foundry IQ KB or connector request, generic agent creation.
python-appservice-deploy
Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.
preset
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
customize
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
capacity
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.