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azure-cloud-architect

Design, review, and validate Azure cloud architectures. Use when choosing Azure compute, storage, networking, or identity services, or applying the Azure Well-Architected Framework to a workload.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구AzureTerraform
설치 방법 보기

포함된 파일(9)

  • SKILL.md5.5 KB
  • references/azure-cost-optimization.md12.6 KB
  • references/azure-decision-trees.md7.0 KB
  • references/azure-services-reference.md16.8 KB
  • references/azure-well-architected.md17.8 KB
  • references/azure-workflows-and-antipatterns.md6.1 KB
  • scripts/azure_architecture_validator.py14.4 KB
  • scripts/azure_cost_estimator.py16.2 KB
  • scripts/azure_waf_scorer.py23.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Azure Cloud Architect

End-to-end Azure-specific architecture: service selection, Well-Architected Framework assessment, identity and networking patterns, cost optimization, and operational defaults. Provider-specific complement to our generic senior-cloud-architect skill — that one covers cross-cloud patterns; this one knows AKS pricing tiers, when to pick Cosmos over SQL DB, and how Front Door differs from Application Gateway.

Core Capabilities

  • Compute selection — decision tree across AKS, App Service, Container Apps, Functions, VMs/VMSS, Batch, Static Web Apps, and API gateways.
  • Data store selection — relational, NoSQL/document, cache, blob, time-series, search, vector, and warehouse decision trees.
  • Networking design — VNets, Private/Service Endpoints, gateways (App Gateway, Front Door, Firewall, NAT, VPN/ER), hub-and-spoke and Private Link patterns.
  • Identity design — Entra ID, Managed Identity (system/user-assigned), Workload Identity, Service Principals, and least-privilege RBAC scoping.
  • WAF assessment — score workloads across Reliability, Security, Cost, Operational Excellence, and Performance Efficiency pillars.
  • Cost optimization — right-sizing, reservations, autoscaling, spot, storage tiering, and egress-reduction levers with anti-pattern detection.

When to Use

SituationSkill applies
Designing an Azure architecture from scratchYes — start with the compute decision tree
Reviewing an existing Azure architectureYes — run WAF assessment via scripts/azure_waf_scorer.py
Validating an ARM/Bicep/Terraform planYes — scripts/azure_architecture_validator.py
Estimating Azure cost for a workloadYes — scripts/azure_cost_estimator.py
Picking compute, data store, networking, or identityYes — see the decision-trees reference
Going to production without WAF reviewDon't — run the WAF scorer first

Clarify First

Before designing or assessing, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Task — design from scratch, review an existing architecture, validate IaC, or estimate cost (selects azure_architecture_validator.py vs azure_cost_estimator.py vs azure_waf_scorer.py)
  • Workload spec — the YAML workload config, or the Bicep/ARM/Terraform files (the input the scripts parse)
  • Priority pillar — reliability, security, cost, operational excellence, or performance (weights the WAF assessment and which recommendations lead)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

ToolPurposeCommand
azure_architecture_validator.pyValidate a Bicep/ARM/YAML workload for anti-patterns and missing best-practice settingspython scripts/azure_architecture_validator.py --bicep ./infra/*.bicep
azure_cost_estimator.pyEstimate monthly Azure cost from a YAML workload specpython scripts/azure_cost_estimator.py --workload-config workload.yaml
azure_waf_scorer.pyScore a workload against the five Well-Architected pillarspython scripts/azure_waf_scorer.py --workload-config workload.yaml

All scripts: stdlib only, argparse CLI, JSON or markdown output (--format).

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

Related skills

  • engineering/senior-cloud-architect — generic multi-cloud architecture patterns
  • engineering/aws-solution-architect — AWS counterpart
  • engineering/gcp-cloud-architect — GCP counterpart
  • engineering/kubernetes-operator — for AKS operator-pattern workloads
  • ra-qm-team/information-security-manager-iso27001 — for compliance-mapped controls (Azure has built-in Defender / Compliance Manager)
  • ra-qm-team/soc2-compliance-expert — Azure-specific SOC 2 evidence collection

리뷰

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댓글

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같은 저장소의 스킬

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

borghei/Claude-Skills9482026년 10월 7일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

borghei/Claude-Skills9482026년 10월 7일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

borghei/Claude-Skills9482026년 10월 7일 업데이트

aeo

무료

Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

borghei/Claude-Skills9482026년 10월 7일 업데이트

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