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Azure Cloud Architect Skill for Claude

> 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.

31k tokens
context cost
the whole folder, loaded on every use
9
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
447
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/borghei/Claude-Skills --skill azure-cloud-architect

What comes with it

117 117 bytes besides the instruction
references/azure-cost-optimization.md
references/azure-decision-trees.md
references/azure-services-reference.md
references/azure-well-architected.md
references/azure-workflows-and-antipatterns.md
scripts/azure_architecture_validator.py
scripts/azure_cost_estimator.py
scripts/azure_waf_scorer.py

The instruction itself

7 sections, as written by the author

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

| Situation | Skill applies |

|-----------|---------------|

| Designing an Azure architecture from scratch | Yes — start with the compute decision tree |

| Reviewing an existing Azure architecture | Yes — run WAF assessment via scripts/azure_waf_scorer.py |

| Validating an ARM/Bicep/Terraform plan | Yes — scripts/azure_architecture_validator.py |

| Estimating Azure cost for a workload | Yes — scripts/azure_cost_estimator.py |

| Picking compute, data store, networking, or identity | Yes — see the decision-trees reference |

| Going to production without WAF review | Don'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

| Tool | Purpose | Command |

|------|---------|---------|

| azure_architecture_validator.py | Validate a Bicep/ARM/YAML workload for anti-patterns and missing best-practice settings | python scripts/azure_architecture_validator.py --bicep ./infra/*.bicep |

| azure_cost_estimator.py | Estimate monthly Azure cost from a YAML workload spec | python scripts/azure_cost_estimator.py --workload-config workload.yaml |

| azure_waf_scorer.py | Score a workload against the five Well-Architected pillars | python 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:

  • references/azure-decision-trees.md — full compute, data store, networking, and identity decision trees with building-block, gateway, and RBAC tables. Read when selecting services or designing topology.
  • references/azure-workflows-and-antipatterns.md — the five WAF pillars, cost levers and anti-patterns, the design/review/migrate workflows, Azure-specific anti-patterns, and tooling outputs. Read when running a design or review.
  • references/azure-services-reference.md — per-service depth: tiers, SLAs, limits, when to upgrade. Read when choosing a specific SKU or tier.
  • references/azure-well-architected.md — 5-pillar WAF assessment with the 10-question checklist per pillar, common findings, and remediations. Read during a WAF review.
  • references/azure-cost-optimization.md — full cost lever catalog, anti-patterns, and detection heuristics. Read when optimizing spend.
  • 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

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How to use it

Copy the folder

Take borghei/azure-cloud-architect from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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