跳到正文
cccskills
免费在 GitHub 公开

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.

中文简介正在准备中。当前显示原文说明。

集成工具AzureTerraform
查看安装方法

包含的文件(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(原文)

安装前可以查看交给 AI 代理的指令内容。

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

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".

中文简介正在准备中。当前显示原文说明。

borghei/Claude-Skills9622026年10月7日 更新

Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."

中文简介正在准备中。当前显示原文说明。

borghei/Claude-Skills9622026年10月7日 更新

Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.

中文简介正在准备中。当前显示原文说明。

borghei/Claude-Skills9622026年10月7日 更新

Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.

中文简介正在准备中。当前显示原文说明。

borghei/Claude-Skills9622026年10月7日 更新

Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.

中文简介正在准备中。当前显示原文说明。

borghei/Claude-Skills9622026年10月7日 更新

aeo

免费

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.

中文简介正在准备中。当前显示原文说明。

borghei/Claude-Skills9622026年10月7日 更新

查看 borghei 的全部技能

报告此技能的问题