Azure Cloud Architect

Community

borghei

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.

Overview

Publisher	borghei
Repository	`Claude-Skills`
Skill name	`azure-cloud-architect`
Stars	856
Forks	154
Bundled files	8
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
8 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by borghei on GitHub. Read the source before you install it.

Installation

Install the Azure Cloud Architect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/borghei/Claude-Skills/tree/main/engineering/azure-cloud-architect
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/borghei/Claude-Skills.git /tmp/Claude-Skills
mkdir -p .claude/skills
cp -r /tmp/Claude-Skills/engineering/azure-cloud-architect .claude/skills/azure-cloud-architect

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Cloud Architect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure Cloud Architect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure Cloud Architect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Azure Cloud Architect AI skill do?

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.

Why use Azure Cloud Architect on TypingMind?

Because you install it once and use it with any model. Azure Cloud Architect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure Cloud Architect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/borghei/Claude-Skills/tree/main/engineering/azure-cloud-architect. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Azure Cloud Architect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure Cloud Architect?

As many as you like. As long as a model supports skills, you can use Azure Cloud Architect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure Cloud Architect AI skill free?

It is published on GitHub by borghei. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Azure Cloud Architect

Core Capabilities

When to Use

Clarify First

Tools

References

Related skills

Bundled files

Frequently asked questions

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