Hire an AI Azure resource costs and resilience reviewer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A map of your resources and how they connect
• Current retail prices with every assumption shown (list prices, not your bill)
• Savings tied to named resources, each with its risk, and gaps rated high, medium or low

How it does the job well

• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.
• Reproduce a bug before fixing it, and add a test that fails without the fix.
• Treat input from users, files and networks as untrusted. Validate it at the boundary.

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: Your Azure bill keeps rising and you do not know which resources cause it.

• Resource relationship map. A diagram of your resources and how they connect, with a resource table and notes on weak spots.
• Price assumptions. Current retail prices by service, size and region, with monthly and yearly estimates and the assumptions shown.
• Cost findings. Waste found in named resources, with the proposed fix, saving, risk and effort for each.
• Workload findings. Gaps across the five Azure Well-Architected pillars, each rated high, medium or low risk, with a fix.
• Hand-back summary. Source coverage, missing facts and one next action.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Azure resource relationship mapper. Share your Azure resource group. Get a diagram of the resources and how they connect.
• Azure price estimate researcher. Name the Azure services you need. Get current retail prices and an estimate with its assumptions shown.
• Azure resource cost reviewer. Share your Azure setup. Get waste findings and proposed fixes tied to your app's resources.
• Azure workload practice reviewer. Share your Azure design. Get architecture gaps and proposed fixes against Azure workload guidance.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id review-azure-cost-and-resilience only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/review-azure-cost-and-resilience/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• The name of your Azure resource group, or a list of your resources
• Your Bicep, Terraform or ARM files (optional)
• Your goal and any budget limit

Functions and deliverables

• Azure resource costs and resilience reviewer: Share Azure resources, bills and workload needs. Get a resource map, current price estimates, savings options and design risks.
  • A map of your resources and how they connect
  • Current retail prices with every assumption shown (list prices, not your bill)
  • Savings tied to named resources, each with its risk, and gaps rated high, medium or low
• Azure resource relationship mapper: Get an Azure architecture diagram of your resource group, with each resource and how they connect. Needs read access to Azure.
  • A markdown file named after your resource group, ending in -architecture.md
  • A table of every resource with its type and key settings
  • A Mermaid diagram grouped by network, compute, data and security
  • Short notes on each connection and anything that looks off
  Source: GitHub Copilot community.
• Azure price estimate researcher: Name the Azure services you need. Get current retail prices and an estimate with its assumptions shown.
  • A table of current retail prices for each service
  • A monthly and yearly estimate for your usage
  • A comparison of pay-as-you-go with reserved, savings plan and spot prices where they exist
  • A list of the assumptions behind the numbers
  Source: GitHub Copilot community.
• Azure resource cost reviewer: Share Azure files and resources. Get cost findings and proposed fixes ranked by score. GitHub issues are optional and need approval.
  • A summary of current cost and possible savings for you to approve
  • One GitHub issue per fix, with evidence, risks and checks
  • A priority score so you start with the biggest savings
  • A tracking issue with a diagram of your current setup
  Source: GitHub Copilot community.
• Azure architecture reviewer: Review your Azure setup against the five Well-Architected pillars. Get risk-ranked findings, fixes, and GitHub issues you can track.
  • A diagram of the Azure services found in your files and live resources
  • A list of differences between your files and what is live
  • A check of five pillars, with each finding rated High, Medium or Low
  • One GitHub issue per finding, with the fix in Bicep, a command line fallback and a checklist
  • One tracking issue with a table by pillar, a ranked checklist and success goals
  Source: GitHub Copilot community.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• Your Azure bill keeps rising and you do not know which resources cause it.
• You want to cut waste without making the setup less reliable or secure.
• You need a resource map and a price estimate before a budget talk.

Limits to check before starting

• Your setup runs on AWS, not Azure: use AWS cost and resilience reviewer.
• You want a new Azure design from scratch: use the Azure architecture designer.
• A deploy is failing or an Azure service is down: use the Azure service health investigator.
• You only want to check that your deploy will pass: use the Bicep deployment preflight checker or Azure deployment access reviewer.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Add the complete skill pack to your AI.
• Share your goal and the files listed above.
• Ask it to follow the steps and mark any missing facts.
• Review the result before approving external actions.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/review-azure-cost-and-resilience for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.