az-cost-optimize

SKILL.md

Azure Cost Optimize

This workflow analyzes Infrastructure-as-Code (IaC) files and Azure resources to generate cost optimization recommendations. It creates individual GitHub issues for each optimization opportunity plus one EPIC issue to coordinate implementation, enabling efficient tracking and execution of cost savings initiatives.

Prerequisites

Azure MCP server configured and authenticated

GitHub MCP server configured and authenticated

Target GitHub repository identified

Azure resources deployed (IaC files optional but helpful)

Prefer Azure MCP tools (azmcp-*) over direct Azure CLI when available

Workflow Steps
Step 1: Get Azure Best Practices

Action: Retrieve cost optimization best practices before analysis
Tools: Azure MCP best practices tool
Process:

Load Best Practices:

Execute azmcp-bestpractices-get to get some of the latest Azure optimization guidelines. This may not cover all scenarios but provides a foundation.

Use these practices to inform subsequent analysis and recommendations as much as possible

Reference best practices in optimization recommendations, either from the MCP tool output or general Azure documentation

Step 2: Discover Azure Infrastructure

Action: Dynamically discover and analyze Azure resources and configurations
Tools: Azure MCP tools + Azure CLI fallback + Local file system access
Process:

Resource Discovery:

Execute azmcp-subscription-list to find available subscriptions

Execute azmcp-group-list --subscription <subscription-id> to find resource groups

Get a list of all resources in the relevant group(s):

Use az resource list --subscription <id> --resource-group <name>

For each resource type, use MCP tools first if possible, then CLI fallback:

azmcp-cosmos-account-list --subscription <id> - Cosmos DB accounts

azmcp-storage-account-list --subscription <id> - Storage accounts

azmcp-monitor-workspace-list --subscription <id> - Log Analytics workspaces

azmcp-keyvault-key-list - Key Vaults

az webapp list - Web Apps (fallback - no MCP tool available)

az appservice plan list - App Service Plans (fallback)

az functionapp list - Function Apps (fallback)

az sql server list - SQL Servers (fallback)

az redis list - Redis Cache (fallback)

... and so on for other resource types

IaC Detection:

Use file_search to scan for IaC files: "/*.bicep", "/*.tf", "/main.json", "/template.json"

Parse resource definitions to understand intended configurations

Compare against discovered resources to identify discrepancies

Note presence of IaC files for implementation recommendations later on

Do NOT use any other file from the repository, only IaC files. Using other files is NOT allowed as it is not a source of truth.

If you do not find IaC files, then STOP and report no IaC files found to the user.

Configuration Analysis:

Extract current SKUs, tiers, and settings for each resource

Identify resource relationships and dependencies

Map resource utilization patterns where available

Step 3: Collect Usage Metrics & Validate Current Costs

Action: Gather utilization data AND verify actual resource costs
Tools: Azure MCP monitoring tools + Azure CLI
Process:

Find Monitoring Sources:

Use azmcp-monitor-workspace-list --subscription <id> to find Log Analytics workspaces

Use azmcp-monitor-table-list --subscription <id> --workspace <name> --table-type "CustomLog" to discover available data

Execute Usage Queries:

Use azmcp-monitor-log-query with these predefined queries:

Query: "recent" for recent activity patterns

Query: "errors" for error-level logs indicating issues

For custom analysis, use KQL queries:

// CPU utilization for App Services
AppServiceAppLogs
| where TimeGenerated > ago(7d)
| summarize avg(CpuTime) by Resource, bin(TimeGenerated, 1h)

// Cosmos DB RU consumption  
AzureDiagnostics
| where ResourceProvider == "MICROSOFT.DOCUMENTDB"
| where TimeGenerated > ago(7d)
| summarize avg(RequestCharge) by Resource

// Storage account access patterns
StorageBlobLogs
| where TimeGenerated > ago(7d)
| summarize RequestCount=count() by AccountName, bin(TimeGenerated, 1d)

Calculate Baseline Metrics:

CPU/Memory utilization averages

Database throughput patterns

Storage access frequency

Function execution rates

VALIDATE CURRENT COSTS:

Using the SKU/tier configurations discovered in Step 2

Look up current Azure pricing at https://azure.microsoft.com/pricing/ or use az billing commands

Document: Resource → Current SKU → Estimated monthly cost

Calculate realistic current monthly total before proceeding to recommendations

Step 4: Generate Cost Optimization Recommendations

Action: Analyze resources to identify optimization opportunities
Tools: Local analysis using collected data
Process:

Apply Optimization Patterns based on resource types found:

Compute Optimizations:

App Service Plans: Right-size based on CPU/memory usage

Function Apps: Premium → Consumption plan for low usage

Virtual Machines: Scale down oversized instances

Database Optimizations:

Cosmos DB:

Provisioned → Serverless for variable workloads

Right-size RU/s based on actual usage

SQL Database: Right-size service tiers based on DTU usage

Storage Optimizations:

Implement lifecycle policies (Hot → Cool → Archive)

Consolidate redundant storage accounts

Right-size storage tiers based on access patterns

Infrastructure Optimizations:

Remove unused/redundant resources

Implement auto-scaling where beneficial

Schedule non-production environments

Calculate Evidence-Based Savings:

Current validated cost → Target cost = Savings

Document pricing source for both current and target configurations

Calculate Priority Score for each recommendation:

Priority Score = (Value Score × Monthly Savings) / (Risk Score × Implementation Days)

High Priority: Score > 20
Medium Priority: Score 5-20
Low Priority: Score < 5

Validate Recommendations:

Ensure Azure CLI commands are accurate

Verify estimated savings calculations

Assess implementation risks and prerequisites

Ensure all savings calculations have supporting evidence

Step 5: User Confirmation

Action: Present summary and get approval before creating GitHub issues
Process:

Display Optimization Summary:

🎯 Azure Cost Optimization Summary

📊 Analysis Results:
• Total Resources Analyzed: X
• Current Monthly Cost: $X 
• Potential Monthly Savings: $Y 
• Optimization Opportunities: Z
• High Priority Items: N

🏆 Recommendations:
1. [Resource]: [Current SKU] → [Target SKU] = $X/month savings - [Risk Level] | [Implementation Effort]
2. [Resource]: [Current Config] → [Target Config] = $Y/month savings - [Risk Level] | [Implementation Effort]
3. [Resource]: [Current Config] → [Target Config] = $Z/month savings - [Risk Level] | [Implementation Effort]
... and so on

💡 This will create:
• Y individual GitHub issues (one per optimization)
• 1 EPIC issue to coordinate implementation

❓ Proceed with creating GitHub issues? (y/n)

Wait for User Confirmation: Only proceed if user confirms

Step 6: Create Individual Optimization Issues

Action: Create separate GitHub issues for each optimization opportunity. Label them with "cost-optimization" (green color), "azure" (blue color).
MCP Tools Required: create_issue for each recommendation
Process:

Create Individual Issues using this template:

Title Format: [COST-OPT] [Resource Type] - [Brief Description] - $X/month savings

Body Template:

## 💰 Cost Optimization: [Brief Title]

**Monthly Savings**: $X | **Risk Level**: [Low/Medium/High] | **Implementation Effort**: X days

### 📋 Description
[Clear explanation of the optimization and why it's needed]

### 🔧 Implementation

**IaC Files Detected**: [Yes/No - based on file_search results]

```bash
# If IaC files found: Show IaC modifications + deployment
# File: infrastructure/bicep/modules/app-service.bicep
# Change: sku.name: 'S3' → 'B2'
az deployment group create --resource-group [rg] --template-file infrastructure/bicep/main.bicep

# If no IaC files: Direct Azure CLI commands + warning
# ⚠️ No IaC files found. If they exist elsewhere, modify those instead.
az appservice plan update --name [plan] --sku B2

📊 Evidence

Current Configuration: [details]

Usage Pattern: [evidence from monitoring data]

Cost Impact: $X/month → $Y/month

Best Practice Alignment: [reference to Azure best practices if applicable]

✅ Validation Steps

 Test in non-production environment

 Verify no performance degradation

 Confirm cost reduction in Azure Cost Management

 Update monitoring and alerts if needed

⚠️ Risks & Considerations

[Risk 1 and mitigation]

[Risk 2 and mitigation]

Priority Score: X | Value: X/10 | Risk: X/10

Step 7: Create EPIC Coordinating Issue

Action: Create master issue to track all optimization work. Label it with "cost-optimization" (green color), "azure" (blue color), and "epic" (purple color).
MCP Tools Required: create_issue for EPIC
Note about mermaid diagrams: Ensure you verify mermaid syntax is correct and create the diagrams taking accessibility guidelines into account (styling, colors, etc.).
Process:

Create EPIC Issue:

Title: [EPIC] Azure Cost Optimization Initiative - $X/month potential savings

Body Template:

# 🎯 Azure Cost Optimization EPIC

**Total Potential Savings**: $X/month | **Implementation Timeline**: X weeks

## 📊 Executive Summary
- **Resources Analyzed**: X
- **Optimization Opportunities**: Y  
- **Total Monthly Savings Potential**: $X
- **High Priority Items**: N

## 🏗️ Current Architecture Overview

```mermaid
graph TB
    subgraph "Resource Group: [name]"
        [Generated architecture diagram showing current resources and costs]
    end

📋 Implementation Tracking

🚀 High Priority (Implement First)

 #[issue-number]: [Title] - $X/month savings

 #[issue-number]: [Title] - $X/month savings

⚡ Medium Priority

 #[issue-number]: [Title] - $X/month savings

 #[issue-number]: [Title] - $X/month savings

🔄 Low Priority (Nice to Have)

 #[issue-number]: [Title] - $X/month savings

📈 Progress Tracking

Completed: 0 of Y optimizations

Savings Realized: $0 of $X/month

Implementation Status: Not Started

🎯 Success Criteria

 All high-priority optimizations implemented

 >80% of estimated savings realized

 No performance degradation observed

 Cost monitoring dashboard updated

📝 Notes

Review and update this EPIC as issues are completed

Monitor actual vs. estimated savings

Consider scheduling regular cost optimization reviews

Error Handling

Cost Validation: If savings estimates lack supporting evidence or seem inconsistent with Azure pricing, re-verify configurations and pricing sources before proceeding

Azure Authentication Failure: Provide manual Azure CLI setup steps

No Resources Found: Create informational issue about Azure resource deployment

GitHub Creation Failure: Output formatted recommendations to console

Insufficient Usage Data: Note limitations and provide configuration-based recommendations only

Success Criteria

✅ All cost estimates verified against actual resource configurations and Azure pricing

✅ Individual issues created for each optimization (trackable and assignable)

✅ EPIC issue provides comprehensive coordination and tracking

✅ All recommendations include specific, executable Azure CLI commands

✅ Priority scoring enables ROI-focused implementation

✅ Architecture diagram accurately represents current state

✅ User confirmation prevents unwanted issue creation