Claude Cursor Skill

cost-optimization

Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.

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Part of wshobson/agents — 170 skills

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skills CLI npx skills add https://github.com/wshobson/agents/tree/main/plugins/cloud-infrastructure/skills/cost-optimization
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install wshobson-agents@llmmart
Git git clone https://github.com/wshobson/agents.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole wshobson/agents collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Cloud Cost Optimization

Strategies and patterns for optimizing cloud costs across AWS, Azure, GCP, and OCI.

Purpose

Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.

When to Use

  • Reduce cloud spending
  • Right-size resources
  • Implement cost governance
  • Optimize multi-cloud costs
  • Meet budget constraints

Cost Optimization Framework

1. Visibility

  • Implement cost allocation tags
  • Use cloud cost management tools
  • Set up budget alerts
  • Create cost dashboards

2. Right-Sizing

  • Analyze resource utilization
  • Downsize over-provisioned resources
  • Use auto-scaling
  • Remove idle resources

3. Pricing Models

  • Use reserved capacity
  • Leverage spot/preemptible instances
  • Implement savings plans
  • Use committed use discounts

4. Architecture Optimization

  • Use managed services
  • Implement caching
  • Optimize data transfer
  • Use lifecycle policies

AWS Cost Optimization

Reserved Instances

Savings: 30-72% vs On-Demand
Term: 1 or 3 years
Payment: All/Partial/No upfront
Flexibility: Standard or Convertible

Savings Plans

Compute Savings Plans: 66% savings
EC2 Instance Savings Plans: 72% savings
Applies to: EC2, Fargate, Lambda
Flexible across: Instance families, regions, OS

Spot Instances

Savings: Up to 90% vs On-Demand
Best for: Batch jobs, CI/CD, stateless workloads
Risk: 2-minute interruption notice
Strategy: Mix with On-Demand for resilience

S3 Cost Optimization

resource "aws_s3_bucket_lifecycle_configuration" "example" {
  bucket = aws_s3_bucket.example.id

  rule {
    id     = "transition-to-ia"
    status = "Enabled"

    transition {
      days          = 30
      storage_class = "STANDARD_IA"
    }

    transition {
      days          = 90
      storage_class = "GLACIER"
    }

    expiration {
      days = 365
    }
  }
}

Azure Cost Optimization

Reserved VM Instances

  • 1 or 3 year terms
  • Up to 72% savings
  • Flexible sizing
  • Exchangeable

Azure Hybrid Benefit

  • Use existing Windows Server licenses
  • Up to 80% savings with RI
  • Available for Windows and SQL Server

Azure Advisor Recommendations

  • Right-size VMs
  • Delete unused resources
  • Use reserved capacity
  • Optimize storage

GCP Cost Optimization

Committed Use Discounts

  • 1 or 3 year commitment
  • Up to 57% savings
  • Applies to vCPUs and memory
  • Resource-based or spend-based

Sustained Use Discounts

  • Automatic discounts
  • Up to 30% for running instances
  • No commitment required
  • Applies to Compute Engine, GKE

Preemptible VMs

  • Up to 80% savings
  • 24-hour maximum runtime
  • Best for batch workloads

OCI Cost Optimization

Flexible Shapes

  • Scale OCPUs and memory independently
  • Match instance sizing to workload demand
  • Reduce wasted capacity from fixed VM shapes

Commitments and Budgets

  • Use annual commitments for predictable spend
  • Set compartment-level budgets with alerts
  • Track monthly forecasts with OCI Cost Analysis

Preemptible Capacity

  • Use preemptible instances for batch and ephemeral workloads
  • Keep interruption-tolerant autoscaling groups
  • Mix with standard capacity for critical services

Tagging Strategy

AWS Tagging

locals {
  common_tags = {
    Environment = "production"
    Project     = "my-project"
    CostCenter  = "engineering"
    Owner       = "team@example.com"
    ManagedBy   = "terraform"
  }
}

resource "aws_instance" "example" {
  ami           = "ami-12345678"
  instance_type = "t3.medium"

  tags = merge(
    local.common_tags,
    {
      Name = "web-server"
    }
  )
}

Reference: See references/tagging-standards.md

Cost Monitoring

Budget Alerts

# AWS Budget
resource "aws_budgets_budget" "monthly" {
  name              = "monthly-budget"
  budget_type       = "COST"
  limit_amount      = "1000"
  limit_unit        = "USD"
  time_period_start = "2024-01-01_00:00"
  time_unit         = "MONTHLY"

  notification {
    comparison_operator        = "GREATER_THAN"
    threshold                  = 80
    threshold_type            = "PERCENTAGE"
    notification_type         = "ACTUAL"
    subscriber_email_addresses = ["team@example.com"]
  }
}

Cost Anomaly Detection

  • AWS Cost Anomaly Detection
  • Azure Cost Management alerts
  • GCP Budget alerts
  • OCI Budgets and Cost Analysis

Architecture Patterns

Pattern 1: Serverless First

  • Use Lambda/Functions for event-driven
  • Pay only for execution time
  • Auto-scaling included
  • No idle costs

Pattern 2: Right-Sized Databases

Development: t3.small RDS
Staging: t3.large RDS
Production: r6g.2xlarge RDS with read replicas

Pattern 3: Multi-Tier Storage

Hot data: S3 Standard
Warm data: S3 Standard-IA (30 days)
Cold data: S3 Glacier (90 days)
Archive: S3 Deep Archive (365 days)

Pattern 4: Auto-Scaling

resource "aws_autoscaling_policy" "scale_up" {
  name                   = "scale-up"
  scaling_adjustment     = 2
  adjustment_type        = "ChangeInCapacity"
  cooldown              = 300
  autoscaling_group_name = aws_autoscaling_group.main.name
}

resource "aws_cloudwatch_metric_alarm" "cpu_high" {
  alarm_name          = "cpu-high"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = "2"
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = "60"
  statistic           = "Average"
  threshold           = "80"
  alarm_actions       = [aws_autoscaling_policy.scale_up.arn]
}

Cost Optimization Checklist

  • Implement cost allocation tags
  • Delete unused resources (EBS, EIPs, snapshots)
  • Right-size instances based on utilization
  • Use reserved capacity for steady workloads
  • Implement auto-scaling
  • Optimize storage classes
  • Use lifecycle policies
  • Enable cost anomaly detection
  • Set budget alerts
  • Review costs weekly
  • Use spot/preemptible instances
  • Optimize data transfer costs
  • Implement caching layers
  • Use managed services
  • Monitor and optimize continuously

Tools

  • AWS: Cost Explorer, Cost Anomaly Detection, Compute Optimizer
  • Azure: Cost Management, Advisor
  • GCP: Cost Management, Recommender
  • OCI: Cost Analysis, Budgets, Cloud Advisor
  • Multi-cloud: CloudHealth, Cloudability, Kubecost

Related Skills

  • terraform-module-library - For resource provisioning
  • multi-cloud-architecture - For cloud selection
Files (agents)
  • references
    • tagging-standards.md 832 B
      # Cloud Tagging Standards
      
      ## Required Tags
      
      - `Environment`: dev, staging, production
      - `Owner`: team or individual responsible for the workload
      - `CostCenter`: finance or reporting identifier
      - `Project`: product or initiative name
      - `ManagedBy`: terraform, opentofu, pulumi, or manual
      
      ## Provider Notes
      
      - AWS: standardize tags for Cost Explorer, CUR, and automation policies
      - Azure: align tags with management groups, subscriptions, and Azure Policy
      - GCP: combine labels and resource hierarchy for billing attribution
      - OCI: apply defined tags at the compartment and resource level for chargeback
      
      ## Best Practices
      
      1. Publish an approved tag dictionary and naming rules.
      2. Enforce tags with policy and CI validation.
      3. Inherit tags from shared modules whenever possible.
      4. Audit for missing or inconsistent tags weekly.
      
  • SKILL.md 6.7 KB
    ---
    name: cost-optimization
    description: Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
    ---
    
    # Cloud Cost Optimization
    
    Strategies and patterns for optimizing cloud costs across AWS, Azure, GCP, and OCI.
    
    ## Purpose
    
    Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.
    
    ## When to Use
    
    - Reduce cloud spending
    - Right-size resources
    - Implement cost governance
    - Optimize multi-cloud costs
    - Meet budget constraints
    
    ## Cost Optimization Framework
    
    ### 1. Visibility
    
    - Implement cost allocation tags
    - Use cloud cost management tools
    - Set up budget alerts
    - Create cost dashboards
    
    ### 2. Right-Sizing
    
    - Analyze resource utilization
    - Downsize over-provisioned resources
    - Use auto-scaling
    - Remove idle resources
    
    ### 3. Pricing Models
    
    - Use reserved capacity
    - Leverage spot/preemptible instances
    - Implement savings plans
    - Use committed use discounts
    
    ### 4. Architecture Optimization
    
    - Use managed services
    - Implement caching
    - Optimize data transfer
    - Use lifecycle policies
    
    ## AWS Cost Optimization
    
    ### Reserved Instances
    
    ```
    Savings: 30-72% vs On-Demand
    Term: 1 or 3 years
    Payment: All/Partial/No upfront
    Flexibility: Standard or Convertible
    ```
    
    ### Savings Plans
    
    ```
    Compute Savings Plans: 66% savings
    EC2 Instance Savings Plans: 72% savings
    Applies to: EC2, Fargate, Lambda
    Flexible across: Instance families, regions, OS
    ```
    
    ### Spot Instances
    
    ```
    Savings: Up to 90% vs On-Demand
    Best for: Batch jobs, CI/CD, stateless workloads
    Risk: 2-minute interruption notice
    Strategy: Mix with On-Demand for resilience
    ```
    
    ### S3 Cost Optimization
    
    ```hcl
    resource "aws_s3_bucket_lifecycle_configuration" "example" {
      bucket = aws_s3_bucket.example.id
    
      rule {
        id     = "transition-to-ia"
        status = "Enabled"
    
        transition {
          days          = 30
          storage_class = "STANDARD_IA"
        }
    
        transition {
          days          = 90
          storage_class = "GLACIER"
        }
    
        expiration {
          days = 365
        }
      }
    }
    ```
    
    ## Azure Cost Optimization
    
    ### Reserved VM Instances
    
    - 1 or 3 year terms
    - Up to 72% savings
    - Flexible sizing
    - Exchangeable
    
    ### Azure Hybrid Benefit
    
    - Use existing Windows Server licenses
    - Up to 80% savings with RI
    - Available for Windows and SQL Server
    
    ### Azure Advisor Recommendations
    
    - Right-size VMs
    - Delete unused resources
    - Use reserved capacity
    - Optimize storage
    
    ## GCP Cost Optimization
    
    ### Committed Use Discounts
    
    - 1 or 3 year commitment
    - Up to 57% savings
    - Applies to vCPUs and memory
    - Resource-based or spend-based
    
    ### Sustained Use Discounts
    
    - Automatic discounts
    - Up to 30% for running instances
    - No commitment required
    - Applies to Compute Engine, GKE
    
    ### Preemptible VMs
    
    - Up to 80% savings
    - 24-hour maximum runtime
    - Best for batch workloads
    
    ## OCI Cost Optimization
    
    ### Flexible Shapes
    
    - Scale OCPUs and memory independently
    - Match instance sizing to workload demand
    - Reduce wasted capacity from fixed VM shapes
    
    ### Commitments and Budgets
    
    - Use annual commitments for predictable spend
    - Set compartment-level budgets with alerts
    - Track monthly forecasts with OCI Cost Analysis
    
    ### Preemptible Capacity
    
    - Use preemptible instances for batch and ephemeral workloads
    - Keep interruption-tolerant autoscaling groups
    - Mix with standard capacity for critical services
    
    ## Tagging Strategy
    
    ### AWS Tagging
    
    ```hcl
    locals {
      common_tags = {
        Environment = "production"
        Project     = "my-project"
        CostCenter  = "engineering"
        Owner       = "team@example.com"
        ManagedBy   = "terraform"
      }
    }
    
    resource "aws_instance" "example" {
      ami           = "ami-12345678"
      instance_type = "t3.medium"
    
      tags = merge(
        local.common_tags,
        {
          Name = "web-server"
        }
      )
    }
    ```
    
    **Reference:** See `references/tagging-standards.md`
    
    ## Cost Monitoring
    
    ### Budget Alerts
    
    ```hcl
    # AWS Budget
    resource "aws_budgets_budget" "monthly" {
      name              = "monthly-budget"
      budget_type       = "COST"
      limit_amount      = "1000"
      limit_unit        = "USD"
      time_period_start = "2024-01-01_00:00"
      time_unit         = "MONTHLY"
    
      notification {
        comparison_operator        = "GREATER_THAN"
        threshold                  = 80
        threshold_type            = "PERCENTAGE"
        notification_type         = "ACTUAL"
        subscriber_email_addresses = ["team@example.com"]
      }
    }
    ```
    
    ### Cost Anomaly Detection
    
    - AWS Cost Anomaly Detection
    - Azure Cost Management alerts
    - GCP Budget alerts
    - OCI Budgets and Cost Analysis
    
    ## Architecture Patterns
    
    ### Pattern 1: Serverless First
    
    - Use Lambda/Functions for event-driven
    - Pay only for execution time
    - Auto-scaling included
    - No idle costs
    
    ### Pattern 2: Right-Sized Databases
    
    ```
    Development: t3.small RDS
    Staging: t3.large RDS
    Production: r6g.2xlarge RDS with read replicas
    ```
    
    ### Pattern 3: Multi-Tier Storage
    
    ```
    Hot data: S3 Standard
    Warm data: S3 Standard-IA (30 days)
    Cold data: S3 Glacier (90 days)
    Archive: S3 Deep Archive (365 days)
    ```
    
    ### Pattern 4: Auto-Scaling
    
    ```hcl
    resource "aws_autoscaling_policy" "scale_up" {
      name                   = "scale-up"
      scaling_adjustment     = 2
      adjustment_type        = "ChangeInCapacity"
      cooldown              = 300
      autoscaling_group_name = aws_autoscaling_group.main.name
    }
    
    resource "aws_cloudwatch_metric_alarm" "cpu_high" {
      alarm_name          = "cpu-high"
      comparison_operator = "GreaterThanThreshold"
      evaluation_periods  = "2"
      metric_name         = "CPUUtilization"
      namespace           = "AWS/EC2"
      period              = "60"
      statistic           = "Average"
      threshold           = "80"
      alarm_actions       = [aws_autoscaling_policy.scale_up.arn]
    }
    ```
    
    ## Cost Optimization Checklist
    
    - [ ] Implement cost allocation tags
    - [ ] Delete unused resources (EBS, EIPs, snapshots)
    - [ ] Right-size instances based on utilization
    - [ ] Use reserved capacity for steady workloads
    - [ ] Implement auto-scaling
    - [ ] Optimize storage classes
    - [ ] Use lifecycle policies
    - [ ] Enable cost anomaly detection
    - [ ] Set budget alerts
    - [ ] Review costs weekly
    - [ ] Use spot/preemptible instances
    - [ ] Optimize data transfer costs
    - [ ] Implement caching layers
    - [ ] Use managed services
    - [ ] Monitor and optimize continuously
    
    ## Tools
    
    - **AWS:** Cost Explorer, Cost Anomaly Detection, Compute Optimizer
    - **Azure:** Cost Management, Advisor
    - **GCP:** Cost Management, Recommender
    - **OCI:** Cost Analysis, Budgets, Cloud Advisor
    - **Multi-cloud:** CloudHealth, Cloudability, Kubecost
    
    
    ## Related Skills
    
    - `terraform-module-library` - For resource provisioning
    - `multi-cloud-architecture` - For cloud selection
    

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