PaperclipOrg
Claude Agent Skill · by Wshobson

Cost Optimization

Tackles the bread-and-butter problem of runaway cloud bills with concrete optimization strategies across AWS, Azure, GCP, and OCI. Covers the full spectrum from

Install
Terminal · npx
$npx skills add https://github.com/wshobson/agents --skill cost-optimization
Works with Paperclip

How Cost Optimization fits into a Paperclip company.

Cost Optimization drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md313 lines
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---name: cost-optimizationdescription: 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-DemandTerm: 1 or 3 yearsPayment: All/Partial/No upfrontFlexibility: Standard or Convertible``` ### Savings Plans ```Compute Savings Plans: 66% savingsEC2 Instance Savings Plans: 72% savingsApplies to: EC2, Fargate, LambdaFlexible across: Instance families, regions, OS``` ### Spot Instances ```Savings: Up to 90% vs On-DemandBest for: Batch jobs, CI/CD, stateless workloadsRisk: 2-minute interruption noticeStrategy: Mix with On-Demand for resilience``` ### S3 Cost Optimization ```hclresource "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 ```hcllocals {  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 Budgetresource "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 RDSStaging: t3.large RDSProduction: r6g.2xlarge RDS with read replicas``` ### Pattern 3: Multi-Tier Storage ```Hot data: S3 StandardWarm data: S3 Standard-IA (30 days)Cold data: S3 Glacier (90 days)Archive: S3 Deep Archive (365 days)``` ### Pattern 4: Auto-Scaling ```hclresource "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