cost-optimization
- Use it when:
- Your cloud bill keeps growing and you want savings, or tags and budgets that keep spending visible.
- It does:
- Organizes savings into four levers, visibility, right-sizing, pricing models and architecture, with each provider's discount options for AWS, Azure, GCP and OCI.
- You get:
- Terraform snippets for S3 lifecycle rules, cost tags, an AWS budget alert and CPU-triggered auto-scaling, plus a 15-item checklist.
- Discounts:
- AWS reserved capacity and savings plans up to 72%, spot up to 90%, GCP committed use up to 57% and preemptible VMs up to 80%.
- Tag standard:
- A reference file requires Environment, Owner, CostCenter, Project and ManagedBy tags on every resource.
TL;DR
A reference, not a workflow. Its framework has four parts: visibility (cost allocation tags, budget alerts, dashboards), right-sizing (downsizing, auto-scaling, removing idle resources), pricing models (reserved capacity, spot or preemptible instances, savings plans, committed use discounts) and architecture (managed services, caching, data transfer, lifecycle policies). For each provider it lists the main options: AWS Reserved Instances, Savings Plans and Spot; Azure reserved VMs, Hybrid Benefit for existing Windows Server and SQL Server licenses, and Advisor; GCP committed and sustained use discounts and preemptible VMs; OCI flexible shapes, commitments, compartment budgets and preemptible capacity. Four architecture patterns follow: serverless first, databases sized per environment, storage tiered from S3 Standard to Deep Archive, and auto-scaling. A reference file sets the required tags, how each provider uses them for billing, and rules such as enforcing tags in CI and auditing them weekly. It names cost tools per provider and across clouds, such as Cost Explorer, Azure Advisor, Kubecost and CloudHealth, and points to terraform-module-library for provisioning.
Summarizes the skill at 4bde316.
npx skills add https://github.com/wshobson/agents --skill cost-optimization skills.sh pageSource on GitHubAll skills from wshobson/agents
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- First seen
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Weekly values
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|---|---|
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| Category |
Deploy, provision, observe and debug cloud apps. Everything for building software: planning, coding, debugging, design, frameworks, cloud, agents and skills. |
|---|---|
| Type |
Knowledge and rules the agent applies while it works: best practices, design principles, API references. |
| Effect |
Adds no side effects of its own. It shapes how the agent thinks, writes or reviews. Guidelines and routers are always effect:none, even when the task they support edits code. |
| Needs |
Instructions only. Works in any agent that supports skills. |
| Publisher |
Everyone else. Unverified vendor claims default to community. |
| Topics | |
| Stack |
SKILL.md
From wshobson/agents at 4bde316. Checked daily, last changed Nov 6, 2026.
Frontmatter
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
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 provisioningmulti-cloud-architecture- For cloud selection