cost-optimization — скилл для Claude Code: установка
Скилл помогает разбирать расходы AWS, Azure, GCP и OCI, находить лишние ресурсы и настраивать правила контроля бюджета.
/plugin marketplace add wshobson/agents
/plugin install cloud-infrastructure@claude-code-workflowsЧто делает cost-optimization
cost-optimization описывает подход к снижению расходов в AWS, Azure, GCP и OCI. Он помогает распределять затраты по тегам, проверять загрузку ресурсов, удалять простаивающие мощности, выбирать reserved capacity и spot или preemptible instances.
Внутри есть framework с этапами видимости расходов, right-sizing, выбора модели оплаты и настройки архитектуры. Также приведены примеры Terraform для тегов, бюджетных уведомлений, lifecycle policies и auto-scaling, а для облаков перечислены Cost Explorer, Azure Advisor, GCP Recommender и OCI Cost Analysis.
Скилл стоит поставить, если вы управляете облачной инфраструктурой или расходами в нескольких провайдерах. Для разовой оценки без доступа к данным биллинга его рекомендации придётся проверять вручную.
Какие задачи решает
- найти простаивающие облачные ресурсы
- сопоставить расходы с проектами и командами
- настроить бюджетные уведомления
- подобрать reserved или preemptible capacity
- разнести данные по уровням хранения
Примеры запросов агенту
- Проанализируй расходы AWS и найди ресурсы, которые можно right-size.
- Предложи tagging strategy для Terraform и настрой budget alerts.
- Сравни варианты reserved capacity и spot instances для batch workload.
Как установить cost-optimization
1Через маркетплейс плагинов
/plugin marketplace add wshobson/agents/plugin install cloud-infrastructure@claude-code-workflowsКоманда ставит плагин cloud-infrastructure целиком, со всеми его скиллами
2Через Skills CLI (Vercel)
npx skills add wshobson/agents --skill cost-optimization -a claude-code3Вручную: положить файлы
~/.claude/skills/cost-optimization/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/cost-optimization/
1Через Skills CLI (Vercel)
npx skills add wshobson/agents --skill cost-optimization -a codex2Вручную: положить файлы
.agents/skills/cost-optimization/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add wshobson/agents --skill cost-optimization -a cursor2Вручную: положить файлы
.cursor/skills/cost-optimization/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install wshobson/agents cost-optimization2Вручную: положить файлы
.github/skills/cost-optimization/SKILL.mdКомандой платформы
gemini skills install https://github.com/wshobson/agents --path plugins/cloud-infrastructure/skills/cost-optimizationВручную: положить файлы
.devin/skills/cost-optimization/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Claude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
Из того же набора213
Весь набор →Похожие
Вопросы о cost-optimization
Как установить cost-optimization в Claude Code?
Добавьте marketplace командой /plugin marketplace add wshobson/agents, затем установите cloud-infrastructure командой /plugin install cloud-infrastructure@claude-code-workflows. Также доступна установка через npx skills add wshobson/agents --skill cost-optimization -a claude-code.
Что делает cost-optimization?
Скилл помогает анализировать расходы AWS, Azure, GCP и OCI, уменьшать перерасход ресурсов и задавать правила cost governance. Он охватывает теги, right-sizing, модели оплаты, storage lifecycle и бюджетные уведомления.
Какие инструменты для анализа расходов он учитывает?
Для AWS указаны Cost Explorer, Cost Anomaly Detection и Compute Optimizer; для Azure — Cost Management и Advisor. Также перечислены GCP Recommender, OCI Cost Analysis и мультиоблачные CloudHealth, Cloudability и Kubecost.
Исходный текст
SKILL.md · MIT
---
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 = "[email protected]"
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 = ["[email protected]"]
}
}
```
### 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
© Seth Hobson · MIT · wshobson/agents