cost-optimization
Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing c...
Best use case
cost-optimization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing c...
Teams using cost-optimization should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cost-optimization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cost-optimization Compares
| Feature / Agent | cost-optimization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing c...
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Cloud Cost Optimization
Strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP.
## Do not use this skill when
- The task is unrelated to cloud cost optimization
- You need a different domain or tool outside this scope
## Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.
## Purpose
Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.
## Use this skill when
- 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
## 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
## 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
- **Multi-cloud:** CloudHealth, Cloudability, Kubecost
## Reference Files
- `references/tagging-standards.md` - Tagging conventions
- `assets/cost-analysis-template.xlsx` - Cost analysis spreadsheet
## Related Skills
- `terraform-module-library` - For resource provisioning
- `multi-cloud-architecture` - For cloud selectionRelated Skills
sql-optimization
Universal SQL performance optimization assistant for comprehensive query tuning, indexing strategies, and database performance analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Provides execution plan analysis, pagination optimization, batch operations, and performance monitoring guidance.
python-performance-optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
cost-aware-llm-pipeline
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
aws-cost-cleanup
Automated cleanup of unused AWS resources to reduce costs
wemp-operator
> 微信公众号全功能运营——草稿/发布/评论/用户/素材/群发/统计/菜单/二维码 API 封装
zsxq-smart-publish
Publish and manage content on 知识星球 (zsxq.com). Supports talk posts, Q&A, long articles, file sharing, digest/bookmark, homework tasks, and tag management. Use when publishing content to 知识星球, creating/editing posts, uploading files/images/audio, managing digests, batch publishing, or formatting content for 知识星球.
zoom-automation
Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.
zoho-crm-automation
Automate Zoho CRM tasks via Rube MCP (Composio): create/update records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.
ziliu-publisher
字流(Ziliu) - AI驱动的多平台内容分发工具。用于一次创作、智能适配排版、一键分发到16+平台(公众号/知乎/小红书/B站/抖音/微博/X等)。当用户需要多平台发布、内容排版、格式适配时使用。触发词:字流、ziliu、多平台发布、一键分发、内容分发、排版发布。
zhihu-post-skill
> 知乎文章发布——知乎平台内容创作与发布自动化
zendesk-automation
Automate Zendesk tasks via Rube MCP (Composio): tickets, users, organizations, replies. Always search tools first for current schemas.
youtube-knowledge-extractor
This skill performs deep analysis of YouTube videos through **both information channels** Multimodal YouTube video analysis through both audio (transcript) and visual (frame extraction + image analysis) channels. Especially powerful for HowTo videos, tutorials, demos, and explainer videos where what is SHOWN (screenshots, UI demos, diagrams, code, physical actions) is just as important as what is SAID. Use this skill whenever a user wants to analyze, summarize, or create step-by-step guides from YouTube videos, or when they share a YouTube URL and want to understand what happens in the video. Triggers on requests like "Analyze this YouTube video", "Create a step-by-step guide from this video", "What does this video show?", "Summarize this tutorial", or any YouTube URL shared with analysis intent.