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Agent Skill · 开发

cost-optimization(成本优化)

cost-optimization 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

cost-optimization 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 16,848。

总安装 465周安装 19GitHub Stars 52下载 150核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cost-optimization(成本优化)
来源仓库:https://github.com/aaaaqwq/claude-code-skills
仓库路径:skills/cost-optimization
安装命令:
npx skills add https://github.com/aaaaqwq/claude-code-skills --skill cost-optimization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aaaaqwq/claude-code-skills --skill cost-optimization

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

cost-optimization 提供云成本优化的系统性策略,适用于 AWS、Azure 和 GCP 环境中的资源调优与预算管理。适用于降低云支出、实现资源合理配置、建立成本治理机制以及满足预算约束的场景。基于可见性、合理化、治理和多云协同三大框架,提供可验证的成本优化步骤与最佳实践。

安装命令:npx skills add https://github.com/aaaaqwq/claude-code-skills --skill cost-optimization。建议确认权限范围和维护状态,避免触发联网、命令执行或文件读写等敏感操作。

来源仓库内路径:skills/cost-optimization

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/aaaaqwq/claude-code-skills --skill cost-optimi…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

cost-optimization 怎么安装?

在终端执行 npx skills add https://github.com/aaaaqwq/claude-code-skills --skill cost-optimization。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

cost-optimization 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

cost-optimization 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

cost-optimization 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

cost-optimization 用的人多吗?

公开数据是累计安装 465 次,近一周安装 19 次,下载 150 次,GitHub 52 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

cost-optimization 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
cost-optimization

成本优化 · 本页

46552调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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

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

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
  • 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 selection

平台分布

Codex

33.16%
按下载量换算50

Claude

32.26%
按下载量换算48

Cursor

18.08%
按下载量换算27

Gemini CLI

10.42%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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