[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71MPE4KVQ87SRJRJ5WDAM7W":3,"skill-same:01M71MPE4KVQ87SRJRJ5WDAM7W":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":15,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":6,"updatedAt":65,"install":66,"resources":79},"01M71MPE4KVQ87SRJRJ5WDAM7W","cost-optimization",null,"Use when reducing cloud spend on AWS, Azure, GCP or OCI through rightsizing, cost allocation tags, reserved capacity, spot instances and savings plans. Infrastructure spend, not LLM API cost (see llm-cost-optimizer).","monoes\u002Fmonomind","monoes","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F276744696?v=4","01M71KWF37AJ2NJBP3KWBVSDGT","01M71KWF4CSF8ADH0M6HA09VD2",23,"MIT","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fcost-optimization\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fcost-optimization\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000040","财务","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"cost optimization","Optimization","cost","when","github","llm api","allocation","tags","plans","spot","optimizer","instances","cloud spend","use","api","gcp","infrastructure","azure","capacity","oci","2026-11-10 10:56:23",false,"---\nname: cost-optimization\ndescription: \"Use when reducing cloud spend on AWS, Azure, GCP or OCI through rightsizing, cost allocation tags, reserved capacity, spot instances and savings plans. Infrastructure spend, not LLM API cost (see llm-cost-optimizer).\"\ntags: [\"devops\",\"finance\",\"cloud\"]\ntools: []\nlicense: MIT\nsource: https:\u002F\u002Fgithub.com\u002Fwshobson\u002Fagents\nsource_path: \"plugins\u002Fcloud-infrastructure\u002Fskills\u002Fcost-optimization\"\nsource_commit: 4236bb91f8395b0435f1d8b8baf9e8e4c69a8620\n---\n# Cloud Cost Optimization\n\nStrategies and patterns for optimizing cloud costs across AWS, Azure, GCP, and OCI.\n\n## Purpose\n\nImplement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.\n\n## When to Use\n\n- Reduce cloud spending\n- Right-size resources\n- Implement cost governance\n- Optimize multi-cloud costs\n- Meet budget constraints\n\n## Cost Optimization Framework\n\n### 1. Visibility\n\n- Implement cost allocation tags\n- Use cloud cost management tools\n- Set up budget alerts\n- Create cost dashboards\n\n### 2. Right-Sizing\n\n- Analyze resource utilization\n- Downsize over-provisioned resources\n- Use auto-scaling\n- Remove idle resources\n\n### 3. Pricing Models\n\n- Use reserved capacity\n- Leverage spot\u002Fpreemptible instances\n- Implement savings plans\n- Use committed use discounts\n\n### 4. Architecture Optimization\n\n- Use managed services\n- Implement caching\n- Optimize data transfer\n- Use lifecycle policies\n\n## AWS Cost Optimization\n\n### Reserved Instances\n\n```\nSavings: 30-72% vs On-Demand\nTerm: 1 or 3 years\nPayment: All\u002FPartial\u002FNo upfront\nFlexibility: Standard or Convertible\n```\n\n### Savings Plans\n\n```\nCompute Savings Plans: 66% savings\nEC2 Instance Savings Plans: 72% savings\nApplies to: EC2, Fargate, Lambda\nFlexible across: Instance families, regions, OS\n```\n\n### Spot Instances\n\n```\nSavings: Up to 90% vs On-Demand\nBest for: Batch jobs, CI\u002FCD, stateless workloads\nRisk: 2-minute interruption notice\nStrategy: Mix with On-Demand for resilience\n```\n\n### S3 Cost Optimization\n\n```hcl\nresource \"aws_s3_bucket_lifecycle_configuration\" \"example\" {\n  bucket = aws_s3_bucket.example.id\n\n  rule {\n    id     = \"transition-to-ia\"\n    status = \"Enabled\"\n\n    transition {\n      days          = 30\n      storage_class = \"STANDARD_IA\"\n    }\n\n    transition {\n      days          = 90\n      storage_class = \"GLACIER\"\n    }\n\n    expiration {\n      days = 365\n    }\n  }\n}\n```\n\n## Azure Cost Optimization\n\n### Reserved VM Instances\n\n- 1 or 3 year terms\n- Up to 72% savings\n- Flexible sizing\n- Exchangeable\n\n### Azure Hybrid Benefit\n\n- Use existing Windows Server licenses\n- Up to 80% savings with RI\n- Available for Windows and SQL Server\n\n### Azure Advisor Recommendations\n\n- Right-size VMs\n- Delete unused resources\n- Use reserved capacity\n- Optimize storage\n\n## GCP Cost Optimization\n\n### Committed Use Discounts\n\n- 1 or 3 year commitment\n- Up to 57% savings\n- Applies to vCPUs and memory\n- Resource-based or spend-based\n\n### Sustained Use Discounts\n\n- Automatic discounts\n- Up to 30% for running instances\n- No commitment required\n- Applies to Compute Engine, GKE\n\n### Preemptible VMs\n\n- Up to 80% savings\n- 24-hour maximum runtime\n- Best for batch workloads\n\n## OCI Cost Optimization\n\n### Flexible Shapes\n\n- Scale OCPUs and memory independently\n- Match instance sizing to workload demand\n- Reduce wasted capacity from fixed VM shapes\n\n### Commitments and Budgets\n\n- Use annual commitments for predictable spend\n- Set compartment-level budgets with alerts\n- Track monthly forecasts with OCI Cost Analysis\n\n### Preemptible Capacity\n\n- Use preemptible instances for batch and ephemeral workloads\n- Keep interruption-tolerant autoscaling groups\n- Mix with standard capacity for critical services\n\n## Tagging Strategy\n\n### AWS Tagging\n\n```hcl\nlocals {\n  common_tags = {\n    Environment = \"production\"\n    Project     = \"my-project\"\n    CostCenter  = \"engineering\"\n    Owner       = \"team@example.com\"\n    ManagedBy   = \"terraform\"\n  }\n}\n\nresource \"aws_instance\" \"example\" {\n  ami           = \"ami-12345678\"\n  instance_type = \"t3.medium\"\n\n  tags = merge(\n    local.common_tags,\n    {\n      Name = \"web-server\"\n    }\n  )\n}\n```\n\n**Reference:** See `references\u002Ftagging-standards.md`\n\n## Cost Monitoring\n\n### Budget Alerts\n\n```hcl\n# AWS Budget\nresource \"aws_budgets_budget\" \"monthly\" {\n  name              = \"monthly-budget\"\n  budget_type       = \"COST\"\n  limit_amount      = \"1000\"\n  limit_unit        = \"USD\"\n  time_period_start = \"2024-01-01_00:00\"\n  time_unit         = \"MONTHLY\"\n\n  notification {\n    comparison_operator        = \"GREATER_THAN\"\n    threshold                  = 80\n    threshold_type            = \"PERCENTAGE\"\n    notification_type         = \"ACTUAL\"\n    subscriber_email_addresses = [\"team@example.com\"]\n  }\n}\n```\n\n### Cost Anomaly Detection\n\n- AWS Cost Anomaly Detection\n- Azure Cost Management alerts\n- GCP Budget alerts\n- OCI Budgets and Cost Analysis\n\n## Architecture Patterns\n\n### Pattern 1: Serverless First\n\n- Use Lambda\u002FFunctions for event-driven\n- Pay only for execution time\n- Auto-scaling included\n- No idle costs\n\n### Pattern 2: Right-Sized Databases\n\n```\nDevelopment: t3.small RDS\nStaging: t3.large RDS\nProduction: r6g.2xlarge RDS with read replicas\n```\n\n### Pattern 3: Multi-Tier Storage\n\n```\nHot data: S3 Standard\nWarm data: S3 Standard-IA (30 days)\nCold data: S3 Glacier (90 days)\nArchive: S3 Deep Archive (365 days)\n```\n\n### Pattern 4: Auto-Scaling\n\n```hcl\nresource \"aws_autoscaling_policy\" \"scale_up\" {\n  name                   = \"scale-up\"\n  scaling_adjustment     = 2\n  adjustment_type        = \"ChangeInCapacity\"\n  cooldown              = 300\n  autoscaling_group_name = aws_autoscaling_group.main.name\n}\n\nresource \"aws_cloudwatch_metric_alarm\" \"cpu_high\" {\n  alarm_name          = \"cpu-high\"\n  comparison_operator = \"GreaterThanThreshold\"\n  evaluation_periods  = \"2\"\n  metric_name         = \"CPUUtilization\"\n  namespace           = \"AWS\u002FEC2\"\n  period              = \"60\"\n  statistic           = \"Average\"\n  threshold           = \"80\"\n  alarm_actions       = [aws_autoscaling_policy.scale_up.arn]\n}\n```\n\n## Cost Optimization Checklist\n\n- [ ] Implement cost allocation tags\n- [ ] Delete unused resources (EBS, EIPs, snapshots)\n- [ ] Right-size instances based on utilization\n- [ ] Use reserved capacity for steady workloads\n- [ ] Implement auto-scaling\n- [ ] Optimize storage classes\n- [ ] Use lifecycle policies\n- [ ] Enable cost anomaly detection\n- [ ] Set budget alerts\n- [ ] Review costs weekly\n- [ ] Use spot\u002Fpreemptible instances\n- [ ] Optimize data transfer costs\n- [ ] Implement caching layers\n- [ ] Use managed services\n- [ ] Monitor and optimize continuously\n\n## Tools\n\n- **AWS:** Cost Explorer, Cost Anomaly Detection, Compute Optimizer\n- **Azure:** Cost Management, Advisor\n- **GCP:** Cost Management, Recommender\n- **OCI:** Cost Analysis, Budgets, Cloud Advisor\n- **Multi-cloud:** CloudHealth, Cloudability, Kubecost\n\n\n## Related Skills\n\n- `terraform-module-library` - For resource provisioning\n- `multi-cloud-architecture` - For cloud selection\n","# Cloud Cost Optimization\n\nStrategies and patterns for optimizing cloud costs across AWS, Azure, GCP, and OCI.\n\n## Purpose\n\nImplement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.\n\n## When to Use\n\n- Reduce cloud spending\n- Right-size resources\n- Implement cost governance\n- Optimize multi-cloud costs\n- Meet budget constraints\n\n## Cost Optimization Framework\n\n### 1. Visibility\n\n- Implement cost allocation tags\n- Use cloud cost management tools\n- Set up budget alerts\n- Create cost dashboards\n\n### 2. Right-Sizing\n\n- Analyze resource utilization\n- Downsize over-provisioned resources\n- Use auto-scaling\n- Remove idle resources\n\n### 3. Pricing Models\n\n- Use reserved capacity\n- Leverage spot\u002Fpreemptible instances\n- Implement savings plans\n- Use committed use discounts\n\n### 4. Architecture Optimization\n\n- Use managed services\n- Implement caching\n- Optimize data transfer\n- Use lifecycle policies\n\n## AWS Cost Optimization\n\n### Reserved Instances\n\n```\nSavings: 30-72% vs On-Demand\nTerm: 1 or 3 years\nPayment: All\u002FPartial\u002FNo upfront\nFlexibility: Standard or Convertible\n```\n\n### Savings Plans\n\n```\nCompute Savings Plans: 66% savings\nEC2 Instance Savings Plans: 72% savings\nApplies to: EC2, Fargate, Lambda\nFlexible across: Instance families, regions, OS\n```\n\n### Spot Instances\n\n```\nSavings: Up to 90% vs On-Demand\nBest for: Batch jobs, CI\u002FCD, stateless workloads\nRisk: 2-minute interruption notice\nStrategy: Mix with On-Demand for resilience\n```\n\n### S3 Cost Optimization\n\n```hcl\nresource \"aws_s3_bucket_lifecycle_configuration\" \"example\" {\n  bucket = aws_s3_bucket.example.id\n\n  rule {\n    id     = \"transition-to-ia\"\n    status = \"Enabled\"\n\n    transition {\n      days          = 30\n      storage_class = \"STANDARD_IA\"\n    }\n\n    transition {\n      days          = 90\n      storage_class = \"GLACIER\"\n    }\n\n    expiration {\n      days = 365\n    }\n  }\n}\n```\n\n## Azure Cost Optimization\n\n### Reserved VM Instances\n\n- 1 or 3 year terms\n- Up to 72% savings\n- Flexible sizing\n- Exchangeable\n\n### Azure Hybrid Benefit\n\n- Use existing Windows Server licenses\n- Up to 80% savings with RI\n- Available for Windows and SQL Server\n\n### Azure Advisor Recommendations\n\n- Right-size VMs\n- Delete unused resources\n- Use reserved capacity\n- Optimize storage\n\n## GCP Cost Optimization\n\n### Committed Use Discounts\n\n- 1 or 3 year commitment\n- Up to 57% savings\n- Applies to vCPUs and memory\n- Resource-based or spend-based\n\n### Sustained Use Discounts\n\n- Automatic discounts\n- Up to 30% for running instances\n- No commitment required\n- Applies to Compute Engine, GKE\n\n### Preemptible VMs\n\n- Up to 80% savings\n- 24-hour maximum runtime\n- Best for batch workloads\n\n## OCI Cost Optimization\n\n### Flexible Shapes\n\n- Scale OCPUs and memory independently\n- Match instance sizing to workload demand\n- Reduce wasted capacity from fixed VM shapes\n\n### Commitments and Budgets\n\n- Use annual commitments for predictable spend\n- Set compartment-level budgets with alerts\n- Track monthly forecasts with OCI Cost Analysis\n\n### Preemptible Capacity\n\n- Use preemptible instances for batch and ephemeral workloads\n- Keep interruption-tolerant autoscaling groups\n- Mix with standard capacity for critical services\n\n## Tagging Strategy\n\n### AWS Tagging\n\n```hcl\nlocals {\n  common_tags = {\n    Environment = \"production\"\n    Project     = \"my-project\"\n    CostCenter  = \"engineering\"\n    Owner       = \"team@example.com\"\n    ManagedBy   = \"terraform\"\n  }\n}\n\nresource \"aws_instance\" \"example\" {\n  ami           = \"ami-12345678\"\n  instance_type = \"t3.medium\"\n\n  tags = merge(\n    local.common_tags,\n    {\n      Name = \"web-server\"\n    }\n  )\n}\n```\n\n**Reference:** See `references\u002Ftagging-standards.md`\n\n## Cost Monitoring\n\n### Budget Alerts\n\n```hcl\n# AWS Budget\nresource \"aws_budgets_budget\" \"monthly\" {\n  name              = \"monthly-budget\"\n  budget_type       = \"COST\"\n  limit_amount      = \"1000\"\n  limit_unit        = \"USD\"\n  time_period_start = \"2024-01-01_00:00\"\n  time_unit         = \"MONTHLY\"\n\n  notification {\n    comparison_operator        = \"GREATER_THAN\"\n    threshold                  = 80\n    threshold_type            = \"PERCENTAGE\"\n    notification_type         = \"ACTUAL\"\n    subscriber_email_addresses = [\"team@example.com\"]\n  }\n}\n```\n\n### Cost Anomaly Detection\n\n- AWS Cost Anomaly Detection\n- Azure Cost Management alerts\n- GCP Budget alerts\n- OCI Budgets and Cost Analysis\n\n## Architecture Patterns\n\n### Pattern 1: Serverless First\n\n- Use Lambda\u002FFunctions for event-driven\n- Pay only for execution time\n- Auto-scaling included\n- No idle costs\n\n### Pattern 2: Right-Sized Databases\n\n```\nDevelopment: t3.small RDS\nStaging: t3.large RDS\nProduction: r6g.2xlarge RDS with read replicas\n```\n\n### Pattern 3: Multi-Tier Storage\n\n```\nHot data: S3 Standard\nWarm data: S3 Standard-IA (30 days)\nCold data: S3 Glacier (90 days)\nArchive: S3 Deep Archive (365 days)\n```\n\n### Pattern 4: Auto-Scaling\n\n```hcl\nresource \"aws_autoscaling_policy\" \"scale_up\" {\n  name                   = \"scale-up\"\n  scaling_adjustment     = 2\n  adjustment_type        = \"ChangeInCapacity\"\n  cooldown              = 300\n  autoscaling_group_name = aws_autoscaling_group.main.name\n}\n\nresource \"aws_cloudwatch_metric_alarm\" \"cpu_high\" {\n  alarm_name          = \"cpu-high\"\n  comparison_operator = \"GreaterThanThreshold\"\n  evaluation_periods  = \"2\"\n  metric_name         = \"CPUUtilization\"\n  namespace           = \"AWS\u002FEC2\"\n  period              = \"60\"\n  statistic           = \"Average\"\n  threshold           = \"80\"\n  alarm_actions       = [aws_autoscaling_policy.scale_up.arn]\n}\n```\n\n## Cost Optimization Checklist\n\n- [ ] Implement cost allocation tags\n- [ ] Delete unused resources (EBS, EIPs, snapshots)\n- [ ] Right-size instances based on utilization\n- [ ] Use reserved capacity for steady workloads\n- [ ] Implement auto-scaling\n- [ ] Optimize storage classes\n- [ ] Use lifecycle policies\n- [ ] Enable cost anomaly detection\n- [ ] Set budget alerts\n- [ ] Review costs weekly\n- [ ] Use spot\u002Fpreemptible instances\n- [ ] Optimize data transfer costs\n- [ ] Implement caching layers\n- [ ] Use managed services\n- [ ] Monitor and optimize continuously\n\n## Tools\n\n- **AWS:** Cost Explorer, Cost Anomaly Detection, Compute Optimizer\n- **Azure:** Cost Management, Advisor\n- **GCP:** Cost Management, Recommender\n- **OCI:** Cost Analysis, Budgets, Cloud Advisor\n- **Multi-cloud:** CloudHealth, Cloudability, Kubecost\n\n\n## Related Skills\n\n- 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Covers discovering workflows from code, handoff contracts, timeouts, recovery paths and observable state.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fworkflow-architect\u002FSKILL.md",[235],{"categoryNo":213,"categoryName":214,"taxonomyType":131,"taxonomyTypeDesc":132},[218,237,238,239,240,241,242,192,243,244,142,245,246,247,112,28,248,249,199,250],"workflow architect","architect","trees","user journeys","branch","planning","map","workflows","architecture","state","leadership","recovery","operations","systems"]