Cloud Cost Optimization

Overview

This public intake copy packages

plugins/antigravity-awesome-skills/skills/cost-optimization

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Cloud Cost Optimization Strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Purpose, Cost Optimization Framework, AWS Cost Optimization, Azure Cost Optimization, GCP Cost Optimization, Tagging Strategy.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• The task is unrelated to cloud cost optimization
• You need a different domain or tool outside this scope
• Reduce cloud spending
• Right-size resources
• Implement cost governance
• Optimize multi-cloud costs

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Clarify goals, constraints, and required inputs.
2. Apply relevant best practices and validate outcomes.
3. Provide actionable steps and verification.
4. If detailed examples are required, open resources/implementation-playbook.md.
5. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
6. Read the overview and provenance files before loading any copied upstream support files.
7. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.

Imported Workflow Notes

Imported: 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

  .

Imported: Purpose

Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.

Examples

Example 1: Ask for the upstream workflow directly

Use @cost-optimization-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @cost-optimization-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @cost-optimization-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @cost-optimization-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills/skills/cost-optimization

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @comprehensive-review-pr-enhance-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @computer-use-agents-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @computer-vision-expert-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @concise-planning-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Reference Files

• references/tagging-standards.md

  - Tagging conventions

• assets/cost-analysis-template.xlsx

  - Cost analysis spreadsheet

Imported: 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

Imported: 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
    }
  }
}

Imported: 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

Imported: 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

Imported: 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

Imported: 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

Imported: 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]
}

Imported: 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

Imported: Tools

• AWS: Cost Explorer, Cost Anomaly Detection, Compute Optimizer
• Azure: Cost Management, Advisor
• GCP: Cost Management, Recommender
• Multi-cloud: CloudHealth, Cloudability, Kubecost

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.