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    terraform cost preflight

    1

    Flags high-cost Terraform anti-patterns and infrastructure trade-offs before you hit apply.

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    See it in action

    You say

    Review this terraform plan for cost anti-patterns: [paste of terraform plan output showing 3 NAT gateways and an io2 EBS volume]

    Your agent does

    The plan provisions 3 NAT gateways (aws_nat_gateway.a/b/c). This trades high availability for increased base charges. Is AZ-level failover required for this workload? Also, EBS volume db_data uses io2; check if gp3 meets your IOPS needs to reduce costs. Use Infracost for precise dollar deltas.

    What you get

    Detect NAT gateway sprawl in multi-AZ deployments before provisioning.Identify orphaned EBS volumes and snapshots in plan diffs.Flag missing S3 lifecycle policies and CloudWatch retention settings.Identify over-provisioned RDS and Lambda resources in infrastructure code.

    About this skill

    The problem

    Infrastructure as Code allows for rapid deployment, but it also makes it easy to accidentally provision expensive, idle, or over-sized resources. Standard Terraform plans show what will change, but they don't flag the architectural anti-patterns that lead to massive cloud bills.

    What it does

    • Scans Terraform plans and diffs for nine specific high-cost anti-patterns including NAT gateway sprawl and orphaned EBS volumes.
    • Identifies over-provisioned RDS instances, oversized Lambda memory, and missing S3 lifecycle policies.
    • Flags unused Elastic IPs, idle load balancers, and CloudWatch log groups lacking retention policies.
    • Maps every finding to a specific resource address and defines the cost-vs-availability trade-off.
    • Redirects authoritative pricing calculations to Infracost to ensure you never rely on stale, static data.

    Frameworks & tools

    Terraform, OpenTofu, AWS, GCP, Azure, Infracost.

    Why this beats prompting it yourself

    A generic prompt often yields vague advice or inaccurate pricing hallucinations. This skill uses a strict semantic layer to catch specific, hard-to-spot patterns while maintaining a clear boundary between architectural logic and real-time pricing data.

    Use cases

    • Reviewing a Terraform plan for NAT gateway sprawl across multiple availability zones.
    • Audit of EBS volumes and snapshots to find unattached or orphaned storage.
    • Checking new RDS or Lambda resource blocks for over-provisioning before deployment.
    • Ensuring all new CloudWatch log groups include a defined retention period.

    Known limitations

    Does not calculate authoritative dollar figures. Limited to nine specific anti-patterns; it is not an exhaustive FinOps suite or a real-time monitoring tool.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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