Can I use this with Terraform or OpenTofu?

Absolutely. The skill includes HCL (Terraform) code snippets for implementing budgets, lifecycle rules, auto-scaling policies, and tagging standards.

Does this skill support multi-cloud environments?

Yes, it includes specific optimization strategies and infrastructure-as-code patterns for Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

How does this skill help with cost visibility?

It provides standardized tagging strategies and monitoring configurations to ensure every cloud resource is correctly attributed to specific projects or cost centers.

Does it provide advice on Spot or Preemptible instances?

Yes, it includes guidance on when to leverage Spot (AWS), Preemptible (GCP), or Low-priority (Azure) VMs to achieve up to 90% savings on stateless workloads.

Cloud Cost Optimization

Name: Cloud Cost Optimization
Author: HermeticOrmus

byHermeticOrmus

Cloud Infrastructure

Optimizes cloud infrastructure spending through resource rightsizing, strategic tagging, and efficient pricing models across AWS, Azure, and GCP.

About

This skill empowers Claude to analyze and reduce cloud expenses by implementing systematic cost optimization frameworks. It provides actionable patterns for resource rightsizing, implementing tagging strategies for visibility, utilizing reserved capacity, and configuring automated lifecycle policies. By integrating FinOps best practices directly into the development workflow, it helps teams reduce waste and implement cost governance without sacrificing system performance or reliability.

Key Features

0 GitHub stars
Multi-cloud optimization strategies for AWS, Azure, and GCP
Automated storage lifecycle and tagging policy generation
Resource rightsizing and idle resource identification patterns
Pricing model optimization including Reserved Instances and Savings Plans
Budget monitoring and cost anomaly detection configuration

Use Cases

Reducing monthly cloud billing through architectural rightsizing
Implementing enterprise-wide resource tagging for granular cost allocation
Configuring automated storage transitions to lower-cost archival tiers

About

Key Features

0 GitHub stars
Multi-cloud optimization strategies for AWS, Azure, and GCP
Automated storage lifecycle and tagging policy generation
Resource rightsizing and idle resource identification patterns
Pricing model optimization including Reserved Instances and Savings Plans
Budget monitoring and cost anomaly detection configuration

Use Cases

Reducing monthly cloud billing through architectural rightsizing
Implementing enterprise-wide resource tagging for granular cost allocation
Configuring automated storage transitions to lower-cost archival tiers