Detect cloud providers and IaC patterns to generate architecture plans.
npx skills add https://github.com/jlaws/dotfiles --skill cmd-cloud-cloud
Please help me install this Agent Skill. Skill: cmd-cloud-cloud Source: https://github.com/jlaws/dotfiles/tree/main/.agents/skills/cmd-cloud-cloud Command: npx skills add https://github.com/jlaws/dotfiles --skill cmd-cloud-cloud
Streamline cloud architecture planning by analyzing repository context to produce scalable, cost-aware deployment designs and clear guidance for implementation.
Provide a cloud architecture plan for a new project by first detecting the cloud provider and IaC patterns in your repository, then outlining a scalable design and cost considerations.
High-intent search queries and answers about installing and using this skill.
Cost-optimized cloud architecture planning scans repository context for Terraform, CDK, CloudFormation, and Helm configurations to detect the cloud provider, identify IaC patterns, and generate an actionable, scalable deployment plan.
Yes, the architecture planning process adapts to multi-cloud environments by detecting IaC patterns across AWS, GCP, and Azure, then proposing scalable architectures and deployment plans tailored to project constraints and cost considerations.
The best way to define deployment scope is by analyzing repository context to automatically detect the cloud provider and identify IaC patterns like Terraform or CloudFormation, which then scopes the actionable architecture plan.
To generate a scalable deployment plan, the tool scans your repository to detect the cloud provider and IaC patterns, then outlines a scalable architecture design adapted to your project size, constraints, and cost requirements.
Yes, you can analyze existing Helm and Terraform configurations together, as the scanning mechanism detects multiple IaC patterns within the repository context to scope the architecture work and produce cost-aware deployment guidance.
You need automated cloud architecture planning when managing projects with multiple IaC patterns and cloud providers, as it streamlines the detection of configurations and produces scalable, cost-aware deployment designs faster than manual analysis.
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