Can I use this for multi-cloud environments?

Yes, the skill provides a detailed breakdown of all detected providers and includes a confidence score for each, making it ideal for managing multi-cloud repositories.

How does the Cloud Provider Detector achieve 95% accuracy?

It uses a weighted multi-signal approach, analyzing Terraform resource types, package manifests like package.json or requirements.txt, CLI config files, and Docker base images to confirm the provider.

How fast is the detection process?

It is highly optimized for performance, scanning typical projects in under 100ms using efficient glob patterns and streaming file reads.

Can I add support for other providers like DigitalOcean?

Absolutely. The skill is designed to be extensible through a patterns.json file where you can define custom signals and weights for any additional cloud provider.

Cloud Provider Detector

Name: Cloud Provider Detector
Author: FortiumPartners

byFortiumPartners

Infraestructura en la Nube

Automatically identifies AWS, Google Cloud, and Azure environments with high accuracy using multi-signal analysis.

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This skill enables Claude Code to intelligently scan project repositories and determine the target cloud platform by analyzing infrastructure-as-code files, package manifests, configuration directories, and Docker images. By calculating a weighted confidence score, it allows AI development agents to dynamically load the appropriate domain-specific cloud skills, ensuring that infrastructure tasks are performed with the correct provider context and best practices.

Características Principales

Automatic identification of AWS, GCP, and Azure environments
Dynamic skill loading trigger for infrastructure-specific workflows
Weighted confidence scoring for 95%+ detection accuracy
Extensible pattern system for adding custom cloud providers
Multi-signal analysis of Terraform, NPM, Python, and Docker files
0 GitHub stars

Casos de Uso

Initializing specialized AI agents with provider-specific knowledge
Auditing multi-cloud repositories to identify infrastructure footprint
Automating documentation generation for existing cloud environments

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Características Principales

Automatic identification of AWS, GCP, and Azure environments
Dynamic skill loading trigger for infrastructure-specific workflows
Weighted confidence scoring for 95%+ detection accuracy
Extensible pattern system for adding custom cloud providers
Multi-signal analysis of Terraform, NPM, Python, and Docker files
0 GitHub stars

Casos de Uso

Initializing specialized AI agents with provider-specific knowledge
Auditing multi-cloud repositories to identify infrastructure footprint
Automating documentation generation for existing cloud environments