Can it detect secrets and sensitive data during extraction?

Yes, it intelligently identifies potential secrets like API keys, passwords, and tokens, flagging them for secure handling and preventing them from being hardcoded in templates.

Can this help with cloud migrations?

Absolutely. By extracting patterns from one provider's configuration, it helps identify the core architectural components that need to be mapped to a new environment or provider.

Does it work with existing directory structures?

Yes, it performs structure analysis to understand file organization, module boundaries, and resource relationships across your entire codebase.

Which IaC frameworks does this skill support?

It supports a wide range of popular frameworks including Terraform, Pulumi, CloudFormation, AWS CDK, Azure Bicep, Helm, and Kubernetes YAML.

How does it handle variable naming and type inference?

The skill analyzes usage context and naming conventions to suggest descriptive variable names and automatically infers data types (string, integer, boolean) and allowed values.

Pattern Extraction

Name: Pattern Extraction
Author: Lobbi-Docs

byLobbi-Docs

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Cloud Infrastructure

Detects and extracts reusable patterns from infrastructure code to automate template generation and standardization.

About

This skill empowers developers to transform existing infrastructure-as-code (IaC) into reusable templates by intelligently identifying hardcoded values, environment-specific configurations, and structural patterns. It supports major frameworks like Terraform, Pulumi, and Kubernetes, allowing for the automatic extraction of variables, naming conventions, and resource relationships to accelerate the creation of standardized, production-ready template libraries from existing production code.

Key Features

1 GitHub stars
Automatic variable schema generation with type inference and validation constraints
Deep structural analysis of resource hierarchies and module dependencies
Multi-framework technology stack detection for Terraform, CloudFormation, K8s, and Bicep
Context-aware mapping of cloud-specific patterns like regions, CIDR blocks, and instance types
Intelligent identification of hardcoded strings, environment indicators, and sensitive secrets

Use Cases

Standardizing naming conventions and tagging strategies across multi-cloud environments
Converting production-grade IaC into modular, reusable infrastructure templates
Automating the migration of hardcoded configurations into dynamic variable schemas

About

Key Features

1 GitHub stars
Automatic variable schema generation with type inference and validation constraints
Deep structural analysis of resource hierarchies and module dependencies
Multi-framework technology stack detection for Terraform, CloudFormation, K8s, and Bicep
Context-aware mapping of cloud-specific patterns like regions, CIDR blocks, and instance types
Intelligent identification of hardcoded strings, environment indicators, and sensitive secrets

Use Cases

Standardizing naming conventions and tagging strategies across multi-cloud environments
Converting production-grade IaC into modular, reusable infrastructure templates
Automating the migration of hardcoded configurations into dynamic variable schemas