analyze-extension-intent

To structure an extension idea into a data model, you capture requirements through focused questions and return a validated intent object that defines entity types, population methods, and cross-references for downstream scaffolding.

You need a custom entity when information requires standalone data modeling, whereas a sidecar is used for supplementary data attached to existing structures. Intent structuring determines the appropriate shape based on conceptual relationships.

ECR decomposition is a recursive compositional reasoning method that breaks eligible systems into measurable atoms, patterns, and reusable structures during entity modeling to ensure comprehensive and validated system architecture.

You classify scope and population methods by applying structured capture rules and eligibility checks to the extension concept, generating an output schema that defines data relationships and measurable components.

Yes, intent structuring applies to recursive system decomposition and learning capture routes by validating conceptual structure and returning the appropriate data shape, references, and decomposition when applicable.

Structured intent objects require defined eligibility checks and capture rules, meaning vague concepts without clear measurable atoms or cross-references may fail validation and require further conceptual decomposition before scaffolding.