07-data-model

Catalog persistent data entities and storage occurrences across multi-repo codebases.

1|Updated Apr 11, 2026

Search Tags:#architecture #capabilities #mapping #entity #cross-project #data-model #risk-analysis

Ready to run

npx skills add https://github.com/vishalkhondre/unearth --skill 07-data-model

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: 07-data-model
Source: https://github.com/vishalkhondre/unearth/tree/main/skills/07-data-model
Command: npx skills add https://github.com/vishalkhondre/unearth --skill 07-data-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Discovers all persistent data entities in the codebase, identifies how and where they are stored, and traces ownership to capabilities for architectural clarity.

Core Features & Use Cases

Inventory of storage entities with storage technology, collection/table names, and key fields.
Mapping of entities to owning capabilities and cross-project usage, including projections and DTOs.
Risk and hotspot analysis highlighting missing constraints, cross-store references, and high-coupling entities.

Quick Start

Run a full data-model discovery across the repository to inventory entities, storage, projections, and ownership.

Frequently Asked Questions about 07-data-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I map data entities to their owning capabilities in a multi-repo architecture?▼

To map data entities to owning capabilities in a multi-repo architecture, this skill inventories persistent storage occurrences and traces cross-project usage. It outputs an entity inventory detailing ownership by capability, storage technology, and cross-store references.

What is the best way to inventory all persistent data entities and their storage technology across a codebase?▼

The best way to inventory all persistent data entities is to run a full data-model discovery across the repository. This process catalogs storage occurrences, mapping each entity to its specific storage technology, collection or table names, and key fields.

How does cross-project data coupling analysis work for identifying architectural hotspots?▼

Cross-project data coupling analysis works by mapping storage types, projections, and cross-project entity usage to reveal coupling hotspots. It identifies high-coupling entities and missing constraints to highlight architectural risks across multiple repositories.

Can I trace cross-store references and projections for DTOs across my repositories?▼

Yes, you can trace cross-store references and projections for DTOs across repositories. The analysis maps entities to owning capabilities and cross-project usage, including projections and DTOs, to expose coupling and reference patterns.

Does this data-model discovery work for multi-repo architectures where capabilities own entities?▼

Yes, this data-model discovery specifically applies to multi-repo architectures where capabilities own entities. It maps storage types and cross-project usage to reveal coupling hotspots and suggest architectural constraints.

Why do I need suggested constraints for high-coupling entities in my data model?▼

You need suggested constraints for high-coupling entities to mitigate architectural risks identified during hotspot analysis. The data-model discovery highlights missing constraints and cross-store references, providing recommendations to reduce coupling and ensure stability.