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data-model

Define data requirements, schema types, and governance policies for enterprise data architectures.

2|Updated Mar 15, 2026
Search Tags:#ontology#knowledge-graph#data-model#data-governance#er-schema#vector-schema
One-click install
npx skills add https://github.com/Modular-Earth-LLC/solutions-architecture-agent --skill data-model-modular-earth-llc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-model
Source: https://github.com/Modular-Earth-LLC/solutions-architecture-agent/tree/main/skills/data-model
Command: npx skills add https://github.com/Modular-Earth-LLC/solutions-architecture-agent --skill data-model-modular-earth-llc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data modeling and governance policy design enable scalable, auditable, and consistent data architectures across enterprise systems by translating business requirements into formal schemas and governance rules.

Core Features & Use Cases

  • ER/schema design: Design entity-relationship models with constraints, keys, and indexes supporting migration-friendly evolution.
  • Vector/Knowledge schemas: Define collection metadata, chunking strategy, embedding approaches, and ontology alignment for robust retrieval across domains.
  • Governance & compliance: Document retention, access controls, PII handling, and audit trails to meet regulatory and policy requirements.

Quick Start

Provide a complete data-model design for the current architecture context and generate the corresponding schemas and governance policies.

Frequently Asked Questions about data-model

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

FAQPage Schema
How do I design an ER schema with data governance policies for enterprise systems?▼

Design an ER schema with governance by defining entity-relationship constraints, keys, and indexes. This translates business requirements into formal schemas and governance rules to support scalable, auditable enterprise data architectures.

What is the best way to structure a vector schema and chunking strategy for a knowledge graph?▼

Structuring a vector schema for a knowledge graph involves defining collection metadata, chunking strategy, embedding approaches, and ontology alignment. This ensures robust retrieval capabilities and consistent domain alignment across your data landscapes.

How do I document data governance compliance for PII handling and retention?▼

Document data governance compliance for PII handling by defining retention policies, access controls, and audit trails. This meets regulatory requirements and ensures your data architecture maintains strict policy compliance and auditable records.

Can I apply ontology alignment to vector schemas across different domains?▼

Yes, you can apply ontology alignment to vector schemas across different domains. Defining collection metadata and embedding approaches ensures robust retrieval and consistent knowledge graph mapping across your enterprise data landscapes.

Does data model design support migration-friendly schema evolution?▼

Yes, data model design supports migration-friendly schema evolution. It includes prerequisites validation, core design, validation rules, and migration considerations to ensure your ER models and schemas evolve consistently without disruption.

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