domain-review

Review MD-DDL domain models for structural correctness and modeling quality.

1|Updated Feb 7, 2026

Search Tags:#validation #data modeling #quality check #data governance #domain review #md-ddl

Ready to run

npx skills add https://github.com/Semprini/md-ddl --skill domain-review-semprini

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: domain-review
Source: https://github.com/Semprini/md-ddl/tree/main/agents/agent-ontology/skills/domain-review
Command: npx skills add https://github.com/Semprini/md-ddl --skill domain-review-semprini

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures your MD-DDL domain models are structurally sound and adhere to best practices, catching errors before they impact downstream processes.

Core Features & Use Cases

Structural Conformance: Verifies adherence to the MD-DDL specification for syntax, formatting, and linking.
Decision Quality Checks: Assesses the quality of modeling decisions regarding granularity, temporal tracking, existence, mutability, and more.
Standards & Regulatory Alignment: Checks if domain concepts align with industry standards and regulatory requirements.
Use Case: Before deploying a new customer domain model, use this Skill to perform a comprehensive review, ensuring all relationships are correctly defined, temporal aspects are handled appropriately, and governance policies are accurately reflected.

Quick Start

Run a full review of the 'customer' domain model.

Frequently Asked Questions about domain-review

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

FAQPage Schema

How do I validate MD-DDL domain models for structural correctness?▼

To validate MD-DDL domain models, this Skill performs a comprehensive review checking structural conformance against the specification, verifying syntax, formatting, and linking accuracy to catch errors before downstream impact.

What is domain review in data governance and when do I need it?▼

Domain review in data governance is a comprehensive audit of domain models assessing modeling decisions, granularity, temporal tracking, and regulatory posture. You need it before deploying new domains to ensure best practices.

How do I perform a quality check on data modeling decisions like temporal tracking and mutability?▼

Quality checks on data modeling decisions are performed by assessing relationship granularity, temporal tracking, existence, mutability, and conceptual-to-logical realization, identifying critical, major, and minor findings with remediation suggestions.

Can I check if my domain concepts align with industry standards and regulatory requirements?▼

Yes, you can check standards and regulatory alignment by reviewing domain concepts against industry standards and regulatory requirements, verifying that governance policies are accurately reflected within the MD-DDL domain structure.

What's the best way to audit domain models before deployment to catch structural errors?▼

The best way to audit domain models before deployment is running a full review that validates relationship definitions, temporal aspects, and governance policies, categorizing findings by severity to guide remediation.

Does this domain review process work without external dependencies?▼

Yes, the domain review process works without external dependencies, operating independently to analyze MD-DDL domain models and generate findings based solely on structural conformance and decision quality assessments.