Review MD-DDL domain models for structural correctness and modeling quality.
npx skills add https://github.com/Semprini/md-ddl --skill domain-review-semprini
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
This Skill ensures your MD-DDL domain models are structurally sound and adhere to best practices, catching errors before they impact downstream processes.
Run a full review of the 'customer' domain model.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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.
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