data-schema-knowledge-modeling

SKILL.md

Create rigorous, validated models of entities, relationships, and constraints that enable correct system implementation, knowledge representation, and semantic reasoning.

Data schema & knowledge modeling is the process of formally defining:

Ask user for domain description, core use cases (what queries/operations will this support), existing data (if migration/integration), performance/scale requirements, and technology constraints (SQL vs NoSQL vs graph database). Understanding use cases shapes the model - OLTP vs OLAP vs graph traversal require different designs. See Schema Types for guidance.

Use when designing database schemas, need to model domain entities and relationships clearly, building knowledge graphs or ontologies, creating API data models, defining system boundaries and invariants, migrating between data models, establishing taxonomies or hierarchies, user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when scattered/inconsistent data structures need formalization. Source: zpankz/mcp-skillset.

View raw