neo4j-modeling-skill

To design a Neo4j graph data model from a relational schema, translate tables to node labels and foreign keys to relationship types. This skill plans the migration by mapping relational/document concepts to appropriate graph structures, including selecting intermediate nodes for n-ary facts.

Common Neo4j graph modeling anti-patterns include generic labels, missing constraints, and supernode risks. This skill reviews your schema to detect these issues, assesses structural quality, and recommends concrete refactoring steps to improve traversal correctness and query performance.

You need intermediate nodes in a Neo4j graph database when modeling n-ary facts or complex relationships that require additional properties. This skill provides graph modeling guidance to choose node labels, relationship types, and properties, indicating exactly when to introduce intermediate nodes.

Constraints and indexes for a Neo4j schema should enforce data integrity and optimize query traversal. This skill inspects your existing schema, produces a structured assessment, and recommends specific constraints and indexes using Neo4j best practices to eliminate anti-patterns.

No, this skill cannot write or optimize Cypher queries. It specifically designs, reviews, and refactors Neo4j graph data models by aligning labels, relationships, and properties with domain intent, delegating Cypher authoring and other domains to dedicated skills.

The best way to review and refactor a Neo4j schema for performance is to inspect labels, relationship types, and properties against domain intent. This skill identifies anti-patterns like supernodes and missing constraints, recommending concrete schema enforcement and refactoring plans.