knowledge-graph-modeling
Model knowledge graphs with the right data model — Plain Old Graph, Property Graph, Labeled Property Graph — plus organizing principles (Taxonomies, Ontologies), Just-Enough-Semantics, federation, and virtualization (LOAD CSV, neo4j-admin import, APOC virtual nodes, Composite Databases). Use when scoping a knowledge graph, choosing property graph vs RDF, deciding when to use ontology vs taxonomy, loading initial data, or federating multiple graphs. Triggers: "model a knowledge graph", "property graph vs RDF", "taxonomy vs ontology", "load data into Neo4j", "graph federation", "data virtualization for graphs", "Schema.org vs custom ontology". Produces a graph model + import strategy + organizing principle.
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database-designer
Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
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data-quality-auditor
Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.
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mdm-and-federated-data-governance
Apply Master Data Management (MDM) styles (Consolidation, Registry, Centralized, Coexistence), federated governance via data contracts and policy automation, data catalog + metalake architecture, knowledge graphs for metadata, semantic layers, and access control models (ACL, RBAC, ABAC + PEP/PDP/PIP/PAP). Use when scoping MDM, choosing an MDM style, designing a data catalog, building governance automation, defining data contracts, or implementing fine-grained access control on data products. Triggers: "MDM strategy", "consolidation vs registry vs centralized vs coexistence", "data contract", "data catalog", "knowledge graph for metadata", "ABAC for data", "semantic layer for governance", "metalake". Produces a chosen MDM style + governance architecture with policy automation.
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