Does it support both RDF and Property Graphs?

Yes, it supports both Labeled Property Graph (LPG) and RDF triplestore models, providing query language support for both Cypher and SPARQL.

Is it suitable for GenAI and RAG applications?

Absolutely. It includes specific patterns for Knowledge Graph RAG (Retrieval-Augmented Generation), including vector index queries and entity extraction patterns.

Which graph databases are supported by this skill?

It covers 12 major technologies: Neo4j, AWS Neptune, Dgraph, JanusGraph, TigerGraph, Memgraph, TypeDB, Apache AGE, NebulaGraph, Blazegraph, and Stardog.

Can this skill help with query optimization?

Yes, it provides detailed guidance on profiling, indexing (including range, text, and vector indexes), and optimizing Cypher, Gremlin, and SPARQL queries.

Graph Database Specialist

Name: Graph Database Specialist
Author: rnavarych

byrnavarych

•

数据库管理

Provides production-level guidance and implementation patterns for 12 major graph database technologies.

This skill equips Claude with specialized expertise in graph data modeling, query optimization, and architectural selection across 12 different graph databases including Neo4j, AWS Neptune, and TigerGraph. It provides deep operational knowledge of query languages like Cypher, Gremlin, and SPARQL, enabling users to implement complex knowledge graphs, fraud detection systems, and recommendation engines with production-grade patterns and optimized indexing strategies.

主要功能

01Selection frameworks to choose between RDF triplestores and Property Graphs

02Advanced query optimization for Cypher, Gremlin, SPARQL, GSQL, and DQL

03Cross-platform support for 12 graph databases including Neo4j, Neptune, and Dgraph

04Production-ready modeling patterns for fraud detection and RAG

0511 GitHub stars

06Operational guidance for indexing, memory management, and clustering

使用场景

01Building enterprise-grade knowledge graphs for GenAI and RAG applications

02Implementing real-time fraud detection and cyclic transfer analysis

03Designing collaborative filtering recommendation engines using graph traversals

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add rnavarych/alpha-engineer graph-databases

For use in Claude.ai and ChatGPT

主要功能

01Selection frameworks to choose between RDF triplestores and Property Graphs

02Advanced query optimization for Cypher, Gremlin, SPARQL, GSQL, and DQL

03Cross-platform support for 12 graph databases including Neo4j, Neptune, and Dgraph

04Production-ready modeling patterns for fraud detection and RAG

0511 GitHub stars

06Operational guidance for indexing, memory management, and clustering

使用场景

01Building enterprise-grade knowledge graphs for GenAI and RAG applications

02Implementing real-time fraud detection and cyclic transfer analysis

03Designing collaborative filtering recommendation engines using graph traversals

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add rnavarych/alpha-engineer graph-databases

For use in Claude.ai and ChatGPT