How does it help with query performance?

It provides best practices for bounded traversals, indexing strategies, and techniques for avoiding 'supernodes' to ensure queries remain fast as your data grows.

Does this skill support Amazon Neptune?

Yes, it provides guidance for AWS-native graph deployments using Amazon Neptune, including support for Gremlin and SPARQL languages.

Can I use this for AI and RAG applications?

Absolutely. It includes specific patterns for building Knowledge Graphs to enrich LLM context through hybrid vector and graph search integrations.

When should I use a graph database instead of SQL?

Use a graph database when your queries involve deep traversals (4+ hops), variable relationships, or complex pathfinding that would require expensive multiple joins in a relational database.

Does it include support for PostgreSQL extensions?

Yes, it covers Apache AGE for developers who want to add graph capabilities to their existing PostgreSQL infrastructure without migrating data.

Graph Database Implementation

Name: Graph Database Implementation
Author: ancoleman

byancoleman

•

158

データベース管理

Implements and optimizes graph database architectures for relationship-heavy data models like social networks and recommendation engines.

概要

This skill provides specialized guidance for architecting and querying graph databases where relationships are first-class citizens. It covers selection criteria for Neo4j, ArangoDB, and Neptune, offering optimized Cypher query patterns for deep traversals, pathfinding, and complex pattern matching. Whether building knowledge graphs for RAG-based AI applications or implementing fraud detection systems, this skill ensures scalable data modeling and performance optimization through bounded traversals and efficient indexing.

主な機能

Graph data modeling best practices to avoid supernodes and bottlenecks
Multi-database support including Neo4j, ArangoDB, and Amazon Neptune
158 GitHub stars
Advanced Cypher query patterns for deep traversals and pathfinding
Integration patterns for AI-driven Knowledge Graphs and RAG systems
Implementation guides for fraud detection and recommendation engines

ユースケース

Developing knowledge graphs to enhance LLM context and RAG performance
Building social networks with complex connection and recommendation logic
Implementing fraud detection systems through circular flow and pattern analysis

概要

主な機能

Graph data modeling best practices to avoid supernodes and bottlenecks
Multi-database support including Neo4j, ArangoDB, and Amazon Neptune
158 GitHub stars
Advanced Cypher query patterns for deep traversals and pathfinding
Integration patterns for AI-driven Knowledge Graphs and RAG systems
Implementation guides for fraud detection and recommendation engines

ユースケース

Developing knowledge graphs to enhance LLM context and RAG performance
Building social networks with complex connection and recommendation logic
Implementing fraud detection systems through circular flow and pattern analysis