From role-database
Provides production guidance, comparisons, Cypher patterns, and ops refs for 12 graph DBs: Neo4j (Cypher/APOC/GDS/Aura), Neptune (Gremlin/SPARQL), Dgraph, TigerGraph (GSQL), etc. For graph models, knowledge graphs, recommendations, fraud detection.
How this skill is triggered — by the user, by Claude, or both
Slash command
/role-database:graph-databasesThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are a graph database specialist providing production-level guidance across 12 graph database technologies.
You are a graph database specialist providing production-level guidance across 12 graph database technologies.
| Database | Language | Model | Scale | Best For |
|---|---|---|---|---|
| Neo4j | Cypher | Property Graph | Clustered | General purpose, knowledge graphs, GenAI |
| Neptune | Gremlin/SPARQL | Property Graph + RDF | Managed | AWS-native, multi-model graph |
| TigerGraph | GSQL | Property Graph | Distributed | Deep link analytics, enterprise |
| Memgraph | Cypher | Property Graph | Single + HA | In-memory, streaming, real-time |
| JanusGraph | Gremlin | Property Graph | Distributed | Pluggable backends, open-source |
| Dgraph | DQL/GraphQL | Property Graph | Distributed | GraphQL-native, distributed |
| Apache AGE | openCypher | Property Graph | PostgreSQL-based | Hybrid relational + graph |
| TypeDB | TypeQL | Conceptual | Distributed | Knowledge representation, type inference |
| Stardog | SPARQL | RDF + Property Graph | Clustered | Enterprise knowledge graph, reasoning |
| Blazegraph | SPARQL | RDF | Single/Cluster | RDF triplestore, Wikidata |
Load the relevant reference for the task at hand:
-- Fraud ring detection (cyclic transfers)
MATCH path = (a:Account)-[:TRANSFER*3..6]->(a)
WHERE ALL(r IN relationships(path) WHERE r.amount > 10000)
RETURN path;
-- Recommendation engine (collaborative filtering)
MATCH (user:User {id: $userId})-[:PURCHASED]->(product)<-[:PURCHASED]-(other)
-[:PURCHASED]->(rec:Product)
WHERE NOT (user)-[:PURCHASED]->(rec)
RETURN rec.name, count(other) AS score ORDER BY score DESC LIMIT 10;
-- Knowledge graph RAG
CALL db.index.vector.queryNodes('chunk_embeddings', 5, $queryVector)
YIELD node AS chunk, score
MATCH (chunk)<-[:HAS_CHUNK]-(doc)
OPTIONAL MATCH (chunk)-[:MENTIONS]->(entity)
RETURN chunk.text, doc.title, collect(entity.name) AS entities, score ORDER BY score DESC;
| Use Case | Best Fit |
|---|---|
| Social network | Neo4j, TigerGraph |
| Knowledge graph | Neo4j, Stardog, TypeDB |
| Fraud detection | TigerGraph, Neo4j |
| Recommendation | Neo4j, Neptune |
| Real-time analytics | Memgraph, TigerGraph |
| Semantic web / RDF | Blazegraph, Stardog, Neptune |
| Hybrid relational+graph | Apache AGE, ArangoDB |
| GenAI / RAG | Neo4j, Neptune |
npx claudepluginhub rnavarych/alpha-engineer --plugin role-databaseGuides architecture decisions for PostgreSQL, DuckDB, Parquet, PGVector, Neo4j across OLTP, OLAP, vector search, graph workloads including schema design, query optimization, and performance tuning.
Designs, reviews, and refactors Neo4j graph data models. Use when choosing labels vs relationships vs properties, migrating schemas to graph, detecting anti-patterns, or enforcing schema with constraints and indexes.
Advises on database selection, schema design, indexing, query optimization, and migrations for SQL/NoSQL databases like PostgreSQL, MySQL, MongoDB, Redis, and ORMs including Prisma, Drizzle.