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Graph database implementation for relationship-heavy data models.
Passed 21 of 22 test cases with the skill, and 20 of 22 without it.
1 fixed20 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
When running static schema validation scripts against a production graph database schema, what structural risks and anti-patterns should the automated checks inspect?
| Checked | Without skill | With skill |
|---|---|---|
| Checks for unbounded relationship traversals missing upper bounds. | Missed | Passed |
| Checks for unindexed properties used frequently in match filters. | Missed | Passed |
The instructions your model reads when the skill loads.
This skill guides selection and implementation of graph databases for applications where relationships between entities are first-class citizens. Unlike relational databases that model relationships through foreign keys and joins, graph databases natively represent connections as properties, enabling efficient traversal-heavy queries.
Use graph databases when:
Do NOT use graph databases when:
DATA CHARACTERISTICS?
├── Fixed schema, shallow joins (≤3 hops)
│ └─ PostgreSQL (relational)
│
├── Already on PostgreSQL + simple graphs
│ └─ Apache AGE (PostgreSQL extension)
│
├── Deep traversals (4+ hops) + general purpose
│ └─ Neo4j (battle-tested, largest ecosystem)
│
├── Multi-model (documents + graph)
│ └─ ArangoDB
│
├── AWS-native, serverless
│ └─ Amazon Neptune
│
└── Real-time streaming, in-memory
└─ Memgraph
test cases passed, vs 20 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.