When to Use

Running GDS algorithms against embedded GDS plugin through Python client (graphdatascience)
Running GDS algorithms through CALL gds.* Cypher procedures
Aura Pro, self-managed Neo4j, local Neo4j, or offline DBMS with GDS plugin installed
Projecting named in-memory graphs, running centrality/community/similarity/path/embedding algorithms
Chaining algorithms via mutate mode; building FastRP → KNN pipelines
Writing node embeddings for Neo4j vector indexes / structural similarity search
Memory estimation before large graph operations

When NOT to Use

Aura Graph Analytics Sessions / AGA / GdsSessions / AuraGraphDataScience → neo4j-aura-graph-analytics-skill
AuraDB Cypher API with { memory: ... } or { sessionId: ... } → neo4j-aura-graph-analytics-skill
Cypher query authoring → neo4j-cypher-skill
Driver/connection setup → neo4j-driver-python-skill
GraphRAG retrieval → neo4j-graphrag-skill
Creating/querying vector indexes over written embeddings → neo4j-vector-index-skill