neo4j-aura-graph-analytics-skill

What problem does it solve?

This Skill eliminates the friction of setting up and running Graph Data Science (GDS) analytics on Neo4j Aura Graph Analytics by handling authentication, session lifecycle, graph projection, algorithm execution, and result retrieval in one coherent workflow.

Core Features & Use Cases

• Aura Graph Analytics (AGA) session management: Authenticate with Aura API credentials, estimate memory tiers, create/get sessions with TTL, verify connectivity, reconnect safely, list, and delete sessions to stop billing.
• Remote projection and AGA-specific Cypher API: Project graphs from connected Neo4j into ephemeral GDS sessions using gds.v2.graph.project(..., query) and gds.graph.project.remote(...), and support AuraDB Cypher API projection using memory or sessionId.
• Run algorithms end-to-end (mutate/stream/write): Execute GDS algorithms across mutate, stream, and write modes; poll async jobs when needed; retrieve results and optionally write back to the connected Neo4j database; handle common failure modes like session expiry and memory limits.
• Standalone graph construction: Build graphs from Pandas/Spark DataFrames using gds.v2.graph.construct() and run analytics without a Neo4j database connection.

Quick Start

Ask the AI to run Aura Graph Analytics for your dataset by creating a GDS session, projecting your Neo4j data remotely (or constructing from DataFrames), running PageRank with v2 endpoints, streaming results, then cleaning up the session to avoid ongoing charges.