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neo4j-aura-graph-analytics-skill

Execute Neo4j Aura Graph Analytics workflows for graph projection, algorithm runs, and result retrieval.

101|35|Updated Jan 20, 2026
Search Tags:#session management#aura#neo4j#gds#graph analytics#remote projection#page rank
One-click install
npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-aura-graph-analytics-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neo4j-aura-graph-analytics-skill
Source: https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-aura-graph-analytics-skill
Command: npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-aura-graph-analytics-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about neo4j-aura-graph-analytics-skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run Neo4j GDS algorithms on AuraDB without installing plugins?▼

Neo4j Aura Graph Analytics provides serverless, isolated GDS compute through ephemeral sessions. You authenticate with Aura API credentials, project your database remotely into a session, run algorithms like PageRank, and stream results back without embedded plugins.

Can I run graph analytics on DataFrames without connecting to a Neo4j database?▼

Yes, standalone graph construction allows building graphs directly from Pandas or Spark DataFrames using gds.v2.graph.construct(). You can execute GDS algorithms on these isolated graphs without requiring an active Neo4j database connection.

How do I manage GDS session lifecycle to stop billing on Aura?▼

GDS session lifecycle management involves creating sessions with TTL, verifying connectivity, and safely deleting sessions when analytics complete. Deleting the session stops ongoing billing and cleans up the isolated compute environment.

Does remote projection support writing algorithm results back to Neo4j?▼

Remote projection projects graphs from connected Neo4j into ephemeral GDS sessions. Algorithm execution supports mutate, stream, and write modes, allowing you to retrieve results and optionally write them directly back to the connected Neo4j database.

What are the limitations of running graph analytics in Aura sessions?▼

Common failure modes include session expiry and memory limit constraints. The workflow handles async polling for long-running jobs and uses validation helpers for safe cleanup, but you must manage memory tier estimation and session TTL to avoid interruptions.

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