Neo4j Snowflake Graph Analytics Skill

# Neo4j Graph Analytics for Snowflake Skill

An [Agent Skill](https://agentskills.io/specification) that helps AI agents work with [Neo4j Graph Analytics for Snowflake](https://neo4j.com/docs/snowflake-graph-analytics/current/) — a Snowflake Native Application that brings graph algorithms directly into Snowflake via SQL procedures.

## What this skill covers

- Installing Neo4j Graph Analytics from the Snowflake Marketplace
- Setting up the required privileges and roles
- The **project → compute → write** pattern for running algorithms
- SQL syntax for all available graph algorithms
- Projection configuration (node tables, relationship tables, orientation)
- Chaining algorithms together
- Working with views when column names don't match requirements
- Troubleshooting common errors

## Use this skill when

- Writing SQL to run graph algorithms on Snowflake tables
- Setting up Neo4j Graph Analytics for the first time
- Choosing the right algorithm for a business problem (fraud detection, recommendations, entity resolution, etc.)
- Configuring compute pool sizes for jobs
- Troubleshooting privilege or projection errors

## Available algorithms

| Category | Algorithms |
|---|---|
| Community Detection | WCC, Louvain, Leiden, K-Means, Triangle Count |
| Centrality | PageRank, Article Rank, Betweenness, Degree |
| Pathfinding | Dijkstra, Delta-Stepping, BFS, Yen's, Max Flow, FastPath |
| Similarity | Node Similarity, Filtered Node Similarity, KNN, Filtered KNN |
| Node Embeddings | FastRP, Node2Vec, HashGNN |
| Graph ML | GraphSAGE (node classification & embeddings, train & predict) |

## Quick example

```sql
CALL Neo4j_Graph_Analytics.graph.wcc('CPU_X64_XS', {
    'project': {
        'nodeTables': ['MY_DB.MY_SCHEMA.NODES'],
        'relationshipTables': {
            'MY_DB.MY_SCHEMA.RELATIONSHIPS': {
                'sourceTable': 'MY_DB.MY_SCHEMA.NODES',
                'targetTable': 'MY_DB.MY_SCHEMA.NODES',
                'orientation': 'NATURAL'
            }
        }
    },
    'compute': { 'consecutiveIds': true },
    'write': [{
        'nodeLabel': 'NODES',
        'outputTable': 'MY_DB.MY_SCHEMA.NODES_COMPONENTS'
    }]
});
```

## Resources

- [Neo4j Graph Analytics for Snowflake documentation](https://neo4j.com/docs/snowflake-graph-analytics/current/)
- [Snowflake Marketplace listing](https://app.snowflake.com/marketplace/listing/GZTDZH40CN/neo4j-neo4j-graph-analytics)
- [Example: Basket analysis on TPC-H data](https://github.com/neo4j-product-examples/snowflake-graph-analytics/tree/main/basket-analysis)

---
name: neo4j-snowflake-graph-analytics-skill
description: Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN,
  Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when
  running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App
  ("GDS Snowflake", "graph algorithms in Snowflake", "Neo4j Graph Analytics").
  Covers installation, privilege setup, project-compute-write pattern, and SQL CALL syntax.
  Does NOT cover Cypher or Neo4j DBMS queries — use neo4j-cypher-skill.
  Does NOT cover Aura Graph Analytics — use neo4j-aura-graph-analytics-skill.
  Does NOT cover self-managed GDS — use neo4j-gds-skill.
version: 1.0.1
allowed-tools: Bash WebFetch
---

Snowflake Native App — graph algorithm power inside Snowflake. Data stays in Snowflake; project into a graph, run algorithms via SQL `CALL`, results written back to Snowflake tables.

**Docs:** https://neo4j.com/docs/snowflake-graph-analytics/current/

---

## When to Use
- Running graph algorithms / GDS in Snowflake
- Data in Snowflake tables
- On-demand / pipeline workloads — ephemeral sessions, pay per session-minute
- Full isolation from the live database during analytics

## When NOT to Use
- **Aura Pro with embedded GDS plugin** → `neo4j-gds-skill`
- **Aura Graph Analytics** → `neo4j-aura-graph-analytics-skill`
- **Self-managed Neo4j with embedded GDS plugin** → `neo4j-gds-skill`
-