neo4j-getting-started-skill

Orchestrate a Neo4j project lifecycle from database provisioning to a runnable graph application.

101|35|Updated Jan 20, 2026

Search Tags:#app generation #data import #mcp integration #neo4j #graph rag #graph modeling #cypher 25

Ready to run

npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-getting-started-skill

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: neo4j-getting-started-skill
Source: https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-getting-started-skill
Command: npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-getting-started-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill eliminates the hard startup work of getting a Neo4j graph from zero into a running, explorable application by orchestrating provisioning, schema design, data loading, visualization, query generation, and app creation.

Core Features & Use Cases

End-to-end Neo4j onboarding: guides you through prerequisites, context capture, database provisioning or connection, graph modeling, constraint setup, and data import.
Use-case driven outputs: generates a Cypher query library plus a runnable artifact such as a Jupyter notebook, Streamlit dashboard, FastAPI backend, or a GraphRAG pipeline.
Validation and guardrails: enforces success gates like schema completeness, data presence, query correctness, and a required visual graph entry point.

Quick Start

Run the skill in an empty project directory by invoking /neo4j-getting-started-skill and tell it your domain and desired app type, for example I want a beginner-friendly social graph friend recommendation notebook on Aura Free with synthetic data.

Frequently Asked Questions about neo4j-getting-started-skill

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

FAQPage Schema

How do I build a Neo4j graph application from scratch?▼

To build a Neo4j graph application from scratch, this skill orchestrates provisioning, graph modeling, data loading, and query generation to produce a runnable artifact like a Jupyter notebook, Streamlit dashboard, or FastAPI backend.

How does GraphRAG document ingestion work with Neo4j?▼

GraphRAG document ingestion with Neo4j works by optionally processing document data during the loading stage to populate the graph, which is then queried using validated Cypher queries to support retrieval-augmented generation pipelines.

Can I use this to model a graph schema and apply constraints on Aura Free?▼

Yes, you can model a graph schema and apply constraints on Aura Free, as the skill supports schema-first modeling, constraint setup, and database provisioning across Aura, local Docker, and Desktop environments.

What is the best way to generate Cypher queries for a new Neo4j project?▼

The best way to generate Cypher queries for a new Neo4j project is to use an orchestrated skill that validates CYPHER 25 syntax, enforces data presence success gates, and creates a dedicated query library.

Do I need Docker to load synthetic data into a Neo4j database?▼

You do not need Docker exclusively, as the skill supports connecting to existing databases or provisioning new ones on Aura Free or Desktop, allowing you to load synthetic or demo data without a local Docker setup.

What are the limitations when importing document data into a Neo4j graph?▼

Limitations when importing document data include enforcing success gates for schema completeness and data presence, requiring resumable stage execution, and needing a root SKILL.md YAML frontmatter to orchestrate the GraphRAG ingestion pipeline.