What problem does it solve? Setting up a new Neo4j project involves many disconnected steps — provisioning a database, designing a graph model, importing data, writing Cypher queries, and scaffolding an application. This Skill automates that entire zero-to-running-app journey in a single guided or autonomous session. ## Core Features & Use Cases - End-to-end 8-stage pipeline: prerequisites, context collection, provisioning (Aura Free/Pro, Docker, Desktop, or existing instance), schema modeling, data loading, visual exploration, query library generation, and app building. - Flexible data sources and outputs: load demo datasets, synthetic data, CSVs, or documents via a GraphRAG pipeline, then produce a Jupyter notebook, Streamlit dashboard, FastAPI service, or MCP server integration. - Resumable autonomous or HITL execution: tracks progress in progress.md, resumes interrupted sessions, and runs fully unattended when all context variables are provided upfront. - Use Case: Ask for a fraud detection graph on local Docker with synthetic data and a Streamlit app, and receive a running database, validated Cypher query library, and runnable dashboard within minutes. ## Quick Start Start a session in an empty directory and ask the agent to run the neo4j-getting-started skill for your domain, for example: build a friend recommendation graph for a beginner using Aura Free with synthetic data and a notebook.
