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 app — which is slow and error-prone for newcomers. This Skill automates the entire journey from zero to a running graph application in a single guided session. ## Core Features & Use Cases - End-to-end 8-stage pipeline: prerequisites, context collection, database provisioning (Aura Free/Pro, Docker, Desktop, or existing), schema modeling, data loading, visual exploration, query library generation, and app building. - Flexible data sources and outputs: load demo datasets, generate synthetic CSV data, import your own CSVs, or build a GraphRAG knowledge graph from documents; output a Jupyter notebook, Streamlit dashboard, FastAPI service, GraphRAG pipeline, or MCP server config. - HITL and autonomous modes with resumability: runs interactively with approval checkpoints or fully autonomously when all context is provided, and writes progress.md so interrupted sessions resume where they left off. - Use Case: A developer says "fraud detection for a fintech startup, local Docker, synthetic data, FastAPI" and receives a provisioned database, a validated Cypher query library, and a runnable API in about 15 minutes. ## Quick Start Start a session in an empty directory and ask the agent to run the neo4j-getting-started skill for your domain, use case, database target, data source, and desired app type.
