Generate realistic synthetic data for a Neo4j property graph by asking the user the right questions, then writing a Faker script tailored to their answers. Trigger when the user wants to populate Neo4j, generate fake/synthetic/test data for a graph schema, or build a dataset to test detection logic (fraud rings, entity resolution, anomalies). Trigger when the user uploads an arrows.app schema or describes a graph model and asks for sample data. Do NOT trigger for schema design (use graph-schema-studio).
Generate realistic synthetic data for a Neo4j property graph by asking the user the right questions, then writing a Faker script tailored to their answers. Trigger when the user wants to populate Neo4j, generate fake/synthetic/test data for a graph schema, or build a dataset to test detection logic (fraud rings, entity resolution, anomalies). Trigger when the user uploads an arrows.app schema or describes a graph model and asks for sample data. Do NOT trigger for schema design (use graph-schema-studio).
Graph Data Workflow
A six-step conversational flow: scale → use case → cardinality → preview →
patterns → script. Claude asks; the user answers; a Faker script is written
and run.
Read assets/docs/WORKFLOW.md before starting. It has the question
templates, the order, and the rules about pausing for confirmation.
Helper: scripts/build_script.py — takes a JSON config and emits a
standalone Python script using Faker.
