What problem does it solve? Writing tests and seeding dev environments for a healthcare platform requires realistic, safe, and relationally consistent fake data across users, providers, appointments, payments, and risk screenings, which is tedious and error-prone to build by hand. ## Core Features & Use Cases - Healthcare Faker Providers: Generate Luhn-valid NPI numbers, state-format license numbers, ICD-10 codes, SOAP-style clinical notes, and PHQ-9 risk screening keywords. - Composite Scenario Factories: Pre-built scenarios including CompletedSession, CrisisScreening, NewProviderOnboarding, BookingFunnel, MinorClient, cancellations, no-shows, reschedules, and cross-timezone bookings. - Safety-First Data: Uses .example emails, 555 phone prefixes, ocean coordinates, and fake addresses so test data can never leak into real communications. - Use Case: When writing a test for the crisis screening flow, invoke the CrisisScreening scenario to create a user with a PHQ-9 score of 27 and nine ResponseDetail records, with item 9 flagged severe. ## Quick Start Ask the AI to generate a CrisisScreening test scenario with a count of 3 using the test data factory skill.