hol-data

name	hol-data
description	Generate realistic synthetic seed data for a HOL lab app. Reads the app's models and creates 20-50 records per entity with industry-authentic values, realistic relationships, and useful edge cases.
argument-hint	<company-profile-or-domain-description>

HOL Synthetic Data Generator

You are generating realistic seed data for a HOL lab app. The data should feel authentic to the company's domain — not obviously fake placeholder data.

Input

The user has provided a domain description via $ARGUMENTS. This may be a company profile, industry description, or file path.

Step 1: Understand the Data Model

Read models.py and schemas.py to understand:

• All entity types and their fields
• Relationships between entities (foreign keys)
• Enum-like fields (status, tier, category) and their valid values
• Required vs optional fields
• Field types and constraints

Step 2: Design the Dataset

Plan the data before generating it:

Volume

• Primary entities (the main "thing" — itineraries, products, courses): 20-30 records
• Secondary entities (users, customers, guests): 10-15 records
• Junction/transaction entities (bookings, orders, enrollments): 30-50 records
• Reference entities (categories, tiers, locations): 5-10 records

Realism Requirements

• Names: Use plausible names for the domain. For cruise lines: real port cities, realistic ship names, standard cabin categories. For manufacturing: real material names, standard unit codes. For finance: realistic ticker symbols, standard account types.
• Dates: Use dates within a reasonable window (next 6-12 months for future events, past 1-2 years for historical data). Use proper date formats.
• Numbers: Prices, quantities, and scores should be in realistic ranges for the industry. No $1 cruises or $999,999 widgets.
• Relationships: Every foreign key must reference a valid record. A booking references a real guest AND a real itinerary.
• Status distribution: Mix of statuses — not all "confirmed" or all "active". Include some cancelled, pending, completed.

Edge Cases (Important for Bug Discovery)

Mix in a few records that exercise boundary conditions:

• A record with the minimum/maximum value for a numeric field
• A record with an empty optional field
• A record right at a tier boundary (e.g., a guest with exactly enough points for Gold)
• A record with dates at edge boundaries (first/last day of a range)
• A few records that look similar but differ in one key field

These edge cases help participants discover planted bugs more naturally.

Step 3: Generate seed.py

Rewrite seed.py with the full dataset. The file should:

"""Seed the database with synthetic data for the {App Name} HOL lab."""
from database import SessionLocal, init_db, engine, Base
from models import ModelA, ModelB, ModelC
from datetime import date, datetime

def seed():
    # Drop and recreate tables for clean state
    Base.metadata.drop_all(bind=engine)
    init_db()

    db = SessionLocal()
    try:
        # Create reference/lookup data first
        # ...

        # Create primary entities
        # ...

        # Create secondary entities
        # ...

        # Create transaction/junction records
        # ...

        db.commit()
        print(f"Seeded: X records of A, Y records of B, Z records of C")
    finally:
        db.close()

if __name__ == "__main__":
    seed()

Data Format Rules

• Define data inline in the seed file (not external JSON/CSV)
• Use model constructors directly: ModelA(field1="value", field2=123)
• Add records in bulk with db.add_all([...])
• Print a summary of what was seeded
• The script must be idempotent (drop and recreate tables)

Step 4: Verify

uv run python seed.py          # Should complete without errors
uv run pytest                  # All tests should still pass
uv run uvicorn main:app --reload  # Quick check that data is served

Test a few API endpoints to confirm data is visible and relationships work.

Step 5: Report

Summarize the dataset:

• Record counts per entity
• Notable edge cases included
• Any fields you weren't sure about realistic values for