ln-723-seed-data-generator

Paths: File paths (references/, ../ln-*) are relative to this skill directory.

ln-723-seed-data-generator

Type: L3 Worker Category: 7XX Project Bootstrap

Universal seed data generator with two modes: MIGRATE (parse existing ORM schemas) or GENERATE (create from entity definitions). Outputs to any target format (C#, TypeScript, Python, JSON, SQL).

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Purpose & Scope

Aspect	Description
Input	ORM schema files (MIGRATE) or entity list (GENERATE)
Output	Seed data files in target format
Modes	MIGRATE: parse existing ORM → seed data. GENERATE: entity definitions → seed data

Scope boundaries:

• Parses ORM schema definitions or accepts entity lists
• Generates seed data in requested target format
• Creates realistic sample data using faker libraries
• Does not generate database migrations, EF Core configs, or ORM models

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Mode Selection

Mode	When	Input	Source
MIGRATE	TRANSFORM pipeline — existing ORM schemas found	ORM schema files	Drizzle, Prisma, TypeORM, EF Core, SQLAlchemy, Django ORM
GENERATE	CREATE pipeline — no existing schemas	Entity list from ln-700 Phase 0	User-provided or starter template (User, Role)

If GENERATE mode receives no entity list, generate starter template with User (id, name, email, role, createdAt) and Role (id, name, description).

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Target Formats

Format	Output File	Faker Library	Use Case
C# MockData	MockData.cs	Bogus	.NET projects
TypeScript fixtures	seed.ts	Faker.js	Node/React projects
Python factories	factories.py	Faker (Python)	Django/Flask projects
JSON	seed.json	—	API testing, import scripts
SQL	seed.sql	—	Direct DB seeding

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Workflow

Phase	Name	Actions	Output
1	Parse/Define	1A: Parse ORM schema (MIGRATE) or 1B: Accept entity list (GENERATE)	Entity model
2	Map Types	Apply universal type mapping to target format	Target type definitions
3	Generate Seed Data	Create seed files with faker-based realistic data	Seed data files
4	Verify	Validate relationships, check syntax	Valid seed files

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Phase 1: Parse/Define

1A: MIGRATE Mode — Parse ORM Schema

Step	Action	Reference
1A.1	Locate schema file(s)	—
1A.2	Auto-detect ORM type	orm_patterns.md — ORM Auto-Detection table
1A.3	Extract table/model definitions	orm_patterns.md — per-ORM parsing section
1A.4	Extract column definitions with types	orm_patterns.md
1A.5	Identify constraints (PK, FK, nullable, unique)	orm_patterns.md
1A.6	Extract enum definitions	orm_patterns.md

1B: GENERATE Mode — Accept Entity Definitions

Step	Action	Reference
1B.1	Receive entity list from orchestrator (or use starter template)	—
1B.2	Parse entity definitions (name, fields, types)	—
1B.3	Infer relationships from field names (userId → FK to User)	relationship_mapping.md
1B.4	Apply default constraints (id = PK, *Id = FK)	—

Output: Entity model with columns, types, and constraints.

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Phase 2: Map Types

Convert entity types to target format types.

Step	Action	Reference
2.1	Select target format (from orchestrator params)	—
2.2	Map column types to target format	type_mapping.md — Universal Type Mapping table
2.3	Determine nullable status per target	type_mapping.md
2.4	Map foreign keys and relationships	relationship_mapping.md
2.5	Transform names to target convention	See Name Conventions table below

Name Conventions by Target:

Target	Class/Model	Property/Field	File
C#	PascalCase singular	PascalCase	PascalCase.cs
TypeScript	PascalCase singular	camelCase	camelCase.ts
Python	PascalCase singular	snake_case	snake_case.py
JSON	camelCase	camelCase	kebab-case.json
SQL	snake_case plural	snake_case	snake_case.sql

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Phase 3: Generate Seed Data

Create seed files with realistic data using faker libraries.

Step	Action	Reference
3.1	Determine generation order (parents → children)	relationship_mapping.md
3.2	Generate IDs (GUIDs/UUIDs) for all entities	data_generation.md
3.3	Generate field values using faker	data_generation.md, type_mapping.md — Faker Integration
3.4	Ensure FK relationships valid (child references existing parent ID)	relationship_mapping.md
3.5	Write seed file in target format	—

Faker integration rule: All generated seed files MUST use faker libraries for realistic data with deterministic seeding (fixed seed value for reproducibility).

Target	Faker Setup
C#	var faker = new Bogus.Faker(); Randomizer.Seed = new Random(42);
TypeScript	import { faker } from '@faker-js/faker'; faker.seed(42);
Python	from faker import Faker; fake = Faker(); Faker.seed(42)

Generation order by dependency:

Order	Entity Type	Generate After
1	Root entities (no FK)	First
2	First-level children	Parents exist
3	Second-level children	Grandparents exist
N	Deepest children	All ancestors exist

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Phase 4: Verify

Check	Method	Expected
Syntax valid	Language-specific check	No syntax errors
FKs valid	Cross-reference	All FKs point to existing IDs
Types correct	Type analysis	Proper types for target format
Names follow convention	Pattern check	Per-target naming convention
Faker deterministic	Re-run with same seed	Identical output

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Supported ORM Detection

ORM	Detection Pattern	Ecosystem
Drizzle	pgTable(), mysqlTable(), sqliteTable()	Node.js
Prisma	model X { syntax in .prisma files	Node.js
TypeORM	@Entity(), @Column() decorators	Node.js
EF Core	DbContext, DbSet<>, [Table] attributes	.NET
SQLAlchemy	Base = declarative_base(), Column()	Python
Django ORM	models.Model, models.CharField()	Python

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Entity Transformation Rules

Source	Target	Transformation
Table name (plural, snake)	Class name (singular, Pascal)	user_profiles → UserProfile
Column name (snake)	Property name (target convention)	created_at → CreatedAt / createdAt / created_at
Enum name	Enum type (Pascal)	status_enum → StatusEnum
FK column	Navigation property	user_id → UserId / userId

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Sample Data Guidelines

Field Type	Sample Count	Distribution
Root entities	3-5 items	Varied status/priority
Child entities	5-10 items	Distributed across parents
Leaf entities	10-20 items	Realistic variety

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Critical Rules

• Single Responsibility: Generate only seed data, no ORM models or migrations
• Idempotent: Can re-run with same seed to produce identical output
• Valid Relationships: All FKs must reference existing parent IDs
• Faker Required: Use faker libraries for realistic data, never random strings
• Deterministic Seeding: Fixed seed value (42) for reproducibility across re-runs
• Generation Order: Parents before children, always
• Mode Awareness: MIGRATE parses files; GENERATE accepts definitions — never mix

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Definition of Done

• Mode determined (MIGRATE or GENERATE)
• Entity model extracted/defined with all fields and constraints
• Target format selected and type mappings applied
• Seed data files generated with faker-based realistic values
• Deterministic seeding verified (re-run produces identical output)
• Foreign keys reference valid parent IDs
• Names follow target format conventions
• Sample data includes 5-10 items per entity

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Risk Mitigation

Risk	Detection	Mitigation
Unknown ORM type	Auto-detection fails	Log warning, ask orchestrator for ORM hint
Invalid type mapping	Unknown column type	Use string as fallback, log warning
FK mismatch	FK references non-existent ID	Generate parents first, validate after
No entity list in GENERATE	Empty input	Use starter template (User, Role)
Name collision	Duplicate class/table names	Prefix with feature name
Circular references	Self-referencing with cycles	Limit depth, validate graph

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Reference Files

File	Purpose
references/orm_patterns.md	ORM auto-detection and schema parsing patterns (Drizzle, Prisma, TypeORM, EF Core, SQLAlchemy, Django)
references/type_mapping.md	Universal type mapping (ORM-agnostic → C#, TypeScript, Python) + Faker integration
references/data_generation.md	Realistic sample data patterns and generation rules
references/relationship_mapping.md	FK handling, generation order, relationship inference

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Version: 3.0.0 Last Updated: 2026-02-07