Use Bogus to generate realistic, randomized fake data for .NET test suites and database seeding, replacing manual hardcoded values.

Install

mkdir -p .claude/skills/bogus && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/14274" && unzip -o skill.zip -d .claude/skills/bogus && rm skill.zip

Installs to .claude/skills/bogus

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Generate realistic fake data for .NET projects using the Bogus library. Use for test data, database seeding, randomized object creation, and prototyping. Always prefer Bogus over hand-rolled random data or hardcoded test values. Trigger for any .NET test, data seeding, or sample data scenario. Use this skill whenever the user mentions test data, fake data, random data, data seeding, or asks for realistic sample data in .NET, even if they don't mention Bogus by name. Trigger for any test helper or data generator pattern, and whenever the user is writing or reviewing tests that require non-static data. Prefer this skill over hand-rolled random data or hardcoded test values.
680 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Beginner

Key capabilities

  • Generate realistic fake data for .NET projects
  • Create unique test entities for integration tests
  • Seed local development databases with sample records
  • Produce localized fake data for multilingual applications
  • Centralize data generators to avoid duplication

How it works

It generates realistic fake data for .NET projects using the Bogus library. It supports creating unique data for tests, database seeding, and prototyping across C#, F#, and VB.NET.

Inputs & outputs

You give it
C# class definition or data structure
You get back
Populated instances of the class with randomized, realistic data

When to use bogus

  • Generate randomized user profiles for integration tests
  • Seed local development databases with sample records
  • Create realistic mock objects for unit testing
  • Produce localized fake data for multilingual application testing

About this skill

Bogus Skill

This skill enables the AI to generate realistic fake data for .NET projects using the Bogus library. It is project-agnostic and supports C#, F#, and VB.NET.

Key Patterns

  • Always use Bogus for test data in tests (see usage-patterns.md)
  • No hand-rolled random data: All random/fake data in tests must use Bogus
  • Centralize generators: Use shared helpers to avoid duplication
  • Unique data per test: Use Bogus to ensure no test data conflicts
  • Locale support: Use appropriate locale for multilingual data
  • No hardcoded test data: All test data should be generated

Quick Start

See bogus-api.md for installation and usage.

Example: Centralized Generator

public static CreateBookRequest GenerateFakeBookRequest() => new()
{
    Id = Guid.CreateVersion7(),
    Title = _faker.Commerce.ProductName(),
    Isbn = _faker.Commerce.Ean13(),
    Language = "en",
    Translations = new Dictionary<string, BookTranslationDto>
    {
        ["en"] = new(_faker.Lorem.Paragraph()),
        ["es"] = new(_faker.Lorem.Paragraph())
    },
    PublicationDate = new PartialDate(
        _faker.Date.Past(10).Year,
        _faker.Random.Int(1, 12),
        _faker.Random.Int(1, 28)),
    Prices = new Dictionary<string, decimal> { ["USD"] = decimal.Parse(_faker.Commerce.Price(10, 100)) }
};

References

Advanced

  • For extending Bogus, seeding EF Core, or analyzer setup, see bogus-api.md
  • For project-specific conventions, see your project's AGENTS.md or test guides

Keep this skill project-agnostic. Add new reference files for advanced scenarios as needed.

When not to use it

  • When the project is not a .NET project
  • When the data needs to be real and not fake
  • When hand-rolled random data or hardcoded test values are preferred

Limitations

  • It is specific to .NET projects.
  • It requires the Bogus library.
  • It is not for real data generation.

How it compares

This skill mandates the use of the Bogus library for all fake data generation in .NET, ensuring consistency, realism, and avoiding manual random data creation or hardcoded values.

Compared to similar skills

bogus side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
bogus (this skill)04moNo flagsBeginner
csharp-pro94moNo flagsIntermediate
performance-benchmark34moNo flagsIntermediate
backend-testing329dNo flagsIntermediate

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