generating-test-data
Generates type-safe, reproducible test data and fixtures for various test frameworks.
Install
mkdir -p .claude/skills/generating-test-data && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4770" && unzip -o skill.zip -d .claude/skills/generating-test-data && rm skill.zipInstalls to .claude/skills/generating-test-data
Activation
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Generate realistic test data including edge cases and boundary conditions.Key capabilities
- →Generate factory functions for data models
- →Create edge case data variants for testing
- →Build relationship factories for connected entity graphs
- →Generate database seed files for integration tests
- →Validate generated data against schemas
How it works
The skill reads project data models to create factory functions that produce valid default instances. It then generates edge case variants and relationship graphs to ensure complete test coverage.
Inputs & outputs
When to use generating-test-data
- →Generate database seed datasets
- →Create factory functions for unit tests
- →Populate test databases with edge cases
- →Setup fixtures for integration testing
About this skill
Test Data Generator
Overview
Generate realistic, type-safe test data including fixtures, factory functions, seed datasets, and edge case values. Supports Faker.js, Factory Bot patterns, Fishery (TypeScript factories), pytest fixtures, and database seed scripts.
Prerequisites
- Data generation library installed (Faker.js/@faker-js/faker, Fishery, factory-boy for Python, or JavaFaker)
- Database schema or TypeScript/Python type definitions for the data models
- Test framework with fixture support (Jest, pytest, JUnit)
- Seed management for reproducible random data (
faker.seed()) - Database client for seed data insertion (if generating database fixtures)
Instructions
- Read the project's data models, TypeScript interfaces, database schemas, or ORM definitions to understand the shape of all entities.
- For each entity, create a factory function that produces a valid default instance:
- Use Faker methods matched to field semantics (e.g.,
faker.person.fullName()for names,faker.internet.email()for emails). - Provide sensible defaults for required fields.
- Allow overrides via a partial parameter for test-specific customization.
- Set a deterministic seed for reproducibility (
faker.seed(12345)).
- Use Faker methods matched to field semantics (e.g.,
- Generate edge case data variants for each entity:
- Empty values: Empty strings, null, undefined, empty arrays.
- Boundary values: Maximum string length, integer overflow, zero, negative numbers.
- Unicode and i18n: Names with accents, CJK characters, RTL text, emoji.
- Adversarial inputs: SQL injection strings, XSS payloads, excessively long strings.
- Temporal edge cases: Leap years, timezone boundaries, epoch zero, far-future dates.
- Create relationship factories that build connected entity graphs:
- A user factory that also creates associated addresses and orders.
- Configurable depth to avoid infinite recursion.
- Lazy evaluation for optional relationships.
- Generate database seed files for integration tests:
- SQL insert scripts or ORM seed functions.
- Idempotent operations (use
ON CONFLICTorINSERT IF NOT EXISTS). - Separate seed sets for different test scenarios (empty state, populated state, edge cases).
- Write fixture files in JSON, YAML, or TypeScript for static test data:
- Group fixtures by test scenario.
- Include both valid and invalid data sets.
- Validate generated data against the schema to ensure factories remain in sync with model changes.
Output
- Factory function files (one per entity) in
test/factories/ortests/factories/ - Edge case data collections covering boundaries and adversarial inputs
- Database seed scripts for integration test environments
- JSON/YAML fixture files for static test data
- Factory index file exporting all factories for easy test imports
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Factory produces invalid data | Schema changed but factory not updated | Add a validation step that runs the factory output through the schema validator |
| Duplicate unique values | Faker generates collisions in small datasets | Use sequential IDs or append a counter; increase Faker's unique retry limit |
| Database seed fails on foreign key | Seed insertion order violates referential integrity | Sort seed operations topologically by dependency; disable FK checks during seeding |
| Factory recursion overflow | Circular relationships (User -> Order -> User) | Limit relationship depth; use lazy references; break cycles with ID-only references |
| Non-deterministic test failures | Random seed not set consistently | Call faker.seed() in beforeAll or at factory module level; document seed values |
Examples
TypeScript factory with Fishery:
import { Factory } from 'fishery';
import { faker } from '@faker-js/faker';
interface User {
id: string;
name: string;
email: string;
role: 'admin' | 'user';
createdAt: Date;
}
export const userFactory = Factory.define<User>(({ sequence }) => ({
id: `user-${sequence}`,
name: faker.person.fullName(),
email: faker.internet.email(),
role: 'user',
createdAt: faker.date.past(),
}));
// Usage:
const user = userFactory.build();
const admin = userFactory.build({ role: 'admin' });
const users = userFactory.buildList(10);
pytest fixture factory:
import pytest
from faker import Faker
fake = Faker()
Faker.seed(42)
@pytest.fixture
def make_user():
def _make_user(**overrides):
defaults = {
"name": fake.name(),
"email": fake.email(),
"age": fake.random_int(min=18, max=99),
}
return {**defaults, **overrides}
return _make_user
def test_user_validation(make_user):
user = make_user(age=17)
assert validate_age(user) is False
Edge case data collection:
export const edgeCases = {
strings: ['', ' ', '\t\n', 'a'.repeat(10000), '<script>alert(1)</script>', # 10000: 10 seconds in ms
"Robert'); DROP TABLE users;--", '\u0000null\u0000byte'],
numbers: [0, -0, -1, Number.MAX_SAFE_INTEGER, NaN, Infinity, -Infinity],
dates: [new Date(0), new Date('2024-02-29'), new Date('9999-12-31')], # 2024: 9999 = configured value
};
Resources
- Faker.js: https://fakerjs.dev/
- Fishery (TypeScript factories): https://github.com/thoughtbot/fishery
- factory_boy (Python): https://factoryboy.readthedocs.io/
- Chance.js: https://chancejs.com/
- Test data management patterns: https://martinfowler.com/bliki/ObjectMother.html
Prerequisites
Limitations
- →Factory recursion overflow on circular relationships
- →Duplicate unique values in small datasets
- →Database seed failures due to referential integrity
How it compares
Unlike manual data creation, this workflow uses deterministic seeds and schema-aware factories to ensure reproducibility and consistency across test environments.
Compared to similar skills
generating-test-data side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| generating-test-data (this skill) | 1 | 27d | Review | Intermediate |
| lint-and-validate | 6 | 6mo | Review | Beginner |
| agent-implementer-sparc-coder | 1 | 6mo | Review | Intermediate |
| langsmith-evaluator | 0 | 4mo | Review | Intermediate |
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Example prompts that trigger this skill in your AI assistant.
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