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Found 7 Skills
Generate realistic, consistent test data using factories, fixtures, and fake data libraries. Use for test data, fixtures, mock data, faker, test builders, and seed data generation.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Utilize AI to assist in testing activities, including test data generation, defect root cause analysis, test prioritization, and intelligent test recommendation. The default output format is Markdown, and you can request Excel/CSV/JSON formats instead. This skill applies to AI-assisted testing scenarios.
Creates test fixtures, mock data, and test scenarios for unit and integration tests. Use when setting up test data, creating mocks, or generating test fixtures.
Create or update database seed scripts for development and testing environments. Use when setting up test data, initializing development databases, creating demo environments, resetting to known state, or generating realistic sample data.
Property-based testing with Hypothesis for discovering edge cases automatically. Use when testing invariants, finding boundary conditions, implementing stateful testing, or validating data transformations.
Generate synthetic test data with edge cases for ETL pipeline testing.