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Found 8 Skills
Create and manage test data with factory patterns, fixture strategies, data anonymization, and synthetic data generation. Covers Fishery (TypeScript), FactoryBot (Ruby), Factory Boy (Python), database seeding, cleanup strategies, and GDPR-compliant data handling. Use when: "test data," "fixtures," "factories," "seed data," "synthetic data," "test database," "data anonymization." Not for: migration/integrity testing of the DB itself — use database-testing; environment provisioning and database branching strategy — use test-environments. Related: test-environments, database-testing, api-testing, unit-testing.
Generate synthetic training data when you don't have enough real examples. Use when you're starting from scratch with no data, need a proof of concept fast, have too few examples for optimization, can't use real customer data for privacy or compliance, need to fill gaps in edge cases, have unbalanced categories, added new categories, or changed your schema. Covers DSPy synthetic data generation, quality filtering, and bootstrapping from zero.
Prepare inputs for MTHDS methods. Use when user says "prepare inputs", "create inputs", "use my files", "generate test data", "template", "synthesize inputs", "mock inputs", "I have a PDF/image/document to use", "make sample data", or wants to create inputs.json for running a .mthds pipeline. Handles user-provided files, synthetic data generation, placeholder templates, and mixed approaches. Defaults to automatic mode.
Prepare inputs for MTHDS methods. Use when user says "prepare inputs", "create inputs", "use my files", "generate test data", "template", "synthesize inputs", "mock inputs", "I have a PDF/image/document to use", "make sample data", or wants to create inputs.json for running a .mthds pipeline. Handles user-provided files, synthetic data generation, placeholder templates, and mixed approaches. Defaults to automatic mode.
Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.
Plan comprehensive test data management including synthetic data generation, data anonymization, versioning, and environment-specific strategies.
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.
Strategic test data generation, management, and privacy compliance. Use when creating test data, handling PII, ensuring GDPR/CCPA compliance, or scaling data generation for realistic testing scenarios.