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Found 5 Skills
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.
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.
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.
Plan comprehensive test data management including synthetic data generation, data anonymization, versioning, and environment-specific strategies.