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Found 18 Skills
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.
Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
Synthetic-truth controls for analysis pipelines. Use before running any statistical fit, solver, audit, or search on real data — the pipeline must first prove it can recover a known planted answer and catch a deliberately corrupted input.