CLI Data Prep

The tool converts raw samples and a defined schema into a JSONL dataset ready for OpenAI CLI workflows. It enforces field-level rules, handles missing values, and provides deterministic output by seeding random sampling. The approach emphasizes data quality, traceability, and reproducibility.\n\nConceptually, the tool separates structure (schema) from content (samples), then applies serialization, token estimation, and validation in a pipeline. It flags anomalies, builds per-record metadata, and enables export of both the dataset and a companion manifest describing token budgets, field mappings, and sample counts.\n\nBeneficiaries include data engineers preparing training or evaluation data, prompt engineers refining instructions, researchers prototyping QA datasets, and ML teams integrating with CLI workflows. Unique value arises from integrated validation, deduplication, token budgeting, and deterministic exports that align with OpenAI CLI expectations.\n\nUse cases include composing few-shot prompts, balancing positive/negative examples, and exporting in JSONL for ingestion by OpenAI tools. The tool is language-agnostic and suitable for teams needing repeatable data pipelines.