Dataset Curator

Overview

This skill covers the full lifecycle of dataset creation and curation for machine learning and LLM tasks. It addresses dataset schema design, data collection strategies, quality filtering, deduplication, class imbalance mitigation, stratified train/val/test splits, annotation guideline writing, and dataset card documentation. Good datasets are the foundation of reliable models — this skill helps teams avoid the most common data quality pitfalls that lead to poor generalization, evaluation leakage, and biased models.

When to Use

• Designing a new dataset schema for a classification, extraction, or generation task
• Cleaning and deduplicating a raw dataset before model training
• Planning annotation guidelines for human labelers or LLM-assisted labeling
• Addressing class imbalance in a training set (oversampling, undersampling, weighting)
• Creating stratified train/val/test splits without leakage between splits
• Writing a dataset card (model card for data) for reproducibility and documentation
• Auditing an existing dataset for quality, coverage, and potential biases
• Combining multiple data sources into a single unified dataset

When NOT to Use

• Training or fine-tuning a model (use model training skills)
• Running SQL or analytical queries on a production database (use data analysis skills)
• Building real-time data pipelines or streaming ETL (use data engineering skills)