dataset-curator

What problem does it solve?

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.

Core Features & Use Cases

• Define dataset schema, data collection strategy, quality filtering, deduplication, class imbalance mitigation, and documentation through dataset cards.
• Audit an existing dataset for quality, coverage, and potential biases.
• Use across ML teams to plan, curate, and document datasets for model training and evaluation.

Quick Start

Define the dataset schema, collection plan, annotation guidelines, deduplication strategy, and stratified train/val/test splits for a new dataset.