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dataset-curator

Manage ML/AI dataset lifecycle including cleaning, splitting, versioning, and QA.

Updated May 24, 2026
Search Tags:#quality assurance#data cleaning#ML pipeline#dataset management#data versioning
One-click install
npx skills add https://github.com/haJ1t/senior-dev-squad-skills --skill dataset-curator-haj1t
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dataset-curator
Source: https://github.com/haJ1t/senior-dev-squad-skills/tree/main/plugins/ai-ml-eng-pro/skills/dataset-curator
Command: npx skills add https://github.com/haJ1t/senior-dev-squad-skills --skill dataset-curator-haj1t

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill ensures datasets for ML/AI projects are reproducible, well-documented, and free from common pitfalls, improving the quality of training and evaluation datasets.

Core Features & Use Cases

  • Dataset Lifecycle Management: Full lifecycle management from data collection to quality assurance.
  • Versioning and Documentation: Versioning for reproducibility and datasheets for documentation.
  • Class Balance and Bias Awareness: Ensures balanced and unbiased datasets.
  • Use Case: Use this Skill to prepare a new dataset for ML training or clean and prepare an existing dataset for model consumption.

Quick Start

Start the dataset-curator skill with 'dataset-curator prepare dataset my_dataset'.

Frequently Asked Questions about dataset-curator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage the full ML dataset lifecycle including collection and cleaning?▼

Manage the ML dataset lifecycle by handling data collection, cleaning, augmentation, splitting, versioning, and quality assurance to ensure reproducible and well-documented training pipelines.

What is dataset versioning and when do I need it for ML pipelines?▼

Dataset versioning tracks changes in data over time. You need it for ML pipelines to ensure reproducibility, maintain consistent model evaluations, and document the exact data used during training.

How do I prevent data leakage and label noise when preparing datasets?▼

Prevent data leakage and label noise during dataset preparation by applying built-in quality assurance checks that ensure proper splitting, class balance, and accurate metadata documentation.

Does this dataset management approach handle class balance and bias awareness?▼

Yes, dataset management includes class balance and bias awareness checks to ensure datasets remain unbiased and properly distributed before being used for ML model training.

What's the best way to document datasets for AI reproducibility?▼

The best way to document datasets for AI reproducibility is to generate datasheets for documentation, enforce data versioning, and maintain strict metadata records throughout the data lifecycle.

Can I use this to clean an existing dataset for model consumption?▼

Yes, you can clean and prepare an existing dataset for model consumption by applying data cleaning, augmentation, and quality assurance steps to ensure it is free from common pitfalls.

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