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domino-datasets

Create, version, and share Domino Datasets with snapshots and tags.

6|3|Updated Feb 19, 2026
Search Tags:#data storage#data sharing#reproducibility#snapshot#data versioning#domino datasets
One-click install
npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-datasets-dominodatalab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: domino-datasets
Source: https://github.com/dominodatalab/domino-claude-plugin/tree/main/skills/datasets
Command: npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-datasets-dominodatalab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the management of Domino Datasets, enabling users to efficiently store, version, share, and access their data for reproducible data science workflows.

Core Features & Use Cases

  • Dataset Management: Create, upload, and organize data within Domino Projects.
  • Versioning: Utilize snapshots and tags for reproducible data states.
  • Sharing: Share datasets across projects and with collaborators.
  • Access: Understand and utilize correct paths for accessing data in different project types (DFS and Git-based).
  • Use Case: Ensure your machine learning model training is reproducible by creating a snapshot of your training dataset before each training run and tagging it with the model version.

Quick Start

Use the domino-datasets skill to create a new dataset named 'customer-churn-data' with the description 'Raw data for churn prediction'.

Frequently Asked Questions about domino-datasets

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

FAQPage Schema
How do I version datasets for reproducible machine learning training runs?▼

Dataset versioning for reproducible training uses snapshots to capture data states and tags to label them with model versions. This ensures consistent data alignment before each training iteration.

What is the best way to share datasets across multiple data science projects?▼

Sharing datasets across projects is managed through configurable access permissions. This allows collaborators to efficiently access and utilize shared data storage without duplicating files.

Does dataset access work differently for DFS and Git-based project types?▼

Dataset access paths differ between DFS and Git-based project types. Understanding and utilizing the correct path structure ensures proper data retrieval for each specific project configuration.

How can I optimize data handling when reading large datasets?▼

Optimizing data handling for large datasets is achieved through efficient data formats and chunked reading. This approach prevents memory overload and accelerates processing performance.

Can I create and organize data storage directly within Domino Projects?▼

Creating and organizing data within Domino Projects is fully supported. Users can directly upload files, structure data storage, and manage dataset metadata for high-performance access.

When do I need to create a dataset snapshot?▼

Creating a dataset snapshot is needed when establishing a reproducible baseline for data analysis or model training. It captures the exact data state at a specific point in time for future reference.

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