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huggingface-dataset-publishing

Publish Hugging Face datasets with image and metadata handling.

7|1|Updated May 9, 2026
Search Tags:#hugging face#dataset validation#dataset publishing#image features#dataset card#push to hub
One-click install
npx skills add https://github.com/black-yt/skills --skill huggingface-dataset-publishing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-dataset-publishing
Source: https://github.com/black-yt/skills/tree/main/huggingface-dataset-publishing
Command: npx skills add https://github.com/black-yt/skills --skill huggingface-dataset-publishing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you create, upload, verify, and maintain Hugging Face Datasets without breaking image features, metadata fields, or dataset viewer behavior. It is especially useful when your dataset includes images, multiple images per row, JSON metadata, or needs careful Hub-side validation and README maintenance.

Core Features & Use Cases

  • Dataset Construction: Build datasets locally with the correct datasets.Features, including Image and Sequence(Image()) for single-image and multi-image records.
  • Upload and Verification: Push datasets to the Hub, then reload them with load_dataset to confirm splits, configs, image decoding, and metadata parsing.
  • Repository Maintenance: Clone dataset repos to manage README.md, dataset cards, and small metadata updates with git.
  • Safety and Quality Checks: Prevent token leakage, validate JSON metadata, inspect dataset viewer rendering, and avoid committing temporary files or private paths.

Quick Start

Ask me to publish your Hugging Face dataset safely by checking the feature schema, uploading it, and verifying the result from the Hub.

Frequently Asked Questions about huggingface-dataset-publishing

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

FAQPage Schema
How do I publish a Hugging Face dataset with multiple images per row without breaking the dataset viewer?▼

To publish a Hugging Face dataset with multiple images per row, you must define the correct `datasets.Features` using `Sequence(Image())` for multi-image records. This ensures proper image decoding and metadata parsing during Hub-side validation and viewer rendering.

What is the best way to verify a dataset uploads correctly to the Hugging Face Hub?▼

The best way to verify a Hugging Face Hub upload is to reload the dataset using `load_dataset` after pushing. This round-trip verification confirms that splits, configs, image decoding, and JSON metadata parsing all function correctly from the Hub.

How do I maintain a dataset card and README for a published Hugging Face repository?▼

You maintain a dataset card and README by cloning the Hugging Face dataset repository locally to manage `README.md` and JSON metadata. This git-based approach allows safe, small metadata updates without disrupting the main dataset features.

Why does my Hugging Face dataset upload fail or leak sensitive tokens?▼

Hugging Face dataset uploads can fail or leak tokens due to improper feature schemas or unsafe repository updates. Applying safety checks to prevent token leakage, validate JSON metadata, and avoid committing temporary private paths ensures a safe upload.

Can I use this approach to validate JSON metadata fields before pushing to the Hugging Face Hub?▼

Yes, you can validate JSON metadata fields before pushing to the Hugging Face Hub. The process includes safety and quality checks that inspect JSON metadata formatting and prevent committing temporary files or private paths to the repository.

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