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hugging-face-datasets

Create, configure, and query Hugging Face Hub datasets with SQL-based transformations.

4|Updated Jan 12, 2026
One-click install
npx skills add https://github.com/Bennoo/classification_experience --skill hugging-face-datasets-bennoo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-datasets
Source: https://github.com/Bennoo/classification_experience/tree/main/.claude/skills/hugging-face-datasets
Command: npx skills add https://github.com/Bennoo/classification_experience --skill hugging-face-datasets-bennoo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires duckdb>=1.0.0, huggingface_hub>=0.20.0, datasets>=2.14.0, pandas>=2.0.0.

What problem does it solve?

This Skill provides a complete workflow to create and manage datasets on the Hugging Face Hub, including repository initialization, configuration with system prompts, streaming row updates, and SQL-based data manipulation. It is designed to work alongside the HF MCP server to enable end-to-end dataset workflows.

Core Features & Use Cases

  • Initialize and configure dataset repositories on Hugging Face Hub with detailed system prompts and metadata.
  • Stream updates to datasets by uploading new rows in JSONL format, with template-based validation for chat, classification, QA, and other formats.
  • Query, transform, and export datasets using a DuckDB-backed SQL manager, supporting describe, sample, histogram, unique, and join operations.
  • Push transformed results back to the Hub or export to Parquet/JSONL for downstream analytics and modeling.
  • Templates and JSON schema validation ensure consistent, high-quality data generation for training and evaluation.

Quick Start

  • uv run scripts/dataset_manager.py init --repo_id "your-username/dataset-name"
  • uv run scripts/dataset_manager.py quick_setup --repo_id "your-username/dataset-name" --template chat
  • uv run scripts/dataset_manager.py add_rows --repo_id "your-username/dataset-name" --split train --rows_json '[{"messages": [{"role":"user","content":"Hello"}]}]'
  • uv run scripts/sql_manager.py query --dataset "your-username/dataset-name" --sql "SELECT * FROM data LIMIT 5"

Frequently Asked Questions about hugging-face-datasets

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

FAQPage Schema
How do I create and initialize a Hugging Face dataset repository?▼

To create a Hugging Face dataset repository, use the dataset manager init command with your target repo ID. You can then apply a quick setup command with a chat template to automatically configure system prompts and metadata for the new dataset.

How do I add new rows to a Hugging Face dataset in JSONL format?▼

You can add new rows to a Hugging Face dataset by streaming updates via the add_rows command. Provide your repo ID, target split name, and a JSON array of row objects to populate the dataset.

Can I run SQL queries to transform Hugging Face datasets?▼

Yes, you can run SQL queries to transform Hugging Face datasets using a DuckDB-backed SQL manager. It supports describe, sample, histogram, unique, and join operations for data manipulation.

Do I need DuckDB and pandas to query Hugging Face Hub datasets?▼

Yes, querying Hugging Face Hub datasets requires DuckDB and pandas dependencies. The SQL manager uses DuckDB to execute transformations, while pandas and huggingface_hub handle data structures and remote repository interactions.

How do I export transformed Hugging Face datasets to Parquet?▼

You can export transformed Hugging Face datasets to Parquet or JSONL formats using the SQL manager export command. This allows you to save query results for downstream analytics and modeling.

What is the best way to validate chat data formats for Hugging Face datasets?▼

The best way to validate chat data formats for Hugging Face datasets is using built-in templates and JSON schema validation. This ensures consistent, high-quality data generation for training and evaluation across chat, classification, and QA formats.

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