hugging-face-datasets

Overview

This skill provides tools to manage datasets on the Hugging Face Hub with a focus on creation, configuration, content management, and SQL-based data manipulation. It is designed to complement the existing Hugging Face MCP server by providing dataset editing and querying capabilities.

Integration with HF MCP Server

• Use HF MCP Server for: Dataset discovery, search, and metadata retrieval
• Use This Skill for: Dataset creation, content editing, SQL queries, data transformation, and structured data formatting

Version

2.1.0

Dependencies

• huggingface_hub
• duckdb (for SQL queries)
• datasets (for pushing query results to Hub)
• json (built-in)
• time (built-in)

Core Capabilities

1. Dataset Lifecycle Management

• Initialize: Create new dataset repositories with proper structure
• Configure: Store detailed configuration including system prompts and metadata
• Stream Updates: Add rows efficiently without downloading entire datasets

2. SQL-Based Dataset Querying (NEW)

Query any Hugging Face dataset using DuckDB SQL via scripts/sql_manager.py:

• Direct Queries: Run SQL on datasets using the hf:// protocol
• Schema Discovery: Describe dataset structure and column types
• Data Sampling: Get random samples for exploration
• Aggregations: Count, histogram, unique values analysis
• Transformations: Filter, join, reshape data with SQL
• Export & Push: Save results locally or push to new Hub repos

3. Multi-Format Dataset Support

Supports diverse dataset types through template system:

• Chat/Conversational: Chat templating, multi-turn dialogues, tool usage examples
• Text Classification: Sentiment analysis, intent detection, topic classification
• Question-Answering: Reading comprehension, factual QA, knowledge bases
• Text Completion: Language modeling, code completion, creative writing
• Tabular Data: Structured data for regression/classification tasks
• Custom Formats: Flexible schema definition for specialized needs

4. Quality Assurance Features

• JSON Validation: Ensures data integrity during uploads
• Batch Processing: Efficient handling of large datasets
• Error Recovery: Graceful handling of upload failures and conflicts

Usage Instructions

The skill includes two Python scripts:

• scripts/dataset_manager.py - Dataset creation and management
• scripts/sql_manager.py - SQL-based dataset querying and transformation

Prerequisites

• huggingface_hub library: uv add huggingface_hub
• duckdb library (for SQL): uv add duckdb
• datasets library (for pushing): uv add datasets
• HF_TOKEN environment variable must be set with a Write-access token
• Activate virtual environment: source .venv/bin/activate

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SQL Dataset Querying (sql_manager.py)

Query, transform, and push Hugging Face datasets using DuckDB SQL. The hf:// protocol provides direct access to any public dataset (or private with token).

Quick Start

# Query a dataset
python scripts/sql_manager.py query \
  --dataset "cais/mmlu" \
  --sql "SELECT * FROM data WHERE subject='nutrition' LIMIT 10"

# Get dataset schema
python scripts/sql_manager.py describe --dataset "cais/mmlu"

# Sample random rows
python scripts/sql_manager.py sample --dataset "cais/mmlu" --n 5

# Count rows with filter
python scripts/sql_manager.py count --dataset "cais/mmlu" --where "subject='nutrition'"

SQL Query Syntax

Use data as the table name in your SQL - it gets replaced with the actual hf:// path:

-- Basic select
SELECT * FROM data LIMIT 10

-- Filtering
SELECT * FROM data WHERE subject='nutrition'

-- Aggregations
SELECT subject, COUNT(*) as cnt FROM data GROUP BY subject ORDER BY cnt DESC

-- Column selection and transformation
SELECT question, choices[answer] AS correct_answer FROM data

-- Regex matching
SELECT * FROM data WHERE regexp_matches(question, 'nutrition|diet')

-- String functions
SELECT regexp_replace(question, '\n', '') AS cleaned FROM data

Common Operations

1. Explore Dataset Structure

# Get schema
python scripts/sql_manager.py describe --dataset "cais/mmlu"

# Get unique values in column
python scripts/sql_manager.py unique --dataset "cais/mmlu" --column "subject"

# Get value distribution
python scripts/sql_manager.py histogram --dataset "cais/mmlu" --column "subject" --bins 20

2. Filter and Transform

# Complex filtering with SQL
python scripts/sql_manager.py query \
  --dataset "cais/mmlu" \
  --sql "SELECT subject, COUNT(*) as cnt FROM data GROUP BY subject HAVING cnt > 100"

# Using transform command
python scripts/sql_manager.py transform \
  --dataset "cais/mmlu" \
  --select "subject, COUNT(*) as cnt" \
  --group-by "subject" \
  --order-by "cnt DESC" \
  --limit 10

3. Create Subsets and Push to Hub

# Query and push to new dataset
python scripts/sql_manager.py query \
  --dataset "cais/mmlu" \
  --sql "SELECT * FROM data WHERE subject='nutrition'" \
  --push-to "username/mmlu-nutrition-subset" \
  --private

# Transform and push
python scripts/sql_manager.py transform \
  --dataset "ibm/duorc" \
  --config "ParaphraseRC" \
  --select "question, answers" \
  --where "LENGTH(question) > 50" \
  --push-to "username/duorc-long-questions"

4. Export to Local Files

# Export to Parquet
python scripts/sql_manager.py export \
  --dataset "cais/mmlu" \
  --sql "SELECT * FROM data WHERE subject='nutrition'" \
  --output "nutrition.parquet" \
  --format parquet

# Export to JSONL
python scripts/sql_manager.py export \
  --dataset "cais/mmlu" \
  --sql "SELECT * FROM data LIMIT 100" \
  --output "sample.jsonl" \
  --format jsonl

5. Working with Dataset Configs/Splits

# Specify config (subset)
python scripts/sql_manager.py query \
  --dataset "ibm/duorc" \
  --config "ParaphraseRC" \
  --sql "SELECT * FROM data LIMIT 5"

# Specify split
python scripts/sql_manager.py query \
  --dataset "cais/mmlu" \
  --split "test" \
  --sql "SELECT COUNT(*) FROM data"

# Query all splits
python scripts/sql_manager.py query \
  --dataset "cais/mmlu" \
  --split "*" \
  --sql "SELECT * FROM data LIMIT 10"

6. Raw SQL with Full Paths

For complex queries or joining datasets:

python scripts/sql_manager.py raw --sql "
  SELECT a.*, b.* 
  FROM 'hf://datasets/dataset1@~parquet/default/train/*.parquet' a
  JOIN 'hf://datasets/dataset2@~parquet/default/train/*.parquet' b
  ON a.id = b.id
  LIMIT 100
"

Python API Usage

from sql_manager import HFDatasetSQL

sql = HFDatasetSQL()

# Query
results = sql.query("cais/mmlu", "SELECT * FROM data WHERE subject='nutrition' LIMIT 10")

# Get schema
schema = sql.describe("cais/mmlu")

# Sample
samples = sql.sample("cais/mmlu", n=5, seed=42)

# Count
count = sql.count("cais/mmlu", where="subject='nutrition'")

# Histogram
dist = sql.histogram("cais/mmlu", "subject")

# Filter and transform
results = sql.filter_and_transform(
    "cais/mmlu",
    select="subject, COUNT(*) as cnt",
    group_by="subject",
    order_by="cnt DESC",
    limit=10
)

# Push to Hub
url = sql.push_to_hub(
    "cais/mmlu",
    "username/nutrition-subset",
    sql="SELECT * FROM data WHERE subject='nutrition'",
    private=True
)

# Export locally
sql.export_to_parquet("cais/mmlu", "output.parquet", sql="SELECT * FROM data LIMIT 100")

sql.close()

HF Path Format

DuckDB uses the hf:// protocol to access datasets:

hf://datasets/{dataset_id}@{revision}/{config}/{split}/*.parquet

Examples:

• hf://datasets/cais/mmlu@~parquet/default/train/*.parquet
• hf://datasets/ibm/duorc@~parquet/ParaphraseRC/test/*.parquet

The @~parquet revision provides auto-converted Parquet files for any dataset format.

Useful DuckDB SQL Functions

-- String functions
LENGTH(column)                    -- String length
regexp_replace(col, '\n', '')     -- Regex replace
regexp_matches(col, 'pattern')    -- Regex match
LOWER(col), UPPER(col)           -- Case conversion

-- Array functions  
choices[0]                        -- Array indexing (0-based)
array_length(choices)             -- Array length
unnest(choices)                   -- Expand array to rows

-- Aggregations
COUNT(*), SUM(col), AVG(col)
GROUP BY col HAVING condition

-- Sampling
USING SAMPLE 10                   -- Random sample
USING SAMPLE 10 (RESERVOIR, 42)   -- Reproducible sample

-- Window functions
ROW_NUMBER() OVER (PARTITION BY col ORDER BY col2)

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Dataset Creation (dataset_manager.py)

Recommended Workflow

1. Discovery (Use HF MCP Server):

# Use HF MCP tools to find existing datasets
search_datasets("conversational AI training")
get_dataset_details("username/dataset-name")

2. Creation (Use This Skill):

# Initialize new dataset
python scripts/dataset_manager.py init --repo_id "your-username/dataset-name" [--private]

# Configure with detailed system prompt
python scripts/dataset_manager.py config --repo_id "your-username/dataset-name" --system_prompt "$(cat system_prompt.txt)"

3. Content Management (Use This Skill):

# Quick setup with any template
python scripts/dataset_manager.py quick_setup \
  --repo_id "your-username/dataset-name" \
  --template classification

# Add data with template validation
python scripts/dataset_manager.py add_rows \
  --repo_id "your-username/dataset-name" \
  --template qa \
  --rows_json "$(cat your_qa_data.json)"

Template-Based Data Structures

1. Chat Template (--template chat)

{
  "messages": [
    {"role": "user", "content": "Natural user request"},
    {"role": "a

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