Dataset Quality Audit
Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting...
Install
npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -g -a claude-code -y- Repository
- zebbern/claude-code-guide4.7k
- Category
- Documents
- Updated
- 4d ago
- License
- MIT
- Context cost
- Lightabout 887 tokens
- Runs scripts
- Yes, review before use
What it does
Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and actionable suggestions. Triggered when users ask to check data quality, find missing or duplicate values, detect outliers, validate formats, profile data, or clean data.
dataset-quality-audit
A data quality auditing tool that runs 12-dimension quality checks on tabular data, producing per-dimension scores (0, 100), an overall grade, and actionable fix suggestions.
Capabilities
| Dimension | Description |
|---|---|
| Missing Values | Count and percentage of null/NaN values per column |
| Duplicate Rows | Number and percentage of fully duplicated rows |
| Type Consistency | Mixed types within a single column (e.g., numbers mixed with text) |
| Value Range / Outliers | Outlier detection using the IQR method |
| Format Compliance | Consistency of date, email, phone number, and other formatted fields |
| Uniqueness Constraints | Whether ID-type columns contain duplicates |
| Whitespace Issues | Leading/trailing spaces, empty strings, whitespace-only values |
| Constant Columns | Columns with only a single unique value (zero information) |
| Distribution Skewness | Whether numeric columns have excessive skewness |
| Column Naming | Spaces, special characters, or inconsistent casing in column names |
| Cardinality Anomalies | Unusually high or low number of unique values |
| Cross-Column Consistency | Logical checks across columns (e.g., start date before end date) |
Quick Start
# Basic quality check
python3 scripts/data_quality_checker.py data.csv
# Save report as JSON
python3 scripts/data_quality_checker.py data.csv --output report.json
# Specify ID columns (for uniqueness checks)
python3 scripts/data_quality_checker.py users.csv --id-columns "user_id,email"
# Specify date columns (for format checks)
python3 scripts/data_quality_checker.py orders.csv --date-columns "created_at,updated_at"
Detailed Usage
Basic Invocation
python3 scripts/data_quality_checker.py <data-file> [options]
