# Dataset Quality Audit

Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting...

- Repository: https://github.com/zebbern/claude-code-guide (4691 stars)
- Category: Documents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -g -a claude-code -y`
Codex: `npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -g -a codex -y`
Cursor: `npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -g -a cursor -y`
Gemini CLI: `npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as 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

```bash
# 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

```bash
python3 scripts/data_quality_checker.py <data-file> [options]
```

### Parameters

The rest: https://github.com/zebbern/claude-code-guide/blob/HEAD/skills/dataset-quality-audit/SKILL.md