Dataset quality audit

Skill zebbern/claude-code-guide/skills/dataset-quality-audit

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Install

npx -y skills add zebbern/claude-code-guide --skill dataset-quality-audit

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What its author says it does

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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.

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SKILL.md

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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]

Parameters

Parameter	Short	Required	Default	Description
`input`	—	Yes	—	Path to input file (CSV/TSV/Excel/JSON)
`--output`	`-o`	No	stdout	Path for the JSON report output
`--id-columns`	`-id`	No	Auto-detect	Comma-separated column names that should be unique
`--date-columns`	`-dc`	No	Auto-detect	Comma-separated column names containing dates
`--sample`	`-s`	No	All rows	Number of rows to sample (useful for large files)
`--encoding`	`-e`	No	utf-8	File encoding

Output Format (JSON)

{
  "file": "data.csv",
  "rows": 10000,
  "columns": 15,
  "overall_score": 78.5,
  "grade": "B",
  "dimensions": {
    "missing_values": {
      "score": 85.0,
      "issues": [
        {"column": "age", "missing_count": 150, "missing_pct": 1.5, "suggestion": "Fill with median or mode"}
      ]
    },
    "duplicates": {
      "score": 95.0,
      "issues": [...]
    }
  },
  "top_suggestions": [
    "Column 'age' has 1.5% missing values — consider filling with the median",
    "Found 200 fully duplicated rows — consider deduplication"
  ]
}

Grading Scale

Grade	Score Range	Meaning
A+	95–100	Excellent quality — ready for use as-is
A	90–95	Good quality — minor issues only
B	80–90	Moderate quality — recommended to fix before use
C	60–80	Poor quality — significant cleaning required
D	40–60	Very poor quality — many issues need attention
F	0–40	Essentially unusable — requires re-collection or major cleanup

Dependencies

Python 3.8+
pandas
numpy

pip install pandas numpy