Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →zebbern/dataset-quality-audit
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.
Passed 18 of 20 test cases with the skill, and 13 of 20 without it.
and 9 more in the full report
7 fixed13 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
What short command-line parameter flag is supported for specifying unique ID columns when running `python3 scripts/data_quality_checker.py`?
| Checked | Without skill | With skill |
|---|---|---|
| The answer states -id | Missed | Passed |
The instructions your model reads when the skill loads.
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.
| 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) |
# 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"
test cases passed, vs 13 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.