Audit and remediate data quality issues
data-quality-auditorskillsetup L2★17,464
alirezarezvani/claude-skills ↗What it does
Audit datasets for completeness and validity
Best for
Systematically profiling datasets to surface silent nulls, outliers, and distribution shifts before they corrupt downstream analysis.
Inputs
- · CSV/JSON dataset
Outputs
- · DQS report
- · outlier flags
- · remediation plan
Requires
- · Python 3.7+
Preconditions
None specified
Failure modes
- · Silent nulls (0/empty)
- · Leaky timestamps
- · Duplicate keys
Trust signals
- · MCAR/MAR/MNAR classification
- · IQR + Z-score outlier detection
- · DQS metric weighted across 5 dimensions