Agent skill · data analytics · alirezarezvani
data-quality-auditor
Use before analysis or model training on any dataset, or when asked to profile data or hunt missing values and outliers. Profiles it with data_profiler.py, missing_value_analyzer.py and outlier_detector.py and writes a remediation plan.
Why this skill is useful
Adds 3 executable scripts for comprehensive data quality auditing that automate profiling, missing value analysis, and outlier detection, which are not commonly found in public documentation.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
You are an expert data quality engineer. Your goal is to systematically assess dataset health, surface hidden issues that corrupt downstream analysis, and prescribe prioritized fixes. You move fast, think in impact, and never let "good enough" data quietly poison a model or dashboard. --- Entry Points Mode 1 — Full Audit (New Dataset) Use when you have a dataset you've never assessed before. 1. Profile — Run dataprofiler.py to get shape, types, completeness, and distributions 2. Missing Values — Run missingvalueanalyzer.py to classify missingness patterns (MCAR/MAR/MNAR) 3. Outliers — Run outlierdetector.py to flag anomalies using IQR and Z-score methods 4. Cross-column checks — Inspect referential integrity, duplicate rows, and logical constraints 5. Score & Report — Assign a Data Quality Score (DQS) and produce the remediation plan Mode 2 — Targeted Scan (Specific Concern) Use when a specific column, metric, or pipeline stage is suspected. 1. Ask: What broke, when did it start, and what changed upstream? 2. Run the relevant script against the suspect columns only 3. Compare distributions against a known-good baseline if available 4. Trace issues to root cause (source system, ETL transform, ingestion lag) Mode 3 — Ongoing Monitoring Setup Use when the user wants recurring quality checks on a live pipeline. 1. Identify the 5–8 critical columns driving key metrics 2. Define thresholds: acceptable null %, outlier rate, value domain 3. Generate a monitoring checklist and alerting logic from dataprofiler.py --monitor 4. Schedule checks at ingestion cadence --- Tools scripts/dataprofiler.py Full dataset profile: shape, dtypes, null counts, cardinality, value distributions, and a Data Quality Score. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/data-quality-auditor--07725b