csv-data-auditor_skill

Overview

This skill validates and audits CSV data for quality, consistency, and completeness. It performs structural checks, header and row consistency analysis, data type validation, and data-quality diagnostics to produce a structured audit report. The output highlights critical issues, warnings, and actionable recommendations. Use it to quickly assess CSV readiness for analysis or ingestion pipelines.

How this skill works

The auditor inspects file-level characteristics (existence, encoding, delimiter, quoting) and verifies header integrity and row consistency. It samples and analyzes each column for expected data types (numeric, date/time, text, boolean) and computes missing value statistics, duplicates, outliers, and cross-column consistency checks. The tool can run in-memory or process large files in chunks, and it emits a structured report with counts, examples, and recommended fixes. A simple Python validation script can be used or adapted to automate checks and integrate into pipelines.

When to use it

• Before importing CSVs into databases or analytics systems
• As part of an ETL/ingestion validation step for production pipelines
• When preparing datasets for machine learning or reporting
• To investigate data quality issues reported by consumers
• When standardizing formats across multiple CSV sources

Best practices

• Always detect and specify encoding; try UTF-8, Latin-1, or use a detector like chardet
• Validate and normalize headers: remove duplicates, enforce naming conventions, and strip special characters
• Process large files in chunks or with Dask to avoid memory exhaustion
• Standardize date/time formats (prefer ISO 8601) and normalize boolean representations
• Document transformations, keep originals, and automate periodic audits

Example use cases

• Audit a monthly export from a CRM to find missing contact emails and duplicate IDs
• Validate product feed CSVs for an e-commerce ETL before loading into inventory systems
• Scan survey response files for inconsistent date formats and truncated rows
• Preflight a large dataset for a machine-learning job to detect outliers and null-heavy columns
• Integrate into CI pipelines to reject malformed CSV uploads