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Found 53 Skills
Multi-layer validation pattern - validates data at EVERY layer it passes through to make bugs structurally impossible, not just caught.
Validate and audit CSV data for quality, consistency, and completeness. Use when you need to check CSV files for data issues, missing values, or format inconsistencies.
Chapter 2 데이터 수집 품질 기준 및 검증 방법
Use this for SQL queries, database schema design, ETL pipelines, data transformations (pandas/Spark), and data validation.
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.