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Found 15 Skills
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
Clean, profile, validate, reshape, and document messy tabular, text, JSON, and relational data through an evidence-first, reproducible workflow. Use when preparing data for analysis, reporting, modeling, ingestion, migration, or matching, including AI-suggested repair, entity-match review, score calibration limits, and reversible repair ledgers. Do not use for statistical modeling, dashboard design, or operating a named data platform; route those tasks to data-scientist, data-engineering, or the relevant tool skill.
Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.