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newsc2newsc2/claude-skills/data-quality/SKILL.md

data-quality

Skill0 starsChanged 30 days agoNo licenceView on GitHub

Use this skill for any data quality task: validating datasets, profiling data, cleaning messy or scraped data, detecting anomalies, building validation suites, enforcing schemas, writing data contracts, deduplicating records, standardizing formats, auditing completeness, tracking lineage, or designing well-structured tables. Triggers: 'validate this data', 'check data quality', 'profile this dataset', 'clean this data', 'find duplicates', 'check for nulls', 'detect anomalies', 'schema validation', 'data contract', 'why does this data look wrong', 'audit this table', 'freshness check', 'is this data trustworthy', 'deduplicate', 'data lineage', 'SCD', 'data completeness', or any request involving data validation, cleaning, profiling, or quality assessment. Also use when ingesting unfamiliar datasets or building pipeline quality gates. Do NOT use for analytics/dashboards (use data-analysis), ML model training, or ETL orchestration.

13 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

10 rules. 0 are common, 10 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 11 edited versions we read keep these.

  • 10

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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