data-quality-checker
Validate data quality checker operations. Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category. Use when working with data quality checker functionality. Trigger with phrases like "data quality checker", "data checker", "data". '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill data-quality-checker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Quality Checker ## Overview This skill provides automated assistance for data quality checker tasks within the Data Pipelines domain. ## When to Use This skill activates automatically when you: - Mention "data quality checker" in your request - Ask about data quality checker patterns or best practices - Need help with data pipeline skills covering etl, data transformation, workflow orchestr
What does the data-quality-checker skill do?
Validate data quality checker operations. Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category. Use when working with data quality checker functionality. Trigger with phrases like "data quality checker", "data checker", "data". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill data-quality-checker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.