preprocessing-data-with-automated-pipelines
Process automate data cleaning, transformation, and validation for ML tasks. Use when requesting "preprocess data", "clean data", "ETL pipeline", or "data transformation". Trigger with relevant phrases based on skill purpose. '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill preprocessing-data-with-automated-pipelines --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 Preprocessing Pipeline Construct and execute automated data preprocessing pipelines for cleaning, transforming, and validating ML-ready datasets. ## Overview construct and execute automated data preprocessing pipelines, ensuring data quality and readiness for machine learning. It streamlines the data preparation process by automating common tasks such as data cleaning, transformation, and v
What does the preprocessing-data-with-automated-pipelines skill do?
Process automate data cleaning, transformation, and validation for ML tasks. Use when requesting "preprocess data", "clean data", "ETL pipeline", or "data transformation". Trigger with relevant phrases based on skill purpose. '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill preprocessing-data-with-automated-pipelines --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.