data-cleaning-pipeline
Systematic data cleaning workflows for research datasets
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning-pipeline --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 Cleaning Pipeline A skill for building systematic, reproducible data cleaning pipelines for research datasets. Covers common data quality issues, step-by-step cleaning workflows, handling missing values, detecting and treating outliers, validating data integrity, and documenting cleaning decisions for reproducibility. ## The Data Cleaning Workflow ### Pipeline Overview Data cleaning should
What does the data-cleaning-pipeline skill do?
Systematic data cleaning workflows for research datasets
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning-pipeline --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 brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 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.