data-cleaning
Clean and transform messy data for analysis in Python, R, or Stata
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning --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 ## Purpose This skill helps economists clean, transform, and prepare datasets for analysis in Python, R, or Stata. It emphasizes reproducibility, proper documentation, and handling common data quality issues found in economic research. ## When to Use - Cleaning raw survey or administrative data - Merging multiple data sources - Handling missing values, duplicates, and outliers - Cr
What does the data-cleaning skill do?
Clean and transform messy data for analysis in Python, R, or Stata
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning --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.