missing-data-handling
Diagnose missing data patterns and apply appropriate imputation strategies
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill missing-data-handling --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.
# Missing Data Handling A skill for diagnosing missing data mechanisms, selecting appropriate imputation strategies, and conducting sensitivity analyses. Covers everything from simple imputation to multiple imputation and modern machine learning approaches. ## Missing Data Mechanisms ### Rubin's Classification Understanding the mechanism determines the appropriate handling strategy: | Mechanism |
What does the missing-data-handling skill do?
Diagnose missing data patterns and apply appropriate imputation strategies
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill missing-data-handling --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.