stata-data-provenance
Track dataset lineage, transformation steps, merge logic, and reproducibility risks in Stata workflows. Use when the user needs to explain where data came from, how it changed, or why a pipeline can be trusted.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stata-data-provenance --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 Provenance Use this skill when lineage and reproducibility matter. 1. Map the sequence of source files and transformations. 2. Flag untracked merges, overwrites, and silent sample restrictions. 3. Produce a concise provenance narrative a coauthor can audit. Read `references/lineage.md` for the provenance checklist.
What does the stata-data-provenance skill do?
Track dataset lineage, transformation steps, merge logic, and reproducibility risks in Stata workflows. Use when the user needs to explain where data came from, how it changed, or why a pipeline can be trusted.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stata-data-provenance --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.