Data Provenance
SkillDatabases & dataTrack 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.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Data Provenance skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/64-tmonk-mcp-stata/skills/stata-data-provenance/SKILL.md and read by ahel’s review.
Use this skill when lineage and reproducibility matter.
- Map the sequence of source files and transformations.
- Flag untracked merges, overwrites, and silent sample restrictions.
- Produce a concise provenance narrative a coauthor can audit.
Read references/lineage.md for the provenance checklist.
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
stata-data-provenance- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
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