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.
Use Data Provenance in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the Data Provenance skill
Details
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.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
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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