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Data audit

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/29-quarcs-lab-project20XXy/dot-claude/skills/data-audit

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Install
npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-audit

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One thing to look at

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What its author says it does

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Scans notebooks for data file references and verifies each file exists on disk. Use when checking for broken data paths.

SKILL.md

1.8 KB, as published. Nobody here has run it

Audit Data References

Scan all notebooks for data file references and verify they exist on disk.

Steps

  1. Scan all .ipynb files in notebooks/ for data loading patterns:

    • Python: pd.read_csv(...), pd.read_stata(...), pd.read_excel(...), pd.read_parquet(...), open(...), np.loadtxt(...)
    • R: read.csv(...), read_csv(...), read.dta(...), haven::read_dta(...), readxl::read_excel(...), load(...)
    • Stata: use "...", import delimited "...", import excel "...", insheet using "..."
    • Also check the .md Jupytext pairs for the same patterns
  2. Extract every referenced file path and normalize it:

    • Resolve relative paths from the notebook's directory (notebooks/)
    • Resolve paths using DATA_DIR, RAW_DATA_DIR from config.py / config.R
  3. Check that each referenced file exists in data/rawData/ or data/

  4. Scan data/rawData/ and data/ for all data files present on disk

  5. Report three categories:

    Resolved — referenced and found:

    • File path, which notebook references it, line/cell number

    Broken — referenced but not found:

    • File path as written in code, which notebook, suggested fix (closest matching file, or note that it may need to be downloaded)

    Undocumented — on disk but never referenced by any notebook:

    • File path in data/rawData/ or data/ that no notebook loads
  6. Print a summary: total references, resolved, broken, undocumented files

Error handling

  • If no notebooks exist, report "No notebooks found" and stop.
  • If data/rawData/ does not exist, warn but continue checking data/.

Keep looking

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