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data-audit

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

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/29-quarcs-lab-project20XXy/dot-claude/skills/data-audit/SKILL.md

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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/.