Audit a dataset

Use this skill before statistics, model training, or external publication. The goal is a compact, machine-readable audit plus explicit decisions about every issue that could invalidate downstream results.

Workflow

1. Load records without silently coercing values. Preserve source row IDs.
2. Call audit_rows on a representative or complete list of row mappings.
3. Inspect missingness and observed type mixtures column by column.
4. Resolve duplicate records and non-unique identifiers deliberately.
5. If a split column exists, check both stable IDs and grouping entities for train/validation/test overlap.
6. Record accepted exceptions, then rerun the audit and save the JSON result next to the cleaned dataset.

Import and run

Hyphenated Skill directories are loaded with importlib:

from importlib import import_module

audit_rows = import_module("audit-dataset.kernel").audit_rows
report = audit_rows(
    rows,
    target="label",
    id_columns=("sample_id",),
    group_columns=("patient_id",),
    split_column="split",
)

rows must be a sequence of mappings. The report contains row and column counts, per-column missing/type/unique summaries, duplicate row and ID counts, target frequencies, and split-leakage examples.

Interpretation

• Mixed numeric/string types usually indicate parsing or sentinel-value bugs.
• Missingness is a property of both the data and the collection process; do not impute before checking whether it correlates with label, site, or time.
• Duplicate IDs are not automatically duplicate observations. Decide whether repeated measures are expected and group them during splitting.
• Any patient, molecule scaffold, time series, or near-duplicate entity shared across evaluation boundaries can inflate performance even when row IDs differ.
• A clean structural audit does not establish representativeness, label validity, causal identifiability, or ethical suitability.

Required output

Report the checks performed, blocking findings, accepted exceptions, and the exact source artifact/version. Never describe a dataset as clean without naming the leakage keys and missing-value policy that were checked.