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audit-dataset skillA

audit-dataset is agent-read markdown (skill) from pku-yuangroup/openai4s: Audit tabular datasets before analysis or training for schema drift, missing values, duplicate rows or IDs, target imbalance, and entity or group leakage across splits using pure-stdlib helpers..

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# 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`:

```python
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
…

Read the whole file at its exact version.

How to install

Latest version
mdr add pku-yuangroup/openai4s/audit-dataset@git:20260828.e9753be
Exact content
mdr add pku-yuangroup/openai4s/audit-dataset@sha256:ae890673ebdcddf1

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

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Versions

versioncommittedcommitsizeaudit
git:20260828.e9753be latest2026-08-28 e9753be 2,545 BA view · diff
git:20260714.76ac5862026-07-14 76ac586 2,488 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (2545 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

pku-yuangroup/openai4s · 586 stars · license MIT · pushed 2026-09-23 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_mw5h3trf76gsgzhc
GET https://markdownregistry.com/api/v1/resolve?ref=pku-yuangroup/openai4s/audit-dataset
GET https://markdownregistry.com/api/v1/blob/ae890673ebdcddf1f76d507afa4068d8ed9d6d3a3d5f153af03b073d5ae14fae

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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