# Dataset Audit

> Audit a Hugging Face dataset before training or evaluation. Use before any training job, when choosing between datasets, or when a KeyError or format mismatch appears during training.

- **Type:** Skill
- **Install:** `agentstack add skill-boeschj-ml-intern-plugin-dataset-audit`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [boeschj](https://agentstack.voostack.com/s/boeschj)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [boeschj](https://github.com/boeschj)
- **Source:** https://github.com/boeschj/ml-intern-plugin/tree/main/skills/dataset-audit
- **Website:** https://github.com/boeschj/ml-intern-plugin#quickstart

## Install

```sh
agentstack add skill-boeschj-ml-intern-plugin-dataset-audit
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

Before working with any dataset, audit it. Do not assume you know what the data looks like, inspect it. Looking at data is the best way to boost model performance and the cheapest way to prevent failed jobs.

One call does the audit:

```
hf-dataset-inspect  [--config C] [--split S] [--rows N]
```

It returns validity status, configs and splits, the schema, row counts, sample rows, and parquet info as one JSON object.

Check, in order:
1. The dataset loads at all (status). If it does not, tell the user; never substitute a different dataset silently.
2. Columns match the training method:
   - SFT: "messages", "text", or "prompt"/"completion"
   - DPO: "prompt", "chosen", "rejected"
   - GRPO: "prompt"
3. Row counts per split are sane for the plan (enough for training, an eval split exists when evaluation is expected).
4. Sample rows look right: read several end to end. Surface anything notable to the user: class imbalance, missing values, unexpected formats, duplicates, truncated text, license or gating surprises.

Private datasets and ones behind an access request need HF auth; if the inspect call reports an auth error, run `/hf-doctor` first.

Record the verified column names in your plan and assert them at the top of every training script. The audit is a prerequisite for /hf-jobs preflight; a job submitted against unverified columns is a wasted GPU hour.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [boeschj](https://github.com/boeschj)
- **Source:** [boeschj/ml-intern-plugin](https://github.com/boeschj/ml-intern-plugin)
- **License:** Apache-2.0
- **Homepage:** https://github.com/boeschj/ml-intern-plugin#quickstart

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-boeschj-ml-intern-plugin-dataset-audit
- Seller: https://agentstack.voostack.com/s/boeschj
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.