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.
Install
$ agentstack add skill-boeschj-ml-intern-plugin-dataset-audit ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-boeschj-ml-intern-plugin-dataset-audit) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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:
- The dataset loads at all (status). If it does not, tell the user; never substitute a different dataset silently.
- Columns match the training method:
- SFT: "messages", "text", or "prompt"/"completion"
- DPO: "prompt", "chosen", "rejected"
- GRPO: "prompt"
- Row counts per split are sane for the plan (enough for training, an eval split exists when evaluation is expected).
- 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
- Source: 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.
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Versions
- v0.1.0 Imported from the upstream source.