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Ejecuta cualquier Skill en Manus con un clic

inspect-dataset

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Actualizado28 de abril de 2026 a las 08:03

Audit a Hugging Face dataset before training to confirm splits, columns, format, sample rows, distributions, and duplicates. Triggered before any training/fine-tuning script runs, when a user mentions a new dataset, or when you hit a KeyError / format mismatch in a training job.

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mybigday

mybigday/ml-intern-kit

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SKILL.md

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name	inspect-dataset
description	Audit a Hugging Face dataset before training to confirm splits, columns, format, sample rows, distributions, and duplicates. Triggered before any training/fine-tuning script runs, when a user mentions a new dataset, or when you hit a KeyError / format mismatch in a training job.

inspect-dataset

Never train on a dataset you haven't audited. Looking at the data is the single most reliable way to avoid format mismatches, KeyError-on-step-1 failures, and silently-bad runs.

When to fire

Before any train_sft.py / train_dpo.py / train_grpo.py invocation.
When the user says "use dataset X" — audit X immediately.
When a training job fails with KeyError, ValueError: column not found, or bad samples.

What to run

python scripts/inspect_dataset.py <repo_id>             # default audit
python scripts/inspect_dataset.py <repo_id> --split train --n 5
python scripts/inspect_dataset.py <repo_id> --config wikitext-2-raw-v1

The script prints schema, row counts per split, length statistics on text / prompt / messages columns, duplicate counts, label distribution, and n sample rows.

What to verify against the chosen training method

Method	Required columns
SFT	`messages` (chat) or `text` or `prompt`+`completion`
DPO	`prompt`, `chosen`, `rejected`
GRPO	`prompt` (+ a reward function)

If the columns don't match the method, stop. Tell the user; don't silently remap or substitute the dataset.

Things to surface to the user

Class imbalance, missing values, duplicate rows.
Sequence-length p95 — drives the max_seq_length choice.
Dataset license / gating.
Any unexpected nesting (e.g. messages already wrapped in a list of dicts vs. flat text).

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name	inspect-dataset
description	Audit a Hugging Face dataset before training to confirm splits, columns, format, sample rows, distributions, and duplicates. Triggered before any training/fine-tuning script runs, when a user mentions a new dataset, or when you hit a KeyError / format mismatch in a training job.

inspect-dataset

Never train on a dataset you haven't audited. Looking at the data is the single most reliable way to avoid format mismatches, KeyError-on-step-1 failures, and silently-bad runs.

When to fire

Before any train_sft.py / train_dpo.py / train_grpo.py invocation.
When the user says "use dataset X" — audit X immediately.
When a training job fails with KeyError, ValueError: column not found, or bad samples.

What to run

python scripts/inspect_dataset.py <repo_id>             # default audit
python scripts/inspect_dataset.py <repo_id> --split train --n 5
python scripts/inspect_dataset.py <repo_id> --config wikitext-2-raw-v1

The script prints schema, row counts per split, length statistics on text / prompt / messages columns, duplicate counts, label distribution, and n sample rows.

What to verify against the chosen training method

Method	Required columns
SFT	`messages` (chat) or `text` or `prompt`+`completion`
DPO	`prompt`, `chosen`, `rejected`
GRPO	`prompt` (+ a reward function)

If the columns don't match the method, stop. Tell the user; don't silently remap or substitute the dataset.

Things to surface to the user

Class imbalance, missing values, duplicate rows.
Sequence-length p95 — drives the max_seq_length choice.
Dataset license / gating.
Any unexpected nesting (e.g. messages already wrapped in a list of dicts vs. flat text).