marin: Skill for Claude Code

Register a Hugging Face dataset

Inspect the schema without downloading the full dataset:

uv run lib/marin/tools/get_hf_dataset_schema.py <dataset_name> [options]

For programmatic inspection:

from marin.tools.get_hf_dataset_schema import get_schema

schema = get_schema(dataset_name="wikitext", config_name="wikitext-103-v1")

Use repo-managed dependencies. For a one-off inspection without a provisioned environment, add --with datasets --with pyyaml to uv run.

If the result says a config is required, select one of available_configs and retry with --config_name. Add --trust_remote_code only after inspecting the dataset repository and accepting its code-execution boundary. The tool streams; do not replace it with a full dataset download.

If the dataset cannot be found, stop and report the identifier, path, or access failure instead of guessing a replacement.

Choose the text field from the reported schema. Prefer an exact text field, then a field containing text, then another string field. Inspect sample_row to verify the content; it may be empty for some datasets. The result also reports splits, text_field_candidates, and features.

Add a leaf module under experiments/datasets/ using the lazy builders in marin.experiment.data:

• expose <name>_dataset() for one corpus;
• expose <name>_datasets() -> dict[str, ...] for a keyed family;
• for Hugging Face subsets, follow experiments/datasets/nemotron.py and return one keyed handle per subset.

Validate the selected config, splits, text mapping, and one sample before adding tokenization or downstream experiment configuration.