openai/huggingface-datasets
plugins/hugging-face/skills/datasets/SKILL.md
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
$ install --global
skillsauthnpx skillsauth add openai/plugins huggingface-datasetsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- huggingface-datasets
- description:
- Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Hugging Face Dataset Viewer
Use this skill to execute read-only Dataset Viewer API calls for dataset exploration and extraction.
Core workflow
- Optionally validate dataset availability with
/is-valid. - Resolve
config+splitwith/splits. - Preview with
/first-rows. - Paginate content with
/rowsusingoffsetandlength(max 100). - Use
/searchfor text matching and/filterfor row predicates. - Retrieve parquet links via
/parquetand totals/metadata via/sizeand/statistics.
Defaults
- Base URL:
https://datasets-server.huggingface.co - Default API method:
GET - Query params should be URL-encoded.
offsetis 0-based.lengthmax is usually100for row-like endpoints.- Gated/private datasets require
Authorization: Bearer <HF_TOKEN>.
Dataset Viewer
Validate dataset:/is-valid?dataset=<namespace/repo>List subsets and splits:/splits?dataset=<namespace/repo>Preview first rows:/first-rows?dataset=<namespace/repo>&config=<config>&split=<split>Paginate rows:/rows?dataset=<namespace/repo>&config=<config>&split=<split>&offset=<int>&length=<int>Search text:/search?dataset=<namespace/repo>&config=<config>&split=<split>&query=<text>&offset=<int>&length=<int>Filter with predicates:/filter?dataset=<namespace/repo>&config=<config>&split=<split>&where=<predicate>&orderby=<sort>&offset=<int>&length=<int>List parquet shards:/parquet?dataset=<namespace/repo>Get size totals:/size?dataset=<namespace/repo>Get column statistics:/statistics?dataset=<namespace/repo>&config=<config>&split=<split>Get Croissant metadata (if available):/croissant?dataset=<namespace/repo>
Pagination pattern:
curl "https://datasets-server.huggingface.co/rows?dataset=stanfordnlp/imdb&config=plain_text&split=train&offset=0&length=100"
curl "https://datasets-server.huggingface.co/rows?dataset=stanfordnlp/imdb&config=plain_text&split=train&offset=100&length=100"
When pagination is partial, use response fields such as num_rows_total, num_rows_per_page, and partial to drive continuation logic.
Search/filter notes:
/searchmatches string columns (full-text style behavior is internal to the API)./filterrequires predicate syntax inwhereand optional sort inorderby.- Keep filtering and searches read-only and side-effect free.
Querying Datasets
Use npx parquetlens with Hub parquet alias paths for SQL querying.
Parquet alias shape:
hf://datasets/<namespace>/<repo>@~parquet/<config>/<split>/<shard>.parquet
Derive <config>, <split>, and <shard> from Dataset Viewer /parquet:
curl -s "https://datasets-server.huggingface.co/parquet?dataset=cfahlgren1/hub-stats" \
| jq -r '.parquet_files[] | "hf://datasets/\(.dataset)@~parquet/\(.config)/\(.split)/\(.filename)"'
Run SQL query:
npx -y -p parquetlens -p @parquetlens/sql parquetlens \
"hf://datasets/<namespace>/<repo>@~parquet/<config>/<split>/<shard>.parquet" \
--sql "SELECT * FROM data LIMIT 20"
SQL export
- CSV:
--sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.csv' (FORMAT CSV, HEADER, DELIMITER ',')" - JSON:
--sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.json' (FORMAT JSON)" - Parquet:
--sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.parquet' (FORMAT PARQUET)"
Creating and Uploading Datasets
Use one of these flows depending on dependency constraints.
Zero local dependencies (Hub UI):
- Create dataset repo in browser:
https://huggingface.co/new-dataset - Upload parquet files in the repo "Files and versions" page.
- Verify shards appear in Dataset Viewer:
curl -s "https://datasets-server.huggingface.co/parquet?dataset=<namespace>/<repo>"
Low dependency CLI flow (npx @huggingface/hub / hfjs):
- Set auth token:
export HF_TOKEN=<your_hf_token>
- Upload parquet folder to a dataset repo (auto-creates repo if missing):
npx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data
- Upload as private repo on creation:
npx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data --private
After upload, call /parquet to discover <config>/<split>/<shard> values for querying with @~parquet.
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