hugging-face-dataset-viewer

name	hugging-face-dataset-viewer
description	Query Hugging Face datasets through the Dataset Viewer API for splits, rows, full-text search, filters, and parquet links (read-only). USE WHEN you need read-only exploration or extraction from a Hugging Face dataset.
cluster	ai-agents-meta
version	1.0.0
origin	antigravity-awesome-skills (MIT)
source	https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets
risk	unknown

Hugging Face Dataset Viewer

When to Use

Use this skill when you need read-only exploration of a Hugging Face dataset through the Dataset Viewer API.

Use this skill to execute read-only Dataset Viewer API calls for dataset exploration and extraction.

Core workflow

1. Optionally validate dataset availability with /is-valid.
2. Resolve config + split with /splits.
3. Preview with /first-rows.
4. Paginate content with /rows using offset and length (max 100).
5. Use /search for text matching and /filter for row predicates.
6. Retrieve parquet links via /parquet and totals/metadata via /size and /statistics.

Defaults

• Base URL: https://datasets-server.huggingface.co
• Default API method: GET
• Query params should be URL-encoded.
• offset is 0-based.
• length max is usually 100 for 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:

• /search matches string columns (full-text style behavior is internal to the API).
• /filter requires predicate syntax in where and optional sort in orderby.
• 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.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.