Huggingface Datasets

by huggingfaceabc20ae526d8No licenseListed Oct 8, 2026Updated Oct 8, 2026

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.

Instructions onlyData & Analytics
AI-generated overview

Guides read-only Hugging Face Dataset Viewer API calls to explore, search, filter, and download dataset rows.

What it does
This skill documents a workflow for the Hugging Face Dataset Viewer API, covering endpoints for validating datasets, listing subsets and splits, previewing first rows, paginating rows, searching text, filtering with predicates, and retrieving parquet links, size totals, and column statistics. It also describes creating and uploading datasets, including parquet uploads and agent session traces, and notes pagination fields used to continue partial results. It produces API requests and retrieved dataset metadata or rows rather than local files.
When to use it
Use it when you need to inspect or extract data from a Hugging Face dataset through the Dataset Viewer API, such as resolving configs and splits, paging through rows, or running text searches and filters. It is also relevant when uploading parquet files or agent traces to a dataset repository.
Requirements
Network access to the Hugging Face Dataset Viewer API and the Hub. Gated or private datasets require an HF_TOKEN bearer credential. The upload flows use the Hub web UI or npx @huggingface/hub, and the trace flow uses the hf CLI. No scripts are included; the skill is instructions only.

Hugging Face Dataset Viewer

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:

bash
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.

For CLI-based parquet URL discovery or SQL, use the hf-cli skill with hf datasets parquet and hf datasets sql.

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:
bash
curl -s "https://datasets-server.huggingface.co/parquet?dataset=<namespace>/<repo>"

Low dependency CLI flow (npx @huggingface/hub / hfjs):

  • Set auth token:
bash
export HF_TOKEN=<your_hf_token>
  • Upload parquet folder to a dataset repo (auto-creates repo if missing):
bash
npx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data
  • Upload as private repo on creation:
bash
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.

Agent Traces

The Hub supports raw agent session traces from Claude Code, Codex, and Pi Agent. Upload them to Hugging Face Datasets as original JSONL files and the Hub can auto-detect the trace format, tag the dataset as Traces, and enable the trace viewer for browsing sessions, turns, tool calls, and model responses. Common local session directories:

  • Claude Code: ~/.claude/projects
  • Codex: ~/.codex/sessions
  • Pi: ~/.pi/agent/sessions

Default to private dataset repos because traces can contain prompts, file paths, tool outputs, secrets, or PII. Preserve the raw .jsonl files and nest them by project/cwd instead of uploading every session at the dataset root.

bash
hf repos create <namespace>/<repo> --type dataset --private --exist-okhf upload <namespace>/<repo> ~/.codex/sessions codex/<project-or-cwd> --type dataset

Source and attribution

Source:huggingface/skillsinskills/huggingface-datasetsat commitabc20ae

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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