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Agent Skills/Dataset Viewer workflows
Hugging FaceModelsSKILL.mdVerified source

Agent Skill

Dataset Viewer workflows

Fetch dataset metadata, split rows, filtered samples, and viewer API results for data tasks.

Install this skillView repository

Vendor-authored source · Apache-2.0 license.

Raw SKILL.mdInstall the Tokens& Agent Pack

Skill specification

Declared by Hugging Face in the package front matter. Trigger conditions are what the coding agent matches on before it loads the skill.

View package fields
Dataset Viewer workflows SKILL.md front matter fields
Skill namehuggingface-datasets
Trigger conditionsUse 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 huggingface-datasets

In a terminal with Node.js, npm and Git, run the command for your agent. The Skills CLI installs the complete package directory, including referenced files within it. Review its install prompt, then start a new agent session. A skill package does not set up an MCP server connection.

Claude Code

.claude/skills/huggingface-datasets/SKILL.md

Project skills are committed with the repo. Use the user directory for a personal install across every project.

Project install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'claude-code'
Install for all projects instead

Personal install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'claude-code' --global

Codex

.agents/skills/huggingface-datasets/SKILL.md

Codex reads `.agents/skills/` as its primary location, which is also the cross-platform default other clients honour.

Project install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'codex'
Install for all projects instead

Personal install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'codex' --global

Cursor

.agents/skills/huggingface-datasets/SKILL.md

Cursor also loads `.agents/skills/`, `.claude/skills/`, and `.codex/skills/`, so one committed copy can serve several clients.

Project install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'cursor'
Install for all projects instead

Personal install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'cursor' --global

Gemini CLI

.agents/skills/huggingface-datasets/SKILL.md

Gemini CLI reads `.agents/skills/` first when both directories exist.

Project install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'gemini-cli'
Install for all projects instead

Personal install

npx skills add 'https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets' --skill 'huggingface-datasets' --agent 'gemini-cli' --global

SKILL.md

View raw source

Published by Hugging Face under Apache-2.0. Rendered from the package in github.com/huggingface/skills/tree/main/skills/huggingface-datasets.

Read full skill instructions

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)

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.

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:
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.

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.

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

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Docs

:
/croissant?dataset=<namespace/repo>