Huggingface Datasets

OrganizationPopular

huggingface

huggingface-datasets

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.

Overview

Publisher	huggingface
Repository	`skills`
Skill name	`huggingface-datasets`
Stars	11.1K
Forks	744
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by huggingface on GitHub. Read the source before you install it.

Installation

Install the Huggingface Datasets AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/huggingface/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/huggingface-datasets .claude/skills/huggingface-datasets

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Huggingface Datasets in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Huggingface Datasets on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Huggingface Datasets is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 + split with /splits.
Preview with /first-rows.
Paginate content with /rows using offset and length (max 100).
Use /search for text matching and /filter for row predicates.
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-ok
hf upload <namespace>/<repo> ~/.codex/sessions codex/<project-or-cwd> --type dataset

Frequently asked questions

What does the Huggingface Datasets AI skill do?

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.

Why use Huggingface Datasets on TypingMind?

Because you install it once and use it with any model. Huggingface Datasets is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Huggingface Datasets in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huggingface/skills/tree/main/skills/huggingface-datasets. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Huggingface Datasets?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Huggingface Datasets?

As many as you like. As long as a model supports skills, you can use Huggingface Datasets with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Huggingface Datasets AI skill free?

Yes. It is published on GitHub by huggingface under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Hugging Face Dataset Viewer

Core workflow

Defaults

Dataset Viewer

Creating and Uploading Datasets

Agent Traces

Frequently asked questions

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