huggingface-datasets
Helps your agent work with Hugging Face datasets. It can fetch metadata, filter data, and download files.
What it does
This tool lets you explore and download data from a Hugging Face "dataset" (a collection of data). You can view parts of the data, search for specific information, filter results, and get information about the dataset's size and structure.
When to use it
- ✓When you need to see how a dataset is organized.
- ✓When you want to find specific information within a dataset.
- ✓When you need to download parts of a dataset in Parquet format.
Example of what to ask your AI
“Show the first 10 rows of the 'stanfordnlp/imdb' dataset in the 'plain_text' config and 'train' split.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool connects to a service called Hugging Face Dataset Viewer. It sends specific requests to this service to fetch information like metadata, data rows, search results, filters, and download links.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill huggingface-datasetsOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/skills/huggingface-datasets/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
When to Use
- Use when this upstream workflow matches the user's stated goal.
- Use when the task requires the procedures documented in this skill.
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.
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
Examples
…
FAQ
What is a "dataset" on Hugging Face?
It's a place where data (like text, images, audio) is stored and shared, often for training artificial intelligence models.
What are "config" and "split"?
These are ways to organize data within a dataset. 'Config' might be a different format of the data, and 'split' usually divides the data into training, validation, or test sets.
Can I modify data with this tool?
No, this tool is only for reading and exploring data. It does not allow you to make changes to the datasets.
Details
Original description by the author
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.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4665
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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