hugging-face-dataset-viewer
Query Hugging Face datasets through the Dataset Viewer API for splits, rows, search, filters, and parquet links.
Best use case
hugging-face-dataset-viewer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Query Hugging Face datasets through the Dataset Viewer API for splits, rows, search, filters, and parquet links.
Teams using hugging-face-dataset-viewer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/hugging-face-dataset-viewer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hugging-face-dataset-viewer Compares
| Feature / Agent | hugging-face-dataset-viewer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Query Hugging Face datasets through the Dataset Viewer API for splits, rows, search, filters, and parquet links.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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: ```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. ## Querying Datasets Use `npx parquetlens` with Hub parquet alias paths for SQL querying. Parquet alias shape: ```text hf://datasets/<namespace>/<repo>@~parquet/<config>/<split>/<shard>.parquet ``` Derive `<config>`, `<split>`, and `<shard>` from Dataset Viewer `/parquet`: ```bash 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: ```bash 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: ```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`. ## 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.
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