어떤 스킬인가요?
큰 학습 자료를 전부 내려받기 전에 어떤 열과 내용이 있는지 확인하고 싶을 때 사용합니다. 데이터셋의 config와 split을 찾은 다음 일부 행을 읽고 필요한 조건으로 검색할 수 있습니다.
공개 자료라도 이용 조건과 문서 설명을 확인해야 합니다. 검색할 열과 필요한 범위를 알려주면 필터와 페이지 단위 조회를 구성합니다. 접근 제한이나 비공개 자료는 별도 토큰이 필요하며 인증 없이 조회할 수 있다고 가정하지 않습니다.
huggingface-datasets · 스킬 지원 에이전트
Dataset Viewer API로 데이터셋 분할, 행 미리보기, 검색·필터와 다운로드 경로를 조회합니다.
큰 학습 자료를 전부 내려받기 전에 어떤 열과 내용이 있는지 확인하고 싶을 때 사용합니다. 데이터셋의 config와 split을 찾은 다음 일부 행을 읽고 필요한 조건으로 검색할 수 있습니다.
공개 자료라도 이용 조건과 문서 설명을 확인해야 합니다. 검색할 열과 필요한 범위를 알려주면 필터와 페이지 단위 조회를 구성합니다. 접근 제한이나 비공개 자료는 별도 토큰이 필요하며 인증 없이 조회할 수 있다고 가정하지 않습니다.
huggingface-datasets로 아래 공개 데이터셋의 split과 열을 확인해줘. 처음 10행과 한국어 텍스트 검색 방법, parquet 다운로드 경로를 정리해줘.
받을 수 있는 결과샘플·검색 결과·추출 경로
스킬 지원 에이전트와 API 요청 환경이 필요합니다. 제한·비공개 자료는 Hugging Face 인증이 필요합니다.
현재 사용 중인 스킬 지원 에이전트에 아래 요청을 붙여넣으세요. SKILL.md만 복사하면 보조 파일이 빠질 수 있어요.
https://github.com/huggingface/skills 에서 skills/huggingface-datasets 스킬을 현재 에이전트의 스킬 폴더에 설치해줘. SKILL.md와 이 스킬이 참조하는 scripts·references·assets와 공통 파일을 함께 유지하고, 필요한 실행 환경과 추가 연결을 확인한 뒤 알려줘. 설치가 끝나면 huggingface-datasets 스킬을 인식하는지 확인해줘.
“huggingface-datasets 스킬을 사용해줘. 입력 자료: 데이터셋 ID·조건·필요한 분할”처럼 요청하세요. 위의 예시를 내 자료에 맞게 바꿔도 좋아요.
예상 결과는 ‘샘플·검색 결과·추출 경로’입니다. 필요한 내용이 포함됐는지 확인하세요. 입력 자료의 사실·숫자와 결과를 대조하고, 부족한 부분을 이어서 요청하세요.
제작자 원본 2026-10-04 확인 · 설치 안내 2026-10-04 확인 · 실제 설치·실행 미검증
모델 사용 요금과 연결 앱 요금은 이용 중인 서비스의 정책을 따릅니다.
설치 안내 출처--- name: huggingface-datasets description: 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. --- # 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.--- name: huggingface-datasets description: 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. --- # 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-ok hf upload <namespace>/<repo> ~/.codex/sessions codex/<project-or-cwd> --type dataset ```
Original skill: huggingface/skills/skills/huggingface-datasets/SKILL.md
Source: https://github.com/huggingface/skills/blob/ca0325bb20b2d0a1b2efa893670c4c72f79e707b/skills/huggingface-datasets/SKILL.md
License: Apache-2.0
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SKILL.md 본문입니다. 원문에서 참조하는 스크립트·보조 파일은 원본 패키지에 포함되어 있습니다.