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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.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=huggingface-skills-skills-huggingface-datasets-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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 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: 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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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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