🛠️ Hugging Face Papers
Hugging FaceやarXivに掲載されているAI関連
📺 まず動画で見る(YouTube)
▶ 【衝撃】最強のAIエージェント「Claude Code」の最新機能・使い方・プログラミングをAIで効率化する超実践術を解説! ↗
※ jpskill.com 編集部が参考用に選んだ動画です。動画の内容と Skill の挙動は厳密には一致しないことがあります。
📜 元の英語説明(参考)
Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.
🇯🇵 日本人クリエイター向け解説
Hugging FaceやarXivに掲載されているAI関連
※ jpskill.com 編集部が日本のビジネス現場向けに補足した解説です。Skill本体の挙動とは独立した参考情報です。
下記のコマンドをコピーしてターミナル(Mac/Linux)または PowerShell(Windows)に貼り付けてください。 ダウンロード → 解凍 → 配置まで全自動。
mkdir -p ~/.claude/skills && cd ~/.claude/skills && curl -L -o hugging-face-papers.zip https://jpskill.com/download/2995.zip && unzip -o hugging-face-papers.zip && rm hugging-face-papers.zip
$d = "$env:USERPROFILE\.claude\skills"; ni -Force -ItemType Directory $d | Out-Null; iwr https://jpskill.com/download/2995.zip -OutFile "$d\hugging-face-papers.zip"; Expand-Archive "$d\hugging-face-papers.zip" -DestinationPath $d -Force; ri "$d\hugging-face-papers.zip"
完了後、Claude Code を再起動 → 普通に「動画プロンプト作って」のように話しかけるだけで自動発動します。
💾 手動でダウンロードしたい(コマンドが難しい人向け)
- 1. 下の青いボタンを押して
hugging-face-papers.zipをダウンロード - 2. ZIPファイルをダブルクリックで解凍 →
hugging-face-papersフォルダができる - 3. そのフォルダを
C:\Users\あなたの名前\.claude\skills\(Win)または~/.claude/skills/(Mac)へ移動 - 4. Claude Code を再起動
⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。
🎯 このSkillでできること
下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。
📦 インストール方法 (3ステップ)
- 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
- 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
- 3. 展開してできたフォルダを、ホームフォルダの
.claude/skills/に置く- · macOS / Linux:
~/.claude/skills/ - · Windows:
%USERPROFILE%\.claude\skills\
- · macOS / Linux:
Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。
詳しい使い方ガイドを見る →- 最終更新
- 2026-05-17
- 取得日時
- 2026-05-17
- 同梱ファイル
- 1
💬 こう話しかけるだけ — サンプルプロンプト
- › Hugging Face Papers を使って、最小構成のサンプルコードを示して
- › Hugging Face Papers の主な使い方と注意点を教えて
- › Hugging Face Papers を既存プロジェクトに組み込む方法を教えて
これをClaude Code に貼るだけで、このSkillが自動発動します。
📖 Claude が読む原文 SKILL.md(中身を展開)
この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。
Hugging Face Paper Pages
Hugging Face Paper pages (hf.co/papers) is a platform built on top of arXiv (arxiv.org), specifically for research papers in the field of artificial intelligence (AI) and computer science. Hugging Face users can submit their paper at hf.co/papers/submit, which features it on the Daily Papers feed (hf.co/papers). Each day, users can upvote papers and comment on papers. Each paper page allows authors to:
- claim their paper (by clicking their name on the
authorsfield). This makes the paper page appear on their Hugging Face profile. - link the associated model checkpoints, datasets and Spaces by including the HF paper or arXiv URL in the model card, dataset card or README of the Space
- link the Github repository and/or project page URLs
- link the HF organization. This also makes the paper page appear on the Hugging Face organization page.
Whenever someone mentions a HF paper or arXiv abstract/PDF URL in a model card, dataset card or README of a Space repository, the paper will be automatically indexed. Note that not all papers indexed on Hugging Face are also submitted to daily papers. The latter is more a manner of promoting a research paper. Papers can only be submitted to daily papers up until 14 days after their publication date on arXiv.
The Hugging Face team has built an easy-to-use API to interact with paper pages. Content of the papers can be fetched as markdown, or structured metadata can be returned such as author names, linked models/datasets/spaces, linked Github repo and project page.
When to Use
- User shares a Hugging Face paper page URL (e.g.
https://huggingface.co/papers/2602.08025) - User shares a Hugging Face markdown paper page URL (e.g.
https://huggingface.co/papers/2602.08025.md) - User shares an arXiv URL (e.g.
https://arxiv.org/abs/2602.08025orhttps://arxiv.org/pdf/2602.08025) - User mentions a arXiv ID (e.g.
2602.08025) - User asks you to summarize, explain, or analyze an AI research paper
Parsing the paper ID
It's recommended to parse the paper ID (arXiv ID) from whatever the user provides:
| Input | Paper ID |
|---|---|
https://huggingface.co/papers/2602.08025 |
2602.08025 |
https://huggingface.co/papers/2602.08025.md |
2602.08025 |
https://arxiv.org/abs/2602.08025 |
2602.08025 |
https://arxiv.org/pdf/2602.08025 |
2602.08025 |
2602.08025v1 |
2602.08025v1 |
2602.08025 |
2602.08025 |
This allows you to provide the paper ID into any of the hub API endpoints mentioned below.
Fetch the paper page as markdown
The content of a paper can be fetched as markdown like so:
curl -s "https://huggingface.co/papers/{PAPER_ID}.md"
This should return the Hugging Face paper page as markdown. This relies on the HTML version of the paper at https://arxiv.org/html/{PAPER_ID}.
There are 2 exceptions:
- Not all arXiv papers have an HTML version. If the HTML version of the paper does not exist, then the content falls back to the HTML of the Hugging Face paper page.
- If it results in a 404, it means the paper is not yet indexed on hf.co/papers. See Error handling for info.
Alternatively, you can request markdown from the normal paper page URL, like so:
curl -s -H "Accept: text/markdown" "https://huggingface.co/papers/{PAPER_ID}"
Paper Pages API Endpoints
All endpoints use the base URL https://huggingface.co.
Get structured metadata
Fetch the paper metadata as JSON using the Hugging Face REST API:
curl -s "https://huggingface.co/api/papers/{PAPER_ID}"
This returns structured metadata that can include:
- authors (names and Hugging Face usernames, in case they have claimed the paper)
- media URLs (uploaded when submitting the paper to Daily Papers)
- summary (abstract) and AI-generated summary
- project page and GitHub repository
- organization and engagement metadata (number of upvotes)
To find models linked to the paper, use:
curl https://huggingface.co/api/models?filter=arxiv:{PAPER_ID}
To find datasets linked to the paper, use:
curl https://huggingface.co/api/datasets?filter=arxiv:{PAPER_ID}
To find spaces linked to the paper, use:
curl https://huggingface.co/api/spaces?filter=arxiv:{PAPER_ID}
Claim paper authorship
Claim authorship of a paper for a Hugging Face user:
curl "https://huggingface.co/api/settings/papers/claim" \
--request POST \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $HF_TOKEN" \
--data '{
"paperId": "{PAPER_ID}",
"claimAuthorId": "{AUTHOR_ENTRY_ID}",
"targetUserId": "{USER_ID}"
}'
- Endpoint:
POST /api/settings/papers/claim - Body:
paperId(string, required): arXiv paper identifier being claimedclaimAuthorId(string): author entry on the paper being claimed, 24-char hex IDtargetUserId(string): HF user who should receive the claim, 24-char hex ID
- Response: paper authorship claim result, including the claimed paper ID
Get daily papers
Fetch the Daily Papers feed:
curl -s -H "Authorization: Bearer $HF_TOKEN" \
"https://huggingface.co/api/daily_papers?p=0&limit=20&date=2017-07-21&sort=publishedAt"
- Endpoint:
GET /api/daily_papers - Query parameters:
p(integer): page numberlimit(integer): number of results, between 1 and 100date(string): RFC 3339 full-date, for example2017-07-21week(string): ISO week, for example2024-W03month(string): month value, for example2024-01submitter(string): filter by submittersort(enum):publishedAtortrending
- Response: list of daily papers
List papers
List arXiv papers sorted by published date:
curl -s -H "Authorization: Bearer $HF_TOKEN" \
"https://huggingface.co/api/papers?cursor={CURSOR}&limit=20"
- Endpoint:
GET /api/papers - Query parameters:
cursor(string): pagination cursorlimit(integer): number of results, between 1 and 100
- Response: list of papers
Search papers
Perform hybrid semantic and full-text search on papers:
curl -s -H "Authorization: Bearer $HF_TOKEN" \
"https://huggingface.co/api/papers/search?q=vision+language&limit=20"
This searches over the paper title, authors, and content.
- Endpoint:
GET /api/papers/search - Query parameters:
q(string): search query, max length 250limit(integer): number of results, between 1 and 120
- Response: matching papers
Index a paper
Insert a paper from arXiv by ID. If the paper is already indexed, only its authors can re-index it:
curl "https://huggingface.co/api/papers/index" \
--request POST \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $HF_TOKEN" \
--data '{
"arxivId": "{ARXIV_ID}"
}'
- Endpoint:
POST /api/papers/index - Body:
arxivId(string, required): arXiv ID to index, for example2301.00001
- Pattern:
^\d{4}\.\d{4,5}$ - Response: empty JSON object on success
Update paper links
Update the project page, GitHub repository, or submitting organization for a paper. The requester must be the paper author, the Daily Papers submitter, or a papers admin:
curl "https://huggingface.co/api/papers/{PAPER_OBJECT_ID}/links" \
--request POST \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $HF_TOKEN" \
--data '{
"projectPage": "https://example.com",
"githubRepo": "https://github.com/org/repo",
"organizationId": "{ORGANIZATION_ID}"
}'
- Endpoint:
POST /api/papers/{paperId}/links - Path parameters:
paperId(string, required): Hugging Face paper object ID
- Body:
githubRepo(string, nullable): GitHub repository URLorganizationId(string, nullable): organization ID, 24-char hex IDprojectPage(string, nullable): project page URL
- Response: empty JSON object on success
Error Handling
- 404 on
https://huggingface.co/papers/{PAPER_ID}ormdendpoint: the paper is not indexed on Hugging Face paper pages yet. - 404 on
/api/papers/{PAPER_ID}: the paper may not be indexed on Hugging Face paper pages yet. - Paper ID not found: verify the extracted arXiv ID, including any version suffix
Fallbacks
If the Hugging Face paper page does not contain enough detail for the user's question:
- Check the regular paper page at
https://huggingface.co/papers/{PAPER_ID} - Fall back to the arXiv page or PDF for the original source:
https://arxiv.org/abs/{PAPER_ID}https://arxiv.org/pdf/{PAPER_ID}
Notes
- No authentication is required for public paper pages.
- Write endpoints such as claim authorship, index paper, and update paper links require
Authorization: Bearer $HF_TOKEN. - Prefer the
.mdendpoint for reliable machine-readable output. - Prefer
/api/papers/{PAPER_ID}when you need structured JSON fields instead of page markdown.
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.