tomevault-io Hugging Face Daily Papers
Fetch Hugging Face Daily Papers for a target date, extract per-paper full metadata from the embedded Svelte hydration JSON, classify papers by domain, and save the result as a Markdown + JSON report.
When to use
Use this skill when the user asks to:
- 抓取某天的 Hugging Face 每日论文列表
- 获取某天 Daily Papers 的标题、点赞量、arXiv ID、评论数、GitHub Star 等
- 按领域分类输出论文清单
- 将结果保存为 Markdown 文档
Critical insight
The HF Daily Papers page is a Svelte SPA. The server-rendered HTML does not contain human-readable paper data. Instead, all paper metadata lives in a single JSON blob inside a data-props attribute:
<div class="SVELTE_HYDRATER contents"
data-target="DailyPapers"
data-props='{"dailyPapers":[{...21 papers...}], ...}'>
Do NOT use web_fetch for this page. The Markdown/text conversion loses arXiv IDs, author links, and structured metadata. Instead, use download_file to get the raw HTML, then extract JSON with Python (see below).
Workflow
1) Determine the target date
- Default to yesterday if no date specified.
- The page uses format:
https://huggingface.co/papers/date/YYYY-MM-DD - Daily Papers are updated on workdays only. If the user requests a weekend date, warn that the list may not have been updated.
2) Download raw HTML
Use download_file (NOT web_fetch):
download_file(
url="https://huggingface.co/papers/date/YYYY-MM-DD",
dest="hf_papers_YYYY-MM-DD.html",
overwrite=true
)
This preserves the full DOM including the data-props JSON blob.
3) Extract JSON with Python
Use the bundled script or the following inline Python pattern:
import json, re
with open("hf_papers_YYYY-MM-DD.html", "r", encoding="utf-8") as f:
html = f.read()
# Locate the JSON blob inside data-props
dp_start = html.rfind('data-props="', 0, html.find('"dailyPapers"') + 50000)
json_start = dp_start + len('data-props="')
json_end = html.find('"><section', json_start)
json_str = html[json_start:json_end]
json_str = json_str.replace(""", '"').replace("&", "&")
data = json.loads(json_str)
papers = data["dailyPapers"]
4) Extract per-paper fields
From each entry in data.dailyPapers[i]:
| Field | Path | Example |
|---|---|---|
| arXiv ID | paper.id |
"2605.00658" |
| Title | title (or paper.title) |
"UniVidX: A Unified..." |
| Upvotes | paper.upvotes |
71 |
| Comments | numComments |
2 |
| GitHub Stars | paper.githubStars |
52 (may be absent) |
| Organization | organization.name |
"ByteDance" (may be absent) |
| HF URL | https://huggingface.co/papers/{paper.id} |
— |
| arXiv URL | https://arxiv.org/abs/{paper.id} |
— |
| AI Summary | paper.ai_summary |
Short one-liner |
| Keywords | paper.ai_keywords |
Array of strings |
| Authors | paper.authors[].name |
Array |
| Author participation | isAuthorParticipating |
true/false |
| Submitted by | submittedBy.fullname |
"taesiri" |
| Thumbnail | thumbnail |
Image URL |
5) Save output files
Save both a structured JSON and a polished Markdown report:
| File | Format | Purpose |
|---|---|---|
hf_daily_YYYY-MM-DD.json |
JSON | Machine-readable, full metadata |
hf_daily_YYYY-MM-DD.md |
Markdown | Human-readable report, domain-classified |
6) Classify by domain
Use the paper's primary contribution to assign one main domain. Common domains include:
- Agent / AI Systems / Tool Use / RAG
- Reinforcement Learning / Reward Modeling
- LLM Training / Distillation / Inference Efficiency / Safety
- Vision-Language / Multimodal / Robotics
- 3D / Graphics / World Models / Video Generation
- Memory / Cognitive Architectures
- Continual / Incremental Learning
- Healthcare / Biology
- Benchmarks / Evaluation
Each paper gets exactly one domain. Total entries must equal the total paper count.
Output structure (Markdown)
# 🤗 Hugging Face Daily Papers — YYYY-MM-DD
**共 N 篇论文** | 数据来源: https://huggingface.co/papers/date/YYYY-MM-DD
| # | 👍 | arXiv ID | 论文标题 | 💬 | ⭐ | 机构 |
|:--:|:--:|:--|------|:--:|:--:|------|
| 1 | 71 | `2605.00658` | [UniVidX: ...](https://arxiv.org/abs/2605.00658) | 2 | 52 | — |
---
## 论文速览
### 1. Paper Title
**arXiv:** `2605.xxxxx` | **👍 71** | **🏢 Organization**
> AI summary one-liner
**关键词:** kw1 · kw2 · kw3
---
Quality bar
- Do NOT invent arXiv IDs. Every ID must come from the JSON.
- Do NOT use
web_fetchfor this task — usedownload_file+ Python. - Verify the paper count:
len(papers)must equal 21 (or the day's actual count). - Keep each overview concise and factual.
- Preserve the original paper title verbatim.
- Cross-check: the total entries in the Markdown table must equal the paper count in the header.
Update cadence
- Hugging Face Daily Papers are typically updated on workdays only.
- If the user requests a weekend date, first check whether a date page exists. If not, report that the list may not have been updated that day and ask whether to use the previous workday.
Bundled resources
- Reference:
references/extraction-notes.md— detailed extraction method and edge cases
Read references/extraction-notes.md for the full Python extraction script and troubleshooting guide.
Source: ChangweiXu/PyClaego — distributed by TomeVault.