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("&quot;", '"').replace("&amp;", "&")

data = json.loads(json_str)
papers = data["dailyPapers"]

4) Extract per-paper fields

From each entry in data.dailyPapers[i]:

5) Save output files

Save both a structured JSON and a polished Markdown report:

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_fetch for this task — use download_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.