Daily Paper Generator

Daily Paper Generator

Overview

Automate the workflow of discovering, reviewing, and summarizing recent research papers on arXiv related to EEG decoding, brain-computer interfaces, and neural foundation models.

Core workflow:

1. Search arXiv for recent papers (within 3 months) using Chrome browser
2. Retrieve paper metadata from arXiv pages
3. Evaluate paper quality using structured criteria
4. Select top 3 papers
5. Generate structured summaries with Chinese and English reviews
6. Save results as Markdown files in daily paper/ directory

When to Use

Use this skill when:

• User asks to "generate daily paper" or "find recent EEG papers"
• User wants to discover research on EEG decoding, speech decoding from EEG, or brain foundation models
• User needs paper reviews with both Chinese and English summaries
• User wants to track recent arXiv publications in neuro/AI intersection

Output Format

Each paper summary follows this structure (see example/daily paper example.md for complete example):

1. Header Section

# Paper Title

## 作者及单位
Author list
Institution

## arXiv 链接
https://arxiv.org/abs/ARXIV_ID

**发表日期**: YYYY-MM-DD
**arXiv ID**: XXXX.XXXXX
**分类**: cs.LG, q-bio.NC, eess.SP

2. Review Sections

中文评语 (~300 words):

• Background (1-2 sentences): Research context and importance
• Challenges (2-3 sentences): Problems with existing methods
• Contribution (1-2 sentences): Core contribution of this work
• Method (2-3 sentences): Key technical details
• Results (2-3 sentences): Main findings and metrics
• Analysis & Limitations (1-2 sentences): Significance and limitations

English Review (fluent academic English):

• Concise summary following the same structure as Chinese review
• Use natural academic prose (avoid AI-like patterns)
• Apply scientific writing best practices

3. Main Figure Section

## 主图
[预留论文主图位置]

4. Metadata Table

## 论文元数据

| 项目 | 内容 |
|------|------|
| **标题** | Paper Title |
| **第一作者** | First Author Name |
| **作者列表** | Full author list |
| **第一作者单位** | Institution |
| **发表日期** | YYYY-MM-DD |
| **arXiv 链接** | https://arxiv.org/abs/ID |
| **PDF 链接** | https://arxiv.org/pdf/ID |
| **分类** | cs.LG, q-bio.NC, eess.SP |

5. Integrated Format (for publishing)

## 整合格式

Daily Paper MMDD

Paper Title

https://arxiv.org/abs/ARXIV_ID

[Chinese Review]

[English Review]

6. Appendix

## 附录

**github连接：** [Available/Not Available]

**补充说明**

[Key insights, impact points]

**Sources:**
- [arXiv Abstract](URL)
- [arXiv HTML](URL)
- [Paperverse Review](URL) (if available)

Quick Reference

Workflow

Step 1: Search arXiv Using Chrome

Search keywords (see references/keywords.md for full list):

• EEG decoding: EEG decoding, brain decoding, neural decoding
• Speech decoding: speech decoding from EEG, EEG speech reconstruction
• Foundation models: EEG foundation model, large EEG model, brain foundation model

Method: Use Chrome browser with arXiv search

1. Navigate to arXiv search using Chrome MCP tools:

   • URL: https://arxiv.org/search/
   • Add search parameters: ?searchtype=all&query=KEYWORDS&abstracts=show&order=-announced_date_first

2. Search URL pattern:

   https://arxiv.org/search/?searchtype=all&query=EEG+decoding&abstracts=show&order=-announced_date_first
   https://arxiv.org/search/?searchtype=all&query=EEG+foundation+model&abstracts=show&order=-announced_date_first
   

3. Time filtering: Use date filters or sort by announced_date_first to get recent papers

4. Extract paper information from search results:

   • Paper title
   • Authors
   • arXiv ID
   • Abstract preview
   • Publication date

Step 2: Retrieve Paper Details

For each candidate paper, navigate to its arXiv abs page and extract:

URL pattern: https://arxiv.org/abs/ARXIV_ID

Extract from page:

• Title (from <h1> tag)
• Authors (from .authors element)
• Abstract (from blockquote.abstract)
• Submission date (from .dateline)
• arXiv ID (from URL or page)
• Categories (from .subjects)
• Comments (if present)
• First author institution (if available in comments or author affiliations)

Step 3: Evaluate Paper Quality

Review each paper using the 5-dimension criteria in references/quality-criteria.md:

Scoring: Rate each dimension 1-5, calculate weighted sum.

Process:

1. Screen by title/abstract for relevance
2. Navigate to full paper page for detailed review
3. Score each dimension
4. Rank by total score
5. Select top 3

Step 4: Generate Paper Summaries

For each selected paper, create a summary following the structure in example/daily paper example.md:

Required sections:

1. Title (H1 heading)
2. 作者及单位 (Authors and Institution)
3. arXiv 链接 (with metadata: date, ID, categories)
4. 中文评语 (Chinese review, ~300 words)
5. English Review (fluent academic English)
6. 主图 (placeholder for main figure)
7. 论文元数据 (metadata table)
8. 整合格式 (integrated format for publishing)
9. 附录 (appendix with github link,补充说明, sources)

Writing Chinese review (see references/writing-style.md):

• Background: 研究背景和重要性
• Challenges: 现有方法的不足
• Contribution: 本工作的核心贡献
• Method: 关键技术细节
• Results: 主要发现和指标
• Analysis & Limitations: 意义和局限性

Writing English review:

• Apply scientific-writing skill best practices
• Use anti-AI writing principles (natural, varied sentence structure)
• Keep concise and direct
• Avoid formulaic transitions ("furthermore", "moreover", "additionally")

Step 5: Save Output

Create Markdown files in the daily paper/ directory:

daily paper/
├── 2025-01-26-1430-paper-1.md
├── 2025-01-26-1430-paper-2.md
└── 2025-01-26-1430-paper-3.md

Filename format: YYYY-MM-DD-HHMM-paper-N.md

Important: 使用时间戳（精确到分钟）避免覆盖之前生成的文件。

Example Output

See example/daily paper example.md for a complete example of the DeeperBrain paper summary with all sections properly formatted.

Additional Resources

Reference Files

• references/keywords.md - Complete search keyword list and arXiv URL patterns
• references/quality-criteria.md - Detailed 5-dimension evaluation criteria with scoring rubrics
• references/writing-style.md - Chinese review structure, templates, and example analysis

Example Files

• example/daily paper example.md - Complete output example with all sections
• scripts/arxiv_search.py - Legacy Python script (deprecated, use Chrome instead)

Chrome MCP Tools

Use Chrome MCP tools for browser automation:

• Navigation: Open arXiv search and paper pages
• Screenshot: Capture pages for analysis
• Tabs: Manage multiple arXiv pages
• Content extraction: Parse paper metadata from HTML

Important Notes

1. Time range: Search focuses on papers from the last 3 months (check submission dates)
2. Link format: Use arXiv abs page links (https://arxiv.org/abs/ID), not direct PDF links
3. Review length: Chinese reviews should be approximately 300 words
4. Quality focus: Prioritize content quality (innovation, method, experiments) over quantitative metrics
5. Bilingual output: Both Chinese and English reviews are required for each paper
6. Chrome required: This workflow uses Chrome browser automation via MCP tools
7. Complete format: Ensure all 9 sections are included in each summary
8. Consistent naming: Use Daily Paper MMDD format in integrated section