---
name: daily-paper-generator-5a3bd2fbebd2
description: Use when the user asks to generate daily paper digests on a general topic. This skill supports both arXiv and bioRxiv (or either one), then produces structured Chinese/English summaries for selected papers.
version: 0.5.1
---

# Daily Paper Generator

## Overview

Discover, screen, and summarize recent papers for any research topic.

Supported sources:
- arXiv
- bioRxiv
- both (`--source both`)

Core workflow:
1. Define topic query and time window
2. Search papers from arXiv / bioRxiv
3. Select Top 10 candidates per field
4. Score and narrow to Top 3 per field
5. Choose Top 1 per field
6. Generate bilingual summaries
7. Save outputs to `daily paper/`

## When to Use

Use this skill when:
- The user asks for a daily/weekly paper digest on any topic
- The user wants recent papers from arXiv and/or bioRxiv
- The user needs structured bilingual notes for reading and tracking

## Output Format

Each summary should contain:
1. Paper title
2. Authors and venue/source
3. Link(s) and date
4. Chinese review (~300 words)
5. English review (concise academic prose)
6. Metadata table
7. Appendix (optional resources)

## Quick Reference

| Task | Method |
|---|---|
| Search papers | Use `scripts/arxiv_search.py` with `--source arxiv|biorxiv|both` |
| Topic selection | Use general-topic queries from `references/keywords.md` |
| Evaluate quality | Use `references/quality-criteria.md` |
| Write Chinese review | Use `references/writing-style.md` |
| Write English review | Follow scientific writing best practices |

## Workflow

### Step 1: Define query

Choose a concrete topic query. Examples:
- `test-time adaptation for medical imaging`
- `multimodal foundation model for healthcare`
- `protein language model interpretability`

### Step 2: Search arXiv and/or bioRxiv

Use helper script:

```bash
python skills/daily-paper-generator/scripts/arxiv_search.py \
  --query "test-time adaptation for medical imaging" \
  --source both \
  --months 1 \
  --max-results 80 \
  --output /tmp/papers.json
```

Notes:
- `--source arxiv`: arXiv only
- `--source biorxiv`: bioRxiv only
- `--source both`: merge both sources and sort by date

### Step 3: Top 10 candidate selection (per field)

For each candidate paper:
1. Check topic relevance from title + abstract
2. Remove obviously off-topic papers
3. Keep **Top 10 candidates** for this field

Minimum rule:
- Do not jump directly from raw search results to final paper.
- Keep an explicit Top 10 list first.

### Step 4: Top 3 quality shortlist (per field)

For the Top 10 pool:
1. Score each paper with `references/quality-criteria.md`
2. Rank by weighted score
3. Keep **Top 3**

### Step 5: Final Top 1 selection (per field)

For the Top 3 shortlist:
1. Compare novelty + method completeness + experimental credibility
2. Check practical impact for the field
3. Select **Top 1** as the final pick

Required output trace:
- Top 10 candidate list
- Top 3 scored shortlist (with weighted scores)
- Final Top 1 and one-paragraph selection rationale

### Step 6: Generate bilingual summaries

For each selected paper, generate:
- 中文评语：背景、挑战、贡献、方法、结果、局限
- English Review: concise, factual, non-formulaic

### Step 7: Save output

Recommended directory and naming:

```text
daily paper/
  YYYY-MM-DD-HHMM-paper-1.md
  YYYY-MM-DD-HHMM-paper-2.md
  YYYY-MM-DD-HHMM-paper-3.md
```

## Additional Resources

- `references/keywords.md`: general-topic query templates
- `references/quality-criteria.md`: scoring rubric
- `references/writing-style.md`: review writing style
- `example/daily paper example.md`: output example
- `scripts/arxiv_search.py`: arXiv + bioRxiv search helper

## Important Notes

1. Use explicit topic queries, avoid single-word vague queries.
2. Keep the time window explicit (`--months N`).
3. Distinguish source in metadata (`arxiv` vs `biorxiv`).
4. Use the fixed narrowing rule: **Top 10 -> Top 3 -> Top 1** (per field).
5. If a paper lacks robust evaluation, mark confidence and limitations clearly.
6. Do not fabricate unavailable fields (institution/GitHub/code links).


---
Source attribution: Galaxy-Dawn
Original project: https://github.com/Galaxy-Dawn/claude-scholar
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: Galaxy-Dawn
Original project: https://github.com/Galaxy-Dawn/claude-scholar
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Education & Research, Marketing
Roles: Content Marketer, Data Analyst, Researcher
Specialties: Specialty not specified
Tools: Arxiv, Biorxiv
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: c4a3b75b13dbcb27607a1e5f10e76dba3822d94404edca165f46b288d10f42b6