daily-paper-generator

name	daily-paper-generator
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.
metadata	{"author":"galaxy-dawn"}

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
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:

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:

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).

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Source: galaxy-dawn/claude-scholar — distributed by TomeVault.