Daily Paper Generator
SkillSearchLets your agent find new arXiv and bioRxiv papers on a topic and write ranked bilingual summaries.
Use Daily Paper Generator in Claude, ChatGPT or Ahel Desktop
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Then ask your AI: use the Daily Paper Generator skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
About this skill
Use when the user asks to "generate daily paper", "search arXiv for EEG papers", "find EEG decoding papers", "review brain-computer interface papers", or wants to create paper summaries for EEG/brain decoding/speech decoding research. This skill automates searching arXiv for recent papers on EEG dec
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/33-Galaxy-Dawn-claude-scholar/skills/daily-paper-generator/SKILL.md and read by ahel’s review.
Overview
Discover, screen, and summarize recent papers for any research topic.
Supported sources:
- arXiv
- bioRxiv
- both (
--source both)
Core workflow:
- Define topic query and time window
- Search papers from arXiv / bioRxiv
- Select Top 10 candidates per field
- Score and narrow to Top 3 per field
- Choose Top 1 per field
- Generate bilingual summaries
- 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:
- Paper title
- Authors and venue/source
- Link(s) and date
- Chinese review (~300 words)
- English review (concise academic prose)
- Metadata table
- 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 imagingmultimodal foundation model for healthcareprotein 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:
- Check topic relevance from title + abstract
- Remove obviously off-topic papers
- 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:
- Score each paper with
references/quality-criteria.md - Rank by weighted score
- Keep Top 3
Step 5: Final Top 1 selection (per field)
For the Top 3 shortlist:
- Compare novelty + method completeness + experimental credibility
- Check practical impact for the field
- 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 templatesreferences/quality-criteria.md: scoring rubricreferences/writing-style.md: review writing styleexample/daily paper example.md: output examplescripts/arxiv_search.py: arXiv + bioRxiv search helper
Important Notes
- Use explicit topic queries, avoid single-word vague queries.
- Keep the time window explicit (
--months N). - Distinguish source in metadata (
arxivvsbiorxiv). - Use the fixed narrowing rule: Top 10 -> Top 3 -> Top 1 (per field).
- If a paper lacks robust evaluation, mark confidence and limitations clearly.
- Do not fabricate unavailable fields (institution/GitHub/code links).
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
daily-paper-generator- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
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