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Galaxy-Dawn/claude-scholar/skills/daily-paper-generator/SKILL.md

daily-paper-generator

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.

Source repository stars
4,981
Declared platforms
0
Static risk flags
1
Last source update
2026-07-17
Source checked
2026-08-04

Decision brief

What it does—and where it fits

This skill supports both arXiv and bioRxiv (or either one), then produces structured Chinese/English summaries for selected papers.

Best for

  • 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

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/Galaxy-Dawn/claude-scholar --skill "skills/daily-paper-generator"
Safe inspection promptEditorial

Inspect the Agent Skill "daily-paper-generator" from https://github.com/Galaxy-Dawn/claude-scholar/blob/2f7766fd541a723d4ddc6230b3277f948d61b093/skills/daily-paper-generator/SKILL.md at commit 2f7766fd541a723d4ddc6230b3277f948d61b093. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Workflow

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

    test-time adaptation for medical imagingmultimodal foundation model for healthcareprotein language model interpretability
  2. 02

    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

    test-time adaptation for medical imagingmultimodal foundation model for healthcareprotein language model interpretability
  3. 03

    Step 2: Search arXiv and/or bioRxiv

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

    --source arxiv: arXiv only--source biorxiv: bioRxiv only--source both: merge both sources and sort by date
  4. 04

    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

    Check topic relevance from title + abstractRemove obviously off-topic papersKeep Top 10 candidates for this field
  5. 05

    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

    Score each paper with references/quality-criteria.mdRank by weighted scoreKeep Top 3

Permission review

Static risk signals and limitations

Runs scripts

medium · line 64

The documentation asks the agent to run terminal commands or scripts.

python skills/daily-paper-generator/scripts/arxiv_search.py \

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score87/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars4,981SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
Galaxy-Dawn/claude-scholar
Skill path
skills/daily-paper-generator/SKILL.md
Commit
2f7766fd541a723d4ddc6230b3277f948d61b093
License
MIT
Collected
2026-08-04
Default branch
main
View the original SKILL.md

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

TaskMethod
Search papersUse scripts/arxiv_search.py with `--source arxiv
Topic selectionUse general-topic queries from references/keywords.md
Evaluate qualityUse references/quality-criteria.md
Write Chinese reviewUse references/writing-style.md
Write English reviewFollow 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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