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nature-literature-pipeline

by @yuan1z0825v
4.5(120)

An automated academic literature monitoring pipeline that searches multiple sources (arXiv, OpenAlex, Crossref, Semantic Scholar) daily, scores papers using a six-dimension rubric (topic match, methodology, journal quality, etc.), generates concise digests, and delivers them to Feishu/Telegram with archiving and deduplication. It saves researchers hours of manual screening and ensures they never miss important publications in their field.

literature-reviewautomationresearchdaily-digestacademic-searchGitHub
Installation
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-literature-pipeline
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Before / After Comparison

1 组
Before

Users manually search multiple databases (arXiv, PubMed, Google Scholar) daily, reading titles and abstracts to filter relevant papers, typically taking over 2 hours, with high risk of missing important papers or re-reading duplicates.

After

This skill automatically retrieves candidate papers from multiple sources, scores them, and delivers only the top 5 digest summaries. Users spend only 15 minutes reviewing the daily push, with automatic deduplication.

SKILL.md

Nature Literature Pipeline

A complete, production-tested automated literature pipeline. Not just "search for papers" — it's a structured engine that scores, classifies, reads, delivers, and archives research papers daily.

What It Does

Cron (daily trigger, e.g. 08:30)
  │
  ├─ ① SEARCH (30 candidates)
  │   arXiv / OpenAlex / Crossref / Semantic Scholar (auto-degradation)
  │
  ├─ ② COARSE FILTER (30 → 5)
  │   Six-dimension scoring: topic match × 35 + methodology × 20
  │   + journal quality × 15 + network relevance × 10
  │   + applied value × 10 + archival value × 10
  │
  ├─ ③ FINE READ (top 5)
  │   Abstract-level or full-text. Source level tagged:
  │   Full-text / Abstract only / Metadata only
  │
  ├─ ④ DELIVER
  │   Formatted digest to Feishu/Telegram/etc.
  │   🏅 rank | title | journal | ⭐ score | 💡 one-liner
  │   🔬 methods | 📊 key results | 🧭 commentary
  │
  └─ ⑤ ARCHIVE
      DOI/arXiv de-dup → classify → write notes → update index

Quick Start

After installing, tell your agent:

My research area is [X], keywords: [Y], deliver to [feishu group name], archive to [path]

The agent will configure keywords, delivery target, and archive path automatically.

Then set up a daily cron job:

Set up a daily literature push at 08:30 Beijing time, 30 candidates, top 5 delivered

Architecture

The skill is organized in two layers:

LayerPurposeFiles
EngineScoring, classification, note templates, gap analysisreferences/scoring-system.md, references/gap-analysis.md, references/note-template.md
ApplicationDaily cron pipeline, delivery formatting, archival workflowreferences/push-format.md, references/cron-setup.md, references/review-compilation-workflow.md

Configuration

All domain-specific content is configurable:

  • Keywords — your research keywords (English + Chinese)
  • Scoring weights — adjust the six dimensions for your field
  • Classification rules — define your own tier system (A-E or custom)
  • Delivery target — Feishu group, Telegram channel, email, etc.
  • Archive path — local vault/wiki directory

A config template is provided in templates/literature-push-template.md.

Built-in Safeguards

  • Score validation: Each dimension capped, total recalculated — no 11/10 allowed
  • Triple de-duplication: DOI / arXiv ID / OpenAlex ID
  • Graceful degradation: Semantic Scholar down → auto-switch to OpenAlex + Crossref + arXiv
  • Read-only archive: Daily pipeline writes to raw/ literature directory only; never modifies wiki/knowledge base without user approval

Related Skills

  • nature-academic-search — ad-hoc literature search (complementary; this skill adds structured daily automation)
  • nature-citation — CNS citation export (for importing pipeline discoveries into manuscripts)
  • zotero — library management (for long-term organization of pipeline outputs)
  • arxiv — arXiv API (used as a search source)

References

ReferencePurpose
references/scoring-system.mdSix-dimension scoring rubric with weights, caps, and evaluation logic
references/gap-analysis.mdMethodology for identifying research gaps through systematic literature survey
references/note-template.mdStandardized literature note format with YAML frontmatter
references/push-format.mdDaily digest message template with field guidelines and example
references/cron-setup.mdCron job creation, verification, and manual fallback procedures
references/review-compilation-workflow.mdEnd-to-end workflow for concentrated literature review writing

Pitfalls

  1. Keyword drift: Review keywords monthly — research directions evolve
  2. Score inflation: Subagents may inflate scores; always validate arithmetic
  3. Duplicate creep: Classic papers will reappear; maintain a dedup index
  4. Wiki safety: Pipeline writes to raw/ only; wiki integration is manual
  5. Cron locality: Hermes cron is local, not cloud — machine must be running

nature-literature-pipeline 技能

English

nature-literature-pipeline 用于搭建持续运行的文献发现管线:多源检索、六维评分、精读摘要、推送和归档,适合每天或每周跟踪一个研究方向。

适合用它做什么

  • 为固定主题建立自动化文献监测。
  • 从 arXiv、OpenAlex、Crossref、Semantic Scholar 等来源收集候选论文。
  • 对候选文献按方向匹配、方法价值、期刊质量、网络关联、工程价值和归档价值打分。
  • 把 Top 文献整理成可推送的中文/英文精读摘要。
  • 维护 DOI、arXiv ID、主题标签和已读状态,减少重复阅读。

与 nature-academic-search 的关系

nature-academic-search 适合一次性检索和引用核查;nature-literature-pipeline 适合持续订阅和周期性推送。前者回答“现在帮我找”,后者回答“持续帮我盯”。

典型请求

  • “每天早上帮我跟踪海洋混凝土氯离子扩散和机器学习方向的新论文。”
  • “为这个关键词组建一条每周推送的文献管线。”
  • “把过去 7 天候选论文按六维评分筛到 Top 5,并归档。”

你需要提供

  • 研究主题、关键词、排除词、重点期刊或重点作者。
  • 推送频率、每次候选数量和最终精读数量。
  • 输出位置,例如 Markdown 目录、飞书/Telegram 接口或本地归档文件夹。

产出

  • 候选文献表和去重后的 Top 列表。
  • 每篇重点论文的精读卡片:问题、方法、关键数据、局限和与你课题的关系。
  • 可复用的管线配置、归档索引和失败/降级说明。

边界

  • 自动推送依赖本机 cron、消息接口或用户配置的调度器。
  • 无法访问全文时,会明确标注 Abstract only 或 Metadata only。
  • 不会把评分当作论文质量的最终判断;高分候选仍需要人工阅读确认。

相关技能

  • nature-academic-search:一次性多源检索、引用指标和严格他引审计。
  • nature-reader:把候选论文做成全文中英对照阅读材料。

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Installs11.6K
Rating4.5 / 5.0
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Updated2026年10月23日
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Timeline

Created2026年7月26日
Last Updated2026年10月23日
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