---
id: gh-nature-literature-pipeline
name: "nature-literature-pipeline"
url: https://skills.yangsir.net/skill/gh-nature-literature-pipeline
author: yuan1z0825
domain: science
tags: ["literature-review", "automation", "research", "daily-digest", "academic-search"]
install_count: 11600
rating: 4.50 (120 reviews)
github: https://github.com/yuan1z0825/nature-skills/tree/main/skills/nature-literature-pipeline
---

# nature-literature-pipeline

> 自动化的学术文献监测管线，每天从arXiv、OpenAlex等多源检索文献，通过六维评分筛选Top论文，生成精读摘要后推送到飞书/Telegram等渠道，并归档去重。适合研究人员持续跟踪特定方向，节省手动筛选时间，确保不漏关键文献。

**Stats**: 11,600 installs · 4.5/5 (120 reviews)

## Before / After 对比

### 每日文献筛选时间对比

**Before**:

用户需要每天手动登录多个数据库（arXiv、PubMed、Google Scholar等），阅读标题和摘要筛选相关文献，通常耗时2小时以上，且容易遗漏重要论文或重复阅读已看过的文献。

**After**:

此技能自动从多个来源检索候选论文，评分排序后只推送Top5精读摘要，用户只需花15分钟浏览推送即可掌握当日重要文献，且自动去重避免重复。

| Metric | Before | After | Change |
|---|---|---|---|
| 每日文献筛选时间 | 120分钟 | 15分钟 | -87.5% |

## Readme

# 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:

| Layer | Purpose | Files |
|-------|---------|-------|
| **Engine** | Scoring, classification, note templates, gap analysis | `references/scoring-system.md`, `references/gap-analysis.md`, `references/note-template.md` |
| **Application** | Daily cron pipeline, delivery formatting, archival workflow | `references/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

| Reference | Purpose |
|-----------|---------|
| `references/scoring-system.md` | Six-dimension scoring rubric with weights, caps, and evaluation logic |
| `references/gap-analysis.md` | Methodology for identifying research gaps through systematic literature survey |
| `references/note-template.md` | Standardized literature note format with YAML frontmatter |
| `references/push-format.md` | Daily digest message template with field guidelines and example |
| `references/cron-setup.md` | Cron job creation, verification, and manual fallback procedures |
| `references/review-compilation-workflow.md` | End-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](README_EN.md)

`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。


---
*Source: https://skills.yangsir.net/skill/gh-nature-literature-pipeline*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-nature-literature-pipeline/markdown*