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Literature Search

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This skill should be used when mining academic literature from PubMed, OpenAlex or Semantic Scholar — searching papers by topic or keywords, deduplicating multi-source results, filtering by relevance or citation count, enriching missing abstracts, and exporting RIS, BibTeX or GB/T 7714 citations. 用于多源学术文献检索、去重、相关性筛选、摘要富集与参考文献格式导出的场景。

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  • Added November 7, 2026
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npx -y skills add ssinder/bioinformatics-agent-skills --skill literature-search --agent claude-code

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SKILL.md
---
name: literature-search
description: This skill should be used when mining academic literature from PubMed, OpenAlex or Semantic Scholar — searching papers by topic or keywords, deduplicating multi-source results, filtering by relevance or citation count, enriching missing abstracts, and exporting RIS, BibTeX or GB/T 7714 citations. 用于多源学术文献检索、去重、相关性筛选、摘要富集与参考文献格式导出的场景。
license: MIT
compatibility: Python 3.9+;需要 requests(联网检索时);无第三方库也可对本地 CSV 做去重打分
metadata:
  version: "1.0"
  last-reviewed: "2026-10-25"
  skill-author: sinder
---

## Overview

在 PubMed、OpenAlex、Semantic Scholar 三个公开文献库上按主题或关键词检索论文,把异构结果归一化到统一 schema,按 DOI 与标题模糊匹配两级去重,再按相关性、引用数与年份做筛选,为缺摘要的文献从三个源依次补全摘要,最后导出可直接投稿使用的引用格式。

所有主题、关键词、年份范围与数据源都是命令行参数,不绑定任何具体疾病或课题,因此可用于任何研究方向的系统综述式检索。

## When to Use This Skill

- 需要围绕某个主题快速摸清 PubMed 上的文献分布(如 `antimicrobial peptide`、`protein structure prediction`)
- 需要跨多个文献库做覆盖更全的检索并合并去重
- 手头已有文献 CSV,但需要重新打分、筛选、清洗
- 手头有一批文献缺摘要,需要从 OpenAlex / Semantic Scholar 补全
- 需要把文献列表导出为 RIS / BibTeX / GB/T 7714 参考文献格式

## Quick Start

```bash
export PYTHONPATH="$PWD/packages"

# 1) 单源检索(PubMed)
python skills/literature-search/scripts/search_literature.py \
    --topic "antimicrobial peptide" --source pubmed \
    --date-range 2020: --max-results 50 \
    --output results/amp_pubmed.csv

# 2) 三源联合检索 + 摘要富集 + 相关性过滤
python skills/literature-search/scripts/search_literature.py \
    --topic "protein structure prediction" \
    --keywords "AlphaFold" "molecular dynamics" \
    --source pubmed openalex semantic-scholar \
    --date-range 2018:2024 --min-citations 20 --enrich-abstracts \
    --sort-by relevance --output results/prediction.csv

# 3) 离线处理已有 CSV(不联网):重新打分 + 去重 + 排除词筛选
python skills/literature-search/scripts/search_literature.py \
    --input results/raw.csv --keywords "antifungal" \
    --exclude-terms "review" "protocol" \
    --min-relevance 3 --output results/filtered.csv

# 4) 导出引用格式
python skills/literature-search/scripts/export_citations.py \
    --input results/prediction.csv --format bibtex ris gbt7714 \
    --output-dir results/citations
```

## Workflow

1. **检索**:按 `--source` 逐源调用 API
   - PubMed:`esearch.fcgi` 拿 PMID → `esummary.fcgi` 批量拿元数据
   - OpenAlex:`works` 端点,用 `filter=from_publication_date:...,to_publication_date:...` 做年份过滤
   - Semantic Scholar:`graph/v1/paper/search`,用 `year=起始-结束` 做年份过滤
2. **归一化**:所有记录统一为 `title/authors/year/doi/journal/abstract/citations/source/pmid/url/type/oa_status`
3. **打分**:按关键词命中(标题权重 3.0、摘要权重 1.0)+ 引用数对数饱和 + 近期加成,得到 `relevance`
4. **去重**:DOI 精确匹配优先,其次标题归一化后的 `difflib` 模糊匹配(默认阈值 0.92);被去掉的记录信息合并回保留记录(补摘要、累计引用、标记多来源)
5. **筛选**:相关性与引用数阈值 + 包含/排除词 + 年份区间
6. **摘要富集**(可选):PubMed(efetch XML) → OpenAlex(倒排索引重建) → Semantic Scholar(abstract/tldr) 依次尝试
7. **导出**:统一 schema CSV(UTF-8 BOM,Excel 直开)+ 可选 JSON

```mermaid
flowchart TD
    A[topic/keywords] --> B[PubMed]
    A --> C[OpenAlex]
    A --> D[Semantic Scholar]
    B --> E[归一化 schema]
    C --> E
    D --> E
    E --> F[相关性打分]
    F --> G[DOI/标题两级去重]
    G --> H[阈值与关键词筛选]
    H --> I[摘要富集]
    I --> J[CSV + JSON]
    J --> K[RIS/BibTeX/GB-T 7714]
```

## Parameters

| 参数 | 说明 | 默认值 |
| --- | --- | --- |
| `--topic` | 自由文本检索主题(作为主检索式) | 无 |
| `--keywords` | 附加关键词,AND 收窄检索 + 参与相关性打分 | 空 |
| `--source` | 数据源,可多选:`pubmed` / `openalex` / `semantic-scholar` | `pubmed` |
| `--date-range` | 年份区间 `START:END`,如 `2018:2024`、`2020:`、`:2015` | 不限 |
| `--max-results` | 每源每查询最大返回条数 | 50 |
| `--input` | 已有 CSV(提供时不联网) | 无 |
| `--output` | 输出 CSV 路径 | `<root>/results/literature_results.csv` |
| `--json-output` | 额外输出完整 JSON | 无 |
| `--min-citations` | 最低引用数 | 0 |
| `--min-relevance` | 最低相关性得分 | 0 |
| `--min-abstract-length` | 最短摘要长度 | 0 |
| `--include-terms` / `--exclude-terms` | 必含 / 必排关键词 | 空 |
| `--title-similarity` | 标题模糊去重阈值 | 0.92 |
| `--enrich-abstracts` | 开启摘要富集 | 关闭 |
| `--sort-by` | `relevance` / `citations` / `year` | `relevance` |
| `--email` | 联系邮箱(OpenAlex 礼貌池、NCBI 建议提供) | 无 |
| `--ncbi-api-key` / `--s2-api-key` | API Key(环境变量 `NCBI_API_KEY` / `S2_API_KEY` 优先) | 无 |

## Outputs

| 文件 | 说明 |
| --- | --- |
| `literature_results.csv` | 主结果,UTF-8 BOM,列:`title, authors, year, doi, journal, abstract, citations, source, pmid, url, type, oa_status, relevance, query` |
| `<name>.json` | 完整记录(含解析出的字段,便于二次处理) |
| `references.bib` | BibTeX 格式,`@article{作者姓+年份+首词+序号}` 稳定键 |
| `references.ris` | RIS 格式,EndNote / Zotero / Mendeley 可直接导入 |
| `references.txt` | GB/T 7714-2015 顺序编码制 或 中文编号纯文本 |

## Validation

- **检索覆盖**:确认三个源各自返回条数符合预期;某个源为 0 条通常是年份过滤过窄或 API Key 缺失导致限速
- **去重有效性**:去重前后条数差应接近各源之间真实重复量;`source` 列出现 `A+B` 说明该文献被多个源命中
- **摘要完整度**:摘要富集后检查 `abstract` 列空值率;OpenAlex 摘要来自倒排索引重建,若原库无摘要则仍然为空
- **相关性合理性**:抽查 `relevance` 排序前 20 条,确认主题相关;`--min-relevance` 阈值建议先设为 0 观察分布再收紧
- **引用格式**:导入 Zotero/EndNote 检查作者名、卷期页码是否被正确解析;GB/T 7714 输出需人工核对卷(期)与页码(部分源不提供)
- **引用计数口径**:`citations` 各源定义不同(PubMed 为 pmcrefcount,OpenAlex 为 cited_by_count),跨源比较时只作相对参考

## References

- `references/methodology.md` —— 检索策略设计、各源 API 参数、相关性评分与去重算法原理
- `references/troubleshooting.md` —— 常见报错(限速、空结果、编码、重复条目)与排错步骤

Files in this skill

  • SKILL.md7.1 KB
  • references/methodology.md8.9 KB
  • references/troubleshooting.md8.1 KB
  • requirements.txt367 B
  • scripts/export_citations.py4.4 KB
  • scripts/literature_core.py34.8 KB
  • scripts/search_literature.py10 KB

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