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nature-academic-search

by @yuan1z0825v
4.5(120)

此技能是 Claude Code 的学术搜索工具,能够集成 CrossRef、PubMed、arXiv 等多个文献数据库,实现多源并发搜索、按ID获取详情、格式化引用以及文献管理。它帮助研究人员高效地查找、整理和引用学术文献,大幅提升科研效率和文献调研的准确性。

academic-searchliterature-reviewresearchcitation-managementdata-retrievalGitHub
安装方式
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-academic-search
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Before / After 效果对比

1 组
使用前

研究人员需要手动在不同的学术数据库中进行搜索,并逐一整理、格式化引用,耗费大量时间和精力,且容易出错。

使用后

该 Skill 能够自动整合多源搜索结果,快速获取文献详情并按需格式化引用,显著减少了文献调研和管理的时间。

SKILL.md

Academic Search — Router

This skill is split into two layers:

  • A static layer under static/ that holds versioned, reusable content fragments (the MCP tool inventory and shared modules, and source routing plus operational rules).
  • A dynamic layer (this file plus manifest.yaml) that detects which workflow the user needs and loads that workflow, reaching for shared modules and scripts only when a step needs them.

Do not try to apply the search logic from memory or from this router. Always load fragments from disk as described below.

Routing protocol

Follow these five steps every time the skill is invoked.

1. Load the manifest and the core layer

Read manifest.yaml. It declares the workflow axis, the allowed values, and the file paths each value maps to.

Also read every file listed under always_load:

  • static/core/tools.md — the MCP tool inventory (core search, extended search, PubMed utilities) and the shared-module map.
  • static/core/routing-and-ops.md — the T1→T2→T3 source routing quick guide, environment setup, error handling, and limitations.

2. Detect the workflow

Map the user's need to one or more workflow values:

  • multi-source-search — find literature across sources.
  • citation-verification — verify citations extracted from a document.
  • mesh-strategy — build a MeSH/PubMed search strategy.
  • citation-file-mgmt — convert/manage .nbib/.ris/.bib files.
  • reference-mgmt — BibTeX, related-article discovery, ID conversion.

A combined request (for example search then export) may need more than one. State the detected workflow(s) in one short line before proceeding.

3. Load the matching workflow fragment(s)

Read the file mapped for each detected workflow (under references/workflows/). Do not read every workflow. Each workflow file links to the shared modules it needs.

4. Run the workflow using the loaded material

Apply the loaded material in this order:

  1. Core tools and routing (core/tools.md, core/routing-and-ops.md) — which MCP tool for which need, and the T1→T2→T3 fallback chain that is the standard execution order across all workflows.
  2. The workflow fragment — its specific steps.
  3. Shared modules and scripts on demand (dedup, citation parser, search strategy, RIS/BibTeX format, format converter).

Report specific tool failures and continue with remaining tools; broaden terms when there are no results; fall back to manual generation from MCP-fetched metadata if a script fails twice.

5. Reach for references only when needed

The files under references/ (and scripts/) are deep references, not defaults. Open them on demand per the references.on_demand table in the manifest — for example references/source-tiers.md for the full reliability classification, references/dedup-engine.md / references/citation-parser.md / references/search-strategy.md / references/ris-bibtex-format.md for the shared modules, and scripts/academic_search.py (no-MCP fallback discovery search) / scripts/format-converter.py / scripts/preflight.py for the tooling.

Why this split

  • The static layer is versioned and reviewable; the workflow files and shared modules were already factored this way.
  • The dynamic layer keeps each invocation cheap: only the workflow the user needs enters context, instead of all five plus every module.
  • The router itself is short on purpose. Update fragments and references, not this file, when adding scope.
  • This structure mirrors the other nature-* skills (nature-writing, nature-polishing, nature-reader, nature-paper2ppt, nature-figure, nature-citation, nature-response, nature-data).

Academic Search

Claude Code 的学术搜索技能包,集成 CrossRef、PubMed、arXiv、Scopus、ScienceDirect 文献数据源。

功能

  • 多源并发搜索: 默认查询 CrossRef / PubMed / arXiv,并合并返回结果
  • 按 ID 获取详情: 支持 DOI、PMID、arXiv ID 自动识别
  • 格式化引用: APA / Nature / IEEE / Vancouver 等风格
  • MeSH 词表查询: 构建精准 PubMed 检索式
  • 文献管理脚本: .nbib / .ris / .bib / .enw 格式互转
  • Scopus / ScienceDirect: 支持论文、作者、机构、期刊、引用概览、PlumX 指标和 ScienceDirect 元数据检索

MCP 运行

插件默认通过 uv 启动隔离运行环境:

uv run --no-project --directory <mcp-server> --with "mcp>=1.0.0,<2.0.0" --with "requests>=2.28.0,<3.0.0" --with "toml>=0.10.2,<2.0.0" --with "lxml>=4.9.0,<6.0.0" --with "pybliometrics>=4.4.1,<5.0.0" python academic_search_server.py

PubMed 需要在环境变量 PUBMED_EMAIL 或 mcp-server/config.toml 中配置邮箱。Scopus / ScienceDirect 是可选 provider,复用本机 pybliometrics 配置,默认读取 ~/.config/pybliometrics.cfg;不要把 Elsevier API key 写入插件文件。search_papers 只有在 sources 显式包含 scopus / sciencedirect 时才会调用 Elsevier-backed provider,以避免无意消耗 Elsevier API 配额。

MCP Tools

Tool说明
search_papers默认三源并发搜索;可显式添加 Scopus / ScienceDirect
get_paper_by_id按 DOI/PMID/arXiv ID 获取详情
get_citation格式化引用生成
lookup_meshMeSH 词表查询
search_scopusScopus 高级检索
get_scopus_abstractScopus 摘要/详情元数据
get_scopus_citation_overviewScopus 引用概览
search_scopus_authors / get_scopus_author作者检索与详情
search_scopus_affiliations / get_scopus_affiliation机构检索与详情
search_scopus_serial_titles / get_scopus_serial_title期刊/连续出版物检索与详情
get_scopus_plumx_metricsPlumX 指标
search_sciencedirectScienceDirect 检索
get_sciencedirect_article_metadataScienceDirect 文章元数据

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统计数据

安装量15.0K
评分4.5 / 5.0
版本
更新日期2026年10月23日
对比案例1 组

用户评分

4.5(120)
5
37%
4
43%
3
13%
2
5%
1
2%

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🤖claude-code

时间线

创建2026年6月13日
最后更新2026年10月23日
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