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nature-academic-search

Search literature across sources, verify or manage citations, and build MeSH strategies or citation-impact audits. Use for 文献检索、引文核对、参考文献管理、严格他引 and evidence-backed citer profiles; not for translating a paper or drafting manuscript prose.

インストール方法を見る

含まれるファイル(53)

  • SKILL.md3.5 KB
  • agents/openai.yaml265 B
  • config/mcp-snippet.json502 B
  • config/settings-snippet.json59 B
  • config/triggers-academic-search.toml978 B
  • install.sh4.3 KB
  • manifest.yaml3.1 KB
  • mcp-server/academic_search_server.py20.9 KB
  • mcp-server/config.toml249 B
  • mcp-server/README.md1.9 KB
  • mcp-server/requirements.txt134 B
  • mcp-server/sources/__init__.py352 B
  • mcp-server/sources/arxiv.py11.4 KB
  • mcp-server/sources/crossref.py6.9 KB
  • mcp-server/sources/elsevier_common.py2.4 KB
  • mcp-server/sources/pubmed.py13.1 KB
  • mcp-server/sources/sciencedirect.py6.9 KB
  • mcp-server/sources/scopus.py19.0 KB
  • mcp-server/tests/__init__.py40 B
  • mcp-server/tests/test_arxiv_error_entries.py1.4 KB
  • mcp-server/tests/test_citation_errors.py1.6 KB
  • mcp-server/tests/test_elsevier_live.py2.6 KB
  • mcp-server/tests/test_mcp_tools.py2.6 KB
  • mcp-server/tests/test_pubmed_parsing.py894 B
  • mcp-server/tests/test_scopus_empty_result.py1006 B
  • mcp-server/tests/test_script_converters.py2.9 KB
  • mcp-server/tests/test_sources.py30.5 KB
  • mcp-server/utils/__init__.py359 B
  • mcp-server/utils/config.py1.4 KB
  • mcp-server/utils/errors.py620 B
  • mcp-server/utils/logging.py1.1 KB
  • README_EN.md2.7 KB
  • README.md2.4 KB
  • references/citation-parser.md2.7 KB
  • references/dedup-engine.md1.9 KB
  • references/pubmed-28344011.bib3.3 KB
  • references/pubmed-28344011.nbib11.9 KB
  • references/pubmed-28344011.ris3.4 KB
  • references/ris-bibtex-format.md4.5 KB
  • references/search-strategy.md2.0 KB
  • references/source-tiers.md2.7 KB
  • references/workflows/wf1-multi-source-search.md1.9 KB
  • references/workflows/wf2-citation-verification.md1.6 KB
  • references/workflows/wf3-mesh-strategy.md538 B
  • references/workflows/wf4-citation-file-mgmt.md1.9 KB
  • references/workflows/wf5-reference-mgmt.md1.3 KB
  • references/workflows/wf6-strict-other-citation-impact-audit.md11.0 KB
  • scripts/academic_search.py20.4 KB
  • scripts/converters.py18.5 KB
  • scripts/format-converter.py19.2 KB
  • scripts/preflight.py3.7 KB
  • static/core/routing-and-ops.md5.0 KB
  • static/core/tools.md3.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Academic Search — Router

Routing protocol

For local citation-file conversion with complete supplied records, load references/workflows/wf4-citation-file-mgmt.md and its format reference directly. Source lookup, API setup, and network preflight apply only when retrieval or verification is needed; do not require them for a requested offline conversion.

For a new task, load the core and matching resources below. Reuse already loaded guidance on follow-ups; load more only when the task needs it.

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.
  • strict-other-citation-impact-audit — determine strict independent other-citations, build article-level citation metric tables, identify high-profile citers (academy members, presidents/deans, talent-award holders, fellows, field leaders), and extract how they cited the target paper.

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 for source retrieval.
  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.

レビュー

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