nature-academic-search

Name: nature-academic-search
Author: yuan1z0825

github.com/yuan1z0825Productivity51.2k15.5k installsUpdated 1d ago

About nature-academic-search

A literature-search skill for academic discovery, citation checks, PubMed strategies, and reference-file workflows. It routes requests to relevant search or citation-management guidance and includes a no-MCP fallback script for discovery search.

Key features

01Searches literature across sources
02Verifies citations from documents
03Builds MeSH and PubMed strategies
04Converts and manages citation files
05Supports reference and citation-impact workflows

Use cases

01Finding papers across research sources
02Checking references extracted from a document
03Building a PubMed search query
04Converting or managing RIS and BibTeX files

MCP support

The skill describes an MCP tool inventory for core and extended search and PubMed utilities. It routes requests to those tools and provides a no-MCP fallback discovery-search script.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-academic-search

After installing, type/nature-academic-searchin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

install.sh

manifest.yaml

agents/

openai.yaml

config/

mcp-snippet.json

settings-snippet.json

triggers-academic-search.toml

mcp-server/

academic_search_server.py

config.toml

README.md

requirements.txt

mcp-server/sources/

__init__.py

arxiv.py

crossref.py

elsevier_common.py

pubmed.py

sciencedirect.py

scopus.py

mcp-server/tests/

__init__.py

test_arxiv_error_entries.py

test_citation_errors.py

test_elsevier_live.py

test_format_converter_hardening.py

test_mcp_tools.py

test_pubmed_parsing.py

test_scopus_empty_result.py

test_script_converters.py

test_sources.py

mcp-server/utils/

__init__.py

config.py

errors.py

Details

From the project README

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:

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.
The workflow fragment — its specific steps.
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.

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Details

Author: yuan1z0825
Repository: yuan1z0825/nature-skills
Category: Productivity
Installs: 15,542
GitHub stars: 51,194

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