S2-首创的AI研究工作流程发现引擎。

基于语义学者API构建的三种研究工作流——可作为Python库、Claude代码技能或16工具MCP服务器提供。

安装

作为 Claude Code 插件

手册

工作流程

/expand-references — 从种子论文中发现相关工作

输入1–3篇种子论文。返回精选桶：基础论、方法论、近期论文、调查论文、桥梁论文和最近邻论文。由S2推荐API支持，包含正负种子。

/trace-citations — 绘制论文引用邻域图

选择一篇重点论文。用上下文片段、意图标签和影响标志追踪其引用和参考文献。返回基础、后裔、桥接连接以及可选的第二跳扩展。

/paper-triage — 将模糊查询筛选入候选名单

输入一个模糊的查询。引擎会同时运行自动补全、相关性搜索、批量搜索和摘录提取，然后对其进行评分和排名，并附有解释。

快速启动

作为克劳德代码技能（复制并使用）

每个技能都是完全自成体系——不需要pip install。

semantic-scholar-skills

S2-first discovery engine for AI research workflows.

Three research workflows built on the Semantic Scholar API — available as a Python library, Claude Code skills, or a 16-tool MCP server.

Installation

As Claude Code Plugin

claude plugin install github:zongmin-yu/semantic-scholar-skills

Manual

cp -r skills/* ~/.claude/skills/

Workflows

/expand-references — Discover related work from seed papers

Feed 1–3 seed papers. Get back curated buckets: foundational, methodological, recent, survey, bridge papers, and closest neighbors. Powered by S2’s Recommendations API with positive/negative seeds.

/trace-citations — Map a paper’s citation neighborhood

Pick a focal paper. Trace its citations and references with context snippets, intent labels, and influence flags. Returns foundations, descendants, bridge connections, and optional second-hop expansion.

/paper-triage — Triage an ambiguous query into a shortlist

Type a vague query. The engine runs autocomplete, relevance search, bulk search, and snippet extraction in parallel, then scores and ranks into a shortlist with explanations.

Quick Start

As Claude Code skills (copy & use)

# Copy any skill to your personal skills directory
cp -r skills/expand-references ~/.claude/skills/

# Then in Claude Code:
/expand-references Attention Is All You Need

Each skill is fully self-contained — no pip install required. The bundled standalone runtime uses only Python stdlib.

As a Python library

pip install semantic-scholar-skills

core library only: installs core/, engine/, and the stdlib-backed standalone/ runtime.

import asyncio
from semantic_scholar_skills.core import get_default_client, cleanup_client
from semantic_scholar_skills.engine import paper_triage

async def main():
    client = get_default_client()
    try:
        result = await paper_triage(client, "retrieval augmented generation")
        for paper in result.shortlist[:5]:
            print(f"{paper.score:.2f}  {paper.paper.title}")
    finally:
        await cleanup_client()

asyncio.run(main())

As an MCP server

pip install "semantic-scholar-skills[mcp]"
semantic-scholar-skills-mcp

full MCP stack: installs fastmcp, fastapi, and uvicorn in addition to the core library.

16 tools covering papers, authors, citations, recommendations, and snippets. Works with Claude Desktop, Cursor, and any MCP client.

Architecture

              Claude Code Skills          MCP Server (16 tools)