Agent skill

双引擎论文搜索

使用 OpenAlex 与 AnySearch 两个真实数据源并行搜索、交叉匹配和输出可追溯论文元数据。

XiaoMaColtAIgithub.com/XiaoMaColtAIGitHub ↗
claude-codecodexships scripts
Install
npx skills add XiaoMaColtAI/math-modeling-skill --skill paper_search --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 1 KB
Bundled scripts: yes
Path: tools/paper_search/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 590
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 双引擎论文搜索 ## 数据源 - OpenAlex:结构化学术元数据。 - AnySearch Academic:学术垂直搜索,支持当前 MCP Markdown 响应解析。 默认并行调用两个引擎。DOI 相同的记录直接交叉验证;无 DOI 时仅在标题高度相似且年份相容时合并。同一引擎中标题规范化后相同的预印本与正式出版记录也会折叠,并优先保留引用信息和元数据更完整的记录。交叉匹配结果、OpenAlex 独有结果和 AnySearch 独有结果分开输出。 融合时按查询词覆盖率过滤和重排,相关性优先于引用量,避免高被引但主题无关的论文挤占结果。包含多个专业术语时,候选文献至少命中两个有效查询词;这一阈值兼顾缺少摘要的元数据,不能代替人工核验。物理、材料和光学主题应组合使用材料名、机理名与模型名,例如 `Sellmeier 4H-SiC Fabry-Perot`;结果过少时逐步放宽查询,不直接接受无关结果。 ## 使用 ```powershell python scripts/hybrid_scholar.py --query "robust optimization vehicle routing" --limit 10 --json ``` 如 AnySearch 需要鉴权: ```powershell $env:ANYSEARCH_API_KEY = "<密钥>" python scripts/hybrid_scholar.py --query "analytic hierarchy process" --limit 8 ``` 诊断单个引擎时可用 `--openalex-only` 或 `--anysearch-only`;正式文献检索默认不得只运行一个引擎。 ## 核验规则 1. 搜索结果只用于发现候选文献。 2. 引用前打开 DOI 或出版机构页面核对作者、题名、年份、期刊/会议、卷期页。 3. 不把引用量当作正确性的证明。 4. 不根据标题或摘要编造不存在的结论。 5. 输出中保留 `sources` 和 `cross_validated` 状态。

What's inside
Steps it walks through
  1. 数据源
  2. 使用
  3. 核验规则
Ships with 3 files
  • scripts/anysearch_academic.py
  • scripts/hybrid_scholar.py
  • scripts/openalex_scholar.py
More from math-modeling-skill
All skills →
About this skill
What does the 双引擎论文搜索 skill do?

使用 OpenAlex 与 AnySearch 两个真实数据源并行搜索、交叉匹配和输出可追溯论文元数据。

How do I install it?

Run `npx skills add XiaoMaColtAI/math-modeling-skill --skill paper_search --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From XiaoMaColtAI/math-modeling-skill, a repository with 590 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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