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Agent Skill · 研究检索

openalex-paper-search(openalex 论文搜索)

openalex-paper-search 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

openalex-paper-search 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 23,589。

总安装 4,234周安装 180GitHub Stars 3下载 1,483核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:openalex-paper-search(openalex 论文搜索)
来源仓库:https://github.com/kortix-ai/kortix-registry
仓库路径:skills/openalex-paper-search
安装命令:
npx skills add https://github.com/kortix-ai/kortix-registry --skill openalex-paper-search
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/kortix-ai/kortix-registry --skill openalex-paper-search

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。通过 npx skills add 命令从 GitHub 安装使用。安装前需确认权限范围和维护状态,注意是否触发联网或文件读写。

openalex-paper-search 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/openalex-paper-search

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/kortix-ai/kortix-registry --skill openalex-pap…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

openalex-paper-search 怎么安装?

在终端执行 npx skills add https://github.com/kortix-ai/kortix-registry --skill openalex-paper-search。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

openalex-paper-search 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

openalex-paper-search 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

openalex-paper-search 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

openalex-paper-search 用的人多吗?

公开数据是累计安装 4,234 次,近一周安装 180 次,下载 1,483 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

openalex-paper-search 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
openalex-paper-search

openalex 论文搜索 · 本页

4,2343接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Academic Paper Search (OpenAlex)

Search 240M+ scholarly works using the OpenAlex API -- completely free, no API key required, no SDK needed. Just curl or bash with URL construction.

Full docs: https://docs.openalex.org

Quick Start

OpenAlex is a REST API. You query it by constructing URLs and fetching them with curl. All responses are JSON.

# Search for papers about "transformer architecture"
curl -s "https://api.openalex.org/works?search=transformer+architecture&per_page=5&mailto=agent@kortix.ai" | python3 -m json.tool

Important: Always include mailto=agent@kortix.ai (or any valid email) in every request. Without it, you're limited to 1 request/second. With it, you get 10 requests/second (the "polite pool").

Core Concepts

Entities

OpenAlex has these entity types (all queryable):

EntityEndpointCountDescription
Works/works240M+Papers, articles, books, datasets, theses
Authors/authors90M+People who create works
Sources/sources250K+Journals, repositories, conferences
Institutions/institutions110K+Universities, research orgs
Topics/topics4K+Research topics (hierarchical)

Work Object -- Key Fields

When you fetch a work, these are the most useful fields:

id                        OpenAlex ID (e.g., "https://openalex.org/W2741809807")
doi                       DOI URL
title / display_name      Paper title
publication_year          Year published
publication_date          Full date (YYYY-MM-DD)
cited_by_count            Number of incoming citations
fwci                      Field-Weighted Citation Impact (normalized)
type                      article, preprint, review, book, dataset, etc.
language                  ISO 639-1 code (e.g., "en")
is_retracted              Boolean
open_access.is_oa         Boolean -- is it freely accessible?
open_access.oa_url        Direct URL to free version
authorships               List of authors with names, institutions, ORCIDs
abstract_inverted_index   Abstract as inverted index (needs reconstruction)
referenced_works          List of OpenAlex IDs this work cites (outgoing)
related_works             Algorithmically related works
cited_by_api_url          API URL to get works that cite this one (incoming)
topics                    Assigned research topics with scores
keywords                  Extracted keywords with scores
primary_location          Where the work is published (journal, repo)
best_oa_location          Best open access location with PDF link

Reconstructing Abstracts

OpenAlex stores abstracts as inverted indexes for legal reasons. To get plaintext, reconstruct:

import json, sys
# Read the abstract_inverted_index from a work object
inv_idx = work["abstract_inverted_index"]
if inv_idx:
    words = [""] * (max(max(positions) for positions in inv_idx.values()) + 1)
    for word, positions in inv_idx.items():
        for pos in positions:
            words[pos] = word
    abstract = " ".join(words)

Or in bash with python3 -c:

# Pipe a work JSON into this to extract the abstract
echo "$WORK_JSON" | python3 -c "
import json,sys
w=json.load(sys.stdin)
idx=w.get('abstract_inverted_index',{})
if idx:
    words=['']*( max(max(p) for p in idx.values())+1 )
    for word,positions in idx.items():
        for pos in positions: words[pos]=word
    print(' '.join(words))
"

Searching for Papers

Basic Keyword Search

Searches across titles, abstracts, and fulltext. Uses stemming and stop-word removal.

# Simple search
curl -s "https://api.openalex.org/works?search=large+language+models&mailto=agent@kortix.ai"

# With per_page limit
curl -s "https://api.openalex.org/works?search=CRISPR+gene+editing&per_page=10&mailto=agent@kortix.ai"

Boolean Search

Use uppercase AND, OR, NOT with parentheses and quoted phrases:

# Complex boolean query
curl -s "https://api.openalex.org/works?search=(reinforcement+learning+AND+%22robot+control%22)+NOT+simulation&mailto=agent@kortix.ai"

# Exact phrase match (use double quotes, URL-encoded as %22)
curl -s "https://api.openalex.org/works?search=%22attention+is+all+you+need%22&mailto=agent@kortix.ai"

Search Specific Fields

# Title only
curl -s "https://api.openalex.org/works?filter=title.search:transformer&mailto=agent@kortix.ai"

# Abstract only
curl -s "https://api.openalex.org/works?filter=abstract.search:protein+folding&mailto=agent@kortix.ai"

# Title and abstract combined
curl -s "https://api.openalex.org/works?filter=title_and_abstract.search:neural+scaling+laws&mailto=agent@kortix.ai"

# Fulltext search (subset of works)
curl -s "https://api.openalex.org/works?filter=fulltext.search:climate+tipping+points&mailto=agent@kortix.ai"

Filtering

Filters are the most powerful feature. Combine them with commas (AND) or pipes (OR).

Most Useful Filters

# By publication year
?filter=publication_year:2024
?filter=publication_year:2020-2024
?filter=publication_year:>2022

# By citation count
?filter=cited_by_count:>100        # highly cited
?filter=cited_by_count:>1000       # landmark papers

# By open access
?filter=is_oa:true                 # only open access
?filter=oa_status:gold             # gold OA only

# By type
?filter=type:article               # journal articles
?filter=type:preprint              # preprints
?filter=type:review                # review articles

# By language
?filter=language:en                # English only

# Not retracted
?filter=is_retracted:false

# Has abstract
?filter=has_abstract:true

# Has downloadable PDF
?filter=has_content.pdf:true

# By author (OpenAlex ID)
?filter=author.id:A5023888391

# By institution (OpenAlex ID)
?filter=institutions.id:I27837315  # e.g., University of Michigan

# By DOI
?filter=doi:https://doi.org/10.1038/s41586-021-03819-2

# By indexed source
?filter=indexed_in:arxiv           # arXiv papers
?filter=indexed_in:pubmed          # PubMed papers
?filter=indexed_in:crossref        # Crossref papers

Combining Filters

# AND: comma-separated
?filter=publication_year:>2022,cited_by_count:>50,is_oa:true,type:article

# OR: pipe-separated within a filter
?filter=publication_year:2023|2024

# NOT: prefix with !
?filter=type:!preprint

# Combined example: highly-cited OA articles from 2023-2024, not preprints
curl -s "https://api.openalex.org/works?filter=publication_year:2023-2024,cited_by_count:>50,is_oa:true,type:!preprint&search=machine+learning&per_page=10&mailto=agent@kortix.ai"

Sorting

# Most cited first
?sort=cited_by_count:desc

# Most recent first
?sort=publication_date:desc

# Most relevant first (only when using search)
?sort=relevance_score:desc

# Multiple sort keys
?sort=publication_year:desc,cited_by_count:desc

Pagination

Two modes: basic paging (for browsing) and cursor paging (for collecting all results).

# Basic paging (limited to 10,000 results)
?page=1&per_page=25
?page=2&per_page=25

# Cursor paging (unlimited, for collecting everything)
?per_page=100&cursor=*                    # first page
?per_page=100&cursor=IlsxNjk0ODc...      # next page (cursor from previous response meta)

The cursor for the next page is in response.meta.next_cursor. When it's null, you've reached the end.

Select Fields

Reduce response size by selecting only the fields you need:

# Only get IDs, titles, citation counts, and DOIs
?select=id,display_name,cited_by_count,doi,publication_year

# Minimal metadata for scanning
?select=id,display_name,publication_year,cited_by_count,open_access

Citation Graph Traversal

Find what a paper cites (outgoing references)

# Get works cited BY a specific paper
curl -s "https://api.openalex.org/works?filter=cited_by:W2741809807&per_page=25&mailto=agent@kortix.ai"

Find what cites a paper (incoming citations)

# Get works that CITE a specific paper
curl -s "https://api.openalex.org/works?filter=cites:W2741809807&sort=cited_by_count:desc&per_page=25&mailto=agent@kortix.ai"

Find related works

# Get related works (algorithmic, based on shared concepts)
curl -s "https://api.openalex.org/works?filter=related_to:W2741809807&per_page=25&mailto=agent@kortix.ai"

Citation chain: follow the references

  1. Get a seminal paper by DOI
  2. Find its referenced_works (what it cites)
  3. Find who cites it (filter=cites:WORK_ID)
  4. For the most cited citers, repeat

This is how you build a literature graph around a topic.

Author Lookup

# Search for an author
curl -s "https://api.openalex.org/authors?search=Yann+LeCun&mailto=agent@kortix.ai"

# Get an author's works (by OpenAlex author ID)
curl -s "https://api.openalex.org/works?filter=author.id:A5064850633&sort=cited_by_count:desc&per_page=10&mailto=agent@kortix.ai"

# Get an author by ORCID
curl -s "https://api.openalex.org/authors/orcid:0000-0001-6187-6610?mailto=agent@kortix.ai"

Lookup by External ID

# By DOI
curl -s "https://api.openalex.org/works/doi:10.1038/s41586-021-03819-2?mailto=agent@kortix.ai"

# By PubMed ID
curl -s "https://api.openalex.org/works/pmid:14907713?mailto=agent@kortix.ai"

# By arXiv ID (via DOI)
curl -s "https://api.openalex.org/works/doi:10.48550/arXiv.2303.08774?mailto=agent@kortix.ai"

# Batch lookup: up to 50 IDs at once
curl -s "https://api.openalex.org/works?filter=doi:https://doi.org/10.1234/a|https://doi.org/10.1234/b|https://doi.org/10.1234/c&mailto=agent@kortix.ai"

Open Access & PDF Access

# Find OA papers with direct PDF links
curl -s "https://api.openalex.org/works?search=quantum+computing&filter=is_oa:true,has_content.pdf:true&select=id,display_name,open_access,best_oa_location&per_page=5&mailto=agent@kortix.ai"

The best_oa_location.pdf_url field gives a direct PDF link when available. The open_access.oa_url gives the best available OA landing page or PDF.

Practical Workflows

Literature Survey on a Topic

# 1. Find the most-cited papers on a topic
curl -s "https://api.openalex.org/works?search=retrieval+augmented+generation&sort=cited_by_count:desc&filter=publication_year:>2020,type:article,has_abstract:true&per_page=20&select=id,display_name,publication_year,cited_by_count,doi,authorships,abstract_inverted_index&mailto=agent@kortix.ai"

# 2. For the top papers, explore their citation graphs
curl -s "https://api.openalex.org/works?filter=cites:W4285719527&sort=cited_by_count:desc&per_page=10&select=id,display_name,publication_year,cited_by_count,doi&mailto=agent@kortix.ai"

# 3. Find recent papers building on this work
curl -s "https://api.openalex.org/works?filter=cites:W4285719527,publication_year:>2023&sort=publication_date:desc&per_page=10&mailto=agent@kortix.ai"

Find Landmark/Seminal Papers

# Highly cited + search term
curl -s "https://api.openalex.org/works?search=attention+mechanism+neural+networks&filter=cited_by_count:>500,type:article&sort=cited_by_count:desc&per_page=10&select=id,display_name,publication_year,cited_by_count,doi&mailto=agent@kortix.ai"

Find Recent Preprints

# Latest preprints on a topic
curl -s "https://api.openalex.org/works?search=multimodal+large+language+models&filter=type:preprint,publication_year:2025&sort=publication_date:desc&per_page=15&mailto=agent@kortix.ai"

Find Review Articles

# Review/survey papers on a topic
curl -s "https://api.openalex.org/works?search=federated+learning&filter=type:review,cited_by_count:>20&sort=cited_by_count:desc&per_page=10&mailto=agent@kortix.ai"

Author Analysis

# 1. Find the author
curl -s "https://api.openalex.org/authors?search=Geoffrey+Hinton&select=id,display_name,works_count,cited_by_count,last_known_institutions&mailto=agent@kortix.ai"

# 2. Get their most influential papers
curl -s "https://api.openalex.org/works?filter=author.id:A5068082743&sort=cited_by_count:desc&per_page=10&select=id,display_name,publication_year,cited_by_count,doi&mailto=agent@kortix.ai"

# 3. Get their recent work
curl -s "https://api.openalex.org/works?filter=author.id:A5068082743,publication_year:>2023&sort=publication_date:desc&per_page=10&mailto=agent@kortix.ai"

Saving Results to Disk

When doing deep research, save paper data to disk for later processing:

# Save search results as JSON
curl -s "https://api.openalex.org/works?search=topic&per_page=50&mailto=agent@kortix.ai" > research/papers/topic-search.json

# Extract and save a clean summary
curl -s "https://api.openalex.org/works?search=topic&per_page=50&select=id,display_name,publication_year,cited_by_count,doi,authorships&mailto=agent@kortix.ai" | python3 -c "
import json, sys
data = json.load(sys.stdin)
for w in data.get('results', []):
    authors = ', '.join(a['author']['display_name'] for a in w.get('authorships', [])[:3])
    if len(w.get('authorships', [])) > 3: authors += ' et al.'
    print(f\"[{w.get('cited_by_count',0)} cites] {w['display_name']} ({w.get('publication_year','?')}) - {authors}\")
    if w.get('doi'): print(f\"  DOI: {w['doi']}\")
    print()
" > research/papers/topic-summary.txt

For deep research, save individual paper metadata to your sources-index.md and raw data to sources/:

# Save a paper's full metadata
curl -s "https://api.openalex.org/works/W2741809807?mailto=agent@kortix.ai" > research/sources/001-paper-title.json

Rate Limits

PoolRateHow to get it
Common1 req/secNo email provided
Polite10 req/secAdd mailto=your@email.com to requests
PremiumHigherPaid API key via api_key param

Always use the polite pool. Add &mailto=agent@kortix.ai to every request.

Tips

  • Use select aggressively to reduce response size and speed up requests
  • Use per_page=100 (max) when collecting lots of results to minimize request count
  • Use cursor paging (cursor=*) when you need more than 10,000 results
  • Batch DOI lookups with OR syntax: filter=doi:DOI1|DOI2|DOI3 (up to 50)
  • Reconstruct abstracts using the inverted index -- don't skip this, abstracts are gold
  • Follow citation chains to find seminal works and recent developments
  • Filter by has_abstract:true when you need abstracts (not all works have them)
  • Filter by indexed_in:arxiv or indexed_in:pubmed to target specific repositories
  • Sort by cited_by_count:desc to find the most influential papers first
  • Combine search + filters for precise results: search gives relevance, filters give precision

平台分布

Codex

36.33%
按下载量换算539

Claude

32.19%
按下载量换算477

Cursor

16.85%
按下载量换算250

Gemini CLI

8.23%
按下载量换算122

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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