Academic Research Kernel
概览
作者提供的说明（英文原文）
Deterministic research state, evidence validation and statistical recomputation.
PopNx MCP 的三大支柱
这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。
- 用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
- 我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
- 使用情况i非常新↓ 138/周 · ★ –
它能帮你做什么
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
- 连接方式: 在你的电脑上运行
- 传输方式: stdio
- 分类: 搜索与 AI 记忆
安装
选择你使用的 AI 应用，然后复制设置。
安装之前
- 基础检查：1 项中通过 1 项
- 在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
- 注册中心没有列出需要填写的密钥。
你需要
- PyPI已安装 Python 的 uv（用于 uvx 命令）
你用的是哪个应用？
在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"academic-research-kernel": {
"command": "uvx",
"args": [
"academic-research-kernel",
"mcp"
]
}
}
}在终端中运行这条命令
claude mcp add academic-research-kernel -- uvx academic-research-kernel mcp或者自己复制设置
粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json
{
"mcpServers": {
"academic-research-kernel": {
"command": "uvx",
"args": [
"academic-research-kernel",
"mcp"
]
}
}
}浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。
或者自己复制设置
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"academic-research-kernel": {
"type": "stdio",
"command": "uvx",
"args": [
"academic-research-kernel",
"mcp"
]
}
}
}浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。
这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上，无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。
它能做什么
它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。
基础检查i
我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 xngg1021 验证，该账户拥有此仓库
- … 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
- … 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
- – 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
- – 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
- … 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表
与“搜索与 AI 记忆”中的其他服务器比较i
- 星标数多于此分类中其他 5689 个服务器的 0%
- 发布到注册中心的时间比此分类中其他 5689 个服务器的 57% 更新
- 基础检查结果等于或优于此分类中其他 5689 个服务器的 100%
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。
常见问题
Academic Research Kernel 安全吗？
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。
它需要哪些密钥或代码？
注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。
它在我的电脑上运行还是在云端？
它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。
它还在维护吗？
GitHub 仓库最后更新于–，最新版本（2.1.0）于31天前进入注册中心。
哪些应用可以使用它？
支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
有什么可以替代它？
同一分类中有：Context7, Codebase Memory, Exa
替代选择
同一分类中其他作者的服务器，按星标数排序。
如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
作者的 README
打开 README
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
academic-research-kernel
English · 简体中文 · 繁體中文 · 日本語 · 한국어 · Deutsch · Français · Español · Português · Русский · Bahasa Indonesia · Italiano · हिन्दी · العربية · বাংলা · اردو · Tiếng Việt · Türkçe · فارسی · Kiswahili · Polski
This repository is a practical toolkit for verifying scholarly sources, checking statistical numbers, and tracking research evidence with AI agents. When researchers use language models to read papers or draft literature reviews, models often invent citations, misread tables, or lose track of which page supports a specific claim. To prevent this, academic-research-kernel packages 14 scholarly workflows and 12 deterministic verification tools behind a standard Model Context Protocol (MCP) server and command-line interface. You can use it to check paper identities and retractions, recompute reported statistics, read papers into source-linked reports, manage systematic review screening logs, and record every claim alongside its supporting evidence and negative results. It works directly with Claude Code, Cursor, Codex, Gemini CLI, and Hermes Agent. Validation scope and external service limits are documented in the project audit.
Author: Junfu Shi (SJF, xngg1021), Hermes Agent. License: Source Lineage License 1.0.
Quick start
If you want your AI agent to read a paper, check its numbers, and generate a report, install the package with PDF support and export the paper-research workflow into your agent's skill directory; if you want to connect the 12 verification tools as a background MCP server for an MCP client, install uv and start the stdio server:
# 1. Read PDF papers and generate reports with your local AI agentuv tool install "academic-research-kernel[research]==2.2.0" academic-research-kernel research skill --output ./paper-research
2. Or start the background stdio MCP server for an MCP client
uvx academic-research-kernel@2.2.0 mcp
Note that running mcp in a terminal waits silently for JSON-RPC requests from an MCP client rather than opening an interactive menu. The 2.2.0 release page lists package hashes and publication status. If you download the release wheel, you can also run it locally with uvx --from ./academic_research_kernel-2.2.0-py3-none-any.whl academic-research-kernel mcp. The package supports Python 3.10–3.14, and uvx --python 3.12 selects a specific Python version. The base installation includes all 12 verification tools and loads statistical libraries on demand when statistical recomputation is called.
Verify installation
uvx academic-research-kernel@2.2.0 doctoruvx academic-research-kernel@2.2.0 --version
The doctor command runs offline and checks runtime dependencies, JSON schemas, and all 12 tool definitions. Leaving optional external APIs unconfigured will not fail the check. Pass doctor --json if you need machine-readable output.
Package install
uv tool install academic-research-kernel==2.2.0academic-research-kernel doctor academic-research-kernel mcp
You can also install the package with pipx install academic-research-kernel==2.2.0 or python -m pip install academic-research-kernel==2.2.0 inside a virtual environment. Installing with [full] adds libraries used in extended scientific computing examples. Installing with [research] adds pypdf==6.19.0 and fonttools==4.65.0 so PDF font decoding behaves identically across clean environments on Python 3.10 and newer. Installing with [qa] adds repository test tools on top of those parsing libraries. HTML, XML, DOCX, and XLSX reading use the Python standard library, while GPU backends and paid scholarly databases remain optional.
Hermes
Make sure uv is on the Hermes process PATH, then install and enable the plugin:
hermes plugins install xngg1021/academic-research-kernel --no-enablehermes plugins enable academic-skills
The plugin keeps the academic-skills name for compatibility and includes all 14 skills, including paper-research. Its mcp.json configuration runs uv run --frozen --no-dev --no-editable with Python 3.12 and the repository's uv.lock file, storing its isolated environment under ${PLUGIN_DATA}/runtime. On first run, it installs its own dependencies without modifying Hermes or system Python packages. If you prefer installing individual skills without the plugin runtime, see Integration & Portable Usage.
Run from source
git clone https://github.com/xngg1021/academic-research-kernel.gitcd academic-research-kernel uv sync --frozen --no-dev --no-editable uv run --frozen --no-dev --no-editable academic-research-kernel doctor uv run --frozen --no-dev --no-editable python scripts/mcp_server.py
See distribution and release for dependency groups, reproducible build steps, and release verification checkpoints.
License
Snapshots containing this notice apply Source Lineage License 1.0 to the covered material and rights described in LICENSE-APPLICATION.md. The initial SLL commit and the subsequent boundary-recording commit are recorded in LICENSE-HISTORY.md and SOURCE-LINEAGE.md. Later snapshots that keep this notice carry the same license scope.
Historical snapshots through commit 439ce4a29d6d2d9137308d7f9f045863e9f0c5b3 were distributed under the MIT License. Users of those earlier snapshots keep their MIT permissions and do not need to migrate to SLL. The previous MIT license text and tests/upstream/LICENSE are preserved unchanged.
SLL permits use, study, modification, commercial use, redistribution, and proprietary extensions, provided you keep the required license and source-lineage notices. It is not copyleft and does not require disclosing your source code. Running a network service without distributing software copies does not trigger the source-lineage notice requirement. The license includes no express patent grant, and third-party files remain under their original licenses. The English LICENSE text is authoritative; LicenseRef-Source-Lineage-1.0 is a local identifier rather than an SPDX or OSI-approved code. Contribution rules are separate from downstream license rights.
Skills
| Skill | Version | What it does |
|---|---|---|
skills/academic-source-verification | 1.2.0 | Check paper metadata and citation counts across databases, inspect corrections or retractions, find open-access full text, and verify that a local PDF matches the cited work |
skills/literature-analysis | 1.3.0 | Twelve everyday workflows: find similar papers, check text overlap, search for counter-evidence, summarize author histories, run mock peer review, check reasoning flaws, build comparison tables, suggest target journals, export BibTeX, read bilingually, spot research gaps, and plan reproduction |
skills/academic-writing | 1.1.1 | Revise academic prose, format citations (ISO 690, APA, MLA, Chicago, IEEE, AMA, and regional standards), check journal author guidelines, prepare submission files, and verify anonymization for blind review |
skills/math-computation | 1.2.1 | Route symbolic, numerical, and statistical calculation tasks to verified Python tools, accompanied by four domain reference guides |
skills/quantitative-paper-audit | 1.1.0 | Recompute reported effect sizes, p-values, confidence intervals, odds or risk ratios, and statistical power from summary numbers to catch reporting errors |
skills/research-reproducibility | 1.0.1 | Audit whether a paper's code, data, dependencies, and reported metrics can be reproduced across a 14-step checklist |
skills/systematic-review-meta-analysis | 1.0.1 | Record PRISMA search and screening logs, convert effect sizes, test heterogeneity, pool fixed or random effects, and run sensitivity and publication-bias checks |
skills/literature-watch | 1.1.0 | Monitor OpenAlex and Crossref weekly for new papers by topic, author, or cited DOI, deduplicating results so only new works are reported |
skills/retraction-watch | 1.1.0 | Recheck a DOI watchlist weekly against OpenAlex retraction flags and Crossref update records, reporting only papers whose status changed |
skills/research-object-identity | 1.1.0 | Normalize DOIs, arXiv IDs, and file hashes into canonical records, verify resource status, and trace how derived files were produced from raw sources |
skills/claim-evidence-graph | 1.0.0 | Link each scientific claim to the exact source passage, table, or calculation record that supports or contradicts it |
skills/decision-ledger | 1.0.0 | Keep an append-only Research Decision Log of methodological choices, failed attempts, reasons for dropping a direction, and later outcome revisions |
skills/paper-research | 1.0.0 | Read a paper and its supplements from a DOI, URL, or local folder, verify extracted numbers, save structured research state, and generate source-linked Chinese reports or two-paper comparisons |
skills/cross-review-five | 2.0.0 | Coordinate independent blind reviews across multiple language models or subagents, merge overlapping points, assign cross-challenges, and grade issues from P0 to P3 while preserving unresolved dissent |
Across the 14 skills, 22 Markdown reference guides are loaded on demand when a task requires them.
Scholarly Standards & Multi-Profile Baseline
Citation formats, reporting checklists, and metadata rules depend on your target journal, funder, discipline, and region. This repository uses ISO 690:2021 (bibliographic references), ISO 5127:2017 (documentation vocabulary), and W3C PROV (provenance records) as its international baseline, alongside regional standards such as GB/T 7714-2025 in Mainland China, UNE-ISO 690:2024 in Spain, and DIN ISO 690:2021 in Germany, plus disciplinary guides including APA 7th, IEEE, ACM, Vancouver, Chicago, PRISMA 2020, and ICMJE. A target journal's own instructions always override default settings. See the Scholarly Standards Architecture and Natural Terminology Guide.
When saving research state, the kernel checks that DOIs and external identifiers match, prevents contradictory statistical verdicts on the same check, and tracks unresolved questions in a shared review list. Cache verification rebuilds state directly from saved source packages and hash bindings rather than trusting summary lists, so deleting both a record and its cache entry will still be caught during replay. Details are recorded in the four-blocker closeout.
Integration & Portable Usage
1. Universal Agent Plugins v1 & Model Context Protocol (MCP)
This repository follows the vendor-neutral Agent Plugins v1 format (plugin.json) and exposes its verification and statistical tools through a standard stdio MCP server (mcp.json / scripts/mcp_server.py). You can load it directly in Claude Code, Cursor, Codex, Gemini CLI, or any other MCP-compatible client. Although the repository is named academic-research-kernel, the plugin name academic-skills and existing MCP aliases remain unchanged so existing configurations keep working:
# 只显示了开头部分；其余内容请在仓库中阅读