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Cnki Journal Index

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cookjohn
cnki-journal-index

Query journal indexing/inclusion status on CNKI - check which databases include a journal (北大核心, CSSCI, CSCD, SCI, EI, etc.), get impact factors and evaluation data. Use when user asks about a journal's level, indexing, or ranking.

Overview

Publishercookjohn
Repositorycnki-skills
Skill namecnki-journal-index
Stars
984
Forks
84
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cookjohn on GitHub. Read the source before you install it.

Installation

Install the Cnki Journal Index AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cookjohn/cnki-skills/tree/master/skills/cnki-journal-index
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cookjohn/cnki-skills.git /tmp/cnki-skills
mkdir -p .claude/skills
cp -r /tmp/cnki-skills/skills/cnki-journal-index .claude/skills/cnki-journal-index
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cnki Journal Index in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cnki Journal Index on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cnki Journal Index is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CNKI Journal Indexing Query (收录查询)

Check which databases index a journal and extract evaluation metrics from its CNKI detail page.

Arguments

$ARGUMENTS is either:

  • A journal name (will search first, then navigate to detail)
  • A CNKI journal detail URL (containing navi.cnki.net/knavi/detail)

Steps

1. Navigate to the journal detail page

If URL provided: Navigate directly.

  • Use mcp__chrome-devtools__navigate_page with the URL.
  • Use mcp__chrome-devtools__wait_for with text ["该刊被以下数据库收录"] and timeout 15000.

If journal name provided: Search first.

  • Navigate to https://navi.cnki.net/knavi
  • Search for the journal (same as cnki-journal-search steps 2-4)
  • Click the first matching journal title link
  • Use mcp__chrome-devtools__list_pages to find and select the new detail tab
  • Wait for the detail page to load

2. Check for captcha

Take snapshot. If "拖动下方拼图完成验证" found, notify user.

3. Extract journal info via JavaScript

Use mcp__chrome-devtools__evaluate_script with this function:

javascript
() => {
  const body = document.body.innerText;

  // Journal title
  const titleEl = document.querySelector('h3.titbox, h3.titbox1');
  const titleText = titleEl?.innerText?.trim() || '';
  const titleParts = titleText.split('\n').map(s => s.trim()).filter(Boolean);
  const nameCN = titleParts[0] || '';
  const nameEN = titleParts[1] || '';

  // Indexing tags - extract from text between title and "基本信息"
  const tagText = body.match(/Chinese.*?\n\n([\s\S]*?)\n\n基本信息/)?.[1]
    || body.match(new RegExp(nameCN + '[\\s\\S]*?\\n\\n([\\s\\S]*?)\\n\\n基本信息'))?.[1]
    || '';
  const knownTags = ['北大核心','CSSCI','CSCD','SCI','EI','CAS','JST','WJCI','AMI','Scopus','卓越期刊','网络首发'];
  const indexedIn = knownTags.filter(tag => tagText.includes(tag) || body.includes(tag));

  // Basic info
  const sponsor = body.match(/主办单位[::]\s*(.+?)(?=\n)/)?.[1] || '';
  const frequency = body.match(/出版周期[::]\s*(\S+)/)?.[1] || '';
  const issn = body.match(/ISSN[::]\s*(\S+)/)?.[1] || '';
  const cn = body.match(/CN[::]\s*(\S+)/)?.[1] || '';

  // Publication info
  const collection = body.match(/专辑名称[::]\s*(.+?)(?=\n)/)?.[1] || '';
  const paperCount = body.match(/出版文献量[::]\s*(.+?)(?=\n)/)?.[1] || '';

  // Evaluation info
  const impactComposite = body.match(/复合影响因子[::]\s*([\d.]+)/)?.[1] || '';
  const impactComprehensive = body.match(/综合影响因子[::]\s*([\d.]+)/)?.[1] || '';

  // "该刊被以下数据库收录" section - click "更多介绍" first if needed
  const moreBtn = Array.from(document.querySelectorAll('a')).find(a => a.innerText?.includes('更多介绍'));
  const hasMoreIntro = !!moreBtn;

  return {
    nameCN,
    nameEN,
    indexedIn,
    sponsor,
    frequency,
    issn,
    cn,
    collection,
    paperCount,
    impactComposite,
    impactComprehensive,
    hasMoreIntro,
    rawTagText: tagText.substring(0, 200)
  };
}

4. Get detailed indexing info (optional)

If the extraction shows hasMoreIntro: true, click the "更多介绍" link to expand detailed indexing information, then take a new snapshot to capture the expanded content.

The expanded section typically lists specific database inclusions with years, such as:

  • 北大核心期刊(2023年版)
  • CSCD中国科学引文数据库来源期刊(2023-2024年度)
  • EI 工程索引(美)

5. Check "统计与评价" tab (for detailed metrics)

If the user wants detailed evaluation data:

  • Find and click the "统计与评价" link/tab in the snapshot
  • Wait for the statistics section to load
  • Extract additional metrics (H-index, citation distribution, etc.)

6. Present results

## {nameCN} ({nameEN})

**收录数据库:** {indexedIn joined by " | "}

**基本信息:**
- ISSN: {issn}
- CN: {cn}
- 主办单位: {sponsor}
- 出版周期: {frequency}
- 专辑: {collection}
- 出版文献量: {paperCount}

**评价指标:**
- 复合影响因子 (2025版): {impactComposite}
- 综合影响因子 (2025版): {impactComprehensive}

**收录情况:**
{For each tag in indexedIn: "- ✓ {tag}"}

Verified Page Structure

The journal detail page (navi.cnki.net/knavi/detail) has:

DataLocation
Titleh3.titbox.titbox1 — first line CN, second line EN
Indexing tagsText nodes after title: "北大核心", "EI", "CSCD", etc.
"被以下数据库收录"h4 heading, below tags
Basic infoText patterns: "主办单位:", "ISSN:", "CN:", "出版周期:"
Publication infoText patterns: "专辑名称:", "出版文献量:"
Evaluation infoText patterns: "复合影响因子:", "综合影响因子:"
Detailed indexingExpandable via "更多介绍" link
Stats tab"统计与评价" tab link
Detail page opens inNew tab — use list_pages + select_page

Common Indexing Databases

TagFull Name
北大核心北京大学中文核心期刊
CSSCI中文社会科学引文索引
CSCD中国科学引文数据库
SCIScience Citation Index
EIEngineering Index
CASChemical Abstracts Service
JSTJapan Science and Technology Agency
WJCIWorld Journal Clout Index
AMI中国人文社会科学期刊 AMI 综合评价
ScopusElsevier Scopus

Frequently asked questions

What does the Cnki Journal Index AI skill do?

Query journal indexing/inclusion status on CNKI - check which databases include a journal (北大核心, CSSCI, CSCD, SCI, EI, etc.), get impact factors and evaluation data. Use when user asks about a journal's level, indexing, or ranking.

Why use Cnki Journal Index on TypingMind?

Because you install it once and use it with any model. Cnki Journal Index is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cnki Journal Index in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cookjohn/cnki-skills/tree/master/skills/cnki-journal-index. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cnki Journal Index?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cnki Journal Index?

As many as you like. As long as a model supports skills, you can use Cnki Journal Index with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cnki Journal Index AI skill free?

It is published on GitHub by cookjohn. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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