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Cnki Paper Detail

Community
cookjohn
cnki-paper-detail

Extract full paper details from a CNKI paper page including title, authors, affiliations, abstract, keywords, fund, classification. Use when the user needs detailed information about a specific paper.

Overview

Publishercookjohn
Repositorycnki-skills
Skill namecnki-paper-detail
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 Paper Detail 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-paper-detail
  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-paper-detail .claude/skills/cnki-paper-detail
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cnki Paper Detail 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 Paper Detail 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 Paper Detail 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 Paper Detail Extraction

Extract complete metadata from a CNKI paper detail page.

Arguments

$ARGUMENTS is optionally a CNKI paper detail URL (containing kcms2/article/abstract). If not provided, assumes the current page is already a paper detail page.

Steps

1. Navigate to the paper page (if URL provided)

If $ARGUMENTS contains a URL:

  • Use mcp__chrome-devtools__navigate_page with the URL.
  • Use mcp__chrome-devtools__wait_for with text ["摘要"] and timeout 15000.

2. Check for captcha

Use mcp__chrome-devtools__take_snapshot. If "拖动下方拼图完成验证" found, notify user:

CNKI 正在显示滑块验证码。请在 Chrome 浏览器中手动完成拼图验证,完成后告诉我继续。

3. Extract paper metadata via JavaScript

Use mcp__chrome-devtools__evaluate_script with this function:

javascript
() => {
  const brief = document.querySelector('.brief');
  if (!brief) return { error: 'Paper detail section (.brief) not found' };

  // Title
  const title = brief.querySelector('h1')?.innerText?.trim()
    ?.replace(/\s*附视频\s*$/, '')  // remove "附视频" suffix
    ?.replace(/\s*网络首发\s*$/, ''); // remove "网络首发" suffix

  // Authors - first h3.author contains author links with sup tags
  const authorH3s = brief.querySelectorAll('h3.author');
  const authorSection = authorH3s[0];
  const authors = [];
  if (authorSection) {
    const authorLinks = authorSection.querySelectorAll('a');
    authorLinks.forEach(a => {
      const name = a.innerText?.replace(/\d+$/, '').trim();
      const supMatch = a.innerText?.match(/(\d+)$/);
      const affiliationNum = supMatch ? supMatch[1] : '';
      authors.push({ name, affiliationNum });
    });
  }

  // Affiliations - second h3.author contains org links
  const affiliations = [];
  if (authorH3s.length > 1) {
    const orgLinks = authorH3s[1].querySelectorAll('a');
    orgLinks.forEach(a => {
      affiliations.push(a.innerText?.trim());
    });
  }

  // Abstract
  const abstractEl = document.querySelector('.abstract-text');
  const abstract = abstractEl?.innerText?.trim() || '';

  // Keywords
  const keywordsP = document.querySelector('p.keywords');
  const keywords = keywordsP
    ? Array.from(keywordsP.querySelectorAll('a')).map(a => a.innerText?.replace(/;$/, '').trim())
    : [];

  // Fund
  const fundsP = document.querySelector('p.funds');
  const fund = fundsP?.innerText?.trim() || '';

  // Classification code
  const clcCode = document.querySelector('.clc-code');
  const classification = clcCode?.innerText?.trim() || '';

  // Journal/source
  const docTop = document.querySelector('.doc-top');
  const journal = docTop?.querySelector('a')?.innerText?.trim() || '';

  // Online first / publication info
  const headTime = document.querySelector('.head-time');
  const pubInfo = headTime?.innerText?.trim() || '';

  // Is online first?
  const isOnlineFirst = !!brief.querySelector('.icon-shoufa');

  // Article outline/TOC
  const catalogList = document.querySelector('.catalog-list, .catalog-listDiv');
  const toc = catalogList?.innerText?.trim() || '';

  // Citation network counts
  const citationTabs = document.querySelectorAll('ul.module-tab.tpl_lieteratures li');
  const citationInfo = {};
  citationTabs.forEach(li => {
    const id = li.getAttribute('data-id');
    const text = li.innerText?.trim();
    const countMatch = text.match(/(\d+)/);
    if (id) {
      citationInfo[id] = {
        label: text.replace(/\d+/, '').trim(),
        count: countMatch ? parseInt(countMatch[1]) : 0
      };
    }
  });

  return {
    title,
    authors,
    affiliations,
    abstract,
    keywords,
    fund,
    classification,
    journal,
    pubInfo,
    isOnlineFirst,
    toc,
    citationInfo
  };
}

4. Format and present the output

## {title} {isOnlineFirst ? "[网络首发]" : ""}

**Authors:**
{For each author: "- {name} ({affiliation})"}

**Affiliations:**
{For each affiliation: "- {affiliation}"}

**Journal:** {journal}
**Publication Info:** {pubInfo}

**Abstract:**
{abstract}

**Keywords:** {keywords joined by ", "}

**Fund:** {fund}
**Classification:** {classification}

**Citation Network:**
{For each citation type: "- {label}: {count}"}

5. Fallback: snapshot-based parsing

If JS extraction fails, use mcp__chrome-devtools__take_snapshot and parse the accessibility tree:

  • Title: heading level 1 element
  • Authors: link elements whose URLs contain kcms2/author/detail
  • Affiliations: link elements whose URLs contain kcms2/organ/detail
  • Abstract: StaticText following "摘要:"
  • Keywords: link elements whose URLs contain kcms2/keyword/detail
  • Fund: link elements following "基金资助:"
  • Classification: StaticText following "分类号:"

Verified DOM Selectors

DataSelectorNotes
Paper section.briefMain paper info container
Title.brief h1May contain icons, clean text needed
Authors.brief h3.author:first-of-type aText has superscript numbers (e.g., "张三1")
Affiliations.brief h3.author:nth-of-type(2) aText starts with "N." (e.g., "1.北京大学")
Abstract.abstract-textFull abstract text
Keywordsp.keywords aSemicolon-separated keyword links
Fundp.fundsFund information text
Classification.clc-codeCLC classification codes
Journal.doc-top aSource journal link
Online first.brief .icon-shoufaPresent if paper is online first
Citation tabsul.module-tab.tpl_lieteratures lidata-id attr identifies type

Frequently asked questions

What does the Cnki Paper Detail AI skill do?

Extract full paper details from a CNKI paper page including title, authors, affiliations, abstract, keywords, fund, classification. Use when the user needs detailed information about a specific paper.

Why use Cnki Paper Detail on TypingMind?

Because you install it once and use it with any model. Cnki Paper Detail 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 Paper Detail in TypingMind?

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

Which AI models can use Cnki Paper Detail?

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 Paper Detail?

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

Is the Cnki Paper Detail 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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