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Cnki Parse Results

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cookjohn
cnki-parse-results

Parse current CNKI search results page into structured paper data (title, authors, journal, date, citations). Use after a search has been performed and you need to extract the results.

Overview

Publishercookjohn
Repositorycnki-skills
Skill namecnki-parse-results
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 Parse Results 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-parse-results
  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-parse-results .claude/skills/cnki-parse-results
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cnki Parse Results 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 Parse Results 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 Parse Results 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 Parse Search Results

Extract structured paper data from the current CNKI search results page.

Prerequisites

The current Chrome page must be a CNKI search results page (URL contains kns.cnki.net and page shows "条结果").

Steps

1. Verify we are on a results page

Use mcp__chrome-devtools__take_snapshot. Verify the page contains "条结果". If not, inform the user that no search results page is currently open.

Check for captcha ("拖动下方拼图完成验证") - if found, notify user to solve it manually.

2. Extract results via JavaScript

Use mcp__chrome-devtools__evaluate_script with this function:

javascript
() => {
  const rows = document.querySelectorAll('.result-table-list tbody tr');
  const checkboxes = document.querySelectorAll('.result-table-list tbody input.cbItem');
  const results = Array.from(rows).map((row, index) => {
    const nameCell = row.querySelector('td.name');
    const titleLink = nameCell?.querySelector('a.fz14');
    const authorCell = row.querySelector('td.author');
    const sourceCell = row.querySelector('td.source');
    const dateCell = row.querySelector('td.date');
    const dataCell = row.querySelector('td.data');
    const quoteCell = row.querySelector('td.quote');
    const downloadCell = row.querySelector('td.download');
    const isOnlineFirst = !!nameCell?.querySelector('.marktip');

    return {
      number: index + 1,
      title: titleLink?.innerText?.trim() || '',
      url: titleLink?.href || '',
      exportId: checkboxes[index]?.value || '',
      authors: Array.from(authorCell?.querySelectorAll('a.KnowledgeNetLink') || []).map(a => a.innerText?.trim()),
      journal: sourceCell?.querySelector('a')?.innerText?.trim() || '',
      date: dateCell?.innerText?.trim() || '',
      database: dataCell?.innerText?.trim() || '',
      citations: quoteCell?.innerText?.trim() || '',
      downloads: downloadCell?.innerText?.trim() || '',
      isOnlineFirst: isOnlineFirst
    };
  });

  const totalText = document.querySelector('.pagerTitleCell')?.innerText || '';
  const totalMatch = totalText.match(/([\d,]+)/);
  const pageInfo = document.querySelector('.countPageMark')?.innerText || '';

  return {
    papers: results,
    totalCount: totalMatch ? totalMatch[1] : 'unknown',
    pageInfo: pageInfo
  };
}

3. Present results

Format as a numbered list:

CNKI search results ({totalCount} total, page {pageInfo}):

1. {title} {isOnlineFirst ? "[网络首发]" : ""}
   Authors: {authors joined by "; "}
   Journal: {journal} | Date: {date} | Type: {database}
   Citations: {citations} | Downloads: {downloads}
   URL: {url}

2. ...

4. Fallback: snapshot-based parsing

If JavaScript returns empty (DOM structure changed), use mcp__chrome-devtools__take_snapshot and parse the accessibility tree manually:

Look for the repeating pattern:

  • checkbox → StaticText (number) → link with URL containing kcms2/article/abstract (title) → links with URL containing kcms2/author/detail (authors) → link with URL containing navi.cnki.net/knavi/detail (journal) → StaticText (date) → StaticText (database type)

Verified DOM Selectors (CNKI uses jQuery, stable semantic class names)

DataSelectorNotes
Table.result-table-list tbody trEach row = one paper
Checkboxinput.cbItemvalue = export encrypted ID
Numbertd.seqRow sequence number
Titletd.name a.fz14Paper title link
Authorstd.author a.KnowledgeNetLinkAuthor name links
Journaltd.source aJournal/source link
Datetd.datePublication date text
DB Typetd.dataDatabase type (期刊/学位论文)
Citationstd.quoteCitation count
Downloadstd.downloadDownload count
Online 1sttd.name .marktip"网络首发" label
Total.pagerTitleCell"共找到 X 条结果"
Page.countPageMark"1/300" format

Frequently asked questions

What does the Cnki Parse Results AI skill do?

Parse current CNKI search results page into structured paper data (title, authors, journal, date, citations). Use after a search has been performed and you need to extract the results.

Why use Cnki Parse Results on TypingMind?

Because you install it once and use it with any model. Cnki Parse Results 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 Parse Results in TypingMind?

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

Which AI models can use Cnki Parse Results?

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 Parse Results?

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

Is the Cnki Parse Results 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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