cnki-paper-detail

cnki-paper-detail is a skill for Claude Code from YuanyuanMa03/academic-research-skills. It costs 42 tokens per session (1,592 once invoked), scanned A, original, MIT.

A paper-detail extractor for CNKI, a Chinese academic database. It collects the main information shown on a specific paper page, including the title, authors, affiliations, abstract, keywords, funding, and classification.

In plain words
What is it for?
Use it to gather structured details from a CNKI paper when preparing research notes, bibliographies, or datasets.
Why use it?
It saves developers from copying publication details field by field from CNKI pages. It provides a consistent set of metadata for one paper.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the cnki-paper-detail plugin — 1 skill shipped together

Good fit Use it to gather structured details from a CNKI paper when preparing research notes, bibliographies, or datasets.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yuanyuanma03/academic-research-skills/cnki-paper-detail
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add YuanyuanMa03/academic-research-skills --skill cnki-paper-detail
Clone the repo
git clone --depth 1 https://github.com/YuanyuanMa03/academic-research-skills

Made for: Claude Code.

Or install cnki-paper-detail, the plugin that ships this one along with the rest of its 1 skill.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for cnki-paper-detail

README.md
[![agentmods](https://agentmods.dev/badge/skills/yuanyuanma03/academic-research-skills/cnki-paper-detail/github.svg)](https://agentmods.dev/skills/yuanyuanma03/academic-research-skills/cnki-paper-detail)
Your own site
<a href="https://agentmods.dev/skills/yuanyuanma03/academic-research-skills/cnki-paper-detail"><img src="https://agentmods.dev/badge/skills/yuanyuanma03/academic-research-skills/cnki-paper-detail/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for cnki-paper-detail

Your own site · 80×15
<a href="https://agentmods.dev/skills/yuanyuanma03/academic-research-skills/cnki-paper-detail"><img src="https://agentmods.dev/badge/skills/yuanyuanma03/academic-research-skills/cnki-paper-detail.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,592 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01592
Opus 5 $0.00021 $0.00796
Sonnet 5 $0.00008 $0.00318
Haiku 4.5 $0.00004 $0.00159

Measured 12d ago against content hash 15e087b6b4be, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

cnki-paper-detail scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/cnki-paper-detail/skills/cnki-paper-detail/SKILL.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

() => {
  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
  };
}

Read the full file on GitHub · 184 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 184 lines · 42 tokens per session scan A 15e087b6b4be

Subscribe to this mod's changes

cnki-paper-detail is a skill published in the GitHub repository YuanyuanMa03/academic-research-skills (63 stars, last pushed 22d ago), licensed MIT. It adds 42 tokens to every session and 1,592 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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