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Gs Advanced Search

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cookjohn
gs-advanced-search

Perform advanced Google Scholar search with filters - author, journal, date range, exact phrase, title-only. Constructs proper URL parameters from natural language. Use for precise filtered searches.

Overview

Publishercookjohn
Repositorygs-skills
Skill namegs-advanced-search
Stars
529
Forks
26
Bundled files
Instructions only
LicenseMIT
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 Gs Advanced Search 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/gs-skills/tree/master/skills/gs-advanced-search
  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/gs-skills.git /tmp/gs-skills
mkdir -p .claude/skills
cp -r /tmp/gs-skills/skills/gs-advanced-search .claude/skills/gs-advanced-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gs Advanced Search 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 Gs Advanced Search 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 Gs Advanced Search 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.

Google Scholar Advanced Search

Construct and execute a Google Scholar search using URL parameters based on the user's natural language description.

Arguments

$ARGUMENTS is a natural language description of the search criteria, e.g.:

  • "Search for Einstein's papers on relativity after 2020"
  • "Find reviews about CRISPR in Nature"
  • "Search for exact phrase 'machine learning' in title only"

Step 1: Parse search criteria into URL parameters

Map the user's intent to Google Scholar URL parameters:

CriteriaParameterExample
Keywordsqq=gastric+cancer
Authoras_sauthorsas_sauthors="Albert Einstein"
Journal/Sourceas_publicationas_publication=Nature
Start yearas_yloas_ylo=2020
End yearas_yhias_yhi=2025
Exact phraseas_epqas_epq=machine+learning
Any of these words (OR)as_oqas_oq=immunotherapy+checkpoint
Exclude wordsas_eqas_eq=review
Search scopeas_occtas_occt=title (title only) / as_occt=any (anywhere)
Results per pagenumnum=10 (default) or num=20 (max)
Languagehlhl=en or hl=zh-CN

Construction examples:

  • "Einstein 2020 年以后在 Nature 上的论文" → scholar?as_sauthors=Einstein&as_publication=Nature&as_ylo=2020&hl=en
  • "标题包含 CRISPR 的论文" → scholar?q=CRISPR&as_occt=title&hl=en
  • "精确搜索 'deep learning' 排除 review" → scholar?as_epq=deep+learning&as_eq=review&hl=en
  • "搜索 immunotherapy 或 checkpoint 相关论文" → scholar?as_oq=immunotherapy+checkpoint&hl=en

Notes:

  • When as_sauthors, as_publication, etc. are used, q can be omitted or used for additional keywords
  • Always include hl=en for consistent results
  • Use num=10 (default) to minimize CAPTCHA risk

Step 2: Navigate

Use mcp__chrome-devtools__navigate_page:

  • url: https://scholar.google.com/scholar?{CONSTRUCTED_PARAMS}

Step 3: Extract results (evaluate_script)

Same extraction script as gs-search step 2:

javascript
async () => {
  for (let i = 0; i < 20; i++) {
    if (document.querySelector('#gs_res_ccl') || document.querySelector('#gs_captcha_ccl')) break;
    await new Promise(r => setTimeout(r, 500));
  }

  if (document.querySelector('#gs_captcha_ccl') || document.body.innerText.includes('unusual traffic')) {
    return { error: 'captcha', message: 'Google Scholar requires CAPTCHA verification. Please complete it in your browser, then tell me to continue.' };
  }

  const items = document.querySelectorAll('#gs_res_ccl .gs_r.gs_or.gs_scl');
  const results = Array.from(items).map((item, i) => {
    const titleEl = item.querySelector('.gs_rt a');
    const meta = item.querySelector('.gs_a')?.textContent || '';
    const parts = meta.split(' - ');
    const authors = parts[0]?.trim() || '';
    const journalYear = parts[1]?.trim() || '';
    const citedByEl = item.querySelector('.gs_fl a[href*="cites"]');

    return {
      n: i + 1,
      title: titleEl?.textContent?.trim() || item.querySelector('.gs_rt')?.textContent?.trim() || '',
      href: titleEl?.href || '',
      authors,
      journalYear,
      citedBy: citedByEl?.textContent?.match(/\d+/)?.[0] || '0',
      citedByUrl: citedByEl?.href || '',
      dataCid: item.getAttribute('data-cid') || '',
      fullTextUrl: (item.querySelector('.gs_ggs a') || item.querySelector('.gs_or_ggsm a'))?.href || '',
      snippet: item.querySelector('.gs_rs')?.textContent?.trim()?.substring(0, 200) || ''
    };
  });

  const totalText = document.querySelector('#gs_ab_md')?.textContent?.trim() || '';
  const currentUrl = window.location.href;
  return { total: totalText, resultCount: results.length, currentUrl, results };
}

Step 4: Report

Advanced search on Google Scholar:
Query parameters: {list the parameters used}
{total}

1. {title}
   Authors: {authors} | {journalYear}
   Cited by: {citedBy} | [Full text]({fullTextUrl})
   Data-CID: {dataCid}

2. ...

Always show the constructed URL parameters so the user understands how the query was built.

Notes

  • This skill uses 2 tool calls: navigate_page + evaluate_script
  • Google Scholar does NOT support publication type filtering (review, clinical trial, etc.) — use keywords instead
  • Impact factor is not available in Google Scholar — use citation count as a proxy
  • The key difference from gs-search is URL parameter construction — this skill translates natural language to Google Scholar query parameters

Frequently asked questions

What does the Gs Advanced Search AI skill do?

Perform advanced Google Scholar search with filters - author, journal, date range, exact phrase, title-only. Constructs proper URL parameters from natural language. Use for precise filtered searches.

Why use Gs Advanced Search on TypingMind?

Because you install it once and use it with any model. Gs Advanced Search 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 Gs Advanced Search in TypingMind?

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

Which AI models can use Gs Advanced Search?

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 Gs Advanced Search?

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

Is the Gs Advanced Search AI skill free?

Yes. It is published on GitHub by cookjohn under the MIT license. 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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