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research-topic-extractor

Extract structured review topics, keywords, and core questions from files, images, URLs, and text.

2.7k|251|Updated Feb 12, 2024
Search Tags:#literature review#content analysis#keyword extraction#topic extraction#pdf analysis#Academic research
One-click install
npx skills add https://github.com/huangwb8/ChineseResearchLaTeX --skill research-topic-extractor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-topic-extractor
Source: https://github.com/huangwb8/ChineseResearchLaTeX/tree/main/skills/research-topic-extractor
Command: npx skills add https://github.com/huangwb8/ChineseResearchLaTeX --skill research-topic-extractor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Starting a literature review requires a well-defined topic, searchable keywords, and concrete research questions, but deriving these from scattered notes, PDFs, images, or web pages is tedious and inconsistent. This Skill converts any input source into a structured topic package ready for downstream literature review workflows.

Core Features & Use Cases

  • Multi-source input analysis: Accepts natural language descriptions, text files (Markdown, TXT, TeX), PDFs, Word documents, images, web URLs, and entire folders, automatically detecting the input type.
  • Structured three-part output: Always produces a one-sentence topic, 5-10 English standard search terms, and 2-5 specific core research questions, formatted as plain text, YAML, or JSON.
  • Downstream integration: Output feeds directly into research-literature-review, with keywords supplementing search strategies and core questions defining review scope.
  • Use Case: A researcher has a grant proposal PDF and wants to start a literature review. The Skill reads the PDF, extracts the topic, standard English keywords for PubMed or Web of Science retrieval, and the specific challenges to investigate.

Quick Start

Ask the AI to extract a structured review topic with keywords and core questions from your research notes file or a short description of your research interest.

Frequently Asked Questions about research-topic-extractor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract a literature review topic from a PDF?▼

Provide the PDF file path and ask for topic extraction. The Skill reads the document, prioritizing the title, abstract, and conclusion, then outputs a one-sentence topic, 5-10 English keywords, and 2-5 core research questions.

What input formats does research topic extraction support?▼

It supports natural language descriptions, text files (Markdown, TXT, TeX, RST), PDF and Word documents, images (PNG, JPG, GIF, WebP), web URLs, and folders. Folders are scanned recursively and their contents merged for analysis.

Can I get the extracted topic in JSON or YAML format?▼

Yes, specify the output format when requesting extraction. Plain text is the default for readability, while YAML and JSON provide structured topic, keywords, and core_questions fields suitable for passing to downstream skills programmatically.

Why is the extracted topic too broad or inaccurate?▼

Broad topics result from vague input such as a single term like CRISPR. Add the research object, core method, application scenario, and the specific challenge you care about to get a precise, actionable topic.

What happens when a URL or image cannot be parsed?▼

The Skill degrades gracefully: for failed URL parsing it asks you to paste the page text or provide a PDF, and for unclear images it asks for a short text description. It does not install extra script dependencies.

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