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extracting-keywords

Extract keywords, detect language, and generate embeddings from documents.

26|2|Updated Jun 8, 2026
Search Tags:#document analysis#text analysis#language detection#keyword extraction#embedding generation
One-click install
npx skills add https://github.com/xberg-io/plugins --skill extracting-keywords-xberg-io
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: extracting-keywords
Source: https://github.com/xberg-io/plugins/tree/main/plugins/xberg/.cursor-plugin/skills/extracting-keywords
Command: npx skills add https://github.com/xberg-io/plugins --skill extracting-keywords-xberg-io

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the need for quickly and accurately extracting keywords, detecting document language, and generating embeddings, providing valuable insights for document analysis.

Core Features & Use Cases

  • Keyword Extraction: Extract keywords (YAKE/RAKE) from documents.
  • Language Detection: Detect document language with precision.
  • Embeddings: Generate vector embeddings for text.
  • Use Case: For a researcher compiling a literature review, this Skill can extract keywords from documents, detect the primary language, and generate embeddings for efficient organization and analysis.

Quick Start

Run the xberg extract paper.pdf --keywords --detect-language --embed command to extract keywords, detect language, and generate embeddings for a document.

Frequently Asked Questions about extracting-keywords

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

FAQPage Schema
How do I extract keywords from a document for research analysis?▼

You can extract keywords from a document by running the command line tool with YAKE or RAKE algorithms. This provides quick text insights for literature reviews and content categorization.

What is the best way to detect document language and generate text embeddings?▼

The best way to detect document language and generate text embeddings is to use a combined text analysis command. Running the tool with detection and embedding flags processes these tasks together for efficient document organization.

Can I use YAKE and RAKE algorithms for keyword extraction in my content categorization workflow?▼

Yes, you can use YAKE and RAKE algorithms for keyword extraction in your content categorization workflow. The tool supports both algorithms to organize documents based on their text content.

Do I need to configure algorithms before I extract keywords and detect language?▼

Yes, you need to configure algorithms before you extract keywords and detect language. The tool requires configuration for its keyword extraction and language detection algorithms to ensure precise text analysis.

How do I generate vector embeddings from a PDF for document organization?▼

To generate vector embeddings from a PDF for document organization, run the command line tool with the embed flag. This creates text embeddings that enable efficient categorization and analysis of your research documents.

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