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chunking

Split text into chunks by size, overlap, and chunker type.

26|2|Updated Jun 8, 2026
Search Tags:#RAG ingestion#text chunking#chunking strategies#LLM context windows#tokenizer-based sizing
One-click install
npx skills add https://github.com/xberg-io/plugins --skill chunking-xberg-io
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chunking
Source: https://github.com/xberg-io/plugins/tree/main/plugins/xberg/.cursor-plugin/skills/chunking
Command: npx skills add https://github.com/xberg-io/plugins --skill chunking-xberg-io

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of splitting extracted text into manageable chunks for optimal LLM context windows or RAG ingestion, enhancing the efficiency of AI processing.

Core Features & Use Cases

  • Text Chunking: Splits text into chunks based on size, overlap, and chunker type (text, markdown, yaml, semantic).
  • Token-based Sizing: Allows chunking by tokens for specific models, optimizing for model window size.
  • Config File Alternative: Offers configuration via a TOML file for flexible settings.
  • Programmatic Access: Provides Python API for programmatic control and integration.
  • Use Case: When preparing documents for AI processing, this Skill can be used to split large text files into smaller chunks that fit within the model's context window, improving processing speed and accuracy.

Quick Start

Run the chunk command with the text you want to chunk, specifying the chunk size and overlap. For example: xberg chunk --text "Your text here" --chunk-size 500 --chunk-overlap 100

Frequently Asked Questions about chunking

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

FAQPage Schema
How do I split text into chunks for RAG ingestion?▼

To split text for RAG ingestion, use the `chunk` command to divide extracted text into manageable chunks. You specify the chunk size and overlap, optimizing the text for model context windows.

What chunking strategies are available for LLM context windows?▼

Available chunking strategies include text, markdown, yaml, and semantic chunking. These strategies allow you to split text based on structure and content type for optimal LLM processing.

Can I size text chunks by tokens for a specific LLM model?▼

Yes, you can size text chunks by tokens for specific models. Token-based sizing optimizes the chunks according to the exact model window size, ensuring the text fits within the LLM context limits.

How do I configure text chunking settings via a TOML file?▼

You can configure text chunking settings via a TOML file as an alternative to command line arguments. This provides flexible settings management for splitting text into chunks.

Is there a Python API for programmatic text chunking control?▼

Yes, a Python API is provided for programmatic control and integration. This allows you to implement text chunking directly within your Python scripts for RAG ingestion pipelines.

What is the best way to handle markdown text chunking for AI processing?▼

The best way to handle markdown text chunking is to use the markdown chunker type. This strategy splits text into chunks while respecting the markdown structure, improving processing speed and accuracy.

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