How does it handle tables and structured data?

It recommends row-based or section-based chunking that preserves headers in every chunk to ensure the AI maintains context for the data contained in the rows.

Does this skill support different embedding models?

Yes, it provides specific configurations optimized for popular models including OpenAI's text-embedding-3, Voyage-2, and Cohere embed-v3, accounting for their token limits.

When should I use the Chunking Advisor?

Use it when setting up a new RAG pipeline, if you're experiencing poor search results, or when working with complex document types like code, tables, and legal contracts.

What is a document chunking strategy?

A chunking strategy is the method of breaking down large documents into smaller pieces (chunks) so that a RAG system can retrieve only the most relevant sections to answer a specific query.

Can it provide ready-to-use implementation code?

Absolutely. It generates Python snippets using libraries like LangChain for fixed-size and semantic splitting, as well as custom logic for hierarchical chunking.

Chunking Advisor

Name: Chunking Advisor
Author: floflo777

byfloflo777

•

Ciencia de Datos y ML

Recommends optimal document chunking strategies to improve retrieval quality and accuracy in RAG pipelines.

Chunking Advisor is a specialized utility for RAG developers designed to optimize document segmentation for better AI retrieval. It analyzes various content types—ranging from technical documentation and legal contracts to product catalogs and structured tables—to suggest the most effective chunking strategy, size, and overlap based on your specific embedding model. By providing implementation-ready code snippets and decision trees, it helps engineers eliminate retrieval noise, preserve semantic context, and build more reliable AI search systems.

Características Principales

01Context-aware chunking recommendations for diverse document types

02Ready-to-use Python implementation code for LangChain and custom logic

03Diagnostic checklists to identify and fix poor retrieval performance

0417 GitHub stars

05Hierarchical and semantic chunking strategies for complex data structures

06Embedding model-specific optimization for OpenAI, Voyage, and Cohere

Casos de Uso

01Adjusting chunk sizes and overlap when switching to new embedding models

02Optimizing retrieval accuracy in an existing RAG-based application

03Configuring initial indexing for diverse document sets like PDFs, code, and legal text

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add floflo777/claude-rag-skills chunking-advisor

For use in Claude.ai and ChatGPT

Download Skill