Rag chunking strategy

Skill a5c-ai/babysitter/library/specializations/ai-agents-conversational/skills/rag-chunking-strategy

Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration

Install

npx -y skills add a5c-ai/babysitter --skill rag-chunking-strategy

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What its author says it does

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Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

SKILL.md

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RAG Chunking Strategy Skill

Capabilities

Implement multiple document chunking strategies
Configure semantic chunking based on content boundaries
Set up recursive character text splitting
Design fixed-size chunking with overlap
Implement document-aware chunking (markdown, code, etc.)
Optimize chunk sizes for retrieval quality

Target Processes

rag-pipeline-implementation
chunking-strategy-design

Implementation Details

Chunking Strategies

RecursiveCharacterTextSplitter: Hierarchical splitting with separators
SemanticChunker: Embedding-based semantic boundaries
TokenTextSplitter: Token-aware splitting
MarkdownHeaderTextSplitter: Structure-aware markdown splitting
CodeSplitter: Language-aware code chunking

Configuration Options

Chunk size (characters or tokens)
Chunk overlap percentage
Separator hierarchy
Embedding model for semantic chunking
Document type detection

Best Practices

Match chunk size to embedding model limits
Use appropriate overlap for context preservation
Test retrieval quality with different strategies
Consider document structure in strategy selection

Dependencies

langchain-text-splitters
sentence-transformers (for semantic chunking)