Stats
Actions
Tags
From rag-skills
Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
How this skill is triggered — by the user, by Claude, or both
Slash command
/rag-skills:chunkingThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this parent skill when the main RAG problem is how to split source material into retrievable units. Route to the child skill that best matches the document shape, retrieval failure, and production constraints.
Use this parent skill when the main RAG problem is how to split source material into retrievable units. Route to the child skill that best matches the document shape, retrieval failure, and production constraints.
Poor chunking causes retrieval misses, fragmented answers, duplicated context, and weak citations. RAG systems need chunking strategies that preserve meaning while staying efficient for indexing and retrieval.
Use semantic chunking for prose, hierarchical chunking for structured documents, and code-specific guidance for APIs or source repositories.
Use sliding windows when answers cross boundaries, contextual headers when chunks lack source context, and semantic chunking when fixed windows split ideas.
Measure retrieval recall, answer groundedness, duplicate context rate, and citation usefulness before changing embeddings or vector databases.
npx claudepluginhub goodnight77/rag-skills --plugin rag-skillsDocument chunking implementations and benchmarking tools for RAG pipelines including fixed-size, semantic, recursive, and sentence-based strategies. Use when implementing document processing, optimizing chunk sizes, comparing chunking approaches, benchmarking retrieval performance, or when user mentions chunking, text splitting, document segmentation, RAG optimization, or chunk evaluation.
Generates chunking strategies for RAG systems: 256-1024 token sizes, 10-20% overlaps, semantic boundaries; validates coherence and evaluates precision/recall metrics. For vector DBs and large documents.
Provides chunking strategies for RAG document processing pipelines: fixed-size, semantic, recursive methods with Python examples, document-type recommendations, and best practices.