Organizes and prioritizes retrieved documents using vector similarity metrics to optimize RAG performance.
The Ranking skill is a specialized utility for Retrieval-Augmented Generation (RAG) systems that sorts and filters documents based on pre-computed similarity metrics like cosine distance and similarity scores. It acts as a bridge between the initial retrieval phase and the final generation phase, allowing Claude to refine document relevance without performing additional database queries. By calculating similarity scores and applying thresholds, this skill ensures that only the most pertinent information is passed to the LLM, reducing noise, lowering token costs, and improving the overall accuracy of AI-generated responses.
主な機能
010 GitHub stars
02Top-k document selection to optimize context window usage and reduce costs
03Hybrid ranking support for prioritizing specific data collections
04Automated sorting by similarity score (descending) or distance (ascending)
05Configurable similarity threshold filtering to eliminate low-quality matches
06Local processing of existing metadata without additional database overhead
ユースケース
01Reducing token consumption by filtering large retrieval sets down to the most relevant nodes
02Prioritizing internal documentation over general FAQ data in multi-source retrieval
03Optimizing RAG pipelines by pruning irrelevant search results before generation
