Can I use this for multi-document analysis?

Yes, the skill provides specific patterns for passing multiple documents to Claude simultaneously while maintaining distinct citations for each source.

Does this skill help with API costs?

Yes, it includes guidance on using prompt caching (ephemeral cache) for large documents to reduce token costs during repeated queries.

How does this skill improve RAG accuracy?

It implements 'Contextual Retrieval,' which prepends specific context to each document chunk before embedding, often resulting in 49-67% better retrieval performance.

What citation formats are supported?

The skill includes implementation patterns for standard academic formats like APA, MLA, and Chicago, as well as character-level indexing for text documents.

Claude Citations & RAG Retrieval

Name: Claude Citations & RAG Retrieval
Author: markus41

bymarkus41

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Ciencia de Datos y ML

Implements document-based citations and Retrieval-Augmented Generation (RAG) patterns to ensure grounded, verifiable AI responses.

This skill empowers developers to build trustworthy AI applications by integrating document citations and RAG workflows directly into Claude's interactions. It provides a comprehensive framework for source attribution, document grounding, and contextual retrieval, which can improve retrieval accuracy by up to 67%. Whether you are building research tools, multi-document Q&A systems, or academic analysis engines, this skill provides the necessary patterns for character-level citations, custom content blocks, and efficient prompt caching for large-scale knowledge bases.

Características Principales

019 GitHub stars

02Support for multiple citation formats including APA, MLA, and Chicago

03Automated citation integrity validation and character-level extraction

04Native document citation enabling for verifiable AI responses

05Efficient prompt caching strategies for large document processing

06Contextual retrieval patterns to significantly improve RAG accuracy

Casos de Uso

01Implementing reliable RAG pipelines for enterprise knowledge bases

02Building research and analysis tools that require precise source attribution

03Developing multi-document Q&A systems with grounded facts

Características Principales

019 GitHub stars

02Support for multiple citation formats including APA, MLA, and Chicago

03Automated citation integrity validation and character-level extraction

04Native document citation enabling for verifiable AI responses

05Efficient prompt caching strategies for large document processing

06Contextual retrieval patterns to significantly improve RAG accuracy

Casos de Uso

01Implementing reliable RAG pipelines for enterprise knowledge bases

02Building research and analysis tools that require precise source attribution

03Developing multi-document Q&A systems with grounded facts