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rag-systems

4.9

by majiayu000

192Favorites
72Upvotes
0Downvotes

Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization

rag

4.9

Rating

0

Installs

AI & LLM

Category

Quick Review

The skill provides a solid foundation for RAG system implementation with clear parameter schemas, practical code examples, and useful reference tables for chunking strategies and embedding costs. The description adequately covers core RAG capabilities, and the task knowledge includes concrete implementation steps with LangChain. Structure is clean and well-organized with appropriate sections. However, novelty is moderate—while RAG systems add value, much of this guidance (chunking strategies, embedding model selection, vector DB setup) could be accomplished by a capable CLI agent with sufficient context. The skill is most valuable for standardizing best practices and providing quick-reference tables rather than solving uniquely complex problems that would otherwise require many tokens.

LLM Signals

Description coverage7
Task knowledge8
Structure7
Novelty4

GitHub Signals

49
7
1
1
Last commit 0 days ago

Publisher

majiayu000

majiayu000

Skill Author

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Publisher

majiayu000 avatar
majiayu000

Skill Author

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