Public skill
rag-architect
Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
Package contents
Manifest
Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
02c361246432282004a40003f8dc80959c8503c1d66c24933d0c2ca6faa56f98- SKILL.md4,506 bytes
- chunking_optimizer.py29,512 bytes
- rag_pipeline_designer.py28,221 bytes
- references/chunking_strategies_comparison.md10,181 bytes
- references/embedding_model_benchmark.md12,729 bytes
- references/rag_evaluation_framework.md15,137 bytes
- retrieval_evaluator.py23,603 bytes
Release history