RAG Failure Diagnostics & Architect

What it does

This skill transforms your AI agent into a Retrieval Architect capable of diagnosing why RAG systems fail. Instead of blindly tuning chunk sizes or embedding models, it analyzes whether a query's failure is structural (requiring a Knowledge Graph, temporal index, or structured query) or merely a tuning issue. It provides three specialized modes: DIAGNOSE to classify failing queries, ARCHITECT to design hybrid retrieval pipelines, and SCHEMA to build institutional memory layers that capture causal relationships vector search ignores.

Why use this skill

RAG systems often fail "quietly"—returning plausible but incorrect answers because vector search cannot handle multi-hop reasoning, temporal changes, or aggregations. This skill prevents the common mistake of over-indexing on semantic similarity. It helps developers move beyond basic vector stores to build sophisticated, production-ready context engines that understand "why" and "when," not just "what."

What it supports

• RAG & Vector DBs: Hybrid search, reranking, and chunking strategies.
• Knowledge Graphs: Designing GraphRAG architectures for multi-hop and causal reasoning.
• Hybrid Routers: Building logic to dispatch queries between SQL, Vector, and Graph stores.
• Metadata & Temporal Ops: Constructing schemas for event-sourced or time-aware retrieval.