What This Skill Does

Most RAG systems fail silently — not because the model is weak, but because the retrieval architecture assumes every query is a lookup. This skill teaches you to design hybrid context layers that match the retrieval strategy to the query type, so your agent gets the right kind of context every time.

Problems It Solves

• The RAG-for-everything trap — routing relational and temporal queries through vector search causes silent structural failure, expensive reranking, and answers that get worse as you add more context.

• Multi-hop blindness — "Which teams own services that depend on the deprecated API?" is an entity-traversal query, not a lookup. Vector RAG cannot answer it accurately.

• Missing organizational causation — questions like "What decisions led to this incident?" require a temporal event graph, not a document chunk.

• Context-reasoning mismatch — good context routed to a weak reasoning tier, or long context with no causal structure, produces hallucinations at scale.

What You Get

The skill defines a three-layer context model:

• Layer 1 — Factual Store (Vector RAG): Single-fact, single-document point queries — the only case where RAG is structurally correct.

• Layer 2 — Relational Store (Knowledge Graph): Entity relationships, dependency chains, and multi-hop queries that require traversal across linked nodes.

• Layer 3 — Temporal/Episodic Store (Timeline Index): Event sequences, causal decision chains, and "how did we get here" queries that require timestamped structure.

You also get a query router decision tree — a concrete classification step that routes every incoming query to the correct layer before any retrieval begins, plus a phased implementation roadmap for teams migrating from RAG-only systems.

Who Should Use This

Teams building AI agents over enterprise knowledge bases, architecture decision records, incident histories, or any organizational system where the agent must answer relational or causal questions — not just fact lookups.