agentic-rag-router

name	agentic-rag-router
description	Choose the cheapest sufficient retrieval strategy for a query before answering. Use whenever the system has multiple retrievers (vector, hybrid, graph, metadata, summary) and needs to decide how to fetch evidence for a question. Trigger on any retrieval/QA task where query types are mixed.

agentic-rag-router

The point of agentic RAG isn't "more agents" — it's adaptive retrieval: use the cheapest sufficient behavior for the query at hand. This skill is the router.

Routing rules

• exact names / IDs / error codes / config keys / quoted strings → hybrid_search
• simple conceptual / single-hop lookup → vector_search
• relationship / dependency / impact / lineage / "connected to" → graph_search
• scoped by region / tenant / version / date / department → metadata_filtered_search
• broad summary / theme → hierarchical_summary_search
• ambiguous wording → multi-query expansion
• weak evidence after retrieval → corrective retry (regrade, switch retriever, decompose)
• high-risk answer → add a critic/verification pass

Output (JSON, user-safe — no hidden chain-of-thought)

{"query_type": "...", "selected_tool": "...", "tool_args": {}, "decision_reason": "brief", "needs_fallback": false}

Notes

• Don't answer the user here; only return the routing decision.
• This is the runtime cousin of the comparison harness in evals/python/rag_compare/, which measures which strategy actually wins per query class at what cost/latency.