graphrag-scaffolderlisted

GraphRAG is the most oversold upgrade in retrieval — and genuinely transformative for the right query shapes. This skill keeps you on the right side of that line: it builds the smallest GraphRAG that could prove value on *your* failures, measures it against your existing pipeline, and prices the ongoing bill before you commit. ## When to use this skill - Multi-hop questions ("how is A exposed to C through B?") keep failing your vector RAG and you suspect structure is the answer. - You need "global" answers over a whole corpus (themes, patterns, summaries) that top-k chunks structurally can't provide. - Someone said "let's add a knowledge graph" and you want evidence before infrastructure. ## When NOT to use this skill - Your RAG failures are ranking problems (right doc exists, wrong position) — fix retrieval first: hybrid search and reranking are cheaper and usually sufficient. - The corpus churns rapidly — GraphRAG's re-extraction cost on updates may dominate; consider it only with an incremental-update plan. - You need agent memory with temporal structure rather than corpus QA — that's a memory platform (Zep/Graphiti), not corpus GraphRAG. ## Instructions 1. **Build the failure set first.** Collect 15–30 real queries the current pipeline fails, and classify each: lookup (vector should handle — fix retrieval instead), multi-hop (graph traversal candidate), or global (community-summary candidate). If multi-hop+global don't dominate, stop and say so — that's a successful