Individual skill
graphrag
Combine knowledge graphs with retrieval-augmented generation — graph construction, community summaries, and graph-guided retrieval. Use when RAG needs to answer questions spanning many documents.
skills/graphrag/SKILL.md
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# GraphRAG
Standard RAG retrieves isolated chunks; questions spanning many documents ("how do these themes
connect?", "summarize the whole corpus on X") need structure. GraphRAG builds a knowledge graph
from the corpus and retrieves along its structure — entities, relationships, and community
summaries — giving the generator a map, not just fragments.
## Overview
The pipeline: extract entities and relationships from documents into a graph, detect communities
(clusters of densely connected entities), summarize each community, and at query time retrieve
relevant entities, relationships, and summaries — traversing the graph to gather connected
context. Local queries ("what does the doc say about X?") use entity neighborhoods; global queries
("what are the main themes?") use community summaries. It's RAG with a structural index on top.