graph-rag

Installation
SKILL.md

Default output: return only the result, blockers, and required evidence. Omit preambles, process narration, repeated context, confidence scores, and follow-up offers. Use at most five bullets unless a required artifact or schema needs more.

Graph RAG

Overview

Use graph-native retrieval when the answer depends on relationships, not just similar text. Graph RAG works well for entity-heavy systems, architecture questions, causal chains, and multi-hop queries that plain vector retrieval often misses.

When to Use

  • The user asks how two concepts connect
  • The answer depends on paths, dependencies, or neighborhoods
  • Important context is split across multiple files or documents
  • Vector search returns individually relevant chunks but weak overall explanations
  • You already have entities, references, or graph structure available

Retrieval Patterns

Entity Anchor Retrieval

Installs
3
GitHub Stars
8
First Seen
May 7, 2026
graph-rag — v1truv1us/ai-eng-system