Individual skill
graph-rag
Use when needing global or relational understanding of codebases or knowledge corpora. Keywords: GraphRAG, knowledge graph, entity extraction, community summarization, graph traversal, Microsoft GraphRAG.
skills/engineering/graph-rag/SKILL.md
Source commit 1c04afc2a760ed58cea1308851e7626f76c4ff44
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- Last checked
- Nov 6, 2026
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- Repository last updated
- Oct 24, 2026
- License
- Apache-2.0
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- claude-code Repository topic
- agent-skills Repository topic
- agentic-workflow Repository topic
- skill-md Repository topic
- ai-agent Repository topic
- ai-workflows Repository topic
- codex Repository topic
- coding-agent Repository topic
- developer-tools Repository topic
- gemini-cli Repository topic
- engineering Derived category
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# GraphRAG Skill
The `graph-rag` skill provides structured methods for building and querying Knowledge Graphs (KG) from text and code. Unlike standard RAG, which relies on vector similarity, GraphRAG leverages structural relationships between entities to answer complex, multi-hop, and global queries.
## When to Use This Skill
- When you need to understand the "big picture" of a massive repository or documentation set.
- When standard semantic search (vector-only) fails to retrieve relevant information for relational or aggregate questions (e.g., "What are the common design patterns used in this project?").
- When you need to trace dependencies across codebases for safe refactoring.
- When you need to generate high-quality summaries of knowledge clusters (communities).
## Limitations of Standard Vector-Only RAG
Standard RAG (Retrieval-Augmented Generation) has significant blind spots: