What is graph-rag-knowledge-expert?

A guide to combining a knowledge graph with vector search to answer questions about connected information. A knowledge graph stores entities and their relationships, while vector search finds text with similar meaning.

Use it for multi-step relationship questions, linked enterprise data, global summaries, and knowledge bases containing structured entities and dependencies.

Regular vector search can miss links between several related entities and can struggle with questions about an entire collection of documents. Graph-based retrieval makes those relationships available during search.

graph-rag-knowledge-expert — Skill for Cursor by roedyrustam

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add roedyrustam/vibes-plug --skill graph-rag-knowledge-expert

Clone the repo

git clone --depth 1 https://github.com/roedyrustam/vibes-plug

Made for: Cursor.

Or install vibes-plug, the plugin that ships this one along with the rest of its 178 skills, 9 agents, 2 MCP servers.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/graph-rag-knowledge-expert/github.svg)](https://agentmods.dev/skills/roedyrustam/vibes-plug/graph-rag-knowledge-expert)

Your own site

<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/graph-rag-knowledge-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/graph-rag-knowledge-expert/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/graph-rag-knowledge-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/graph-rag-knowledge-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,273 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00045	$0.03273
Opus 5.5	$0.00018	$0.01309
Sonnet 5.5	$0.00009	$0.00655
Haiku 4.5	$0.00005	$0.00327

Measured 7d ago against content hash c7ae5eb6e764, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

graph-rag-knowledge-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/skills/graph-rag-knowledge-expert/SKILL.md · 300 lines

How it starts

The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GraphRAG & Knowledge Graph Expert (2026 Edition)

English | Bahasa Indonesia

English

Orchestration & Integration

Upstream Vector & Hybrid Engines: vector-db-rag-expert, search-engine-expert, xerj-search-expert
Relational & Graph Storage: database-orm-expert, living-codebase-ast-graph
LLM Reasoning & Pipelines: ai-llm-integration-expert, frontier-ai-models-expert, context-window-engineer
Security & Hallucination Guardrails: ai-safety-governance-expert, prompt-injection-firewall, anti-slop
Lifecycle & Governance: zero-to-prod-orchestrator, session-memory-manager

Description

Expert guide for implementing Knowledge Graph-augmented Retrieval (GraphRAG), solving the fatal weaknesses of vector search: multi-hop reasoning, relationship discovery, and global corpus understanding. Covers Microsoft GraphRAG, Neo4j Text2Cypher, FalkorDB, and hybrid Vector + Graph retrieval pipelines.

Trigger Conditions

Complex multi-hop queries where entities are linked through multiple intermediate nodes.
High hallucination rate using standard vector RAG on interconnected data.
Global corpus queries requiring domain-wide thematic summarization across thousands of documents.
Enterprise knowledge bases containing explicitly structured relational entities (e.g., organizations, code dependencies, regulatory rules).

1. Why GraphRAG Over Pure Vector Search?

Capability	Pure Vector Search (RAG)	GraphRAG (Graph + Vector)
Direct Similarity ("What is X?")	🟢 Fast, accurate	🟢 High accuracy
Multi-Hop Traversal ("How does X affect Z via Y?")	🔴 Blind (returns fragmented chunks)	🟢 Explores interconnected graph edges
Global Corpus Query ("What are the main themes across all documents?")	🔴 Fails (limited to Top-K chunks)	🟢 Hierarchical Community Summaries
Hallucination Rate on Complex Queries	🔴 Moderate to High (context stitching)	🟢 Grounded in explicit knowledge edges

Read the full file on GitHub · 300 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago Changed · +58 lines · -17 tokens per session c7ae5eb6e764
22d ago Changed 5b813c139397
23d ago Changed · +1 lines cf32d7c05e13
26d ago Changed · +40 lines eac37f60bd07
1mo ago Changed 33337c5c4927
1mo ago Changed 959b3d9e2b71
1mo ago Changed ce9ddf65b5b7
1mo ago Changed · -6 lines · +47 tokens per session 3a5c707a9555
1mo ago Changed · +6 lines · -47 tokens per session 07f13b4269ce
2mo ago First seen · 201 lines · 62 tokens per session scan A 8097ae60e367

Subscribe to this mod's changes

graph-rag-knowledge-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (126 stars, last pushed 2d ago), licensed MIT. It adds 45 tokens to every session and 3,273 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-12.

Data and AI LLM RAG and vectors ai-agents antigravity developer-tools prompt-engineering skills

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