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graph-engineering

Teaches an agent graph engineering — both halves. Knowledge graphs (ontology design, entity/relation/event extraction, fusion, GraphRAG/memory serving; distilled and translated from Southeast University's graduate Knowledge Graph course, npubird/KnowledgeGraphCourse, 4.4K stars) and task graphs (agent orchestration — parallel fan-out, verifier separation, the stop rule, human gates). Use when asked to build a knowledge graph, extract entities/relations from text, design an ontology, dedupe/merge entities, add graph memory or GraphRAG to an agent, orchestrate multi-agent workflows as a graph, or LEARN graph engineering — in teaching mode the agent explains each stage with worked examples and generates visual diagram artifacts.

graph-engineering/SKILL.md

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Jul 23, 2026
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# Graph Engineering

Graph engineering is the discipline of designing the structures agents work through — not the
prompts. It has two halves:

1. **Knowledge graphs** — what agents remember. Nodes are entities and facts, edges are
   relationships with time and provenance. This file's 9-stage pipeline covers it, distilled
   from Southeast University's graduate KG course
   (https://github.com/npubird/KnowledgeGraphCourse, Prof. Peng Wang), translated to English
   and adapted for LLM-era agents.
2. **Task graphs** — how agents work. Nodes are jobs, edges are execution dependencies:
   parallel fan-out, separate verifier contexts, the stop rule, the human gate.
   Read [references/task-graphs.md](references/task-graphs.md) when the request is about
   orchestrating agents rather than building memory.