codejunkie99/graph-engineering

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記録された最新のソース活動: 2026年7月23日 14:10

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# Graph Engineering **The discipline of designing the structures AI 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. Ontology → extraction → fusion → serving. 2. **Task graphs** — how agents *work*. Nodes are jobs, edges are execution dependencies. Parallel fan-out, separate verifiers, the stop rule, the human gate. Prompt engineers steered the model's words. Loop engineers steered its iterations. Graph engineers steer its **topology**. This repo turns Southeast University's graduate Knowledge Graph course ([npubird/KnowledgeGraphCourse](https://github.com/npubird/KnowledgeGraphCourse), 4.4K★, taught in Chinese since 2019) — plus the modern agent-orchestration research behind task graphs — into things you can actually use today. ## What's inside | Path | What it is | |---|---| | [`graph-engineering/`](graph-engineering/) | **The skill.** Hand it to your agent (Claude Code / any skill-compatible harness) — it learns the full 9-stage knowledge-graph pipeline, the task-graph patterns, and a teaching mode that explains every stage with diagrams drawn from *your* domain. | | [`graph-engineering/references/`](graph-engineering/references/) | The distilled course: [curriculum map](graph-engineering/references/curriculum.md) (translated, with links to the original Chinese decks), [modeling](graph-engineering/references/modeling.md), [extraction](graph-engineering/references/extraction.md), [fusion + GraphRAG](graph-engineering/references/fusion-and-llm.md), [task graphs](graph-engineering/references/task-graphs.md). | | [`WORKFLOWS.md`](WORKFLOWS.md) | Nine paste-ready prompt blocks — a `/kg-tutor` that teaches you the whole course interactively, plus eight single-purpose tools (`/kg-scope` → `/kg-rag`) that chain into a full build. | | [`dist/graph-engineering.skill`](dist/) | The packaged skill file. | ## Install (two commands) ```bash git clone https://github.com/codejunkie99/graph-engineering.git cp -r graph-engineering/graph-engineering ~/.claude/skills/ ``` Then ask your agent to *build* ("build a knowledge graph from my docs") or to *teach* ("teach me graph engineering") — teaching mode walks the pipeline stage by stage with worked examples and generated diagrams, using your own project as the running example. ## The 9-stage pipeline ```mermaid flowchart LR A[1 scope] --> B[2 representation] --> C[3 ontology] --> D[4 entities] D --> E[5 relations] --> F[6 events] --> G[7 quality gate] G --> H[8 fusion] --> I[9 serve to LLMs] ``` Model the domain **before** extracting. Fuse **before** storing. Verify at every stage. A knowledge graph is a product with a schema, not a pile of triples. ## The task-graph rules (the other half) - Delete **fake edges**: an arrow is real only when work flows through it. - The **diamond**: split → parallel workers → *separate* verifier contexts → one owned merge. - The **stop rule** (Google DeepMind × MIT, 180 configurations): teams win ~80% on work that splits; every team configuration loses on sequential work. The shape of the work decides. - The **human gate**: your approval sits exactly where a mistake is expensive to undo. Details: [references/task-graphs.md](graph-engineering/references/task-graphs.md) ## Credits The knowledge-graph half is an independent English distillation of 东南大学《知识图谱》研究生课程 (Southeast University's graduate Knowledge Graph course), Prof. Peng Wang — [npubird/KnowledgeGraphCourse](https://github.com/npubird/KnowledgeGraphCourse). All original lecture PDFs are in Chinese and remain in the original repo; none are redistributed here. Task-graph material draws on Google DeepMind × MIT's ["Towards a Science of Scaling Agent Systems"](https://research.google/blog/towards-a-science-of-scaling-agent-systems-when-and-why-agent-systems-work/) and Anthropic's published multi-agent engineering work. MIT licensed. Built by [@Av1dlive](https://x.com/Av1dlive).

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graph-engineering

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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,…

原文の言語: 英語

2026-07-23

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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/codejunkie99/graph-engineering.