codejunkie99

Graph Engineering — Data skill for Claude Code

Data community

Graph engineering for AI agents: the 9-stage knowledge-graph pipeline (translated from SEU's graduate course) + task-graph orchestration patterns, as a Claude skill with teaching mode and paste-ready.

How to install Graph Engineering

This entry records only its repository, not the path inside it, so there is no exact command to give. Open codejunkie99/graph-engineering and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Graph Engineering does

Alternatives in Data

Age — Apache AGE (Graph Database) Reference 573 ★
Team Tasks — Multi-agent pipeline coordination: Linear, DAG, and Debate modes for AI agent orchestration 446 ★
Wiki All — Run the full llmwiki pipeline end-to-end: build → graph → export all → lint 375 ★

README

Graph Engineering

**The discipline of designing the structures AI agents work through — not the prompts.**

It has two halves:

Knowledge graphs — what agents remember. Nodes are entities and facts, edges are relationships with time and provenance. Ontology → extraction → fusion → serving.
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/`	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/`	The distilled course: curriculum map (translated, with links to the original Chinese decks), modeling, extraction, fusion + GraphRAG, task graphs.
`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`	The packaged skill file.