jamesob
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local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
为什么值得关注
它将知识图谱与智能体编排这两个原本分离的领域融合为一套连贯的实践，其基础来自一门长期的大学课程，并得到 Google DeepMind 和 MIT 最新研究的验证。
适合谁使用
- 构建智能体系统的 AI 工程师
- 寻求现代模式的知识图谱从业者
- AI 与数据工程领域的学生和研究人员
- 使用兼容技能文件的智能体框架（如 Claude Code）的开发者
典型使用场景
- 按照 9 阶段流水线从文档构建知识图谱
- 利用自己的项目作为示例交互式地教授图工程
- 设计包含扇形拆分、独立验证器和人机门控的任务图
- 评估智能体团队何时适合拆分工作而非顺序执行
项目优势
- 包含即用型技能文件，可向兼容的智能体教授完整的流水线
- 基于成熟的研究生课程（4.4K 星）并得到智能体扩展经验研究的支持
- 清晰的 9 阶段流水线，每个阶段都有验证，避免“三元组堆叠”陷阱
- 提供即用提示块，既可用于教学也可用于单一用途工具
使用前须知
- 需要兼容技能文件的智能体插件（目前仅演示了 Claude Code）
- 原始课程材料为中文；本仓库提供英文精简版，但未重新分发原始 PDF
- 任务图规则来自新兴研究，可能随着领域发展而演进
README 快速开始
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, 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. |
Install (two commands)
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 exa
项目描述
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 workflows
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kill-ai-slop
Kill AI Slop is a multilingual field guide and agent skill that catalogues 33 common AI-generated design tells with interactive before-after demos, and provides a tool to scan and fix web projects.