ontology

Manage structured agent memory through a typed knowledge graph.

2|Updated Mar 10, 2026

Search Tags:#knowledge graph #ontology #graph database #entity relationship #skill integration #structured memory

Ready to run

npx skills add https://github.com/caoronglin/copaw-skills --skill ontology-caoronglin

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ontology
Source: https://github.com/caoronglin/copaw-skills/tree/main/skills/ontology
Command: npx skills add https://github.com/caoronglin/copaw-skills --skill ontology-caoronglin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill tackles the challenge of managing complex, interconnected information by providing a structured, typed knowledge graph for agent memory and skill composability.

Core Features & Use Cases

Structured Memory: Represent entities (like people, projects, tasks) and their relationships in a graph format.
Cross-Skill Data Sharing: Enables different skills to access and update a shared, consistent knowledge base.
Use Case: Imagine you're managing a project. You can use this Skill to "remember" that "Task X belongs to Project Y" and "Person Z is assigned to Task X". Later, you can ask "What are all the tasks for Project Y?" or "Who is working on Task X?".

Quick Start

Use the ontology skill to remember that the project 'CoPaw' has the status 'active'.

Frequently Asked Questions about ontology

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I manage structured agent memory for complex project data?▼

To manage structured agent memory, you can use a typed knowledge graph to represent entities like people, projects, and tasks. This approach allows an AI agent to store and retrieve interconnected project information consistently.

How does a typed knowledge graph work for cross-skill data sharing?▼

A typed knowledge graph enables cross-skill data sharing by allowing different skills to access and update a shared knowledge base. This ensures consistent state management and entity relationships across multiple automated workflows.

How do I create and query entity relationships in a knowledge graph?▼

You create and query entity relationships in a knowledge graph by utilizing a Python script for graph operations and schema validation. This allows you to define typed connections, such as assigning a person to a task, and later query those specific relationships.

What is the best way to structure interconnected information for AI state management?▼

The best way to structure interconnected information for AI state management is to represent entities and their relationships in a graph format. This provides a typed schema that validates data and enables complex relational queries for agent memory.

Do I need a graph database to use a typed knowledge graph for agent memory?▼

No, you do not need an external graph database to use this typed knowledge graph. It utilizes a built-in Python script for graph operations and schema validation to manage structured memory directly.

What are the limitations of using a Python script for knowledge graph schema validation?▼

Using a Python script for knowledge graph schema validation means operations are bound by script execution constraints and local memory limits. It is designed for agent memory and skill composability rather than serving as a distributed graph database.