memory-graph

Organize research data into a semantic knowledge graph with search and visualization.

Updated Apr 16, 2026

Search Tags:#data visualization #knowledge graph #semantic search #research findings #research organization

Ready to run

npx skills add https://github.com/yu13130122297/helloCat --skill memory-graph-yu13130122297

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-graph
Source: https://github.com/yu13130122297/helloCat/tree/main/src/skills/memory-graph
Command: npx skills add https://github.com/yu13130122297/helloCat --skill memory-graph-yu13130122297

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured way to organize and access research findings by utilizing a knowledge graph, enhancing research efficiency and comprehension.

Core Features & Use Cases

Automatic Knowledge Recording: Distills key findings into a knowledge graph after completing related quests.
Semantic Search: Enables natural language search of research history, linking concepts, experiments, and results.
Graph Visualization: Offers visual representations of relationships between research nodes.
For example, a researcher can visualize how experiments relate to hypotheses, or find all papers citing a specific concept.

Quick Start

Connect your research workflow to the knowledge graph and perform semantic searches to explore related work and findings.

Frequently Asked Questions about memory-graph

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

FAQPage Schema

How does a semantic knowledge graph help with research organization?▼

A semantic knowledge graph for research organization captures relationships among concepts, experiments, and findings to structure data. It enables natural language search and visual mapping of how specific research nodes interconnect.

What is the best way to visually map relationships between research findings?▼

Visually mapping research findings is best achieved through graph visualization features that render links between research nodes. This allows you to trace how experiments relate to hypotheses or find papers citing a specific concept.

How do I use natural language search to find related research papers?▼

To find related research papers, use semantic search to query your research history in natural language. This links concepts, experiments, and results to retrieve relevant findings without requiring exact keyword matches.

Can I automatically record research findings into a knowledge graph?▼

You can automatically record research findings into a knowledge graph by completing related quests. The system distills key findings into structured nodes and relationships for comprehensive research insights.

Do I need to enable knowledge graph features in configuration for semantic search?▼

You must enable knowledge graph features in configuration to utilize semantic search. Activating these features allows the system to track multiple node types and relationships for detailed research insights.

Are there limitations when tracking relationships across multiple research projects?▼

A limitation when tracking relationships across multiple research projects is the reliance on enabling knowledge graph features in configuration. Comprehensive relationship tracking requires structured knowledge management to maintain accurate semantic links.