Does this skill support specific AI frameworks?

Yes, it includes specialized documentation and approaches for frameworks like CrewAI, along with general technical stack recommendations for various agent architectures.

When should I use GraphRAG over standard Vector RAG?

You should use GraphRAG when your AI agent needs to understand complex, non-linear relationships between entities that simple semantic similarity search cannot effectively capture.

What are Temporal Knowledge Graphs in AI memory?

Temporal Knowledge Graphs are advanced memory structures that track how information and relationships change over time, allowing agents to maintain a chronological history of events and states.

What is the architecting-memory skill?

It is a specialized Claude Code skill that provides architectural patterns and implementation guides for building persistent, sophisticated memory systems for AI agents.

Architecting Memory

Name: Architecting Memory
Author: Git-Fg

byGit-Fg

•

データサイエンスとML

Designs and implements evolutionary persistent memory architectures for AI agent systems using RAG and Knowledge Graphs.

概要

This skill provides a comprehensive framework for building sophisticated memory systems for AI agents, moving beyond basic semantic search. It guides developers through a structured progression from Vector RAG for semantic retrieval to GraphRAG for complex relational understanding, and finally to Temporal Knowledge Graphs for time-series data management. By offering decision matrices and specific design patterns, it helps architects select the optimal tech stack and implementation strategy to ensure agents maintain long-term context, deep relational awareness, and historical accuracy.

主な機能

Step-by-step progression from Vector RAG to Temporal Knowledge Graphs
Specific implementation patterns for relational and time-series data
Tech stack recommendations for building persistent AI memory layers
Integration guides for CrewAI and specialized agent memory models
1 GitHub stars
Architecture decision matrix for selecting optimal memory models

ユースケース

Implementing a relational knowledge graph to improve RAG accuracy in complex domains
Designing a long-term memory system for an autonomous coding agent
Building time-aware agent memory to track state changes over long-running tasks

概要

主な機能

Step-by-step progression from Vector RAG to Temporal Knowledge Graphs
Specific implementation patterns for relational and time-series data
Tech stack recommendations for building persistent AI memory layers
Integration guides for CrewAI and specialized agent memory models
1 GitHub stars
Architecture decision matrix for selecting optimal memory models

ユースケース

Implementing a relational knowledge graph to improve RAG accuracy in complex domains
Designing a long-term memory system for an autonomous coding agent
Building time-aware agent memory to track state changes over long-running tasks