AI Runtime Memory FAQs

Question 1

How does SQL-style querying benefit my AI development workflow?

Accepted Answer

It allows for high-precision information retrieval. You can use commands like WHERE, ORDER BY, and LIMIT to filter project events by date, tags, or type, ensuring the AI assistant accesses exactly the context it needs without exceeding token limits.

Question 2

What are the three layers of the memory architecture?

Accepted Answer

The system mimics human cognition with: 1. Short-term memory (current session context), 2. Long-term memory (cross-project technical knowledge and graphs), and 3. Episodic memory (specific project history and timeline events).

Question 3

What does the AI Runtime Memory skill do?

Accepted Answer

It provides a hierarchical memory architecture (Short-term, Long-term, and Episodic) that enables Claude to store, manage, and recall project history. It uses SQL-style querying to retrieve specific technical decisions, events, and context from previous sessions.

Question 4

When should I use this Claude Code skill?

Accepted Answer

This skill is best for long-term projects where maintaining a 'source of truth' for architectural decisions, meeting notes, and error logs is critical. It is essential when you need the AI to 'remember' context across different days or development phases.

Question 5

How is the memory stored and managed?

Accepted Answer

Memory is stored locally using a structured directory system with Markdown files and YAML front matter. This makes the data human-readable, version-control friendly, and easily accessible via the provided Python CLI tools and shell scripts.

AI Runtime Memory

AI Runtime Memory

Key Features

Use Cases

Key Features

Use Cases