Can I search my project history using SQL commands?

Yes, the system provides a CLI tool that supports SQL-style syntax to filter events by date, tags, type, or specific metadata conditions.

What makes this memory system 'layered'?

It mirrors human cognition by separating immediate session context (short-term), cross-project technical knowledge (long-term), and specific project events (episodic).

How is memory data actually stored?

Events are stored as individual Markdown files with YAML front matter within a structured directory hierarchy, ensuring the data is portable and human-readable.

Does this skill help with large-scale project context?

Absolutely. It solves the context window limitation by allowing the AI to selectively retrieve relevant historical data rather than loading the entire project history at once.

AI Runtime Layered Memory System

Name: AI Runtime Layered Memory System
Author: Dwsy

byDwsy

Productivity & Workflow

Empowers Claude with a multi-layered cognitive memory system for project history tracking, SQL-style event querying, and intelligent knowledge retrieval.

About

The AI Runtime Layered Memory System simulates human cognitive processes by organizing information into short-term, long-term, and episodic memory layers. It allows developers and AI agents to maintain persistent context across sessions through a structured YAML-based event system. By supporting SQL-style queries for technical decisions, project milestones, and historical events, it ensures that critical project context is never lost, facilitating better decision-making and seamless long-term project maintenance.

Key Features

Three-tier memory architecture including Short-term, Long-term, and Episodic layers
Automated timeline management and intelligence retrieval for project history
SQL-style event querying with support for WHERE, ORDER BY, and LIMIT clauses
Structured YAML-based event tracking with tags, timestamps, and types
Integrated CLI tools for searching, statistics, and cognitive workflow commands
0 GitHub stars

Use Cases

Tracking architectural decisions and technical debt to maintain long-term project integrity
Querying historical project events to understand the context behind specific code changes
Building a persistent knowledge base for team-wide technical documentation and knowledge transfer

About

Key Features

Three-tier memory architecture including Short-term, Long-term, and Episodic layers
Automated timeline management and intelligence retrieval for project history
SQL-style event querying with support for WHERE, ORDER BY, and LIMIT clauses
Structured YAML-based event tracking with tags, timestamps, and types
Integrated CLI tools for searching, statistics, and cognitive workflow commands
0 GitHub stars

Use Cases

Tracking architectural decisions and technical debt to maintain long-term project integrity
Querying historical project events to understand the context behind specific code changes
Building a persistent knowledge base for team-wide technical documentation and knowledge transfer