memory-refresh

Archive session files and convert recurring knowledge into summaries.

1|Updated Mar 12, 2026

Search Tags:#memory management #ai optimization #context window #knowledge archiving #session summarization #memory index

Ready to run

npx skills add https://github.com/bketelsen/truenas-mcp --skill memory-refresh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-refresh
Source: https://github.com/bketelsen/truenas-mcp/tree/main/.gaai/core/skills/cross/memory-refresh
Command: npx skills add https://github.com/bketelsen/truenas-mcp --skill memory-refresh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing and optimizing an AI's growing memory, ensuring it remains efficient, precise, and within manageable context limits without losing valuable information.

Core Features & Use Cases

Memory Maintenance: Archives old session files and converts recurring knowledge into concise summaries.
Context Window Management: Frees up valuable context window space by archiving and summarizing, crucial for maintaining AI performance during long tasks or projects.
Use Case: After completing a major development sprint, activate this skill to archive all session logs, summarize key decisions made during the sprint into a new summary file, and update the memory index, ensuring the AI's knowledge base is clean and up-to-date for the next phase.

Quick Start

Run the memory-refresh skill to clean up and optimize the AI's memory.

Frequently Asked Questions about memory-refresh

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

FAQPage Schema

How do I optimize AI context window pressure during long coding sessions?▼

To optimize AI context window pressure, you need to archive old session files and convert recurring knowledge into concise summaries. This process frees up valuable context space by applying category-aware compaction rules to manage summary lifecycles efficiently.

What is the best way to archive AI session files after a development sprint?▼

The best way to archive AI session files after a development sprint is to run periodic memory maintenance. This archives the session logs, summarizes key decisions into a new file, and updates the memory index to reflect the current active memory status.

How does session summarization work for AI memory management?▼

Session summarization for AI memory management works by converting recurring knowledge from active sessions into concise summaries. It applies category-aware compaction rules to ensure the AI retains critical information without exceeding manageable context limits.

When should I perform memory maintenance on my AI coding agent?▼

You should perform memory maintenance on your AI coding agent when you notice context window pressure or after completing major project milestones. Archiving session files and summarizing key decisions ensures the knowledge base remains clean and up-to-date for the next phase.

Does memory optimization delete active session knowledge for AI agents?▼

Memory optimization does not delete active session knowledge. Instead, it archives old session files and converts recurring knowledge into concise summaries, updating the memory index to preserve valuable information while reducing context window pressure.