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memory-hygiene

Prune old AI agent ideas and archive them after a 7-day TTL.

Updated Aug 23, 2026
Search Tags:#memory management#cron job#agent operations#data pruning#ai hygiene#idea preservation
One-click install
npx skills add https://github.com/jasonqween/orgrimmar-constitution --skill memory-hygiene-jasonqween
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-hygiene
Source: https://github.com/jasonqween/orgrimmar-constitution/tree/main/_archive/kaelthas/skills/memory-hygiene
Command: npx skills add https://github.com/jasonqween/orgrimmar-constitution --skill memory-hygiene-jasonqween

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the need to maintain a clean and efficient working memory for AI agents by regularly pruning old or irrelevant information while ensuring valuable ideas and historical data are preserved.

Core Features & Use Cases

  • Automated Memory Compaction: Regularly cleans up the agent's working memory.
  • Idea Preservation: Ensures that important ideas and change history are not lost during pruning.
  • Scheduled Rotation: Implements a weekly rotation for idea pruning based on a Time-To-Live (TTL) policy.
  • Use Case: When an agent's memory is becoming cluttered, or after a significant operation, this skill can be invoked to tidy up without losing critical insights or past actions.

Quick Start

Run the weekly ideas clean script with a 7-day dry run.

Frequently Asked Questions about memory-hygiene

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

FAQPage Schema
How do I automate AI agent memory management and prune old ideas?▼

You can automate AI agent memory management by running a scheduled cron job that enforces a 7-day TTL, pruning expired ideas from the working memory and moving them to an archive.

What is AI memory hygiene and why is it needed for agent operations?▼

AI memory hygiene is the process of cleaning an agent's working memory by pruning old or irrelevant data. It is needed to maintain operational efficiency and compact memory while preserving essential content.

How do I preserve important ideas when cleaning up working memory?▼

To preserve important ideas during memory cleanup, you can use an automated pruning script that archives expired ideas rather than deleting them, ensuring valuable history and insights are retained.

Can I run a memory data pruning script as a dry run before committing changes?▼

Yes, you can perform a 7-day dry run of the weekly ideas clean script to simulate the pruning and archiving process, allowing you to validate data integrity and format before actual execution.

Does this memory compaction approach work for scheduled weekly rotations?▼

Yes, the memory compaction approach is designed for scheduled weekly rotations, enforcing a Time-To-Live policy to automatically rotate and archive ideas, maintaining a compact agent activity record.

What happens to expired ideas after the 7-day TTL expires?▼

After the 7-day TTL expires, expired ideas are automatically moved to an archive, ensuring the working memory stays compact while maintaining a comprehensive record of historical agent activity.

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