memory-management

Decode internal company jargon using tiered memory in CLAUDE.md and memory/.

145|36|Updated Feb 26, 2026

Search Tags:#collaboration #context #memory #acronyms #jargon #decoding

Ready to run

npx skills add https://github.com/w95/awesome-claude-corporate-skills --skill memory-management-w95

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-management
Source: https://github.com/w95/awesome-claude-corporate-skills/tree/main/07-operations/memory-management
Command: npx skills add https://github.com/w95/awesome-claude-corporate-skills --skill memory-management-w95

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables Claude to understand and use internal company jargon, acronyms, nicknames, and project codenames, making it function like a true colleague rather than an outsider.

Core Features & Use Cases

Personalized Decoding: Translates shorthand like "PSR" or "Todd" into full terms and names.
Tiered Memory System: Uses a lean CLAUDE.md for frequent terms and a comprehensive memory/ directory for deep knowledge.
Use Case: When a user says "ask todd about the PSR for phoenix," Claude can correctly identify "Todd" as Todd Martinez (Finance Lead), "PSR" as Pipeline Status Report, and "Phoenix" as the DB migration project, enabling a precise response.

Quick Start

Use the memory management skill to decode the phrase "ask todd about the PSR for oracle".

Frequently Asked Questions about memory-management

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

FAQPage Schema

How do I teach AI to understand internal company jargon and acronyms?▼

You decode internal company jargon by implementing a tiered memory system that maps shorthand, acronyms, and project codenames to their full meanings. This enables the AI to translate terms like "PSR" into "Pipeline Status Report," functioning like a true colleague.

How does a tiered memory system work for AI context management?▼

A tiered memory system manages context by using a lean working memory file for frequent terms and a deep memory directory for comprehensive knowledge. This split supports dynamic learning and recall of contextual information during AI-driven collaboration.

Can I use a working memory file to store frequent team shorthand for AI?▼

Yes, you can store frequent team shorthand in a lean working memory file. This file acts as the primary reference for the AI to quickly recall high-traffic acronyms and nicknames without scanning deeper memory directories.

What is the best way to decode internal nicknames and project codenames for AI collaboration?▼

The best way to decode nicknames and project codenames is using a personalized decoding system that maps shorthand like "Todd" to Todd Martinez and "Phoenix" to the DB migration project. This enables precise, context-aware responses.

When should I move decoded terms from working memory to a deep memory directory?▼

You should move decoded terms to a deep memory directory when they require comprehensive context beyond simple shorthand mapping. The deep memory directory handles complex contextual information, keeping the working memory lean for frequent terms.