Verified:GitHub

memory-merger

Merge mature domain memory files into instruction files with user approval.

Updated Nov 21, 2025

Search Tags:#prompt engineering #knowledge management #workflow optimization #memory consolidation #domain learning #instruction merging

Ready to run

npx skills add https://github.com/lluppesms/dadabase.demo --skill memory-merger-lluppesms

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-merger
Source: https://github.com/lluppesms/dadabase.demo/tree/main/.github/skills/memory-merger
Command: npx skills add https://github.com/lluppesms/dadabase.demo --skill memory-merger-lluppesms

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines knowledge management by merging valuable insights from memory files into instruction files, ensuring that important lessons are preserved and readily accessible without redundancy.

Core Features & Use Cases

Automated Merging: Consolidates "mature" learnings from domain-specific memory files into corresponding instruction files.
Scope Management: Supports merging into both global (user-level) and workspace-specific instruction files.
Quality Assurance: Adheres to a strict 10/10 quality bar for merged instructions, focusing on zero knowledge loss, minimal redundancy, and maximum scannability.
Use Case: After refining a set of prompts for a specific domain like 'git-workflow', use this skill to integrate those refined prompts into the main instruction set for that domain, making them permanently available.

Quick Start

Use the memory merger skill to merge global memories for the clojure domain.

Frequently Asked Questions about memory-merger

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

FAQPage Schema

How do I consolidate domain memory into instruction files without losing knowledge?▼

To consolidate domain memory into instruction files, this Skill merges mature learnings while preserving knowledge and minimizing redundancy. It parses input, reads both files, analyzes content, and updates instructions upon user approval to ensure zero knowledge loss.

What is the best way to merge refined prompts into a permanent instruction set?▼

The best way to merge refined prompts into a permanent instruction set is using automated memory consolidation. This process integrates mature learnings into your main instruction files, making them permanently available while adhering to a strict 10/10 quality bar for maximum scannability.

Can I merge memory files into both global and workspace-specific instruction sets?▼

Yes, you can merge memory files into both global and workspace-specific instruction sets. This Skill supports scope management for both user-level and workspace-level consolidation, requiring your approval for proposed memories before updating any files.

How does memory consolidation ensure quality when merging domain learnings?▼

Memory consolidation ensures quality by iterating until a 10/10 quality bar is met. It focuses on zero knowledge loss, minimal redundancy, and maximum scannability, proposing updates and refining instructions only after receiving explicit user approval.

Do I need to approve proposed memories before they are merged into my instruction files?▼

Yes, you need to approve proposed memories before they are merged into your instruction files. The Skill operates by analyzing content and proposing updates, requiring explicit user approval before modifying the target instruction files.

When should I use memory consolidation for knowledge management?▼

You should use memory consolidation for knowledge management after refining a set of prompts or learnings for a specific domain. It streamlines workflow optimization by integrating those mature insights into your main instruction set, preventing redundancy and ensuring permanent accessibility.