Verified:GitHub

memory

Recall learned patterns and transfer knowledge across clients.

3|Updated Feb 17, 2026

Search Tags:#knowledge management #AI learning #knowledge base #documentation audit #pattern recall #cross-project learning

Ready to run

npx skills add https://github.com/martineserios/thebrana --skill memory-martineserios

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/martineserios/thebrana/tree/main/system/skills/memory
Command: npx skills add https://github.com/martineserios/thebrana --skill memory-martineserios

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a unified interface to manage the AI's learned knowledge, ensuring it remains accurate, accessible, and useful across projects.

Core Features & Use Cases

Pattern Recall: Search and retrieve learned patterns based on queries or project context.
Cross-Client Pollination: Transfer useful patterns between different projects or clients.
Knowledge Auditing: Review the health and consistency of the knowledge base, identifying outdated or contradictory information.
Use Case: After encountering a recurring bug, use this Skill to recall past solutions and apply them to the current problem, or to check if a similar issue has been resolved in another project.

Quick Start

Recall patterns related to user authentication in the current project.

Frequently Asked Questions about memory

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

FAQPage Schema

How do I recall learned patterns from past projects to solve a current bug?▼

To recall learned patterns, query the AI knowledge base using specific project context or issue descriptions. The system retrieves previously successful solutions, allowing you to apply past resolutions directly to current recurring problems.

Can I transfer AI knowledge across different clients and projects?▼

Cross-client pollination allows you to transfer useful AI knowledge and learned patterns between different projects or clients. This ensures successful solutions are shared and applied across your entire software engineering portfolio.

What is the best way to audit documentation for contradictions in a knowledge base?▼

The best way to audit documentation for contradictions is to run a knowledge health review. This process identifies outdated or contradictory information within the knowledge base, ensuring your documentation remains accurate and consistent.

How does cross-project learning work for recurring software engineering issues?▼

Cross-project learning works by extracting and storing resolutions as learned patterns. When a similar issue arises, the knowledge system recalls these patterns, enabling the AI to apply previously successful cross-project solutions automatically.

Do I need any specific dependencies to manage my AI's knowledge base?▼

No specific dependencies are required to manage your AI's knowledge base. The system operates independently using its internal scripts and references to handle pattern recall, cross-pollination, and documentation audits.