ralph-memories

Name: ralph-memories
Availability: InStock
Author: Happyverse-Team

Store and retrieve project memories in .agent/memories.md via Ralph CLI commands.

Updated Jan 29, 2026

Search Tags:#architecture #context #codebase #learning #patterns #memory

Ready to run

npx skills add https://github.com/Happyverse-Team/video-content-generator --skill ralph-memories

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ralph-memories
Source: https://github.com/Happyverse-Team/video-content-generator/tree/main/.claude/skills/ralph-memories
Command: npx skills add https://github.com/Happyverse-Team/video-content-generator --skill ralph-memories

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Solves the problem of losing context across sessions by storing memories in a centralized file, .agent/memories.md.

Core Features & Use Cases

Persistent memories: store patterns, architectural decisions, and project context to reference later.
Cross-session learning: surface relevant insights when planning features or debugging.
Use Case: When exploring a large codebase, memory helps recall why a particular directory structure was chosen and what conventions exist.

Quick Start

Create a memory entry: ralph tools memory add "Respond to new feature request: adopt memory persistence for patterns" -t context --tags memory,pattern
List memories: ralph tools memory list
Prime context for next tasks: ralph tools memory prime --budget 2000

Frequently Asked Questions about ralph-memories

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

FAQPage Schema

How do I persist project context and architectural decisions across coding sessions?▼

Persist project context across sessions by storing architectural decisions and patterns in a centralized memory file. This ensures insights are saved for reference during future feature planning or debugging tasks.

What is the best way to save codebase patterns for reuse in future development tasks?▼

Saving codebase patterns is best handled by storing memories in a centralized markdown file. You can create tagged memory entries to surface relevant architectural context and conventions when needed.

How do I use CLI commands to manage and tag project memories?▼

Manage project memories using CLI commands to add entries with specific types and tags, list stored items, and prime context for upcoming tasks within a defined token budget.

Can I recall architectural context and directory structure conventions from previous sessions?▼

Recalling architectural context from previous sessions is possible by storing insights in a centralized memory file. This helps retrieve why specific directory structures and conventions were chosen.

Does cross-session learning work for debugging recurring problems in a large codebase?▼

Cross-session learning works for debugging by preserving project context and recurring problem solutions in a centralized file. This stored memory surfaces relevant insights during future debugging tasks.