memory-manager

Store and recall ADRs, phase context, and learnings across SDLC sessions.

Updated Jan 15, 2026

Search Tags:#learning #memory #SDLC #ADR #project-context #decision-tracking

Ready to run

npx skills add https://github.com/arbgjr/smart_alarm --skill memory-manager-arbgjr

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-manager
Source: https://github.com/arbgjr/smart_alarm/tree/main/.claude/skills/memory-manager
Command: npx skills add https://github.com/arbgjr/smart_alarm --skill memory-manager-arbgjr

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, and includes scripts (resource) components.

What problem does it solve?

Memory-manager provides persistent project context, ADRs, and learnings to maintain continuity across sessions.

Core Features & Use Cases

Save and recall phase context, decisions (ADRs), and learnings for a SDLC project.
Cross-session artifact storage under .agentic_sdlc with scripts to manage ADRs and learnings.
Supports migration from legacy memory to the new structure.

Quick Start

Create a new project memory space and begin storing ADRs, phase context, and learnings. Then remember to migrate from legacy memory on first use.

Frequently Asked Questions about memory-manager

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

FAQPage Schema

How do I maintain project context across multiple SDLC sessions?▼

To maintain project context across sessions, you can use a persistent storage utility to save phase context, ADRs, and learnings. This ensures continuity by storing artifacts in a structured directory layout for later recall.

What is the best way to track architectural decisions and learnings across development phases?▼

Tracking architectural decisions and learnings is best handled by saving them as ADRs and indexed learnings within a persistent project memory structure. This allows you to recall specific phase context and historical decisions during later development stages.

Do I need Python and PyYAML to manage project memory and ADRs?▼

Yes, you need Python and the PyYAML dependency to operate these project memory and ADR management scripts. The utilities rely on PyYAML to parse and store artifacts within the structured memory layout.

Can I migrate legacy project memory into a new SDLC tracking structure?▼

Yes, you can migrate legacy project memory into the new structure. The memory management utilities support migrating from legacy memory layouts on first use, ensuring your historical ADRs and phase context are preserved in the new directory format.

How does saving ADRs and phase context improve software development continuity?▼

Saving ADRs and phase context improves development continuity by persisting decisions and environmental states across sessions. Indexed learnings and recalled context prevent knowledge loss, allowing future sessions to resume work with full historical awareness.

What limitations exist when using file-based scripts for decision tracking?▼

File-based decision tracking relies on a local directory structure, meaning artifacts are stored as files requiring PyYAML to parse. Without a centralized database, managing cross-project memory requires manual synchronization of these local storage directories.