agent-memory

Store and recall project context across development sessions using markdown files.

1|Updated Jan 14, 2026
One-click install
npx skills add https://github.com/DecentralizedGeo/stac-manager --skill agent-memory-decentralizedgeo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-memory
Source: https://github.com/DecentralizedGeo/stac-manager/tree/main/.github/skills/agent-memory
Command: npx skills add https://github.com/DecentralizedGeo/stac-manager --skill agent-memory-decentralizedgeo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams preserve decisions, architectural notes, and user preferences across development sessions, preventing context loss.

Core Features & Use Cases

  • Centralized long-term memory for episodic decisions, architectural maps, and preferences.
  • Stepwise memory lifecycle: recall, consolidation, and temporal chaining.
  • Initialization guidance and safe update rules to avoid drift and noise.

Quick Start

Initialize the memory store by creating .github/memory/ with episodic.md, semantic.md, procedural.md, and preferences.md, then begin logging context updates after milestones.

Frequently Asked Questions about agent-memory

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

FAQPage Schema
How do I maintain long-term project context across development sessions?

You maintain long-term project context by storing decisions, architecture notes, and user preferences in a structured memory directory. This prevents context loss by consolidating episodic, semantic, procedural, and preference data for recall during planning and coding.

What files do I need to set up a persistent memory store for software architecture?

Setting up a persistent memory store requires creating a .github/memory/ directory containing four files: episodic.md, semantic.md, procedural.md, and preferences.md. These files capture decisions, architectural maps, and user preferences.

When should I update my project memory to avoid context drift?

Update project memory incrementally after reaching development milestones. Following safe update rules and logging context at these checkpoints prevents drift and noise, ensuring stored decisions and architecture notes remain accurate.

Does this memory lifecycle approach support temporal chaining for past decisions?

Yes, the memory lifecycle supports temporal chaining. It operates through a stepwise process of recall, consolidation, and temporal chaining to connect past episodic decisions with current architectural context during development.

What is the best way to preserve user preferences and procedural knowledge in a codebase?

The best way to preserve preferences and procedural knowledge is logging them into dedicated markdown files within a .github/memory/ directory. This centralizes long-term memory, capturing both user preferences and procedural steps.

Can I use this memory approach without initializing a .github/memory/ directory?

No, initializing the .github/memory/ directory is required. This directory houses the episodic, semantic, procedural, and preferences markdown files needed to store and recall project context safely.