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memory-retrieve

Retrieve agent-specific context from procedural, feedback, and conceptual memory files.

2|Updated Jun 16, 2026
Search Tags:#project memory#memory retrieval#agent-based systems#contextual information#MALMAS pattern
One-click install
npx skills add https://github.com/PandaWithAPlan/mas --skill memory-retrieve-pandawithaplan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-retrieve
Source: https://github.com/PandaWithAPlan/mas/tree/main/development-team/global-config/skills/memory-retrieve
Command: npx skills add https://github.com/PandaWithAPlan/mas --skill memory-retrieve-pandawithaplan

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of quickly finding the most relevant information from a project's multi-layered memory, enabling agents to make informed decisions without unnecessary context overload.

Core Features & Use Cases

  • Contextual Memory Retrieval: Extracts relevant information from procedural, feedback, and conceptual memory layers.
  • Agent-Specific Memory: Filters memory based on the agent's ID, ensuring that only relevant information is retrieved.
  • Use Case: When an agent receives a task, this Skill helps them quickly understand the current status, limitations, and recommendations from the project's memory.

Quick Start

Invoke the memory-retrieve skill to load the relevant context for your current task.

Frequently Asked Questions about memory-retrieve

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

FAQPage Schema
How do I retrieve relevant context from project memory for a specific agent?▼

To retrieve project memory, invoke the skill to load relevant procedural, feedback, and conceptual information filtered by the specific agent ID, preventing context overload for informed decision-making.

What is the MALMAS pattern in agent-based systems?▼

The MALMAS pattern is an agent-based system architecture that utilizes multi-layered memory. This skill is optimized for MALMAS environments to help agents quickly understand task status, limitations, and recommendations.

How do I filter procedural and feedback memory layers by agent ID?▼

Filtering memory layers by agent ID is handled automatically during the contextual information retrieval process, ensuring agents only receive memory records relevant to their specific tasks and operational scope.

Do I need existing memory files to extract contextual information?▼

Yes, you need existing procedural, feedback, and conceptual memory files. The skill requires access to these pre-established project memory layers to successfully extract and filter relevant context.

What's the best way to prevent context overload in multi-agent systems?▼

The best way to prevent context overload is using agent-specific memory retrieval. By filtering multi-layered memory by agent ID, agents receive only the relevant contextual information needed for their current task.

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