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frostaura
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memory

Store and recall agent learnings using MCP memory tools with JSON fallback.

32|26|Updated Jun 4, 2025
Search Tags:#recall#mcp#memory#persistence#knowledge-management#agent-memory
One-click install
npx skills add https://github.com/frostaura/ai.toolkit.gaia --skill memory-frostaura
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/frostaura/ai.toolkit.gaia/tree/main/.github/skills/memory
Command: npx skills add https://github.com/frostaura/ai.toolkit.gaia --skill memory-frostaura

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Memory management for AI agents: persisting learnings and enabling fast recall across sessions, with a default to memory tools and a JSON fallback when tools are unavailable.

Core Features & Use Cases

  • Structured memory with categories (fix, pattern, config, decision, warning, context) and recall/remember operations.
  • Default to memory tools for memory life-cycle: recall at task start, store significant learnings, and query past work for faster problem solving.
  • Fallback to docs/memories.json with strict JSON validity when memory tools are unavailable to ensure continuity.

Quick Start

Ask the AI to begin a memory session, recall related past learnings at the start of a task, and store a significant takeaway using the recommended categories.

Frequently Asked Questions about memory

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

FAQPage Schema
How do I persist and recall knowledge across agent sessions?▼

To persist and recall knowledge across agent sessions, store significant learnings using predefined categories like fix, pattern, and decision, then query past work at the start of a new task for faster problem solving.

What is the best way to manage agent memory when MCP tools are unavailable?▼

When MCP tools are unavailable, agent memory management falls back to docs/memories.json, ensuring continuity through strict JSON validity, atomic edits, and validation for persistent knowledge storage.

How do I structure agent memory for fast recall during development tasks?▼

Structure agent memory for fast recall by organizing learnings into predefined categories such as fix, pattern, config, decision, warning, and context, which streamlines querying during planning and development.

Can I use this memory system for session-scoped knowledge without external dependencies?▼

Yes, you can manage session-scoped knowledge without external dependencies by defaulting to MCP tools, or utilizing the atomic JSON file fallback to maintain strict data validity when tools are missing.

Why does my agent memory recall fail when using a JSON fallback?▼

Agent memory recall using the JSON fallback fails if strict JSON validity is not maintained, which is why the system enforces atomic edits and validation when writing to docs/memories.json.

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