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jstoup111
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memory

Store and retrieve categorized memory entries across AI agent sessions.

5|1|Updated Mar 30, 2026
Search Tags:#session management#memory management#context retention#AI recall#knowledge persistence
One-click install
npx skills add https://github.com/jstoup111/ai-conductor --skill memory-jstoup111
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/jstoup111/ai-conductor/tree/main/skills/memory
Command: npx skills add https://github.com/jstoup111/ai-conductor --skill memory-jstoup111

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill ensures the agent recalls relevant context and persists significant learnings, preventing repetition and knowledge loss.

Core Features & Use Cases

  • Persistent Memory: Stores and retrieves categorized memory entries across sessions.
  • Recall Protocol: Starts each session with a recall process to refresh context.
  • Persist Protocol: Persists significant decisions and learnings during work.
  • Staleness Detection: Flags potentially outdated memory entries for review.
  • Use Case: Ideal for software development, where agents need to recall past decisions and patterns to avoid repeating mistakes.

Quick Start

At the start of a session, use the 'memory' skill to recall relevant context.

Frequently Asked Questions about memory

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

FAQPage Schema
How does AI context retention work across multiple sessions?▼

Knowledge persistence for AI agents is managed through a structured persist protocol that saves significant decisions and learnings into categorized memory entries. This file-based storage supports structured data retrieval to maintain context across sessions.

What is the best way to prevent an AI agent from repeating past development mistakes?▼

To persist AI learnings across sessions, you trigger a persist protocol during work to save significant decisions into file-based memory storage. This enables structured data retrieval and immediate knowledge persistence for future recall.

How do I recall past decisions when starting a new AI session?▼

Session management for AI requires file-based memory storage and structured data retrieval to persist categorized context. It is ideal for software development environments where agents must track significant learnings and detect staleness in outdated entries.

How do I detect outdated context in AI memory management?▼

You detect outdated context in AI memory management using built-in staleness detection. This feature automatically flags potentially outdated memory entries for review, ensuring the recalled context remains accurate and relevant for current tasks.

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