skills.rest
marktantongco
marktantongcoCOMMUNITY
Explore all skills by this author →
GitHub

persistent-memory

Store and retrieve structured memory entries across AI agent sessions.

Updated May 3, 2026
Search Tags:#AI architecture#memory management#persistence#memory architecture#AI context retention
One-click install
npx skills add https://github.com/marktantongco/remotion-video-pro --skill persistent-memory-marktantongco
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: persistent-memory
Source: https://github.com/marktantongco/remotion-video-pro/tree/main/skills/persistent-memory
Command: npx skills add https://github.com/marktantongco/remotion-video-pro --skill persistent-memory-marktantongco

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of retaining information across AI agent sessions, enabling continuity in long-running projects and multi-turn workflows.

Core Features & Use Cases

  • Cross-Session Memory Persistence: Allows AI agents to remember and maintain context over extended periods.
  • Memory Architecture: Defines architecture with three tiers (short-term, working, and long-term memory).
  • Privacy Controls: Ensures sensitive data is protected with privacy enforcement and control features.
  • Memory Types: Categorizes memory into user preferences, project context, learned patterns, and decisions log.
  • Integration Points: Integrates with agent lifecycle for initialization, task completion, and session end.

Quick Start

Set up the persistent-memory skill to ensure that your AI agent's memory persists across sessions.

Frequently Asked Questions about persistent-memory

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

FAQPage Schema
How do I maintain AI context retention across multiple sessions?▼

To maintain AI context retention across sessions, you need a cross-session memory persistence solution that structures storage and retrieval for long-term agent memory. This ensures continuity in long-running projects and multi-turn workflows.

What is the best memory architecture for long-running AI agents?▼

An effective memory architecture for AI agents uses three tiers: short-term, working, and long-term memory. This structure categorizes data into user preferences, project context, learned patterns, and a decisions log.

How do I implement semantic search and relevance scoring for AI memory retrieval?▼

Implementing semantic search for AI memory retrieval requires a pipeline that scores memory relevance to the current context. This pipeline filters stored memory entries to surface the most applicable agent context during task execution.

Can I enforce privacy controls on sensitive data within AI agent memory?▼

Yes, you can enforce privacy controls on sensitive data within AI agent memory. Structured memory entries require privacy enforcement to protect user preferences and project context throughout the memory lifecycle.

When do I need to integrate memory lifecycle management with my AI agent?▼

You need to integrate memory lifecycle management with your AI agent at initialization, task completion, and session end. These integration points ensure memory persistence and context continuity across extended operational periods.

Related Skills

Explore More Software Engineering Skills→
latestaiagentslatestaiagents
5

agent-memory-systems

Implement working, short-term, and long-term memory modules for AI agents.

Official
Advanced
AcosmiAcosmi
6

memory-search

Search long-term memory by keyword with semantic retrieval and relevance scores.

Community
Intermediate
parisgroup-aiparisgroup-ai
1

session-close

Persist session learnings by updating CLAUDE.md and writing a session brief.

Official
Advanced
AJBcodingAJBcoding
4

moai-cc-memory

Manage session memory and persist context across Claude Code sessions.

Community
Advanced