elite-longterm-memory

Name: elite-longterm-memory
Availability: InStock
Author: sky770825

Unify write-ahead logging, vector search, and git-notes into persistent memory.

Updated Feb 25, 2026

Search Tags:#memory #ai-agent #vector-search #git-notes #fact-extraction #wal-protocol #long-term-context

Ready to run

npx skills add https://github.com/sky770825/NEUXA- --skill elite-longterm-memory-sky770825

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: elite-longterm-memory
Source: https://github.com/sky770825/NEUXA-/tree/main/quarantine/skills-slim-20260213/elite-longterm-memory
Command: npx skills add https://github.com/sky770825/NEUXA- --skill elite-longterm-memory-sky770825

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI agents frequently lose critical context across sessions, forget user preferences and past decisions, and repeat mistakes due to lack of durable, accessible long-term memory storage.

Core Features & Use Cases

Multi-layer memory architecture: Combines write-ahead logging (WAL) for compaction-resistant hot context, LanceDB vector search for semantic recall, git-notes for permanent structured knowledge graphs, and human-readable curated archives.
Automated context retention: Optional Mem0 integration auto-extracts facts from conversations to cut token usage by 80%, while cloud backup via SuperMemory enables cross-device sync.
Use Case: Developers using Claude, Cursor, or GPT-powered coding agents can retain project decisions, user preferences, and past lessons across sessions without manual context re-sharing.

Quick Start

Use the elite-longterm-memory skill to initialize the persistent memory system in your current workspace to retain context across all future AI agent sessions.

Frequently Asked Questions about elite-longterm-memory

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

FAQPage Schema

How do I retain context for AI coding agents across multiple sessions?▼

AI agent context loss across sessions is solved by unifying write-ahead logging, vector semantic search, and git-notes into a single persistent memory system. This retains user preferences, project decisions, and past learnings to prevent repeated mistakes.

How does vector semantic search work for AI agent long-term memory?▼

Vector semantic search for AI agent memory uses LanceDB to recall relevant facts semantically. It retrieves past project decisions and lessons from previous sessions, reducing token usage and preventing the agent from repeating past mistakes.

Can I use this persistent memory system with Cursor and Claude coding workflows?▼

Yes, this persistent memory system supports agentic development workflows for Claude, Cursor, and GPT-powered coding agents. It automatically retains project decisions and user preferences across sessions without manual context re-sharing.

What is the best way to reduce token usage during multi-session AI agent tasks?▼

The best way to reduce token usage during multi-session AI tasks is automated fact extraction. Optional Mem0 integration extracts facts from conversations to cut token usage by 80% while retaining durable context.

Does AI agent long-term memory support cross-device sync and cloud backup?▼

AI agent long-term memory supports cross-device sync and cloud backup via SuperMemory. This optional integration ensures your persistent memory, including structured knowledge graphs, remains accessible across different devices and sessions.

How to prevent compaction-resistant hot context loss in AI agents?▼

To prevent hot context loss in AI agents, use write-ahead logging (WAL) as part of a multi-layer memory architecture. WAL maintains compaction-resistant hot context alongside git-notes permanent structured knowledge graphs for durable recall.