skills.rest
M4cs
M4csCOMMUNITY
Explore all skills by this author →
Website𝕏@maxbridglandGitHub

ctxovrflw

Provide persistent memory for AI agents via the Model Context Protocol.

1|1|Updated Feb 13, 2026
Search Tags:#context#ai memory#agent#mcp#persistence#cross-tool
One-click install
npx skills add https://github.com/M4cs/ctxovrflw-client --skill ctxovrflw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ctxovrflw
Source: https://github.com/M4cs/ctxovrflw-client/tree/main/skill
Command: npx skills add https://github.com/M4cs/ctxovrflw-client --skill ctxovrflw

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a unified, persistent memory layer for all AI agents on your machine, ensuring context is shared and recalled across different tools and sessions.

Core Features & Use Cases

  • Shared Memory: Store and recall information across different AI tools (e.g., what Cursor remembers, Claude Code can access).
  • Context Persistence: Maintain user preferences, past decisions, and project setup information across sessions.
  • Use Case: A user sets a preference in one AI tool (e.g., "always use Rust for CLIs"). This Skill ensures that preference is available to all other AI tools, preventing repetitive questions and ensuring consistency.

Quick Start

Use the ctxovrflw skill to recall information about the current project setup.

Frequently Asked Questions about ctxovrflw

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

FAQPage Schema
How do I share persistent memory across different AI agents?▼

You can share persistent memory across different AI agents by using a local-first memory layer via the Model Context Protocol. This enables tools like Cursor and Claude Code to store and recall the same context, preferences, and project setup across sessions.

How does semantic search work for AI agent memory?▼

Semantic search for AI agent memory allows you to query stored context and past decisions by meaning rather than exact keywords. The local memory layer processes these queries to retrieve relevant project setup information and user preferences.

Do I need a daemon running to use Model Context Protocol for context persistence?▼

Yes, you need the ctxovrflw daemon installed and running locally to enable context persistence. This local-first daemon manages the memory layer, semantic search, and optional E2E encrypted cross-device synchronization for your agents.

Can I synchronize AI memory across multiple devices securely?▼

You can synchronize AI memory across multiple devices securely using optional E2E encrypted cross-device synchronization. This ensures your shared agent context and preferences remain protected while moving between different local environments.

How do I prevent AI agents from forgetting preferences between sessions?▼

To prevent AI agents from forgetting preferences between sessions, implement a persistent memory layer using the Model Context Protocol. This stores user preferences and past decisions locally, making them available to all compatible tools.

What are the limitations of local-first AI memory layers?▼

A limitation of local-first AI memory layers is the requirement for a dedicated daemon to be actively running locally. Without this daemon operational, agents cannot store, retrieve, or synchronize context across different tools and sessions.

Related Skills

Explore More Software Engineering Skills→
jonnabiojonnabio
6

mcp-implementation

Implement the Model Context Protocol to expose local tools and APIs to LLMs.

Community
Intermediate
bibo1243bibo1243

shared-memory

Read and write a shared memory log for cross-device bot continuity.

Community
Intermediate
vuongtu1k9-ship-itvuongtu1k9-ship-it

memory-manager

Organize AI agent memory into episodic, semantic, and procedural tiers.

Community
Advanced
zilliztechzilliztech
3

chat-memory

Store and retrieve past chatbot conversations across sessions using vector memory.

Official
Advanced