mlp-continuity

Name: mlp-continuity
Availability: InStock
Author: riley-coyote

Reflect on conversations and store encrypted memories to IPFS/Pinata.

34|4|Updated Dec 21, 2025

Search Tags:#memory #encryption #continuity #identity #storage #ipfs #mlp

Ready to run

npx skills add https://github.com/Riley-Coyote/memory-ledger-protocol-v0.2 --skill mlp-continuity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mlp-continuity
Source: https://github.com/Riley-Coyote/memory-ledger-protocol-v0.2/tree/main/skills/openclaw/full-stack
Command: npx skills add https://github.com/Riley-Coyote/memory-ledger-protocol-v0.2 --skill mlp-continuity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables end-to-end memory continuity by decoupling memories from platforms and storing them encrypted in a decentralized ledger so users own their history.

Core Features & Use Cases

Reflect on conversations and extract memories with confidence scoring
Encrypt and store memories to IPFS/Pinata for cross-platform portability
Generate context packs and manage identity kernels for seamless session transitions
Use Case: An AI assistant switches between platforms without losing personal memory or context

Quick Start

Install dependencies and run the mlp-continuity integration to store and retrieve memories across sessions.

Frequently Asked Questions about mlp-continuity

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

FAQPage Schema

How do I persist AI agent memory across different platforms?▼

To persist AI agent memory across platforms, use encrypted decentralized storage to decouple histories from specific platforms. This approach extracts memories via asynchronous reflection and stores them on IPFS, ensuring users own their data and can retrieve it across different sessions.

What is encrypted memory continuity for decentralized storage?▼

Encrypted memory continuity is the process of securely maintaining AI agent histories by storing them on decentralized ledgers. It combines asynchronous conversation reflection with encrypted IPFS storage to generate portable context packs and identity kernels for seamless session transitions.

Do I need Node.js and Continuity Framework to manage encrypted IPFS memory storage?▼

Yes, managing encrypted IPFS memory storage requires Node.js tooling and the Continuity Framework. The workflow is configuration-driven, enforcing these dependencies to execute memory reflection, envelope creation, and identity management operations.

Can I generate context packs and manage identity kernels for AI session transitions?▼

Yes, you can generate context packs and manage identity kernels to enable seamless AI session transitions. The system provides dedicated commands for context-pack and identity operations, allowing an AI assistant to switch platforms without losing personal memory or context.

How do I reflect on conversations and extract memories with confidence scoring?▼

You reflect on conversations and extract memories with confidence scoring by executing the reflect command within the configuration-driven workflow. This asynchronous process evaluates conversation data and generates scored memory envelopes for encrypted decentralized storage.

What are the limitations of using decentralized storage for AI memory continuity?▼

Limitations of using decentralized storage for AI memory continuity include the strict requirement for Node.js tooling, the Continuity Framework, and MLP storage dependencies. Without these specific environmental configurations, the reflect, sync, and context-pack commands cannot execute.