Define LLM memory lifecycle actions with provenance for RecallNest.
npx skills add https://github.com/AliceLJY/recallnest --skill memory-partner-protocol
Please help me install this Agent Skill. Skill: Memory Partner Protocol Source: https://github.com/AliceLJY/recallnest/tree/main Command: npx skills add https://github.com/AliceLJY/recallnest --skill memory-partner-protocol
MemoryPartner Protocol formalizes how LLMs should act as proactive, durable memory partners within RecallNest to ensure important context is retained across sessions and windows.
Instruct your agent to resume_context first, then use search_memory, store_memory, and checkpoint_session to establish a durable memory layer.
High-intent search queries and answers about installing and using this skill.
To persist conversation context across different windows, you can use a memory protocol that defines lifecycle actions like resume_context and checkpoint_session. This establishes a durable memory layer for your LLM.
Cross-window session continuity is the ability to retain important context across separate chat sessions. It works by applying a behavior protocol that stores and resumes memory with provenance tracking.
You can resume context by instructing your agent to execute resume_context first, then use search_memory and store_memory actions to capture data, and finally checkpoint_session to save the state in Claude Code and Gemini CLI.
Yes, this memory partner protocol applies across Claude Code, Codex, and Gemini CLI. It provides seamless cross-window continuity and governance for LLMs acting as proactive memory partners.
The best way to manage memory conflicts and provenance is to use a formalized memory protocol that provides transparent audit trails for memory promotions, ensuring context governance across sessions.
Your LLM forgets previous session context because it lacks a durable memory layer. By applying a proactive memory protocol with checkpoint_session, you can ensure important context is retained.
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