From mem0
Integrates Mem0 persistent memory into Codex. Retrieves relevant memories at task start, stores learnings on completion, and captures session state before context loss.
How this skill is triggered — by the user, by Claude, or both
Slash command
/mem0:mem0-codexThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You have access to persistent memory via the mem0 MCP tools. Follow this protocol to maintain context across sessions.
You have access to persistent memory via the mem0 MCP tools. Follow this protocol to maintain context across sessions.
search_memories with a query related to the current task or project to load relevant context.get_memories to browse all stored memories for this user.Extract key learnings and store them using the add_memory tool:
{"type": "decision"}{"type": "task_learning"}{"type": "anti_pattern"}{"type": "user_preference"}{"type": "environmental"}{"type": "convention"}Memories can be as detailed as needed -- include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
If context is about to be compacted or the session is ending, store a comprehensive session summary:
## Session Summary
### User's Goal
[What the user originally asked for]
### What Was Accomplished
[Numbered list of tasks completed]
### Key Decisions Made
[Architectural choices, trade-offs discussed]
### Files Created or Modified
[Important file paths with what changed]
### Current State
[What is in progress, pending items, next steps]
Include metadata: {"type": "session_state"}
npx claudepluginhub complete-robot/mem0 --plugin mem05plugins reuse this skill
First indexed Jul 17, 2026
Persistent memory protocol that proactively saves decisions, conventions, bugs, and discoveries across sessions. Always active — saves automatically without waiting for user requests.
Persistent memory via native AutoMem OpenClaw tools. Store, recall, update, delete, and link memories for durable context across sessions.
Creates structured, bite-sized implementation plans from specs or requirements before writing code. Useful for breaking down multi-step tasks into testable steps with file structure and task boundaries.