Skills 导航

Use the MemOS Lite memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Use task_summary when you need full task context, skill_get for experience guides, and memory_timeline to expand around a memory hit.

Lightweight cognitive memory system for AI agents by Automaton. Auto-save conversations, quick recall, session management.

Set up the full OpenClaw agent memory system with 3-tier memory (HOT/WARM/COLD), daily logs, semantic search (QMD), and lossless context management (Lossless Claw). Use when onboarding a new agent, setting up memory for a fresh OpenClaw instance, or when asked to install the memory system on a new agent. Triggers on "set up memory", "install memory system", "onboard new agent memory", "memory setup", "agent onboarding".

Stop agents from "forgetting, mixing projects, and rotting over time" by giving them a practical memory operating system: global memory, project memory, promotion rules, validation cases, and a maintenance loop.

Long-term memory, learning, and self-evolution for the agent. Activates on session start (SOUL.md/USER.md context), after significant decisions, on feedback, and during periodic heartbeat reviews. Maintains MEMORY.md, daily logs, learnings corpus, and behavioral patterns.

This skill should be used when working with memory-lancedb-pro, a production-grade long-term memory MCP plugin for OpenClaw AI agents. Use when installing, configuring, or using any feature of memory-lancedb-pro including Smart Extraction, hybrid retrieval, memory lifecycle management, multi-scope isolation, self-improvement governance, or any MCP memory tools (memory_recall, memory_store, memory_forget, memory_update, memory_stats, memory_list, self_improvement_log, self_improvement_extract_skill, self_improvement_review).

Systematic memory management for long-running AI agents. Implements a five-tier lifecycle — heartbeat micro-attention, nightly consolidation, weekly reflection, monthly archiving, and yearly wisdom distillation. Use when setting up a new agent's memory system, improving an existing agent's memory quality, or when the agent's MEMORY.md is growing too large and context quality is degrading. Triggers on "set up memory", "memory management", "improve memory", "memory lifecycle", "nightly consolidation", "sleep cycle", "memory housekeeping".

记忆管理技能 - 五层时间架构 + 三类记忆标签 + 最小化写入 + 压缩检测

知识沉淀自动化技能。扫描近期日记，识别可沉淀知识，自动写入知识库。触发时机：cron 定时任务或手动调用。使用方法：加载 skill 后读取 references/spec.md 获取详细规范。

多实例记忆共享，多个 Agent 之间同步记忆

Emotional processing layer for AI agents. Persistent emotional states that influence behavior and responses. Part of the AI Brain series.

记忆管理技能 - 三层时间架构 + 自动搜索/提取/会话笔记统一 Hook + 记忆巩固（整合 auto-dream）

AI 记忆栈架构 - 符合 2026 前沿的 AI 记忆系统。微调+RAG+ 上下文三层设计，mirrors 人类记忆工作方式。

Persistent local cognitive memory for OpenClaw via a Node adapter and FastAPI engine.

Structured persistent memory system using an Obsidian vault with daily journals, project docs, knowledge base, and self-improvement logging. Use when: (1) Setting up a new OpenClaw agent's memory system, (2) Agent needs persistent memory across sessions, (3) Organizing project documentation, daily journals, or knowledge base, (4) Logging errors, learnings, or feature requests for continuous improvement, (5) User says 'set up memory', 'initialize vault', 'create journal', 'log this error', 'remember this', or 'update memory'. Also covers semantic search setup and the promotion pipeline for learnings into brain files.

An Agent long-term memory guide for OpenClaw, Codex, and Obsidian workflows. Covers MEMORY.md, daily notes, session recovery, memory distillation, and optional OpenViking support.

Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.

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基于 LanceDB 向量数据库的智能记忆系统，为 OpenClaw Agent 提供长期记忆和语义检索能力。

Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Available tools: memory_search, memory_get, memory_write_public, task_summary, skill_get, skill_search, skill_install, skill_publish, skill_unpublish, memory_timeline, memory_viewer.

You have access to Cortex, a self-organizing knowledge graph for persistent memory. Use it to remember facts, decisions, goals, patterns, and observations across sessions. Knowledge is stored as nodes in a graph that auto-links, decays stale information, detects contradictions, and computes trust

Distill repeated user preferences, successful patterns, and durable working rules into reusable memory notes or prompt-ready context blocks. Use when a user wants to capture habits, preserve preferences, summarize lessons from prior work, or convert raw conversation/task outcomes into structured memory.