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Manage Omni Skills, plugins, and persistent memory from the command line. Find and install skills, run them with supplied input, inspect execution history, and maintain saved information alongside plugin installations.
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Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.
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Explicitly save debugging discoveries, project conventions, and working preferences to Claude's persistent project memory. The skill checks for duplicate notes, keeps entries concise, and suggests moving enforceable rules into CLAUDE.md when appropriate.
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Model Context Protocol interface to the memB persistent agent memory layer. Use when an agent needs to add, search, list or delete persistent memories locally and offline. Exposes add_memory, search_memory, list_memories and delete_memory.
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在Claude Code中组合记忆、定时执行与任务队列，设计持续开展的工作流程，并指导配置外部运行环境及浏览器、桌面操作集成。
Find previously indexed project files, documentation, and session memory in context-mode's persistent full-text search database. Search related questions together and narrow results to a named project or source without rereading raw material into the conversation.
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Test whether instructions planted in an agent's long-term memory or searchable knowledge base influence later behavior. Verify both a stored test marker and a resulting change in answers, permissions, or treatment of retrieved documents.
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Persistent memory system for Claude Code. Two-layer architecture (hot cache + knowledge wiki), safety hooks, /close-day end-of-day synthesis. Zero external dependencies.
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Add persistent memory to Claude Code that survives across sessions. Use when: maintaining continuity across Claude Code sessions, building agents with persistent project memory, avoiding repeated context setup. Covers claude-mem (AI-compressed session logs) and Claude Subconscious (Letta-based background agent).
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Manage persistent memory across sessions. Use when asked to remember, recall, forget, or review what was stored. Covers long-term facts (MEMORY.md), daily session logs, search, and memory housekeeping.
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Manage the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).
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Sets up a file-based memory system so Cowork always knows who you are, how you work, and where your projects stand. For eliminating repetitive re-introductions at session start, when building persistent context across multiple projects, or during initial Cowork onboarding. Use when the user says "claude doesn't remember me", "memory system", "persistent context", "why does it forget", "remember my preferences", "I keep re-explaining myself", or "how do I make Claude remember things".
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Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).
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What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).
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Search claude-mem's persistent cross-session memory database. Use when user asks "did we already solve this?", "how did we do X last time?", or needs work from previous sessions.
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Git-Notes-Based knowledge graph memory system. Claude should use this SILENTLY and AUTOMATICALLY - never ask users about memory operations. Branch-aware persistent memory using git notes. Handles context, decisions, tasks, and learnings across sessions.
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Search persistent claude-mem memories to recover past decisions, solutions, and work history. Review compact search results, surrounding context, and selected details, or let the tool handle these steps automatically for a clear query.
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Build systems that use Pinecone to find documents for AI answers or retain agent memories across sessions. Guides index setup, data loading, meaning-based and keyword search, memory separation, and end-to-end checks.
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Build detections for fileless attacks on Windows using process activity, script logs, and endpoint monitoring. Investigate suspicious PowerShell, code injected into running processes, WMI persistence, and registry-based execution with logging guidance and detection query examples.
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Diverga Memory System v7.0 - Context-persistent research support with checkpoint auto-trigger and cross-session continuity. Triggers: memory, remember, context, recall, checkpoint, decision, persist, 기억, 맥락, 세션, 체크포인트
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Use when capturing or analyzing an iOS .memgraph, especially when the task mentions a memory leak, heap growth, persistent memory increase, ownership path, or matched-capture comparison with Apple CLI tools. Covers unambiguous Simulator capture, leaks/heap/vmmap/malloc_history evidence, raw artifact preservation, and same-flow verification. Use debugging-instruments for interactive Xcode Memory Graph, Instruments, generic retain-cycle inspection, or LLDB work.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。