“persistent memory”的搜索结果

F Agent Memory: A Git-Native Persistent Memory Solution for AI Coding Agents and Project Knowledge Management OKF Agent Memory introduces a standardiz

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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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Detects fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.

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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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Architecture and systems design for agents that retain bounded state across sessions. Covers durable event history, owned state, derived memory, retrieval indexes, framework evaluation, recovery, deletion, and rebuild behavior. Uses constructed implementation examples and workload calibration rather than universal vendor, latency, similarity, retention, or cost claims. 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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Manage persistent memory for autonomous coding. Use when storing/retrieving knowledge, managing Graphiti integration, persisting learnings, or accessing episodic memory.

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Cross-session memory — store, recall and query project knowledge across sessions. Detects whatever persistent memory is available (MemPalace, claude-mem, or file-only fallback) and uses the best one present.

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Detects fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.

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Search persistent Claude-Mem records on cmem.ai for context from earlier sessions across agents. Narrow keyword searches to relevant observations, then explain past work, decisions, and dates in plain language.

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• Recalling what happened in the previous session
• Finding the reasoning behind a project decision
• Retrieving past records about a file or error

Coordinate specialized AI agents under a lead coordinator for research, development, and review. Configure voting rules, share persistent knowledge, assign tasks, monitor progress, and pause or resume sessions through Claude Flow.

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• Divide application development among specialized agents
• Research and compare GraphQL and REST
• Coordinate reviewers for a pull request

Guides agents in choosing Cherry Studio tools for research, document conversion, memory, notifications, and other tasks. It explains when to use app tools or local runtimes, with checks for availability, configuration, and approval.

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• Choose the right Cherry Studio tool for a task
• Read or convert local Office and PDF files
• Set up scheduled tasks and notifications

Keep a short, persistent work journal so an agent can resume across sessions. Record completed work, current status, next steps, and unsuccessful attempts, then read the latest entry when restarting or summarize the work when closing it.

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• Recording progress at the end of a session
• Resuming work from the latest journal entry
• Saving key decisions and unsuccessful attempts

Extend a NanoClaw agent's own environment with packages, external tools, or specialist agents. The skill chooses the workflow for each change, edits memory and standing instructions, and delegates source changes to a builder for review.

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• Adding an external tool for reading RSS feeds
• Installing persistent system or global npm packages
• Updating memory or standing behavior instructions

Track work across sessions with persistent issues, dependencies, and progress notes. Use the bd command-line tool to find unblocked work, claim issues, record completion, and recover task context after conversation compaction.

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• Track feature work across multiple sessions
• Find work that has no unresolved blockers
• Recover task context after conversation compaction

Inspect Google Cloud Filestore usage and free space, then propose capacity increases or decreases based on configured thresholds and storage tier limits. Apply resizing after confirmation to address low space or reduce excess capacity.

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• Check free space across Filestore instances
• Increase capacity when storage is running low
• Reduce excess capacity on eligible storage tiers

Diagnose why an AI workflow becomes inconsistent despite receiving extensive information. Through guided questions, define what to retain, retrieve when needed, or exclude, and develop a memory structure and a research-to-implementation process with focused context.

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• Diagnosing vague answers despite extensive input
• Defining information boundaries between agents
• Planning persistent memory and on-demand retrieval

This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences across sessions using MCP memory server integration

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Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.

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Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries

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INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

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Debug iOS apps and profile performance using LLDB, the interactive Memory Graph Debugger, and Instruments. Use for crashes, retain-cycle inspection, hangs, build failures, and generic CPU, memory, energy, or network profiling. Use ios-memgraph-analysis for .memgraph capture, leaks CLI ownership paths, or persistent heap growth; use ios-ettrace-performance for ETTrace capture and JSON.

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Give an AI agent persistent, cross-session long-term memory through MindMemOS. Covers installing and authenticating the mindmemos CLI, the full command interface (add / search / get / update / delete / feedback / dreaming) with parameters and examples, guidance on which capability to use when, plus a Python SDK example. To wire memory into a specific agent host (OpenClaw, DeepSeek Harness, Codex, Claude, etc.), see references/.

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Sync session learnings to persistent memory incrementally, with a report. "save what we learned this session"

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Claude Mind - Search and manage Claude's persistent memory stored in a single portable .mv2 file

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