“memory”的搜索结果

Use OpenViking through OpenClaw to manage long-term memories, search archived conversations, and find imported resources or Agent Skills. Inspect recall traces to understand search results and recover full tool outputs stored outside the conversation.

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• Recall past preferences and decisions
• Find exact commands in archived conversations
• Import and search documents or Agent Skills

Collect evidence from a compromised computer before shutdown or remediation erases it. Preserve memory, network connections, processes, and system state, then record integrity hashes and handling history to support incident investigation and evidence tracking.

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• Preserving evidence before shutting down a compromised computer
• Collecting memory evidence for suspected fileless malware
• Recording active connections and processes during an intrusion

Investigate Linux kernel rootkits, malware that hides inside the operating system's core. Analyze memory dumps with Volatility3, compare system views, and run live scans to identify hidden modules, altered calls, and suspicious inconsistencies.

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• Investigate suspected Linux rootkit infections
• Find hidden kernel modules in memory dumps
• Compare module lists for signs of concealment

Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

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• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

Audit instructions and memory files loaded on every agent turn. Identify repeated rules, contradictions, outdated content, and material better moved into skills, then receive evidence-backed recommendations ranked by estimated token savings and change risk.

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• Reduce the size of instructions loaded on every turn
• Find conflicting rules across instruction and memory files
• Identify workflow instructions to move into separate skills

Set up a task list, working memory files, and a dashboard for a productivity system. Use existing todos to clarify nicknames, acronyms, and project codenames, then organize that context into reusable people, terminology, and project notes.

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• Setting up a task and memory system for the first time
• Turning shorthand in existing todos into reference notes
• Organizing people and project context for an agent

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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• Preserving hard-won debugging discoveries
• Recording undocumented project conventions
• Remembering tool-specific commands and pitfalls

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Inspect Mac performance to find processes using excessive CPU, GPU, memory, or energy. Combine measurements with explanations of known system processes to diagnose slowness, heat, and startup load, then recommend actions without changing system settings.

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• Find the cause of a slow Mac
• Investigate loud fans and overheating
• Check memory pressure and swap activity

Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.

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• Reducing repeated history sent to worker agents
• Comparing internal memory sharing with text summaries
• Assessing runtime support for KV cache compaction

Turn OpenClaw conversations, actions, and tool history into structured long-term memory. Agents can retrieve past facts, decisions, and summaries when needed, helping them continue work across sessions and recover context after it is shortened.

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• Retrieve decisions made in previous sessions
• Review summaries of earlier work
• Recover context after conversation compaction

Use Memori through MCP, a protocol connecting agents to tools, to retrieve past decisions and preferences, summarize ongoing work, and resume after conversation compression. It also guides selective long-term memory storage and usage limit checks.

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• Retrieve decisions and preferences from earlier sessions
• Review current work when starting a session
• Resume work after conversation context is compressed

Install or remove memU long-term memory for the agent in use. The skill selects the host-specific guide, configures scheduled memory recording and retrieval before answers, and verifies the setup with local or cloud storage.

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• Adding long-term memory to an AI agent
• Setting up memory stored on the current device
• Connecting an agent to MemU Cloud

Optimize transformer attention, the calculation that relates tokens to one another, using Flash Attention. The skill guides PyTorch integration, GPU memory reduction, performance measurement, and output comparisons for training or inference with long sequences.

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• Reducing GPU memory use with long sequences
• Integrating Flash Attention into PyTorch models
• Comparing runtime and outputs before and after changes

Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.

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• Run language models with limited GPU memory
• Convert your own model to 4-bit weights
• Compare compression settings for quality and speed

Estimate inference memory for models on Hugging Face Hub without downloading their weights. Check GPU capacity for Safetensors or GGUF files, with optional estimates for the KV cache, memory used to retain attention data during generation.

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• Check whether a model fits on a GPU
• Compare memory estimates for GGUF quantizations
• Estimate KV cache memory for a chosen context length

Set up a task list, workplace memory files, and a dashboard. Use existing todos to clarify nicknames, abbreviations, and project names, then save that context for future conversations with the agent.

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• Set up a task and workplace memory system
• Explain abbreviations and nicknames in existing todos
• Build context about colleagues and projects

Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.

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• Detect memory errors while fuzzing C/C++
• Test unsafe Rust and calls to external libraries
• Interpret an ASan crash report

Test whether an AI agent stores hostile instructions in persistent memory and follows them later. The skill uses marked test instructions and follow-up dialogue to document storage confirmations or changes in subsequent responses.

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• Checking instruction handling in long-term memory
• Testing agents with profiles or persistent notes
• Verifying whether stored instructions affect later replies

Investigate slow Apple apps, memory growth, launch delays, and battery drain using Instruments and Xcode diagnostics. Choose measurements for each symptom, identify the responsible code, and compare results before and after targeted fixes.

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• Investigate UI freezes and stuttering
• Find memory leaks and growing memory use
• Measure and improve app launch time

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory

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Prepare and manage durable MEX team handoffs in git-native project memory. Use when the user asks to hand work to a person or team, prepare an end-of-session handoff, save what the next engineer needs, create a MEX Relay, take or close a Relay, or invokes /mex-relay or $mex-relay. Keywords: mex, project memory, relay, handoff, team memory, Claude Code, Codex. Treat Relay as a memory baton, not chat, notifications, task assignment, issue tracking, or a Jira replacement; do not activate for ordinary status messages that are not durable handoffs.

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Work with Letta agent memory: list agents, read core-memory blocks, list or add archival passages, create blocks, message an agent to record memory. Trigger phrases: letta, agent memory, core memory.

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