'memo' 검색 결과

Evaluates task complexity and selects a small set of existing skills for work spanning multiple areas. Uses agent-memory-mcp to record and retrieve successful combinations, while keeping simple tasks within the agent’s ordinary capabilities.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Selecting skills for a complex task
• Coordinating work across UI, databases, and deployment
• Reusing successful skill combinations

Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Save architecture decisions for later reference
• Search previously recorded implementation patterns
• Retrieve design notes by key

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI assistant that answers from documents
• Keep agent facts and decisions across sessions
• Combine meaning-based search with exact terminology

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Review board memos and strategy briefs with available Claude, Codex, and Gemini models. Compare concerns, supporting arguments, and votes, then turn disagreements into questions to resolve before committing to a major business decision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing fundraising terms before signing
• Checking the reasoning behind a major strategic pivot
• Examining an acquisition memo before a decision

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Keeps work preferences, corrections, and ongoing goals in a personal Kortix folder so agents can refer to them across sessions. Users can inspect, edit, or delete the saved Markdown files and request specific details be forgotten.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Keeping response preferences across sessions
• Remembering corrections to the agent's approach
• Carrying ongoing goals and deadlines into future tasks

Guide file storage and context retention in Kortix shared folders. Identify accessible folders, save lasting outputs, keep user and team notes across sessions, and handle conflicting file copies according to the documented sharing and synchronization rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving outputs for use after the session ends
• Reusing user notes and preferences across sessions
• Keeping shared context for the team

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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