'memo' 검색 결과

Design memory systems that help AI agents retrieve relevant past information. Choose short-term and long-term memory structures, compare vector storage options, and review document splitting and retrieval strategies for different kinds of knowledge and experience.

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• Design memory for information used across conversations
• Choose storage for experiences, facts, and procedures
• Review retrieval when an agent misses saved information

Retrieve past work, decisions, and lessons from agentmemory using keyword, meaning, and relationship searches. It expands selected records, groups findings by session, and prioritizes important observations so earlier context can inform the current task.

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• Recalling an earlier decision and its context
• Finding how a similar issue was handled
• Reviewing one project's or agent's history

Helps reduce GPU memory use by loading large language models in 4-bit or 8-bit formats, a technique called quantization. Covers bitsandbytes configuration, memory checks, QLoRA fine-tuning, and training with memory-saving optimizers.

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• Load a larger model within available GPU memory
• Compare 4-bit and 8-bit quantization settings
• Fine-tune a quantized model with QLoRA

Draft an internal investigation memo from the investigation log, or update an existing draft as new information arrives. The workflow flags outstanding priority sources and shows changes before revising the memo.

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• Prepare the first memo from an investigation log
• Update a draft after new information arrives
• Check outstanding priority sources before drafting

Draft or review formal investment memos for listed stocks. Organize evidence, valuation, downside scenarios, and conditions for revisiting a recommendation into documents suited to investment committees, portfolio managers, clients, or research teams.

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• Preparing an investment committee memo
• Combining earnings and valuation analysis into a memo
• Reviewing evidence and downside risks in an existing memo

[omh] Project fact to remember across future sessions: capture one bounded durable project or product memory candidate through explicit remember, refuse, or defer review; for existing Hermes memory use omh-memory-sync, and for a past decision use decision-recall. Use when the user says: memory-new, new memory, project memory, product memory, remember this project, remember this product, do not save, do not save this token.

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Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data structures for memory, handle large datasets efficiently, or diagnose memory issues. Covers object sizing, generator patterns, efficient data structures, and memory profiling strategies.

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Use at write time to vector/embedding memory — Grove content-addressed insertion, chroma/pgvector upserts, memory-consolidation promoting session traces to MEMORY.md, or embedding externally-ingested content. Scores each candidate record against a fixed panel of sentinel queries and quarantines any record that would become the nearest neighbor of too many unrelated queries — a hub — whether from adversarial poisoning or accidental over-generality. This is the memory-record-side sibling of skill-injection-guardian (file-side) and the write-side complement of memory-use-warrant (read-side). Quarantine, never silently drop; a human reviews. Backed by the admission-time hubness gate (arxiv 2606.19692v1). Triggers on inserting into vector memory, consolidating memory, and embedding stranger content.

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End-of-session pass that persists durable facts from the CURRENT session into .claude/memory/. Runs IN-SESSION in the main agent (never via Task — a subagent starts cold with no transcript), prompted by the Stop hook's nudge. Triggers on "persist durable facts from this session", "run memory-extraction", "flush session memory", or "extract memory before close". Reads the in-context transcript plus existing memory, applies the durable-fact predicate below, and writes one file per fact plus one MEMORY.md index line — append-or-update, never clobbering a consumer's existing memory.

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Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

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Orchestrate persistent agent memory across coding sessions, repos, and tools. Load when the user asks to remember, recall context, save project memory, create a handoff, manage global memory, update memory, compact memory, audit memory, forget memory, continue from prior sessions, or before commit/push/git operations that checkpoint project state.

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Mac에서 명령줄 도구 memo로 Apple Notes 메모를 만들고 검색·편집합니다. 폴더 이동과 내보내기를 지원하며 iCloud로 다른 Apple 기기에서도 볼 수 있습니다.

• Apple 기기 간에 메모를 공유합니다
• 기존 메모를 유사 검색으로 찾습니다
• 메모를 폴더별로 정리합니다

Manage stored agent memories for backups, project transfers, and team knowledge sharing. Work with RVF, a portable memory format, to export and import records, upgrade formats, inspect storage statistics, and transfer patterns between local projects.

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• Back up stored agent memories
• Transfer memories and patterns between projects
• Share stored knowledge with a team

Bring exported memories from another AI assistant into Claude. Organize profile, project, relationship, and topic facts, exclude sensitive details and instructions, then ask for confirmation before adding information without replacing existing memories.

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• Move stored context from ChatGPT or Gemini to Claude
• Carry active project details into Claude's memory
• Review sensitive information before importing

Plan how an AI application should use Hindsight memory. Identify useful information to retain, define user-scoping tags and memory bank settings, and specify how retrieved facts and synthesized knowledge should reach the model.

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• Plan memory that carries conversation context across sessions
• Design tags that separate each user's memories
• Plan summaries of customer preferences and recurring issues

Investigate security incidents using memory dumps, saved copies of RAM. The skill guides memory acquisition, process and network analysis, suspicious code detection, and evidence extraction with Volatility and related tools, including checks for capture integrity.

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• Capturing RAM before shutting down a system under investigation
• Finding suspicious processes and network connections in memory
• Extracting executables and strings for malware analysis

Draft investment committee memos for private equity deals, investments in privately held companies. Combine due diligence findings, financial analysis, deal terms, return scenarios, and risks into a structured document with a clear recommendation.

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• Preparing approval materials for an investment committee
• Combining due diligence findings into a deal memo
• Comparing base, upside, and downside return scenarios

Diagnose missing recall, failed captures, and connection errors in the OpenViking memory plugin for Codex. Check installation, configuration, authentication, and server readiness, then use the findings to guide fixes and verify that memory works again.

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• Investigate missing recall or unsaved conversations
• Check plugin status after installation or updates
• Diagnose 401 and 403 authentication errors

Retrieve earlier decisions, preferences, and saved resources from OpenViking across sessions. Store durable information, import documents or repositories, and manage deletion, with guidance on how project memory is shared between directories and repository clones.

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• Recall decisions from earlier sessions
• Save preferences and lasting decisions
• Import files, URLs, or Git repositories for later use

Calculate how to divide serving memory between recurrent state and KV cache, two memory pools used by hybrid models. Use startup logs and workload settings to recommend a ratio, estimate concurrency, and identify the limiting pool.

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• Choosing a memory ratio for a hybrid model
• Investigating limits on concurrent requests
• Recalculating memory allocation for context length

Acquire Linux memory with LiME and examine it using Volatility 3. Extract processes, network connections, command history, loaded kernel modules, and signs of injected code to support investigations of compromised Linux systems.

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• Acquiring memory from a compromised Linux system
• Comparing process lists to investigate hidden processes
• Reviewing command history and network connections

Analyze memory dumps with Volatility3 to find heap spraying, an attack that fills memory with attacker-controlled data. Examine suspicious allocations and repeated byte patterns, extract suspicious code, and report affected processes and memory regions in JSON.

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• Investigating suspected heap spraying in a memory dump
• Extracting suspicious code from memory
• Organizing evidence for detection rule development

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

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• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Help Claude interpret workplace shorthand by storing people, acronyms, project names, and preferences in local files. Keep frequent references in CLAUDE.md and detailed context in memory/, so requests can be understood using recorded workplace knowledge.

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• Recording internal acronyms and terminology
• Linking nicknames to people and roles
• Looking up project codenames and background