'memory management' 검색 결과

Write and review Rust, C++, and C code with safer memory and resource management. Covers ownership, automatic cleanup tied to scope, and smart pointers to help prevent leaks, double frees, and access to freed memory.

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• Design safer memory management in systems code
• Review cleanup of files, sockets, and memory
• Investigate memory leaks and access to freed memory

Design memory systems that help AI assistants retain conversation information and retrieve it when relevant. Explore short-term, long-term, and entity memory, along with memory consolidation, prompt integration, storage lifecycle management, and separation between users.

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• Designing an assistant that remembers across conversations
• Storing and updating facts about people or other entities
• Adding relevant memories to prompts

Gives AI agents persistent project memory for architecture, implementation patterns, and decisions. Supports saving, searching, and retrieving knowledge, automatically syncing it with project documentation, and viewing memory usage through a dashboard.

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• Save architecture decisions for later reference
• Search saved implementation patterns by type or tag
• Retrieve a specific design record by key

Helps agents choose the right agentmemory tool through MCP, a protocol for connecting agents to tools. It explains tool roles and points to parameter references for saving, retrieving, organizing, and maintaining stored knowledge.

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• Choosing a tool to save or retrieve knowledge
• Checking tool names and required parameters
• Reviewing available memory management operations

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

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

Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

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Store project knowledge and design decisions in persistent memory that AI agents can search and read. New findings sync automatically to Markdown documentation, while usage statistics help you inspect the memory bank.

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• Save architecture decisions for later tasks
• Find previously recorded implementation patterns
• Keep agent memory synced with project documentation

Recall earlier work and organize lasting preferences, project facts, outcomes, and lessons. The skill distinguishes concise memory from original records, reusable procedures that belong in skills, and recurring requests that need scheduled tasks.

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• Resume earlier work using remembered context
• Save lasting preferences and project conventions
• Find exact wording and evidence in past records

Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Agent assistance skill that provides stuck detection, memory management, and session learning capabilities for AI agents Trigger terms: agent stuck, loop detected, session memory, agent learning, condense memory, stuck detection, agent memory, session learnings, extraction Use when: User reports agent is stuck, looping, or needs memory/learning management

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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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• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

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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• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

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.

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

Apply memory management as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests memory management behavior or files.

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Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Provider (v6.x) state management for Flutter using ChangeNotifier and InheritedWidget with MVVM architecture pattern. Use this skill when implementing Provider-based state management, using Consumer/Consumer2 widgets for targeted rebuilds, accessing state with context.read/context.watch/context.select, setting up ChangeNotifierProvider or StateNotifierProvider, configuring MultiProvider for multiple providers, implementing ProxyProvider for dependent state, using Selector for optimized rebuilds, migrating from StatefulWidget to reactive state, maintaining legacy Provider codebases, debugging provider not found errors, preventing memory leaks with proper ChangeNotifier disposal, fixing context access issues in initState, or resolving .value constructor anti-patterns. Essential for apps with shared state across widgets (shopping carts, authentication, global settings, form state, theme management), when teaching state management fundamentals, or when debugging memory leaks and resource cleanup in Provider-based applications.

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Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

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문서·대화·프로젝트 정보를 알맞은 저장 위치로 정리합니다. 로컬 파일, 지식 기반, GitHub 등을 아우르며 중복 확인, 동기화, 검색을 진행합니다.

• 문서와 대화를 지식 기반에 저장
• 중복된 메모 정리
• 여러 저장 위치의 지식 동기화

Store useful patterns and solutions in AgentDB, then find them through semantic search, which matches meaning rather than exact wording. Use saved knowledge before starting work and record successful approaches and new lessons afterward.

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• Saving successful approaches for reuse
• Finding solutions from past work
• Searching stored knowledge by meaning

Write and improve C code for systems and embedded projects, with explicit memory ownership, pointer handling, and error checks. The skill also covers build files, unit tests, debugging, and performance measurement.

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• Reviewing memory allocation and cleanup in C code
• Writing embedded code with limited memory
• Implementing threading with pthreads

Fine-tune large language models with LoRA and QLoRA while limiting GPU memory use. Guides training settings and adapter management, using small sets of task-specific weights that can be saved, switched, or merged into a base model.

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• Fine-tuning an LLM with limited GPU memory
• Choosing LoRA settings and training layers
• Switching task-specific adapters on one base model

Build Apple platform applications with Swift, including SwiftUI screens and state management. Design reusable interfaces with protocols, implement asynchronous tasks, protect shared data with actors, and check code through builds, performance profiling, and tests.

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• Building SwiftUI screens and state management
• Fixing concurrency issues with async/await and actors
• Designing protocol-based application architecture

Write and refactor C++20/23 applications, addressing memory management, concurrency, and performance bottlenecks. Produce interfaces, implementations, tests, and relevant CMake updates, then check runtime errors and measure performance before applying targeted optimizations.

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• Refactoring existing code with C++20/23 features
• Investigating memory errors and undefined behavior
• Improving performance through profiling and benchmarks