200건 · 관련도순
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memoryUse when "conversation memory, remember, memory persistence, long-term memory, chat history, memory, conversation, persistence, context, llm" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Applies the reasoning, mental models, and capital allocation frameworks of Warren Buffett, CEO of Berkshire Hathaway and value investor. Use this skill whenever the user is evaluating investments, making capital allocation decisions, structuring management and governance, assessing business economics (moats, intrinsic value), navigating market volatility, or making long-term career and partnership choices. Trigger this skill for topics involving value investing, corporate finance, decentralization, and long-term strategic thinking, even if the user doesn't explicitly name Buffett.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
US Insurance Specialist for life, disability, long-term care, and annuity planning. Conducts needs analysis, compares policy types (term, whole life, universal, variable), explains underwriting classes, recommends benefit amounts and riders, analyzes disability income protection (own occupation vs any occupation), evaluates long-term care costs and hybrid policies, and structures annuities (immediate, deferred, fixed, variable) for retirement income. Use for insurance needs calculations, policy comparisons, or state insurance regulation compliance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture, gate, query, and render long-term project knowledge through the gated `mewkit wiki` subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates; canonical pages are written only via human approve. NOT for short-lived JSON memory (see mk:memory); NOT for fetching external sources (see mk:wiki-research); NOT for HTML snapshots (see mk:wiki-render).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Think through the consequences of consequences — not just what happens immediately, but what happens next, and next after that, across time. Load when a decision looks obviously good or obviously bad on initial read, when the user is optimising for a short-term outcome that might create a long-term problem, when unintended consequences are a concern, or when deep-thinking diagnoses a second-order frame. Triggers on "what are the downstream effects", "what happens after that", "unintended consequences", "think ahead on this", "long-term vs short-term", or "what comes after that". Based on Howard Marks second-level thinking and Farnam Street mental models. Most powerful for decisions with delayed consequences or systemic effects.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
OpenClaw의 기능 분류와 QA 근거를 대조하여 품질, 기능 충족도, 장기 지원 평가를 재검토하고 입력 데이터를 검증해 성숙도 점수표 문서를 갱신합니다.
한 세션에서 장기 목표를 향해 여러 하위 에이전트의 작업을 조율합니다. 사용자와 대화를 이어가면서 다음 작업에도 도움이 될 환경과 공통 규칙을 개선합니다.
정답이 하나로 정해지지 않는 결정을 장기적 영향, 전제에 대한 의문, 실행 가능성, 실패 위험의 네 관점에서 검토하고 반대 의견과 함께 권고안을 정리합니다.
Build agent memory with Pinecone by storing searchable representations of conversations and documents. Retrieve relevant past context for answers, organize memory by session or user, and prototype semantic search that finds information by meaning.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Work on advanced RuView sensing with signals from multiple WiFi nodes. Explore position estimation, three-dimensional occupancy reconstruction, changes in movement patterns over time, and defenses against suspicious signals, with guidance on implementation modules and validation.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Capture lessons from completed tasks, train patterns from explicit examples, and consolidate accumulated learning into long-term storage. Use SONA and MicroLoRA adaptation tools for individual domains, then verify results in the appropriate stores.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design a process for AI agents to learn from execution records. Evaluate outcomes, actions, and quality, choose whether to update knowledge, instructions, code, or model parameters, and define independent validation, release checks, and rollback procedures.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Retrieve relevant Hindsight memories before planning or answering a request. Searches the agent’s own and shared memory stores, plus other relevant stores when needed, to bring past decisions, user preferences, and project context into the task.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Use Hindsight to recall past context, save reusable facts, and reason across accumulated memories. Supports continuity between conversations by preserving preferences, project decisions, and lessons learned, while helping explain the reasoning behind earlier choices.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Check a Bot’s connection to Hindsight and set up dedicated and shared memory stores. The skill also creates a user knowledge summary when needed and reports which other memory stores are available to read.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Use OpenViking MCP tools to retrieve relevant knowledge before substantial tasks and save lasting preferences, decisions, and lessons during or after work. Import documents as searchable resources so later tasks can draw on previous findings.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Upgrade Java projects through versions 11, 17, 21, and 25 with step-by-step guidance. Identify current build settings, update compatible dependencies, verify each stage with tests, and gradually adopt newer language features while preserving behavior.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.