61건 · 관련도순
Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.
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.
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.
Retrieve compacted ReasoningBank context for cost-analysis queries and summarize retrieved memories, reported token savings, cache hit rate, and package availability. The savings figure is an estimate from the integration, not a measured comparison against a baseline.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Implement agents that use recorded task outcomes to choose future approaches. ReasoningBank supports matching past patterns, comparing strategies, retaining experience in AgentDB, and tracking how strategy success and learning change over time.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build agents that record task outcomes and use past experience to choose future approaches. Implement pattern matching, strategy comparisons, persistent learning records, and metrics for checking how recommendations and results change over time.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when scripting mGBA emulator automation in Lua - console API, ROM and save state operations, frame callbacks, input handling, memory reading for cheats and RAM watches, sockets, or TAS and speedrunner tooling
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Manage code changes across multiple AI agents and record their operations and outcomes. Use those records to find successful patterns, review past failures, and get suggestions for similar development or deployment tasks.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Für Verfügbarkeit Wartungsfenster: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query DrugBank for comprehensive drug and target information
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Parse local DrugBank XML for drug info, interactions, targets, and properties. Search by ID/name/CAS, extract DDIs with severity, map targets/enzymes/transporters, compute SMILES similarity. Primary via local XML; REST API rate-limited (3k/month dev). For live bioactivity use chembl-database-bioactivity; for compound properties use pubchem-compound-search.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.