52건 · 관련도순
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.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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 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.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Self-learning SEO content pipeline: dashboard, multi-agent queue, token budgets, research → write → audit → publish. StoryBrand/Cialdini/JTBD-style frameworks; config-driven. Use for content factory, batch articles, or scaled publishing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user asks to "build a feature", "fix a bug", "implement something", "start a dev cycle", types "/dev", or describes a software task that requires design, implementation, testing, and shipping. Orchestrates the full software development lifecycle from interrogation through shipping, with self-learning that improves over time.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Self-learning pre/post-task hooks and model/task routing from the Ruflo CLI, on a ruOS desktop. Use to add coordination, formatting, and learning around Claude Code operations, or to route work by complexity/cost.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use ruLake as the agent's working memory. Auto-invoke /rulake-memory:memory-recall when the agent needs to look up something it might already know; auto-invoke /rulake-memory:memory-remember after the agent learns or decides something worth pinning. Closes the self-learning loop without requiring the agent author to wire it explicitly.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run and grow the OpenAmer Agent-to-Agent swarm: identity, trust, node-to-node ask, signed skill/insight sharing, and the autonomous self-learning loop.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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.