ReasoningBank Intelligence

What problem does it solve? AI agents typically repeat the same mistakes because they cannot learn from past task outcomes. This Skill implements ReasoningBank's adaptive learning system so agents can record experiences, recognize patterns, and select better strategies over time. ## Core Features & Use Cases - Experience Recording & Pattern Recognition: Log task outcomes with rich context and learn patterns with triggers, actions, and confidence scores. - Strategy Optimization: Compare multiple approaches for a task type and recommend the highest-scoring strategy based on historical performance. - Meta-Learning & Transfer Learning: Learn about learning itself and transfer knowledge between similar domains, such as from JavaScript to TypeScript code review. - Use Case: Build a self-improving code review agent that records each review's outcome, learns which analysis approach finds the most bugs per language, and automatically selects the optimal strategy for each new task. ## Quick Start Initialize ReasoningBank with AgentDB persistence, record a task outcome with its context, then request a recommended strategy for the next task of that type.