What problem does it solve? AI agents typically repeat the same mistakes because they cannot retain knowledge across tasks. This Skill implements ReasoningBank's adaptive learning system so agents can record task outcomes, recognize recurring patterns, and select better strategies over time. ## Core Features & Use Cases - Experience Recording & Strategy Recommendation: Log task outcomes with context and metrics, then query the optimal strategy for a given task type and situation. - Pattern Recognition & Meta-Learning: Learn patterns from triggers and actions, match them against current situations, and perform meta-learning and transfer learning across domains. - AgentDB Persistence: Store learned patterns and experiences in AgentDB with vector search for semantic pattern matching and long-term retention. - Use Case: Build a self-improving code review agent that records each review's outcome, compares strategies like TDD-first versus debug-first, and automatically recommends the highest-scoring approach for the next task. ## Quick Start Initialize ReasoningBank with AgentDB persistence, record a task outcome with its context and metrics, then request a recommended strategy for the next similar task.
