What problem does it solve? AI agents typically repeat the same mistakes because they cannot retain or learn from past task outcomes. This Skill implements ReasoningBank's adaptive learning system so agents record experiences, recognize recurring patterns, and select better strategies over time. ## Core Features & Use Cases - Experience Recording & Strategy Recommendation: Log task outcomes with context and query the optimal strategy for a given task type and situation. - Pattern Recognition & Matching: Learn patterns from data (e.g., errors spiking after deployments) and match them against current situations to trigger actions. - Meta-Learning & Transfer Learning: Learn about the learning process 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, compares strategies like TDD-first versus debug-first, and automatically recommends the highest-scoring approach for the next task. ## Quick Start Ask the agent to initialize ReasoningBank with AgentDB persistence, record a task outcome, and recommend the best strategy for your next task.
