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ReasoningBank Intelligence

Implement adaptive learning for AI agents using ReasoningBank pattern recognition and strategy optimization.

1|1|Updated Nov 28, 2025
Search Tags:#ai agents#adaptive learning#reasoningbank#agentdb#strategy optimization#pattern recognition#meta-learning
One-click install
npx skills add https://github.com/33may/robotics --skill reasoningbank-intelligence-33may
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank Intelligence
Source: https://github.com/33may/robotics/tree/main/humanoid/.claude/skills/reasoningbank-intelligence
Command: npx skills add https://github.com/33may/robotics --skill reasoningbank-intelligence-33may

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentic-flow, agentdb.

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 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 match learned patterns against new situations. - Strategy Optimization: Compare multiple approaches for a task type and recommend the highest-scoring strategy based on historical success. - 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 the next review. ## Quick Start Ask the AI to initialize ReasoningBank with AgentDB persistence and record a task outcome so it can recommend the optimal strategy for your next task.

Frequently Asked Questions about ReasoningBank Intelligence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I make an AI agent learn from past task outcomes?▼

Use ReasoningBank's recordExperience method to log each task's approach, outcome metrics, and context. After accumulating enough experiences, call recommendStrategy to get the best-performing approach for similar future tasks.

How to compare different strategies for the same task type?▼

Call compareStrategies with the task type and a list of strategy names. ReasoningBank scores each strategy based on recorded outcomes and returns them ranked, so you can pick the highest-scoring one.

Does ReasoningBank require AgentDB for storage?▼

ReasoningBank can run without persistence, but AgentDB is recommended for durable storage and vector-based semantic pattern matching. Configure it with the agentdb adapter and enable vector search for better pattern retrieval.

Why is ReasoningBank giving poor strategy recommendations?▼

Poor recommendations usually mean insufficient training data. ReasoningBank needs roughly 100 or more recorded experiences per task type, with rich context and consistent outcome metrics, before its recommendations become reliable.

Can ReasoningBank transfer learning between different domains?▼

Yes, the transferKnowledge method applies learned patterns from one domain to a similar one, such as from JavaScript to TypeScript code review, using a similarity score to control how much knowledge carries over.

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