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ReasoningBank Intelligence

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

11|Updated Jan 20, 2026
Search Tags:#ai agents#adaptive learning#reasoningbank#agentdb#strategy optimization#pattern recognition#meta-learning
One-click install
npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill reasoningbank-intelligence-ishandutta2007
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank Intelligence
Source: https://github.com/ishandutta2007/claude-agent-orchestration/tree/main/.claude/skills/reasoningbank-intelligence
Command: npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill reasoningbank-intelligence-ishandutta2007

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentic-flow, agentdb.

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 & 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 triggers and actions, then match them against current situations with confidence scores. - 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, compares strategies like TDD-first versus debug-first, and automatically recommends the highest-scoring approach for the next task. ## Quick Start Ask the AI to initialize ReasoningBank with AgentDB persistence and record a task outcome so it can recommend the best strategy for your next code review.

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 with the task type and context to get the best-performing approach.

How to compare different problem-solving strategies programmatically?▼

Call compareStrategies with a task type and a list of strategy names, such as tdd_approach or debug_first. The result ranks strategies by score so you can select the highest-performing one for your context.

Does ReasoningBank work with AgentDB for persistence?▼

Yes, ReasoningBank integrates with AgentDB v1.0.4+ by setting the adapter to agentdb during initialization. This enables durable storage of experiences and vector-based semantic pattern matching.

Why is ReasoningBank giving poor strategy recommendations?▼

Poor recommendations usually mean insufficient training data; you need at least 100 recorded experiences per task type. Also ensure you record both successes and failures with rich context to improve pattern matching quality.

Can learned knowledge transfer between different task domains?▼

Yes, the transferKnowledge method applies learning from one domain to another, such as from JavaScript to TypeScript code review, using a similarity score. Higher similarity values produce more reliable transfers.

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