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ReasoningBank Intelligence

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

Updated Aug 18, 2026
Search Tags:#adaptive learning#reasoningbank#agentdb#strategy optimization#pattern recognition#meta-learning#agentic-flow
One-click install
npx skills add https://github.com/wandreandrade2018-maker/claude-config --skill reasoningbank-intelligence-wandreandrade2018-maker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank Intelligence
Source: https://github.com/wandreandrade2018-maker/claude-config/tree/main/.claude/skills/reasoningbank-intelligence
Command: npx skills add https://github.com/wandreandrade2018-maker/claude-config --skill reasoningbank-intelligence-wandreandrade2018-maker

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 & Pattern Recognition: Log task outcomes with 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 picks the best strategy for the next review. ## Quick Start Ask the agent to initialize ReasoningBank with AgentDB persistence and record the outcome of your next task so it can recommend an optimal strategy for future runs.

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 work with AgentDB for persistence?▼

Yes, ReasoningBank integrates with AgentDB v1.0.4+ as its storage adapter. Configure the storage type as agentdb with a database path, and optionally enable vector search for semantic pattern matching.

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, before its recommendations become reliable.

What are the limitations of ReasoningBank pattern matching?▼

Pattern matching can become slow without vector indexing enabled in AgentDB, and memory grows unbounded without TTL or pruning for old experiences. Low-confidence learnings should also be filtered with thresholds.

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