reasoningbank-intelligence

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning…

INSTALLATION
npx skills add https://github.com/ruvnet/ruflo --skill reasoningbank-intelligence
Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

ReasoningBank Intelligence

What This Skill Does

Implements ReasoningBank's adaptive learning system for AI agents to learn from experience, recognize patterns, and optimize strategies over time. Enables meta-cognitive capabilities and continuous improvement.

Prerequisites

  • agentic-flow v1.5.11+
  • AgentDB v1.0.4+ (for persistence)
  • Node.js 18+

Quick Start

import { ReasoningBank } from 'agentic-flow$reasoningbank';

// Initialize ReasoningBank

const rb = new ReasoningBank({

persist: true,

learningRate: 0.1,

adapter: 'agentdb' // Use AgentDB for storage

});

// Record task outcome

await rb.recordExperience({

task: 'code_review',

approach: 'static_analysis_first',

outcome: {

success: true,

metrics: {

bugs_found: 5,

time_taken: 120,

false_positives: 1

}

},

context: {

language: 'typescript',

complexity: 'medium'

}

});

// Get optimal strategy

const strategy = await rb.recommendStrategy('code_review', {

language: 'typescript',

complexity: 'high'

});

## Core Features

### 1. Pattern Recognition

// Learn patterns from data

await rb.learnPattern({

pattern: 'api_errors_increase_after_deploy',

triggers: ['deployment', 'traffic_spike'],

actions: ['rollback', 'scale_up'],

confidence: 0.85

});

// Match patterns

const matches = await rb.matchPatterns(currentSituation);


### 2. Strategy Optimization

// Compare strategies

const comparison = await rb.compareStrategies('bug_fixing', [

'tdd_approach',

'debug_first',

'reproduce_then_fix'

]);

// Get best strategy

const best = comparison.strategies[0];

console.log(Best: ${best.name} (score: ${best.score}));


### 3. Continuous Learning

// Enable auto-learning from all tasks

await rb.enableAutoLearning({

threshold: 0.7, // Only learn from high-confidence outcomes

updateFrequency: 100 // Update models every 100 experiences

});


## Advanced Usage

### Meta-Learning

// Learn about learning

await rb.metaLearn({

observation: 'parallel_execution_faster_for_independent_tasks',

confidence: 0.95,

applicability: {

task_types: ['batch_processing', 'data_transformation'],

conditions: ['tasks_independent', 'io_bound']

}

});


### Transfer Learning

// Apply knowledge from one domain to another

await rb.transferKnowledge({

from: 'code_review_javascript',

to: 'code_review_typescript',

similarity: 0.8

});


### Adaptive Agents

// Create self-improving agent

class AdaptiveAgent {

async execute(task: Task) {

// Get optimal strategy

const strategy = await rb.recommendStrategy(task.type, task.context);

// Execute with strategy

const result = await this.executeWithStrategy(task, strategy);

// Learn from outcome

await rb.recordExperience({

task: task.type,

approach: strategy.name,

outcome: result,

context: task.context

});

return result;

}

}


## Integration with AgentDB

// Persist ReasoningBank data

await rb.configure({

storage: {

type: 'agentdb',

options: {

database: '.$reasoning-bank.db',

enableVectorSearch: true

}

}

});

// Query learned patterns

const patterns = await rb.query({

category: 'optimization',

minConfidence: 0.8,

timeRange: { last: '30d' }

});


## Performance Metrics

// Track learning effectiveness

const metrics = await rb.getMetrics();

console.log(

Total Experiences: ${metrics.totalExperiences}

Patterns Learned: ${metrics.patternsLearned}

Strategy Success Rate: ${metrics.strategySuccessRate}

Improvement Over Time: ${metrics.improvement}

);

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