cldcde: Skill for Claude Code

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']
  }
});