/neural-patterns

Neural Pattern Training

Purpose

Continuously improve coordination through neural network learning.

How Training Works

1. Automatic Learning

Every successful operation trains the neural networks:

• Edit patterns for different file types
• Search strategies that find results faster
• Task decomposition approaches
• Agent coordination patterns

2. Manual Training

Tool: mcp__claude-flow__neural_train
Parameters: {
  "pattern_type": "coordination",
  "training_data": "successful task patterns",
  "epochs": 50
}

3. Pattern Types

**Cognitive Patterns:**

• Convergent: Focused problem-solving
• Divergent: Creative exploration
• Lateral: Alternative approaches
• Systems: Holistic thinking
• Critical: Analytical evaluation
• Abstract: High-level design

4. Improvement Tracking

Tool: mcp__claude-flow__neural_status
Result: {
  "patterns": {
    "convergent": 0.92,
    "divergent": 0.87,
    "lateral": 0.85
  },
  "improvement": "5.3% since last session",
  "confidence": 0.89
}

Pattern Analysis

Tool: mcp__claude-flow__neural_patterns
Parameters: {
  "action": "analyze",
  "operation": "recent_edits"
}

Benefits

• 🧠 Learns your coding style
• 📈 Improves with each use
• 🎯 Better task predictions
• ⚡ Faster coordination

CLI Usage

# Train neural patterns via CLI
npx claude-flow neural train --type coordination --epochs 50

# Check neural status
npx claude-flow neural status

# Analyze patterns
npx claude-flow neural patterns --analyze