MCP Standards

# 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 ```bash # 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 ```