neural-training

name	neural-training
description	Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation. Use when: pattern learning, model optimization, knowledge transfer, adaptive routing. Skip when: simple tasks, no learning required, one-off operations.

Neural Training Skill

Purpose

Train and optimize neural patterns using SONA, MoE, and EWC++ systems.

When to Trigger

• Training new patterns
• Optimizing agent routing
• Knowledge consolidation
• Pattern recognition tasks

Intelligence Pipeline

1. RETRIEVE — Fetch relevant patterns via HNSW (150x-12,500x faster)
2. JUDGE — Evaluate with verdicts (success$failure)
3. DISTILL — Extract key learnings via LoRA
4. CONSOLIDATE — Prevent catastrophic forgetting via EWC++

Components

Component	Purpose	Performance
SONA	Self-optimizing adaptation	<0.05ms
MoE	Expert routing	8 experts
HNSW	Pattern search	150x-12,500x
EWC++	Prevent forgetting	Continuous
Flash Attention	Speed	2.49x-7.47x

Commands

Train Patterns

npx claude-flow neural train --model-type moe --epochs 10

Check Status

npx claude-flow neural status

View Patterns

npx claude-flow neural patterns --type all

Predict

npx claude-flow neural predict --input "task description"

Optimize

npx claude-flow neural optimize --target latency

Best Practices

1. Use pretrain hook for batch learning
2. Store successful patterns after completion
3. Consolidate regularly to prevent forgetting
4. Route based on task complexity