neural-training

# 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 ```bash npx claude-flow neural train --model-type moe --epochs 10 ``` ### Check Status ```bash npx claude-flow neural status ``` ### View Patterns ```bash npx claude-flow neural patterns --type all ``` ### Predict ```bash npx claude-flow neural predict --input "task description" ``` ### Optimize ```bash 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