neural-training
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.
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- ruvnet/ruflo
- Last source activity
- February 7, 2026 at 17:30
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- English
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- 75,283
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- 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
```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
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