About neural-training
Neural Training Skill trains and optimizes neural patterns with SONA, MoE, and EWC++. It is intended for pattern learning, agent routing, and knowledge consolidation, with commands for training, status checks, prediction, and optimization.
Key features
- 01Retrieve patterns with HNSW
- 02Evaluate pattern outcomes
- 03Distill learnings with LoRA
- 04Consolidate learning with EWC++
- 05Commands for training, prediction, and optimization
Use cases
- 01Train new neural patterns
- 02Route agents by task complexity
- 03Consolidate accumulated knowledge
- 04Recognize and predict task patterns
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/neural-trainingin Claude Code to confirm it loaded.
Details
From the project READMENeural 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
- RETRIEVE — Fetch relevant patterns via HNSW (150x-12,500x faster)
- JUDGE — Evaluate with verdicts (success$failure)
- DISTILL — Extract key learnings via LoRA
- 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
- Use pretrain hook for batch learning
- Store successful patterns after completion
- Consolidate regularly to prevent forgetting
- Route based on task complexity
Comments
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