neural-training

neural-training (neural-training)

🌟 TỔNG QUAN

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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

🛠️ PHÁC ĐỒ VẬN HÀNH (OPERATIONAL PROTOCOL)

🔍 Phase 1: Investigation (Thẩm định)

• Xác minh tham số đầu vào dựa trên Schema.
• Kiểm tra bối cảnh hệ thống liên quan.

🚀 Phase 2: Action (Thực thi)

• Triệu hồi logic thực thi trong logic.py.
• Trả về kết quả và chắt lọc kinh nghiệm.

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⚠️ SAI LẦM THƯỜNG GẶP (COMMON PITFALLS)

• Chưa ghi nhận.

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TRUNG THÀNH - CHÍNH XÁC - TỐI THƯỢNG 💎🦾