neural-training

Original：🇺🇸 English
Translated

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.

6installs
Sourceruvnet/claude-flow
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NPX Install

npx skill4agent add ruvnet/claude-flow neural-training

Tags

Translated version includes tags in frontmatter
neural-pattern-trainingmoe-systemewc-plussona-architectureknowledge-consolidation

SKILL.md Content

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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

ComponentPurposePerformance
SONASelf-optimizing adaptation<0.05ms
MoEExpert routing8 experts
HNSWPattern search150x-12,500x
EWC++Prevent forgettingContinuous
Flash AttentionSpeed2.49x-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