Neural Training

by ruvnet6051f6702b61No license74K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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.

Instructions onlyAI & Agents
AI-generated overview

Trains and optimizes neural patterns using SONA, MoE, and EWC++ through claude-flow commands.

What it does
This skill describes a neural pattern training workflow built around SONA, MoE, and EWC++ components. It outlines an intelligence pipeline of retrieve, judge, distill, and consolidate steps, and lists claude-flow commands for training, checking status, viewing patterns, predicting, and optimizing. It is an instructions-only skill with no bundled scripts.
When to use it
Use it for pattern learning, model optimization, knowledge transfer, or adaptive routing tasks. It is intended to be skipped for simple tasks, one-off operations, or work that requires no learning.
Requirements
Requires the claude-flow CLI, invoked via npx, and network access to fetch it. No scripts are shipped; the skill contains instructions only.

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

Source and attribution

Source:ruvnet/rufloin.agents/skills/neural-trainingat commit6051f67

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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