Skill Market

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.

GitHub
githubcommunityclaude
0.0
0 installs73.4K GitHub starsby ruvnet

Skill Introduction

Overview
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.

Core value

Turns reusable Other know-how into an installable skill, helping users complete github, community, claude work faster.

Target users

  • Developers, testers, and maintainers who handle Other tasks in Focus Code.
  • Teams that already trust workflows or content from ruvnet.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community, claude that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Other, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, claude, so it can be filtered by concrete task intent.

Install and use

Install
Copy Install Command
focus install neural-training-4867c2
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SKILL.md

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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

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

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