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smithery/ruvnet

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.

Installation

$ npx skills add smithery/ruvnet --skill neural-training
View on skills.sh ↗ Website ↗

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Gemini CLI Not declared
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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,735 B
  • docs SUMMARY.md 313 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

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