ruvnet / neural-training
Install for your project team
Run this command in your project directory to install the skill for your entire team:
mkdir -p .claude/skills/neural-training && curl -L -o skill.zip "https://fastmcp.me/Skills/Download/4036" && unzip -o skill.zip -d .claude/skills/neural-training && rm skill.zipProject Skills
This skill will be saved in .claude/skills/neural-training/ and checked into git. All team members will have access to it automatically.
Important: Please verify the skill by reviewing its instructions before using it.
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.
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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 ```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