neural-training
A toolkit for advanced neural network training, model optimization, and knowledge retention using state-of-the-art architectures.
Install
mkdir -p .claude/skills/neural-training && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4036" && unzip -o skill.zip -d .claude/skills/neural-training && rm skill.zipInstalls to .claude/skills/neural-training
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Train and optimize neural patterns using SONA
- →Route tasks using Mixture of Experts
- →Consolidate knowledge to prevent catastrophic forgetting via EWC++
- →Search patterns using HNSW
- →Predict model behavior based on task inputs
How it works
The skill uses a pipeline involving HNSW for retrieval, LoRA for distillation, and EWC++ for consolidation to manage and optimize neural patterns.
Inputs & outputs
When to use neural-training
- →Train custom neural patterns
- →Optimize agent model routing
- →Consolidate model knowledge to prevent forgetting
- →Predict model behavior based on task inputs
About this skill
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
- RETRIEVE — Fetch relevant patterns via HNSW (150x-12,500x faster)
- JUDGE — Evaluate with verdicts (success$failure)
- DISTILL — Extract key learnings via LoRA
- 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
- Use pretrain hook for batch learning
- Store successful patterns after completion
- Consolidate regularly to prevent forgetting
- Route based on task complexity
When not to use it
- →Simple tasks
- →No learning required
- →One-off operations
Limitations
- →Not suitable for simple or non-learning tasks
How it compares
It provides a specialized architecture for continuous learning and expert routing rather than standard model training.
Compared to similar skills
neural-training side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| neural-training (this skill) | 1 | 6mo | Review | Advanced |
| quant-analyst | 103 | 2mo | No flags | Advanced |
| umap-learn | 6 | 2mo | Review | Intermediate |
| embedding-strategies | 8 | 2mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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