neural-train

Neural Training

Train and manage SONA neural patterns for self-learning.

When to use

After completing a successful task, use this skill to capture what worked and train the intelligence system so future tasks benefit from learned patterns.

Steps

1. Check current neural status — call mcp__claude-flow__neural_status to see active patterns and training state
2. Start a trajectory — call mcp__claude-flow__hooks_intelligence_trajectory-start with the task context
3. Record steps — for each significant action, call mcp__claude-flow__hooks_intelligence_trajectory-step
4. End trajectory — call mcp__claude-flow__hooks_intelligence_trajectory-end with outcome (success/failure)
5. Train patterns — call mcp__claude-flow__neural_train with --pattern-type coordination --epochs 10
6. Store patterns — call mcp__claude-flow__hooks_intelligence_pattern-store to persist learnings
7. Verify — call mcp__claude-flow__neural_patterns to confirm patterns were stored

CLI alternative

npx @claude-flow/cli@latest neural train --pattern-type coordination --epochs 10
npx @claude-flow/cli@latest neural patterns --list
npx @claude-flow/cli@latest neural status
npx @claude-flow/cli@latest hooks pretrain --model-type moe --epochs 10

SONA adaptation

For real-time micro-adaptation (<0.05ms), use:

• mcp__claude-flow__ruvllm_sona_create to initialize a SONA instance
• mcp__claude-flow__ruvllm_sona_adapt to adapt weights based on feedback