neural-train

Neural Training

Train and consolidate neural patterns. Implements the DISTILL and CONSOLIDATE phases of the 4-step intelligence pipeline.

When to use

• After completing a successful task — capture what worked.
• After accumulating ≥10 task completions — run consolidation to fold patterns into long-term storage.
• When training a new domain — create a MicroLoRA adapter for it.

Standard flow (DISTILL)

1. Check current neural status — mcp__claude-flow__neural_status.
2. Start a trajectory — mcp__claude-flow__hooks_intelligence_trajectory-start with the task context.
3. Record steps — for each significant action, mcp__claude-flow__hooks_intelligence_trajectory-step.
4. End trajectory — mcp__claude-flow__hooks_intelligence_trajectory-end with verdict: pass|fail|partial.
5. Learn from the trajectory — mcp__claude-flow__hooks_intelligence_learn.
6. Train patterns — mcp__claude-flow__neural_train with --pattern-type coordination --epochs 10.
7. Store patterns — mcp__claude-flow__hooks_intelligence_pattern-store.
8. Verify — mcp__claude-flow__neural_patterns to confirm.

SONA adaptation (single-domain, <0.05ms)