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__plugin_ruflo-core_ruflo__neural_status`. 2. **Start a trajectory** — `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start` with the task context. 3. **Record steps** — for each significant action, `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step`. 4. **End trajectory** — `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end` with `verdict: pass|fail|partial`. 5. **Learn from the trajectory** — `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_learn`. 6. **Train patterns** — `mcp__plugin_ruflo-core_ruflo__neural_train` with `--pattern-type coordination --epochs 10`. 7. **Store patterns** — `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store`. 8. **Verify** — `mcp__plugin_ruflo-core_ruflo__neural_patterns` to confirm. ## SONA adaptation (single-domain, <0.05ms) For real-time micro-adaptation: ```bash mcp tool call ruvllm_sona_create --json -- '{"domain": "coding"}' mcp tool call ruvllm_sona_adapt --json -- '{"feedback": {"score": 0.9, "trajectory": "..."}}' ``` ## MicroLoRA adaptation (multi-domain) When you have ≥3 distinct domains, create a MicroLoRA adapter per domain rather than overloading SONA: ```bash # Create the adapter mcp tool call ruvllm_microlora_create --json -- '{"domain": "frontend"}' # Adapt with feedback mcp tool call ruvllm_microlora_adapt --json -- '{"adapter": "frontend", "feedback": {...}}' # CONSOLIDATE phase: apply EWC++ on weight deltas to prevent catastrophic forgetting mcp tool call ruvllm_microlora_adapt --json -- '{"adapter": "frontend", "consolidate": true}' ``` The `--consolidate` flag is the EWC++ trigger. Without it, fresh training overwrites older domains. ## CONSOLIDATE phase (separate from training) After every ~10 trajectory completions, run a full consolidation pass: ```bash mcp tool call agentdb_consolidate --json mcp tool call neural_compress --json # storage efficiency ``` This folds patterns into long-term storage under EWC++ semantics. ## Bootstrapping from scratch If the system has no learned patterns yet: ```bash mcp tool call hooks_pretrain --json -- '{"modelType": "moe", "epochs": 10}' mcp tool call hooks_build-agents --json -- '{"agentTypes": "coder,tester"}' ``` `hooks_pretrain` writes to the `patterns` (plural) namespace — distinct from the `pattern` (singular) ReasoningBank target. See `ruflo-agentdb` ADR-0001 for the namespace convention. ## Reset (testing only) To wipe intelligence state (e.g., for benchmarking): ```bash mcp tool call hooks_intelligence-reset --json ``` ## CLI alternatives ```bash 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 neural compress npx @claude-flow/cli@latest hooks pretrain --model-type moe --epochs 10 npx @claude-flow/cli@latest hooks build-agents --agent-types coder,tester ```