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neural-train

neural-train

github.com/ruvnetAgent workflows80.0k748 installsUpdated today
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About neural-train

neural-train is a Ruflo skill for learning patterns from completed tasks and consolidating them into long-term storage. It guides trajectory capture, pattern training, and SONA or MicroLoRA adaptation.

Key features

  1. 01Records task trajectories and outcomes
  2. 02Learns and trains coordination patterns
  3. 03Stores and verifies learned patterns
  4. 04Supports SONA and MicroLoRA adaptation
  5. 05Consolidates patterns with EWC++
  6. 06Offers CLI alternatives for training

Use cases

  1. 01Capturing what worked after a task
  2. 02Consolidating patterns from completed trajectories
  3. 03Adapting models to a new domain

MCP support

Uses Ruflo MCP tools for neural status, trajectory capture, learning, training, pattern storage, and consolidation; it also documents MCP calls for SONA and MicroLoRA adaptation.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ruvnet/ruflo --skill neural-train

After installing, type/neural-trainin Claude Code to confirm it loaded.

Details

From the project README

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:

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:

# 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:

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:

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):

mcp tool call hooks_intelligence-reset --json

CLI alternatives

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

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