MCPInstall

neural-train

by ruvnetProductivity & Automation

Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ruvnet/ruflo --skill neural-train
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads neural-train automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

FAQ

What does the neural-train skill do?

Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline

How do I install neural-train?

Run npx skills add https://github.com/ruvnet/ruflo --skill neural-train in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/neural-train/ (personal) or .claude/skills/neural-train/ (project).

Which AI agents can use neural-train?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is neural-train safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains neural-train?

It is published by ruvnet in ruvnet/ruflo. It has 748 recorded installs.

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