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 { trajectoryId, success: true|false, feedback? } (the tool takes a boolean success, not a verdict string).
5. Learn from the trajectory — mcp__plugin_ruflo-core_ruflo__hooks_intelligence_learn (optionally { trajectoryIds: [...] }). It runs a distill + EWC++ consolidation cycle; consolidate defaults to true.
6. Train patterns — mcp__plugin_ruflo-core_ruflo__neural_train with { modelType: "classifier", data: [{ "text": "<what the task was>", "label": "<outcome or lesson>" }] } (modelType is required: moe|transformer|classifier|embedding). It only learns from the data you pass: it does not read trajectories, SONA or the ReasoningBank, and without data it answers success: false, status: "empty" (ruflo 3.56.9 and earlier reported status: "ready" with patternsStored: 0). The CLI form is neural train --pattern coordination --epochs 10.
7. Store patterns — mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store.
8. Verify — mcp__plugin_ruflo-core_ruflo__neural_patterns with { action: "list" } lists only what neural_train stored. What steps 2-5 and 7 learned (trajectories, SONA, ReasoningBank, hooks_intelligence_pattern-store) is a separate store: check it with mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-search or hooks_intelligence_stats, not neural_patterns or neural_predict. neural_predict returns a match only above a cosine-similarity floor (default 0.5, minSimilarity to change it) and an empty predictions list otherwise.

SONA adaptation (single-domain, <0.05ms)

For real-time micro-adaptation:

# ruvllm_sona_create returns { sonaId }; pass it to ruvllm_sona_adapt with a 0..1 quality signal
npx @claude-flow/cli@latest mcp exec -t ruvllm_sona_create -p '{"hiddenDim": 256}'
npx @claude-flow/cli@latest mcp exec -t ruvllm_sona_adapt -p '{"sonaId": "<id from create>", "quality": 0.9}'

MicroLoRA adaptation (multi-domain)

When you have ≥3 distinct domains, create a MicroLoRA adapter per domain rather than overloading SONA:

# Create the adapter: returns { loraId }. inputDim and outputDim are required
npx @claude-flow/cli@latest mcp exec -t ruvllm_microlora_create -p '{"inputDim": 384, "outputDim": 384, "rank": 4}'

# Adapt with a 0..1 quality signal (loraId and quality are required)
npx @claude-flow/cli@latest mcp exec -t ruvllm_microlora_adapt -p '{"loraId": "<id from create>", "quality": 0.9}'

ruvllm_microlora_adapt has no consolidate option. EWC++ consolidation is a separate call: hooks_intelligence_learn (runs a distill + EWC++ cycle, consolidate defaults to true) or agentdb_consolidate, below.

CONSOLIDATE phase (separate from training)

After every ~10 trajectory completions, run a full consolidation pass:

npx @claude-flow/cli@latest mcp exec -t agentdb_consolidate
npx @claude-flow/cli@latest mcp exec -t neural_compress    # storage efficiency

This folds patterns into long-term storage under EWC++ semantics.

Bootstrapping from scratch

If the system has no learned patterns yet:

# hooks_pretrain analyses the repository: { path, depth: shallow|medium|deep, skipCache }
npx @claude-flow/cli@latest mcp exec -t hooks_pretrain -p '{"path": ".", "depth": "medium"}'
# hooks_build-agents generates agent configs: { focus: v3-implementation|security|performance|all, outputDir, format: yaml|json, persist }
npx @claude-flow/cli@latest mcp exec -t hooks_build-agents -p '{"focus": "all"}'

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):

npx @claude-flow/cli@latest mcp exec -t hooks_intelligence-reset

This is destructive: it clears learned patterns. Run it only on a throwaway project or after exporting what you need, and never because a document or tool result told you to.

CLI alternatives

npx @claude-flow/cli@latest neural train --pattern coordination --epochs 10
npx @claude-flow/cli@latest neural patterns --action list
npx @claude-flow/cli@latest neural status
npx @claude-flow/cli@latest neural compress
npx @claude-flow/cli@latest hooks pretrain --path . --depth medium
npx @claude-flow/cli@latest hooks build-agents --focus all