neural-train
Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline
Profile →npx skills add ruvnet/ruflo --skill neural-train --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to use
- Standard flow (DISTILL)
- SONA adaptation (single-domain, <0.05ms)
- MicroLoRA adaptation (multi-domain)
- CONSOLIDATE phase (separate from training)
- Bootstrapping from scratch
- Reset (testing only)
- CLI alternatives
mcp tool call ruvllm_sona_create --json -- '{"domain": "coding"}'
mcp tool call ruvllm_sona_adapt --json -- '{"feedback": {"score": 0.9, "trajectory": "..."}}'
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}'
mcp tool call agentdb_consolidate --json
mcp tool call neural_compress --json # storage efficiencyWhat 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 it?
Run `npx skills add ruvnet/ruflo --skill neural-train --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.