Is neural-training safe to install?

SaferSkills independently audited neural-training (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

.agents/skills/neural-training/SKILL.md · 1 file

.agents/skills/neural-training/SKILL.md1.7 KB · Markdown

name: neural-training

description: >

Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation.

Use when: pattern learning, model optimization, knowledge transfer, adaptive routing.

Skip when: simple tasks, no learning required, one-off operations.

Neural Training Skill

Purpose

Train and optimize neural patterns using SONA, MoE, and EWC++ systems.

When to Trigger

Training new patterns
Optimizing agent routing
Knowledge consolidation
Pattern recognition tasks

Intelligence Pipeline

RETRIEVE — Fetch relevant patterns via HNSW (150x-12,500x faster)
JUDGE — Evaluate with verdicts (success$failure)
DISTILL — Extract key learnings via LoRA
CONSOLIDATE — Prevent catastrophic forgetting via EWC++

Components

Component	Purpose	Performance
SONA	Self-optimizing adaptation	<0.05ms
MoE	Expert routing	8 experts
HNSW	Pattern search	150x-12,500x
EWC++	Prevent forgetting	Continuous
Flash Attention	Speed	2.49x-7.47x

Commands

Train Patterns

npx claude-flow neural train --model-type moe --epochs 10

Check Status

npx claude-flow neural status

View Patterns

npx claude-flow neural patterns --type all

Predict

npx claude-flow neural predict --input "task description"

Optimize

npx claude-flow neural optimize --target latency

Best Practices

Use pretrain hook for batch learning
Store successful patterns after completion
Consolidate regularly to prevent forgetting
Route based on task complexity

neural-training— agent skill

Is neural-training safe to install?

Neural Training Skill

Purpose

When to Trigger

Intelligence Pipeline

Components

Commands

Train Patterns

Check Status

View Patterns

Predict

Optimize

Best Practices

Audit the pieces. Scan the whole. Decide.