Trader TrainCAUTION
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Overview
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
ef7d4f0535e5OBSERVED · 2026-09-26What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: trader-train
description: Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__neural_train
argument-hint: "<lstm|transformer|nbeats> --symbol <TICKER>"
---
Train neural prediction models using neural-trader's ML engine.
Steps:
1. Ensure neural-trader is available:
`npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader`
2. Train the specified model:
```bash
npx neural-trader --model lstm --symbol TICKER --confidence 0.95
npx neural-trader --model transformer --symbol TICKER --predict
npx neural-trader --model nbeats --symbol TICKER --decompose
```
3. Review training output: loss curves, validation metrics, prediction accuracy
4. Generate predictions with confidence intervals:
```bash
npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d
```
5. Compare model performance across types:
```bash
npx neural-trader --model-compare --symbol TICKER --models "lstm,transformer,nbeats"
```
6. Store model results (canonical `trading-analysis` namespace per ADR-126 Phase 1 — was previously stored to undeclared `trading-models`):
`mcp__plugin_ruflo-core_ruflo__memory_store({ key: "model-MODEL-TICKER-DATE", value: "TRAINING_RESULTS", namespace: "trading-analysis" })`
7. Train SONA on model outcomes:
`mcp__plugin_ruflo-core_ruflo__neural_train({ patternType: "trading-model", epochs: 10 })`Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
crates
plugin/agents
plugin/commands
plugin/skills
Gates applied: no_behavioural_pass.
ef7d4f0535e5full audit observations/trust-audit/skill/ruvnet__trader-train.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-26 | ef7d4f0535e5 | CAUTION | B | 89 | first audit |
Questions
What does the Trader Train skill do?
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Is Trader Train safe to install?
With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Trader Train access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (ef7d4f0535e5), read on 2026-09-26. The repository is watched, and a new audit runs when it changes — this is the first audit.