Skill v1.0.2

version: "1.0.2" 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

1. 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 ``

1. Review training output: loss curves, validation metrics, prediction accuracy
2. Generate predictions with confidence intervals:

``bash npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d ``

1. Compare model performance across types:

``bash npx neural-trader --model-compare --symbol TICKER --models "lstm,transformer,nbeats" ``

1. 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" })

1. Train SONA on model outcomes:

mcp__plugin_ruflo-core_ruflo__neural_train({ patternType: "trading-model", epochs: 10 })