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Reusable instructions for compatible AI agents.
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Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
Detect and classify candlestick patterns from ingested OHLCV data
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation; Ed25519-sign the result for paper→live tamper evidence (ADR-126 Phase 4)
Regulator-grade feature attribution for any LSTM/Transformer signal — single-entry PageRank ranks the top-K features that drove the prediction (ADR-126 Phase 6, ADR-123 single-entry PR)
Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
Ingest and normalize market data into OHLCV vectors with HNSW indexing
Pathfinder traversal of the knowledge graph starting from a seed entity
Workflow creation, execution, and template management. Automates complex multi-step processes with agent coordination. Use when: automating processes, creating reusable workflows, orchestrating multi-step tasks. Skip when: simple single-step tasks, ad-hoc operations.
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Platform labels describe intended use. Read each resource's author credits, requirements and available access methods.