Skill UI

This skill simulates a four-voice council (Architect, Skeptic, Pragmatist, Critic) to resolve complex decisions marked by ambiguity or multiple trade-offs. It is designed for high-level strategic choices, identifying blind spots, and determining go/no-go decisions when a clear winner is absent. It specializes in synthesizing diverse perspectives to mitigate risk.

Expert knowledge for customs declarations, tariff classification, duty optimization, restricted party screening, and regulatory compliance across multiple jurisdictions. Handles HS classification, Incoterms, FTA utilization, and penalty mitigation.

This skill generates a structured, non-advisory worksheet for planning trade ideas, basket adjustments, or comparing prediction markets. It helps users methodically review venues, underliers, constraints, and manual execution steps without placing any trades or providing financial advice. Ideal for the research phase of quantitative finance workflows.

This skill provides a structured framework for researching prediction markets when they are used as data sources, forecasting inputs, or oracle signals for business intelligence. It allows users to perform source-grounded analysis of market-implied probabilities, signal quality (e.g., liquidity, spread, age), and compare these signals against traditional internal KPIs or news sources. Crucially, it strictly separates implied signals from investment advice and trade recommendations.

This skill reviews prediction market, basket, oracle, and trading-agent workflows to ensure compliance, safety, data quality, and privacy. It must be run before any workflow handles user portfolio data, venue authentication, API keys, or trade planning to mitigate execution and regulatory risks across multiple domains.

Provides a product-focused lens for evaluating features, user needs and business trade-offs, helping teams make better product decisions. Note: this file is a Japanese translation placeholder; see the English source skill for full content.

This skill executes comprehensive historical backtesting using the high-performance neural-trader engine. It supports features like walk-forward validation and captures key financial metrics (Sharpe, Drawdown). Critically, it signs the resulting artifact using Ed25519 cryptography, providing non-repudiable tamper evidence required for promoting research models to live trading systems.

This skill dispatches computationally intensive quantitative trading jobs (such as multi-year walk-forward analysis, large Monte-Carlo simulations, parameter sweeping, or deep model training) to a managed cloud agent runtime. It is designed for large-scale, production-grade backtesting that exceeds local machine capacity, ensuring robust execution and artifact management via Anthropic's cloud services.

This skill calculates model interpretability for algorithmic trading signals using PageRank. It constructs a feature-contribution graph from an LSTM or Transformer signal and executes a single-entry PageRank calculation. The result identifies the top-K features that most significantly drove the prediction, providing a "regulator-grade" audit trail essential for compliance with regulations like the EU AI Act.

This tool automates the process of optimizing investment portfolio allocations using a mean-variance engine. It integrates neural prediction models and risk constraints to determine optimal asset weights. Users can set specific risk targets, assess comprehensive risk metrics (variance, correlation), and generate actionable, step-by-step rebalancing plans for professional portfolio management.

This skill performs mean-variance portfolio optimization by solving the linear system Σ · x = μ using the Conjugate Gradient (CG) method. It is significantly faster (40-60x) and numerically robust compared to legacy methods like the Neumann series. It takes a covariance matrix (Σ) and expected return vector (μ) to determine optimal asset weights, providing full provenance tracking for auditability.

A comprehensive tool for analyzing the current market regime (bull, bear, ranging, volatile) using advanced neural models. It detects the prevailing market structure, provides a confidence score, and recommends suitable trading strategies. Ideal for market analysis, backtesting preparation, or general market condition inquiries.