Skill UI

A read-only tool designed to compare prediction-market baskets or themes against the user's existing knowledge base, portfolio notes, research memos, or financial context. It performs gap analysis, flagging potential conflicts, stale assumptions, and areas of missing research. This skill provides structured insights into fit and exposure without offering any investment or trading advice.

This skill provides comprehensive, read-only market intelligence for prediction markets. It helps users research market events, compare different venues (like Polymarket, Kalshi), analyze liquidity, and understand underlying assets. It generates source-grounded briefings, emphasizing context and data quality, and strictly avoids providing investment or trading advice.

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 tool is designed to manage the process of altering an existing, functional feature to meet a new desired specification. It ensures a structured workflow: first, update the existing test cases to reflect the new behavior; second, modify the underlying implementation to pass these updated tests; finally, complete through review and gated commit. Use this when the feature works but its behavior needs adjustment.

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 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.

Assess and manage investment risk using the neural-trader risk engine. This tool calculates critical metrics such as Value at Risk (VaR), Conditional VaR (CVaR), and Sharpe ratio for both single positions and entire portfolios. It also optimizes position sizing using methods like Kelly criterion and checks for market circuit breaker triggers (e.g., high correlation spikes or volatility regime shifts) to ensure robust trading decisions and minimize exposure.

A comprehensive tool for training and evaluating advanced neural network models (including LSTM, Transformer, and N-BEATS) on time-series market data. It facilitates the prediction of financial trends, generating results with confidence intervals, and comparing performance across multiple architectures. Ideal for quantitative trading research and financial forecasting.

This dashboard provides a comprehensive, single-view operational picture of campaign health. It monitors active campaigns by showing real-time health scores, logging auto-corrections (e.g., bid adjustments, budget reallocation), displaying current guardrail rules, and identifying campaigns needing human attention. It quantifies the estimated savings achieved through automated optimizations, helping users trust the automated system while focusing strategic effort on critical issues.

This sophisticated tool optimizes marketing budget allocation across multiple channels using performance data, ROI calculations, and diminishing returns modeling. It analyzes current spend efficiency (ROAS, CPA) and generates a data-driven reallocation plan. Ideal for justifying spend shifts, maximizing investment returns, and creating phased implementation timelines for measurable growth.

This tool generates a comprehensive content repurposing plan by taking a single source piece (e.g., webinar, whitepaper) and adapting it into multiple, platform-specific derivative formats. It ensures maximum ROI from content investments by providing full draft content, a detailed posting schedule, and platform best practices for optimal engagement and reach.