Skill UI

This skill utilizes the Kronos model to perform advanced financial time-series forecasting. It generates quantitative base predictions and then integrates news sentiment analysis to adjust and refine the forecast, making it an essential tool for finance professionals needing actionable market insights.

This skill aggregates real-time financial information, including hot news articles and market trends from multiple sources (e.g., Weibo, Zhihu). It is also capable of retrieving structured data from prediction markets like Polymarket. Use this when you need a unified, comprehensive view of the financial market landscape, spanning breaking news, aggregated sentiment, and forward-looking market predictions.

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 guides the architectural design of an Itô Data Atlas agent for advanced quantitative research. It facilitates market discovery, constructs candidate prediction-market baskets, drafts necessary parameter changes, and integrates human-in-the-loop review. It is strictly for workflow and architectural planning, and does not execute live trading orders.

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 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.

This tool generates sophisticated trading signals by leveraging an advanced anomaly detection engine. It scans financial assets for various market anomalies—such as spikes, drifts, and oscillations—and classifies them. It then integrates neural network prediction and searches historical pattern databases to output highly ranked, actionable trading signals, complete with confidence scores and target parameters.

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.

A rigorous framework designed for academics to vet research questions for high-impact journals. It guides researchers to ensure that the international dimension is the theoretical core, providing 'stress tests' and criteria to prove that cross-national variation generates unique predictions, elevating the work beyond mere cross-country replication.

This guide provides a structured framework for developing robust conceptual arguments in marketing manuscripts. It teaches users how to ground theory in real-world phenomena, specify causal mechanisms, and derive measurable predictions. It addresses both theory-first and empirics-first research routes, helping authors build the minimum sufficient conceptual structure required for high-impact academic publishing.