Skill UI

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

This skill guides the creation of highly auditable, evidence-traceable market research reports. It provides a structured workflow for defining market scope, mapping every claim to primary sources (like official filings and statistics), and conducting rigorous, assumption-driven market sizing (TAM/SAM/SOM) and forecasting scenarios. Essential for professional market analysis and strategic planning.

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.

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.

This guide provides comprehensive standards for submitting papers to top-tier Chinese finance journals, such as Journal of Financial Research. It distinguishes between macro-finance (monetary policy, banking) and micro-finance (corporate governance, capital markets), emphasizing the need for rigorous econometric methods (DID, IV, VAR) and concrete policy relevance for financial systems.

Provides comprehensive methodological guidelines for designing rigorous studies in public policy and marketing. The framework covers both prospective (e.g., realistic experiments) and retrospective (e.g., DiD, RDD) evaluation designs, emphasizing the necessity of establishing a strong counterfactual basis and ensuring policy relevance in data collection and analysis.

A guide for transforming abstract marketing phenomena into rigorous, mathematically testable models. It covers both analytical (game-theoretic) and structural econometric approaches, emphasizing the specification of primitives, deriving equilibria, and establishing clear identification arguments for academic research.