Skill UI

This tool generates comprehensive trading signals by analyzing multiple technical indicators (RSI, MACD, Bollinger Bands, etc.) across various assets. It combines indicators to produce composite BUY/SELL signals, complete with confidence scores and risk management levels. Use it when analyzing crypto or stock assets to identify high-potential trading entry or exit points.

Provides technical analysis for stocks and ETFs using RSI, MACD, Bollinger Bands, and moving averages. Useful for traders who need indicator calculations, buy/sell signals, stock comparisons, and monitoring based on ticker symbols.

Build or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.

This skill connects to the DeepEar Lite platform to fetch real-time, high-frequency financial signals. It retrieves crucial data points including signal titles, sentiment summaries, confidence scores, and detailed reasoning. Use this when you require immediate, actionable insights into volatile market trends, stock performance factors, or comprehensive financial analysis.

This specialized skill provides a robust framework for tracking and updating financial investment signals. It systematically assesses the impact of new market information—such as breaking news or price movements—on existing investment theses. The system determines if a signal is Strengthened, Weakened, Falsified, or Unchanged, providing a crucial layer of continuous signal validation for sophisticated financial decision-making.

This skill generates a sophisticated, single-screen consumer dashboard mimicking a high-end lifestyle publication's dating column. It moves beyond simple swiping mechanics to visualize complex social metrics, mutual matches, and connectivity trends using editorial typography and restrained design. Ideal for concepts requiring data-driven storytelling about personal or community relationships.

This skill provides a highly rigorous framework for analyzing complex public issues, platform governance, and institutional compliance. It is designed to structure evidence boundaries, assess procedural justice, and track public commitments by synthesizing source ledgers and multiple data points. It is intended as a specialized sub-routine triggered by the main CrossFrame suite, not for standalone use. Key focus areas include source verification, identifying weak signals, and mitigating AI compliance performance risks.

This skill generates a comprehensive, structured churn analysis, moving far beyond simple headline rates. It meticulously separates avoidable vs. unavoidable customer losses, identifies high-risk segments (by tier, cohort, etc.), calculates critical metrics like Net Revenue Retention (NRR), and pinpoints actionable early warning signals. The output culminates in prioritized, data-driven intervention recommendations to improve customer retention.

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 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.

This skill computes disproportionality metrics (PRR, ROR, EBGM, IC) using FAERS/OpenFDA drug-event data. It analyzes spontaneous reports to statistically screen for potential drug-reaction associations (Signal of Disproportionate Reporting, SDR). By constructing a 2x2 contingency table, it helps pharmacovigilance professionals identify over-reported adverse events, providing a statistical hypothesis for drug safety assessment.

Analyzes sexual health records, STD screening status, contraception effectiveness, and IIEF-5 scores to surface risk patterns, trends, and signals that warrant professional medical evaluation.