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.

This tool aggregates public sentiment and discussions on any given topic from the last 30 days. It pulls data from major platforms like Reddit, X, YouTube, TikTok, GitHub, and news sources, providing a comprehensive, multi-source view of current trends, public opinion, and industry signals. Ideal for market research, competitive analysis, and understanding rapidly evolving topics.

The Stock Analyzer Skill provides comprehensive technical analysis for stocks and ETFs. It utilizes industry-standard indicators like RSI, MACD, and Bollinger Bands to generate actionable trading signals. Users can perform advanced technical comparisons, monitor portfolio strength, and identify chart patterns through natural language queries, eliminating the need for manual indicator calculation.

NeuroKit2 is a comprehensive Python toolkit designed for processing and analyzing diverse physiological biosignals. It covers multiple domains including cardiovascular (ECG, PPG), neural (EEG), autonomic (EDA), respiratory (RSP), and muscular (EMG) signals. Use this skill for advanced psychophysiology research, complex pattern detection, and real-time clinical monitoring applications.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

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.