Skill UI

This comprehensive tool helps users set, track, and optimize personal health goals using deep data analysis. It validates goals against the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) and tracks progress by correlating diverse health data, including nutrition, exercise, and sleep patterns. Key features include habit formation analysis, motivation assessment, identifying roadblocks, and generating interactive reports with ECharts visualizations, providing a holistic view of wellness journeys.

A comprehensive tool for analyzing long-term health data, tracking trends across multiple dimensions such as weight, vital signs, symptoms, medication adherence, lab results, and mood. It identifies significant changes, correlations between lifestyle factors and health metrics, and provides data-driven insights, risk assessments, and predictive recommendations for proactive health management.

Use this skill to perform read-only API calls for exploring, previewing, searching, and filtering structured datasets hosted on Hugging Face. It allows users to validate dataset availability, list splits, paginate large datasets, and retrieve comprehensive metadata and statistics, making it ideal for data validation and content extraction workflows.

A comprehensive skill for managing the entire lifecycle of datasets on the Hugging Face Hub. It allows users to initialize, configure, and stream updates for datasets of various types (Chat, Text Classification, Tabular). Crucially, it extends data handling capabilities by enabling powerful SQL-based querying and transformation directly on Hub datasets using the DuckDB engine, facilitating advanced data exploration and pipeline creation.

Literature Review Tool helps researchers gather and synthesize key insights from scholarly texts, highlighting patterns and critical arguments to accelerate literature surveying workflows even when structured content is initially unavailable.

Analyze dietary and nutrient data, identify nutrition patterns, assess nutritional status, and generate personalized improvement advice. Also supports correlation analysis with exercise, sleep, weight, blood pressure, and blood sugar data.

Analyzes occupational health data to assess work-related risks, ergonomic issues, symptoms, and trends. Use it to generate personalized workplace health advice, screen for occupational hazards, and relate work factors to sleep, exercise, and mental health.

Analyzes oral health records to detect trends, assess caries and periodontal risks, and generate personalized care advice. Supports linked analysis with nutrition, chronic disease, medication, and eye health data for comprehensive oral care insights.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

Provides a concise quantitative analysis experience for financial or research scenarios by structuring numerical data insights and interpretations.

Placeholder skill entry for risk modeling workflows that outlines basic analytics and scenario planning used to quantify potential losses and guide mitigation decisions.