Skill UI

Guides teams through Juicebox-specific privacy workflows, covering GDPR/CCPA compliance, PII handling, retention, rights management, and access logging for sensitive candidate and personal data.

This skill provides deep analytical insights into aggregated family health data. It assesses genetic predisposition, identifies patterns of familial diseases, calculates weighted risk scores based on historical data, and generates personalized, actionable prevention recommendations. Ideal for generating comprehensive family health reports and trend analysis.

Analyzes exercise logs to identify trends, monitor progress, assess habits, correlate with chronic disease indicators, and generate personalized workout and recovery advice.

Analyzes psychological health data to track PHQ-9/GAD-7 trends, detect emotional patterns, evaluate therapy progress, assess crisis risks, and correlate with sleep, fitness, nutrition, and chronic disease metrics to inform personalized wellness suggestions.

Analyzes dietary and nutrition records to identify trends and deficiencies, compares intake against RDA/DRI, correlates with exercise, sleep, and chronic disease data, and generates prioritized personalized nutrition improvements and habit suggestions.

Analyzes occupational health metrics to spot work-related risks, evaluate ergonomic factors, correlate with sleep/exercise/mental data, and deliver personalized improvement recommendations plus alerts.

This comprehensive skill analyzes sleep data to identify patterns, assess quality, and detect potential sleep disorders. It tracks trends in sleep duration, efficiency, and consistency, and performs advanced correlation analysis with other health metrics like exercise, diet, and mood. The output provides actionable, personalized recommendations based on scientific data, helping users improve their overall sleep health.

Analyzes TCM constitution data to classify body types, evaluate traits, track trends, and generate personalized wellness advice by correlating nutrition, exercise, sleep and chronic-disease indicators.

This technique guides the identification of musicians by prioritizing unique personal life events over discography details (albums or songs). It instructs users to use highly distinctive biographical information—such as career turning points, specific personal relationships, or unusual life incidents—as the primary search anchor. This method significantly improves search accuracy when typical musical clues are insufficient.