Data Analysis

This skill provides AI assistants with deep knowledge about the Addax open-source ETL tool. It covers the project's architecture, plugin system for 20+ data sources (MySQL, PostgreSQL, MongoDB, Kafka, HDFS, etc.), job configuration format, data transformation capabilities, and development guidelines for building custom reader/writer plugins.

Integrates digital health data from Apple Health, Fitbit, and Oura Ring into a local JSON format. Provides intelligent article recommendations from the WellAlly.tech knowledge base based on user health data, covering nutrition, fitness, sleep, mental health, and chronic disease management.

This skill provides comprehensive weight loss analysis tools including BMI calculation, body composition assessment, metabolic rate estimation (BMR/TDEE), and energy deficit tracking. It offers guidance on healthy weight loss pacing and includes standard formulas like Mifflin-St Jeor for calorie calculations. The skill is documentation-focused and requires users to maintain their own data files.

A sleep analysis skill that processes sleep tracking data from local JSON files to identify sleep patterns, assess quality, and detect potential issues like insomnia or sleep apnea risk. It provides personalized recommendations for improving sleep hygiene including bedtime routines, environment optimization, and lifestyle adjustments.

This skill analyzes rehabilitation training data to track recovery progress, identify pain patterns, and evaluate goal achievement. It reads local JSON files containing exercise logs, functional assessments, and pain diaries to generate comprehensive progress reports with trend analysis and personalized recommendations.

This skill analyzes occupational health data to assess workplace health risks including sedentary behavior, screen time exposure, and ergonomic issues. It reads local health tracker JSON files, calculates risk scores across multiple dimensions, and generates comprehensive markdown reports with personalized recommendations for improving workplace health.

A comprehensive nutrition analyzer that processes local dietary records to identify nutritional trends, evaluate nutrient intake against RDA standards, and generate personalized improvement recommendations. It supports correlation analysis with exercise, sleep, and chronic disease data while maintaining clear medical safety boundaries.

This skill analyzes mental health data from local JSON files to track PHQ-9/GAD-7 assessment scores, identify emotional patterns, evaluate crisis risk levels, and correlate mental health with sleep, exercise, and nutrition. It generates comprehensive reports with actionable recommendations while maintaining clear medical safety boundaries.

A comprehensive health trend analysis skill that tracks weight/BMI, symptoms, medications, lab results, and mood/sleep patterns over time. It reads locally stored health data files and generates interactive HTML reports with ECharts visualizations, correlation analysis, and personalized health recommendations.

A comprehensive health goal management skill that analyzes fitness, nutrition, and wellness goals using SMART criteria. It tracks progress, identifies habit patterns, evaluates motivation levels, and generates interactive HTML reports with charts. The skill operates entirely locally on user-provided health data files.

This skill enables querying a nutritional food database containing 50 common foods with detailed nutritional data. It supports food lookups by name (Chinese/English), nutritional comparisons between foods, smart recommendations based on health conditions (diabetes, hypertension, etc.), and automatic nutrition calculations for meals with portion size conversion.

This skill analyzes your fitness and exercise data to identify workout trends, track progress over time, and correlate exercise with health metrics like blood pressure and blood sugar. It provides personalized training recommendations based on WHO/ACSM guidelines while respecting medical safety boundaries.

Analyzes family health history data to identify genetic risk patterns, disease clustering, and provides personalized prevention recommendations. Generates visual reports including family trees and risk heatmaps while maintaining clear medical safety boundaries.

An AI-powered health analysis system that integrates multiple health data sources (fitness, sleep, nutrition, mental health) to identify patterns, predict health risks, and provide personalized recommendations. All data processing happens locally with no external API calls.

Mviz is a data visualization skill that generates clean, professional charts and dashboards from compact JSON specifications or markdown files. It supports 17+ chart types (bar, line, scatter, heatmap, sankey, etc.) and 8 UI components, using a 16-column grid layout system with light/dark theme support.

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.

A comprehensive guide for creating interactive data visualizations using D3.js. Provides detailed code patterns for bar charts, line charts, scatter plots, heatmaps, chord diagrams, force-directed networks, geographic maps, and hierarchical visualizations, along with reference materials for color schemes, scales, and responsive design.

A comprehensive Pinecone integration plugin that enables document-based Q&A with citations through Pinecone Assistant. Users can create assistants, upload documentation files, sync repositories, and chat with their knowledge base. Also includes MCP server tools for vector index management, semantic search, and RAG application development.