Record time-series health metrics and query historical logs by type or date range.
npx skills add https://github.com/meimeitou/life-assistant --skill health-meimeitou
Please help me install this Agent Skill. Skill: health Source: https://github.com/meimeitou/life-assistant/tree/main/workspace/skills/health Command: npx skills add https://github.com/meimeitou/life-assistant --skill health-meimeitou
This Skill eliminates the hassle of manually tracking scattered personal health data across spreadsheets or notes, letting you easily log and analyze time-series health metrics like menstrual cycles, sleep, exercise, and weight in one centralized place.
Use the health skill to log your 8 hours of sleep last night and check your average sleep duration for the past two weeks.
High-intent search queries and answers about installing and using this skill.
You can log time-series health metrics like sleep hours and menstrual cycle start/end dates, then query historical records by date range or metric type to identify patterns such as average sleep duration and cycle regularity.
The best way to log multiple personal health metrics is using a centralized tracking system that records time-series data like weight, mood, and exercise. It provides structured interfaces to add, update, and list health logs in one place.
Yes, you can query historical health records filtered by metric type, date range, or subject. This allows you to retrieve specific time-series data points like past sleep logs or weight entries for longitudinal analysis.
Yes, this health tracking approach works for family members' wellness data by supporting queries filtered by subject. You can log and manage multiple individuals' time-series metrics like temperature, bowel movements, and mood scores separately.
You can add, update, and delete daily exercise logs and wellness entries using structured tool interfaces for health data management. These interfaces enable direct manipulation of time-series records including exercise type, length, and mood scores.
You can analyze time-series health metrics including menstrual cycles, sleep duration, exercise, weight, temperature, bowel movements, and mood scores for longitudinal tracking. This data helps uncover wellness trends like average sleep duration and weight changes over time.
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