Identify patterns and correlations across health data to explain recent trends.
npx skills add https://github.com/compound-life-ai/Turri --skill insights-compound-life-ai
Please help me install this Agent Skill. Skill: insights Source: https://github.com/compound-life-ai/Turri/tree/main/insights Command: npx skills add https://github.com/compound-life-ai/Turri --skill insights-compound-life-ai
Helps users uncover patterns and correlations across health data, then guides structured self-experiments from observation to next steps.
Start an insights session by running a gap_report to assess data readiness and then design a structured experiment workflow.
High-intent search queries and answers about installing and using this skill.
To find correlations across health data, this Skill analyzes Apple Health metrics and nutrition logs to identify patterns. It surfaces actionable insights by cross-referencing variables like sleep and HRV to explain recent trends.
The best way to design a structured self-experiment is using a hypothesis-driven workflow. This Skill generates proactive hypotheses, designs experiments, and guides you through check-ins and analysis cycles for continuous health improvement.
To check if your health data is ready for pattern discovery, run a gap report. This assesses data readiness across your Apple Health metrics and nutrition logs to ensure you have sufficient information for correlation analysis.
You can use health data correlations to explain changing HRV trends. The Skill identifies patterns across activity, sleep, and nutrition metrics to provide context for your recent physiological shifts.
After completing a self-experiment check-in, the Skill analyzes the results and provides clear next steps. This structured workflow ensures you can iterate on your hypothesis for continuous health improvement.
hvkshetryAggregate health data from Oura, Apple Health, wger, Fitbod, and Peloton into a unified dashboard.
Likas07Convert learning content into actionable implementation plans with iterative practice cycles.
heroveAnalyze nutrition logs to generate personalized dietary insights with safety checks.
BordaAnalyze completed ML experiment runs to produce structured retrospectives and hypotheses.
