Support for LLM / AI Integration via Local Data Export or Cloud API

As an advanced user tracking complex protocols (such as alternating peptide cycles and health metrics), I would love the ability to feed my tracking logs into an LLM (Large Language Model) for personalized data analysis. This would allow users to ask an AI questions like, "Analyze my resting heart rate trends against my BPC-157 cycle over the last 30 days," or "Generate an markdown summary of my current peptide stack and dosing schedule to share with my practitioner."

To make this possible, the app needs a reliable way to expose structured tracking data. I'd love to request the implementation of one (or both) of the following approaches on the product roadmap:

1. Option A (Offline-First / Private): Robust JSON/CSV Export Expand the current data export feature to generate a highly structured, clean JSON or CSV file containing all historical injection logs, current schedules, compound names, and synced health metrics. This allows power users to manually drop their data payload directly into an LLM context window.

2. Option B (Cloud-First): Cloud Sync & Developer API Implement a cloud synchronization layer with a basic, authenticated REST API. This would allow users to generate an API key and securely pull their own structured protocol data into external automation tools, local AI agents, or custom dashboards.

Why this adds value: The Regimen app is incredibly powerful for tracking, but allowing users to connect their data to modern AI analysis tools completely unlocks the value of "Health Data Correlation." It elevates the app from a great logging tool to a core dataset for personalized health optimization.