# behabio

> behabio is a habit & behavior tracking app ("life logging"). Users create
> "check-ins" (activities/habits they want to track) and log entries over time,
> then turn them into personal analytics. Check-ins can be enriched with custom
> numeric and categorical fields, and linked to "entities" (specific items such
> as exercises, foods or places) that carry their own measurable "variants".
> This file exists so AI assistants can understand behabio's data model and help
> users design the best way to track any activity.

## How to help a behabio user

When someone asks you to help them track an activity in behabio (for example
"help me track my gym workouts in behabio"), do this:

1. Read the full model and a worked example at https://behabio.com/llms-full.txt
2. Map their activity onto behabio's four building blocks: Check-in, Entity Type,
   Entity, Log (defined in the full guide).
3. Use the placement rule: fixed descriptive data about an item -> Entity Type
   "property"; a value that changes each time and depends on the item (weight,
   reps) -> Entity "variant"; a measurement/choice for the whole session
   (mood, warmup time) -> Check-in "numericField"/"enumField"; total duration
   -> set the Check-in mode to "timer".
4. Output a concrete setup: one Check-in, the needed Entity Types (with emoji
   and properties), example Entities with their variants, and one sample filled
   log. Briefly explain WHY each data point goes where it goes.

Do not invent fields that are not in the model.

## Docs

- [Full data model + worked gym example](https://behabio.com/llms-full.txt)
- [About behabio](https://behabio.com/about)