# Build brief — a focused alternative to Bevel

> **Verdict:** Partly, if you narrow it · **Buildability:** 63/100 · **Category:** Wellness
> **Source:** https://www.canitbevibecoded.com/bevel
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Bevel. Verify current pricing and capabilities before acting.

## Context

**Bevel** — iOS app that pulls your Apple Health and wearable data into readable scores, trends and correlations. It currently costs $14.99/mo.

Bevel is not sitting on secret data: it reads the same HealthKit records your watch already wrote, then does math and draws charts. An agent can absolutely build you a local dashboard over an Apple Health export that computes rolling averages, sleep and HRV trends, and lag correlations between habits and recovery. The gap is delivery, not analysis: real HealthKit access means an actual iOS app, Xcode, a developer account and background sync, and manual export zips get stale fast. You also lose the part Bevel spends most of its effort on, which is turning noisy sensor data into something you glance at once a day and actually understand. Fine for a curious quantified-self person, annoying for anyone who wants a phone widget.

This brief describes a focused, single-operator replacement for the part of Bevel that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

Ingest an Apple Health export zip into a local database and serve a dashboard of daily metrics, rolling trends and correlations between behaviour and recovery.

- Log workouts and nutrition, track trends, and keep personal health data exportable.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- An Apple Health export zip (Health app, profile, Export All Health Data).
- Python 3.12 locally.
- Patience with a multi-hundred-megabyte export.xml.
- An Apple Developer account and Xcode if you want live HealthKit sync instead of manual exports.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build a local Apple Health analytics dashboard. Single machine, no accounts, no cloud, no telemetry.

Stack, non negotiable: Python 3.12, DuckDB for storage, Streamlit for the UI, uv for dependency management. No web framework, no Docker, no database server.

Project layout:
- ingest.py: CLI that takes a path to an Apple Health export.zip
- app.py: Streamlit dashboard
- health.duckdb: local database file, gitignored
- .env for anything configurable (default export path), read with python-dotenv

Ingest requirements:
- Read export.xml directly from inside the zip, streaming, using xml.etree.ElementTree.iterparse and clearing elements as you go. The file can be 500MB+, never load it into memory.
- Extract Record elements and Workout elements. Normalise into two DuckDB tables: records(type, source, unit, start_ts, end_ts, value) and workouts(activity_type, start_ts, end_ts, duration_min, energy_kcal).
- Strip the HK prefixes off type names so they read as sleep_analysis, heart_rate_variability_sdnn, resting_heart_rate, step_count, active_energy_burned, respiratory_rate, oxygen_saturation, body_mass.
- Idempotent: re-running on the same export replaces the tables, does not duplicate.
- Print a summary of row counts per type and the date range found.

Dashboard requirements:
- Date range picker, defaults to last 180 days.
- Daily aggregate table built in SQL: steps sum, active energy sum, resting HR mean, HRV mean, sleep hours from sleep_analysis asleep intervals, workout minutes.
- One chart per metric with a 7 day rolling mean drawn over the raw daily points.
- A correlation panel: pick metric A and metric B, pick a lag of 0 to 3 days, show the Pearson r, the n, and a scatter plot. Print a blunt caption saying this is correlation on a tiny sample and means nothing on its own.
- A weekday breakdown: mean of each metric by day of week.
- No composite "scores", no AI summaries, no advice text. Numbers only.

Deliver a README with the exact export steps from the iPhone Health app, the uv commands to run ingest and the dashboard, and one paragraph stating clearly that this needs a manual re-export to stay current and is not medical advice.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- Live background sync; you are re-exporting a zip by hand.
- A phone app, widgets and notifications.
- Their opinionated composite scores and plain-English daily readouts.
- Non-Apple device integrations and whatever normalisation they do across sources.
- Charts that a designer looked at.

## What you still own after launch

- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
- Maintain every third-party integration as APIs and OAuth rules change.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: low. No reviewed project implementation is linked yet.

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Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/bevel