Bevel

WORKING SLICE
Import a user-owned Apple Health export zip with streaming XML parsing, inspect unit mappings and view sleep/activity trends. Show baseline comparisons and exploratory correlations with sample counts, without issuing recovery diagnoses.

Build this scoped Bevel-inspired workflow with a documented data model and visible failure states.

Architecture
- Python, FastAPI and server-rendered HTML with HTMX for a local interface.
- SQLite for manifests and job state, with an explicit worker process and immutable source files.

Prerequisites and limits
A Python virtual environment, writable input/output directories and sufficient disk for both originals and outputs. Bind the service to localhost. A user-exported Apple Health zip; streaming XML and zip-bomb limits are required. No HealthKit entitlement or live health access is needed.
Outside this release: Live HealthKit access, medical guidance and validated recovery scoring are excluded.

Data model and correctness
Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits
Invariant: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
Save a job manifest with input hash, parameters and state. Write to temporary outputs, then atomically finalize only successful results; resume unfinished jobs without replacing originals.

Security and privacy
Bound file sizes and processing time, reject path traversal, and use argument arrays for subprocesses. Treat imported text as data and redact confidential source content from logs.

Recovery and export
Export sources, manifests and outputs with checksums. Keep failed-job diagnostics and allow retry into a new output path; restore the database and file directory together.

Implementation order
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Import a user-owned Apple Health export zip with streaming XML parsing, inspect unit mappings and view sleep/activity trends. Show baseline comparisons and exploratory correlations with sample counts, without issuing recovery diagnoses. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Live HealthKit access, medical guidance and validated recovery scoring are excluded.
2. Phase 2 — Durable model. Model Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
3. Phase 3 — Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Save a job manifest with input hash, parameters and state. Write to temporary outputs, then atomically finalize only successful results; resume unfinished jobs without replacing originals.
4. Phase 4 — Permissions and integration failure. Bound file sizes and processing time, reject path traversal, and use argument arrays for subprocesses. Treat imported text as data and redact confidential source content from logs. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Export sources, manifests and outputs with checksums. Keep failed-job diagnostics and allow retry into a new output path; restore the database and file directory together. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries.
Use real source data or clearly labeled fixtures. Explain unsupported input and provider failures; do not fabricate analytics, delivery receipts, accuracy claims or security guarantees.

Optional agent guidance
Optional external skill: [modern-python](https://github.com/trailofbits/skills/blob/main/plugins/modern-python/skills/modern-python/SKILL.md) — Set up Python projects with pyproject.toml, dependency management, linting, typing and automated checks. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [sharp-edges](https://github.com/trailofbits/skills/blob/main/plugins/sharp-edges/skills/sharp-edges/SKILL.md) — Review security-sensitive APIs and configuration for dangerous defaults and easy-to-misuse interfaces. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — Review web interfaces for accessibility, keyboard focus, forms, navigation and interaction quality. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Project rule — data model: Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits
Project rule — preserve this invariant: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
Project rule — acceptance evidence: Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries.

# AGENTS.md — Build guide for Bevel

## Project scope
Import a user-owned Apple Health export zip with streaming XML parsing, inspect unit mappings and view sleep/activity trends. Show baseline comparisons and exploratory correlations with sample counts, without issuing recovery diagnoses.

Catalogue verdict: kinda. 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.
Use the implementation prompt below to define the deliverable. Complete each phase's acceptance checks before extending the scope.

## Working agreement
- Inspect the repository and its existing instructions before choosing paths, dependencies or commands. Keep one coherent stack and explain changes to the proposed architecture.
- Plan a vertical slice that accepts a real input and produces the useful output described below. Persist only the state the prompt calls for; respect memory-only and upstream-managed workflows. Use fixtures only when they are clearly labelled.
- After scaffolding, document the actual install, development, check and build commands in README and keep them synchronized with the package or project manifest. Do not report commands as successful unless they ran.
- Work in small steps. At handoff, list implemented flows, checks actually performed, remaining blockers, and any credentials or provider setup the owner must supply.
- Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.

## Prerequisites
- A Python virtual environment, writable input/output directories and sufficient disk for both originals and outputs. Bind the service to localhost. A user-exported Apple Health zip; streaming XML and zip-bomb limits are required. No HealthKit entitlement or live health access is needed.
- Implementation components: Python, FastAPI and server-rendered HTML with HTMX for a local interface. SQLite for manifests and job state, with an explicit worker process and immutable source files.
- Scope boundary: Live HealthKit access, medical guidance and validated recovery scoring are excluded.

## Stack and architecture
- Python, FastAPI and server-rendered HTML with HTMX for a local interface.
- SQLite for manifests and job state, with an explicit worker process and immutable source files.
- Domain model: Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits

## Security and data integrity
- Bound file sizes and processing time, reject path traversal, and use argument arrays for subprocesses. Treat imported text as data and redact confidential source content from logs.
- Correctness boundary: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
- Save a job manifest with input hash, parameters and state. Write to temporary outputs, then atomically finalize only successful results; resume unfinished jobs without replacing originals.
- Export sources, manifests and outputs with checksums. Keep failed-job diagnostics and allow retry into a new output path; restore the database and file directory together.

## Agent implementation rules
- Project rule — data model: Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits
- Project rule — preserve this invariant: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
- Project rule — acceptance evidence: Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries.

## Optional agent skills and references
- Optional external skill: [modern-python](https://github.com/trailofbits/skills/blob/main/plugins/modern-python/skills/modern-python/SKILL.md) — Set up Python projects with pyproject.toml, dependency management, linting, typing and automated checks. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
- Optional external skill: [sharp-edges](https://github.com/trailofbits/skills/blob/main/plugins/sharp-edges/skills/sharp-edges/SKILL.md) — Review security-sensitive APIs and configuration for dangerous defaults and easy-to-misuse interfaces. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
- Optional external skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — Review web interfaces for accessibility, keyboard focus, forms, navigation and interaction quality. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.

Read the linked SKILL.md and its dependencies before adding a skill. Select only the skills matching this project's runtime and task; their documentation does not supply API access, credentials or approval to perform external actions. Pin the reviewed revision where the tool supports it. Follow the chosen agent's documented project-level installation mechanism.

## Distribution ideas
These are optional planning notes. Obtain the owner's approval before publishing or contacting anyone.
- Demonstrate the actual Bevel-inspired workflow with owned or clearly labeled sample data: Import a user-owned Apple Health export zip with streaming XML parsing, inspect unit mappings and view sleep/activity trends. Show baseline comparisons and exploratory correlations with sample counts, without issuing recovery diagnoses.
- Publish a reproducible walkthrough with this observable result: Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries.
- Explain who can operate this scoped tool, its setup and ongoing costs, and these remaining product gaps: Live HealthKit access, medical guidance and validated recovery scoring are excluded. Avoid guaranteed savings, performance scores or implied endorsement.

## Engineering roadmap
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Import a user-owned Apple Health export zip with streaming XML parsing, inspect unit mappings and view sleep/activity trends. Show baseline comparisons and exploratory correlations with sample counts, without issuing recovery diagnoses. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Live HealthKit access, medical guidance and validated recovery scoring are excluded.
2. Phase 2 — Durable model. Model Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
3. Phase 3 — Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Save a job manifest with input hash, parameters and state. Write to temporary outputs, then atomically finalize only successful results; resume unfinished jobs without replacing originals.
4. Phase 4 — Permissions and integration failure. Bound file sizes and processing time, reject path traversal, and use argument arrays for subprocesses. Treat imported text as data and redact confidential source content from logs. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Export sources, manifests and outputs with checksums. Keep failed-job diagnostics and allow retry into a new output path; restore the database and file directory together. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

## Paid-product capabilities outside this build
- 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

## Implementation prompt
WORKING SLICE
Import a user-owned Apple Health export zip with streaming XML parsing, inspect unit mappings and view sleep/activity trends. Show baseline comparisons and exploratory correlations with sample counts, without issuing recovery diagnoses.

Build this scoped Bevel-inspired workflow with a documented data model and visible failure states.

Architecture
- Python, FastAPI and server-rendered HTML with HTMX for a local interface.
- SQLite for manifests and job state, with an explicit worker process and immutable source files.

Prerequisites and limits
A Python virtual environment, writable input/output directories and sufficient disk for both originals and outputs. Bind the service to localhost. A user-exported Apple Health zip; streaming XML and zip-bomb limits are required. No HealthKit entitlement or live health access is needed.
Outside this release: Live HealthKit access, medical guidance and validated recovery scoring are excluded.

Data model and correctness
Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits
Invariant: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
Save a job manifest with input hash, parameters and state. Write to temporary outputs, then atomically finalize only successful results; resume unfinished jobs without replacing originals.

Security and privacy
Bound file sizes and processing time, reject path traversal, and use argument arrays for subprocesses. Treat imported text as data and redact confidential source content from logs.

Recovery and export
Export sources, manifests and outputs with checksums. Keep failed-job diagnostics and allow retry into a new output path; restore the database and file directory together.

Implementation order
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Import a user-owned Apple Health export zip with streaming XML parsing, inspect unit mappings and view sleep/activity trends. Show baseline comparisons and exploratory correlations with sample counts, without issuing recovery diagnoses. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Live HealthKit access, medical guidance and validated recovery scoring are excluded.
2. Phase 2 — Durable model. Model Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
3. Phase 3 — Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Save a job manifest with input hash, parameters and state. Write to temporary outputs, then atomically finalize only successful results; resume unfinished jobs without replacing originals.
4. Phase 4 — Permissions and integration failure. Bound file sizes and processing time, reject path traversal, and use argument arrays for subprocesses. Treat imported text as data and redact confidential source content from logs. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Export sources, manifests and outputs with checksums. Keep failed-job diagnostics and allow retry into a new output path; restore the database and file directory together. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries.
Use real source data or clearly labeled fixtures. Explain unsupported input and provider failures; do not fabricate analytics, delivery receipts, accuracy claims or security guarantees.

Optional agent guidance
Optional external skill: [modern-python](https://github.com/trailofbits/skills/blob/main/plugins/modern-python/skills/modern-python/SKILL.md) — Set up Python projects with pyproject.toml, dependency management, linting, typing and automated checks. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [sharp-edges](https://github.com/trailofbits/skills/blob/main/plugins/sharp-edges/skills/sharp-edges/SKILL.md) — Review security-sensitive APIs and configuration for dangerous defaults and easy-to-misuse interfaces. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — Review web interfaces for accessibility, keyboard focus, forms, navigation and interaction quality. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Project rule — data model: Apple Health import batches, source devices, units, sleep intervals, daily summaries and user-defined habits
Project rule — preserve this invariant: Measurements from different devices and overlapping sleep sources are not added blindly; proprietary recovery scores are not recreated as clinical facts.
Project rule — acceptance evidence: Import the same zip twice without doubling activity; missing sleep nights remain gaps and changing a unit conversion updates only derived summaries.

## Completion evidence
Demonstrate the prompt's acceptance scenarios against the scoped workflow. Include setup from a clean checkout and failure recovery. Check persistence across restart and export/restore only for the state the prompt says to store; for memory-only tools, confirm that temporary content is discarded as specified. Record actual results and remaining limitations. A detailed plan alone does not establish a working replacement.