Vibecode Bevel
track this build5 steps, step by step0%KINDA · weekend project
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.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Bevel. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Bevel indie build ## Goal Build the smallest trustworthy replacement for the core Bevel workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Bevel instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build 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. ## Required capabilities - 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 ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Bevel. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Bevel indie build ## Goal Build the smallest trustworthy replacement for the core Bevel workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Bevel instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build 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. ## Required capabilities - 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 ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Bevel. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Bevel product brief ## Problem 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. ## Product outcome 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. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - 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 ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Bevel capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Bevel indie build ## Goal Build the smallest trustworthy replacement for the core Bevel workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Bevel instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build 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. ## Required capabilities - 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 ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Bevel product brief ## Problem 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. ## Product outcome 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. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - 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 ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Bevel capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
Because the value of health metrics collapses if you have to go fetch them. Bevel's job is to be already updated when you open your phone, with a number you trust and a sentence that explains it. A local notebook that needs a manual export every two weeks answers different questions: interesting once, abandoned by month two. People pay for the part that keeps running when their curiosity does not.
xLive background sync; you are re-exporting a zip by hand
xA phone app, widgets and notifications
xTheir opinionated composite scores and plain-English daily readouts
xNon-Apple device integrations and whatever normalisation they do across sources
xCharts that a designer looked at
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Bevel ($14.99/mo) with one prompt"
Vibecode Bevel
Kinda. The core of Bevel is buildable in a weekend with the prompt on this page, but there are real gaps: Live background sync; you are re-exporting a zip by hand, A phone app, widgets and notifications. Read the honest list above before committing.
How much does Bevel cost?
Bevel costs about $14.99/month (Bevel Pro, checked 2026-08-18), which is $179.88 per year.
What do I lose by replacing Bevel?
Honestly: 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. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Bevel?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.