Vibecode Inbox Zero

You are building a lean indie version of Inbox Zero.

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 =====
# Inbox Zero indie build

## Goal

Build the smallest trustworthy replacement for the core Inbox Zero workflow for one developer or a tiny team.

## Scope

Watch one Gmail inbox, classify and label new threads, create reply drafts for review, and group noisy senders for cleanup.

## 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:
- Outlook and reliable multi-account support
- battle-tested token refresh, push sync, retries, rate-limit handling, and background uptime
- reply-in-your-voice learning with deeper mailbox, calendar, and knowledge-base context
- Slack and Telegram chat, mobile access, meeting briefs, and attachment filing
- hosted security, compliance, team administration, monitoring, and support

If those capabilities are essential, use Inbox Zero 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 me a self-hosted personal Gmail assistant that covers the core Inbox Zero loop.
Use Node 22 + TypeScript + Fastify + better-sqlite3; server-rendered HTML and vanilla JS, no React.
Run on localhost by default, with one Dockerfile for an always-on VPS behind Caddy.
Support exactly one Gmail or Google Workspace mailbox through the Gmail API.
Use a Google OAuth client from .env and request only gmail.modify and gmail.compose; store the refresh token encrypted with an APP_SECRET.
On first run, import 14 days of mail; then poll Gmail history every two minutes and persist the last history ID.
Deduplicate jobs by Gmail message ID, use exponential backoff for transient API failures, and surface a failed-jobs page.
Rules live in rules.md: label definitions, priority senders, newsletters, cold email, and messages that need a reply.
For each new thread, call the OpenAI API with the thread text plus rules.md and require validated JSON: labels, priority, needsReply, summary, and optional draftReply.
Apply Gmail labels automatically, but never archive, delete, unsubscribe, or send without a click.
When needsReply is true, create or update a Gmail draft in the original thread; never send mail from the app.
Build one dashboard with Priority, Needs reply, Newsletters, Cold email, and Failed jobs views.
Add a bulk-cleanup screen grouped by sender with message counts and List-Unsubscribe support; show the exact action and require confirmation.
Add a small analytics page: daily received count, top senders, category totals, and estimated time saved.
Store only IDs, classifications, job state, and draft metadata in SQLite; do not persist full message bodies after processing.
Redact tokens and email contents from logs; add CSRF protection and bind the admin UI to localhost unless ADMIN_TOKEN is set.
Include fixture-based tests for classification parsing, idempotent sync, label application, draft creation, and unsubscribe confirmation.
Out of scope: Outlook, multiple accounts, mobile apps, Slack/Telegram, calendar context, attachment filing, multi-user accounts, billing, and telemetry.
README: exact Google Cloud OAuth setup, scopes and redirect URI, .env example, how to revoke access, Docker/Caddy deployment, backups, and expected LLM costs.
Return finished code with migrations and scripts for dev, test, and start; no pseudocode or TODOs.

## Required capabilities

- Google Cloud OAuth client
- OpenAI API key
- Node 22
- always-on machine or VPS for continuous processing

## 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 Inbox Zero.

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 =====
# Inbox Zero indie build

## Goal

Build the smallest trustworthy replacement for the core Inbox Zero workflow for one developer or a tiny team.

## Scope

Watch one Gmail inbox, classify and label new threads, create reply drafts for review, and group noisy senders for cleanup.

## 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:
- Outlook and reliable multi-account support
- battle-tested token refresh, push sync, retries, rate-limit handling, and background uptime
- reply-in-your-voice learning with deeper mailbox, calendar, and knowledge-base context
- Slack and Telegram chat, mobile access, meeting briefs, and attachment filing
- hosted security, compliance, team administration, monitoring, and support

If those capabilities are essential, use Inbox Zero 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 me a self-hosted personal Gmail assistant that covers the core Inbox Zero loop.
Use Node 22 + TypeScript + Fastify + better-sqlite3; server-rendered HTML and vanilla JS, no React.
Run on localhost by default, with one Dockerfile for an always-on VPS behind Caddy.
Support exactly one Gmail or Google Workspace mailbox through the Gmail API.
Use a Google OAuth client from .env and request only gmail.modify and gmail.compose; store the refresh token encrypted with an APP_SECRET.
On first run, import 14 days of mail; then poll Gmail history every two minutes and persist the last history ID.
Deduplicate jobs by Gmail message ID, use exponential backoff for transient API failures, and surface a failed-jobs page.
Rules live in rules.md: label definitions, priority senders, newsletters, cold email, and messages that need a reply.
For each new thread, call the OpenAI API with the thread text plus rules.md and require validated JSON: labels, priority, needsReply, summary, and optional draftReply.
Apply Gmail labels automatically, but never archive, delete, unsubscribe, or send without a click.
When needsReply is true, create or update a Gmail draft in the original thread; never send mail from the app.
Build one dashboard with Priority, Needs reply, Newsletters, Cold email, and Failed jobs views.
Add a bulk-cleanup screen grouped by sender with message counts and List-Unsubscribe support; show the exact action and require confirmation.
Add a small analytics page: daily received count, top senders, category totals, and estimated time saved.
Store only IDs, classifications, job state, and draft metadata in SQLite; do not persist full message bodies after processing.
Redact tokens and email contents from logs; add CSRF protection and bind the admin UI to localhost unless ADMIN_TOKEN is set.
Include fixture-based tests for classification parsing, idempotent sync, label application, draft creation, and unsubscribe confirmation.
Out of scope: Outlook, multiple accounts, mobile apps, Slack/Telegram, calendar context, attachment filing, multi-user accounts, billing, and telemetry.
README: exact Google Cloud OAuth setup, scopes and redirect URI, .env example, how to revoke access, Docker/Caddy deployment, backups, and expected LLM costs.
Return finished code with migrations and scripts for dev, test, and start; no pseudocode or TODOs.

## Required capabilities

- Google Cloud OAuth client
- OpenAI API key
- Node 22
- always-on machine or VPS for continuous processing

## 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 Inbox Zero.

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 =====
# Inbox Zero product brief

## Problem

The personal Gmail loop is a weekend project: classify new mail, apply labels, draft replies, and surface bulk cleanup. The gap is making it trustworthy enough to sit inside a real inbox every day: OAuth setup and review, token refresh, provider quirks, continuous sync, retries, rate limits, security, and integrations. Inbox Zero itself is open source, so anyone wanting the full product should self-host the official code instead of rebuilding all of it.

## Product outcome

Watch one Gmail inbox, classify and label new threads, create reply drafts for review, and group noisy senders for cleanup.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- Google Cloud OAuth client
- OpenAI API key
- Node 22
- always-on machine or VPS for continuous processing

## Explicit non-goals for v1

- Outlook and reliable multi-account support
- battle-tested token refresh, push sync, retries, rate-limit handling, and background uptime
- reply-in-your-voice learning with deeper mailbox, calendar, and knowledge-base context
- Slack and Telegram chat, mobile access, meeting briefs, and attachment filing
- hosted security, compliance, team administration, monitoring, and support

## 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 me a self-hosted personal Gmail assistant that covers the core Inbox Zero loop.
Use Node 22 + TypeScript + Fastify + better-sqlite3; server-rendered HTML and vanilla JS, no React.
Run on localhost by default, with one Dockerfile for an always-on VPS behind Caddy.
Support exactly one Gmail or Google Workspace mailbox through the Gmail API.
Use a Google OAuth client from .env and request only gmail.modify and gmail.compose; store the refresh token encrypted with an APP_SECRET.
On first run, import 14 days of mail; then poll Gmail history every two minutes and persist the last history ID.
Deduplicate jobs by Gmail message ID, use exponential backoff for transient API failures, and surface a failed-jobs page.
Rules live in rules.md: label definitions, priority senders, newsletters, cold email, and messages that need a reply.
For each new thread, call the OpenAI API with the thread text plus rules.md and require validated JSON: labels, priority, needsReply, summary, and optional draftReply.
Apply Gmail labels automatically, but never archive, delete, unsubscribe, or send without a click.
When needsReply is true, create or update a Gmail draft in the original thread; never send mail from the app.
Build one dashboard with Priority, Needs reply, Newsletters, Cold email, and Failed jobs views.
Add a bulk-cleanup screen grouped by sender with message counts and List-Unsubscribe support; show the exact action and require confirmation.
Add a small analytics page: daily received count, top senders, category totals, and estimated time saved.
Store only IDs, classifications, job state, and draft metadata in SQLite; do not persist full message bodies after processing.
Redact tokens and email contents from logs; add CSRF protection and bind the admin UI to localhost unless ADMIN_TOKEN is set.
Include fixture-based tests for classification parsing, idempotent sync, label application, draft creation, and unsubscribe confirmation.
Out of scope: Outlook, multiple accounts, mobile apps, Slack/Telegram, calendar context, attachment filing, multi-user accounts, billing, and telemetry.
README: exact Google Cloud OAuth setup, scopes and redirect URI, .env example, how to revoke access, Docker/Caddy deployment, backups, and expected LLM costs.
Return finished code with migrations and scripts for dev, test, and start; no pseudocode or TODOs.

## 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 Inbox Zero capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# Inbox Zero indie build

## Goal

Build the smallest trustworthy replacement for the core Inbox Zero workflow for one developer or a tiny team.

## Scope

Watch one Gmail inbox, classify and label new threads, create reply drafts for review, and group noisy senders for cleanup.

## 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:
- Outlook and reliable multi-account support
- battle-tested token refresh, push sync, retries, rate-limit handling, and background uptime
- reply-in-your-voice learning with deeper mailbox, calendar, and knowledge-base context
- Slack and Telegram chat, mobile access, meeting briefs, and attachment filing
- hosted security, compliance, team administration, monitoring, and support

If those capabilities are essential, use Inbox Zero instead of pretending the gap is solved.

# 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

## Original build brief

Build me a self-hosted personal Gmail assistant that covers the core Inbox Zero loop.
Use Node 22 + TypeScript + Fastify + better-sqlite3; server-rendered HTML and vanilla JS, no React.
Run on localhost by default, with one Dockerfile for an always-on VPS behind Caddy.
Support exactly one Gmail or Google Workspace mailbox through the Gmail API.
Use a Google OAuth client from .env and request only gmail.modify and gmail.compose; store the refresh token encrypted with an APP_SECRET.
On first run, import 14 days of mail; then poll Gmail history every two minutes and persist the last history ID.
Deduplicate jobs by Gmail message ID, use exponential backoff for transient API failures, and surface a failed-jobs page.
Rules live in rules.md: label definitions, priority senders, newsletters, cold email, and messages that need a reply.
For each new thread, call the OpenAI API with the thread text plus rules.md and require validated JSON: labels, priority, needsReply, summary, and optional draftReply.
Apply Gmail labels automatically, but never archive, delete, unsubscribe, or send without a click.
When needsReply is true, create or update a Gmail draft in the original thread; never send mail from the app.
Build one dashboard with Priority, Needs reply, Newsletters, Cold email, and Failed jobs views.
Add a bulk-cleanup screen grouped by sender with message counts and List-Unsubscribe support; show the exact action and require confirmation.
Add a small analytics page: daily received count, top senders, category totals, and estimated time saved.
Store only IDs, classifications, job state, and draft metadata in SQLite; do not persist full message bodies after processing.
Redact tokens and email contents from logs; add CSRF protection and bind the admin UI to localhost unless ADMIN_TOKEN is set.
Include fixture-based tests for classification parsing, idempotent sync, label application, draft creation, and unsubscribe confirmation.
Out of scope: Outlook, multiple accounts, mobile apps, Slack/Telegram, calendar context, attachment filing, multi-user accounts, billing, and telemetry.
README: exact Google Cloud OAuth setup, scopes and redirect URI, .env example, how to revoke access, Docker/Caddy deployment, backups, and expected LLM costs.
Return finished code with migrations and scripts for dev, test, and start; no pseudocode or TODOs.

## Required capabilities

- Google Cloud OAuth client
- OpenAI API key
- Node 22
- always-on machine or VPS for continuous processing

## 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.

# 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.

# Inbox Zero product brief

## Problem

The personal Gmail loop is a weekend project: classify new mail, apply labels, draft replies, and surface bulk cleanup. The gap is making it trustworthy enough to sit inside a real inbox every day: OAuth setup and review, token refresh, provider quirks, continuous sync, retries, rate limits, security, and integrations. Inbox Zero itself is open source, so anyone wanting the full product should self-host the official code instead of rebuilding all of it.

## Product outcome

Watch one Gmail inbox, classify and label new threads, create reply drafts for review, and group noisy senders for cleanup.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- Google Cloud OAuth client
- OpenAI API key
- Node 22
- always-on machine or VPS for continuous processing

## Explicit non-goals for v1

- Outlook and reliable multi-account support
- battle-tested token refresh, push sync, retries, rate-limit handling, and background uptime
- reply-in-your-voice learning with deeper mailbox, calendar, and knowledge-base context
- Slack and Telegram chat, mobile access, meeting briefs, and attachment filing
- hosted security, compliance, team administration, monitoring, and support

## 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

## Starting brief

Build me a self-hosted personal Gmail assistant that covers the core Inbox Zero loop.
Use Node 22 + TypeScript + Fastify + better-sqlite3; server-rendered HTML and vanilla JS, no React.
Run on localhost by default, with one Dockerfile for an always-on VPS behind Caddy.
Support exactly one Gmail or Google Workspace mailbox through the Gmail API.
Use a Google OAuth client from .env and request only gmail.modify and gmail.compose; store the refresh token encrypted with an APP_SECRET.
On first run, import 14 days of mail; then poll Gmail history every two minutes and persist the last history ID.
Deduplicate jobs by Gmail message ID, use exponential backoff for transient API failures, and surface a failed-jobs page.
Rules live in rules.md: label definitions, priority senders, newsletters, cold email, and messages that need a reply.
For each new thread, call the OpenAI API with the thread text plus rules.md and require validated JSON: labels, priority, needsReply, summary, and optional draftReply.
Apply Gmail labels automatically, but never archive, delete, unsubscribe, or send without a click.
When needsReply is true, create or update a Gmail draft in the original thread; never send mail from the app.
Build one dashboard with Priority, Needs reply, Newsletters, Cold email, and Failed jobs views.
Add a bulk-cleanup screen grouped by sender with message counts and List-Unsubscribe support; show the exact action and require confirmation.
Add a small analytics page: daily received count, top senders, category totals, and estimated time saved.
Store only IDs, classifications, job state, and draft metadata in SQLite; do not persist full message bodies after processing.
Redact tokens and email contents from logs; add CSRF protection and bind the admin UI to localhost unless ADMIN_TOKEN is set.
Include fixture-based tests for classification parsing, idempotent sync, label application, draft creation, and unsubscribe confirmation.
Out of scope: Outlook, multiple accounts, mobile apps, Slack/Telegram, calendar context, attachment filing, multi-user accounts, billing, and telemetry.
README: exact Google Cloud OAuth setup, scopes and redirect URI, .env example, how to revoke access, Docker/Caddy deployment, backups, and expected LLM costs.
Return finished code with migrations and scripts for dev, test, and start; no pseudocode or TODOs.

## 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.

# 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.

# 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

## 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 Inbox Zero 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