Vibecode Letterboxd

You are building a lean indie version of Letterboxd.

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 =====
# Letterboxd indie build

## Goal

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

## Scope

A local film diary that searches TMDB for a title, logs a watch date, rating and review, and shows your history plus a stats page.

## 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:
- Every other human: friends' ratings, popular reviews, comment threads
- Curated and community lists, which are most of the discovery value
- Aggregate ratings and the histogram that tells you if a film is divisive or just bad
- Mobile apps, offline logging, share cards, streaming availability data
- Importing your existing history back out of anything without a CSV wrangle

If those capabilities are essential, use Letterboxd 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-first personal film diary web app. Single user, no accounts, no cloud, no telemetry.

Stack, non-negotiable:
- Next.js 15 with the App Router, TypeScript, Tailwind.
- SQLite via better-sqlite3, one file at ./data/diary.db, schema created on first run.
- No auth, no external services except TMDB for metadata.

Secrets:
- .env.local with TMDB_API_KEY. Commit .env.example only. Fail loudly with a readable message if the key is missing.

Data model:
- films: tmdb_id (primary key), title, year, director, runtime, poster_path, overview, cached_at.
- entries: id, tmdb_id, watched_on (date), rating (0.5 to 5 in half steps, nullable), review (text, nullable), rewatch (boolean), liked (boolean), created_at.
- watchlist: tmdb_id, added_at, note.

Features in scope:
- Search box that hits TMDB search, shows posters and years, one click to log. Cache the film row locally so the app works offline for anything already logged.
- Log form: watched date defaults to today, half-star rating widget, review textarea, rewatch and liked toggles.
- Diary view grouped by month, newest first, poster grid plus compact list toggle.
- Film detail page: local metadata, every entry you have for it, rating history.
- Watchlist page with add from search and a one-click promote to a diary entry.
- Stats page: films per year and per month, rating distribution histogram, top directors by count, total runtime in hours and days, longest streak of consecutive days with a log.
- CSV import that accepts the Letterboxd export format (Name, Year, Watched Date, Rating, Review, Rewatch) and matches to TMDB with a manual disambiguation queue for anything ambiguous.
- CSV export of everything.

Explicitly out of scope: any social features, follower graphs, comments, aggregate ratings, streaming availability, mobile apps, push notifications, deployment config.

Deliver: working app, README with setup in under five commands, seed script that inserts three sample entries so the stats page is not empty on first load.

## Required capabilities

- TMDB API key for film metadata and posters
- Node 20+
- SQLite (no server to run)

## 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 Letterboxd.

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 =====
# Letterboxd indie build

## Goal

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

## Scope

A local film diary that searches TMDB for a title, logs a watch date, rating and review, and shows your history plus a stats page.

## 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:
- Every other human: friends' ratings, popular reviews, comment threads
- Curated and community lists, which are most of the discovery value
- Aggregate ratings and the histogram that tells you if a film is divisive or just bad
- Mobile apps, offline logging, share cards, streaming availability data
- Importing your existing history back out of anything without a CSV wrangle

If those capabilities are essential, use Letterboxd 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-first personal film diary web app. Single user, no accounts, no cloud, no telemetry.

Stack, non-negotiable:
- Next.js 15 with the App Router, TypeScript, Tailwind.
- SQLite via better-sqlite3, one file at ./data/diary.db, schema created on first run.
- No auth, no external services except TMDB for metadata.

Secrets:
- .env.local with TMDB_API_KEY. Commit .env.example only. Fail loudly with a readable message if the key is missing.

Data model:
- films: tmdb_id (primary key), title, year, director, runtime, poster_path, overview, cached_at.
- entries: id, tmdb_id, watched_on (date), rating (0.5 to 5 in half steps, nullable), review (text, nullable), rewatch (boolean), liked (boolean), created_at.
- watchlist: tmdb_id, added_at, note.

Features in scope:
- Search box that hits TMDB search, shows posters and years, one click to log. Cache the film row locally so the app works offline for anything already logged.
- Log form: watched date defaults to today, half-star rating widget, review textarea, rewatch and liked toggles.
- Diary view grouped by month, newest first, poster grid plus compact list toggle.
- Film detail page: local metadata, every entry you have for it, rating history.
- Watchlist page with add from search and a one-click promote to a diary entry.
- Stats page: films per year and per month, rating distribution histogram, top directors by count, total runtime in hours and days, longest streak of consecutive days with a log.
- CSV import that accepts the Letterboxd export format (Name, Year, Watched Date, Rating, Review, Rewatch) and matches to TMDB with a manual disambiguation queue for anything ambiguous.
- CSV export of everything.

Explicitly out of scope: any social features, follower graphs, comments, aggregate ratings, streaming availability, mobile apps, push notifications, deployment config.

Deliver: working app, README with setup in under five commands, seed script that inserts three sample entries so the stats page is not empty on first load.

## Required capabilities

- TMDB API key for film metadata and posters
- Node 20+
- SQLite (no server to run)

## 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 Letterboxd.

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 =====
# Letterboxd product brief

## Problem

The logging half of Letterboxd is trivial: a table of films, a date, a rating, a note. The half people actually open the app for is everyone else, friends' four-star shrugs, review pages with thousands of jokes, lists that function as film school syllabi, and the year-end stats that only mean something because they are comparable. A one-shot build gives you a private diary with no one in it, and you will still need TMDB to get posters and metadata that do not look broken. If you genuinely only want a watch log, build it and enjoy the speed. If you want the film internet, you are not building the film internet this weekend.

## Product outcome

A local film diary that searches TMDB for a title, logs a watch date, rating and review, and shows your history plus a stats page.

## Target user

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

## Required capabilities

- TMDB API key for film metadata and posters
- Node 20+
- SQLite (no server to run)

## Explicit non-goals for v1

- Every other human: friends' ratings, popular reviews, comment threads
- Curated and community lists, which are most of the discovery value
- Aggregate ratings and the histogram that tells you if a film is divisive or just bad
- Mobile apps, offline logging, share cards, streaming availability data
- Importing your existing history back out of anything without a CSV wrangle

## 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-first personal film diary web app. Single user, no accounts, no cloud, no telemetry.

Stack, non-negotiable:
- Next.js 15 with the App Router, TypeScript, Tailwind.
- SQLite via better-sqlite3, one file at ./data/diary.db, schema created on first run.
- No auth, no external services except TMDB for metadata.

Secrets:
- .env.local with TMDB_API_KEY. Commit .env.example only. Fail loudly with a readable message if the key is missing.

Data model:
- films: tmdb_id (primary key), title, year, director, runtime, poster_path, overview, cached_at.
- entries: id, tmdb_id, watched_on (date), rating (0.5 to 5 in half steps, nullable), review (text, nullable), rewatch (boolean), liked (boolean), created_at.
- watchlist: tmdb_id, added_at, note.

Features in scope:
- Search box that hits TMDB search, shows posters and years, one click to log. Cache the film row locally so the app works offline for anything already logged.
- Log form: watched date defaults to today, half-star rating widget, review textarea, rewatch and liked toggles.
- Diary view grouped by month, newest first, poster grid plus compact list toggle.
- Film detail page: local metadata, every entry you have for it, rating history.
- Watchlist page with add from search and a one-click promote to a diary entry.
- Stats page: films per year and per month, rating distribution histogram, top directors by count, total runtime in hours and days, longest streak of consecutive days with a log.
- CSV import that accepts the Letterboxd export format (Name, Year, Watched Date, Rating, Review, Rewatch) and matches to TMDB with a manual disambiguation queue for anything ambiguous.
- CSV export of everything.

Explicitly out of scope: any social features, follower graphs, comments, aggregate ratings, streaming availability, mobile apps, push notifications, deployment config.

Deliver: working app, README with setup in under five commands, seed script that inserts three sample entries so the stats page is not empty on first load.

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

# Letterboxd indie build

## Goal

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

## Scope

A local film diary that searches TMDB for a title, logs a watch date, rating and review, and shows your history plus a stats page.

## 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:
- Every other human: friends' ratings, popular reviews, comment threads
- Curated and community lists, which are most of the discovery value
- Aggregate ratings and the histogram that tells you if a film is divisive or just bad
- Mobile apps, offline logging, share cards, streaming availability data
- Importing your existing history back out of anything without a CSV wrangle

If those capabilities are essential, use Letterboxd 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 a local-first personal film diary web app. Single user, no accounts, no cloud, no telemetry.

Stack, non-negotiable:
- Next.js 15 with the App Router, TypeScript, Tailwind.
- SQLite via better-sqlite3, one file at ./data/diary.db, schema created on first run.
- No auth, no external services except TMDB for metadata.

Secrets:
- .env.local with TMDB_API_KEY. Commit .env.example only. Fail loudly with a readable message if the key is missing.

Data model:
- films: tmdb_id (primary key), title, year, director, runtime, poster_path, overview, cached_at.
- entries: id, tmdb_id, watched_on (date), rating (0.5 to 5 in half steps, nullable), review (text, nullable), rewatch (boolean), liked (boolean), created_at.
- watchlist: tmdb_id, added_at, note.

Features in scope:
- Search box that hits TMDB search, shows posters and years, one click to log. Cache the film row locally so the app works offline for anything already logged.
- Log form: watched date defaults to today, half-star rating widget, review textarea, rewatch and liked toggles.
- Diary view grouped by month, newest first, poster grid plus compact list toggle.
- Film detail page: local metadata, every entry you have for it, rating history.
- Watchlist page with add from search and a one-click promote to a diary entry.
- Stats page: films per year and per month, rating distribution histogram, top directors by count, total runtime in hours and days, longest streak of consecutive days with a log.
- CSV import that accepts the Letterboxd export format (Name, Year, Watched Date, Rating, Review, Rewatch) and matches to TMDB with a manual disambiguation queue for anything ambiguous.
- CSV export of everything.

Explicitly out of scope: any social features, follower graphs, comments, aggregate ratings, streaming availability, mobile apps, push notifications, deployment config.

Deliver: working app, README with setup in under five commands, seed script that inserts three sample entries so the stats page is not empty on first load.

## Required capabilities

- TMDB API key for film metadata and posters
- Node 20+
- SQLite (no server to run)

## 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.

# Letterboxd product brief

## Problem

The logging half of Letterboxd is trivial: a table of films, a date, a rating, a note. The half people actually open the app for is everyone else, friends' four-star shrugs, review pages with thousands of jokes, lists that function as film school syllabi, and the year-end stats that only mean something because they are comparable. A one-shot build gives you a private diary with no one in it, and you will still need TMDB to get posters and metadata that do not look broken. If you genuinely only want a watch log, build it and enjoy the speed. If you want the film internet, you are not building the film internet this weekend.

## Product outcome

A local film diary that searches TMDB for a title, logs a watch date, rating and review, and shows your history plus a stats page.

## Target user

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

## Required capabilities

- TMDB API key for film metadata and posters
- Node 20+
- SQLite (no server to run)

## Explicit non-goals for v1

- Every other human: friends' ratings, popular reviews, comment threads
- Curated and community lists, which are most of the discovery value
- Aggregate ratings and the histogram that tells you if a film is divisive or just bad
- Mobile apps, offline logging, share cards, streaming availability data
- Importing your existing history back out of anything without a CSV wrangle

## 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 a local-first personal film diary web app. Single user, no accounts, no cloud, no telemetry.

Stack, non-negotiable:
- Next.js 15 with the App Router, TypeScript, Tailwind.
- SQLite via better-sqlite3, one file at ./data/diary.db, schema created on first run.
- No auth, no external services except TMDB for metadata.

Secrets:
- .env.local with TMDB_API_KEY. Commit .env.example only. Fail loudly with a readable message if the key is missing.

Data model:
- films: tmdb_id (primary key), title, year, director, runtime, poster_path, overview, cached_at.
- entries: id, tmdb_id, watched_on (date), rating (0.5 to 5 in half steps, nullable), review (text, nullable), rewatch (boolean), liked (boolean), created_at.
- watchlist: tmdb_id, added_at, note.

Features in scope:
- Search box that hits TMDB search, shows posters and years, one click to log. Cache the film row locally so the app works offline for anything already logged.
- Log form: watched date defaults to today, half-star rating widget, review textarea, rewatch and liked toggles.
- Diary view grouped by month, newest first, poster grid plus compact list toggle.
- Film detail page: local metadata, every entry you have for it, rating history.
- Watchlist page with add from search and a one-click promote to a diary entry.
- Stats page: films per year and per month, rating distribution histogram, top directors by count, total runtime in hours and days, longest streak of consecutive days with a log.
- CSV import that accepts the Letterboxd export format (Name, Year, Watched Date, Rating, Review, Rewatch) and matches to TMDB with a manual disambiguation queue for anything ambiguous.
- CSV export of everything.

Explicitly out of scope: any social features, follower graphs, comments, aggregate ratings, streaming availability, mobile apps, push notifications, deployment config.

Deliver: working app, README with setup in under five commands, seed script that inserts three sample entries so the stats page is not empty on first load.

## 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 Letterboxd 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