Vibecode AppGrowKit

You are building a lean indie version of AppGrowKit.

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 =====
# AppGrowKit indie build

## Goal

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

## Scope

Generate App Store screenshot panels from my real app screens with an image model, and poll Apple's public endpoints daily for my own apps' ranks, ratings, and reviews.

## 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:
- months of rank, rating, and keyword history you cannot backfill
- the 1.7M-app, 36-storefront catalog behind competitor lookups and keyword difficulty
- revenue and download estimates, which are calibrated against that catalog
- keyword volume and competition scores, which need a corpus to be relative to
- the MCP server that answers ASO questions from Claude, Cursor, or ChatGPT
- an accuracy pass on generated screenshots: the QA critic, best-of-2 scoring, and exact-size output

If those capabilities are essential, use SerpBear 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 an App Store screenshot generator and a personal rank tracker to replace AppGrowKit, single user, my own apps only.
Use Node 22, TypeScript, SQLite (better-sqlite3), sharp, and a small React frontend on localhost. Do not offer alternative stacks.

- Screenshots: I upload my real app screens. Compose a 3-panel App Store set by sending each panel to an image model (fal.ai key in .env), passing my screens as reference images so the in-phone UI stays faithful.
- Send every render back to a vision model with a checklist: garbled text, elements sliced by a panel edge, invented star ratings or award badges, phone hardware where the style said none. If it fails, re-render once with the failures pasted in as corrections.
- Composite the headline as real text with sharp, not model-drawn glyphs. Resize output to exactly 1290x2796 (iPhone) and 2064x2752 (iPad).
- Tracking: a config file lists my App Store track IDs and the keywords I care about. A daily job hits Apple's public endpoints (itunes.apple.com/lookup, /search, the RSS chart feeds, and the customerreviews feed) with a real User-Agent and one request every 1.5 seconds.
- Store every poll as a row with a timestamp, never an upsert. Rank history is the whole point and you cannot recover a day you overwrote.
- The page shows my apps' rating and rank over time, new reviews since I last looked, and where I sit for each tracked keyword.
- Out of scope: competitor intelligence, keyword difficulty scores, and revenue estimates. Those need a catalog of hundreds of thousands of apps with months of history. Say so in the README rather than faking them from a single day of data.
- README: the two API keys, cost per screenshot set, and a warning that the tracker is worth little until it has been running for a month.

## Required capabilities

- image model API key (fal.ai or OpenAI)
- vision model API key for the QA pass
- SQLite or Postgres
- a scheduler that actually runs daily

## 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 AppGrowKit.

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 =====
# AppGrowKit indie build

## Goal

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

## Scope

Generate App Store screenshot panels from my real app screens with an image model, and poll Apple's public endpoints daily for my own apps' ranks, ratings, and reviews.

## 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:
- months of rank, rating, and keyword history you cannot backfill
- the 1.7M-app, 36-storefront catalog behind competitor lookups and keyword difficulty
- revenue and download estimates, which are calibrated against that catalog
- keyword volume and competition scores, which need a corpus to be relative to
- the MCP server that answers ASO questions from Claude, Cursor, or ChatGPT
- an accuracy pass on generated screenshots: the QA critic, best-of-2 scoring, and exact-size output

If those capabilities are essential, use SerpBear 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 an App Store screenshot generator and a personal rank tracker to replace AppGrowKit, single user, my own apps only.
Use Node 22, TypeScript, SQLite (better-sqlite3), sharp, and a small React frontend on localhost. Do not offer alternative stacks.

- Screenshots: I upload my real app screens. Compose a 3-panel App Store set by sending each panel to an image model (fal.ai key in .env), passing my screens as reference images so the in-phone UI stays faithful.
- Send every render back to a vision model with a checklist: garbled text, elements sliced by a panel edge, invented star ratings or award badges, phone hardware where the style said none. If it fails, re-render once with the failures pasted in as corrections.
- Composite the headline as real text with sharp, not model-drawn glyphs. Resize output to exactly 1290x2796 (iPhone) and 2064x2752 (iPad).
- Tracking: a config file lists my App Store track IDs and the keywords I care about. A daily job hits Apple's public endpoints (itunes.apple.com/lookup, /search, the RSS chart feeds, and the customerreviews feed) with a real User-Agent and one request every 1.5 seconds.
- Store every poll as a row with a timestamp, never an upsert. Rank history is the whole point and you cannot recover a day you overwrote.
- The page shows my apps' rating and rank over time, new reviews since I last looked, and where I sit for each tracked keyword.
- Out of scope: competitor intelligence, keyword difficulty scores, and revenue estimates. Those need a catalog of hundreds of thousands of apps with months of history. Say so in the README rather than faking them from a single day of data.
- README: the two API keys, cost per screenshot set, and a warning that the tracker is worth little until it has been running for a month.

## Required capabilities

- image model API key (fal.ai or OpenAI)
- vision model API key for the QA pass
- SQLite or Postgres
- a scheduler that actually runs daily

## 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 AppGrowKit.

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 =====
# AppGrowKit product brief

## Problem

Split the product in half and the answer changes. The screenshot generator is genuinely one-shottable: it is an image model behind a prompt, and a weekend of iteration gets you panels good enough to ship. The tracking half is too, if you only care about apps you own, because Apple's public endpoints hand you ratings, reviews, and chart positions for free. What you cannot build is the part you would be paying for: a catalog of 1.7 million apps across 36 storefronts, with 22 million rating observations and 11 million rank observations accumulated over months. 1.25 million of those apps have more than one day of history and 960,000 have ten days or more, which is the difference between a table of apps and a time series. You can start collecting today, and in six months you will have six months of it. Competitor intelligence, keyword difficulty, and revenue estimates are all reads over that history, so they arrive empty on day one and stay thin for a season. Build it if you track a handful of your own apps. Pay if you need to answer questions about apps you have never opened.

## Product outcome

Generate App Store screenshot panels from my real app screens with an image model, and poll Apple's public endpoints daily for my own apps' ranks, ratings, and reviews.

## Target user

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

## Required capabilities

- image model API key (fal.ai or OpenAI)
- vision model API key for the QA pass
- SQLite or Postgres
- a scheduler that actually runs daily

## Explicit non-goals for v1

- months of rank, rating, and keyword history you cannot backfill
- the 1.7M-app, 36-storefront catalog behind competitor lookups and keyword difficulty
- revenue and download estimates, which are calibrated against that catalog
- keyword volume and competition scores, which need a corpus to be relative to
- the MCP server that answers ASO questions from Claude, Cursor, or ChatGPT
- an accuracy pass on generated screenshots: the QA critic, best-of-2 scoring, and exact-size output

## 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 an App Store screenshot generator and a personal rank tracker to replace AppGrowKit, single user, my own apps only.
Use Node 22, TypeScript, SQLite (better-sqlite3), sharp, and a small React frontend on localhost. Do not offer alternative stacks.

- Screenshots: I upload my real app screens. Compose a 3-panel App Store set by sending each panel to an image model (fal.ai key in .env), passing my screens as reference images so the in-phone UI stays faithful.
- Send every render back to a vision model with a checklist: garbled text, elements sliced by a panel edge, invented star ratings or award badges, phone hardware where the style said none. If it fails, re-render once with the failures pasted in as corrections.
- Composite the headline as real text with sharp, not model-drawn glyphs. Resize output to exactly 1290x2796 (iPhone) and 2064x2752 (iPad).
- Tracking: a config file lists my App Store track IDs and the keywords I care about. A daily job hits Apple's public endpoints (itunes.apple.com/lookup, /search, the RSS chart feeds, and the customerreviews feed) with a real User-Agent and one request every 1.5 seconds.
- Store every poll as a row with a timestamp, never an upsert. Rank history is the whole point and you cannot recover a day you overwrote.
- The page shows my apps' rating and rank over time, new reviews since I last looked, and where I sit for each tracked keyword.
- Out of scope: competitor intelligence, keyword difficulty scores, and revenue estimates. Those need a catalog of hundreds of thousands of apps with months of history. Say so in the README rather than faking them from a single day of data.
- README: the two API keys, cost per screenshot set, and a warning that the tracker is worth little until it has been running for a month.

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

# AppGrowKit indie build

## Goal

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

## Scope

Generate App Store screenshot panels from my real app screens with an image model, and poll Apple's public endpoints daily for my own apps' ranks, ratings, and reviews.

## 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:
- months of rank, rating, and keyword history you cannot backfill
- the 1.7M-app, 36-storefront catalog behind competitor lookups and keyword difficulty
- revenue and download estimates, which are calibrated against that catalog
- keyword volume and competition scores, which need a corpus to be relative to
- the MCP server that answers ASO questions from Claude, Cursor, or ChatGPT
- an accuracy pass on generated screenshots: the QA critic, best-of-2 scoring, and exact-size output

If those capabilities are essential, use SerpBear 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 an App Store screenshot generator and a personal rank tracker to replace AppGrowKit, single user, my own apps only.
Use Node 22, TypeScript, SQLite (better-sqlite3), sharp, and a small React frontend on localhost. Do not offer alternative stacks.

- Screenshots: I upload my real app screens. Compose a 3-panel App Store set by sending each panel to an image model (fal.ai key in .env), passing my screens as reference images so the in-phone UI stays faithful.
- Send every render back to a vision model with a checklist: garbled text, elements sliced by a panel edge, invented star ratings or award badges, phone hardware where the style said none. If it fails, re-render once with the failures pasted in as corrections.
- Composite the headline as real text with sharp, not model-drawn glyphs. Resize output to exactly 1290x2796 (iPhone) and 2064x2752 (iPad).
- Tracking: a config file lists my App Store track IDs and the keywords I care about. A daily job hits Apple's public endpoints (itunes.apple.com/lookup, /search, the RSS chart feeds, and the customerreviews feed) with a real User-Agent and one request every 1.5 seconds.
- Store every poll as a row with a timestamp, never an upsert. Rank history is the whole point and you cannot recover a day you overwrote.
- The page shows my apps' rating and rank over time, new reviews since I last looked, and where I sit for each tracked keyword.
- Out of scope: competitor intelligence, keyword difficulty scores, and revenue estimates. Those need a catalog of hundreds of thousands of apps with months of history. Say so in the README rather than faking them from a single day of data.
- README: the two API keys, cost per screenshot set, and a warning that the tracker is worth little until it has been running for a month.

## Required capabilities

- image model API key (fal.ai or OpenAI)
- vision model API key for the QA pass
- SQLite or Postgres
- a scheduler that actually runs daily

## 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.

# AppGrowKit product brief

## Problem

Split the product in half and the answer changes. The screenshot generator is genuinely one-shottable: it is an image model behind a prompt, and a weekend of iteration gets you panels good enough to ship. The tracking half is too, if you only care about apps you own, because Apple's public endpoints hand you ratings, reviews, and chart positions for free. What you cannot build is the part you would be paying for: a catalog of 1.7 million apps across 36 storefronts, with 22 million rating observations and 11 million rank observations accumulated over months. 1.25 million of those apps have more than one day of history and 960,000 have ten days or more, which is the difference between a table of apps and a time series. You can start collecting today, and in six months you will have six months of it. Competitor intelligence, keyword difficulty, and revenue estimates are all reads over that history, so they arrive empty on day one and stay thin for a season. Build it if you track a handful of your own apps. Pay if you need to answer questions about apps you have never opened.

## Product outcome

Generate App Store screenshot panels from my real app screens with an image model, and poll Apple's public endpoints daily for my own apps' ranks, ratings, and reviews.

## Target user

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

## Required capabilities

- image model API key (fal.ai or OpenAI)
- vision model API key for the QA pass
- SQLite or Postgres
- a scheduler that actually runs daily

## Explicit non-goals for v1

- months of rank, rating, and keyword history you cannot backfill
- the 1.7M-app, 36-storefront catalog behind competitor lookups and keyword difficulty
- revenue and download estimates, which are calibrated against that catalog
- keyword volume and competition scores, which need a corpus to be relative to
- the MCP server that answers ASO questions from Claude, Cursor, or ChatGPT
- an accuracy pass on generated screenshots: the QA critic, best-of-2 scoring, and exact-size output

## 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 an App Store screenshot generator and a personal rank tracker to replace AppGrowKit, single user, my own apps only.
Use Node 22, TypeScript, SQLite (better-sqlite3), sharp, and a small React frontend on localhost. Do not offer alternative stacks.

- Screenshots: I upload my real app screens. Compose a 3-panel App Store set by sending each panel to an image model (fal.ai key in .env), passing my screens as reference images so the in-phone UI stays faithful.
- Send every render back to a vision model with a checklist: garbled text, elements sliced by a panel edge, invented star ratings or award badges, phone hardware where the style said none. If it fails, re-render once with the failures pasted in as corrections.
- Composite the headline as real text with sharp, not model-drawn glyphs. Resize output to exactly 1290x2796 (iPhone) and 2064x2752 (iPad).
- Tracking: a config file lists my App Store track IDs and the keywords I care about. A daily job hits Apple's public endpoints (itunes.apple.com/lookup, /search, the RSS chart feeds, and the customerreviews feed) with a real User-Agent and one request every 1.5 seconds.
- Store every poll as a row with a timestamp, never an upsert. Rank history is the whole point and you cannot recover a day you overwrote.
- The page shows my apps' rating and rank over time, new reviews since I last looked, and where I sit for each tracked keyword.
- Out of scope: competitor intelligence, keyword difficulty scores, and revenue estimates. Those need a catalog of hundreds of thousands of apps with months of history. Say so in the README rather than faking them from a single day of data.
- README: the two API keys, cost per screenshot set, and a warning that the tracker is worth little until it has been running for a month.

## 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 AppGrowKit 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