Vibecode AppsFlyer

You are building a lean indie version of AppsFlyer.

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 =====
# AppsFlyer indie build

## Goal

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

## Scope

A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.

## 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:
- Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners
- SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling
- Install and click fraud detection, which depends on cross-advertiser device data you will never have
- Postbacks and audience syncs to thousands of ad partners and MMP-only integrations
- A maintained SDK that survives every OS release, ATT prompt change and privacy policy update
- Deterministic cross-device and cross-platform identity resolution

If those capabilities are essential, use AppsFlyer 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 self-hosted first-party mobile attribution tracker. TypeScript, Fastify, SQLite via better-sqlite3, Vite plus React for the dashboard, Docker Compose for deployment. No accounts, no cloud services, no telemetry.

What it does:
1. Link service: POST /api/links creates a tracked link with campaign, source, medium, and an optional destination path in my app. Each link gets a short id served at GET /l/:id.
2. On click, /l/:id records a click row: timestamp, short id, user agent, platform guess from UA, referrer, IP truncated to /24 for IPv4 and /48 for IPv6, and a random click token set as a cookie. Then 302 to a universal link if the platform is iOS or Android, otherwise to a configured web fallback.
3. Serve /.well-known/apple-app-site-association and /.well-known/assetlinks.json from files in ./public so real deep links work.
4. Ingest endpoint: POST /api/events accepts { deviceId, eventName, clickToken?, platform, appVersion, ts, value? } authenticated by a shared secret in APP_INGEST_KEY. Store raw events untouched in an events table.
5. Attribution job: for each first_open event, attribute deterministically if clickToken matches a click. Otherwise do a probabilistic match against clicks in the last 24 hours on the same truncated IP and platform, and mark it as probabilistic with a confidence field. Never overwrite a deterministic match. Store results in an attributions table so the logic can be re-run idempotently.
6. Dashboard at /: table of campaigns with clicks, installs, deterministic versus probabilistic split, and any named post-install events. Date range picker. CSV export.
7. CLI script: npm run import-spend that loads a CSV of campaign,date,spend so the dashboard can show cost per install for channels I bought myself.

Explicitly out of scope, and say so in the README: SKAdNetwork and AdAttributionKit postbacks, any integration with Meta, Google, TikTok or Apple Search Ads, fraud detection, audience syncing, and a native SDK. The client side is a documented HTTP contract only.

Secrets in .env: APP_INGEST_KEY, PUBLIC_BASE_URL, WEB_FALLBACK_URL. Include a seed script with fake clicks and installs, and vitest tests covering deterministic match, probabilistic match, and the no-double-attribution rule.

## Required capabilities

- A server with a public HTTPS domain for click and postback endpoints
- Ability to add a small SDK or HTTP call to your own iOS/Android app
- Apple App Site Association and Android assetlinks.json files served for deep linking
- Your own ad account exports if you want spend numbers next to installs

## 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 AppsFlyer.

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 =====
# AppsFlyer indie build

## Goal

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

## Scope

A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.

## 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:
- Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners
- SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling
- Install and click fraud detection, which depends on cross-advertiser device data you will never have
- Postbacks and audience syncs to thousands of ad partners and MMP-only integrations
- A maintained SDK that survives every OS release, ATT prompt change and privacy policy update
- Deterministic cross-device and cross-platform identity resolution

If those capabilities are essential, use AppsFlyer 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 self-hosted first-party mobile attribution tracker. TypeScript, Fastify, SQLite via better-sqlite3, Vite plus React for the dashboard, Docker Compose for deployment. No accounts, no cloud services, no telemetry.

What it does:
1. Link service: POST /api/links creates a tracked link with campaign, source, medium, and an optional destination path in my app. Each link gets a short id served at GET /l/:id.
2. On click, /l/:id records a click row: timestamp, short id, user agent, platform guess from UA, referrer, IP truncated to /24 for IPv4 and /48 for IPv6, and a random click token set as a cookie. Then 302 to a universal link if the platform is iOS or Android, otherwise to a configured web fallback.
3. Serve /.well-known/apple-app-site-association and /.well-known/assetlinks.json from files in ./public so real deep links work.
4. Ingest endpoint: POST /api/events accepts { deviceId, eventName, clickToken?, platform, appVersion, ts, value? } authenticated by a shared secret in APP_INGEST_KEY. Store raw events untouched in an events table.
5. Attribution job: for each first_open event, attribute deterministically if clickToken matches a click. Otherwise do a probabilistic match against clicks in the last 24 hours on the same truncated IP and platform, and mark it as probabilistic with a confidence field. Never overwrite a deterministic match. Store results in an attributions table so the logic can be re-run idempotently.
6. Dashboard at /: table of campaigns with clicks, installs, deterministic versus probabilistic split, and any named post-install events. Date range picker. CSV export.
7. CLI script: npm run import-spend that loads a CSV of campaign,date,spend so the dashboard can show cost per install for channels I bought myself.

Explicitly out of scope, and say so in the README: SKAdNetwork and AdAttributionKit postbacks, any integration with Meta, Google, TikTok or Apple Search Ads, fraud detection, audience syncing, and a native SDK. The client side is a documented HTTP contract only.

Secrets in .env: APP_INGEST_KEY, PUBLIC_BASE_URL, WEB_FALLBACK_URL. Include a seed script with fake clicks and installs, and vitest tests covering deterministic match, probabilistic match, and the no-double-attribution rule.

## Required capabilities

- A server with a public HTTPS domain for click and postback endpoints
- Ability to add a small SDK or HTTP call to your own iOS/Android app
- Apple App Site Association and Android assetlinks.json files served for deep linking
- Your own ad account exports if you want spend numbers next to installs

## 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 AppsFlyer.

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 =====
# AppsFlyer product brief

## Problem

You can absolutely build a click tracker: log clicks, generate deep links, receive install pings from your own SDK, join them in SQLite. What you cannot build is the reason AppsFlyer exists, which is that Meta, Google, TikTok and Apple do not hand raw attribution data to random self-hosted endpoints. Those self-attributing networks report only to certified measurement partners, and SKAdNetwork/AdAttributionKit postbacks are aggregated and privacy-thresholded by design. Add install fraud detection, which is a data problem across billions of devices rather than a code problem, and the gap stops being about engineering effort. The honest consolation build is a first-party attribution tracker for channels you control: your own links, emails, influencer codes, organic web to app.

## Product outcome

A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.

## Target user

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

## Required capabilities

- A server with a public HTTPS domain for click and postback endpoints
- Ability to add a small SDK or HTTP call to your own iOS/Android app
- Apple App Site Association and Android assetlinks.json files served for deep linking
- Your own ad account exports if you want spend numbers next to installs

## Explicit non-goals for v1

- Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners
- SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling
- Install and click fraud detection, which depends on cross-advertiser device data you will never have
- Postbacks and audience syncs to thousands of ad partners and MMP-only integrations
- A maintained SDK that survives every OS release, ATT prompt change and privacy policy update
- Deterministic cross-device and cross-platform identity resolution

## 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 self-hosted first-party mobile attribution tracker. TypeScript, Fastify, SQLite via better-sqlite3, Vite plus React for the dashboard, Docker Compose for deployment. No accounts, no cloud services, no telemetry.

What it does:
1. Link service: POST /api/links creates a tracked link with campaign, source, medium, and an optional destination path in my app. Each link gets a short id served at GET /l/:id.
2. On click, /l/:id records a click row: timestamp, short id, user agent, platform guess from UA, referrer, IP truncated to /24 for IPv4 and /48 for IPv6, and a random click token set as a cookie. Then 302 to a universal link if the platform is iOS or Android, otherwise to a configured web fallback.
3. Serve /.well-known/apple-app-site-association and /.well-known/assetlinks.json from files in ./public so real deep links work.
4. Ingest endpoint: POST /api/events accepts { deviceId, eventName, clickToken?, platform, appVersion, ts, value? } authenticated by a shared secret in APP_INGEST_KEY. Store raw events untouched in an events table.
5. Attribution job: for each first_open event, attribute deterministically if clickToken matches a click. Otherwise do a probabilistic match against clicks in the last 24 hours on the same truncated IP and platform, and mark it as probabilistic with a confidence field. Never overwrite a deterministic match. Store results in an attributions table so the logic can be re-run idempotently.
6. Dashboard at /: table of campaigns with clicks, installs, deterministic versus probabilistic split, and any named post-install events. Date range picker. CSV export.
7. CLI script: npm run import-spend that loads a CSV of campaign,date,spend so the dashboard can show cost per install for channels I bought myself.

Explicitly out of scope, and say so in the README: SKAdNetwork and AdAttributionKit postbacks, any integration with Meta, Google, TikTok or Apple Search Ads, fraud detection, audience syncing, and a native SDK. The client side is a documented HTTP contract only.

Secrets in .env: APP_INGEST_KEY, PUBLIC_BASE_URL, WEB_FALLBACK_URL. Include a seed script with fake clicks and installs, and vitest tests covering deterministic match, probabilistic match, and the no-double-attribution rule.

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

# AppsFlyer indie build

## Goal

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

## Scope

A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.

## 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:
- Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners
- SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling
- Install and click fraud detection, which depends on cross-advertiser device data you will never have
- Postbacks and audience syncs to thousands of ad partners and MMP-only integrations
- A maintained SDK that survives every OS release, ATT prompt change and privacy policy update
- Deterministic cross-device and cross-platform identity resolution

If those capabilities are essential, use AppsFlyer 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 self-hosted first-party mobile attribution tracker. TypeScript, Fastify, SQLite via better-sqlite3, Vite plus React for the dashboard, Docker Compose for deployment. No accounts, no cloud services, no telemetry.

What it does:
1. Link service: POST /api/links creates a tracked link with campaign, source, medium, and an optional destination path in my app. Each link gets a short id served at GET /l/:id.
2. On click, /l/:id records a click row: timestamp, short id, user agent, platform guess from UA, referrer, IP truncated to /24 for IPv4 and /48 for IPv6, and a random click token set as a cookie. Then 302 to a universal link if the platform is iOS or Android, otherwise to a configured web fallback.
3. Serve /.well-known/apple-app-site-association and /.well-known/assetlinks.json from files in ./public so real deep links work.
4. Ingest endpoint: POST /api/events accepts { deviceId, eventName, clickToken?, platform, appVersion, ts, value? } authenticated by a shared secret in APP_INGEST_KEY. Store raw events untouched in an events table.
5. Attribution job: for each first_open event, attribute deterministically if clickToken matches a click. Otherwise do a probabilistic match against clicks in the last 24 hours on the same truncated IP and platform, and mark it as probabilistic with a confidence field. Never overwrite a deterministic match. Store results in an attributions table so the logic can be re-run idempotently.
6. Dashboard at /: table of campaigns with clicks, installs, deterministic versus probabilistic split, and any named post-install events. Date range picker. CSV export.
7. CLI script: npm run import-spend that loads a CSV of campaign,date,spend so the dashboard can show cost per install for channels I bought myself.

Explicitly out of scope, and say so in the README: SKAdNetwork and AdAttributionKit postbacks, any integration with Meta, Google, TikTok or Apple Search Ads, fraud detection, audience syncing, and a native SDK. The client side is a documented HTTP contract only.

Secrets in .env: APP_INGEST_KEY, PUBLIC_BASE_URL, WEB_FALLBACK_URL. Include a seed script with fake clicks and installs, and vitest tests covering deterministic match, probabilistic match, and the no-double-attribution rule.

## Required capabilities

- A server with a public HTTPS domain for click and postback endpoints
- Ability to add a small SDK or HTTP call to your own iOS/Android app
- Apple App Site Association and Android assetlinks.json files served for deep linking
- Your own ad account exports if you want spend numbers next to installs

## 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.

# AppsFlyer product brief

## Problem

You can absolutely build a click tracker: log clicks, generate deep links, receive install pings from your own SDK, join them in SQLite. What you cannot build is the reason AppsFlyer exists, which is that Meta, Google, TikTok and Apple do not hand raw attribution data to random self-hosted endpoints. Those self-attributing networks report only to certified measurement partners, and SKAdNetwork/AdAttributionKit postbacks are aggregated and privacy-thresholded by design. Add install fraud detection, which is a data problem across billions of devices rather than a code problem, and the gap stops being about engineering effort. The honest consolation build is a first-party attribution tracker for channels you control: your own links, emails, influencer codes, organic web to app.

## Product outcome

A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.

## Target user

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

## Required capabilities

- A server with a public HTTPS domain for click and postback endpoints
- Ability to add a small SDK or HTTP call to your own iOS/Android app
- Apple App Site Association and Android assetlinks.json files served for deep linking
- Your own ad account exports if you want spend numbers next to installs

## Explicit non-goals for v1

- Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners
- SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling
- Install and click fraud detection, which depends on cross-advertiser device data you will never have
- Postbacks and audience syncs to thousands of ad partners and MMP-only integrations
- A maintained SDK that survives every OS release, ATT prompt change and privacy policy update
- Deterministic cross-device and cross-platform identity resolution

## 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 self-hosted first-party mobile attribution tracker. TypeScript, Fastify, SQLite via better-sqlite3, Vite plus React for the dashboard, Docker Compose for deployment. No accounts, no cloud services, no telemetry.

What it does:
1. Link service: POST /api/links creates a tracked link with campaign, source, medium, and an optional destination path in my app. Each link gets a short id served at GET /l/:id.
2. On click, /l/:id records a click row: timestamp, short id, user agent, platform guess from UA, referrer, IP truncated to /24 for IPv4 and /48 for IPv6, and a random click token set as a cookie. Then 302 to a universal link if the platform is iOS or Android, otherwise to a configured web fallback.
3. Serve /.well-known/apple-app-site-association and /.well-known/assetlinks.json from files in ./public so real deep links work.
4. Ingest endpoint: POST /api/events accepts { deviceId, eventName, clickToken?, platform, appVersion, ts, value? } authenticated by a shared secret in APP_INGEST_KEY. Store raw events untouched in an events table.
5. Attribution job: for each first_open event, attribute deterministically if clickToken matches a click. Otherwise do a probabilistic match against clicks in the last 24 hours on the same truncated IP and platform, and mark it as probabilistic with a confidence field. Never overwrite a deterministic match. Store results in an attributions table so the logic can be re-run idempotently.
6. Dashboard at /: table of campaigns with clicks, installs, deterministic versus probabilistic split, and any named post-install events. Date range picker. CSV export.
7. CLI script: npm run import-spend that loads a CSV of campaign,date,spend so the dashboard can show cost per install for channels I bought myself.

Explicitly out of scope, and say so in the README: SKAdNetwork and AdAttributionKit postbacks, any integration with Meta, Google, TikTok or Apple Search Ads, fraud detection, audience syncing, and a native SDK. The client side is a documented HTTP contract only.

Secrets in .env: APP_INGEST_KEY, PUBLIC_BASE_URL, WEB_FALLBACK_URL. Include a seed script with fake clicks and installs, and vitest tests covering deterministic match, probabilistic match, and the no-double-attribution rule.

## 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 AppsFlyer 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