Vibecode DataFast

You are building a lean indie version of DataFast.

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 =====
# DataFast indie build

## Goal

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

## Scope

Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel.

## 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:
- identity stitching across devices, browsers and cleared storage
- bot and AI-crawler filtering that stays current without you
- one-click installs for Shopify, Webflow, WordPress and 20 other platforms
- the live visitor feed and purchase-likelihood scoring
- the hosted MCP server and CLI for querying the data in plain English

If those capabilities are essential, use PostHog 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 revenue attribution dashboard for one site, to replace DataFast. Requirements:

- Node + Express + better-sqlite3, one process behind Caddy on my own VPS.
  Server-rendered pages, no frontend framework, no build step.
- A tracker snippet under 2 KB: navigator.sendBeacon sends path, referrer and
  any utm_* params, keyed to a first-party visitor id in localStorage. No
  third-party cookies.
- Attribution is the whole point. Per visitor store first-touch and last-touch
  channel, from utm_source/utm_medium/utm_campaign, else by parsing the
  referrer host into google / x / reddit / hn / direct. Never overwrite
  first-touch.
- An /identify endpoint I call after signup with the user's email, which binds
  the anonymous visitor id to a customer row.
- A Stripe webhook for checkout.session.completed, invoice.paid and
  customer.subscription.deleted: verify the signature, match on email, write
  revenue against that visitor. Webhook secret and API key from .env.
- Dashboard on localhost behind one bearer token from .env: a channel table
  with visitors, signups, customers, MRR and revenue per visitor over 7/30/90
  days. Tables and one inline SVG bar chart, nothing else.
- Drop known bots against a user-agent blocklist before anything is counted. No
  accounts, no telemetry, one SQLite file I can copy off the box.
- Out of scope: cross-device identity stitching, multi-touch models, the live
  visitor feed, purchase-likelihood scoring, team seats and an MCP server. One
  domain, single-touch, single-device.
- README: the script tag, the /identify call, `stripe listen` for testing
  webhooks locally, and an honest paragraph on where the numbers lie · a
  phone-to-laptop journey counts as two visitors, cleared localStorage counts
  as a new one, and a customer who pays from a different address never matches
  at all.

## Required capabilities

- hosted server
- database
- tracker script
- Stripe webhook secret
- domain/SSL
- bot filtering

## 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 DataFast.

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 =====
# DataFast indie build

## Goal

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

## Scope

Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel.

## 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:
- identity stitching across devices, browsers and cleared storage
- bot and AI-crawler filtering that stays current without you
- one-click installs for Shopify, Webflow, WordPress and 20 other platforms
- the live visitor feed and purchase-likelihood scoring
- the hosted MCP server and CLI for querying the data in plain English

If those capabilities are essential, use PostHog 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 revenue attribution dashboard for one site, to replace DataFast. Requirements:

- Node + Express + better-sqlite3, one process behind Caddy on my own VPS.
  Server-rendered pages, no frontend framework, no build step.
- A tracker snippet under 2 KB: navigator.sendBeacon sends path, referrer and
  any utm_* params, keyed to a first-party visitor id in localStorage. No
  third-party cookies.
- Attribution is the whole point. Per visitor store first-touch and last-touch
  channel, from utm_source/utm_medium/utm_campaign, else by parsing the
  referrer host into google / x / reddit / hn / direct. Never overwrite
  first-touch.
- An /identify endpoint I call after signup with the user's email, which binds
  the anonymous visitor id to a customer row.
- A Stripe webhook for checkout.session.completed, invoice.paid and
  customer.subscription.deleted: verify the signature, match on email, write
  revenue against that visitor. Webhook secret and API key from .env.
- Dashboard on localhost behind one bearer token from .env: a channel table
  with visitors, signups, customers, MRR and revenue per visitor over 7/30/90
  days. Tables and one inline SVG bar chart, nothing else.
- Drop known bots against a user-agent blocklist before anything is counted. No
  accounts, no telemetry, one SQLite file I can copy off the box.
- Out of scope: cross-device identity stitching, multi-touch models, the live
  visitor feed, purchase-likelihood scoring, team seats and an MCP server. One
  domain, single-touch, single-device.
- README: the script tag, the /identify call, `stripe listen` for testing
  webhooks locally, and an honest paragraph on where the numbers lie · a
  phone-to-laptop journey counts as two visitors, cleared localStorage counts
  as a new one, and a customer who pays from a different address never matches
  at all.

## Required capabilities

- hosted server
- database
- tracker script
- Stripe webhook secret
- domain/SSL
- bot filtering

## 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 DataFast.

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 =====
# DataFast product brief

## Problem

The pageview half of DataFast is the same weekend build as Plausible or Umami. The revenue half is where it stops being a weekend. Attribution is only worth anything if the anonymous visitor who found your launch post on a phone is still recognisably the customer who pays from a laptop three weeks later, and that stitching is exactly what an agent will hand you a naive version of. A localStorage id plus an email match at signup does get you a channel table that is directionally right for a single-domain solo product, which is genuinely worth having. It also quietly under-counts every cross-device path, every privacy browser that clears storage between visits, and every customer who pays with a different address than they signed up with. You can build the dashboard in a weekend. Trusting it enough to move ad spend is the part that keeps costing you weekends.

## Product outcome

Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel.

## Target user

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

## Required capabilities

- hosted server
- database
- tracker script
- Stripe webhook secret
- domain/SSL
- bot filtering

## Explicit non-goals for v1

- identity stitching across devices, browsers and cleared storage
- bot and AI-crawler filtering that stays current without you
- one-click installs for Shopify, Webflow, WordPress and 20 other platforms
- the live visitor feed and purchase-likelihood scoring
- the hosted MCP server and CLI for querying the data in plain English

## 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 revenue attribution dashboard for one site, to replace DataFast. Requirements:

- Node + Express + better-sqlite3, one process behind Caddy on my own VPS.
  Server-rendered pages, no frontend framework, no build step.
- A tracker snippet under 2 KB: navigator.sendBeacon sends path, referrer and
  any utm_* params, keyed to a first-party visitor id in localStorage. No
  third-party cookies.
- Attribution is the whole point. Per visitor store first-touch and last-touch
  channel, from utm_source/utm_medium/utm_campaign, else by parsing the
  referrer host into google / x / reddit / hn / direct. Never overwrite
  first-touch.
- An /identify endpoint I call after signup with the user's email, which binds
  the anonymous visitor id to a customer row.
- A Stripe webhook for checkout.session.completed, invoice.paid and
  customer.subscription.deleted: verify the signature, match on email, write
  revenue against that visitor. Webhook secret and API key from .env.
- Dashboard on localhost behind one bearer token from .env: a channel table
  with visitors, signups, customers, MRR and revenue per visitor over 7/30/90
  days. Tables and one inline SVG bar chart, nothing else.
- Drop known bots against a user-agent blocklist before anything is counted. No
  accounts, no telemetry, one SQLite file I can copy off the box.
- Out of scope: cross-device identity stitching, multi-touch models, the live
  visitor feed, purchase-likelihood scoring, team seats and an MCP server. One
  domain, single-touch, single-device.
- README: the script tag, the /identify call, `stripe listen` for testing
  webhooks locally, and an honest paragraph on where the numbers lie · a
  phone-to-laptop journey counts as two visitors, cleared localStorage counts
  as a new one, and a customer who pays from a different address never matches
  at all.

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

# DataFast indie build

## Goal

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

## Scope

Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel.

## 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:
- identity stitching across devices, browsers and cleared storage
- bot and AI-crawler filtering that stays current without you
- one-click installs for Shopify, Webflow, WordPress and 20 other platforms
- the live visitor feed and purchase-likelihood scoring
- the hosted MCP server and CLI for querying the data in plain English

If those capabilities are essential, use PostHog 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 revenue attribution dashboard for one site, to replace DataFast. Requirements:

- Node + Express + better-sqlite3, one process behind Caddy on my own VPS.
  Server-rendered pages, no frontend framework, no build step.
- A tracker snippet under 2 KB: navigator.sendBeacon sends path, referrer and
  any utm_* params, keyed to a first-party visitor id in localStorage. No
  third-party cookies.
- Attribution is the whole point. Per visitor store first-touch and last-touch
  channel, from utm_source/utm_medium/utm_campaign, else by parsing the
  referrer host into google / x / reddit / hn / direct. Never overwrite
  first-touch.
- An /identify endpoint I call after signup with the user's email, which binds
  the anonymous visitor id to a customer row.
- A Stripe webhook for checkout.session.completed, invoice.paid and
  customer.subscription.deleted: verify the signature, match on email, write
  revenue against that visitor. Webhook secret and API key from .env.
- Dashboard on localhost behind one bearer token from .env: a channel table
  with visitors, signups, customers, MRR and revenue per visitor over 7/30/90
  days. Tables and one inline SVG bar chart, nothing else.
- Drop known bots against a user-agent blocklist before anything is counted. No
  accounts, no telemetry, one SQLite file I can copy off the box.
- Out of scope: cross-device identity stitching, multi-touch models, the live
  visitor feed, purchase-likelihood scoring, team seats and an MCP server. One
  domain, single-touch, single-device.
- README: the script tag, the /identify call, `stripe listen` for testing
  webhooks locally, and an honest paragraph on where the numbers lie · a
  phone-to-laptop journey counts as two visitors, cleared localStorage counts
  as a new one, and a customer who pays from a different address never matches
  at all.

## Required capabilities

- hosted server
- database
- tracker script
- Stripe webhook secret
- domain/SSL
- bot filtering

## 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.

# DataFast product brief

## Problem

The pageview half of DataFast is the same weekend build as Plausible or Umami. The revenue half is where it stops being a weekend. Attribution is only worth anything if the anonymous visitor who found your launch post on a phone is still recognisably the customer who pays from a laptop three weeks later, and that stitching is exactly what an agent will hand you a naive version of. A localStorage id plus an email match at signup does get you a channel table that is directionally right for a single-domain solo product, which is genuinely worth having. It also quietly under-counts every cross-device path, every privacy browser that clears storage between visits, and every customer who pays with a different address than they signed up with. You can build the dashboard in a weekend. Trusting it enough to move ad spend is the part that keeps costing you weekends.

## Product outcome

Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel.

## Target user

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

## Required capabilities

- hosted server
- database
- tracker script
- Stripe webhook secret
- domain/SSL
- bot filtering

## Explicit non-goals for v1

- identity stitching across devices, browsers and cleared storage
- bot and AI-crawler filtering that stays current without you
- one-click installs for Shopify, Webflow, WordPress and 20 other platforms
- the live visitor feed and purchase-likelihood scoring
- the hosted MCP server and CLI for querying the data in plain English

## 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 revenue attribution dashboard for one site, to replace DataFast. Requirements:

- Node + Express + better-sqlite3, one process behind Caddy on my own VPS.
  Server-rendered pages, no frontend framework, no build step.
- A tracker snippet under 2 KB: navigator.sendBeacon sends path, referrer and
  any utm_* params, keyed to a first-party visitor id in localStorage. No
  third-party cookies.
- Attribution is the whole point. Per visitor store first-touch and last-touch
  channel, from utm_source/utm_medium/utm_campaign, else by parsing the
  referrer host into google / x / reddit / hn / direct. Never overwrite
  first-touch.
- An /identify endpoint I call after signup with the user's email, which binds
  the anonymous visitor id to a customer row.
- A Stripe webhook for checkout.session.completed, invoice.paid and
  customer.subscription.deleted: verify the signature, match on email, write
  revenue against that visitor. Webhook secret and API key from .env.
- Dashboard on localhost behind one bearer token from .env: a channel table
  with visitors, signups, customers, MRR and revenue per visitor over 7/30/90
  days. Tables and one inline SVG bar chart, nothing else.
- Drop known bots against a user-agent blocklist before anything is counted. No
  accounts, no telemetry, one SQLite file I can copy off the box.
- Out of scope: cross-device identity stitching, multi-touch models, the live
  visitor feed, purchase-likelihood scoring, team seats and an MCP server. One
  domain, single-touch, single-device.
- README: the script tag, the /identify call, `stripe listen` for testing
  webhooks locally, and an honest paragraph on where the numbers lie · a
  phone-to-laptop journey counts as two visitors, cleared localStorage counts
  as a new one, and a customer who pays from a different address never matches
  at all.

## 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 DataFast 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