Vibecode MarketMaster

You are building a lean indie version of MarketMaster.

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 =====
# MarketMaster indie build

## Goal

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

## Scope

Read one public website, extract a campaign brief, generate coordinated channel copy, review it, and export the approved package.

## 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:
- multi-model campaign review
- generated image and video assets
- live social and email integrations
- scheduled publishing and delivery history
- maintained ad-pattern and platform-limit references

If those capabilities are essential, use MarketMaster 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 personal AI campaign generator inspired by MarketMaster. Requirements:

- Use Node.js 22, Express, server-rendered HTML, and better-sqlite3.
- Run locally on port 4860 with no accounts, analytics, or telemetry.
- Accept one public website URL and fetch only same-domain HTML pages.
- Extract visible product, audience, pricing, proof, brand voice, and CTA text.
- Show the extracted campaign brief in an editable approval form before generation.
- Use one OpenAI or Anthropic model selected with variables in .env.
- Generate one campaign containing three ad concepts, five social posts,
  one five-slide carousel outline, one short-video script, and one email.
- Keep every asset grounded in approved brief fields and flag unsupported claims.
- Add deterministic checks for missing proof, vague CTAs, generic hype, and
  platform character limits; show warnings beside each affected asset.
- Let me edit, approve, and export the package as Markdown and JSON.
- Save briefs and campaigns in SQLite with created dates and approval status.
- Include fixture-based tests for extraction, claim flags, and exports.
- Secrets stay in .env and fetched page text stays on the local machine.
- Out of scope: image/video generation, multi-model review, OAuth integrations,
  automatic publishing, scheduling, collaboration, billing, and hosted accounts.
- Include a README with setup, model costs, robots.txt etiquette, and limitations.

## Required capabilities

- LLM API key
- website text extraction
- SQLite
- hosted or local web app

## 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 MarketMaster.

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 =====
# MarketMaster indie build

## Goal

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

## Scope

Read one public website, extract a campaign brief, generate coordinated channel copy, review it, and export the approved package.

## 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:
- multi-model campaign review
- generated image and video assets
- live social and email integrations
- scheduled publishing and delivery history
- maintained ad-pattern and platform-limit references

If those capabilities are essential, use MarketMaster 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 personal AI campaign generator inspired by MarketMaster. Requirements:

- Use Node.js 22, Express, server-rendered HTML, and better-sqlite3.
- Run locally on port 4860 with no accounts, analytics, or telemetry.
- Accept one public website URL and fetch only same-domain HTML pages.
- Extract visible product, audience, pricing, proof, brand voice, and CTA text.
- Show the extracted campaign brief in an editable approval form before generation.
- Use one OpenAI or Anthropic model selected with variables in .env.
- Generate one campaign containing three ad concepts, five social posts,
  one five-slide carousel outline, one short-video script, and one email.
- Keep every asset grounded in approved brief fields and flag unsupported claims.
- Add deterministic checks for missing proof, vague CTAs, generic hype, and
  platform character limits; show warnings beside each affected asset.
- Let me edit, approve, and export the package as Markdown and JSON.
- Save briefs and campaigns in SQLite with created dates and approval status.
- Include fixture-based tests for extraction, claim flags, and exports.
- Secrets stay in .env and fetched page text stays on the local machine.
- Out of scope: image/video generation, multi-model review, OAuth integrations,
  automatic publishing, scheduling, collaboration, billing, and hosted accounts.
- Include a README with setup, model costs, robots.txt etiquette, and limitations.

## Required capabilities

- LLM API key
- website text extraction
- SQLite
- hosted or local web app

## 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 MarketMaster.

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 =====
# MarketMaster product brief

## Problem

A solo campaign generator that scrapes one site and prompts an LLM for coordinated copy is a realistic weekend build. MarketMaster's paid value is the deeper research, multi-format media generation, deterministic quality gates, independent review, integrations, scheduling, and maintained publishing workflow.

## Product outcome

Read one public website, extract a campaign brief, generate coordinated channel copy, review it, and export the approved package.

## Target user

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

## Required capabilities

- LLM API key
- website text extraction
- SQLite
- hosted or local web app

## Explicit non-goals for v1

- multi-model campaign review
- generated image and video assets
- live social and email integrations
- scheduled publishing and delivery history
- maintained ad-pattern and platform-limit references

## 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 personal AI campaign generator inspired by MarketMaster. Requirements:

- Use Node.js 22, Express, server-rendered HTML, and better-sqlite3.
- Run locally on port 4860 with no accounts, analytics, or telemetry.
- Accept one public website URL and fetch only same-domain HTML pages.
- Extract visible product, audience, pricing, proof, brand voice, and CTA text.
- Show the extracted campaign brief in an editable approval form before generation.
- Use one OpenAI or Anthropic model selected with variables in .env.
- Generate one campaign containing three ad concepts, five social posts,
  one five-slide carousel outline, one short-video script, and one email.
- Keep every asset grounded in approved brief fields and flag unsupported claims.
- Add deterministic checks for missing proof, vague CTAs, generic hype, and
  platform character limits; show warnings beside each affected asset.
- Let me edit, approve, and export the package as Markdown and JSON.
- Save briefs and campaigns in SQLite with created dates and approval status.
- Include fixture-based tests for extraction, claim flags, and exports.
- Secrets stay in .env and fetched page text stays on the local machine.
- Out of scope: image/video generation, multi-model review, OAuth integrations,
  automatic publishing, scheduling, collaboration, billing, and hosted accounts.
- Include a README with setup, model costs, robots.txt etiquette, and limitations.

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

# MarketMaster indie build

## Goal

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

## Scope

Read one public website, extract a campaign brief, generate coordinated channel copy, review it, and export the approved package.

## 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:
- multi-model campaign review
- generated image and video assets
- live social and email integrations
- scheduled publishing and delivery history
- maintained ad-pattern and platform-limit references

If those capabilities are essential, use MarketMaster 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 personal AI campaign generator inspired by MarketMaster. Requirements:

- Use Node.js 22, Express, server-rendered HTML, and better-sqlite3.
- Run locally on port 4860 with no accounts, analytics, or telemetry.
- Accept one public website URL and fetch only same-domain HTML pages.
- Extract visible product, audience, pricing, proof, brand voice, and CTA text.
- Show the extracted campaign brief in an editable approval form before generation.
- Use one OpenAI or Anthropic model selected with variables in .env.
- Generate one campaign containing three ad concepts, five social posts,
  one five-slide carousel outline, one short-video script, and one email.
- Keep every asset grounded in approved brief fields and flag unsupported claims.
- Add deterministic checks for missing proof, vague CTAs, generic hype, and
  platform character limits; show warnings beside each affected asset.
- Let me edit, approve, and export the package as Markdown and JSON.
- Save briefs and campaigns in SQLite with created dates and approval status.
- Include fixture-based tests for extraction, claim flags, and exports.
- Secrets stay in .env and fetched page text stays on the local machine.
- Out of scope: image/video generation, multi-model review, OAuth integrations,
  automatic publishing, scheduling, collaboration, billing, and hosted accounts.
- Include a README with setup, model costs, robots.txt etiquette, and limitations.

## Required capabilities

- LLM API key
- website text extraction
- SQLite
- hosted or local web app

## 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.

# MarketMaster product brief

## Problem

A solo campaign generator that scrapes one site and prompts an LLM for coordinated copy is a realistic weekend build. MarketMaster's paid value is the deeper research, multi-format media generation, deterministic quality gates, independent review, integrations, scheduling, and maintained publishing workflow.

## Product outcome

Read one public website, extract a campaign brief, generate coordinated channel copy, review it, and export the approved package.

## Target user

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

## Required capabilities

- LLM API key
- website text extraction
- SQLite
- hosted or local web app

## Explicit non-goals for v1

- multi-model campaign review
- generated image and video assets
- live social and email integrations
- scheduled publishing and delivery history
- maintained ad-pattern and platform-limit references

## 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 personal AI campaign generator inspired by MarketMaster. Requirements:

- Use Node.js 22, Express, server-rendered HTML, and better-sqlite3.
- Run locally on port 4860 with no accounts, analytics, or telemetry.
- Accept one public website URL and fetch only same-domain HTML pages.
- Extract visible product, audience, pricing, proof, brand voice, and CTA text.
- Show the extracted campaign brief in an editable approval form before generation.
- Use one OpenAI or Anthropic model selected with variables in .env.
- Generate one campaign containing three ad concepts, five social posts,
  one five-slide carousel outline, one short-video script, and one email.
- Keep every asset grounded in approved brief fields and flag unsupported claims.
- Add deterministic checks for missing proof, vague CTAs, generic hype, and
  platform character limits; show warnings beside each affected asset.
- Let me edit, approve, and export the package as Markdown and JSON.
- Save briefs and campaigns in SQLite with created dates and approval status.
- Include fixture-based tests for extraction, claim flags, and exports.
- Secrets stay in .env and fetched page text stays on the local machine.
- Out of scope: image/video generation, multi-model review, OAuth integrations,
  automatic publishing, scheduling, collaboration, billing, and hosted accounts.
- Include a README with setup, model costs, robots.txt etiquette, and limitations.

## 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 MarketMaster 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