Vibecode Rankwise

You are building a lean indie version of Rankwise.

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 =====
# Rankwise indie build

## Goal

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

## Scope

Send a fixed question set through the four answer engine APIs on a schedule, record whether your domain is cited, and diff the score week over week.

## 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:
- the CMS publishing matrix, and the upkeep as WordPress, Shopify, Webflow and Wix each change their APIs
- the editorial layer: topics ranked by demand, citation gap and winnability, then drafted and reviewed rather than dumped
- exact technical diffs (JSON-LD, canonicals, robots rules, llms.txt) instead of generic advice you still have to translate
- months of score history, without which a single week's citation rate tells you nothing
- per-engine weekly scoring across seven on-page dimensions, which costs real API spend every run

If those capabilities are essential, use Elmo 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 local AI answer engine citation tracker for one website. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI plus a server-rendered dashboard
  on localhost. No accounts, no telemetry, everything stays on my machine except the
  API calls.
- site.json holds my domain, brand aliases and competitor domains. questions.json holds
  up to 40 buyer questions I want to be the answer to.
- `rank run` sends every question through OpenAI, Anthropic, Gemini and Perplexity with
  each provider's web search or grounding tool on. Keys live in .env.
- Write one immutable row per run, question and provider: raw answer, cited URLs, model
  id and any error. Never overwrite a previous run, history is the whole point.
- Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and show
  failed cells in the report rather than dropping them silently.
- Normalize every citation to hostname plus canonical path, strip tracking params, then
  compute my citation share and a top 25 cited domains table per engine.
- `rank serve` renders citation rate per engine over time, the questions where a
  competitor is cited and I am not, and a week-over-week diff.
- `rank audit <url>` fetches one page and reports the mechanical gaps only: missing or
  invalid JSON-LD, missing canonical, robots and llms.txt rules blocking AI crawlers,
  and thin heading structure. Print the JSON-LD block it would add.
- `rank export` writes runs and citations to CSV.
- Out of scope: writing articles, publishing to a CMS, and scraping the consumer chat
  UIs. Use the APIs only.
- README: setup, per-run cost estimate, a cron line for the weekly run, and a plain note
  that API answers only approximate what users are actually shown.

## Required capabilities

- OpenAI, Anthropic, Gemini and Perplexity API keys with web search or grounding enabled
- a scheduler for the weekly run
- durable per-run storage so history survives
- an API budget that scales with questions x engines x weeks
- CMS credentials if you want the publishing half too

## 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 Rankwise.

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 =====
# Rankwise indie build

## Goal

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

## Scope

Send a fixed question set through the four answer engine APIs on a schedule, record whether your domain is cited, and diff the score week over week.

## 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:
- the CMS publishing matrix, and the upkeep as WordPress, Shopify, Webflow and Wix each change their APIs
- the editorial layer: topics ranked by demand, citation gap and winnability, then drafted and reviewed rather than dumped
- exact technical diffs (JSON-LD, canonicals, robots rules, llms.txt) instead of generic advice you still have to translate
- months of score history, without which a single week's citation rate tells you nothing
- per-engine weekly scoring across seven on-page dimensions, which costs real API spend every run

If those capabilities are essential, use Elmo 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 local AI answer engine citation tracker for one website. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI plus a server-rendered dashboard
  on localhost. No accounts, no telemetry, everything stays on my machine except the
  API calls.
- site.json holds my domain, brand aliases and competitor domains. questions.json holds
  up to 40 buyer questions I want to be the answer to.
- `rank run` sends every question through OpenAI, Anthropic, Gemini and Perplexity with
  each provider's web search or grounding tool on. Keys live in .env.
- Write one immutable row per run, question and provider: raw answer, cited URLs, model
  id and any error. Never overwrite a previous run, history is the whole point.
- Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and show
  failed cells in the report rather than dropping them silently.
- Normalize every citation to hostname plus canonical path, strip tracking params, then
  compute my citation share and a top 25 cited domains table per engine.
- `rank serve` renders citation rate per engine over time, the questions where a
  competitor is cited and I am not, and a week-over-week diff.
- `rank audit <url>` fetches one page and reports the mechanical gaps only: missing or
  invalid JSON-LD, missing canonical, robots and llms.txt rules blocking AI crawlers,
  and thin heading structure. Print the JSON-LD block it would add.
- `rank export` writes runs and citations to CSV.
- Out of scope: writing articles, publishing to a CMS, and scraping the consumer chat
  UIs. Use the APIs only.
- README: setup, per-run cost estimate, a cron line for the weekly run, and a plain note
  that API answers only approximate what users are actually shown.

## Required capabilities

- OpenAI, Anthropic, Gemini and Perplexity API keys with web search or grounding enabled
- a scheduler for the weekly run
- durable per-run storage so history survives
- an API budget that scales with questions x engines x weeks
- CMS credentials if you want the publishing half too

## 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 Rankwise.

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 =====
# Rankwise product brief

## Problem

The measurement half is a weekend project. Fire a fixed question set at the ChatGPT, Claude, Gemini and Perplexity APIs on a schedule, count whether your domain gets named, store every run, and you have the dashboard. What takes Rankwise past that is the acting half: ranking topics by demand and citation gap, drafting sourced articles, and publishing them into WordPress, Shopify, Webflow or Wix without breaking anything. You can hand-write that loop for one site and one CMS. Doing it across four engines and four CMSes, every week, while the APIs move under you, is the part that stays a product.

## Product outcome

Send a fixed question set through the four answer engine APIs on a schedule, record whether your domain is cited, and diff the score week over week.

## Target user

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

## Required capabilities

- OpenAI, Anthropic, Gemini and Perplexity API keys with web search or grounding enabled
- a scheduler for the weekly run
- durable per-run storage so history survives
- an API budget that scales with questions x engines x weeks
- CMS credentials if you want the publishing half too

## Explicit non-goals for v1

- the CMS publishing matrix, and the upkeep as WordPress, Shopify, Webflow and Wix each change their APIs
- the editorial layer: topics ranked by demand, citation gap and winnability, then drafted and reviewed rather than dumped
- exact technical diffs (JSON-LD, canonicals, robots rules, llms.txt) instead of generic advice you still have to translate
- months of score history, without which a single week's citation rate tells you nothing
- per-engine weekly scoring across seven on-page dimensions, which costs real API spend every run

## 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 local AI answer engine citation tracker for one website. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI plus a server-rendered dashboard
  on localhost. No accounts, no telemetry, everything stays on my machine except the
  API calls.
- site.json holds my domain, brand aliases and competitor domains. questions.json holds
  up to 40 buyer questions I want to be the answer to.
- `rank run` sends every question through OpenAI, Anthropic, Gemini and Perplexity with
  each provider's web search or grounding tool on. Keys live in .env.
- Write one immutable row per run, question and provider: raw answer, cited URLs, model
  id and any error. Never overwrite a previous run, history is the whole point.
- Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and show
  failed cells in the report rather than dropping them silently.
- Normalize every citation to hostname plus canonical path, strip tracking params, then
  compute my citation share and a top 25 cited domains table per engine.
- `rank serve` renders citation rate per engine over time, the questions where a
  competitor is cited and I am not, and a week-over-week diff.
- `rank audit <url>` fetches one page and reports the mechanical gaps only: missing or
  invalid JSON-LD, missing canonical, robots and llms.txt rules blocking AI crawlers,
  and thin heading structure. Print the JSON-LD block it would add.
- `rank export` writes runs and citations to CSV.
- Out of scope: writing articles, publishing to a CMS, and scraping the consumer chat
  UIs. Use the APIs only.
- README: setup, per-run cost estimate, a cron line for the weekly run, and a plain note
  that API answers only approximate what users are actually shown.

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

# Rankwise indie build

## Goal

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

## Scope

Send a fixed question set through the four answer engine APIs on a schedule, record whether your domain is cited, and diff the score week over week.

## 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:
- the CMS publishing matrix, and the upkeep as WordPress, Shopify, Webflow and Wix each change their APIs
- the editorial layer: topics ranked by demand, citation gap and winnability, then drafted and reviewed rather than dumped
- exact technical diffs (JSON-LD, canonicals, robots rules, llms.txt) instead of generic advice you still have to translate
- months of score history, without which a single week's citation rate tells you nothing
- per-engine weekly scoring across seven on-page dimensions, which costs real API spend every run

If those capabilities are essential, use Elmo 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 local AI answer engine citation tracker for one website. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI plus a server-rendered dashboard
  on localhost. No accounts, no telemetry, everything stays on my machine except the
  API calls.
- site.json holds my domain, brand aliases and competitor domains. questions.json holds
  up to 40 buyer questions I want to be the answer to.
- `rank run` sends every question through OpenAI, Anthropic, Gemini and Perplexity with
  each provider's web search or grounding tool on. Keys live in .env.
- Write one immutable row per run, question and provider: raw answer, cited URLs, model
  id and any error. Never overwrite a previous run, history is the whole point.
- Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and show
  failed cells in the report rather than dropping them silently.
- Normalize every citation to hostname plus canonical path, strip tracking params, then
  compute my citation share and a top 25 cited domains table per engine.
- `rank serve` renders citation rate per engine over time, the questions where a
  competitor is cited and I am not, and a week-over-week diff.
- `rank audit <url>` fetches one page and reports the mechanical gaps only: missing or
  invalid JSON-LD, missing canonical, robots and llms.txt rules blocking AI crawlers,
  and thin heading structure. Print the JSON-LD block it would add.
- `rank export` writes runs and citations to CSV.
- Out of scope: writing articles, publishing to a CMS, and scraping the consumer chat
  UIs. Use the APIs only.
- README: setup, per-run cost estimate, a cron line for the weekly run, and a plain note
  that API answers only approximate what users are actually shown.

## Required capabilities

- OpenAI, Anthropic, Gemini and Perplexity API keys with web search or grounding enabled
- a scheduler for the weekly run
- durable per-run storage so history survives
- an API budget that scales with questions x engines x weeks
- CMS credentials if you want the publishing half too

## 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.

# Rankwise product brief

## Problem

The measurement half is a weekend project. Fire a fixed question set at the ChatGPT, Claude, Gemini and Perplexity APIs on a schedule, count whether your domain gets named, store every run, and you have the dashboard. What takes Rankwise past that is the acting half: ranking topics by demand and citation gap, drafting sourced articles, and publishing them into WordPress, Shopify, Webflow or Wix without breaking anything. You can hand-write that loop for one site and one CMS. Doing it across four engines and four CMSes, every week, while the APIs move under you, is the part that stays a product.

## Product outcome

Send a fixed question set through the four answer engine APIs on a schedule, record whether your domain is cited, and diff the score week over week.

## Target user

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

## Required capabilities

- OpenAI, Anthropic, Gemini and Perplexity API keys with web search or grounding enabled
- a scheduler for the weekly run
- durable per-run storage so history survives
- an API budget that scales with questions x engines x weeks
- CMS credentials if you want the publishing half too

## Explicit non-goals for v1

- the CMS publishing matrix, and the upkeep as WordPress, Shopify, Webflow and Wix each change their APIs
- the editorial layer: topics ranked by demand, citation gap and winnability, then drafted and reviewed rather than dumped
- exact technical diffs (JSON-LD, canonicals, robots rules, llms.txt) instead of generic advice you still have to translate
- months of score history, without which a single week's citation rate tells you nothing
- per-engine weekly scoring across seven on-page dimensions, which costs real API spend every run

## 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 local AI answer engine citation tracker for one website. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI plus a server-rendered dashboard
  on localhost. No accounts, no telemetry, everything stays on my machine except the
  API calls.
- site.json holds my domain, brand aliases and competitor domains. questions.json holds
  up to 40 buyer questions I want to be the answer to.
- `rank run` sends every question through OpenAI, Anthropic, Gemini and Perplexity with
  each provider's web search or grounding tool on. Keys live in .env.
- Write one immutable row per run, question and provider: raw answer, cited URLs, model
  id and any error. Never overwrite a previous run, history is the whole point.
- Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and show
  failed cells in the report rather than dropping them silently.
- Normalize every citation to hostname plus canonical path, strip tracking params, then
  compute my citation share and a top 25 cited domains table per engine.
- `rank serve` renders citation rate per engine over time, the questions where a
  competitor is cited and I am not, and a week-over-week diff.
- `rank audit <url>` fetches one page and reports the mechanical gaps only: missing or
  invalid JSON-LD, missing canonical, robots and llms.txt rules blocking AI crawlers,
  and thin heading structure. Print the JSON-LD block it would add.
- `rank export` writes runs and citations to CSV.
- Out of scope: writing articles, publishing to a CMS, and scraping the consumer chat
  UIs. Use the APIs only.
- README: setup, per-run cost estimate, a cron line for the weekly run, and a plain note
  that API answers only approximate what users are actually shown.

## 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 Rankwise 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