Vibecode CueScout

You are building a lean indie version of CueScout.

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 =====
# CueScout indie build

## Goal

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

## Scope

Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.

## 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 continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model
- Google rank badges on matched threads, sourced from a paid search API
- the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number
- weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise
- a hosted, shareable report link you can hand a client without exposing your own infra

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 buyer-question visibility tracker for one product. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry.
- product.json holds my product name, aliases, domain, and up to 5 competitor names.
- `track questions` generates 20 buyer-intent questions from the product name and a one-paragraph description, or accepts a JSON list I supply.
- `track run` sends every question through Perplexity's API and OpenAI's Chat Completions API with web search enabled where supported. Keys live in .env.
- Store one immutable row per run, question, and provider: raw answer, cited URLs, model id, and error text. Never overwrite a prior run.
- Retry transient failures twice with backoff; keep failed cells visible in the report instead of dropping them.
- Detect brand and competitor mentions case-insensitively against the alias list.
- Normalize citation URLs to hostname plus canonical path, strip tracking parameters, then compute a top-sources table and an owned-domain citation rate.
- Compute one GEO score per run: mention rate times citation rate, shown with the raw counts behind it, never just the number.
- `track serve` renders visibility over time by provider, share of voice against each competitor, and the sources table.
- `track export` writes questions, answers, mentions, and citations to CSV.
- Fixture tests for mention detection, URL normalization, and the GEO score calculation.
- Out of scope: Reddit/Hacker News monitoring, Google rank tracking, writing-plan or content generation, hosted sharing, teams, and billing.
- README: setup, per-run API cost estimate, and a cron line for a daily run.

## Required capabilities

- Perplexity API key
- OpenAI API key
- durable per-run storage
- a scheduler for repeat runs

## 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 CueScout.

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 =====
# CueScout indie build

## Goal

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

## Scope

Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.

## 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 continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model
- Google rank badges on matched threads, sourced from a paid search API
- the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number
- weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise
- a hosted, shareable report link you can hand a client without exposing your own infra

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 buyer-question visibility tracker for one product. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry.
- product.json holds my product name, aliases, domain, and up to 5 competitor names.
- `track questions` generates 20 buyer-intent questions from the product name and a one-paragraph description, or accepts a JSON list I supply.
- `track run` sends every question through Perplexity's API and OpenAI's Chat Completions API with web search enabled where supported. Keys live in .env.
- Store one immutable row per run, question, and provider: raw answer, cited URLs, model id, and error text. Never overwrite a prior run.
- Retry transient failures twice with backoff; keep failed cells visible in the report instead of dropping them.
- Detect brand and competitor mentions case-insensitively against the alias list.
- Normalize citation URLs to hostname plus canonical path, strip tracking parameters, then compute a top-sources table and an owned-domain citation rate.
- Compute one GEO score per run: mention rate times citation rate, shown with the raw counts behind it, never just the number.
- `track serve` renders visibility over time by provider, share of voice against each competitor, and the sources table.
- `track export` writes questions, answers, mentions, and citations to CSV.
- Fixture tests for mention detection, URL normalization, and the GEO score calculation.
- Out of scope: Reddit/Hacker News monitoring, Google rank tracking, writing-plan or content generation, hosted sharing, teams, and billing.
- README: setup, per-run API cost estimate, and a cron line for a daily run.

## Required capabilities

- Perplexity API key
- OpenAI API key
- durable per-run storage
- a scheduler for repeat runs

## 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 CueScout.

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 =====
# CueScout product brief

## Problem

The visibility-check loop is the weekend-buildable half: generate buyer questions, run them through Perplexity and ChatGPT, detect brand and competitor mentions, normalize citations, and score a GEO number. That much is close to what a $49/mo plan actually ships, since Basic only covers one engine. What does not fit in a prompt is the continuous Reddit and Hacker News scan that mines buyer questions from real threads instead of guessing them, the writing-plan-to-draft loop that turns a score into dated work, and weeks of trend history without which one run tells you almost nothing.

## Product outcome

Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.

## Target user

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

## Required capabilities

- Perplexity API key
- OpenAI API key
- durable per-run storage
- a scheduler for repeat runs

## Explicit non-goals for v1

- the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model
- Google rank badges on matched threads, sourced from a paid search API
- the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number
- weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise
- a hosted, shareable report link you can hand a client without exposing your own infra

## 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 buyer-question visibility tracker for one product. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry.
- product.json holds my product name, aliases, domain, and up to 5 competitor names.
- `track questions` generates 20 buyer-intent questions from the product name and a one-paragraph description, or accepts a JSON list I supply.
- `track run` sends every question through Perplexity's API and OpenAI's Chat Completions API with web search enabled where supported. Keys live in .env.
- Store one immutable row per run, question, and provider: raw answer, cited URLs, model id, and error text. Never overwrite a prior run.
- Retry transient failures twice with backoff; keep failed cells visible in the report instead of dropping them.
- Detect brand and competitor mentions case-insensitively against the alias list.
- Normalize citation URLs to hostname plus canonical path, strip tracking parameters, then compute a top-sources table and an owned-domain citation rate.
- Compute one GEO score per run: mention rate times citation rate, shown with the raw counts behind it, never just the number.
- `track serve` renders visibility over time by provider, share of voice against each competitor, and the sources table.
- `track export` writes questions, answers, mentions, and citations to CSV.
- Fixture tests for mention detection, URL normalization, and the GEO score calculation.
- Out of scope: Reddit/Hacker News monitoring, Google rank tracking, writing-plan or content generation, hosted sharing, teams, and billing.
- README: setup, per-run API cost estimate, and a cron line for a daily run.

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

# CueScout indie build

## Goal

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

## Scope

Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.

## 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 continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model
- Google rank badges on matched threads, sourced from a paid search API
- the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number
- weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise
- a hosted, shareable report link you can hand a client without exposing your own infra

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 buyer-question visibility tracker for one product. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry.
- product.json holds my product name, aliases, domain, and up to 5 competitor names.
- `track questions` generates 20 buyer-intent questions from the product name and a one-paragraph description, or accepts a JSON list I supply.
- `track run` sends every question through Perplexity's API and OpenAI's Chat Completions API with web search enabled where supported. Keys live in .env.
- Store one immutable row per run, question, and provider: raw answer, cited URLs, model id, and error text. Never overwrite a prior run.
- Retry transient failures twice with backoff; keep failed cells visible in the report instead of dropping them.
- Detect brand and competitor mentions case-insensitively against the alias list.
- Normalize citation URLs to hostname plus canonical path, strip tracking parameters, then compute a top-sources table and an owned-domain citation rate.
- Compute one GEO score per run: mention rate times citation rate, shown with the raw counts behind it, never just the number.
- `track serve` renders visibility over time by provider, share of voice against each competitor, and the sources table.
- `track export` writes questions, answers, mentions, and citations to CSV.
- Fixture tests for mention detection, URL normalization, and the GEO score calculation.
- Out of scope: Reddit/Hacker News monitoring, Google rank tracking, writing-plan or content generation, hosted sharing, teams, and billing.
- README: setup, per-run API cost estimate, and a cron line for a daily run.

## Required capabilities

- Perplexity API key
- OpenAI API key
- durable per-run storage
- a scheduler for repeat runs

## 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.

# CueScout product brief

## Problem

The visibility-check loop is the weekend-buildable half: generate buyer questions, run them through Perplexity and ChatGPT, detect brand and competitor mentions, normalize citations, and score a GEO number. That much is close to what a $49/mo plan actually ships, since Basic only covers one engine. What does not fit in a prompt is the continuous Reddit and Hacker News scan that mines buyer questions from real threads instead of guessing them, the writing-plan-to-draft loop that turns a score into dated work, and weeks of trend history without which one run tells you almost nothing.

## Product outcome

Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.

## Target user

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

## Required capabilities

- Perplexity API key
- OpenAI API key
- durable per-run storage
- a scheduler for repeat runs

## Explicit non-goals for v1

- the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model
- Google rank badges on matched threads, sourced from a paid search API
- the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number
- weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise
- a hosted, shareable report link you can hand a client without exposing your own infra

## 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 buyer-question visibility tracker for one product. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry.
- product.json holds my product name, aliases, domain, and up to 5 competitor names.
- `track questions` generates 20 buyer-intent questions from the product name and a one-paragraph description, or accepts a JSON list I supply.
- `track run` sends every question through Perplexity's API and OpenAI's Chat Completions API with web search enabled where supported. Keys live in .env.
- Store one immutable row per run, question, and provider: raw answer, cited URLs, model id, and error text. Never overwrite a prior run.
- Retry transient failures twice with backoff; keep failed cells visible in the report instead of dropping them.
- Detect brand and competitor mentions case-insensitively against the alias list.
- Normalize citation URLs to hostname plus canonical path, strip tracking parameters, then compute a top-sources table and an owned-domain citation rate.
- Compute one GEO score per run: mention rate times citation rate, shown with the raw counts behind it, never just the number.
- `track serve` renders visibility over time by provider, share of voice against each competitor, and the sources table.
- `track export` writes questions, answers, mentions, and citations to CSV.
- Fixture tests for mention detection, URL normalization, and the GEO score calculation.
- Out of scope: Reddit/Hacker News monitoring, Google rank tracking, writing-plan or content generation, hosted sharing, teams, and billing.
- README: setup, per-run API cost estimate, and a cron line for a daily run.

## 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 CueScout 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